research methods that include surveys case studies and observations are called quizlet

Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other.

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null. The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period.

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them.

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables.

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization.
Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way.

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time.

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures.

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

types of correlation. Scatter plot. Positive negative and no correlation

If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

Types of correlation. Strong, weak, and perfect positive correlation, strong, weak, and perfect negative correlation, no correlation. Graphs or charts ...

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved.

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way.

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view.

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
Natural : Here, spontaneous behavior is recorded in a natural setting.
Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.
Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

Test-retest reliability : assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
Primary data is first-hand data collected for the purpose of the investigation.
Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

Research Methodology

Mixed Methods Research

Conversation Analysis

Discourse Analysis

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

2.2 Research Methods

Learning objectives.

By the end of this section, you should be able to:

Recall the 6 Steps of the Scientific Method
Differentiate between four kinds of research methods: surveys, field research, experiments, and secondary data analysis.
Explain the appropriateness of specific research approaches for specific topics.

Sociologists examine the social world, see a problem or interesting pattern, and set out to study it. They use research methods to design a study. Planning the research design is a key step in any sociological study. Sociologists generally choose from widely used methods of social investigation: primary source data collection such as survey, participant observation, ethnography, case study, unobtrusive observations, experiment, and secondary data analysis , or use of existing sources. Every research method comes with plusses and minuses, and the topic of study strongly influences which method or methods are put to use. When you are conducting research think about the best way to gather or obtain knowledge about your topic, think of yourself as an architect. An architect needs a blueprint to build a house, as a sociologist your blueprint is your research design including your data collection method.

When entering a particular social environment, a researcher must be careful. There are times to remain anonymous and times to be overt. There are times to conduct interviews and times to simply observe. Some participants need to be thoroughly informed; others should not know they are being observed. A researcher wouldn’t stroll into a crime-ridden neighborhood at midnight, calling out, “Any gang members around?”

Making sociologists’ presence invisible is not always realistic for other reasons. That option is not available to a researcher studying prison behaviors, early education, or the Ku Klux Klan. Researchers can’t just stroll into prisons, kindergarten classrooms, or Klan meetings and unobtrusively observe behaviors or attract attention. In situations like these, other methods are needed. Researchers choose methods that best suit their study topics, protect research participants or subjects, and that fit with their overall approaches to research.

As a research method, a survey collects data from subjects who respond to a series of questions about behaviors and opinions, often in the form of a questionnaire or an interview. The survey is one of the most widely used scientific research methods. The standard survey format allows individuals a level of anonymity in which they can express personal ideas.

At some point, most people in the United States respond to some type of survey. The 2020 U.S. Census is an excellent example of a large-scale survey intended to gather sociological data. Since 1790, United States has conducted a survey consisting of six questions to received demographical data pertaining to residents. The questions pertain to the demographics of the residents who live in the United States. Currently, the Census is received by residents in the United Stated and five territories and consists of 12 questions.

Not all surveys are considered sociological research, however, and many surveys people commonly encounter focus on identifying marketing needs and strategies rather than testing a hypothesis or contributing to social science knowledge. Questions such as, “How many hot dogs do you eat in a month?” or “Were the staff helpful?” are not usually designed as scientific research. The Nielsen Ratings determine the popularity of television programming through scientific market research. However, polls conducted by television programs such as American Idol or So You Think You Can Dance cannot be generalized, because they are administered to an unrepresentative population, a specific show’s audience. You might receive polls through your cell phones or emails, from grocery stores, restaurants, and retail stores. They often provide you incentives for completing the survey.

Sociologists conduct surveys under controlled conditions for specific purposes. Surveys gather different types of information from people. While surveys are not great at capturing the ways people really behave in social situations, they are a great method for discovering how people feel, think, and act—or at least how they say they feel, think, and act. Surveys can track preferences for presidential candidates or reported individual behaviors (such as sleeping, driving, or texting habits) or information such as employment status, income, and education levels.

A survey targets a specific population , people who are the focus of a study, such as college athletes, international students, or teenagers living with type 1 (juvenile-onset) diabetes. Most researchers choose to survey a small sector of the population, or a sample , a manageable number of subjects who represent a larger population. The success of a study depends on how well a population is represented by the sample. In a random sample , every person in a population has the same chance of being chosen for the study. As a result, a Gallup Poll, if conducted as a nationwide random sampling, should be able to provide an accurate estimate of public opinion whether it contacts 2,000 or 10,000 people.

After selecting subjects, the researcher develops a specific plan to ask questions and record responses. It is important to inform subjects of the nature and purpose of the survey up front. If they agree to participate, researchers thank subjects and offer them a chance to see the results of the study if they are interested. The researcher presents the subjects with an instrument, which is a means of gathering the information.

A common instrument is a questionnaire. Subjects often answer a series of closed-ended questions . The researcher might ask yes-or-no or multiple-choice questions, allowing subjects to choose possible responses to each question. This kind of questionnaire collects quantitative data —data in numerical form that can be counted and statistically analyzed. Just count up the number of “yes” and “no” responses or correct answers, and chart them into percentages.

Questionnaires can also ask more complex questions with more complex answers—beyond “yes,” “no,” or checkbox options. These types of inquiries use open-ended questions that require short essay responses. Participants willing to take the time to write those answers might convey personal religious beliefs, political views, goals, or morals. The answers are subjective and vary from person to person. How do you plan to use your college education?

Some topics that investigate internal thought processes are impossible to observe directly and are difficult to discuss honestly in a public forum. People are more likely to share honest answers if they can respond to questions anonymously. This type of personal explanation is qualitative data —conveyed through words. Qualitative information is harder to organize and tabulate. The researcher will end up with a wide range of responses, some of which may be surprising. The benefit of written opinions, though, is the wealth of in-depth material that they provide.

An interview is a one-on-one conversation between the researcher and the subject, and it is a way of conducting surveys on a topic. However, participants are free to respond as they wish, without being limited by predetermined choices. In the back-and-forth conversation of an interview, a researcher can ask for clarification, spend more time on a subtopic, or ask additional questions. In an interview, a subject will ideally feel free to open up and answer questions that are often complex. There are no right or wrong answers. The subject might not even know how to answer the questions honestly.

Questions such as “How does society’s view of alcohol consumption influence your decision whether or not to take your first sip of alcohol?” or “Did you feel that the divorce of your parents would put a social stigma on your family?” involve so many factors that the answers are difficult to categorize. A researcher needs to avoid steering or prompting the subject to respond in a specific way; otherwise, the results will prove to be unreliable. The researcher will also benefit from gaining a subject’s trust, from empathizing or commiserating with a subject, and from listening without judgment.

Surveys often collect both quantitative and qualitative data. For example, a researcher interviewing people who are incarcerated might receive quantitative data, such as demographics – race, age, sex, that can be analyzed statistically. For example, the researcher might discover that 20 percent of incarcerated people are above the age of 50. The researcher might also collect qualitative data, such as why people take advantage of educational opportunities during their sentence and other explanatory information.

The survey can be carried out online, over the phone, by mail, or face-to-face. When researchers collect data outside a laboratory, library, or workplace setting, they are conducting field research, which is our next topic.

Field Research

The work of sociology rarely happens in limited, confined spaces. Rather, sociologists go out into the world. They meet subjects where they live, work, and play. Field research refers to gathering primary data from a natural environment. To conduct field research, the sociologist must be willing to step into new environments and observe, participate, or experience those worlds. In field work, the sociologists, rather than the subjects, are the ones out of their element.

The researcher interacts with or observes people and gathers data along the way. The key point in field research is that it takes place in the subject’s natural environment, whether it’s a coffee shop or tribal village, a homeless shelter or the DMV, a hospital, airport, mall, or beach resort.

While field research often begins in a specific setting , the study’s purpose is to observe specific behaviors in that setting. Field work is optimal for observing how people think and behave. It seeks to understand why they behave that way. However, researchers may struggle to narrow down cause and effect when there are so many variables floating around in a natural environment. And while field research looks for correlation, its small sample size does not allow for establishing a causal relationship between two variables. Indeed, much of the data gathered in sociology do not identify a cause and effect but a correlation .

Sociology in the Real World

Beyoncé and lady gaga as sociological subjects.

Sociologists have studied Lady Gaga and Beyoncé and their impact on music, movies, social media, fan participation, and social equality. In their studies, researchers have used several research methods including secondary analysis, participant observation, and surveys from concert participants.

In their study, Click, Lee & Holiday (2013) interviewed 45 Lady Gaga fans who utilized social media to communicate with the artist. These fans viewed Lady Gaga as a mirror of themselves and a source of inspiration. Like her, they embrace not being a part of mainstream culture. Many of Lady Gaga’s fans are members of the LGBTQ community. They see the “song “Born This Way” as a rallying cry and answer her calls for “Paws Up” with a physical expression of solidarity—outstretched arms and ﬁngers bent and curled to resemble monster claws.”

Sascha Buchanan (2019) made use of participant observation to study the relationship between two fan groups, that of Beyoncé and that of Rihanna. She observed award shows sponsored by iHeartRadio, MTV EMA, and BET that pit one group against another as they competed for Best Fan Army, Biggest Fans, and FANdemonium. Buchanan argues that the media thus sustains a myth of rivalry between the two most commercially successful Black women vocal artists.

Participant Observation

In 2000, a comic writer named Rodney Rothman wanted an insider’s view of white-collar work. He slipped into the sterile, high-rise offices of a New York “dot com” agency. Every day for two weeks, he pretended to work there. His main purpose was simply to see whether anyone would notice him or challenge his presence. No one did. The receptionist greeted him. The employees smiled and said good morning. Rothman was accepted as part of the team. He even went so far as to claim a desk, inform the receptionist of his whereabouts, and attend a meeting. He published an article about his experience in The New Yorker called “My Fake Job” (2000). Later, he was discredited for allegedly fabricating some details of the story and The New Yorker issued an apology. However, Rothman’s entertaining article still offered fascinating descriptions of the inside workings of a “dot com” company and exemplified the lengths to which a writer, or a sociologist, will go to uncover material.

Rothman had conducted a form of study called participant observation , in which researchers join people and participate in a group’s routine activities for the purpose of observing them within that context. This method lets researchers experience a specific aspect of social life. A researcher might go to great lengths to get a firsthand look into a trend, institution, or behavior. A researcher might work as a waitress in a diner, experience homelessness for several weeks, or ride along with police officers as they patrol their regular beat. Often, these researchers try to blend in seamlessly with the population they study, and they may not disclose their true identity or purpose if they feel it would compromise the results of their research.

At the beginning of a field study, researchers might have a question: “What really goes on in the kitchen of the most popular diner on campus?” or “What is it like to be homeless?” Participant observation is a useful method if the researcher wants to explore a certain environment from the inside.

Field researchers simply want to observe and learn. In such a setting, the researcher will be alert and open minded to whatever happens, recording all observations accurately. Soon, as patterns emerge, questions will become more specific, observations will lead to hypotheses, and hypotheses will guide the researcher in analyzing data and generating results.

In a study of small towns in the United States conducted by sociological researchers John S. Lynd and Helen Merrell Lynd, the team altered their purpose as they gathered data. They initially planned to focus their study on the role of religion in U.S. towns. As they gathered observations, they realized that the effect of industrialization and urbanization was the more relevant topic of this social group. The Lynds did not change their methods, but they revised the purpose of their study.

This shaped the structure of Middletown: A Study in Modern American Culture , their published results (Lynd & Lynd, 1929).

The Lynds were upfront about their mission. The townspeople of Muncie, Indiana, knew why the researchers were in their midst. But some sociologists prefer not to alert people to their presence. The main advantage of covert participant observation is that it allows the researcher access to authentic, natural behaviors of a group’s members. The challenge, however, is gaining access to a setting without disrupting the pattern of others’ behavior. Becoming an inside member of a group, organization, or subculture takes time and effort. Researchers must pretend to be something they are not. The process could involve role playing, making contacts, networking, or applying for a job.

Once inside a group, some researchers spend months or even years pretending to be one of the people they are observing. However, as observers, they cannot get too involved. They must keep their purpose in mind and apply the sociological perspective. That way, they illuminate social patterns that are often unrecognized. Because information gathered during participant observation is mostly qualitative, rather than quantitative, the end results are often descriptive or interpretive. The researcher might present findings in an article or book and describe what he or she witnessed and experienced.

This type of research is what journalist Barbara Ehrenreich conducted for her book Nickel and Dimed . One day over lunch with her editor, Ehrenreich mentioned an idea. How can people exist on minimum-wage work? How do low-income workers get by? she wondered. Someone should do a study . To her surprise, her editor responded, Why don’t you do it?

That’s how Ehrenreich found herself joining the ranks of the working class. For several months, she left her comfortable home and lived and worked among people who lacked, for the most part, higher education and marketable job skills. Undercover, she applied for and worked minimum wage jobs as a waitress, a cleaning woman, a nursing home aide, and a retail chain employee. During her participant observation, she used only her income from those jobs to pay for food, clothing, transportation, and shelter.

She discovered the obvious, that it’s almost impossible to get by on minimum wage work. She also experienced and observed attitudes many middle and upper-class people never think about. She witnessed firsthand the treatment of working class employees. She saw the extreme measures people take to make ends meet and to survive. She described fellow employees who held two or three jobs, worked seven days a week, lived in cars, could not pay to treat chronic health conditions, got randomly fired, submitted to drug tests, and moved in and out of homeless shelters. She brought aspects of that life to light, describing difficult working conditions and the poor treatment that low-wage workers suffer.

The book she wrote upon her return to her real life as a well-paid writer, has been widely read and used in many college classrooms.

Ethnography

Ethnography is the immersion of the researcher in the natural setting of an entire social community to observe and experience their everyday life and culture. The heart of an ethnographic study focuses on how subjects view their own social standing and how they understand themselves in relation to a social group.

An ethnographic study might observe, for example, a small U.S. fishing town, an Inuit community, a village in Thailand, a Buddhist monastery, a private boarding school, or an amusement park. These places all have borders. People live, work, study, or vacation within those borders. People are there for a certain reason and therefore behave in certain ways and respect certain cultural norms. An ethnographer would commit to spending a determined amount of time studying every aspect of the chosen place, taking in as much as possible.

A sociologist studying a tribe in the Amazon might watch the way villagers go about their daily lives and then write a paper about it. To observe a spiritual retreat center, an ethnographer might sign up for a retreat and attend as a guest for an extended stay, observe and record data, and collate the material into results.

Institutional Ethnography

Institutional ethnography is an extension of basic ethnographic research principles that focuses intentionally on everyday concrete social relationships. Developed by Canadian sociologist Dorothy E. Smith (1990), institutional ethnography is often considered a feminist-inspired approach to social analysis and primarily considers women’s experiences within male- dominated societies and power structures. Smith’s work is seen to challenge sociology’s exclusion of women, both academically and in the study of women’s lives (Fenstermaker, n.d.).

Historically, social science research tended to objectify women and ignore their experiences except as viewed from the male perspective. Modern feminists note that describing women, and other marginalized groups, as subordinates helps those in authority maintain their own dominant positions (Social Sciences and Humanities Research Council of Canada n.d.). Smith’s three major works explored what she called “the conceptual practices of power” and are still considered seminal works in feminist theory and ethnography (Fensternmaker n.d.).

Sociological Research

The making of middletown: a study in modern u.s. culture.

In 1924, a young married couple named Robert and Helen Lynd undertook an unprecedented ethnography: to apply sociological methods to the study of one U.S. city in order to discover what “ordinary” people in the United States did and believed. Choosing Muncie, Indiana (population about 30,000) as their subject, they moved to the small town and lived there for eighteen months.

Ethnographers had been examining other cultures for decades—groups considered minorities or outsiders—like gangs, immigrants, and the poor. But no one had studied the so-called average American.

Recording interviews and using surveys to gather data, the Lynds objectively described what they observed. Researching existing sources, they compared Muncie in 1890 to the Muncie they observed in 1924. Most Muncie adults, they found, had grown up on farms but now lived in homes inside the city. As a result, the Lynds focused their study on the impact of industrialization and urbanization.

They observed that Muncie was divided into business and working class groups. They defined business class as dealing with abstract concepts and symbols, while working class people used tools to create concrete objects. The two classes led different lives with different goals and hopes. However, the Lynds observed, mass production offered both classes the same amenities. Like wealthy families, the working class was now able to own radios, cars, washing machines, telephones, vacuum cleaners, and refrigerators. This was an emerging material reality of the 1920s.

As the Lynds worked, they divided their manuscript into six chapters: Getting a Living, Making a Home, Training the Young, Using Leisure, Engaging in Religious Practices, and Engaging in Community Activities.

When the study was completed, the Lynds encountered a big problem. The Rockefeller Foundation, which had commissioned the book, claimed it was useless and refused to publish it. The Lynds asked if they could seek a publisher themselves.

Middletown: A Study in Modern American Culture was not only published in 1929 but also became an instant bestseller, a status unheard of for a sociological study. The book sold out six printings in its first year of publication, and has never gone out of print (Caplow, Hicks, & Wattenberg. 2000).

Nothing like it had ever been done before. Middletown was reviewed on the front page of the New York Times. Readers in the 1920s and 1930s identified with the citizens of Muncie, Indiana, but they were equally fascinated by the sociological methods and the use of scientific data to define ordinary people in the United States. The book was proof that social data was important—and interesting—to the U.S. public.

Sometimes a researcher wants to study one specific person or event. A case study is an in-depth analysis of a single event, situation, or individual. To conduct a case study, a researcher examines existing sources like documents and archival records, conducts interviews, engages in direct observation and even participant observation, if possible.

Researchers might use this method to study a single case of a foster child, drug lord, cancer patient, criminal, or rape victim. However, a major criticism of the case study as a method is that while offering depth on a topic, it does not provide enough evidence to form a generalized conclusion. In other words, it is difficult to make universal claims based on just one person, since one person does not verify a pattern. This is why most sociologists do not use case studies as a primary research method.

However, case studies are useful when the single case is unique. In these instances, a single case study can contribute tremendous insight. For example, a feral child, also called “wild child,” is one who grows up isolated from human beings. Feral children grow up without social contact and language, which are elements crucial to a “civilized” child’s development. These children mimic the behaviors and movements of animals, and often invent their own language. There are only about one hundred cases of “feral children” in the world.

As you may imagine, a feral child is a subject of great interest to researchers. Feral children provide unique information about child development because they have grown up outside of the parameters of “normal” growth and nurturing. And since there are very few feral children, the case study is the most appropriate method for researchers to use in studying the subject.

At age three, a Ukranian girl named Oxana Malaya suffered severe parental neglect. She lived in a shed with dogs, and she ate raw meat and scraps. Five years later, a neighbor called authorities and reported seeing a girl who ran on all fours, barking. Officials brought Oxana into society, where she was cared for and taught some human behaviors, but she never became fully socialized. She has been designated as unable to support herself and now lives in a mental institution (Grice 2011). Case studies like this offer a way for sociologists to collect data that may not be obtained by any other method.

Experiments

You have probably tested some of your own personal social theories. “If I study at night and review in the morning, I’ll improve my retention skills.” Or, “If I stop drinking soda, I’ll feel better.” Cause and effect. If this, then that. When you test the theory, your results either prove or disprove your hypothesis.

One way researchers test social theories is by conducting an experiment , meaning they investigate relationships to test a hypothesis—a scientific approach.

There are two main types of experiments: lab-based experiments and natural or field experiments. In a lab setting, the research can be controlled so that more data can be recorded in a limited amount of time. In a natural or field- based experiment, the time it takes to gather the data cannot be controlled but the information might be considered more accurate since it was collected without interference or intervention by the researcher.

As a research method, either type of sociological experiment is useful for testing if-then statements: if a particular thing happens (cause), then another particular thing will result (effect). To set up a lab-based experiment, sociologists create artificial situations that allow them to manipulate variables.

Classically, the sociologist selects a set of people with similar characteristics, such as age, class, race, or education. Those people are divided into two groups. One is the experimental group and the other is the control group. The experimental group is exposed to the independent variable(s) and the control group is not. To test the benefits of tutoring, for example, the sociologist might provide tutoring to the experimental group of students but not to the control group. Then both groups would be tested for differences in performance to see if tutoring had an effect on the experimental group of students. As you can imagine, in a case like this, the researcher would not want to jeopardize the accomplishments of either group of students, so the setting would be somewhat artificial. The test would not be for a grade reflected on their permanent record of a student, for example.

And if a researcher told the students they would be observed as part of a study on measuring the effectiveness of tutoring, the students might not behave naturally. This is called the Hawthorne effect —which occurs when people change their behavior because they know they are being watched as part of a study. The Hawthorne effect is unavoidable in some research studies because sociologists have to make the purpose of the study known. Subjects must be aware that they are being observed, and a certain amount of artificiality may result (Sonnenfeld 1985).

A real-life example will help illustrate the process. In 1971, Frances Heussenstamm, a sociology professor at California State University at Los Angeles, had a theory about police prejudice. To test her theory, she conducted research. She chose fifteen students from three ethnic backgrounds: Black, White, and Hispanic. She chose students who routinely drove to and from campus along Los Angeles freeway routes, and who had had perfect driving records for longer than a year.

Next, she placed a Black Panther bumper sticker on each car. That sticker, a representation of a social value, was the independent variable. In the 1970s, the Black Panthers were a revolutionary group actively fighting racism. Heussenstamm asked the students to follow their normal driving patterns. She wanted to see whether seeming support for the Black Panthers would change how these good drivers were treated by the police patrolling the highways. The dependent variable would be the number of traffic stops/citations.

The first arrest, for an incorrect lane change, was made two hours after the experiment began. One participant was pulled over three times in three days. He quit the study. After seventeen days, the fifteen drivers had collected a total of thirty-three traffic citations. The research was halted. The funding to pay traffic fines had run out, and so had the enthusiasm of the participants (Heussenstamm, 1971).

Secondary Data Analysis

While sociologists often engage in original research studies, they also contribute knowledge to the discipline through secondary data analysis . Secondary data does not result from firsthand research collected from primary sources, but are the already completed work of other researchers or data collected by an agency or organization. Sociologists might study works written by historians, economists, teachers, or early sociologists. They might search through periodicals, newspapers, or magazines, or organizational data from any period in history.

Using available information not only saves time and money but can also add depth to a study. Sociologists often interpret findings in a new way, a way that was not part of an author’s original purpose or intention. To study how women were encouraged to act and behave in the 1960s, for example, a researcher might watch movies, televisions shows, and situation comedies from that period. Or to research changes in behavior and attitudes due to the emergence of television in the late 1950s and early 1960s, a sociologist would rely on new interpretations of secondary data. Decades from now, researchers will most likely conduct similar studies on the advent of mobile phones, the Internet, or social media.

Social scientists also learn by analyzing the research of a variety of agencies. Governmental departments and global groups, like the U.S. Bureau of Labor Statistics or the World Health Organization (WHO), publish studies with findings that are useful to sociologists. A public statistic like the foreclosure rate might be useful for studying the effects of a recession. A racial demographic profile might be compared with data on education funding to examine the resources accessible by different groups.

One of the advantages of secondary data like old movies or WHO statistics is that it is nonreactive research (or unobtrusive research), meaning that it does not involve direct contact with subjects and will not alter or influence people’s behaviors. Unlike studies requiring direct contact with people, using previously published data does not require entering a population and the investment and risks inherent in that research process.

Using available data does have its challenges. Public records are not always easy to access. A researcher will need to do some legwork to track them down and gain access to records. To guide the search through a vast library of materials and avoid wasting time reading unrelated sources, sociologists employ content analysis , applying a systematic approach to record and value information gleaned from secondary data as they relate to the study at hand.

Also, in some cases, there is no way to verify the accuracy of existing data. It is easy to count how many drunk drivers, for example, are pulled over by the police. But how many are not? While it’s possible to discover the percentage of teenage students who drop out of high school, it might be more challenging to determine the number who return to school or get their GED later.

Another problem arises when data are unavailable in the exact form needed or do not survey the topic from the precise angle the researcher seeks. For example, the average salaries paid to professors at a public school is public record. But these figures do not necessarily reveal how long it took each professor to reach the salary range, what their educational backgrounds are, or how long they’ve been teaching.

When conducting content analysis, it is important to consider the date of publication of an existing source and to take into account attitudes and common cultural ideals that may have influenced the research. For example, when Robert S. Lynd and Helen Merrell Lynd gathered research in the 1920s, attitudes and cultural norms were vastly different then than they are now. Beliefs about gender roles, race, education, and work have changed significantly since then. At the time, the study’s purpose was to reveal insights about small U.S. communities. Today, it is an illustration of 1920s attitudes and values.

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Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs. quantitative : Will your data take the form of words or numbers?
Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .


Qualitative		to broader populations. .
Quantitative		.

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.


Primary	.	methods.
Secondary

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.


Descriptive		. .
Experimental

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Research methods for collecting data
Research method	Primary or secondary?	Qualitative or quantitative?	When to use
	Primary	Quantitative	To test cause-and-effect relationships.
	Primary	Quantitative	To understand general characteristics of a population.
Interview/focus group	Primary	Qualitative	To gain more in-depth understanding of a topic.
Observation	Primary	Either	To understand how something occurs in its natural setting.
	Secondary	Either	To situate your research in an existing body of work, or to evaluate trends within a research topic.
	Either	Either	To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study.

Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

During an experiment .
Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

Research methods for analyzing data
Research method	Qualitative or quantitative?	When to use
	Quantitative	To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations).
Meta-analysis	Quantitative	To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner.
	Qualitative	To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated.
	Either	To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words).

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Chi square test of independence
Statistical power
Descriptive statistics
Degrees of freedom
Pearson correlation
Null hypothesis
Double-blind study
Case-control study
Research ethics
Data collection
Hypothesis testing
Structured interviews

Research bias

Hawthorne effect
Unconscious bias
Recall bias
Halo effect
Self-serving bias
Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Ch 2: Psychological Research Methods

Children sit in front of a bank of television screens. A sign on the wall says, “Some content may not be suitable for children.”

Have you ever wondered whether the violence you see on television affects your behavior? Are you more likely to behave aggressively in real life after watching people behave violently in dramatic situations on the screen? Or, could seeing fictional violence actually get aggression out of your system, causing you to be more peaceful? How are children influenced by the media they are exposed to? A psychologist interested in the relationship between behavior and exposure to violent images might ask these very questions.

The topic of violence in the media today is contentious. Since ancient times, humans have been concerned about the effects of new technologies on our behaviors and thinking processes. The Greek philosopher Socrates, for example, worried that writing—a new technology at that time—would diminish people’s ability to remember because they could rely on written records rather than committing information to memory. In our world of quickly changing technologies, questions about the effects of media continue to emerge. Is it okay to talk on a cell phone while driving? Are headphones good to use in a car? What impact does text messaging have on reaction time while driving? These are types of questions that psychologist David Strayer asks in his lab.

Watch this short video to see how Strayer utilizes the scientific method to reach important conclusions regarding technology and driving safety.

You can view the transcript for “Understanding driver distraction” here (opens in new window) .

How can we go about finding answers that are supported not by mere opinion, but by evidence that we can all agree on? The findings of psychological research can help us navigate issues like this.

Introduction to the Scientific Method

Learning objectives.

Explain the steps of the scientific method
Describe why the scientific method is important to psychology
Summarize the processes of informed consent and debriefing
Explain how research involving humans or animals is regulated

photograph of the word "research" from a dictionary with a pen pointing at the word.

Scientists are engaged in explaining and understanding how the world around them works, and they are able to do so by coming up with theories that generate hypotheses that are testable and falsifiable. Theories that stand up to their tests are retained and refined, while those that do not are discarded or modified. In this way, research enables scientists to separate fact from simple opinion. Having good information generated from research aids in making wise decisions both in public policy and in our personal lives. In this section, you’ll see how psychologists use the scientific method to study and understand behavior.

The Scientific Process

A skull has a large hole bored through the forehead.

The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

While behavior is observable, the mind is not. If someone is crying, we can see the behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This module explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.

Process of Scientific Research

Flowchart of the scientific method. It begins with make an observation, then ask a question, form a hypothesis that answers the question, make a prediction based on the hypothesis, do an experiment to test the prediction, analyze the results, prove the hypothesis correct or incorrect, then report the results.

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on.

The basic steps in the scientific method are:

Observe a natural phenomenon and define a question about it
Make a hypothesis, or potential solution to the question
Test the hypothesis
If the hypothesis is true, find more evidence or find counter-evidence
If the hypothesis is false, create a new hypothesis or try again
Draw conclusions and repeat–the scientific method is never-ending, and no result is ever considered perfect

In order to ask an important question that may improve our understanding of the world, a researcher must first observe natural phenomena. By making observations, a researcher can define a useful question. After finding a question to answer, the researcher can then make a prediction (a hypothesis) about what he or she thinks the answer will be. This prediction is usually a statement about the relationship between two or more variables. After making a hypothesis, the researcher will then design an experiment to test his or her hypothesis and evaluate the data gathered. These data will either support or refute the hypothesis. Based on the conclusions drawn from the data, the researcher will then find more evidence to support the hypothesis, look for counter-evidence to further strengthen the hypothesis, revise the hypothesis and create a new experiment, or continue to incorporate the information gathered to answer the research question.

Basic Principles of the Scientific Method

Two key concepts in the scientific approach are theory and hypothesis. A theory is a well-developed set of ideas that propose an explanation for observed phenomena that can be used to make predictions about future observations. A hypothesis is a testable prediction that is arrived at logically from a theory. It is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests.

A diagram has four boxes: the top is labeled “theory,” the right is labeled “hypothesis,” the bottom is labeled “research,” and the left is labeled “observation.” Arrows flow in the direction from top to right to bottom to left and back to the top, clockwise. The top right arrow is labeled “use the hypothesis to form a theory,” the bottom right arrow is labeled “design a study to test the hypothesis,” the bottom left arrow is labeled “perform the research,” and the top left arrow is labeled “create or modify the theory.”

Other key components in following the scientific method include verifiability, predictability, falsifiability, and fairness. Verifiability means that an experiment must be replicable by another researcher. To achieve verifiability, researchers must make sure to document their methods and clearly explain how their experiment is structured and why it produces certain results.

Predictability in a scientific theory implies that the theory should enable us to make predictions about future events. The precision of these predictions is a measure of the strength of the theory.

Falsifiability refers to whether a hypothesis can be disproved. For a hypothesis to be falsifiable, it must be logically possible to make an observation or do a physical experiment that would show that there is no support for the hypothesis. Even when a hypothesis cannot be shown to be false, that does not necessarily mean it is not valid. Future testing may disprove the hypothesis. This does not mean that a hypothesis has to be shown to be false, just that it can be tested.

To determine whether a hypothesis is supported or not supported, psychological researchers must conduct hypothesis testing using statistics. Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false. If hypothesis testing reveals that results were “statistically significant,” this means that there was support for the hypothesis and that the researchers can be reasonably confident that their result was not due to random chance. If the results are not statistically significant, this means that the researchers’ hypothesis was not supported.

Fairness implies that all data must be considered when evaluating a hypothesis. A researcher cannot pick and choose what data to keep and what to discard or focus specifically on data that support or do not support a particular hypothesis. All data must be accounted for, even if they invalidate the hypothesis.

Applying the Scientific Method

To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later module, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

Remember that a good scientific hypothesis is falsifiable, or capable of being shown to be incorrect. Recall from the introductory module that Sigmund Freud had lots of interesting ideas to explain various human behaviors (Figure 5). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

(a)A photograph shows Freud holding a cigar. (b) The mind’s conscious and unconscious states are illustrated as an iceberg floating in water. Beneath the water’s surface in the “unconscious” area are the id, ego, and superego. The area just below the water’s surface is labeled “preconscious.” The area above the water’s surface is labeled “conscious.”

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Link to Learning

Why the scientific method is important for psychology.

The use of the scientific method is one of the main features that separates modern psychology from earlier philosophical inquiries about the mind. Compared to chemistry, physics, and other “natural sciences,” psychology has long been considered one of the “social sciences” because of the subjective nature of the things it seeks to study. Many of the concepts that psychologists are interested in—such as aspects of the human mind, behavior, and emotions—are subjective and cannot be directly measured. Psychologists often rely instead on behavioral observations and self-reported data, which are considered by some to be illegitimate or lacking in methodological rigor. Applying the scientific method to psychology, therefore, helps to standardize the approach to understanding its very different types of information.

The scientific method allows psychological data to be replicated and confirmed in many instances, under different circumstances, and by a variety of researchers. Through replication of experiments, new generations of psychologists can reduce errors and broaden the applicability of theories. It also allows theories to be tested and validated instead of simply being conjectures that could never be verified or falsified. All of this allows psychologists to gain a stronger understanding of how the human mind works.

Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in “APA style”) are structured around the scientific method. These papers include an Introduction, which introduces the background information and outlines the hypotheses; a Methods section, which outlines the specifics of how the experiment was conducted to test the hypothesis; a Results section, which includes the statistics that tested the hypothesis and state whether it was supported or not supported, and a Discussion and Conclusion, which state the implications of finding support for, or no support for, the hypothesis. Writing articles and papers that adhere to the scientific method makes it easy for future researchers to repeat the study and attempt to replicate the results.

Ethics in Research

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, as you will read in the Tuskegee Syphilis Study, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound. This section presents how ethical considerations affect the design and implementation of research conducted today.

Research Involving Human Participants

Any experiment involving the participation of human subjects is governed by extensive, strict guidelines designed to ensure that the experiment does not result in harm. Any research institution that receives federal support for research involving human participants must have access to an institutional review board (IRB) . The IRB is a committee of individuals often made up of members of the institution’s administration, scientists, and community members (Figure 6). The purpose of the IRB is to review proposals for research that involves human participants. The IRB reviews these proposals with the principles mentioned above in mind, and generally, approval from the IRB is required in order for the experiment to proceed.

A photograph shows a group of people seated around tables in a meeting room.

An institution’s IRB requires several components in any experiment it approves. For one, each participant must sign an informed consent form before they can participate in the experiment. An informed consent form provides a written description of what participants can expect during the experiment, including potential risks and implications of the research. It also lets participants know that their involvement is completely voluntary and can be discontinued without penalty at any time. Furthermore, the informed consent guarantees that any data collected in the experiment will remain completely confidential. In cases where research participants are under the age of 18, the parents or legal guardians are required to sign the informed consent form.

While the informed consent form should be as honest as possible in describing exactly what participants will be doing, sometimes deception is necessary to prevent participants’ knowledge of the exact research question from affecting the results of the study. Deception involves purposely misleading experiment participants in order to maintain the integrity of the experiment, but not to the point where the deception could be considered harmful. For example, if we are interested in how our opinion of someone is affected by their attire, we might use deception in describing the experiment to prevent that knowledge from affecting participants’ responses. In cases where deception is involved, participants must receive a full debriefing upon conclusion of the study—complete, honest information about the purpose of the experiment, how the data collected will be used, the reasons why deception was necessary, and information about how to obtain additional information about the study.

Dig Deeper: Ethics and the Tuskegee Syphilis Study

Unfortunately, the ethical guidelines that exist for research today were not always applied in the past. In 1932, poor, rural, black, male sharecroppers from Tuskegee, Alabama, were recruited to participate in an experiment conducted by the U.S. Public Health Service, with the aim of studying syphilis in black men (Figure 7). In exchange for free medical care, meals, and burial insurance, 600 men agreed to participate in the study. A little more than half of the men tested positive for syphilis, and they served as the experimental group (given that the researchers could not randomly assign participants to groups, this represents a quasi-experiment). The remaining syphilis-free individuals served as the control group. However, those individuals that tested positive for syphilis were never informed that they had the disease.

While there was no treatment for syphilis when the study began, by 1947 penicillin was recognized as an effective treatment for the disease. Despite this, no penicillin was administered to the participants in this study, and the participants were not allowed to seek treatment at any other facilities if they continued in the study. Over the course of 40 years, many of the participants unknowingly spread syphilis to their wives (and subsequently their children born from their wives) and eventually died because they never received treatment for the disease. This study was discontinued in 1972 when the experiment was discovered by the national press (Tuskegee University, n.d.). The resulting outrage over the experiment led directly to the National Research Act of 1974 and the strict ethical guidelines for research on humans described in this chapter. Why is this study unethical? How were the men who participated and their families harmed as a function of this research?

A photograph shows a person administering an injection.

Learn more about the Tuskegee Syphilis Study on the CDC website .

Research Involving Animal Subjects

This does not mean that animal researchers are immune to ethical concerns. Indeed, the humane and ethical treatment of animal research subjects is a critical aspect of this type of research. Researchers must design their experiments to minimize any pain or distress experienced by animals serving as research subjects.

Whereas IRBs review research proposals that involve human participants, animal experimental proposals are reviewed by an Institutional Animal Care and Use Committee (IACUC) . An IACUC consists of institutional administrators, scientists, veterinarians, and community members. This committee is charged with ensuring that all experimental proposals require the humane treatment of animal research subjects. It also conducts semi-annual inspections of all animal facilities to ensure that the research protocols are being followed. No animal research project can proceed without the committee’s approval.

Introduction to Approaches to Research

Differentiate between descriptive, correlational, and experimental research
Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys
Describe the strength and weaknesses of archival research
Compare longitudinal and cross-sectional approaches to research
Explain what a correlation coefficient tells us about the relationship between variables
Describe why correlation does not mean causation
Describe the experimental process, including ways to control for bias
Identify and differentiate between independent and dependent variables

Three researchers review data while talking around a microscope.

Psychologists use descriptive, experimental, and correlational methods to conduct research. Descriptive, or qualitative, methods include the case study, naturalistic observation, surveys, archival research, longitudinal research, and cross-sectional research.

Experiments are conducted in order to determine cause-and-effect relationships. In ideal experimental design, the only difference between the experimental and control groups is whether participants are exposed to the experimental manipulation. Each group goes through all phases of the experiment, but each group will experience a different level of the independent variable: the experimental group is exposed to the experimental manipulation, and the control group is not exposed to the experimental manipulation. The researcher then measures the changes that are produced in the dependent variable in each group. Once data is collected from both groups, it is analyzed statistically to determine if there are meaningful differences between the groups.

When scientists passively observe and measure phenomena it is called correlational research. Here, psychologists do not intervene and change behavior, as they do in experiments. In correlational research, they identify patterns of relationships, but usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.

Watch It: More on Research

If you enjoy learning through lectures and want an interesting and comprehensive summary of this section, then click on the Youtube link to watch a lecture given by MIT Professor John Gabrieli . Start at the 30:45 minute mark and watch through the end to hear examples of actual psychological studies and how they were analyzed. Listen for references to independent and dependent variables, experimenter bias, and double-blind studies. In the lecture, you’ll learn about breaking social norms, “WEIRD” research, why expectations matter, how a warm cup of coffee might make you nicer, why you should change your answer on a multiple choice test, and why praise for intelligence won’t make you any smarter.

You can view the transcript for “Lec 2 | MIT 9.00SC Introduction to Psychology, Spring 2011” here (opens in new window) .

Descriptive Research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are, naturalistic observation, case studies, and surveys.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

A photograph shows two police cars driving, one with its lights flashing.

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 9).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 10). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

(a) A photograph shows Jane Goodall speaking from a lectern. (b) A photograph shows a chimpanzee’s face.

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 11). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

A sample online survey reads, “Dear visitor, your opinion is important to us. We would like to invite you to participate in a short survey to gather your opinions and feedback on your news consumption habits. The survey will take approximately 10-15 minutes. Simply click the “Yes” button below to launch the survey. Would you like to participate?” Two buttons are labeled “yes” and “no.”

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this chapter: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think It Over

Archival research.

(a) A photograph shows stacks of paper files on shelves. (b) A photograph shows a computer.

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Another approach is cross-sectional research . In cross-sectional research, a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of observing a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals make them different from one another.

To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) (Figure 13).

A photograph shows pack of cigarettes and cigarettes in an ashtray. The pack of cigarettes reads, “Surgeon general’s warning: smoking causes lung cancer, heart disease, emphysema, and may complicate pregnancy.”

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increases over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

Correlational Research

Did you know that as sales in ice cream increase, so does the overall rate of crime? Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone? There is no question that a relationship exists between ice cream and crime (e.g., Harper, 2013), but it would be pretty foolish to decide that one thing actually caused the other to occur.

It is much more likely that both ice cream sales and crime rates are related to the temperature outside. When the temperature is warm, there are lots of people out of their houses, interacting with each other, getting annoyed with one another, and sometimes committing crimes. Also, when it is warm outside, we are more likely to seek a cool treat like ice cream. How do we determine if there is indeed a relationship between two things? And when there is a relationship, how can we discern whether it is attributable to coincidence or causation?

Three scatterplots are shown. Scatterplot (a) is labeled “positive correlation” and shows scattered dots forming a rough line from the bottom left to the top right; the x-axis is labeled “weight” and the y-axis is labeled “height.” Scatterplot (b) is labeled “negative correlation” and shows scattered dots forming a rough line from the top left to the bottom right; the x-axis is labeled “tiredness” and the y-axis is labeled “hours of sleep.” Scatterplot (c) is labeled “no correlation” and shows scattered dots having no pattern; the x-axis is labeled “shoe size” and the y-axis is labeled “hours of sleep.”

Correlation Does Not Indicate Causation

Correlational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about cause and effect . While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest. In the ice cream/crime rate example mentioned earlier, temperature is a confounding variable that could account for the relationship between the two variables.

Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our discussion of the research done by the American Cancer Society and how their research projects were some of the first demonstrations of the link between smoking and cancer. It seems reasonable to assume that smoking causes cancer, but if we were limited to correlational research , we would be overstepping our bounds by making this assumption.

Unfortunately, people mistakenly make claims of causation as a function of correlations all the time. Such claims are especially common in advertisements and news stories. For example, recent research found that people who eat cereal on a regular basis achieve healthier weights than those who rarely eat cereal (Frantzen, Treviño, Echon, Garcia-Dominic, & DiMarco, 2013; Barton et al., 2005). Guess how the cereal companies report this finding. Does eating cereal really cause an individual to maintain a healthy weight, or are there other possible explanations, such as, someone at a healthy weight is more likely to regularly eat a healthy breakfast than someone who is obese or someone who avoids meals in an attempt to diet (Figure 15)? While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality. Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how scientific experiments incorporate methods that eliminate, or control for, alternative explanations, which allow researchers to explore how changes in one variable cause changes in another variable.

Watch this clip from Freakonomics for an example of how correlation does not indicate causation.

You can view the transcript for “Correlation vs. Causality: Freakonomics Movie” here (opens in new window) .

Illusory Correlations

The temptation to make erroneous cause-and-effect statements based on correlational research is not the only way we tend to misinterpret data. We also tend to make the mistake of illusory correlations, especially with unsystematic observations. Illusory correlations , or false correlations, occur when people believe that relationships exist between two things when no such relationship exists. One well-known illusory correlation is the supposed effect that the moon’s phases have on human behavior. Many people passionately assert that human behavior is affected by the phase of the moon, and specifically, that people act strangely when the moon is full (Figure 16).

There is no denying that the moon exerts a powerful influence on our planet. The ebb and flow of the ocean’s tides are tightly tied to the gravitational forces of the moon. Many people believe, therefore, that it is logical that we are affected by the moon as well. After all, our bodies are largely made up of water. A meta-analysis of nearly 40 studies consistently demonstrated, however, that the relationship between the moon and our behavior does not exist (Rotton & Kelly, 1985). While we may pay more attention to odd behavior during the full phase of the moon, the rates of odd behavior remain constant throughout the lunar cycle.

Why are we so apt to believe in illusory correlations like this? Often we read or hear about them and simply accept the information as valid. Or, we have a hunch about how something works and then look for evidence to support that hunch, ignoring evidence that would tell us our hunch is false; this is known as confirmation bias . Other times, we find illusory correlations based on the information that comes most easily to mind, even if that information is severely limited. And while we may feel confident that we can use these relationships to better understand and predict the world around us, illusory correlations can have significant drawbacks. For example, research suggests that illusory correlations—in which certain behaviors are inaccurately attributed to certain groups—are involved in the formation of prejudicial attitudes that can ultimately lead to discriminatory behavior (Fiedler, 2004).

We all have a tendency to make illusory correlations from time to time. Try to think of an illusory correlation that is held by you, a family member, or a close friend. How do you think this illusory correlation came about and what can be done in the future to combat them?

Experiments

Causality: conducting experiments and using the data, experimental hypothesis.

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon (Figure 17).

A photograph shows a child pointing a toy gun.

These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.

We also need to precisely define, or operationalize, what is considered violent and nonviolent. An operational definition is a description of how we will measure our variables, and it is important in allowing others understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts is recorded.

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

A photograph shows three glass bottles of pills labeled as placebos.

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect, you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations (Figure 18).

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 19). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.

A box labeled “independent variable: type of television programming viewed” contains a photograph of a person shooting an automatic weapon. An arrow labeled “influences change in the…” leads to a second box. The second box is labeled “dependent variable: violent behavior displayed” and has a photograph of a child pointing a toy gun.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine who to include. Participants are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 20). If possible, we should use a random sample (there are other types of samples, but for the purposes of this section, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth graders who we want to participate in our experiment.

In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

(a) A photograph shows an aerial view of crowds on a street. (b) A photograph shows s small group of children.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design. With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

Introduction to Statistical Thinking

Psychologists use statistics to assist them in analyzing data, and also to give more precise measurements to describe whether something is statistically significant. Analyzing data using statistics enables researchers to find patterns, make claims, and share their results with others. In this section, you’ll learn about some of the tools that psychologists use in statistical analysis.

Define reliability and validity
Describe the importance of distributional thinking and the role of p-values in statistical inference
Describe the role of random sampling and random assignment in drawing cause-and-effect conclusions
Describe the basic structure of a psychological research article

Interpreting Experimental Findings

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this experiment 100 times, we would expect to find the same results at least 95 times out of 100.

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

Reporting Research

When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA) publishes a manual detailing how to write a paper for submission to scientific journals. Unlike an article that might be published in a magazine like Psychology Today, which targets a general audience with an interest in psychology, scientific journals generally publish peer-reviewed journal articles aimed at an audience of professionals and scholars who are actively involved in research themselves.

A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback—to both the author and the journal editor—regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study’s design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.

Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, and even well-designed research can be improved by the revisions suggested. Peer review also ensures that the research is described clearly enough to allow other scientists to replicate it, meaning they can repeat the experiment using different samples to determine reliability. Sometimes replications involve additional measures that expand on the original finding. In any case, each replication serves to provide more evidence to support the original research findings. Successful replications of published research make scientists more apt to adopt those findings, while repeated failures tend to cast doubt on the legitimacy of the original article and lead scientists to look elsewhere. For example, it would be a major advancement in the medical field if a published study indicated that taking a new drug helped individuals achieve a healthy weight without changing their diet. But if other scientists could not replicate the results, the original study’s claims would be questioned.

Dig Deeper: The Vaccine-Autism Myth and the Retraction of Published Studies

Some scientists have claimed that routine childhood vaccines cause some children to develop autism, and, in fact, several peer-reviewed publications published research making these claims. Since the initial reports, large-scale epidemiological research has suggested that vaccinations are not responsible for causing autism and that it is much safer to have your child vaccinated than not. Furthermore, several of the original studies making this claim have since been retracted.

A published piece of work can be rescinded when data is called into question because of falsification, fabrication, or serious research design problems. Once rescinded, the scientific community is informed that there are serious problems with the original publication. Retractions can be initiated by the researcher who led the study, by research collaborators, by the institution that employed the researcher, or by the editorial board of the journal in which the article was originally published. In the vaccine-autism case, the retraction was made because of a significant conflict of interest in which the leading researcher had a financial interest in establishing a link between childhood vaccines and autism (Offit, 2008). Unfortunately, the initial studies received so much media attention that many parents around the world became hesitant to have their children vaccinated (Figure 21). For more information about how the vaccine/autism story unfolded, as well as the repercussions of this story, take a look at Paul Offit’s book, Autism’s False Prophets: Bad Science, Risky Medicine, and the Search for a Cure.

A photograph shows a child being given an oral vaccine.

Reliability and Validity

Dig deeper: everyday connection: how valid is the sat.

Standardized tests like the SAT are supposed to measure an individual’s aptitude for a college education, but how reliable and valid are such tests? Research conducted by the College Board suggests that scores on the SAT have high predictive validity for first-year college students’ GPA (Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). In this context, predictive validity refers to the test’s ability to effectively predict the GPA of college freshmen. Given that many institutions of higher education require the SAT for admission, this high degree of predictive validity might be comforting.

However, the emphasis placed on SAT scores in college admissions has generated some controversy on a number of fronts. For one, some researchers assert that the SAT is a biased test that places minority students at a disadvantage and unfairly reduces the likelihood of being admitted into a college (Santelices & Wilson, 2010). Additionally, some research has suggested that the predictive validity of the SAT is grossly exaggerated in how well it is able to predict the GPA of first-year college students. In fact, it has been suggested that the SAT’s predictive validity may be overestimated by as much as 150% (Rothstein, 2004). Many institutions of higher education are beginning to consider de-emphasizing the significance of SAT scores in making admission decisions (Rimer, 2008).

In 2014, College Board president David Coleman expressed his awareness of these problems, recognizing that college success is more accurately predicted by high school grades than by SAT scores. To address these concerns, he has called for significant changes to the SAT exam (Lewin, 2014).

Statistical Significance

Coffee cup with heart shaped cream inside.

Does drinking coffee actually increase your life expectancy? A recent study (Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012) found that men who drank at least six cups of coffee a day also had a 10% lower chance of dying (women’s chances were 15% lower) than those who drank none. Does this mean you should pick up or increase your own coffee habit? We will explore these results in more depth in the next section about drawing conclusions from statistics. Modern society has become awash in studies such as this; you can read about several such studies in the news every day.

Conducting such a study well, and interpreting the results of such studies requires understanding basic ideas of statistics , the science of gaining insight from data. Key components to a statistical investigation are:

Planning the study: Start by asking a testable research question and deciding how to collect data. For example, how long was the study period of the coffee study? How many people were recruited for the study, how were they recruited, and from where? How old were they? What other variables were recorded about the individuals? Were changes made to the participants’ coffee habits during the course of the study?
Examining the data: What are appropriate ways to examine the data? What graphs are relevant, and what do they reveal? What descriptive statistics can be calculated to summarize relevant aspects of the data, and what do they reveal? What patterns do you see in the data? Are there any individual observations that deviate from the overall pattern, and what do they reveal? For example, in the coffee study, did the proportions differ when we compared the smokers to the non-smokers?
Inferring from the data: What are valid statistical methods for drawing inferences “beyond” the data you collected? In the coffee study, is the 10%–15% reduction in risk of death something that could have happened just by chance?
Drawing conclusions: Based on what you learned from your data, what conclusions can you draw? Who do you think these conclusions apply to? (Were the people in the coffee study older? Healthy? Living in cities?) Can you draw a cause-and-effect conclusion about your treatments? (Are scientists now saying that the coffee drinking is the cause of the decreased risk of death?)

Notice that the numerical analysis (“crunching numbers” on the computer) comprises only a small part of overall statistical investigation. In this section, you will see how we can answer some of these questions and what questions you should be asking about any statistical investigation you read about.

Distributional Thinking

When data are collected to address a particular question, an important first step is to think of meaningful ways to organize and examine the data. Let’s take a look at an example.

Example 1 : Researchers investigated whether cancer pamphlets are written at an appropriate level to be read and understood by cancer patients (Short, Moriarty, & Cooley, 1995). Tests of reading ability were given to 63 patients. In addition, readability level was determined for a sample of 30 pamphlets, based on characteristics such as the lengths of words and sentences in the pamphlet. The results, reported in terms of grade levels, are displayed in Figure 23.

Table showing patients' reading levels and pahmphlet's reading levels.

Data vary . More specifically, values of a variable (such as reading level of a cancer patient or readability level of a cancer pamphlet) vary.
Analyzing the pattern of variation, called the distribution of the variable, often reveals insights.

Addressing the research question of whether the cancer pamphlets are written at appropriate levels for the cancer patients requires comparing the two distributions. A naïve comparison might focus only on the centers of the distributions. Both medians turn out to be ninth grade, but considering only medians ignores the variability and the overall distributions of these data. A more illuminating approach is to compare the entire distributions, for example with a graph, as in Figure 24.

Bar graph showing that the reading level of pamphlets is typically higher than the reading level of the patients.

Figure 24 makes clear that the two distributions are not well aligned at all. The most glaring discrepancy is that many patients (17/63, or 27%, to be precise) have a reading level below that of the most readable pamphlet. These patients will need help to understand the information provided in the cancer pamphlets. Notice that this conclusion follows from considering the distributions as a whole, not simply measures of center or variability, and that the graph contrasts those distributions more immediately than the frequency tables.

Finding Significance in Data

Even when we find patterns in data, often there is still uncertainty in various aspects of the data. For example, there may be potential for measurement errors (even your own body temperature can fluctuate by almost 1°F over the course of the day). Or we may only have a “snapshot” of observations from a more long-term process or only a small subset of individuals from the population of interest. In such cases, how can we determine whether patterns we see in our small set of data is convincing evidence of a systematic phenomenon in the larger process or population? Let’s take a look at another example.

Example 2 : In a study reported in the November 2007 issue of Nature , researchers investigated whether pre-verbal infants take into account an individual’s actions toward others in evaluating that individual as appealing or aversive (Hamlin, Wynn, & Bloom, 2007). In one component of the study, 10-month-old infants were shown a “climber” character (a piece of wood with “googly” eyes glued onto it) that could not make it up a hill in two tries. Then the infants were shown two scenarios for the climber’s next try, one where the climber was pushed to the top of the hill by another character (“helper”), and one where the climber was pushed back down the hill by another character (“hinderer”). The infant was alternately shown these two scenarios several times. Then the infant was presented with two pieces of wood (representing the helper and the hinderer characters) and asked to pick one to play with.

The researchers found that of the 16 infants who made a clear choice, 14 chose to play with the helper toy. One possible explanation for this clear majority result is that the helping behavior of the one toy increases the infants’ likelihood of choosing that toy. But are there other possible explanations? What about the color of the toy? Well, prior to collecting the data, the researchers arranged so that each color and shape (red square and blue circle) would be seen by the same number of infants. Or maybe the infants had right-handed tendencies and so picked whichever toy was closer to their right hand?

Well, prior to collecting the data, the researchers arranged it so half the infants saw the helper toy on the right and half on the left. Or, maybe the shapes of these wooden characters (square, triangle, circle) had an effect? Perhaps, but again, the researchers controlled for this by rotating which shape was the helper toy, the hinderer toy, and the climber. When designing experiments, it is important to control for as many variables as might affect the responses as possible. It is beginning to appear that the researchers accounted for all the other plausible explanations. But there is one more important consideration that cannot be controlled—if we did the study again with these 16 infants, they might not make the same choices. In other words, there is some randomness inherent in their selection process.

Maybe each infant had no genuine preference at all, and it was simply “random luck” that led to 14 infants picking the helper toy. Although this random component cannot be controlled, we can apply a probability model to investigate the pattern of results that would occur in the long run if random chance were the only factor.

If the infants were equally likely to pick between the two toys, then each infant had a 50% chance of picking the helper toy. It’s like each infant tossed a coin, and if it landed heads, the infant picked the helper toy. So if we tossed a coin 16 times, could it land heads 14 times? Sure, it’s possible, but it turns out to be very unlikely. Getting 14 (or more) heads in 16 tosses is about as likely as tossing a coin and getting 9 heads in a row. This probability is referred to as a p-value . The p-value represents the likelihood that experimental results happened by chance. Within psychology, the most common standard for p-values is “p < .05”. What this means is that there is less than a 5% probability that the results happened just by random chance, and therefore a 95% probability that the results reflect a meaningful pattern in human psychology. We call this statistical significance .

So, in the study above, if we assume that each infant was choosing equally, then the probability that 14 or more out of 16 infants would choose the helper toy is found to be 0.0021. We have only two logical possibilities: either the infants have a genuine preference for the helper toy, or the infants have no preference (50/50) and an outcome that would occur only 2 times in 1,000 iterations happened in this study. Because this p-value of 0.0021 is quite small, we conclude that the study provides very strong evidence that these infants have a genuine preference for the helper toy.

If we compare the p-value to some cut-off value, like 0.05, we see that the p=value is smaller. Because the p-value is smaller than that cut-off value, then we reject the hypothesis that only random chance was at play here. In this case, these researchers would conclude that significantly more than half of the infants in the study chose the helper toy, giving strong evidence of a genuine preference for the toy with the helping behavior.

Drawing Conclusions from Statistics

Generalizability.

Photo of a diverse group of college-aged students.

One limitation to the study mentioned previously about the babies choosing the “helper” toy is that the conclusion only applies to the 16 infants in the study. We don’t know much about how those 16 infants were selected. Suppose we want to select a subset of individuals (a sample ) from a much larger group of individuals (the population ) in such a way that conclusions from the sample can be generalized to the larger population. This is the question faced by pollsters every day.

Example 3 : The General Social Survey (GSS) is a survey on societal trends conducted every other year in the United States. Based on a sample of about 2,000 adult Americans, researchers make claims about what percentage of the U.S. population consider themselves to be “liberal,” what percentage consider themselves “happy,” what percentage feel “rushed” in their daily lives, and many other issues. The key to making these claims about the larger population of all American adults lies in how the sample is selected. The goal is to select a sample that is representative of the population, and a common way to achieve this goal is to select a r andom sample that gives every member of the population an equal chance of being selected for the sample. In its simplest form, random sampling involves numbering every member of the population and then using a computer to randomly select the subset to be surveyed. Most polls don’t operate exactly like this, but they do use probability-based sampling methods to select individuals from nationally representative panels.

In 2004, the GSS reported that 817 of 977 respondents (or 83.6%) indicated that they always or sometimes feel rushed. This is a clear majority, but we again need to consider variation due to random sampling . Fortunately, we can use the same probability model we did in the previous example to investigate the probable size of this error. (Note, we can use the coin-tossing model when the actual population size is much, much larger than the sample size, as then we can still consider the probability to be the same for every individual in the sample.) This probability model predicts that the sample result will be within 3 percentage points of the population value (roughly 1 over the square root of the sample size, the margin of error. A statistician would conclude, with 95% confidence, that between 80.6% and 86.6% of all adult Americans in 2004 would have responded that they sometimes or always feel rushed.

The key to the margin of error is that when we use a probability sampling method, we can make claims about how often (in the long run, with repeated random sampling) the sample result would fall within a certain distance from the unknown population value by chance (meaning by random sampling variation) alone. Conversely, non-random samples are often suspect to bias, meaning the sampling method systematically over-represents some segments of the population and under-represents others. We also still need to consider other sources of bias, such as individuals not responding honestly. These sources of error are not measured by the margin of error.

Cause and Effect

In many research studies, the primary question of interest concerns differences between groups. Then the question becomes how were the groups formed (e.g., selecting people who already drink coffee vs. those who don’t). In some studies, the researchers actively form the groups themselves. But then we have a similar question—could any differences we observe in the groups be an artifact of that group-formation process? Or maybe the difference we observe in the groups is so large that we can discount a “fluke” in the group-formation process as a reasonable explanation for what we find?

Example 4 : A psychology study investigated whether people tend to display more creativity when they are thinking about intrinsic (internal) or extrinsic (external) motivations (Ramsey & Schafer, 2002, based on a study by Amabile, 1985). The subjects were 47 people with extensive experience with creative writing. Subjects began by answering survey questions about either intrinsic motivations for writing (such as the pleasure of self-expression) or extrinsic motivations (such as public recognition). Then all subjects were instructed to write a haiku, and those poems were evaluated for creativity by a panel of judges. The researchers conjectured beforehand that subjects who were thinking about intrinsic motivations would display more creativity than subjects who were thinking about extrinsic motivations. The creativity scores from the 47 subjects in this study are displayed in Figure 26, where higher scores indicate more creativity.

Image showing a dot for creativity scores, which vary between 5 and 27, and the types of motivation each person was given as a motivator, either extrinsic or intrinsic.

In this example, the key question is whether the type of motivation affects creativity scores. In particular, do subjects who were asked about intrinsic motivations tend to have higher creativity scores than subjects who were asked about extrinsic motivations?

Figure 26 reveals that both motivation groups saw considerable variability in creativity scores, and these scores have considerable overlap between the groups. In other words, it’s certainly not always the case that those with extrinsic motivations have higher creativity than those with intrinsic motivations, but there may still be a statistical tendency in this direction. (Psychologist Keith Stanovich (2013) refers to people’s difficulties with thinking about such probabilistic tendencies as “the Achilles heel of human cognition.”)

The mean creativity score is 19.88 for the intrinsic group, compared to 15.74 for the extrinsic group, which supports the researchers’ conjecture. Yet comparing only the means of the two groups fails to consider the variability of creativity scores in the groups. We can measure variability with statistics using, for instance, the standard deviation: 5.25 for the extrinsic group and 4.40 for the intrinsic group. The standard deviations tell us that most of the creativity scores are within about 5 points of the mean score in each group. We see that the mean score for the intrinsic group lies within one standard deviation of the mean score for extrinsic group. So, although there is a tendency for the creativity scores to be higher in the intrinsic group, on average, the difference is not extremely large.

We again want to consider possible explanations for this difference. The study only involved individuals with extensive creative writing experience. Although this limits the population to which we can generalize, it does not explain why the mean creativity score was a bit larger for the intrinsic group than for the extrinsic group. Maybe women tend to receive higher creativity scores? Here is where we need to focus on how the individuals were assigned to the motivation groups. If only women were in the intrinsic motivation group and only men in the extrinsic group, then this would present a problem because we wouldn’t know if the intrinsic group did better because of the different type of motivation or because they were women. However, the researchers guarded against such a problem by randomly assigning the individuals to the motivation groups. Like flipping a coin, each individual was just as likely to be assigned to either type of motivation. Why is this helpful? Because this random assignment tends to balance out all the variables related to creativity we can think of, and even those we don’t think of in advance, between the two groups. So we should have a similar male/female split between the two groups; we should have a similar age distribution between the two groups; we should have a similar distribution of educational background between the two groups; and so on. Random assignment should produce groups that are as similar as possible except for the type of motivation, which presumably eliminates all those other variables as possible explanations for the observed tendency for higher scores in the intrinsic group.

But does this always work? No, so by “luck of the draw” the groups may be a little different prior to answering the motivation survey. So then the question is, is it possible that an unlucky random assignment is responsible for the observed difference in creativity scores between the groups? In other words, suppose each individual’s poem was going to get the same creativity score no matter which group they were assigned to, that the type of motivation in no way impacted their score. Then how often would the random-assignment process alone lead to a difference in mean creativity scores as large (or larger) than 19.88 – 15.74 = 4.14 points?

We again want to apply to a probability model to approximate a p-value , but this time the model will be a bit different. Think of writing everyone’s creativity scores on an index card, shuffling up the index cards, and then dealing out 23 to the extrinsic motivation group and 24 to the intrinsic motivation group, and finding the difference in the group means. We (better yet, the computer) can repeat this process over and over to see how often, when the scores don’t change, random assignment leads to a difference in means at least as large as 4.41. Figure 27 shows the results from 1,000 such hypothetical random assignments for these scores.

Standard distribution in a typical bell curve.

Only 2 of the 1,000 simulated random assignments produced a difference in group means of 4.41 or larger. In other words, the approximate p-value is 2/1000 = 0.002. This small p-value indicates that it would be very surprising for the random assignment process alone to produce such a large difference in group means. Therefore, as with Example 2, we have strong evidence that focusing on intrinsic motivations tends to increase creativity scores, as compared to thinking about extrinsic motivations.

Notice that the previous statement implies a cause-and-effect relationship between motivation and creativity score; is such a strong conclusion justified? Yes, because of the random assignment used in the study. That should have balanced out any other variables between the two groups, so now that the small p-value convinces us that the higher mean in the intrinsic group wasn’t just a coincidence, the only reasonable explanation left is the difference in the type of motivation. Can we generalize this conclusion to everyone? Not necessarily—we could cautiously generalize this conclusion to individuals with extensive experience in creative writing similar the individuals in this study, but we would still want to know more about how these individuals were selected to participate.

Close-up photo of mathematical equations.

Statistical thinking involves the careful design of a study to collect meaningful data to answer a focused research question, detailed analysis of patterns in the data, and drawing conclusions that go beyond the observed data. Random sampling is paramount to generalizing results from our sample to a larger population, and random assignment is key to drawing cause-and-effect conclusions. With both kinds of randomness, probability models help us assess how much random variation we can expect in our results, in order to determine whether our results could happen by chance alone and to estimate a margin of error.

So where does this leave us with regard to the coffee study mentioned previously (the Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012 found that men who drank at least six cups of coffee a day had a 10% lower chance of dying (women 15% lower) than those who drank none)? We can answer many of the questions:

This was a 14-year study conducted by researchers at the National Cancer Institute.
The results were published in the June issue of the New England Journal of Medicine , a respected, peer-reviewed journal.
The study reviewed coffee habits of more than 402,000 people ages 50 to 71 from six states and two metropolitan areas. Those with cancer, heart disease, and stroke were excluded at the start of the study. Coffee consumption was assessed once at the start of the study.
About 52,000 people died during the course of the study.
People who drank between two and five cups of coffee daily showed a lower risk as well, but the amount of reduction increased for those drinking six or more cups.
The sample sizes were fairly large and so the p-values are quite small, even though percent reduction in risk was not extremely large (dropping from a 12% chance to about 10%–11%).
Whether coffee was caffeinated or decaffeinated did not appear to affect the results.
This was an observational study, so no cause-and-effect conclusions can be drawn between coffee drinking and increased longevity, contrary to the impression conveyed by many news headlines about this study. In particular, it’s possible that those with chronic diseases don’t tend to drink coffee.

This study needs to be reviewed in the larger context of similar studies and consistency of results across studies, with the constant caution that this was not a randomized experiment. Whereas a statistical analysis can still “adjust” for other potential confounding variables, we are not yet convinced that researchers have identified them all or completely isolated why this decrease in death risk is evident. Researchers can now take the findings of this study and develop more focused studies that address new questions.

Explore these outside resources to learn more about applied statistics:

Video about p-values: P-Value Extravaganza
Interactive web applets for teaching and learning statistics
Inter-university Consortium for Political and Social Research where you can find and analyze data.
The Consortium for the Advancement of Undergraduate Statistics
Find a recent research article in your field and answer the following: What was the primary research question? How were individuals selected to participate in the study? Were summary results provided? How strong is the evidence presented in favor or against the research question? Was random assignment used? Summarize the main conclusions from the study, addressing the issues of statistical significance, statistical confidence, generalizability, and cause and effect. Do you agree with the conclusions drawn from this study, based on the study design and the results presented?
Is it reasonable to use a random sample of 1,000 individuals to draw conclusions about all U.S. adults? Explain why or why not.

How to Read Research

In this course and throughout your academic career, you’ll be reading journal articles (meaning they were published by experts in a peer-reviewed journal) and reports that explain psychological research. It’s important to understand the format of these articles so that you can read them strategically and understand the information presented. Scientific articles vary in content or structure, depending on the type of journal to which they will be submitted. Psychological articles and many papers in the social sciences follow the writing guidelines and format dictated by the American Psychological Association (APA). In general, the structure follows: abstract, introduction, methods, results, discussion, and references.

Abstract : the abstract is the concise summary of the article. It summarizes the most important features of the manuscript, providing the reader with a global first impression on the article. It is generally just one paragraph that explains the experiment as well as a short synopsis of the results.
Introduction : this section provides background information about the origin and purpose of performing the experiment or study. It reviews previous research and presents existing theories on the topic.
Method : this section covers the methodologies used to investigate the research question, including the identification of participants , procedures , and materials as well as a description of the actual procedure . It should be sufficiently detailed to allow for replication.
Results : the results section presents key findings of the research, including reference to indicators of statistical significance.
Discussion : this section provides an interpretation of the findings, states their significance for current research, and derives implications for theory and practice. Alternative interpretations for findings are also provided, particularly when it is not possible to conclude for the directionality of the effects. In the discussion, authors also acknowledge the strengths and limitations/weaknesses of the study and offer concrete directions about for future research.

Watch this 3-minute video for an explanation on how to read scholarly articles. Look closely at the example article shared just before the two minute mark.

https://digitalcommons.coastal.edu/kimbel-library-instructional-videos/9/

Practice identifying these key components in the following experiment: Food-Induced Emotional Resonance Improves Emotion Recognition.

In this chapter, you learned to

define and apply the scientific method to psychology
describe the strengths and weaknesses of descriptive, experimental, and correlational research
define the basic elements of a statistical investigation

Putting It Together: Psychological Research

Psychologists use the scientific method to examine human behavior and mental processes. Some of the methods you learned about include descriptive, experimental, and correlational research designs.

Watch the CrashCourse video to review the material you learned, then read through the following examples and see if you can come up with your own design for each type of study.

You can view the transcript for “Psychological Research: Crash Course Psychology #2” here (opens in new window).

Case Study: a detailed analysis of a particular person, group, business, event, etc. This approach is commonly used to to learn more about rare examples with the goal of describing that particular thing.

Ted Bundy was one of America’s most notorious serial killers who murdered at least 30 women and was executed in 1989. Dr. Al Carlisle evaluated Bundy when he was first arrested and conducted a psychological analysis of Bundy’s development of his sexual fantasies merging into reality (Ramsland, 2012). Carlisle believes that there was a gradual evolution of three processes that guided his actions: fantasy, dissociation, and compartmentalization (Ramsland, 2012). Read Imagining Ted Bundy (http://goo.gl/rGqcUv) for more information on this case study.

Naturalistic Observation : a researcher unobtrusively collects information without the participant’s awareness.

Drain and Engelhardt (2013) observed six nonverbal children with autism’s evoked and spontaneous communicative acts. Each of the children attended a school for children with autism and were in different classes. They were observed for 30 minutes of each school day. By observing these children without them knowing, they were able to see true communicative acts without any external influences.

Survey : participants are asked to provide information or responses to questions on a survey or structure assessment.

Educational psychologists can ask students to report their grade point average and what, if anything, they eat for breakfast on an average day. A healthy breakfast has been associated with better academic performance (Digangi’s 1999).
Anderson (1987) tried to find the relationship between uncomfortably hot temperatures and aggressive behavior, which was then looked at with two studies done on violent and nonviolent crime. Based on previous research that had been done by Anderson and Anderson (1984), it was predicted that violent crimes would be more prevalent during the hotter time of year and the years in which it was hotter weather in general. The study confirmed this prediction.

Longitudinal Study: researchers recruit a sample of participants and track them for an extended period of time.

In a study of a representative sample of 856 children Eron and his colleagues (1972) found that a boy’s exposure to media violence at age eight was significantly related to his aggressive behavior ten years later, after he graduated from high school.

Cross-Sectional Study: researchers gather participants from different groups (commonly different ages) and look for differences between the groups.

In 1996, Russell surveyed people of varying age groups and found that people in their 20s tend to report being more lonely than people in their 70s.

Correlational Design: two different variables are measured to determine whether there is a relationship between them.

Thornhill et al. (2003) had people rate how physically attractive they found other people to be. They then had them separately smell t-shirts those people had worn (without knowing which clothes belonged to whom) and rate how good or bad their body oder was. They found that the more attractive someone was the more pleasant their body order was rated to be.
Clinical psychologists can test a new pharmaceutical treatment for depression by giving some patients the new pill and others an already-tested one to see which is the more effective treatment.

American Cancer Society. (n.d.). History of the cancer prevention studies. Retrieved from http://www.cancer.org/research/researchtopreventcancer/history-cancer-prevention-study

American Psychological Association. (2009). Publication Manual of the American Psychological Association (6th ed.). Washington, DC: Author.

American Psychological Association. (n.d.). Research with animals in psychology. Retrieved from https://www.apa.org/research/responsible/research-animals.pdf

Arnett, J. (2008). The neglected 95%: Why American psychology needs to become less American. American Psychologist, 63(7), 602–614.

Barton, B. A., Eldridge, A. L., Thompson, D., Affenito, S. G., Striegel-Moore, R. H., Franko, D. L., . . . Crockett, S. J. (2005). The relationship of breakfast and cereal consumption to nutrient intake and body mass index: The national heart, lung, and blood institute growth and health study. Journal of the American Dietetic Association, 105(9), 1383–1389. Retrieved from http://dx.doi.org/10.1016/j.jada.2005.06.003

Chwalisz, K., Diener, E., & Gallagher, D. (1988). Autonomic arousal feedback and emotional experience: Evidence from the spinal cord injured. Journal of Personality and Social Psychology, 54, 820–828.

Dominus, S. (2011, May 25). Could conjoined twins share a mind? New York Times Sunday Magazine. Retrieved from http://www.nytimes.com/2011/05/29/magazine/could-conjoined-twins-share-a-mind.html?_r=5&hp&

Fanger, S. M., Frankel, L. A., & Hazen, N. (2012). Peer exclusion in preschool children’s play: Naturalistic observations in a playground setting. Merrill-Palmer Quarterly, 58, 224–254.

Fiedler, K. (2004). Illusory correlation. In R. F. Pohl (Ed.), Cognitive illusions: A handbook on fallacies and biases in thinking, judgment and memory (pp. 97–114). New York, NY: Psychology Press.

Frantzen, L. B., Treviño, R. P., Echon, R. M., Garcia-Dominic, O., & DiMarco, N. (2013). Association between frequency of ready-to-eat cereal consumption, nutrient intakes, and body mass index in fourth- to sixth-grade low-income minority children. Journal of the Academy of Nutrition and Dietetics, 113(4), 511–519.

Harper, J. (2013, July 5). Ice cream and crime: Where cold cuisine and hot disputes intersect. The Times-Picaune. Retrieved from http://www.nola.com/crime/index.ssf/2013/07/ice_cream_and_crime_where_hot.html

Jenkins, W. J., Ruppel, S. E., Kizer, J. B., Yehl, J. L., & Griffin, J. L. (2012). An examination of post 9-11 attitudes towards Arab Americans. North American Journal of Psychology, 14, 77–84.

Jones, J. M. (2013, May 13). Same-sex marriage support solidifies above 50% in U.S. Gallup Politics. Retrieved from http://www.gallup.com/poll/162398/sex-marriage-support-solidifies-above.aspx

Kobrin, J. L., Patterson, B. F., Shaw, E. J., Mattern, K. D., & Barbuti, S. M. (2008). Validity of the SAT for predicting first-year college grade point average (Research Report No. 2008-5). Retrieved from https://research.collegeboard.org/sites/default/files/publications/2012/7/researchreport-2008-5-validity-sat-predicting-first-year-college-grade-point-average.pdf

Lewin, T. (2014, March 5). A new SAT aims to realign with schoolwork. New York Times. Retreived from http://www.nytimes.com/2014/03/06/education/major-changes-in-sat-announced-by-college-board.html.

Lowry, M., Dean, K., & Manders, K. (2010). The link between sleep quantity and academic performance for the college student. Sentience: The University of Minnesota Undergraduate Journal of Psychology, 3(Spring), 16–19. Retrieved from http://www.psych.umn.edu/sentience/files/SENTIENCE_Vol3.pdf

McKie, R. (2010, June 26). Chimps with everything: Jane Goodall’s 50 years in the jungle. The Guardian. Retrieved from http://www.theguardian.com/science/2010/jun/27/jane-goodall-chimps-africa-interview

Offit, P. (2008). Autism’s false prophets: Bad science, risky medicine, and the search for a cure. New York: Columbia University Press.

Perkins, H. W., Haines, M. P., & Rice, R. (2005). Misperceiving the college drinking norm and related problems: A nationwide study of exposure to prevention information, perceived norms and student alcohol misuse. J. Stud. Alcohol, 66(4), 470–478.

Rimer, S. (2008, September 21). College panel calls for less focus on SATs. The New York Times. Retrieved from http://www.nytimes.com/2008/09/22/education/22admissions.html?_r=0

Rothstein, J. M. (2004). College performance predictions and the SAT. Journal of Econometrics, 121, 297–317.

Rotton, J., & Kelly, I. W. (1985). Much ado about the full moon: A meta-analysis of lunar-lunacy research. Psychological Bulletin, 97(2), 286–306. doi:10.1037/0033-2909.97.2.286

Santelices, M. V., & Wilson, M. (2010). Unfair treatment? The case of Freedle, the SAT, and the standardization approach to differential item functioning. Harvard Education Review, 80, 106–134.

Sears, D. O. (1986). College sophomores in the laboratory: Influences of a narrow data base on social psychology’s view of human nature. Journal of Personality and Social Psychology, 51, 515–530.

Tuskegee University. (n.d.). About the USPHS Syphilis Study. Retrieved from http://www.tuskegee.edu/about_us/centers_of_excellence/bioethics_center/about_the_usphs_syphilis_study.aspx.

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grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing

well-developed set of ideas that propose an explanation for observed phenomena

(plural: hypotheses) tentative and testable statement about the relationship between two or more variables

an experiment must be replicable by another researcher

implies that a theory should enable us to make predictions about future events

able to be disproven by experimental results

implies that all data must be considered when evaluating a hypothesis

committee of administrators, scientists, and community members that reviews proposals for research involving human participants

process of informing a research participant about what to expect during an experiment, any risks involved, and the implications of the research, and then obtaining the person’s consent to participate

purposely misleading experiment participants in order to maintain the integrity of the experiment

when an experiment involved deception, participants are told complete and truthful information about the experiment at its conclusion

committee of administrators, scientists, veterinarians, and community members that reviews proposals for research involving non-human animals

research studies that do not test specific relationships between variables

research investigating the relationship between two or more variables

research method that uses hypothesis testing to make inferences about how one variable impacts and causes another

observation of behavior in its natural setting

inferring that the results for a sample apply to the larger population

when observations may be skewed to align with observer expectations

measure of agreement among observers on how they record and classify a particular event

observational research study focusing on one or a few people

list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

subset of individuals selected from the larger population

overall group of individuals that the researchers are interested in

method of research using past records or data sets to answer various research questions, or to search for interesting patterns or relationships

studies in which the same group of individuals is surveyed or measured repeatedly over an extended period of time

compares multiple segments of a population at a single time

reduction in number of research participants as some drop out of the study over time

relationship between two or more variables; when two variables are correlated, one variable changes as the other does

number from -1 to +1, indicating the strength and direction of the relationship between variables, and usually represented by r

two variables change in the same direction, both becoming either larger or smaller

two variables change in different directions, with one becoming larger as the other becomes smaller; a negative correlation is not the same thing as no correlation

changes in one variable cause the changes in the other variable; can be determined only through an experimental research design

unanticipated outside factor that affects both variables of interest, often giving the false impression that changes in one variable causes changes in the other variable, when, in actuality, the outside factor causes changes in both variables

seeing relationships between two things when in reality no such relationship exists

tendency to ignore evidence that disproves ideas or beliefs

group designed to answer the research question; experimental manipulation is the only difference between the experimental and control groups, so any differences between the two are due to experimental manipulation rather than chance

serves as a basis for comparison and controls for chance factors that might influence the results of the study—by holding such factors constant across groups so that the experimental manipulation is the only difference between groups

description of what actions and operations will be used to measure the dependent variables and manipulate the independent variables

researcher expectations skew the results of the study

experiment in which the researcher knows which participants are in the experimental group and which are in the control group

experiment in which both the researchers and the participants are blind to group assignments

people's expectations or beliefs influencing or determining their experience in a given situation

variable that is influenced or controlled by the experimenter; in a sound experimental study, the independent variable is the only important difference between the experimental and control group

variable that the researcher measures to see how much effect the independent variable had

subjects of psychological research

subset of a larger population in which every member of the population has an equal chance of being selected

method of experimental group assignment in which all participants have an equal chance of being assigned to either group

consistency and reproducibility of a given result

accuracy of a given result in measuring what it is designed to measure

determines how likely any difference between experimental groups is due to chance

statistical probability that represents the likelihood that experimental results happened by chance

Psychological Science is the scientific study of mind, brain, and behavior. We will explore what it means to be human in this class. It has never been more important for us to understand what makes people tick, how to evaluate information critically, and the importance of history. Psychology can also help you in your future career; indeed, there are very little jobs out there with no human interaction!

Because psychology is a science, we analyze human behavior through the scientific method. There are several ways to investigate human phenomena, such as observation, experiments, and more. We will discuss the basics, pros and cons of each! We will also dig deeper into the important ethical guidelines that psychologists must follow in order to do research. Lastly, we will briefly introduce ourselves to statistics, the language of scientific research. While reading the content in these chapters, try to find examples of material that can fit with the themes of the course.

To get us started:

The study of the mind moved away Introspection to reaction time studies as we learned more about empiricism
Psychologists work in careers outside of the typical "clinician" role. We advise in human factors, education, policy, and more!
While completing an observation study, psychologists will work to aggregate common themes to explain the behavior of the group (sample) as a whole. In doing so, we still allow for normal variation from the group!
The IRB and IACUC are important in ensuring ethics are maintained for both human and animal subjects

Psychological Science: Understanding Human Behavior Copyright © by Karenna Malavanti is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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9 Survey research

Survey research is a research method involving the use of standardised questionnaires or interviews to collect data about people and their preferences, thoughts, and behaviours in a systematic manner. Although census surveys were conducted as early as Ancient Egypt, survey as a formal research method was pioneered in the 1930–40s by sociologist Paul Lazarsfeld to examine the effects of the radio on political opinion formation of the United States. This method has since become a very popular method for quantitative research in the social sciences.

The survey method can be used for descriptive, exploratory, or explanatory research. This method is best suited for studies that have individual people as the unit of analysis. Although other units of analysis, such as groups, organisations or dyads—pairs of organisations, such as buyers and sellers—are also studied using surveys, such studies often use a specific person from each unit as a ‘key informant’ or a ‘proxy’ for that unit. Consequently, such surveys may be subject to respondent bias if the chosen informant does not have adequate knowledge or has a biased opinion about the phenomenon of interest. For instance, Chief Executive Officers may not adequately know employees’ perceptions or teamwork in their own companies, and may therefore be the wrong informant for studies of team dynamics or employee self-esteem.

Survey research has several inherent strengths compared to other research methods. First, surveys are an excellent vehicle for measuring a wide variety of unobservable data, such as people’s preferences (e.g., political orientation), traits (e.g., self-esteem), attitudes (e.g., toward immigrants), beliefs (e.g., about a new law), behaviours (e.g., smoking or drinking habits), or factual information (e.g., income). Second, survey research is also ideally suited for remotely collecting data about a population that is too large to observe directly. A large area—such as an entire country—can be covered by postal, email, or telephone surveys using meticulous sampling to ensure that the population is adequately represented in a small sample. Third, due to their unobtrusive nature and the ability to respond at one’s convenience, questionnaire surveys are preferred by some respondents. Fourth, interviews may be the only way of reaching certain population groups such as the homeless or illegal immigrants for which there is no sampling frame available. Fifth, large sample surveys may allow detection of small effects even while analysing multiple variables, and depending on the survey design, may also allow comparative analysis of population subgroups (i.e., within-group and between-group analysis). Sixth, survey research is more economical in terms of researcher time, effort and cost than other methods such as experimental research and case research. At the same time, survey research also has some unique disadvantages. It is subject to a large number of biases such as non-response bias, sampling bias, social desirability bias, and recall bias, as discussed at the end of this chapter.

Depending on how the data is collected, survey research can be divided into two broad categories: questionnaire surveys (which may be postal, group-administered, or online surveys), and interview surveys (which may be personal, telephone, or focus group interviews). Questionnaires are instruments that are completed in writing by respondents, while interviews are completed by the interviewer based on verbal responses provided by respondents. As discussed below, each type has its own strengths and weaknesses in terms of their costs, coverage of the target population, and researcher’s flexibility in asking questions.

Questionnaire surveys

Invented by Sir Francis Galton, a questionnaire is a research instrument consisting of a set of questions (items) intended to capture responses from respondents in a standardised manner. Questions may be unstructured or structured. Unstructured questions ask respondents to provide a response in their own words, while structured questions ask respondents to select an answer from a given set of choices. Subjects’ responses to individual questions (items) on a structured questionnaire may be aggregated into a composite scale or index for statistical analysis. Questions should be designed in such a way that respondents are able to read, understand, and respond to them in a meaningful way, and hence the survey method may not be appropriate or practical for certain demographic groups such as children or the illiterate.

Most questionnaire surveys tend to be self-administered postal surveys , where the same questionnaire is posted to a large number of people, and willing respondents can complete the survey at their convenience and return it in prepaid envelopes. Postal surveys are advantageous in that they are unobtrusive and inexpensive to administer, since bulk postage is cheap in most countries. However, response rates from postal surveys tend to be quite low since most people ignore survey requests. There may also be long delays (several months) in respondents’ completing and returning the survey, or they may even simply lose it. Hence, the researcher must continuously monitor responses as they are being returned, track and send non-respondents repeated reminders (two or three reminders at intervals of one to one and a half months is ideal). Questionnaire surveys are also not well-suited for issues that require clarification on the part of the respondent or those that require detailed written responses. Longitudinal designs can be used to survey the same set of respondents at different times, but response rates tend to fall precipitously from one survey to the next.

A second type of survey is a group-administered questionnaire . A sample of respondents is brought together at a common place and time, and each respondent is asked to complete the survey questionnaire while in that room. Respondents enter their responses independently without interacting with one another. This format is convenient for the researcher, and a high response rate is assured. If respondents do not understand any specific question, they can ask for clarification. In many organisations, it is relatively easy to assemble a group of employees in a conference room or lunch room, especially if the survey is approved by corporate executives.

A more recent type of questionnaire survey is an online or web survey. These surveys are administered over the Internet using interactive forms. Respondents may receive an email request for participation in the survey with a link to a website where the survey may be completed. Alternatively, the survey may be embedded into an email, and can be completed and returned via email. These surveys are very inexpensive to administer, results are instantly recorded in an online database, and the survey can be easily modified if needed. However, if the survey website is not password-protected or designed to prevent multiple submissions, the responses can be easily compromised. Furthermore, sampling bias may be a significant issue since the survey cannot reach people who do not have computer or Internet access, such as many of the poor, senior, and minority groups, and the respondent sample is skewed toward a younger demographic who are online much of the time and have the time and ability to complete such surveys. Computing the response rate may be problematic if the survey link is posted on LISTSERVs or bulletin boards instead of being emailed directly to targeted respondents. For these reasons, many researchers prefer dual-media surveys (e.g., postal survey and online survey), allowing respondents to select their preferred method of response.

Constructing a survey questionnaire is an art. Numerous decisions must be made about the content of questions, their wording, format, and sequencing, all of which can have important consequences for the survey responses.

Response formats. Survey questions may be structured or unstructured. Responses to structured questions are captured using one of the following response formats:

Dichotomous response , where respondents are asked to select one of two possible choices, such as true/false, yes/no, or agree/disagree. An example of such a question is: Do you think that the death penalty is justified under some circumstances? (circle one): yes / no.

Nominal response , where respondents are presented with more than two unordered options, such as: What is your industry of employment?: manufacturing / consumer services / retail / education / healthcare / tourism and hospitality / other.

Ordinal response , where respondents have more than two ordered options, such as: What is your highest level of education?: high school / bachelor’s degree / postgraduate degree.

Interval-level response , where respondents are presented with a 5-point or 7-point Likert scale, semantic differential scale, or Guttman scale. Each of these scale types were discussed in a previous chapter.

Continuous response , where respondents enter a continuous (ratio-scaled) value with a meaningful zero point, such as their age or tenure in a firm. These responses generally tend to be of the fill-in-the blanks type.

Question content and wording. Responses obtained in survey research are very sensitive to the types of questions asked. Poorly framed or ambiguous questions will likely result in meaningless responses with very little value. Dillman (1978) [1] recommends several rules for creating good survey questions. Every single question in a survey should be carefully scrutinised for the following issues:

Is the question clear and understandable ?: Survey questions should be stated in very simple language, preferably in active voice, and without complicated words or jargon that may not be understood by a typical respondent. All questions in the questionnaire should be worded in a similar manner to make it easy for respondents to read and understand them. The only exception is if your survey is targeted at a specialised group of respondents, such as doctors, lawyers and researchers, who use such jargon in their everyday environment. Is the question worded in a negative manner ?: Negatively worded questions such as ‘Should your local government not raise taxes?’ tend to confuse many respondents and lead to inaccurate responses. Double-negatives should be avoided when designing survey questions.

Is the question ambiguous ?: Survey questions should not use words or expressions that may be interpreted differently by different respondents (e.g., words like ‘any’ or ‘just’). For instance, if you ask a respondent, ‘What is your annual income?’, it is unclear whether you are referring to salary/wages, or also dividend, rental, and other income, whether you are referring to personal income, family income (including spouse’s wages), or personal and business income. Different interpretation by different respondents will lead to incomparable responses that cannot be interpreted correctly.

Does the question have biased or value-laden words ?: Bias refers to any property of a question that encourages subjects to answer in a certain way. Kenneth Rasinky (1989) [2] examined several studies on people’s attitude toward government spending, and observed that respondents tend to indicate stronger support for ‘assistance to the poor’ and less for ‘welfare’, even though both terms had the same meaning. In this study, more support was also observed for ‘halting rising crime rate’ and less for ‘law enforcement’, more for ‘solving problems of big cities’ and less for ‘assistance to big cities’, and more for ‘dealing with drug addiction’ and less for ‘drug rehabilitation’. A biased language or tone tends to skew observed responses. It is often difficult to anticipate in advance the biasing wording, but to the greatest extent possible, survey questions should be carefully scrutinised to avoid biased language.

Is the question double-barrelled ?: Double-barrelled questions are those that can have multiple answers. For example, ‘Are you satisfied with the hardware and software provided for your work?’. In this example, how should a respondent answer if they are satisfied with the hardware, but not with the software, or vice versa? It is always advisable to separate double-barrelled questions into separate questions: ‘Are you satisfied with the hardware provided for your work?’, and ’Are you satisfied with the software provided for your work?’. Another example: ‘Does your family favour public television?’. Some people may favour public TV for themselves, but favour certain cable TV programs such as Sesame Street for their children.

Is the question too general ?: Sometimes, questions that are too general may not accurately convey respondents’ perceptions. If you asked someone how they liked a certain book and provided a response scale ranging from ‘not at all’ to ‘extremely well’, if that person selected ‘extremely well’, what do they mean? Instead, ask more specific behavioural questions, such as, ‘Will you recommend this book to others, or do you plan to read other books by the same author?’. Likewise, instead of asking, ‘How big is your firm?’ (which may be interpreted differently by respondents), ask, ‘How many people work for your firm?’, and/or ‘What is the annual revenue of your firm?’, which are both measures of firm size.

Is the question too detailed ?: Avoid unnecessarily detailed questions that serve no specific research purpose. For instance, do you need the age of each child in a household, or is just the number of children in the household acceptable? However, if unsure, it is better to err on the side of details than generality.

Is the question presumptuous ?: If you ask, ‘What do you see as the benefits of a tax cut?’, you are presuming that the respondent sees the tax cut as beneficial. Many people may not view tax cuts as being beneficial, because tax cuts generally lead to lesser funding for public schools, larger class sizes, and fewer public services such as police, ambulance, and fire services. Avoid questions with built-in presumptions.

Is the question imaginary ?: A popular question in many television game shows is, ‘If you win a million dollars on this show, how will you spend it?’. Most respondents have never been faced with such an amount of money before and have never thought about it—they may not even know that after taxes, they will get only about $640,000 or so in the United States, and in many cases, that amount is spread over a 20-year period—and so their answers tend to be quite random, such as take a tour around the world, buy a restaurant or bar, spend on education, save for retirement, help parents or children, or have a lavish wedding. Imaginary questions have imaginary answers, which cannot be used for making scientific inferences.

Do respondents have the information needed to correctly answer the question ?: Oftentimes, we assume that subjects have the necessary information to answer a question, when in reality, they do not. Even if a response is obtained, these responses tend to be inaccurate given the subjects’ lack of knowledge about the question being asked. For instance, we should not ask the CEO of a company about day-to-day operational details that they may not be aware of, or ask teachers about how much their students are learning, or ask high-schoolers, ‘Do you think the US Government acted appropriately in the Bay of Pigs crisis?’.

Question sequencing. In general, questions should flow logically from one to the next. To achieve the best response rates, questions should flow from the least sensitive to the most sensitive, from the factual and behavioural to the attitudinal, and from the more general to the more specific. Some general rules for question sequencing:

Start with easy non-threatening questions that can be easily recalled. Good options are demographics (age, gender, education level) for individual-level surveys and firmographics (employee count, annual revenues, industry) for firm-level surveys.

Never start with an open ended question.

If following a historical sequence of events, follow a chronological order from earliest to latest.

Ask about one topic at a time. When switching topics, use a transition, such as, ‘The next section examines your opinions about…’

Use filter or contingency questions as needed, such as, ‘If you answered “yes” to question 5, please proceed to Section 2. If you answered “no” go to Section 3′.

Other golden rules . Do unto your respondents what you would have them do unto you. Be attentive and appreciative of respondents’ time, attention, trust, and confidentiality of personal information. Always practice the following strategies for all survey research:

People’s time is valuable. Be respectful of their time. Keep your survey as short as possible and limit it to what is absolutely necessary. Respondents do not like spending more than 10-15 minutes on any survey, no matter how important it is. Longer surveys tend to dramatically lower response rates.

Always assure respondents about the confidentiality of their responses, and how you will use their data (e.g., for academic research) and how the results will be reported (usually, in the aggregate).

For organisational surveys, assure respondents that you will send them a copy of the final results, and make sure that you follow up with your promise.

Thank your respondents for their participation in your study.

Finally, always pretest your questionnaire, at least using a convenience sample, before administering it to respondents in a field setting. Such pretesting may uncover ambiguity, lack of clarity, or biases in question wording, which should be eliminated before administering to the intended sample.

Interview survey

Interviews are a more personalised data collection method than questionnaires, and are conducted by trained interviewers using the same research protocol as questionnaire surveys (i.e., a standardised set of questions). However, unlike a questionnaire, the interview script may contain special instructions for the interviewer that are not seen by respondents, and may include space for the interviewer to record personal observations and comments. In addition, unlike postal surveys, the interviewer has the opportunity to clarify any issues raised by the respondent or ask probing or follow-up questions. However, interviews are time-consuming and resource-intensive. Interviewers need special interviewing skills as they are considered to be part of the measurement instrument, and must proactively strive not to artificially bias the observed responses.

The most typical form of interview is a personal or face-to-face interview , where the interviewer works directly with the respondent to ask questions and record their responses. Personal interviews may be conducted at the respondent’s home or office location. This approach may even be favoured by some respondents, while others may feel uncomfortable allowing a stranger into their homes. However, skilled interviewers can persuade respondents to co-operate, dramatically improving response rates.

A variation of the personal interview is a group interview, also called a focus group . In this technique, a small group of respondents (usually 6–10 respondents) are interviewed together in a common location. The interviewer is essentially a facilitator whose job is to lead the discussion, and ensure that every person has an opportunity to respond. Focus groups allow deeper examination of complex issues than other forms of survey research, because when people hear others talk, it often triggers responses or ideas that they did not think about before. However, focus group discussion may be dominated by a dominant personality, and some individuals may be reluctant to voice their opinions in front of their peers or superiors, especially while dealing with a sensitive issue such as employee underperformance or office politics. Because of their small sample size, focus groups are usually used for exploratory research rather than descriptive or explanatory research.

A third type of interview survey is a telephone interview . In this technique, interviewers contact potential respondents over the phone, typically based on a random selection of people from a telephone directory, to ask a standard set of survey questions. A more recent and technologically advanced approach is computer-assisted telephone interviewing (CATI). This is increasing being used by academic, government, and commercial survey researchers. Here the interviewer is a telephone operator who is guided through the interview process by a computer program displaying instructions and questions to be asked. The system also selects respondents randomly using a random digit dialling technique, and records responses using voice capture technology. Once respondents are on the phone, higher response rates can be obtained. This technique is not ideal for rural areas where telephone density is low, and also cannot be used for communicating non-audio information such as graphics or product demonstrations.

Role of interviewer. The interviewer has a complex and multi-faceted role in the interview process, which includes the following tasks:

Prepare for the interview: Since the interviewer is in the forefront of the data collection effort, the quality of data collected depends heavily on how well the interviewer is trained to do the job. The interviewer must be trained in the interview process and the survey method, and also be familiar with the purpose of the study, how responses will be stored and used, and sources of interviewer bias. They should also rehearse and time the interview prior to the formal study.

Locate and enlist the co-operation of respondents: Particularly in personal, in-home surveys, the interviewer must locate specific addresses, and work around respondents’ schedules at sometimes undesirable times such as during weekends. They should also be like a salesperson, selling the idea of participating in the study.

Motivate respondents: Respondents often feed off the motivation of the interviewer. If the interviewer is disinterested or inattentive, respondents will not be motivated to provide useful or informative responses either. The interviewer must demonstrate enthusiasm about the study, communicate the importance of the research to respondents, and be attentive to respondents’ needs throughout the interview.

Clarify any confusion or concerns: Interviewers must be able to think on their feet and address unanticipated concerns or objections raised by respondents to the respondents’ satisfaction. Additionally, they should ask probing questions as necessary even if such questions are not in the script.

Observe quality of response: The interviewer is in the best position to judge the quality of information collected, and may supplement responses obtained using personal observations of gestures or body language as appropriate.

Conducting the interview. Before the interview, the interviewer should prepare a kit to carry to the interview session, consisting of a cover letter from the principal investigator or sponsor, adequate copies of the survey instrument, photo identification, and a telephone number for respondents to call to verify the interviewer’s authenticity. The interviewer should also try to call respondents ahead of time to set up an appointment if possible. To start the interview, they should speak in an imperative and confident tone, such as, ‘I’d like to take a few minutes of your time to interview you for a very important study’, instead of, ‘May I come in to do an interview?’. They should introduce themself, present personal credentials, explain the purpose of the study in one to two sentences, and assure respondents that their participation is voluntary, and their comments are confidential, all in less than a minute. No big words or jargon should be used, and no details should be provided unless specifically requested. If the interviewer wishes to record the interview, they should ask for respondents’ explicit permission before doing so. Even if the interview is recorded, the interviewer must take notes on key issues, probes, or verbatim phrases

During the interview, the interviewer should follow the questionnaire script and ask questions exactly as written, and not change the words to make the question sound friendlier. They should also not change the order of questions or skip any question that may have been answered earlier. Any issues with the questions should be discussed during rehearsal prior to the actual interview sessions. The interviewer should not finish the respondent’s sentences. If the respondent gives a brief cursory answer, the interviewer should probe the respondent to elicit a more thoughtful, thorough response. Some useful probing techniques are:

The silent probe: Just pausing and waiting without going into the next question may suggest to respondents that the interviewer is waiting for more detailed response.

Overt encouragement: An occasional ‘uh-huh’ or ‘okay’ may encourage the respondent to go into greater details. However, the interviewer must not express approval or disapproval of what the respondent says.

Ask for elaboration: Such as, ‘Can you elaborate on that?’ or ‘A minute ago, you were talking about an experience you had in high school. Can you tell me more about that?’.

Reflection: The interviewer can try the psychotherapist’s trick of repeating what the respondent said. For instance, ‘What I’m hearing is that you found that experience very traumatic’ and then pause and wait for the respondent to elaborate.

After the interview is completed, the interviewer should thank respondents for their time, tell them when to expect the results, and not leave hastily. Immediately after leaving, they should write down any notes or key observations that may help interpret the respondent’s comments better.

Biases in survey research

Despite all of its strengths and advantages, survey research is often tainted with systematic biases that may invalidate some of the inferences derived from such surveys. Five such biases are the non-response bias, sampling bias, social desirability bias, recall bias, and common method bias.

Non-response bias. Survey research is generally notorious for its low response rates. A response rate of 15-20 per cent is typical in a postal survey, even after two or three reminders. If the majority of the targeted respondents fail to respond to a survey, this may indicate a systematic reason for the low response rate, which may in turn raise questions about the validity of the study’s results. For instance, dissatisfied customers tend to be more vocal about their experience than satisfied customers, and are therefore more likely to respond to questionnaire surveys or interview requests than satisfied customers. Hence, any respondent sample is likely to have a higher proportion of dissatisfied customers than the underlying population from which it is drawn. In this instance, not only will the results lack generalisability, but the observed outcomes may also be an artefact of the biased sample. Several strategies may be employed to improve response rates:

Advance notification: Sending a short letter to the targeted respondents soliciting their participation in an upcoming survey can prepare them in advance and improve their propensity to respond. The letter should state the purpose and importance of the study, mode of data collection (e.g., via a phone call, a survey form in the mail, etc.), and appreciation for their co-operation. A variation of this technique may be to ask the respondent to return a prepaid postcard indicating whether or not they are willing to participate in the study.

Relevance of content: People are more likely to respond to surveys examining issues of relevance or importance to them.

Respondent-friendly questionnaire: Shorter survey questionnaires tend to elicit higher response rates than longer questionnaires. Furthermore, questions that are clear, non-offensive, and easy to respond tend to attract higher response rates.

Endorsement: For organisational surveys, it helps to gain endorsement from a senior executive attesting to the importance of the study to the organisation. Such endorsement can be in the form of a cover letter or a letter of introduction, which can improve the researcher’s credibility in the eyes of the respondents.

Follow-up requests: Multiple follow-up requests may coax some non-respondents to respond, even if their responses are late.

Interviewer training: Response rates for interviews can be improved with skilled interviewers trained in how to request interviews, use computerised dialling techniques to identify potential respondents, and schedule call-backs for respondents who could not be reached.

Incentives : Incentives in the form of cash or gift cards, giveaways such as pens or stress balls, entry into a lottery, draw or contest, discount coupons, promise of contribution to charity, and so forth may increase response rates.

Non-monetary incentives: Businesses, in particular, are more prone to respond to non-monetary incentives than financial incentives. An example of such a non-monetary incentive is a benchmarking report comparing the business’s individual response against the aggregate of all responses to a survey.

Confidentiality and privacy: Finally, assurances that respondents’ private data or responses will not fall into the hands of any third party may help improve response rates

Sampling bias. Telephone surveys conducted by calling a random sample of publicly available telephone numbers will systematically exclude people with unlisted telephone numbers, mobile phone numbers, and people who are unable to answer the phone when the survey is being conducted—for instance, if they are at work—and will include a disproportionate number of respondents who have landline telephone services with listed phone numbers and people who are home during the day, such as the unemployed, the disabled, and the elderly. Likewise, online surveys tend to include a disproportionate number of students and younger people who are constantly on the Internet, and systematically exclude people with limited or no access to computers or the Internet, such as the poor and the elderly. Similarly, questionnaire surveys tend to exclude children and the illiterate, who are unable to read, understand, or meaningfully respond to the questionnaire. A different kind of sampling bias relates to sampling the wrong population, such as asking teachers (or parents) about their students’ (or children’s) academic learning, or asking CEOs about operational details in their company. Such biases make the respondent sample unrepresentative of the intended population and hurt generalisability claims about inferences drawn from the biased sample.

Social desirability bias . Many respondents tend to avoid negative opinions or embarrassing comments about themselves, their employers, family, or friends. With negative questions such as, ‘Do you think that your project team is dysfunctional?’, ‘Is there a lot of office politics in your workplace?’, ‘Or have you ever illegally downloaded music files from the Internet?’, the researcher may not get truthful responses. This tendency among respondents to ‘spin the truth’ in order to portray themselves in a socially desirable manner is called the ‘social desirability bias’, which hurts the validity of responses obtained from survey research. There is practically no way of overcoming the social desirability bias in a questionnaire survey, but in an interview setting, an astute interviewer may be able to spot inconsistent answers and ask probing questions or use personal observations to supplement respondents’ comments.

Recall bias. Responses to survey questions often depend on subjects’ motivation, memory, and ability to respond. Particularly when dealing with events that happened in the distant past, respondents may not adequately remember their own motivations or behaviours, or perhaps their memory of such events may have evolved with time and no longer be retrievable. For instance, if a respondent is asked to describe his/her utilisation of computer technology one year ago, or even memorable childhood events like birthdays, their response may not be accurate due to difficulties with recall. One possible way of overcoming the recall bias is by anchoring the respondent’s memory in specific events as they happened, rather than asking them to recall their perceptions and motivations from memory.

Common method bias. Common method bias refers to the amount of spurious covariance shared between independent and dependent variables that are measured at the same point in time, such as in a cross-sectional survey, using the same instrument, such as a questionnaire. In such cases, the phenomenon under investigation may not be adequately separated from measurement artefacts. Standard statistical tests are available to test for common method bias, such as Harmon’s single-factor test (Podsakoff, MacKenzie, Lee & Podsakoff, 2003), [3] Lindell and Whitney’s (2001) [4] market variable technique, and so forth. This bias can potentially be avoided if the independent and dependent variables are measured at different points in time using a longitudinal survey design, or if these variables are measured using different methods, such as computerised recording of dependent variable versus questionnaire-based self-rating of independent variables.

Dillman, D. (1978). Mail and telephone surveys: The total design method . New York: Wiley. ↵
Rasikski, K. (1989). The effect of question wording on public support for government spending. Public Opinion Quarterly , 53(3), 388–394. ↵
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology , 88(5), 879–903. http://dx.doi.org/10.1037/0021-9010.88.5.879. ↵
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology , 86(1), 114–121. ↵

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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2.2 Correlational Research Methods

Learning objectives.

By the end of this section, you will be able to:

Describe the different research methods used by psychologists
Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research
Compare longitudinal and cross-sectional approaches to research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied. These can be a series of simple questions, in-depth interviews, or well-controlled experiments.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data, which is discussed later. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

All of the above research methods are correlational designs. This means that researchers can speak to important relationships that might exist between two or more variables of interest. However, correlational data cannot be used to make claims about cause-and-effect relationships because there is no experimental manipulation. In experimental research, which will be discussed later in this chapter, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the generalizability of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

A few of the research designs which psychologists use are discussed in the next few sections. Each research method has it’s own advantages and drawbacks, and is best suited for different types of research questions. When designing a study, it’s important to consider the strengths and weaknesses to design the most valid and reliable study possible.

CLINICAL OR CASE STUDIES

This connection means that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

NATURALISTIC OBSERVATION

For example, to better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

When people know they are being watched, they are less likely to behave naturally. Think about how your driving behavior down a deserted highway during the middle of the day might change if you are suddenly being followed by a police car (figure below).

Seeing a police car behind you would probably affect your driving behavior. (credit: Michael Gil)

The anthropologist Jane Goodall , for example, spent nearly five decades observing the behavior of chimpanzees in Africa (figure below). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

(a) Jane Goodall made a career of conducting naturalistic observations of (b) chimpanzee behavior. (credit “Jane Goodall”: modification of work by Erik Hersman; “chimpanzee”: modification of work by “Afrika Force”/Flickr.com)

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the chapter on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias. Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability: a measure of reliability that assesses the consistency of observations by different observers.

The final drawback of naturalistic observation is the issue of consent to participation in the study. While informed consent will be discussed further in the ethics section of this chapter, participants may not know that they are being observed and thus, do not have the ability to agree or not to being part of the research. In the bathroom example, what if the researcher observed a doctor in a white coat leave without washing their hands? Should the researcher do something? What are the rights of that participant who never consented to being observed in the restroom by a stranger?

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (figure below). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys can be administered in a number of ways, including as electronically administered research, like the survey shown here. (credit: Robert Nyman)

Another potential weakness of surveys is something we touched on earlier in this chapter: People don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

ARCHIVAL RESEARCH

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships.

For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and calculate how long it took them to complete their degrees, as well as course loads, grades, and extracurricular involvement. Archival research could provide important information about who is most likely to complete their education, and it could help identify important risk factors for struggling students (figure below).

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with the research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, their research questions must be tailored so they can be answers within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

LONGITUDINAL AND CROSS-SECTIONAL RESEARCH

Sometimes we want to see how people change over time, as in studies of human development and lifespan. We can use a variety of study designs (surveys, observations, experiments, etc.) at either one time point ( cross-sectional ) or several different times (longitudinal) to answer different questions about how things are at a single moment or how they might change over time.

When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

In cross-sectional research, a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals make them different from one another.

In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

The clinical or case study involves studying just a few individuals for an extended period of time. While this approach provides an incredible depth of information, the ability to generalize these observations to the larger population is problematic.

Naturalistic observation involves observing behavior in a natural setting and allows for the collection of valid, true-to-life information from realistic situations. However, naturalistic observation does not allow for much control and often requires quite a bit of time and money to perform.

Surveys can be administered in a number of ways and make it possible to collect large amounts of data quickly. However, the depth of information that can be collected through surveys is somewhat limited compared to a clinical or case study.

Archival research involves studying existing data sets to answer research questions. However, researchers have no control over what data was collected and how it was collected.

Longitudinal research has been incredibly helpful to researchers who need to collect data on how people change over time, but is a major investment of time and money.

Cross-sectional research compares multiple segments of a population at a single time, but can be confounded by generational effects.

References:

Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology

Review Questions: 1. Sigmund Freud developed his theory of human personality by conducting in-depth interviews over an extended period of time with a few clients. This type of research approach is known as a(n): ________.

a. archival research

b. case study

c. naturalistic observation

2. ________ involves observing behavior in individuals in their natural environments.

3. The major limitation of case studies is ________.

a. the superficial nature of the information collected in this approach

b. the lack of control that the researcher has in this approach

c. the inability to generalize the findings from this approach to the larger population

d. the absence of inter-rater reliability

4. The benefit of naturalistic observation studies is ________.

a. the honesty of the data that is collected in a realistic setting

b. how quick and easy these studies are to perform

c. the researcher’s capacity to make sure that data is collected as efficiently as possible

d. the ability to determine cause and effect in this particular approach

5. Using existing records to try to answer a research question is known as ________.

a. naturalistic observation

b. survey research

c. longitudinal research

d. archival research

6. ________ involves following a group of research participants for an extended period of time.

b. longitudinal research

d. cross-sectional research

7. A(n) ________ is a list of questions developed by a researcher that can be administered in paper form.

8. Longitudinal research is complicated by high rates of ________.

a. deception

b. observation

c. attrition

d. generalization

Critical Thinking Questions:

1. In this section, conjoined twins, Krista and Tatiana, were described as being potential participants in a case study. In what other circumstances would you think that this particular research approach would be especially helpful and why?

2. Presumably, reality television programs aim to provide a realistic portrayal of the behavior displayed by the characters featured in such programs. This section pointed out why this is not really the case. What changes could be made in the way that these programs are produced that would result in more honest portrayals of realistic behavior?

3. Which of the research methods discussed in this section would be best suited to research the effectiveness of the D.A.R.E. program in preventing the use of alcohol and other drugs? Why?

4. Aside from biomedical research, what other areas of research could greatly benefit by both longitudinal and archival research?

Personal Application Questions:

1. A friend of your s is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?

2. As a college student, you are no doubt concerned about the grades that you earn while completing your coursework. If you wanted to know how overall GPA is related to success in life after college, how would you choose to approach this question and what kind of resources would you need to conduct this research?

archival research

clinical or case study

cross-sectional research

generalize

inter-rater reliability

longitudinal research

naturalistic observation

observer bias

population

Answers to Exercises

Review Questions:

1. Case studies might prove especially helpful using individuals who have rare conditions. For instance, if one wanted to study multiple personality disorder then the case study approach with individuals diagnosed with multiple personality disorder would be helpful.

2. The behavior displayed on these programs would be more realistic if the cameras were mounted in hidden locations, or if the people who appear on these programs did not know when they were being recorded.

3. Longitudinal research would be an excellent approach in studying the effectiveness of this program because it would follow students as they aged to determine if their choices regarding alcohol and drugs were affected by their participation in the program.

4. Answers will vary. Possibilities include research on hiring practices based on human resource records, and research that follows former prisoners to determine if the time that they were incarcerated provided any sort of positive influence on their likelihood of engaging in criminal behavior in the future.

archival research: method of research using past records or data sets to answer various research questions, or to search for interesting patterns or relationships

attrition: reduction in number of research participants as some drop out of the study over time

clinical or case study: observational research study focusing on one or a few people

cross-sectional research: compares multiple segments of a population at a single time

generalize: inferring that the results for a sample apply to the larger population

inter-rater reliability: measure of agreement among observers on how they record and classify a particular event

longitudinal research: studies in which the same group of individuals is surveyed or measured repeatedly over an extended period of time

naturalistic observation: observation of behavior in its natural setting

observer bias: when observations may be skewed to align with observer expectations

population: overall group of individuals that the researchers are interested in

sample: subset of individuals selected from the larger population

survey: list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

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Chapter 8 Sampling

Sampling is the statistical process of selecting a subset (called a “sample”) of a population of interest for purposes of making observations and statistical inferences about that population. Social science research is generally about inferring patterns of behaviors within specific populations. We cannot study entire populations because of feasibility and cost constraints, and hence, we must select a representative sample from the population of interest for observation and analysis. It is extremely important to choose a sample that is truly representative of the population so that the inferences derived from the sample can be generalized back to the population of interest. Improper and biased sampling is the primary reason for often divergent and erroneous inferences reported in opinion polls and exit polls conducted by different polling groups such as CNN/Gallup Poll, ABC, and CBS, prior to every U.S. Presidential elections.

The Sampling Process

research methods that include surveys case studies and observations are called quizlet

Figure 8.1. The sampling process

The sampling process comprises of several stage. The first stage is defining the target population. A population can be defined as all people or items ( unit of analysis ) with the characteristics that one wishes to study. The unit of analysis may be a person, group, organization, country, object, or any other entity that you wish to draw scientific inferences about. Sometimes the population is obvious. For example, if a manufacturer wants to determine whether finished goods manufactured at a production line meets certain quality requirements or must be scrapped and reworked, then the population consists of the entire set of finished goods manufactured at that production facility. At other times, the target population may be a little harder to understand. If you wish to identify the primary drivers of academic learning among high school students, then what is your target population: high school students, their teachers, school principals, or parents? The right answer in this case is high school students, because you are interested in their performance, not the performance of their teachers, parents, or schools. Likewise, if you wish to analyze the behavior of roulette wheels to identify biased wheels, your population of interest is not different observations from a single roulette wheel, but different roulette wheels (i.e., their behavior over an infinite set of wheels).

The second step in the sampling process is to choose a sampling frame . This is an accessible section of the target population (usually a list with contact information) from where a sample can be drawn. If your target population is professional employees at work, because you cannot access all professional employees around the world, a more realistic sampling frame will be employee lists of one or two local companies that are willing to participate in your study. If your target population is organizations, then the Fortune 500 list of firms or the Standard & Poor’s (S&P) list of firms registered with the New York Stock exchange may be acceptable sampling frames.

Note that sampling frames may not entirely be representative of the population at large, and if so, inferences derived by such a sample may not be generalizable to the population. For instance, if your target population is organizational employees at large (e.g., you wish to study employee self-esteem in this population) and your sampling frame is employees at automotive companies in the American Midwest, findings from such groups may not even be generalizable to the American workforce at large, let alone the global workplace. This is because the American auto industry has been under severe competitive pressures for the last 50 years and has seen numerous episodes of reorganization and downsizing, possibly resulting in low employee morale and self-esteem. Furthermore, the majority of the American workforce is employed in service industries or in small businesses, and not in automotive industry. Hence, a sample of American auto industry employees is not particularly representative of the American workforce. Likewise, the Fortune 500 list includes the 500 largest American enterprises, which is not representative of all American firms in general, most of which are medium and small-sized firms rather than large firms, and is therefore, a biased sampling frame. In contrast, the S&P list will allow you to select large, medium, and/or small companies, depending on whether you use the S&P large-cap, mid-cap, or small-cap lists, but includes publicly traded firms (and not private firms) and hence still biased. Also note that the population from which a sample is drawn may not necessarily be the same as the population about which we actually want information. For example, if a researcher wants to the success rate of a new “quit smoking” program, then the target population is the universe of smokers who had access to this program, which may be an unknown population. Hence, the researcher may sample patients arriving at a local medical facility for smoking cessation treatment, some of whom may not have had exposure to this particular “quit smoking” program, in which case, the sampling frame does not correspond to the population of interest.

The last step in sampling is choosing a sample from the sampling frame using a well-defined sampling technique. Sampling techniques can be grouped into two broad categories: probability (random) sampling and non-probability sampling. Probability sampling is ideal if generalizability of results is important for your study, but there may be unique circumstances where non-probability sampling can also be justified. These techniques are discussed in the next two sections.

Probability Sampling

Probability sampling is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined. Sample statistics thus produced, such as sample mean or standard deviation, are unbiased estimates of population parameters, as long as the sampled units are weighted according to their probability of selection. All probability sampling have two attributes in common: (1) every unit in the population has a known non-zero probability of being sampled, and (2) the sampling procedure involves random selection at some point. The different types of probability sampling techniques include:

Simple random sampling. In this technique, all possible subsets of a population (more accurately, of a sampling frame) are given an equal probability of being selected. The probability of selecting any set of n units out of a total of N units in a sampling frame is N C n . Hence, sample statistics are unbiased estimates of population parameters, without any weighting. Simple random sampling involves randomly selecting respondents from a sampling frame, but with large sampling frames, usually a table of random numbers or a computerized random number generator is used. For instance, if you wish to select 200 firms to survey from a list of 1000 firms, if this list is entered into a spreadsheet like Excel, you can use Excel’s RAND() function to generate random numbers for each of the 1000 clients on that list. Next, you sort the list in increasing order of their corresponding random number, and select the first 200 clients on that sorted list. This is the simplest of all probability sampling techniques; however, the simplicity is also the strength of this technique. Because the sampling frame is not subdivided or partitioned, the sample is unbiased and the inferences are most generalizable amongst all probability sampling techniques.

Systematic sampling. In this technique, the sampling frame is ordered according to some criteria and elements are selected at regular intervals through that ordered list. Systematic sampling involves a random start and then proceeds with the selection of every k th element from that point onwards, where k = N / n , where k is the ratio of sampling frame size N and the desired sample size n , and is formally called the sampling ratio . It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first k elements on the list. In our previous example of selecting 200 firms from a list of 1000 firms, you can sort the 1000 firms in increasing (or decreasing) order of their size (i.e., employee count or annual revenues), randomly select one of the first five firms on the sorted list, and then select every fifth firm on the list. This process will ensure that there is no overrepresentation of large or small firms in your sample, but rather that firms of all sizes are generally uniformly represented, as it is in your sampling frame. In other words, the sample is representative of the population, at least on the basis of the sorting criterion.

Stratified sampling. In stratified sampling, the sampling frame is divided into homogeneous and non-overlapping subgroups (called “strata”), and a simple random sample is drawn within each subgroup. In the previous example of selecting 200 firms from a list of 1000 firms, you can start by categorizing the firms based on their size as large (more than 500 employees), medium (between 50 and 500 employees), and small (less than 50 employees). You can then randomly select 67 firms from each subgroup to make up your sample of 200 firms. However, since there are many more small firms in a sampling frame than large firms, having an equal number of small, medium, and large firms will make the sample less representative of the population (i.e., biased in favor of large firms that are fewer in number in the target population). This is called non-proportional stratified sampling because the proportion of sample within each subgroup does not reflect the proportions in the sampling frame (or the population of interest), and the smaller subgroup (large-sized firms) is over-sampled . An alternative technique will be to select subgroup samples in proportion to their size in the population. For instance, if there are 100 large firms, 300 mid-sized firms, and 600 small firms, you can sample 20 firms from the “large” group, 60 from the “medium” group and 120 from the “small” group. In this case, the proportional distribution of firms in the population is retained in the sample, and hence this technique is called proportional stratified sampling. Note that the non-proportional approach is particularly effective in representing small subgroups, such as large-sized firms, and is not necessarily less representative of the population compared to the proportional approach, as long as the findings of the non-proportional approach is weighted in accordance to a subgroup’s proportion in the overall population.

Cluster sampling. If you have a population dispersed over a wide geographic region, it may not be feasible to conduct a simple random sampling of the entire population. In such case, it may be reasonable to divide the population into “clusters” (usually along geographic boundaries), randomly sample a few clusters, and measure all units within that cluster. For instance, if you wish to sample city governments in the state of New York, rather than travel all over the state to interview key city officials (as you may have to do with a simple random sample), you can cluster these governments based on their counties, randomly select a set of three counties, and then interview officials from every official in those counties. However, depending on between- cluster differences, the variability of sample estimates in a cluster sample will generally be higher than that of a simple random sample, and hence the results are less generalizable to the population than those obtained from simple random samples.

Matched-pairs sampling. Sometimes, researchers may want to compare two subgroups within one population based on a specific criterion. For instance, why are some firms consistently more profitable than other firms? To conduct such a study, you would have to categorize a sampling frame of firms into “high profitable” firms and “low profitable firms” based on gross margins, earnings per share, or some other measure of profitability. You would then select a simple random sample of firms in one subgroup, and match each firm in this group with a firm in the second subgroup, based on its size, industry segment, and/or other matching criteria. Now, you have two matched samples of high-profitability and low-profitability firms that you can study in greater detail. Such matched-pairs sampling technique is often an ideal way of understanding bipolar differences between different subgroups within a given population.

Multi-stage sampling. The probability sampling techniques described previously are all examples of single-stage sampling techniques. Depending on your sampling needs, you may combine these single-stage techniques to conduct multi-stage sampling. For instance, you can stratify a list of businesses based on firm size, and then conduct systematic sampling within each stratum. This is a two-stage combination of stratified and systematic sampling. Likewise, you can start with a cluster of school districts in the state of New York, and within each cluster, select a simple random sample of schools; within each school, select a simple random sample of grade levels; and within each grade level, select a simple random sample of students for study. In this case, you have a four-stage sampling process consisting of cluster and simple random sampling.

Non-Probability Sampling

Nonprobability sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined. Typically, units are selected based on certain non-random criteria, such as quota or convenience. Because selection is non-random, nonprobability sampling does not allow the estimation of sampling errors, and may be subjected to a sampling bias. Therefore, information from a sample cannot be generalized back to the population. Types of non-probability sampling techniques include:

Convenience sampling. Also called accidental or opportunity sampling, this is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient. For instance, if you stand outside a shopping center and hand out questionnaire surveys to people or interview them as they walk in, the sample of respondents you will obtain will be a convenience sample. This is a non-probability sample because you are systematically excluding all people who shop at other shopping centers. The opinions that you would get from your chosen sample may reflect the unique characteristics of this shopping center such as the nature of its stores (e.g., high end-stores will attract a more affluent demographic), the demographic profile of its patrons, or its location (e.g., a shopping center close to a university will attract primarily university students with unique purchase habits), and therefore may not be representative of the opinions of the shopper population at large. Hence, the scientific generalizability of such observations will be very limited. Other examples of convenience sampling are sampling students registered in a certain class or sampling patients arriving at a certain medical clinic. This type of sampling is most useful for pilot testing, where the goal is instrument testing or measurement validation rather than obtaining generalizable inferences.

Quota sampling. In this technique, the population is segmented into mutually-exclusive subgroups (just as in stratified sampling), and then a non-random set of observations is chosen from each subgroup to meet a predefined quota. In proportional quota sampling , the proportion of respondents in each subgroup should match that of the population. For instance, if the American population consists of 70% Caucasians, 15% Hispanic-Americans, and 13% African-Americans, and you wish to understand their voting preferences in an sample of 98 people, you can stand outside a shopping center and ask people their voting preferences. But you will have to stop asking Hispanic-looking people when you have 15 responses from that subgroup (or African-Americans when you have 13 responses) even as you continue sampling other ethnic groups, so that the ethnic composition of your sample matches that of the general American population. Non-proportional quota sampling is less restrictive in that you don’t have to achieve a proportional representation, but perhaps meet a minimum size in each subgroup. In this case, you may decide to have 50 respondents from each of the three ethnic subgroups (Caucasians, Hispanic-Americans, and African- Americans), and stop when your quota for each subgroup is reached. Neither type of quota sampling will be representative of the American population, since depending on whether your study was conducted in a shopping center in New York or Kansas, your results may be entirely different. The non-proportional technique is even less representative of the population but may be useful in that it allows capturing the opinions of small and underrepresented groups through oversampling.

Expert sampling. This is a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied. For instance, in order to understand the impacts of a new governmental policy such as the Sarbanes-Oxley Act, you can sample an group of corporate accountants who are familiar with this act. The advantage of this approach is that since experts tend to be more familiar with the subject matter than non-experts, opinions from a sample of experts are more credible than a sample that includes both experts and non-experts, although the findings are still not generalizable to the overall population at large.

Snowball sampling. In snowball sampling, you start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria. For instance, if you wish to survey computer network administrators and you know of only one or two such people, you can start with them and ask them to recommend others who also do network administration. Although this method hardly leads to representative samples, it may sometimes be the only way to reach hard-to-reach populations or when no sampling frame is available.

Statistics of Sampling

In the preceding sections, we introduced terms such as population parameter, sample statistic, and sampling bias. In this section, we will try to understand what these terms mean and how they are related to each other.

When you measure a certain observation from a given unit, such as a person’s response to a Likert-scaled item, that observation is called a response (see Figure 8.2). In other words, a response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument. Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences. For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution , which can be used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample). These sample estimates are called sample statistics (a “statistic” is a value that is estimated from observed data). Populations also have means and standard deviations that could be obtained if we could sample the entire population. However, since the entire population can never be sampled, population characteristics are always unknown, and are called population parameters (and not “statistic” because they are not statistically estimated from data). Sample statistics may differ from population parameters if the sample is not perfectly representative of the population; the difference between the two is called sampling error . Theoretically, if we could gradually increase the sample size so that the sample approaches closer and closer to the population, then sampling error will decrease and a sample statistic will increasingly approximate the corresponding population parameter.

If a sample is truly representative of the population, then the estimated sample statistics should be identical to corresponding theoretical population parameters. How do we know if the sample statistics are at least reasonably close to the population parameters? Here, we need to understand the concept of a sampling distribution . Imagine that you took three different random samples from a given population, as shown in Figure 8.3, and for each sample, you derived sample statistics such as sample mean and standard deviation. If each random sample was truly representative of the population, then your three sample means from the three random samples will be identical (and equal to the population parameter), and the variability in sample means will be zero. But this is extremely unlikely, given that each random sample will likely constitute a different subset of the population, and hence, their means may be slightly different from each other. However, you can take these three sample means and plot a frequency histogram of sample means. If the number of such samples increases from three to 10 to 100, the frequency histogram becomes a sampling distribution. Hence, a sampling distribution is a frequency distribution of a sample statistic (like sample mean) from a set of samples , while the commonly referenced frequency distribution is the distribution of a response (observation) from a single sample . Just like a frequency distribution, the sampling distribution will also tend to have more sample statistics clustered around the mean (which presumably is an estimate of a population parameter), with fewer values scattered around the mean. With an infinitely large number of samples, this distribution will approach a normal distribution. The variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic) is called its standard error . In contrast, the term standard deviation is reserved for variability of an observed response from a single sample.

Figure 8.2. Sample Statistic

The mean value of a sample statistic in a sampling distribution is presumed to be an estimate of the unknown population parameter. Based on the spread of this sampling distribution (i.e., based on standard error), it is also possible to estimate confidence intervals for that prediction population parameter. Confidence interval is the estimated probability that a population parameter lies within a specific interval of sample statistic values. All normal distributions tend to follow a 68-95-99 percent rule (see Figure 8.4), which says that over 68% of the cases in the distribution lie within one standard deviation of the mean value (µ + 1σ), over 95% of the cases in the distribution lie within two standard deviations of the mean (µ + 2σ), and over 99% of the cases in the distribution lie within three standard deviations of the mean value (µ + 3σ). Since a sampling distribution with an infinite number of samples will approach a normal distribution, the same 68-95-99 rule applies, and it can be said that:

(Sample statistic + one standard error) represents a 68% confidence interval for the population parameter.
(Sample statistic + two standard errors) represents a 95% confidence interval for the population parameter.
(Sample statistic + three standard errors) represents a 99% confidence interval for the population parameter.

Figure 8.3. The sampling distribution

A sample is “biased” (i.e., not representative of the population) if its sampling distribution cannot be estimated or if the sampling distribution violates the 68-95-99 percent rule. As an aside, note that in most regression analysis where we examine the significance of regression coefficients with p<0.05, we are attempting to see if the sampling statistic (regression coefficient) predicts the corresponding population parameter (true effect size) with a 95% confidence interval. Interestingly, the “six sigma” standard attempts to identify manufacturing defects outside the 99% confidence interval or six standard deviations (standard deviation is represented using the Greek letter sigma), representing significance testing at p<0.01.

Figure 8.4. The 68-95-99 percent rule for confidence interval

Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Psychology Module 1.3: Research Methods Flashcards
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1.2 Research Methods Used by Psychologists. Term. 1 / 34. Descriptive methods. Click the card to flip 👆. Definition. 1 / 34. Observational techniques, case studies, and survey research. The main purpose of all three methods is to provide objective and detailed descriptions of behavior and mental processes.
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2.3 Surveys, Interviews, And Case Studies Flashcards
Study with Quizlet and memorize flashcards containing terms like When a researcher asks people questions and records their answers, it is called?, An in-depth look at a subject that involves observation by the researcher is called?, A questionnaire in which people are asked to write their responses to questions about themselves is called? and more.
Research Methods Chapter 6: Surveys and Observations
Research Methods Chapter 6: Surveys and Observations. survey/poll. Click the card to flip 👆. method of posing questions to people online, on phone, personal interviews, written questionairs. Click the card to flip 👆. 1 / 21.
2.2 Approaches to Research
Describe the different research methods used by psychologists; Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research ... This type of observational study is called naturalistic observation: observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger ...
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Types of descriptive research. Observational method. Case studies. Surveys. Recap. Descriptive research methods are used to define the who, what, and where of human behavior and other ...
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Olivia Guy-Evans, MSc. Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.
Survey Research
Survey research means collecting information about a group of people by asking them questions and analyzing the results. To conduct an effective survey, follow these six steps: Determine who will participate in the survey. Decide the type of survey (mail, online, or in-person) Design the survey questions and layout.
2.2 Research Methods
They use research methods to design a study. Planning the research design is a key step in any sociological study. Sociologists generally choose from widely used methods of social investigation: primary source data collection such as survey, participant observation, ethnography, case study, unobtrusive observations, experiment, and secondary ...
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Describe the different research methods used by psychologists. Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research. Compare longitudinal and cross-sectional approaches to research. There are many research methods available to psychologists in their efforts to understand, describe, and ...
Research Methods
To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations). Meta-analysis. Quantitative. To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner. Thematic analysis.
Ch 2: Psychological Research Methods
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Survey research
9 Survey research. Survey research is a research method involving the use of standardised questionnaires or interviews to collect data about people and their preferences, thoughts, and behaviours in a systematic manner. Although census surveys were conducted as early as Ancient Egypt, survey as a formal research method was pioneered in the 1930 ...
Survey Research: Definition, Types & Methods
Replicable: applying the same methods more than once should achieve similar results. Types: surveys can be exploratory, descriptive, or casual used in both online and offline forms. Data: survey research can generate both quantitative and qualitative data. Impartial: sampling is randomized to avoid bias.
Descriptive Research
Descriptive, or qualitative, methods include the case study, naturalistic observation, surveys, archival research, longitudinal research, and cross-sectional research. ... This type of observational study is called naturalistic observation: observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger ...
6.5 Observational Research
Naturalistic observation is an observational method that involves observing people's behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall's famous research on chimpanzees is a classic example of naturalistic observation.
Module 2: Research Methods in Social Psychology
Step 3 called on the scientist to test their hypothesis. Psychology as a discipline uses five main research designs. These include observational research, case studies, surveys, correlational designs, and experiments. 2.2.1. Observational Research
2.2 Correlational Research Methods
Learning Objectives. By the end of this section, you will be able to: Describe the different research methods used by psychologists. Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research. Compare longitudinal and cross-sectional approaches to research.
Chapter 8 Sampling
Chapter 8 Sampling. Sampling is the statistical process of selecting a subset (called a "sample") of a population of interest for purposes of making observations and statistical inferences about that population. Social science research is generally about inferring patterns of behaviors within specific populations.
Psychology, Psychological Research, Approaches to Research
Approaches to Research. Overview. By the end of this section, you will be able to: Describe the different research methods used by psychologists. Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research. Compare longitudinal and cross-sectional approaches to research.

Research Methods In Psychology

Experimental Design

Experimental Methods

Correlational Studies

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Interview Methods

Observations

Pilot Study

Research Design

Reliability

Meta-Analysis

Peer Review

Types of Data

Features of Science

Statistical Testing

Ethical Issues

2.2 Research Methods

Field Research

Sociology in the Real World

Participant Observation

Ethnography

Institutional Ethnography

Sociological Research

Experiments

Secondary Data Analysis

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Research Methods | Definitions, Types, Examples

Table of contents

Qualitative vs. quantitative data

Primary vs. secondary research

Descriptive vs. experimental data

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Ch 2: Psychological Research Methods

Introduction to the Scientific Method

The Scientific Process

Process of Scientific Research

Basic Principles of the Scientific Method

Applying the Scientific Method

Link to Learning

Ethics in Research

Research Involving Human Participants

Dig Deeper: Ethics and the Tuskegee Syphilis Study

Research Involving Animal Subjects

Introduction to Approaches to Research

Watch It: More on Research

Descriptive Research

Naturalistic Observation

Case Studies

Think It Over

Longitudinal and Cross-Sectional Research

Correlational Research

Correlation Does Not Indicate Causation

Illusory Correlations

Experiments

Designing an Experiment

Independent and Dependent Variables

Selecting and Assigning Experimental Participants

Issues to Consider

Introduction to Statistical Thinking

Interpreting Experimental Findings

Reporting Research

Dig Deeper: The Vaccine-Autism Myth and the Retraction of Published Studies

Reliability and Validity

Statistical Significance

Distributional Thinking

Finding Significance in Data

Drawing Conclusions from Statistics

Cause and Effect

How to Read Research

Putting It Together: Psychological Research

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9 Survey research

Questionnaire surveys

Interview survey