how to write demographics in a research paper example

Reporting Participant Characteristics in a Research Paper

A report on a scientific study using human participants will include a description of the participant characteristics. This is included as a subsection of the “Methods” section, usually called “Participants” or “Participant Characteristics.” The purpose is to give readers information on the number and type of study participants, as a way of clarifying to whom the study findings apply and shedding light on the generalizability of the findings as well as any possible limitations. Accurate reporting is needed for replication studies that might be carried out in the future.

The “Participants” subsection should be fairly short and should tell readers about the population pool, how many participants were included in the study sample, and what kind of sample they represent, such as random, snowball, etc. There is no need to give a lengthy description of the method used to select or recruit the participants, as these topics belong in a separate “Procedures” subsection that is also under “Methods.” The subsection on “Participant Characteristics” only needs to provide facts on the participants themselves.

Report the participants’ genders (how many male and female participants) and ages (the age range and, if appropriate, the standard deviation). In particular, if you are writing for an international audience, specify the country and region or cities where the participants lived. If the study invited only participants with certain characteristics, report this, too. For example, tell readers if the participants all had autism, were left-handed, or had participated in sports within the past year.

Related: Finished preparing the methods sections for your research paper ? Find out why the “Methods” section is so important now!

Next, use your judgment to identify other pieces of information that are relevant to the study. For a detailed tutorial on reporting “Participant Characteristics,” see Alice Frye’s “Method Section: Describing participants.” Frye reminds authors to mention if only people with certain characteristics or backgrounds were included in the study. Did all the participants work at the same company? Were the students at the same school? Did they represent a range of socioeconomic backgrounds? Did they come from both urban and rural backgrounds? Were they physically and emotionally healthy? Similarly, mention if the study sample excluded people with certain characteristics.

If you are going to examine any participant characteristics as factors in the analysis, include a description of these. For instance, if you plan to examine the influence of teachers’ years of experience on their attitude toward new technology, then you should report the range of the teachers’ years of experience. If you plan to study how children’s socioeconomic level relates to their test scores, you should briefly mention that the children in the sample came from low, middle, and high-income backgrounds. Finally, mention whether the participants participated voluntarily. Include information on whether they gave informed consent (if the participants were children, mention that their parents consented to their participation). Also, mention if the participants received any sort of compensation or benefit for their participation, such as money or course credit.

Case Studies and Qualitative Reports

Case studies and qualitative reports may have only a few participants or even a single participant. If there is space to do so, you can write a brief background of each participant in the “Participants” section and include relevant information on the participant’s birthplace, current place of residence, language, and any life experience that is relevant to the study theme. If you have permission to use the participant’s name, do so. Otherwise, use a different name and add a note to readers that the name is a pseudonym. Alternatively, you might label the participants with numbers (e.g., Student 1, Student 2) or letters (e.g., Doctor A, Doctor B, etc.), or use initials to identify them (e.g., KY, JM).

Use Past Tense

Remember to use past tense when writing the “Participants” section . This is because you are describing what the participants’ characteristics were at the time of data collection . By the time your article is published, the participants’ characteristics may have changed. For example, they may be a year older and have more work experience. Their socioeconomic level may have changed since the study. In some cases, participants may even have passed away. While characteristics like gender and race are either unlikely or impossible to change, the whole section is written in the past tense to maintain a consistent style and to avoid making unsupported claims about what the participants’ current status is.

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• AI in Academia
• Infographic
• Manuscripts & Grants
• Reporting Research
• Trending Now

Can AI Tools Prepare a Research Manuscript From Scratch? — A comprehensive guide

As technology continues to advance, the question of whether artificial intelligence (AI) tools can prepare…

Abstract Vs. Introduction — Do you know the difference?

Ross wants to publish his research. Feeling positive about his research outcomes, he begins to…

• Old Webinars
• Webinar Mobile App

Demystifying Research Methodology With Field Experts

Choosing research methodology Research design and methodology Evidence-based research approach How RAxter can assist researchers

• Manuscript Preparation
• Publishing Research

How to Choose Best Research Methodology for Your Study

Successful research conduction requires proper planning and execution. While there are multiple reasons and aspects…

Top 5 Key Differences Between Methods and Methodology

While burning the midnight oil during literature review, most researchers do not realize that the…

How to Draft the Acknowledgment Section of a Manuscript

Discussion Vs. Conclusion: Know the Difference Before Drafting Manuscripts

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

What should universities' stance be on AI tools in research and academic writing?

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Audience

What are Demographic Examples

What is a demographic?

Demographics are statistical data that researchers use to study groups of humans. A demographic refers to distinct characteristics of a population. Researchers use demographic analysis to analyze whole societies or just groups of people. Some examples of demographics are age, sex, education, nationality, ethnicity, or religion, to name a few.

Select your respondents

What are the various examples of demographic segmentation?

Demographic segmentation examples explain how researchers divide a market into smaller groups according to age, gender, family income, race and ethnicity, qualification, marital status, nature of employment, etc.  

It is an extremely tedious task to accommodate customers belonging to different demographics and develop an exhaustive marketing plan. Demographic examples ease creating a strategy for a marketer. Thus, they are one of the most commonly implemented marketing segmentation methods compared to other techniques such as geographic segmentatio n, behavioral segmentation , or psychographic segmentation . As the details required for demographic examples are easily accessible, marketers have gained popularity to gather and analyze immense data in brief periods.

According to demographic diversity, dividing the target audience will help a marketer design an accurate marketing plan that will yield productive results. The products or services that interest a White, 13-year-old boy, might not interest a 40-year-old Asian woman.

Demographic examples:

• Age segmentation – Age is one of the most common demographic segmentation elements. Every age group has its peculiar characteristics and needs. Generally, teenagers might be more inclined towards the latest, good looking cars, but working professionals would require a vehicle that caters to his/her family and fits a particular budget. Every age group has a specific requirement, which will be extremely different from the other age groups’ needs. Babies require a constant supply of diapers, select clothing, formula, and other such products, while toddlers require educational toys, coloring books, products that stimulate their mental and physical growth. Middle-aged adults may invest a lot more in an expensive technological gadget than a teenager. An old-aged person would rather spend their money on buying health-related products. As seen in all these examples, every segment has specific requirements, and organizations can develop marketing strategies based on these requirements to obtain valid results.
• Family segmentation – There is a lot of variation in this segmentation type. A lot of families have one or multiple children. Some have single mothers or fathers; others have gay parents with one or more children; while some are child-free and straight. Child-free families will never purchase products related to children, such as baby lotion, toys, or diapers. A multinational organization that is into developing these products will conduct demographic examples based on the type of family. Single parents will be more inclined to save costs at various products that might not concern most child-free people.
• Gender segmentation – Gender is quite a primary category to conduct segmentation. Every gender has specific characteristics that are distinct and instrumental in decision-making. It is very natural for males, females, transgender people, to have different likes and dislikes. Men might not be as interested in makeup or fashion accessories in a manner that women will be. Gender defines people’s preferences. Females are usually into makeup products, and there are currently more females who show interest in the latest fashion products. Based on these characteristics, makeup, or fashion brands can create a marketing strategy that targets women to get better business results.
• Race and ethnicity segmentation – Race and ethnicity are sensitive categories. Promoting a product depends on that target race or ethnicity as it may be adapted differently by each of these races/ethnicities. People belonging to different races will have different food preferences, clothing habits, and many other attributes. Stereotypical segmentation may hurt sentiments, which may cause harm to a business.
• Family income segmentation – One of the most straightforward segmentation types is based on income. An individual or a family’s income would govern their ability to purchase different cost categories’ products/services. A person who can barely afford to provide food and shelter for his/her family would not afford an iPhone. Companies that offer luxury cars or watches must target customers who have a considerable amount of extra earnings. The most likely target audience of an organization that affordable mobile phones will be mid to low income customers.

Other demographic examples

Here are a few more demographic examples that researchers commonly use:

• Employment status: Business-owner, self-employed, unemployed, employed, retired.
• Living status: Home-owner, rented, lease.
• Education level: Graduate degree, undergrad, college degree, high school.
• Religion: Atheist, Muslim, Christian, Jew, Hindu, Buddhist
• Marital status: Single, married, separated, widow/ widower.
• The number of children: None, 1, 2, 3-5, more than 5.
• Political affiliation: Democrat, republic, independent.
• Nationality: American, Mexican, French, Indian, German

Advantages of demographic segmentation:

There are several advantages of dividing the target audience according to demographics.

• As the census is carried out regularly by the government, demographic data can be easily retrieved. An organization can easily divide data into required categories, creating an effective marketing strategy for each of these demographics.  
• According to the demographic data requirement of an organization, age, gender, income, race, etc. can be adjusted and implemented.
• Factors such as family income, type of family, etc., give insights that will decide the consumers’ purchasing power. An organization can decide whether or not to target a particular group of consumers based on these classifications.
• Considering that a lot of effort goes into dividing a target audience into demographic segments, an organization modifies marketing strategies based on each segment’s requirements. There are high chances of increased customer satisfaction , loyalty, and increased retention rates due to this.
• In the longer run, demographic examples will help in reducing cost and time invested in developing and implementing a marketing strategy as all marketing efforts will be carefully calculated according to the various segments.
• This method is better at understanding a target market and creating policies that pertain to each of these markets.

MORE LIKE THIS

Customer Communication Tool: Types, Methods, Uses, & Tools

Apr 23, 2024

Top 12 Sentiment Analysis Tools for Understanding Emotions

QuestionPro BI: From research data to actionable dashboards within minutes

Apr 22, 2024

21 Best Customer Experience Management Software in 2024

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

Finding Statistics and Demographics

• Searching for Articles
• Books and eBooks
• Searching the Web
• Writing & Citing

Incorporating Statistical Information into your Writing

1. Go to the original source:

Rather than relying on a blog, magazine or newspaper restating a statistic from a larger study or publication, try your best to locate the original report mentioned.  If the short article you are reading cites statistics with no reference to the source, the article ought to be avoided.

For example, on January 12, 2016, a news article from KVUE in Austin mentions, "Affordability issues are expected to continue impacting the number of students within the district, and the report expecting about 600 fewer students in the district each year due to multiple factors." ( https://www.kvue.com/news/austin-isd-releases-annual-demographic-report/39601312 ; screenshot )

However, the article includes a link to the full PDF demographic report on the Austin ISD website ( https://www.austinisd.org ; screenshot ).

2. Provide context:

Introduce the data for the reader by mentioning within the text both the source of the research statistic and the survey or study that was conducted.

For example, using the above example, you might want to mention:

The Austin Independent School District conducts an annual demographics report as well as projection studies for school district planning.  According to their 2015 Ten Year Student Population Projections , the district has " experienced a reduction in student population since SY 2013, primarily at the Prekindergarten and Kindergarten grade levels, and can be attributed to decreasing birth rates and lower births to kindergarten relationship (market share)."

3. Use tables or diagrams in some cases:

If you are citing numerous statistics or lots of information, provide tables or diagrams to visually present the data.  You can include and cite tables from the original source or adapt to create a table or diagram from borrowed sources.  Do not distort or misrepresent the original data.

You will include a caption citation under any figure, map, table, diagram, whether pulled directly from a source or adapted from one or more sources.  

If you embed an image of a map, table or diagram into your paper, make sure the image quality is readable. 

4. Use caution in application:

Be careful how you are applying your statistical information.  Make sure it is relevant to your topic and that you apply the statistic in the correct circumstance.  Do not intentionally or accidentally use statistical sources of information to make faulty assumptions of cause or potential effect.

Helpful Links

• MLA Tables, Figures, and Examples
• Quick Tips on Writing with Statistics
• Writing with Statistics: Overview and Introduction

Know Your Citation Styles

The Publication Manual of the American Psychological Association (APA) is commonly used in the social sciences. It provides two different format styles, one for students and one for professionals. Confirm which style you should use with your instructor. 

Use your APA manual or the links below to learn more about APA requirements.

• APA Style Help - This link leads to the official APA website.
• APA Quick Guide for References - This link also leads to the official APA website.
• APA 7 Formatting and Style Guide - This link leads to the Purdue OWL website.
• Citing AI Image or Text Generators in APA This link leads to the official APA website.
• Citing Government Sources APA Style - This link leads to the WLU government guide.
• Citing AI Sources in APA Style - This article leads to the official APA website. Use this method to cite all AI text generators.

The Chicago Manual of Style (CMS) is commonly used in the humanities. It provides the option of two different documentation styles, so ask whether your instructor requires the author-date style or the notes-bibliography style. 

Use your CMS manual or the links below to learn more about CMS requirements.

• Chicago-Style Citation Quick Guide - This link leads to the official CMS website.
• CMS Format Guide - This link leads to the Purdue OWL website.
• Citing AI Image Generators in CMS This link leads to the official CMS website. Use this method to cite all AI image generators.
• Citing AI Text Generators in CMS This link leads to the official CMS website. Use this method to cite all AI text generators.

The Modern Language Association (MLA) Handbook is commonly used in the humanities. It is particularly popular for English courses, but confirm with your instructor before using it.

Use your MLA manual or the links below to learn more about MLA requirements.

• Using MLA Format - This link leads to the official MLA website.
• MLA Works Cited Quick Guide - This link also leads to the official MLA website.
• MLA Style and Format Guide - This link leads to the Purdue OWL website.
• Citing AI Image or Text Generators in MLA This link leads to the official MLA website.

A Manual for Writers of Research Papers, Theses, and Dissertations (called Turabian style) is a modified form of the Chicago Manual of Style (CMS) and is commonly used for student work in the humanities. Confirm with your instructor before using it.

Use your Turabian manual or the links below to learn more about Turabian requirements.

• Turabian-style Citation Quick Guide - This link leads to the official Turabian website.
• Turabian Student Paper Formatting Tips - This link also leads to the official Turabian website.
• << Previous: Searching the Web
• Last Updated: Feb 28, 2024 2:04 PM
• URL: https://mclennan.libguides.com/stats

© McLennan Community College

1400 College Drive Waco, Texas 76708, USA

+1 (254) 299-8622

• Search Search Please fill out this field.

What Are Demographics?

Understanding demographics, types of demographic information, special considerations.

• Demographics FAQs

The Bottom Line

Demographics: how to collect, analyze, and use demographic data.

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

Yarilet Perez is an experienced multimedia journalist and fact-checker with a Master of Science in Journalism. She has worked in multiple cities covering breaking news, politics, education, and more. Her expertise is in personal finance and investing, and real estate.

Demographics are statistics that describe populations and their characteristics. Demographic analysis is the study of a population-based on factors such as age, race, and sex. Demographic data refers to socioeconomic information expressed statistically, including employment, education, income, marriage rates, birth and death rates, and more.

Governments, corporations, and non-government organizations use demographics to learn more about a population's characteristics for many purposes, including policy development and economic market research . For example, a company that sells high-end RVs may want to reach people nearing or at retirement age and the percentage of those who can afford their products.

Key Takeaways

• Demographic analysis is the collection and analysis of broad characteristics about groups of people and populations.
• Demographic data is very useful for businesses to understand how to market to consumers and plan strategically for future trends in consumer demand.
• The combination of the internet, big data, and artificial intelligence is greatly amplifying the usefulness and application of demographics as a tool for marketing and business strategy.
• Market segments are often grouped by age or generation.
• Demographic information can be used in many ways to learn more about the generalities of a particular population.

Investopedia / Paige McLaughlin

Demographic analysis is the collection and study of data regarding the general characteristics of specific populations . It is frequently used as a business marketing tool to determine the best way to reach customers and assess their behavior. Segmenting a population by using demographics allows companies to determine the size of a potential market.

The use of demographics helps determine whether its products and services are being targeted to that company's most influential consumers. For example, market segments may identify a particular age group, such as baby boomers (born 1946–1964) or millennials (born 1981–1996), with specific buying patterns and characteristics.

The advent of the internet, social media, predictive algorithms, and big data has dramatic implications for collecting and using demographic information. Modern consumers give out a flood of data, sometimes unwittingly, collected and tracked through their online and offline lives by myriad apps, social media platforms, third-party data collectors, retailers, and financial transaction processors.

Combined with the growing field of artificial intelligence, this mountain of collected data can be used to predict and target consumer choices and buying preferences with uncanny accuracy based on their demographic characteristics and past behavior.

For corporate marketing goals, demographic data is collected to build a customer base profile. The common variables gathered in demographic research include age, sex, income level, race, employment, location, homeownership, and level of education. Demographical information makes certain generalizations about groups to identify customers.

Additional demographic factors include gathering data on preferences, hobbies, lifestyle, and more. Governmental agencies collect data when conducting a national census and may use that demographic data to forecast economic patterns and population growth to better manage resources.

You can gather demographic information on a large group and then break it down into smaller subsets for deeper dive into your research.

Most large companies conduct demographic research to determine how to market their product or service and best market to the target audience. It is valuable to know the current customer and where the potential customer may come from in the future. Demographic trends are also significant since the size of different demographic groups changes over time due to economic, cultural, and political circumstances.

This information helps the company decide how much capital to allocate to production and advertising. For example, the aging U.S. population has specific needs that companies want to anticipate. Each market segment can be analyzed for its consumer spending patterns. Older demographic groups spend more on healthcare products and pharmaceuticals, and communicating with these customers differs from that of their younger counterparts.

Why Do Demographics Matter?

Demographics refers to the description or distribution of characteristics of some target audience, customer base, or population. Governments use socioeconomic information to understand the age, racial makeup, and income distribution (among several other variables) in neighborhoods, cities, states, and nations in order to make better public policy decisions.

Companies look to demographics to craft more effective marketing and advertising campaigns and to understand patterns among different audiences.

Who Collects Demographic Data?

The U.S. Census Bureau collects demographic data on the American population every year through the American Community Survey (ACS) and every 10-years via an in-depth count of every American household. Companies use marketing departments or outsource to specialized marketing firms to collect demographics on users, customers, or prospective client groups. Academic researchers also collect demographic data for research purposes using various survey instruments. Political parties and campaigns also collect demographics in order to target messaging for political candidates.

Why Do Businesses Need Demographics?

Demographics are key to businesses today. They help identify the individual members of an audience by selecting key characteristics, wants, and needs. This allows companies to tailor their efforts based on particular segments of their customer base. Online advertising and marketing have made enormous headway over the past decade in using algorithms and big data analysis to micro-target ads on social media to very specific demographics.

How Are Demographic Changes Important for Economists?

Economists recognize that one of the major drivers of economic growth is population growth or decline . There is a straightforward relationship when identifying this: Growth Rate of  Gross Domestic Product  (GDP)=Growth Rate of Population+Growth Rate of  GDP per capita , where GDP per capita is simply GDP divided by population. The more people around, the more available workers there are in the labor force, and also more people to consume items like food, energy, cars, and clothes. There are also demographic problems that lie on the horizon, such as an increasing number of retirees who, while no longer in the workforce, are nonetheless expected to live longer lives. Unfortunately, the number of new births seems to be too low to replace those retirees in the workforce.

Demographics and demographic analysis is used to describe the distribution of characteristics in a society or other population in order to understand them, make policy recommendations, and make predictions about where a society or group is headed in the future. Demographic data can come in many forms, but most often describes the distribution of characteristics found in populations such as age, sex/gender, marital status, household structure, income, wealth, education, religion, and so on - and to see how these are changing over time. Birth and death rates are also used to understand if a population is growing or not, and how this might affect things like economic growth, employment, government programs like social security, and so on.

University of North Florida. " What are Demographics? "

Pew Research Center. " Defining Generations: Where Millennials End and Generation Z Begins ."

United States Census Bureau. " What We Do ."

• Terms of Service
• Editorial Policy
• Privacy Policy
• Your Privacy Choices

How To Write A Research Paper

Step-By-Step Tutorial With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | March 2024

For many students, crafting a strong research paper from scratch can feel like a daunting task – and rightly so! In this post, we’ll unpack what a research paper is, what it needs to do , and how to write one – in three easy steps. 🙂 

Overview: Writing A Research Paper

What (exactly) is a research paper.

• How to write a research paper
• Stage 1 : Topic & literature search
• Stage 2 : Structure & outline
• Stage 3 : Iterative writing
• Key takeaways

Let’s start by asking the most important question, “ What is a research paper? ”.

Simply put, a research paper is a scholarly written work where the writer (that’s you!) answers a specific question (this is called a research question ) through evidence-based arguments . Evidence-based is the keyword here. In other words, a research paper is different from an essay or other writing assignments that draw from the writer’s personal opinions or experiences. With a research paper, it’s all about building your arguments based on evidence (we’ll talk more about that evidence a little later).

Now, it’s worth noting that there are many different types of research papers , including analytical papers (the type I just described), argumentative papers, and interpretative papers. Here, we’ll focus on analytical papers , as these are some of the most common – but if you’re keen to learn about other types of research papers, be sure to check out the rest of the blog .

With that basic foundation laid, let’s get down to business and look at how to write a research paper .

Overview: The 3-Stage Process

While there are, of course, many potential approaches you can take to write a research paper, there are typically three stages to the writing process. So, in this tutorial, we’ll present a straightforward three-step process that we use when working with students at Grad Coach.

These three steps are:

• Finding a research topic and reviewing the existing literature
• Developing a provisional structure and outline for your paper, and
• Writing up your initial draft and then refining it iteratively

Let’s dig into each of these.

Need a helping hand?

Step 1: Find a topic and review the literature

As we mentioned earlier, in a research paper, you, as the researcher, will try to answer a question . More specifically, that’s called a research question , and it sets the direction of your entire paper. What’s important to understand though is that you’ll need to answer that research question with the help of high-quality sources – for example, journal articles, government reports, case studies, and so on. We’ll circle back to this in a minute.

The first stage of the research process is deciding on what your research question will be and then reviewing the existing literature (in other words, past studies and papers) to see what they say about that specific research question. In some cases, your professor may provide you with a predetermined research question (or set of questions). However, in many cases, you’ll need to find your own research question within a certain topic area.

Finding a strong research question hinges on identifying a meaningful research gap – in other words, an area that’s lacking in existing research. There’s a lot to unpack here, so if you wanna learn more, check out the plain-language explainer video below.

Once you’ve figured out which question (or questions) you’ll attempt to answer in your research paper, you’ll need to do a deep dive into the existing literature – this is called a “ literature search ”. Again, there are many ways to go about this, but your most likely starting point will be Google Scholar .

If you’re new to Google Scholar, think of it as Google for the academic world. You can start by simply entering a few different keywords that are relevant to your research question and it will then present a host of articles for you to review. What you want to pay close attention to here is the number of citations for each paper – the more citations a paper has, the more credible it is (generally speaking – there are some exceptions, of course).

Ideally, what you’re looking for are well-cited papers that are highly relevant to your topic. That said, keep in mind that citations are a cumulative metric , so older papers will often have more citations than newer papers – just because they’ve been around for longer. So, don’t fixate on this metric in isolation – relevance and recency are also very important.

Beyond Google Scholar, you’ll also definitely want to check out academic databases and aggregators such as Science Direct, PubMed, JStor and so on. These will often overlap with the results that you find in Google Scholar, but they can also reveal some hidden gems – so, be sure to check them out.

Once you’ve worked your way through all the literature, you’ll want to catalogue all this information in some sort of spreadsheet so that you can easily recall who said what, when and within what context. If you’d like, we’ve got a free literature spreadsheet that helps you do exactly that.

Step 2: Develop a structure and outline

With your research question pinned down and your literature digested and catalogued, it’s time to move on to planning your actual research paper .

It might sound obvious, but it’s really important to have some sort of rough outline in place before you start writing your paper. So often, we see students eagerly rushing into the writing phase, only to land up with a disjointed research paper that rambles on in multiple

Now, the secret here is to not get caught up in the fine details . Realistically, all you need at this stage is a bullet-point list that describes (in broad strokes) what you’ll discuss and in what order. It’s also useful to remember that you’re not glued to this outline – in all likelihood, you’ll chop and change some sections once you start writing, and that’s perfectly okay. What’s important is that you have some sort of roadmap in place from the start.

At this stage you might be wondering, “ But how should I structure my research paper? ”. Well, there’s no one-size-fits-all solution here, but in general, a research paper will consist of a few relatively standardised components:

• Introduction
• Literature review
• Methodology

Let’s take a look at each of these.

First up is the introduction section . As the name suggests, the purpose of the introduction is to set the scene for your research paper. There are usually (at least) four ingredients that go into this section – these are the background to the topic, the research problem and resultant research question , and the justification or rationale. If you’re interested, the video below unpacks the introduction section in more detail. 

The next section of your research paper will typically be your literature review . Remember all that literature you worked through earlier? Well, this is where you’ll present your interpretation of all that content . You’ll do this by writing about recent trends, developments, and arguments within the literature – but more specifically, those that are relevant to your research question . The literature review can oftentimes seem a little daunting, even to seasoned researchers, so be sure to check out our extensive collection of literature review content here .

With the introduction and lit review out of the way, the next section of your paper is the research methodology . In a nutshell, the methodology section should describe to your reader what you did (beyond just reviewing the existing literature) to answer your research question. For example, what data did you collect, how did you collect that data, how did you analyse that data and so on? For each choice, you’ll also need to justify why you chose to do it that way, and what the strengths and weaknesses of your approach were.

Now, it’s worth mentioning that for some research papers, this aspect of the project may be a lot simpler . For example, you may only need to draw on secondary sources (in other words, existing data sets). In some cases, you may just be asked to draw your conclusions from the literature search itself (in other words, there may be no data analysis at all). But, if you are required to collect and analyse data, you’ll need to pay a lot of attention to the methodology section. The video below provides an example of what the methodology section might look like.

By this stage of your paper, you will have explained what your research question is, what the existing literature has to say about that question, and how you analysed additional data to try to answer your question. So, the natural next step is to present your analysis of that data . This section is usually called the “results” or “analysis” section and this is where you’ll showcase your findings.

Depending on your school’s requirements, you may need to present and interpret the data in one section – or you might split the presentation and the interpretation into two sections. In the latter case, your “results” section will just describe the data, and the “discussion” is where you’ll interpret that data and explicitly link your analysis back to your research question. If you’re not sure which approach to take, check in with your professor or take a look at past papers to see what the norms are for your programme.

Alright – once you’ve presented and discussed your results, it’s time to wrap it up . This usually takes the form of the “ conclusion ” section. In the conclusion, you’ll need to highlight the key takeaways from your study and close the loop by explicitly answering your research question. Again, the exact requirements here will vary depending on your programme (and you may not even need a conclusion section at all) – so be sure to check with your professor if you’re unsure.

Step 3: Write and refine

Finally, it’s time to get writing. All too often though, students hit a brick wall right about here… So, how do you avoid this happening to you?

Well, there’s a lot to be said when it comes to writing a research paper (or any sort of academic piece), but we’ll share three practical tips to help you get started.

First and foremost , it’s essential to approach your writing as an iterative process. In other words, you need to start with a really messy first draft and then polish it over multiple rounds of editing. Don’t waste your time trying to write a perfect research paper in one go. Instead, take the pressure off yourself by adopting an iterative approach.

Secondly , it’s important to always lean towards critical writing , rather than descriptive writing. What does this mean? Well, at the simplest level, descriptive writing focuses on the “ what ”, while critical writing digs into the “ so what ” – in other words, the implications. If you’re not familiar with these two types of writing, don’t worry! You can find a plain-language explanation here.

Last but not least, you’ll need to get your referencing right. Specifically, you’ll need to provide credible, correctly formatted citations for the statements you make. We see students making referencing mistakes all the time and it costs them dearly. The good news is that you can easily avoid this by using a simple reference manager . If you don’t have one, check out our video about Mendeley, an easy (and free) reference management tool that you can start using today.

Recap: Key Takeaways

We’ve covered a lot of ground here. To recap, the three steps to writing a high-quality research paper are:

• To choose a research question and review the literature
• To plan your paper structure and draft an outline
• To take an iterative approach to writing, focusing on critical writing and strong referencing

Remember, this is just a b ig-picture overview of the research paper development process and there’s a lot more nuance to unpack. So, be sure to grab a copy of our free research paper template to learn more about how to write a research paper.

You Might Also Like:

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• Search Menu
• Advance articles
• Virtual Issues
• Author Guidelines
• Open Access
• Self-Archiving Policy
• About Journal of Pediatric Psychology
• About the Society of Pediatric Psychology
• Editorial Board
• Student Resources
• Advertising and Corporate Services
• Dispatch Dates
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

• < Previous

Reporting of Demographics, Methodology, and Ethical Procedures in Journals in Pediatric and Child Psychology

• Article contents
• Figures & tables
• Supplementary Data

Sarah K. Sifers, Richard W. Puddy, Jared S. Warren, Michael C. Roberts, Reporting of Demographics, Methodology, and Ethical Procedures in Journals in Pediatric and Child Psychology, Journal of Pediatric Psychology , Volume 27, Issue 1, January 2002, Pages 19–25, https://doi.org/10.1093/jpepsy/27.1.19

• Permissions Icon Permissions

Objective: To identify potential problems in methodology reporting that may limit research interpretations and generalization.

Methods: We examined the rates at which articles in four major journals publishing research in pediatric, clinical child, and child psychology report 18 important demographic, methodological, and ethical information variables, such as participants' gender, socioeconomic status, ethnicity, inclusion/exclusion criteria, and consent and assent procedures.

Results: Overall, participants' ages, genders, and ethnicity were reported at moderate to high rates, whereas socioeconomic status was reported less often. Reports of research methodology frequently did not include information on how and where participants were recruited, the participation/consent rates, or attrition rates. Consent and assent procedures were not frequently described.

Conclusions: There is wide variability in articles reporting key demographic, methodological, and ethical procedure information. Necessary information about characteristics of participation samples, important for drawing conclusions, is lacking in the flagship journals serving the child psychology field.

Among the hallmarks of the sciences, including the science of psychology, are an objective perspective and the ability to evaluate and replicate research methodology. Inherent in these is the comprehensive and accurate description of the research sample, the population from which it is drawn, and the methodology used to gather the data. In recognition of the communication requirements for science, the Publication Manual of the American Psychological Association ( APA, 1994 ) states that, when the participants in a research study are human, certain information should be presented in the Method section of a manuscript considered for publication in a journal (see Section 1.09, pp. 12-15). This necessary information includes details regarding major demographic variables, the number of participants, method of selecting participants, assignment to groups, agreements made, payments made, and the number of participants who withdrew from the study and why. Additionally, this information may include, but is not limited to, ethnicity, level of educational attainment, and type of geographic area in which the participants reside.

The APA Publication Manual has assumed a leading position in dictating publication standards, not only for the primary APA journals but also for the numerous other journals in psychology and related fields. As representative of agreed-upon standards, the Publication Manual presented the reasons for fully describing the research participants:

Appropriate identification of research participants and clientele is critical to the science and practice of psychology, particularly for assessing the results (making comparisons across groups), generalizing the findings, and making comparisons in replications, literature reviews, or secondary data analyses. The sample should be adequately described. (p. 13)

Furthermore, the precise reporting of methods and demographics is especially important when determining the generalizability of research findings with children and adolescents. This is particularly important because psychologically manifested differences as a result of gender, development, or other factors may be more prominent in children and adolescents. Lack of adequate information is a methodological weakness placing considerable constraints on interpretation and conclusions in pediatric and clinical child psychology.

The Publication Manual also states that, in order to be published in an APA journal, either the manuscript or a cover letter to the editor of the journal should indicate that the researchers followed all ethical standards set forth in the APA Ethical Principles of Psychologists and Code of Conduct ( APA, 1992 ). These guidelines seek to ensure the protection of the interests of the participants, as well as providing important information to the consumers of the research.

Given the importance for published articles to present demographic, methodological, and ethics-related information, it is worthwhile to periodically examine psychology publications for compliance to these standards of scientific communication. A few previous reports have provided some information related to the completeness of research articles in describing the characteristics of the sample and the ethical procedures used in the study (e.g., Bernal & Enchautegui-de-Jesus, 1994 ; Betan, Roberts, & McCluskey-Fawcett, 1995 ; Graham, 1992 ; Park, Adams, & Lynch, 1998 ; Phares & Compas, 1992 ; Ponterotto, 1988 ). These reports indicate that there is considerable neglect of methodological information in published articles, with some discrepancy depending on the variable and the specialty. The research methodology literature has long called for comprehensive description of research samples (e.g., Bordens & Abbott, 1996 ; Hersen & Bellack, 1984 ).

Content analyses of journals help discern patterns in the development of a field or subdiscipline and provide objective “snapshots” useful in evaluating its science ( Elkins & Roberts, 1988 ; Peterson, 1996 ; Roberts, McNeal, Randall, & Roberts, 1996 ). They provide the field with an additional tool for assessing its past and current status. This examination is important because it allows for self-correction when oversights are detected, as well as the opportunity to set new directions. Consequently, we applied the technique of journal content analysis to determine the presence of and utility of comprehensive information reported in four publication outlets in pediatric and clinical child psychology. This study focused on the rates at which articles reported key demographic, methodological, and ethical variables such as number of participants, age, gender, ethnicity, socioeconomic status (SES), location of participants, rewards given to participants, exclusion and inclusion criteria, attrition, and consent and assent procedures.

The database included all empirical research articles published during 1997 in Journal of Pediatric Psychology (JPP , 58 articles), Journal of Clinical Child Psychology (JCCP , 52 articles), Child Development ( CD , 94 articles), and Journal of Abnormal Child Psychology ( JACP , 56 articles). Review articles, editorial articles, addresses, case studies, and studies that did not include human participants were excluded from this review. In total, 260 articles were coded and included in this study.

Coding Procedure

The coding procedure was based on the procedure used by previous content analyses ( Betan et al., 1995 ; Elkins & Roberts, 1988 ; Roberts, 1992 ). For articles containing more than one study, the studies were coded separately. Four graduate students read and coded all the articles. Interrater reliability was calculated on over 10% of the articles. Each article was coded using a checklist with 18 items regarding characteristics of the study and its participants. Kappa interrater reliability coefficients are presented, as well as the percent agreement between rates for each coded variable: (1) ages (1.0; 100%), (2) gender (.47; 92%), (3) ethnic distribution (.92, 96%), (4) SES (.55; 77%), (5) identification/selection of sample (e.g., requested, teacher recommended, records:.38, 81%), (6) population (e.g., general/school children, physical disability: 1.0; 100%), (7) setting of sample (e.g., school, psychological clinic, hospital:.29; 77%), (8) method of contacting participants (e.g., via mail, information sent via child:.57; 81%), (9) number of contacts requested (.36; 77%), (10) total contact time (.77; 89%), (11) exclusion/inclusion criteria (.34; 69%), (12) attrition (.35; 81%), (13) reliability of dependent measures used in study (.43; 73%), (14) number of participants (1.0; 100%), (15) location (geographically where sample was recruited:.58; 85%), (16) reward offered for participation or time/expense (1.0; 100%), (17) consent rate (after solicitation, participants who agreed to study versus those who did not: (.74; 88%), and (18) child assent (.47; 92%). Lower kappa coefficients were observed on a number of variables in part due to the lack of variability observed within some variables. (A copy of the decision rules for coding can be obtained from Michael Roberts.)

The frequency and percentage of articles reporting the variables were calculated for each journal individually, as well as for the journals overall (see Table I ). As can be seen in the total column, some variables tend to get reported at a fairly high rate across journals. The number of participants is reported in all the journals at a 100% level. The participants' ages are included very close to that perfect mark, as are the types of population from which the sample is drawn. Also reported at a high rate are setting of the research, the gender of the participants, and the methods of identifying and selecting participants. At the middle levels of reporting overall are characteristics such as participants' ethnicity, SES, exclusion/inclusion criteria used, reliability reporting, number of contacts requested, and methods of contacting participants. Low rates of reporting were found overall for child assent, parent consent, attrition rates, whether rewards were used, the location of the research project, and total contact time.

Frequency and Percentages of Articles Reporting Demographic, Methodological, and Ethical Information

Within these overall trends, the journals varied as to whether each included or omitted some of the information components. One-sample t tests were conducted for each journal to determine if the frequency with which each reported study variable differed significantly from the other journals. The mean of the four journals on each variable was used as the test value (all analyses used two-tailed levels of significance). Based on these analyses, JPP reported identification/selection methods ( t [57] = -3.061, p =.003), setting of the sample ( t [57] = -3.952, p <.001), method of contacting participants ( t [57] = -3.258, p =.002), exclusion/inclusion criteria ( t [57] = -2.475, p =.016), and parental consent ( t [57] = -2.548, p =.014) significantly more frequently than the other journals. JCCP reported gender ( t [51] = -6.202, p <.001), ethnicity ( t [51] = -4.263, p <.001), and child assent procedures ( t [51] = -2.356, p =.022) significantly more frequently than the other journals, whereas it reported significantly less frequently information on total contact time ( t [51] = 2.833, p =.007). CD reported the number of contacts requested ( t [93] = -3.791, p <.001) and total contact time ( t [93] = -2.638, p =.010) significantly more frequently than the other journals, whereas it reported information less frequently than the other journals on ethnicity ( t [93] = 2.114, p =.037), identification/selection methods ( t [93] = 3.245, p =.002), setting of the sample ( t [93] = 2.573, p =.012), method of contacting participants ( t [93] = 2.575, p =.012), parental consent ( t [93] = 3.092, p =.003), and child assent procedures ( t [93] = 3.597, p =.001). The rates of reporting variables in JACP did not differ significantly from the other journals.

A basic demographic description of the participants' gender ranged from a low of 80.4 % ( JACP ) to a high of 98.1% ( JCCP ). The percentage of articles that described the ethnic distribution of the participants varied greatly from a low 52.1% ( CD ) to a high of 84.6% ( JCCP ). CD reported the low of 43.6% of the participants' SES while JPP reported the high of 51.7%. The rate at which articles in this study indicated the geographic location of the sample ranged from the low 31% of JPP to a high of 42.6% of CD . The percentage of studies reporting whether a reward was offered was small and varied from 13.5% ( JCCP ) to 25.9% ( JPP ). The percentage of articles reporting consent rate differed from the low of 27.7% ( CD ) to 58.6% ( JPP ). The rate of reporting child assent also was low and significantly varied 8.5% ( CD ) to 34.6% ( JCCP ). Although just over half of the articles reported inclusion/exclusion criteria, the rate varied significantly from 48.1% ( JCCP ) to 70.7% ( JPP ). Across journals, just over a fourth of the articles reported causes for attrition, although rates differed from 19.6% ( JACP ) to 36.2% ( JPP ).

In general, the results of this study suggest wide variability in the percentage of articles that reported key demographic, methodological, and ethical procedure items. This variability was observed across journals and across variables. The conclusion seems clear that, in general, articles published in flagship journals serving the pediatric and child psychology field do not provide needed information about characteristics of their participation samples. These journals ostensibly adhere to the APA Publication Manual for manuscript preparation, which calls for authors to include this detailed information.

The participants' ages and gender tend to be reported at a fairly high rate. This rate for age is higher than for “adult” research journals such as Health Psychology ( Park et al., 1998 ) likely because these four journals have more of a developmental focus. Ethnicity information, although left out of many articles, seems to be higher than found in previous content analyses and in other specialties in psychology. Ethnicity description is likely present in these later reports because of the many efforts to enhance recognition of diversity issues in psychology research (e.g., Iijima Hall, 1998 ). Of course, the overall percentage of 63.1% indicates only that this information was reported in some form, even if only a general statement of predominant ethnicity, not specific breakdowns. Such global information does not indicate anything about the ethnic representativeness of the sample to the larger population, degree of acculturation, or other aspects, for example. Similarly, the SES information was provided in about half of the studies. Age, gender, ethnicity, and SES are demographic characteristics important to most of the psychological variables under study in these research articles. The omission of even this basic or minimal information restricts the research consumers' ability to draw proper conclusions.

How to report ethnicity and cultural variables for research publications requires further clarification by the field, given the complexities inherent in these phenomena. Our analyses indicated only whether some information was presented, not the precision with which the information was reported. When ethnicity of the participants was indicated in the articles we analyzed, what typically was included was a general statement about race (i.e., African American, Asian American, Hispanic American, Native American, Caucasian, or Euro-American). Unfortunately, the majority of the articles did not describe elements usually included in the concept of ethnicity, such as language, religion, degree of acculturation, and nation of origin. Overall, the field likely would need a minimal standard of reporting ethnicity and culture established by a consensus or editorial degree. When ethnicity might be conceived as a major influence or related to other psychological variables under study, then more elaborate conceptualizations would be needed. Psychologists might benefit from conceptualizations arising from controversies on ethnicity and culture in anthropology and sociology ( Jenkins, 1997 ; Malik, 1996 ; Solomos & Back, 1996 ).

Knowing the rates of consent/participation helps us to discern the representativeness of the sample from the overall population and to draw generalizable conclusions. A low rate of participation may or may not be a problem, depending on the circumstances of recruitment and the psychological variables under study. Too few research articles included this information, which is needed to form any consensus for acceptable ranges of participation.

The attrition rates were significantly under-reported. This information is important in determining the representativeness of the sample. Attrition may indicate whether the procedures biased the results, for example, because participants could not complete all aspects of an experiment or data gathering through fatigue, lack of interest, or alienation. Knowledge of attrition is also critical for evaluating clinical interventions. Essentially, differential attrition can bias results and invalidate research findings or mislead consumers of the research.

Reporting of parental permission/consent and child assent procedures as ethical information remains relatively low, despite the fact that two of the journals ( JPP and JCCP ) have instructions to include these procedures. Some information on how consent/assent procedures were handled may have been conveyed in a submission letter to the editor, and for two of the journals, authors of manuscripts accepted for publication sign an ethics compliance form indicating all procedures comply with the APA ethical code. In no way do we want to imply that these investigators were unethical in their research practices by omitting reports of consent and/or assent ( Roberts & Buckloh, 1995 ), and much of the research in the United States has been reviewed by institutional review boards. We can conclude only that the authors did not report this information. Certainly, in the case where journals report research with infants (e.g., CD ), child assent would be inappropriate. Even though avowal of proper use of consent is required to be published in these journals, reporting consent/assent procedures explicitly models ethical practices in research for fellow scientists and symbolizes adherence to ethical practices in research. Furthermore, perhaps more than the presence of a consent/assent form should be reported. Perhaps researchers should include relevant information such as the power differential between researcher and participant or the information the participant was actually given about the study.

Researchers experience the “judgment calls” of editors and reviewers (and usually make such calls themselves when roles are reversed) when manuscripts are reviewed for acceptance/rejection. Such judgments may include decisions about whether a participant sample is adequate from which to draw conclusions. Missing data about a sample, however, may not be caught in the editorial review and, as evidenced here, articles will be published without important pieces of information. Of course, including some information about the sample may affect submission/publication because aspects of the report sample seemingly fall short of some ill-defined criteria (e.g., about what constitutes a currently acceptable return rate of participation or about what is a necessary ethnic distribution of participants). At this time, there is currently not enough information in the literature on which to make this type of judgment.

In the interest of fairness in the publication process, but more important, for the advancement of the science in pediatric and child psychology, we suggest that all manuscripts be held to a standard of comprehensive reporting. If this happens, the field eventually will have a more complete picture from which to draw conclusions about psychological phenomena.

Commentary on the rates at which articles within a journal report the variables explored in this study is not meant to be a judgment of the quality of the research, journal, or editor. Although not reporting the variables considered in this study does restrict the reader's ability to evaluate articles, justification for these omissions may be reasonable. For example, the researchers may believe that some variables were not crucial to understanding their study. Nonetheless, if a standard of comprehensive reporting were used, then consumers of research would be able to judge for themselves the value of these variables. Furthermore, the journals' submission requirements or editorial review might not encourage the reporting of such variables. On the other hand, the researchers may believe that the editor's or the consumer's perception of the worth of their study may be negatively affected by reporting demographic characteristics that are not consistent with those found in the population of interest or by describing less than ideal methodological variables. These variables may then be submerged or obfuscated through global statements.

A couple of examples may illustrate best the deficiencies of reporting even basic information. One coded article on the psychometric development of a screening instrument for young children failed to report anything on the variables of gender, ethnicity, SES, location, identification/selection of the sample, consent rates, attrition, or reliability. This article passed the editorial review, but we question the use of the measure when the consumer has no knowledge about the group on which it was normed. An article on cross-cultural comparison of a widely used behavior problem checklist failed to indicate the ethnicity of the sample and provided no information on location, SES, attrition, and inclusion/exclusion criteria. As the results indicate, we could describe many articles in which critical information was lacking. Although these articles seem particularly egregious, there may be some benign omissions of information. However, an article author may not know how future researchers and clinicians might use the research findings since they will lack critical aspects of a study. Interpretation of findings is limited by this lack of information.

A primary consequence of research articles failing to report demographic and methodological variables is that consumers are not able to estimate whether the sample is representative of the population of interest or if procedures were adequate. As suggested by Betan et al. ( 1995 ), the more representative a sample is of the population being studied, the more likely the findings will generalize to the desired population. We were interested here in determining generally whether information on those characteristics is being reported; therefore, dichotomous coding of presence or absence of information was used in this study, not assessing the level of detail or meticulousness. Based on these overall results, greater precision could be employed in future work to consider the actual degree of representativeness for one or more of the variables in particular lines of research.

Based on our findings, like others before us, we suggest that journal editors and reviewers require, and researchers follow through by including, more demographic and methodological information in their articles. Describing this information in journal publications would enhance the scientific development of the field and the clinical applicability of the research. We hope that, in the future, the reporting of this demographic and methodological information will attain a 100% level for reporting the key variables needed for conclusions and interpretation in pediatric and child psychology research. The “gold standard” of reporting all these variables in all journal articles is a lofty goal and would necessitate changes in common writing and reviewing processes. As certain issues are highlighted in contemporary research, needs for reporting information may change over time. For example, the recent trend toward ensuring explicit accountability in ethical procedures provides pressure to report practices that might not have been the focus of such attention in the past. Similarly, issues of culture and ethnicity in research seem to assume greater emphasis more recently. Such a higher standard of reporting would require effort on the part of researchers to include such information and diligence on the part of reviewers and editors to ensure such information is included. This time and effort seems small in comparison to other resources invested in the research enterprise, yet it has such potential for advancing scientific rigor within research in pediatric and child psychology.

This article is based on a poster presentation at the Kansas Conference in Clinical Child Psychology, Lawrence, in October 1998.

American Psychological Association (APA) ( 1992 ). Ethical principles of psychologists and code of conduct. American Psychologist , 12 , 1597 -1611.

American Psychological Association (APA) ( 1994 ). Publication manual of the American Psychological Association (4th ed.). Washington, DC: Author.

Bernal, G., & Enchautegui-de-Jesus, N. ( 1994 ). Latinos and Latinas in community psychology: A review of the literature. American Journal of Community Psychology , 22 , 531 -558.

Betan, E. J., Roberts, M. C., & McCluskey-Fawcett, K. ( 1995 ). Rates of participation for clinical child and pediatric psychology research: Issues in methodology. Journal of Clinical Child Psychology , 24 , 227 -235.

Bordens, K. S., & Abbott, B. B. ( 1996 ). Research design and methods: A process analysis (3rd ed.). Mountain View, CA: Mayfield.

Elkins, P. D., & Roberts, M. C. ( 1988 ). Journal of Pediatric Psychology: A content analysis of articles over its first 10 years. Journal of Pediatric Psychology , 13 , 575 -594.

Graham, S. ( 1992 ). “Most of the subjects were White and middle class”: Trends in published research on African Americans in selected APA journals, 1970-1989. American Psychologist , 47 , 629 -639.

Hersen, M., & Bellack, A. S. ( 1984 ). Research in clinical psychology. In A. S. Bellack & M. Hersen (Eds.), Research methods in clinical psychology (pp. 100 -138). New York: Pergamon.

Iijima Hall, C. C. ( 1998 ). Cultural malpractice: The growing obsolescence of psychology with the changing U.S. population. American Psychologist , 52 , 642 -651.

Jenkins, R. ( 1997 ). Rethinking ethnicity: Arguments and explorations . London: Sage.

Malik, K. ( 1996 ). The meaning of race: Race, history and culture in Western society . New York: New York University Press.

Park, T. L., Adams, S. G., & Lynch, J. ( 1998 ). Sociodemographic factors in health psychology research: 12 years in review. Health Psychology , 17 , 381 -383.

Peterson, L. ( 1996 ). Establishing the study of development as a dynamic force in health psychology. Health Psychology , 15 , 155 -157.

Phares, V., & Compas, B. E. ( 1992 ). The role of fathers in child and adolescent psychopathology: Make room for daddy. Psychological Bulletin , 111 , 387 -412.

Ponterotto, J. G. ( 1988 ). Racial/ethnic minority research in the Journal of Counseling Psychology: A content analysis and methodological critique. Journal of Counseling Psychology , 35 , 410 -418.

Roberts, M. C. ( 1992 ). Vale dictum: An editor's view of the field of pediatric psychology and its journal. Journal of Pediatric Psychology , 17 , 785 -805.

Roberts, M. C., & Buckloh, L. M. ( 1995 ). Five points and a lament about Range and Cotton's “Reports of assent and permission in research with children: Illustrations and suggestions.” Ethics & Behavior , 5 , 333 -344.

Roberts, M. C., McNeal, R. E., Randall, C. J., & Roberts, J. C. ( 1996 ). A necessary reemphasis on integrating explicative research with the pragmatics of pediatric psychology. Journal of Pediatric Psychology , 21 , 107 -114.

Solomos, J., & Back, L. ( 1996 ). Racism and society . New York: St. Martin's Press.

• child psychology
• ethnic group
• generalization (psychology)
• socioeconomic factors
• experimental attrition

Email alerts

More on this topic, related articles in pubmed, citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1465-735X
• Print ISSN 0146-8693
• Copyright © 2024 Society of Pediatric Psychology
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Writing with Descriptive Statistics

Welcome to the Purdue OWL

This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.

Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

Usually there is no good way to write a statistic. It rarely sounds good, and often interrupts the structure or flow of your writing. Oftentimes the best way to write descriptive statistics is to be direct. If you are citing several statistics about the same topic, it may be best to include them all in the same paragraph or section.

The mean of exam two is 77.7. The median is 75, and the mode is 79. Exam two had a standard deviation of 11.6.

Overall the company had another excellent year. We shipped 14.3 tons of fertilizer for the year, and averaged 1.7 tons of fertilizer during the summer months. This is an increase over last year, where we shipped only 13.1 tons of fertilizer, and averaged only 1.4 tons during the summer months. (Standard deviations were as followed: this summer .3 tons, last summer .4 tons).

Some fields prefer to put means and standard deviations in parentheses like this:

If you have lots of statistics to report, you should strongly consider presenting them in tables or some other visual form. You would then highlight statistics of interest in your text, but would not report all of the statistics. See the section on statistics and visuals for more details.

If you have a data set that you are using (such as all the scores from an exam) it would be unusual to include all of the scores in a paper or article. One of the reasons to use statistics is to condense large amounts of information into more manageable chunks; presenting your entire data set defeats this purpose.

At the bare minimum, if you are presenting statistics on a data set, it should include the mean and probably the standard deviation. This is the minimum information needed to get an idea of what the distribution of your data set might look like. How much additional information you include is entirely up to you. In general, don't include information if it is irrelevant to your argument or purpose. If you include statistics that many of your readers would not understand, consider adding the statistics in a footnote or appendix that explains it in more detail.

• Dissertation
• PowerPoint Presentation
• Book Report/Review
• Research Proposal
• Math Problems
• Proofreading
• Movie Review
• Cover Letter Writing
• Personal Statement
• Nursing Paper
• Argumentative Essay
• Research Paper
• Discussion Board Post

How To Write A Statistics Research Paper?

Table of Contents

Naturally, all-encompassing information about the slightest details of the statistical paper writing cannot be stuffed into one guideline. Still, we will provide a glimpse of the basics of the stats research paper.

What is a stats research paper?

One of the main problems of stats academic research papers is that not all students understand what it is. Put it bluntly, it is an essay that provides an analysis of the gathered statistical data to induce the key points of a specified research issue. Thus, the author of the paper creates a construct of the topic by explaining the statistical data.

Writing a statistics research paper is quite challenging because the sources of data for statistical analysis are quite numerous. These are data mining, biostatistics, quality control, surveys, statistical modelling, etc.

Collecting data for the college research paper analysis is another headache. Research papers of this type call for the data taken from the most reliable and relevant sources because no indeterminate information is inadmissible here.

How to create the perfect statistics research paper example?

If you want to create the paper that can serve as a research paper writing example of well-written statistics research paper example, then here is a guideline that will help you to master this task.

Select the topic

Obviously, work can’t be written without a topic. Therefore, it is essential to come up with the theme that promises interesting statistics, and a possibility to gather enough data for the research. Access to the reliable sources of the research data is also a must.

If you are not confident about the availability of several sources concerning the chosen topic, you’d better choose something else.

Remember to jot down all the needed information for the proper referencing when you use a resource

Data collection

The duration of this stage depends on the number of data sources and the chosen methodology of the data collection. Mind that once you have chosen the method, you should stick to it. Naturally, it is essential to explain your choice of the methodology in your statistics research paper.

Outlining the paper

Creating a rough draft of the paper is your chance to save some time and nerves. Once you’ve done it, you get a clear picture of what to write about and what points should be worked through.

The intro section

This is, perhaps, the most important part of the paper. As this is the most scientific paper from all the papers you will have to write in your studies, it calls for the most logical and clear approach. Thus, your intro should consist of:

• Opening remarks about the field of the research.
• Credits to other researchers who worked on this theme.
• The scientific motivation for the new research .
• An explanation of why existing researches are not sufficient.
• The thesis statement , aka the core idea of the text.

The body of the text (research report, as they say in statistics)

Believe it or not, but many professional writers start such papers from the body. Here you have to place the Methodology Section where you establish the methods of data collection and the results of it. Usually, all main graphs or charts are placed here as a way to convey the results. All additional materials are gathered in the appendices.

The next paragraph of the paper will be the Evaluation of the gathered data . And that’s where the knowledge on how to read statistics in a research paper can come in handy. If you have no clue how to do it, you’re in trouble, to be honest. At least, you should know three concepts: odds ratios, confidence intervals, and p values. You can start searching for them on the web or in B.S.Everitt’s Dictionary of Statistics.

And the last section of the body is Discussion . Here, as the name suggests, you have to discuss the analysis and the results of the research.

The conclusion

This section requires only several sentences where you summarise the findings and highlight the importance of the research. You may also include a suggestion on how to continue or deepen the research of the issue.

Tips on how to write a statistics paper example

Here are some life hacks and shortcuts that you may use to boost your paper:

• Many sources where you take the statistical data , do offer it with the interpretation. Do not waste time on calculations and take the interpretation from there.
• Visuals are the must: always include a graph, chart, or a table to visualize your words.
• If you do not know the statistical procedure and how to interpret the results , never use it in the paper.
• Always put the statistics at the end of the sentence.
• If your paper requires the presentation of your calculations and you are not confident with it, ask a pro to help you.
• Different types of statistical data require proper formatting. Cite statistics properly according to the chosen format.

…Final thoughts

We hope that our guideline on how to write a statistics paper example unveiled the mystery of writing such papers.

But, in the case you still dread stats essays, here is a sound solution: entrust your task to the professionals! Order a paper at trustworthy writing service and enjoy saved time and the great result.

Psst… You can hand this work to a writer and have a completely stress-free evening. It would take like 3 minutes. And your perfect statistics research paper would be done on time!

Psychology Essay Topics

How to Write Essays about Beauty

I Want to Write My Essay, But I Can’t Figure Out How

How to Write a Demographics Report

by Danielle Smyth

Published on 8 Nov 2019

A business may initially believe that its products, goods and services will appeal to everyone, and on some level, this can be true. However, there will always be people who are more likely to be interested in one product over another, and learning how to focus on and cater to these particular markets is one of the best ways to be successful in today’s market landscape.

Rather than trying to make products that are acceptable to everyone, companies now work to make products that are exceptional to the key portions of the market that will want them. This approach requires a lot of data and a good understanding of demographics.

What Are Demographics?

Demographics are meaningful ways to divide a population to gain a better understanding of its characteristics. These are social and socioeconomic factors that can reveal how people in different situations will react to a proposal of some sort (mainly, the goods and services that are offered to them).

Demographics reveal broad characteristics about these groups and work in averages, so they aren’t a full capture and obviously will not represent the situations of every single individual within the demographic. They are meant to show trends and general behaviors rather than individualistic specifics. Demographic breakdowns usually begin with the following characteristics:

• Age: Populations are often broken up by age or by generation (baby boomers, millennials and so on) since one’s life condition can drastically change over the course of one’s life. It’s also generally accepted that generations behave differently in the marketplace and have different sets of values when it comes to their lifestyle.
• Gender: Since men and women are under different sets of societal pressures and expectations, it makes sense that their behavior toward the marketplace might differ. In addition, it’s important to include options for those who may not identify as either gender, as their priorities may differ greatly from the male/female demographic.
• Race: Again, since individuals of different racial backgrounds carry different sets of morals, ideals and values and also face very different societal expectations, racial demographics can be very revealing when looking at overall preferences and the way individuals make their choices.
• Marital status and number of children: Single individuals will spend their money differently than those who are married. Likewise, families have different spending priorities than those who choose to be childless. These demographics can be important breakdowns to determine target markets.
• Education level: Level of education doesn’t always correlate with individual income, but it can correlate with decision-making processes and different mindsets. In markets where these factors are relevant, individuals with doctorates may prove to behave differently than those with a GED.
• Annual income: As expected, those who earn more will have completely different market habits than those who earn less. Individuals below the poverty line will have their own set of behaviors, as will those who make comfortably more than they need for cost of living.

Other Types of Demographics

Other demographics that may be used depending on the report in question include location, home ownership (own vs. rent), political or religious affiliation, disabilities or occupation.

When retrieving this sort of data, surveys may also ask individuals to self-identify in a number of additional categories. This can include affinity categories such as cooking fans, car enthusiasts, sports fans and so on. It can also include asking individuals to categorize themselves into lower, upper or middle class, for example.

While demographics like income and living status might allow the analyst to sort individuals into class segregation, asking the individuals to sort themselves can also reveal valuable data on how these individuals view their own situations and preferences.

Why Create a Demographics Report?

Businesses may ask for a demographics report for a number of reasons. Executives may want to look at their sales demographics to see whether research and development can be done to better customize existing products for existing target markets, and they may want to look at where there may be sales opportunities for new products as well.

Marketing demographics can show where the company’s advertising strategies are having the most impact and whether resources can be better targeted on the most profitable market segments. In cases of the customer service or consumer social responsibility departments, demographics can reveal key ways they can make their outreach more effective to existing customers and to the business’s community.

The most common reason companies undertake the construction of a demographics report is to plan new marketing strategies or new product lines . Demographics are incredibly important within the field of marketing. It’s been shown that generic marketing designed to accommodate everyone is less effective than crafting a campaign targeted specifically to those who are most likely to buy.

Because marketing and especially advertisements can be so subjective, marketing experts can easily develop strategic advertisements meant to appeal to a certain socioeconomic group. Demographics reports are also used to develop future projections about the market. These predictions are then used to develop new products, hoping to meet the future needs of the consumer base.

Demographic Analysis Methods

The first step to building a concrete demographics report is to obtain information about the demographics in which the company is interested. In the United States, the Census Bureau conducts a survey census every 10 years to gather demographic information from the nation’s population.

Other organizations and the best demographic websites may conduct formal surveys to focus on specific regions. Analysts can also use local registries that keep records of information on the individuals within their purview. Many industries have organizations or trade associations that collect this data as well and make it available to anyone for a reasonable price.

The growth of the internet has also led to the collection of a substantial amount of individual behavioral information using cookies, apps and online analytics , which provides insight on completely new types of demographic divisions, like individual buying trends, search histories, social media use and spending habits. While the census provides information about social and population demographics — information that for the most part represents an individual’s situation rather than choices — information gathered online can produce valuable information about the way consumers make choices about their time, spending and habits.

Accuracy of Data

When obtaining this data, it’s important to be sure the information can be readily verified. Often, independent surveys and censuses may have internal biases toward certain race, economic class or income demographics. It’s easy for these internalized biases to skew data, which can lead to faulty conclusions.

Government agencies and third parties that have no conflicts of interest can usually provide the most accurate data sets. Also, be sure the data is as up to date as possible. Especially in today’s markets, with changes in technology, demographics can change dramatically over a seemingly short period of time.

With this in mind, if the information is available, it’s best to track demographics over time. This allows analysts to develop trends that will show market changes and help to predict future consumers. Tracking data over time can often be more helpful than simply providing a snapshot of what existed at the time a survey was taken.

Demographic Analysis Report

A company’s first step with a demographics report should be to determine the information for which it is looking and what questions it wants answered . This will help to focus the analysis and give it structure. Identify the goal of the report, what assumptions will have to be made with regard to the data and how the results will be used. Also, identify the target audience: Will this report go directly to a marketing manager, is it for the executive team or is it intended for research and development? This should influence the way the analysis is collated into a report.

From there, competent business data analysts should begin breaking down the data on hand in ways that can answer the questions. For example, if the company is looking to better focus its marketing campaign, the analysts should explore what demographics already purchase the company’s goods and services and compare them to other demographics to see whether there are marketing opportunities.

This should involve identifying the demographics that have made purchases: Which age group uses the company’s services the most? Is there any difference in male and female interest? Do families with children buy these products more often than single individuals? This can require careful statistical analysis of demographic trends in order to make meaningful conclusions. It’s important to allow the data to build a story about the customers rather than going into the report with expectations, which can unintentionally bias results.

Using the Demographics Report

The first step in using the report is to review the results and conclusions with the stakeholders for confirmation. The analytical team should explain their approach, their logic, any assumptions that were made and how they have drawn their conclusions. Occasionally, management might ask for confirmation or for more information. Once the results have been explained to the team that will be using the data, executive management will make decisions on how to change company direction .

Knowing this information can change a company’s marketing strategy. For example, if families are a demographic of interest, the marketing department could consider adding children to television ads, highlighting the family friendliness of the brand or offering deals and specials catering to parents with children.

As an alternate route, if families are already a well-established customer demographic, the marketing department might try to cater its ads and offers to single individuals. The company’s strategic plan should determine whether the company wants to focus its efforts on one sort of target demographic or attempt to add another demographic to its marketing efforts.

Trends Over Time

The demographics report should also include a section looking at trends over time, combining knowledge of the existing market with the demographic information to predict the future needs of key consumer segments. The company can then initiate research and development projects to create new products that should meet these future expectations.

Changes in demographics over time is also important. For example, a store that depends on local sales will want to watch demographics like income, age or household status so it can understand its own region and adjust marketing, prices and products as necessary.

Benefits of Demographics Reporting

At some point, all types of businesses can benefit from a demographics report, from smaller entrepreneurial startups to large corporate companies. Whether the marketing team consists of one person or an entire department, understanding which types of consumers are most likely to purchase the goods and services offered is a fundamental piece of success.

Even without direct marketing, this understanding helps business owners determine which parts of their products are the most important to consumers. This lets them focus resources on those pieces to further improve and differentiate their business. Luckily, there are resources available online to obtain this data as well as a number of companies that can happily perform this kind of analysis.

Demography Research Paper

Sample Demography Research Paper. Browse other research paper examples and check the list of research paper topics for more inspiration. If you need a research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our custom research paper writing service for professional assistance. We offer high-quality assignments for reasonable rates.

This research paper defines, circumscribes, and reviews the field of demography, providing insight into the breadth of issues covered by this interdisciplinary specialization. Attention is first directed to the discipline of demography, its definition, and conceptual and methodological character, 1 while later sections focus specifically on the various resources of demography. In addition to describing the resources and issues encompassed by the field, the paper also identifies what the authors believe to be three research areas requiring future attention. Finally, demography has not always been viewed primarily as a subfield of sociology. This issue is also explored in this research paper.

Academic Writing, Editing, Proofreading, And Problem Solving Services

Get 10% off with 24start discount code, what is demography.

When professors introduce demography and its subject matter in their graduate and undergraduate courses, many find useful what Bogue (1969) has proposed as the three basic demographic questions: (1) How large (or small) is the population? (2) How is the population composed in terms of the demographic characteristics of age and sex, and two additional characteristics closely aligned to demography, namely, race and marital status? and (3) How is the population distributed spatially? Answers to these questions are typically formulated in terms of the effects of the three demographic processes of fertility, mortality, and migration/mobility. A consideration of these materials leads to defining demography generally as the scientific study of the size, composition, and spatial distribution of human populations, and the changes that occur in these phenomena through the processes of fertility, mortality, and migration (Poston 2000).

The subject matter of demography is often divided into formal or mathematical demography and social demography or population studies (Hauser and Duncan 1959a). Formal demography may be distinguished from social demography by the substantive foci of the independent and dependent variables. Both approaches endeavor to model dependent variables that are demographic in nature; that is, they are concerned with one of the demographic processes of fertility, mortality, or migration or one of the demographic characteristics of age and sex. However, the independent variables of formal demography are also demographic, whereas those of social demography are nondemographic.

To illustrate, a formal demographer might examine among populations the influence of age composition on the birth rate or, alternately, the influence of the birth rate on age composition. Another illustration of a formal demographic exercise would be an analysis among cities of the effects of the sex composition of in-migrants on city death rates. In contrast, a social demographer might study the influence of a sociological independent variable, such as social class, on the death rate; or the effects of a social psychological variable, such as attitudes about motherhood, on desired and intended fertility; or the effects of a geographic variable, such as annual rainfall, on population density; or the influence of an economic variable, such as economic or livelihood opportunities, on the migration rate (Kammeyer and Ginn 1986). Social demography is necessarily broader in scope and orientation than formal demography. As Preston (1993) has written, it includes “research of any disciplinary stripe on the causes and consequences of population change” (p. 593).

Schofield and Coleman (1986) have brought these two approaches together, as follows:

The subject matter of demography may be imagined as being arranged within a sphere with a hard mathematical core and a softer socio-economic and biological rind. The core represents the specific technical property of demography; the mathematical theory which deals with statics and dynamics of population; vital rates in relation to the age structure, dynamics, growth and their perturbations, and all the techniques of measurement, analysis and substitution that follow. . . . But this hard core of demography does not touch the surface of the real world directly, except through measurement and reconstruction. It does so only when the population is made specific. An outer structure of theory and fact is then necessary to explain and predict that population’s response, through the specific agencies of independent biological, social and economic causes and consequences of population trends. In this outer region of demography, the numerical techniques and ideas of demography act as an interdisciplinary common currency. Demography, which deals with the hardest (biological) facts in social science, enables material from one subject to be used in conjunction with material drawn from another. This permits the risks of the fundamental human events of birth and death to be analyzed interchangeably by ideas which may draw on sociology, geography, history, biology and other subjects. (P. 5)

Demographers, however, do not always agree about the boundaries and restrictions of their field. Caldwell (1996) states the problem succinctly as follows:

What demography is and what demographers should be confined to doing remains a difficult area in terms not only of the scope of professional interests, but also of the coverage aimed at in the syllabuses for students and in what is acceptable for journals in the field. (P. 305)

In the United States, most graduate training programs in demography are located in departments of sociology, although this is not the case in many other countries. Some U.S. demographers thus argue that demography is best treated as a subdiscipline or specialization of sociology owing to this organizational relationship (Moore 1959:833). The late Kingsley Davis (1948), who served at different times as president of both the Population Association of America and the American Sociological Association, wrote in 1948 in his classic sociology textbook, Human Society, that “the science of population, sometimes called demography, represents a fundamental approach to the understanding of human society” (p. 551). The relationship between sociology and demography is hence a fundamental one: “Society is both a necessary and sufficient cause of population trends” (pp. 553–54).

Others subscribe to a broader purview of the discipline, particularly social demography, claiming that demography is not a specialization of sociology, or of any discipline, but a discipline in its own right. Consider the definition of demography in today’s most popular demography textbook, Population: An Introduction to Concepts and Issues, by John Weeks (2005), now in its ninth edition: “Demography is concerned with virtually everything that influences, or can be influenced by population size, distribution, processes, structures, or characteristics” (p. 5). It is no wonder that J. M. Stycos (1987) observed that “as a field with its own body of interrelated concepts, techniques, journals and professional associations, demography is clearly a discipline” (p. 616). J. C. Caldwell (1996) also reached this conclusion, but more for methodological reasons:

Demography will remain a distinct discipline because of its approach: its demand that conclusions be in keeping with observable and testable data in the real world, that these data be used as shrewdly as possible to elicit their real meanings, and that the study should be representative of sizable or significant and definable populations. (P. 333)

Earlier in this research paper, demography was defined as the scientific study of the size, composition, and spatial distribution of human populations and the changes that occur in these phenomena through the processes of fertility, mortality, and migration. How this activity, the study of population, is carried out and the results it produces depend on a set of disciplinary resources (Micklin and Poston 2005).

Demographic theories and models are statements of the evident or hypothesized course, causes, and/or consequences of demographic phenomena at varying levels of aggregation (Coale and Trussell 1996; Coleman and Schofield 1986; Hauser and Duncan 1959b). Demographic methods comprise a body of procedures and techniques for collecting, evaluating, adjusting, estimating, and analyzing demographic data, while demographic materials consist of the sources of raw data such as censuses, vital registrationsystems, population registers, and sample surveys (Hauser and Duncan 1959a; also see Siegel and Swanson 2004). The infrastructure of demography consists of the professional organizations, modes of disseminating ideas and research findings, and institutional sources of research support that influence the kinds of work done under the banner of the discipline and how the results are portrayed and received. Finally, demographic praxis refers to the use of demographic data and research findings by governments, businesses, and other organizations for predicting, planning, monitoring, and evaluating a wide range of demographic and nondemographic conditions, events, and trends (Siegel 2002). Each of these resources is discussed in detail in the next section. 3 This will serve as a further introduction to the subject matter of demography and how demographic research is carried out.

More Demography Research Papers:

The resources of demography, demographic theories and models.

In the last 50 years or so, a variety of views have been presented about the nature and status of demographic theory. In 1952, demographer Rupert Vance lamented the “poverty” of theory in demography. A decade later Robert Gutman (1960) wrote “in defense” of population theory, contending that “demography . . . continues to offer illuminating theoretical statements which organize knowledge, lead to the acquisition of new knowledge, and help in the solution of population problems” (p. 333). Hauser and Duncan (1959b) identified several important population theories, including those derived from Malthus, optimum population theory, demographic transition theory, and psychosocial theories of fertility. But they concluded by stating that “demographers in general may have much to gain from additional allocation of energy to deliberate efforts directed toward theory-construction in conjunction with the conduct of empirical research” (p. 104).

Recent assessments of the discipline of demography are less ambivalent about the adequacy of population theories. Writing in 1979, Charles Nam argued,

The issues of demographic journals today are replete with theoretically based articles, in stark contrast to those of the past. We no longer fall behind our fellow disciplines in theoretical development, and a merging of lower-order propositions into a theoretical whole is now as conceivable in demography as in any of the social sciences. (Pp. 490–91)

Yet a decade and a half later Eileen Crimmins (1993) stated that “although our theoretical approaches are considerably more complex now than in the past, demography still has highly developed theories in only a few areas. Fertility behavior is the exception” (p. 587). Other population scientists point to demographic transition theory as the theoretical staple of the discipline (Caldwell 1997; Kirk 1996; Lee 2003).

Although a variety of new or reformulated population theories have been proposed in recent decades, their clarification and evaluation remain a challenge for the field. On the other hand, demography has such an abundance of both formal theory and discursive theory that its theoretical accomplishments rival those of any of the other social sciences. Regarding formal theory, one need only consider, for instance, the richness and precision of stable population theory. Regarding discursive theory, few social sciences may claim as much theory as one finds in, say, the study of fertility. Prominent theories to explain fertility behavior include demographic transition theory, wealth flows theory, human ecological theory, political economic theory, feminist theory, proximate determinants theory, biosocial theory, relative income theory, and diffusion theory (see Caldwell 1997; Hirschman 1994). Any view among nondemographers that demography is void of theory was incorrect in the past and is incorrect today.

Demographic Methods

There is agreement among demographers about the significant advances that have occurred in the past 50 years in methods of data collection and analysis. In their systematic review of this topic, Hauser and Duncan (1959a) covered standard census procedures, vital registration systems, the sample survey, rudimentary data processing, and several types of administrative record systems. They also discussed techniques for evaluating, adjusting, estimating, and analyzing demographic data.

In the past half-century, improvements have been forthcoming in each of the techniques, partly through the application of advances in electronic information systems. National census taking is increasingly based on statistical sampling theory and techniques, resulting in more efficient and accurate data collection.

In recent decades, the uncertain quality and availability of demographic data have led to the development of a variety of techniques for evaluating, adjusting, estimating, and projecting population parameters (Ahlburg and Lutz 1998; Ahlburg, Lutz, and Vaupel 1998; Brass 1996; Coale and Demeny 1968; Keyfitz 1975, 1981; Siegel and Swanson 2004). Although the results of many of these exercises, particularly population forecasts, are notoriously inaccurate, their use continues.

Demographic Materials

This set of basic disciplinary resources may be divided into primary data sources and data compendia, for example, data banks. The most comprehensive and generalizable primary data source is the national population census. National census coverage has improved considerably since the end of World War II, largely through assistance provided to developing countries by the United Nations and a few other organizations. Among 94 developing countries with a population in the mid-1990s of at least 1 million, only 49 conducted a national population census in the decade of the 1950s, by the 1990s, that figure had risen to 71 countries (Cleland 1996). The content, completeness, and accuracy of information collected through censuses vary widely from one country to the next. Overall, the situation has surely improved worldwide.

Another important source of demographic information is the civil registration system, which typically collects information on demographic events such as births, deaths, and changes of civil status as they occur. Although not 100 percent accurate and complete, vital registration in the more developed nations is far better than in the poor nations. Cleland (1996) contends that although civil registration systems in developing countries are “seriously defective, it would not be correct that the data are of little value to demographers” (p. 435). Techniques have been developed for data adjustment and analysis, yielding a rough notion of trends and differentials in vital events.

Beginning in the 1970s, coordinated cross-national surveys emerged as an important source of demographic information. Between 1974 and 1986, sample surveys of reproductive behavior and related social and psychological indicators were conducted in 62 countries, representing 40 percent of the world’s population, under the auspices of the World Fertility Survey (Cleland and Hobcroft 1985; Cleland and Scott 1987). This effort was succeeded by another coordinated international program of research, the Demographic and Health Surveys, with 170 sample surveys carried out in 69 developing countries between 1986 and 2003. The obvious advantage of these surveys was the opportunity for comparative analysis and generalization of findings beyond a single population.

Less ambitious demographic surveys, typically focusing on a single country or community, have been a part of the demographer’s repertoire for decades. Early studies of fertility include the Indianapolis study (Kiser 1953; Kiser and Whelpton 1953), the Princeton study (Westoff, Potter, and Sagi 1963; Westoff et al. 1961), and surveys of family and reproductive behavior carried out in Puerto Rico (Hill, Stycos, and Back 1959; Stycos 1955). The number of demographic surveys has grown steadily over the years. Examples in the United States include the monthly Current Population Survey, the weekly health interview survey, and the various rounds of the National Survey of Family Growth (NSFG) carried out by the National Center for Health Statistics, the most recent being Cycle 6 conducted in 2002. Another important source of demographic information is the Adolescent Health Survey, which was started in the early 1990s by the Carolina Population Center at the University of North Carolina.

In short, in the past five decades, there has been an enormous increase in the availability of primary demographic data. The various sources differ in terms of data quality, but the trend has been toward better coverage and reduced error in census enumeration and collection of survey data. Moreover, the development of techniques to estimate missing values or reduce measurement error has increased the utility of these sources of demographic information.

Another welcome addition to the disciplinary resources of demography is the growing availability of repositories for demographic data. Some of these collections are longstanding and others are of more recent vintage (for discussion, see Micklin and Poston 2005).

Overall, the volume of demographic and populationrelated information resources has grown dramatically, particularly over the last two decades. The research-oriented demographer has a virtually unlimited access to multiple data banks and statistical yearbooks, many of them via the Internet (see below). Used judiciously, this rapidly increasing set of resources provides a means of examining linkages between population conditions and trends and a wide range of societal phenomena.

The Infrastructure of Demography

The development of any scientific discipline depends to an increasing extent on its organizational infrastructure, which includes several components. In the case of demography, these are four: (1) professional and affiliated organizations; (2) professional journals that serve as outlets for the results of demographic research; (3) Internet sites that facilitate communication among demographers, access to research ideas and reports, and retrieval of demographic data; and (4) the application of knowledge produced to resolve societal problems. Each of these infrastructure components is now discussed.

With respect to the first component, professional organizations, the oldest professional association of population scientists is the International Union for the Scientific Study of Population (IUSSP). The Union was founded officially in Paris in 1928 and in 1947 was reorganized as an association of 147 individual members representing 32 countries. By 2005, the IUSSP had grown to nearly 2,000 members, approximately one-third from developing nations. The IUSSP publishes a set of monographs covering diverse topics related to population; many are the result of scientific meetings sponsored by the IUSSP. The full meetings of the IUSSP are held every four years.

Shortly after the launch of the IUSSP, the Population Association of America (PAA) was organized in 1931 with 38 original members. By 1955, membership numbered 430, and as of the date of its 68th annual meeting in 2005, the organization had approximately 3,000 members. Annual meetings of the PAA are devoted to presentation and discussion of research reports and theoretical papers, some of which are published in the PAA’s official quarterly journal, Demography.

In 1983, the European Association for Population Studies (EAPS) was founded. EAPS organizes conferences, seminars, and workshops; disseminates populationrelated information; and publishes the European Journal of Population.

The Southern Demographic Association (SDA) is a scientific and educational society of demographers that was first organized in 1971 as the Southern Regional Demographic Group. The SDA has approximately 200 members and publishes a journal, Population Research and Policy Review.

These professional associations certainly do not exhaust those that exist worldwide. Their descriptions here are intended to illustrate the variety of activities undertaken by such organizations and to suggest that while not as large as many scientific disciplines, demography is a viable and flourishing profession.

In addition to the above-mentioned professional associations, there are many affiliated organizations that are more or less loosely linked with professional demographic organizations and with the discipline as a whole. They contribute to the activities of demographers via several functions, including (1) the funding of demographic research, (2) the public advocacy of important demographic and population-related issues and/or policy concerns, (3) the dissemination of demographic data and research findings, (4) the provision of population education, and (5) the delivery of services to address population problems and improve population health (see Micklin and Poston 2005 for more discussion).

Another component of infrastructure is demographic periodicals. In the 1950s, demographers had few specialized outlets for their work. Most demographic research was published in journals of sociology and economics. The only demographic journals available were the Italian journal Genus (1934), the Population Index (1935) (which was devoted primarily to bibliographic references), the Population Reference Bureau’s Population Bulletin (1945), the British journal Population Studies (1947), and the Indian journal Population Review (1957). There was a slow but steady increase in the 1960s in periodicals devoted to demography. Studies in Family Planning, published by the Population Council, made its appearance in 1963. A year later, the first issue of the official journal of the PAA, Demography, appeared along with the initial publication of the International Migration Review. In 1969, the Alan Guttmacher Institute issued the first volume of Family Planning Perspectives and followed it in 1975 with the International Family Planning Digest (which would later be called International Family Planning Perspectives ). The Population Council’s creation of the Population and Development Review in 1975 was a major addition to demography’s journal repertoire. Later debuts of demographic journals included Population and Environment (1978), Population Research and Policy Review (1981), the European Journal of Population (1985), Journal of Population Economics (1987), the English edition of the French journal Population (1989), Demographic Research (1999), and Applied Population and Policy (2004). Demographers today have many more opportunities to publish results of their research in discipline-friendly periodicals.

Another infrastructure component is Internet sites that facilitate communication among demographers, access to research ideas and reports, and retrieval of demographic data. Considering the case of demography, one cannot help but be impressed with changes in the infrastructure of the discipline resulting from Internet access (see Gryn 1997). However, given the rate of change of Web site addresses and the addition of new sites, it would be futile here to devote a great deal of space to site references. However, several useful sites will be mentioned that have a likelihood of stability.

The United Nations operates a Population Information Network (POPIN) at http://www.un.org/popin/. POPIN includes a list of relevant publications from the UN and affiliated organizations as well as a list of journals and newsletters with population content. The Population Reference Bureau operates a site (POPNET) (http:// www.popnet.org/) that includes links to a wealth of organizational sources (international, nongovernmental, university centers, associations, directories, “listservs,” and databases). The Office of Population Research of Princeton University provides access to its Population Index site (http://popindex.princeton.edu/index.html) with regular coverage of 400 journals. Finally, the Committee for International Cooperation in National Research in Demography (CICRED) offers access to a wide range of information.

Demographic Praxis

Here the concern is with the applications of demographic knowledge. In recent decades there have been considerable advances in this particular resource of demography. Applied demography is a thriving enterprise, providing employment for a sizeable number of demographers (Micklin 1992; Siegel 2002). Three specific examples of applied demographic activity will be mentioned.

First, demographers serve as advisors, witnesses, and technicians on matters of political apportionment and redistricting. Over time, populations become redistributed within political jurisdictions. Periodically, the decision is made to reassess the correspondence between population distribution and voting districts. In such cases, demographic expertise is invaluable.

Second, the increased size and rate of population growth as well as population density have been linked to environmental deterioration, particularly in less developed nations (Shi 2003; United Nations 2001; York, Rosa, and Dietz 2003). Demographers are frequently called to participate in multidisciplinary teams given the responsibility of developing a plan to halt the environmental damage.

Third, demographers are often asked to provide various types of population forecasts in conjunction with community development programs. Large-scale expansion of transportation facilities and construction of residential structures are likely to change patterns of population growth, distribution, and perhaps composition. Officials need research data to estimate the extent of disruption that will occur.

Research Challenges

There are three areas of demographic research that the authors of this research paper deem to be particularly relevant and important for research in future years. 4 These are areas that to date have received insufficient attention by demographers and, moreover, are areas many consider to be preeminent in terms of their actual or potential contribution to the state of demographic knowledge. They are (1) male fertility, (2) biosocial models of demography, and (3) sexual orientation. This is a short and selective listing. But these are areas that have impressed the authors of this research paper as important, relevant, and challenging. It is not known whether other demographers will agree with the selection.

Male Fertility

Why are males not included in the study of fertility? In discussions in both the scholarly and popular literatures, the methods and numbers pertaining to fertility rates almost always apply only to females but are referred to as fertility rates and fertility numbers, not as female fertility rates and female fertility numbers. In the development and testing of fertility theories in the demographic and social science literatures, the explanations are implicitly based on females but are referred to as fertility theories, not as female fertility theories.

But as everyone knows, biology requires that females and males must both intimately be involved in the production of children. Fertility is not a process that involves only women. So, why have males been ignored in conventional demographic studies of fertility? The answer is not because female and male fertility rates are the same. Although some might believe they should be, in fact they are not, and this is shown below.

It is not at all an understatement that until the past few years virtually all conventional demographic research on fertility has been devoted to analyses of women. Until recently, meetings of the PAA and the IUSSP seldom included sessions on the male side of fertility. Indeed, it has only been since the late 1990s that articles and book chapters on male fertility have started to appear in the demographic literature. In 1998, the journal Demography published a special issue on the topic of male reproduction. In 2000, a major paper appeared in the journal Population and Development Review (Greene and Biddlecom 2000) that evaluated current research and suggested directions for future research on male reproductive roles. And also in 2000, a monograph was published on Fertility and the Male Life-Cycle in the Era of Fertility Decline (Bledsoe, Lerner, and Guyer 2000) based in large part on the papers presented at a 1995 conference of the IUSSP.

POPLINE was consulted a few years ago for a review of the literature on the topic of fertility. The POPLINE search reported more than 75,000 fertility studies conducted between 1950 and 2000. Of these, only 381 dealt with fertility and reproduction behaviors involving males, two-thirds of which were biological and medical in orientation, focusing on such issues as spermatogenesis (e.g., Aitken et al. 1986) and medical and biological aspects of fertility regulation (Singh and Ratnam 1991). The other one-third mainly comprises papers investigating family planning policies (e.g., Adamchak and Adebayo 1987) and fertility regulation (Mbizvo and Adamchak 1992), male attitudes toward fertility and family planning (Micklin 1969), and economic considerations and cultural factors that shape male fertility (Muvandi 1995). Most of the fertility analyses uncovered in the POPLINE search that included males (often along with females) were published in the 1990s.

So, why has conventional demographic research in fertility concentrated largely, if not exclusively, on women? Seven specific reasons may be proposed to justify excluding males from fertility studies (Poston et al. 2005:871–72). First, Greene and Biddlecom (2000) write that the (1) “most important barrier to the inclusion of men in demographic research was normative and reflected the socialization of influential demographers and the research course they set” (p. 83). Men were regarded principally as breadwinners, and “as typically uninvolved in fertility except to impregnate women and to stand in the way of their contraceptive use” (p. 83). This is a gender-related perspective and focuses significantly on the social construction of the male gender role. The reasoning is biological, not sociological. This is hardly a satisfactory justification for ignoring males in fertility studies.

Keyfitz (1977) notes (although does not necessarily endorses) four more reasons. Two of them are that (2) data on parental age at the birth of a child are more frequently collected on registration certificates for the mothers than for the fathers; and (3) when such data are obtained for mothers and fathers, there are a greater number of instances of unreported age data for fathers, and this is especially the situation for births occurring outside marriage.

While it is true that demographic surveys have tended to focus more on women than on men, this situation has improved significantly in recent years. Also, birth registration certificates, particularly in the developed world, now typically include data on both parents. Certificates for births occurring outside marriage, however, occasionally still do omit data on fathers. Finally, Coleman (2000:43) notes that as of 1995, 15 countries in the industrialized world have published, at one or more times in recent years, data and/or rates on male fertility in their demographic yearbooks or related publications.

The next two reasons mentioned by Keyfitz (1977) are (4) the fecundity, and hence, the childbearing years of women occur in a more sharply defined and narrower range (15–49) than they do for men (15–79); and (5) “both the spacing and number of children are less subject to variation among women; a woman can have children only at intervals of 1 or 2 years, whereas a man can have hundreds” (p. 114). The fourth point is true theoretically, and indeed “in polygamous populations a man’s fertility can remain high well into his fifties and sixties; . . . [however], in controlled fertility societies, it peaks . . . with a mode in the mid-twenties” (Coleman 2000:41). This is due in part to low fertility norms in Western societies, as well as to a small average age difference of about two to three years between men and women in first marriages. Regarding the fifth point, Guyer (2000) observes that although biologically a man has the potential for siring dozens more children than a woman, this large difference in number of children ever born only occurs in a few societies and “amongst a tiny minority of the population” (p. 64).

Another reason is that (6) female fertility rates are thought to be more fundamental because they are more physiological; that is, they are more bound by biological limitations, and hence are more influenced by the proximate determinants than are male rates. Indeed, several of the proximate determinants are virtually “man-free” (Coleman 2000:31) and thus less tractable. Also “mothers remember events such as miscarriages and deaths in early childhood more clearly than fathers do, and there is no ambiguity as to whether a child is theirs or not” (Greene and Biddlecom 2000:85). The fact that births are more tractable to mothers than to fathers cannot be ignored. But this fact makes it all the more necessary to include males in fertility studies, if for the only reason that by including males, one would then be able to estimate the degree of false paternity in a population, a subject about which little is known. Moreover, Greene and Biddlecom (2000) observe that “since demographers do not limit themselves to counting but also attempt to explain and predict fertility behavior, this methodological justification is patently weak” (p. 85).

The last reason proposed to justify the exclusion of men in studies of fertility is (7) the incompatibility of male and female fertility rates. Unless the population is closed and has a stable age distribution, the rates will likely be different. The differential rates are due to a host of causes that are well known to demographers, some of which are that more males are born than females, males have higher agespecific death rates than females, males marry at older ages than females, males remarry more quickly than females, and emigration and immigration both are often sex selective. These and other factors act together to produce male and female fertility rates that are not the same.

The United Nations (2002) has assembled a natality database that includes age-specific fertility rates (ASFRs) for males and females for various years in the 1990s. Poston, Baumle, and Micklin (2005) have calculated male and female total fertility rates (TFRs) for 19 countries for 1994. They report that most countries have male TFRs that are actually larger than their female TFRs. For instance, Tunisia and Panama show male TFRs that are 623 and 674 births, respectively, larger than their female TFRs. Among those few countries with larger female TFRs than male TFRs, Australia and the United States show the greatest differences, with female TFRs that are 915 and 201 births, respectively, larger than their male TFRs. Only a few countries, namely, Singapore, Canada, and Denmark, have male and female TFRs that are near equal (see Poston et al. 2005:873 for a similar analysis of the counties of Taiwan).

The fact that male and female fertility rates are not the same makes it all the more important and necessary to analyze male fertility along with female fertility. The factors causing the differentials vary over time in their magnitude and effects on the male and female fertility rates. In some cases, they may well be sex specific and will not be realized or understood empirically unless both male and female rates are investigated.

Biosocial Models of Demography

Biosocial models of demography combine biological variables (e.g., hormonal levels and genetic factors) with social variables to predict demographic outcomes, in particular, those outcomes or processes that are biological in nature, that is, fertility and mortality. Aside from demographic studies of the proximate determinants of fertility, the incorporation of biological variables into explanatory models of demographic processes is not an activity to which demographers have devoted even a modest amount of attention. It is likely that there are proportionally more sociologists than demographers developing and testing biosocial models of human behavior. For whatever reasons, demographers have avoided such developments.

Casterline (1995) is one of a handful of demographers who recognize the importance of incorporating biological thinking into our theories of demography. He observes that demographers “can no longer run away from biosocial models . . . It requires either extraordinary blindness or exceptional stubbornness to fail to recognize that fertility and mortality . . . are determined in part by biological variables” (p. 359).

Casterline (1995) argues that after 1994, the “passive avoidance of biosocial models [among demographers] is no longer an option . . . [owing to Udry’s presidential address in 1994 to the Population Association of America] challenging demographers to take biosocial models seriously” (p. 360). In his address, Udry (1994) reported research showing that “one-fourth of the variance in women’s ‘gendered’behavior” is accounted for by a model comprising “prenatal and adult androgen measures and their interaction” (p. 520). This research (Udry, Morris, and Kovenock 1995) concludes that “gendered behavior is not entirely socially constructed, but partly built on a biological foundation” (p. 367).

Udry is a demographer who, over the years, has developed and tested biosocial models of demographic outcomes. He has published several papers introducing “biosocial models of adolescent sexuality that combine traditional sociological models with models derived from a biological theory of hormone effects” (1988:709; see also Udry, Talbert, and Morris 1986). Weller (1995) notes that just because Udry claims that a “behavior has biological foundations [does not mean he believes] it does not also have social foundations” (p. 281).

Here is a hypothetical equation, proposed by Casterline (1995:360):

D i = hB i + sS i + c ( B i ∗ S i ) + e i

where D is some demographic outcome, B is a vector of biological variables, S is a vector of social variables, h and s are vectors of parameters to be estimated indicating the effects of the biological and social variables, e is a disturbance, and the subscript i refers to individuals.

In the first place, much of demography assumes the parameter h not to be significantly different from zero. But Casterline (1995) counters that the

denial of the existence of parameter h . . . [is] now amply refuted by empirical scientific evidence . . . Scientists . . . must acknowledge that a substantial and solid body of evidence supports the proposition that individual variation in many behaviors is biologically driven . . . The challenge for scientists is to determine the magnitude of parameter h. (P. 361)

In Casterline’s equation, the biological and social variables may be considered as additive and as interacting. The B i * S i interaction would posit that the “effect of biological variables is conditioned by the level of social variables” (Casterline 1995: 361), a point made also by Udry (1994; see also Udry 1995).

Casterline (1995) and Udry (1994, 1996) both admit that biosocial models will have no role in certain demographic studies. Casterline (1995) observes that “a large fraction of the central research questions in social demography concerns secular change and or macro/societal variation, and hence it is not clear that much attention need be given [in such analyses] to biological variables” (p. 368). The role of biosocial models in demography thus depends greatly on the demographic outcome being investigated. Given the results of Udry and several others regarding the empirical importance of biological variables as predictors of certain types of demographic outcomes, it is concluded that demographers can no longer afford to ignore the potential of biological predictors of them.

Sexual Orientation

Policymakers are increasingly focusing attention on issues concerning the gay and lesbian community. This recent surge in interest may be attributed partly to judicial decisions seen as victories for homosexuals, including the Supreme Court’s decision striking down Texas’s law against same-sex sodomy, and the Massachusetts Supreme Court’s ruling that the state constitution requires the state to give same-sex couples marriage rights equal to those of opposite-sex couples ( Goodridge et al. v. Department of Public Health 2003; Lawrence et al. v. Texas 2003). In coming years, policymakers are likely to look to demographers and other social scientists to provide information on the homosexual community to aid them in constructing arguments for or against certain policies. Presently, however, there has been little demographic work done in the area of sexual orientation; many questions are just beginning to be explored, and some remain virtually untouched.

The demography of sexual orientation is underdeveloped due in large part to a lack of representative data sets with samples of sufficient size to answer many of the questions that researchers would like to ask about the homosexual community. Many of the larger surveys conducted of the homosexual population were surveys of convenience, such as those drawn from readership of magazines or newspapers (see the discussion of Black et al. 2000). U.S. researchers seeking representative samples of the gay and lesbian population must rely on the General Social Survey (GSS), the National Health and Social Life Survey (NHSLS), the NSFG—Cycle 6, and the census to explore research questions. Studies conducted using the GSS, the NHSLS, or the NSFG are limited due to the small number of individuals captured in these surveys who either identify as homosexual or who report having engaged in sexual activity with a same-sex partner. In the NHSLS, for instance, the sample consists of 3,432 American men and women but includes only 12 women and 27 men who identify as homosexual. And it includes only 32 women and 45 men who either identify as homosexual and/or had exclusively same-sex sex partners in the past year. The numbers in the NSFG are almost twice as large. However, sample sizes such as these are far too small to conduct many analyses of the homosexual population of interest to demographers, such as their distributions across cities, states, or occupations.

Beginning in 1990, however, the U.S. Census Bureau introduced a change on the long-form questionnaire that resulted in the creation of a large data set of same-sex individuals. The bureau offered respondents the option of identifying individuals living in the household as unmarried partners, after studies indicated the increasing number of opposite-sex and same-sex individuals living in marriagelike relationships in the United States (Baumle, Compton, and Poston, forthcoming; Black et al. 2000). The unmarriedpartner category permits unmarried heterosexual and homosexual couples to identify themselves as a couple.

In the 2000 U.S. Census, 1,188,782 individuals identified themselves as being in same-sex unmarried partner households on the census, 605, 052 males and 586,730 females (Simmons and O’Connell 2003). The addition of this category to the census has opened the door for social scientists to explore a number of issues relating to homosexuals that were previously out of reach due to the paucity of data.

Census data on same-sex partners are limited, however, in that only individuals who choose to identify asunmarried partners on the census questionnaire are captured. Thus, individuals who prefer not to self-identify are not counted. Furthermore, the census question allows data to be collected only for same-sex partners living in the same household, leaving homosexuals who are single unaccounted for. Nonetheless, the advantages of the census data over other data sources renders the census an attractive source for research on homosexuals, and studies attempting to quantify the extent of possible bias have concluded that the problem is not so severe as to warrant abstaining from using census data.

Surprisingly, however, little research has been conducted in this area to date, despite the availability of census data for both 1990 and 2000. And the work that has been done has been dominated by economists rather than demographers. There are a number of important areas of research in the area of sexual orientation, however, in which demographers and other social scientists can and should play an important role in the coming years.

One of the primary concerns of policymakers in both formulating policy goals and determining their impacts will center on the places in which gays and lesbians are located within the country. Data from the 1990 and 2000 U.S. Censuses indicate that there are concentrations of gays and lesbians in virtually all the metropolitan areas of the country. However, with but a few exceptions (Baumle et al., forthcoming; Black et al. 2000, 2002; Gates and Ost 2004;Walther and Poston 2004), there has been little effort among social scientists at indexing these concentrations among the metropolitan areas of the United States and examining the extent to which the indexes are associated with the social, ecological, and political characteristics of the areas. Preliminary research using 2000 data indicate that in most metropolitan areas, the levels of concentrations of partnered lesbians are higher than those of partnered gays. San Francisco is an outlier with many more partnered gays per 1,000 never-married males than partnered lesbians per 1,000 never-married females. Most metropolitan areas show the opposite. Limited research also indicates that ecological characteristics of metropolitan areas reflecting amenities of interest to both homosexuals and heterosexuals are more associated with the levels of homosexual prevalence than are characteristics pertaining to factors important only for homosexuals (Baumle et al., forthcoming; Black et al. 2002). Even less quantitative research has been undertaken regarding the differential concentration of partnered gays and lesbians in the nonmetropolitan and rural areas of the United States (Baumle et al., forthcoming).

Another area of homosexual demography in which there is a major research void is residential segregation. Demographers have paid virtually no attention to patterns of residential segregation of homosexuals from married and unmarried heterosexuals (for an exception, see Baumle et al., forthcoming). Preliminary research indicates that levels of segregation of homosexuals (gays and lesbians treated separately) from unmarried and married heterosexuals are sizable, that lesbians are less segregated from heterosexuals than are gays, and that gays and lesbians are segregated from each other. Extensive demographic research on racial residential segregation of black and Hispanic minorities from the white majority indicates that the segregation is largely involuntary. Early research on the segregation of homosexuals from heterosexuals suggests that the segregation is both involuntary and voluntary, but considerable work remains to be done that would sort out these differences and estimate statistical models to explain them.

For decades, U.S. politicians have been proposing the adoption of a federal law prohibiting discrimination in employment on the basis of sexual orientation. Policymakers might turn to social science research to answer important questions in assessing whether such a law is necessary: Do homosexuals earn less than heterosexuals? Are homosexuals segregated into different occupations than heterosexuals? The majority of studies examining homosexuality and work have focused on the relationship between sexual orientation and income. Once controls are introduced for individual characteristics, most research finds that gay men earn less than heterosexual men (Badgett 1995; Baumle et al., forthcoming; Black et al. 2003; Klawitter and Flatt 1998). Findings about the earnings of lesbians are mixed (Badgett 1995; Baumle et al., forthcoming; Klawitter and Flatt 1998). Research is ongoing concerning income differences between homosexuals and heterosexuals, but there is no clear consensus as to the cause of the income differences if they do exist.

Badgett (1995) finds that occupational differences account for some of the income differences between homosexuals and heterosexuals. Occupational segregation, therefore, is another area in which future research needs to be conducted in assessing whether inequalities exist in the workplace between homosexuals and heterosexuals. Baumle et al. (forthcoming) have explored the manner in which homosexuals and heterosexuals are segregated in professional occupations. They find that partnered homosexuals are overrepresented in the professions as a whole and appear to be concentrated within fields that are focused on creativity, psychology/counseling, and law/social work. Partnered homosexuals are underrepresented primarily in the engineering and teaching professions. Additional research needs to be conducted to determine the cause of such occupational segregation, as well as to examine segregation in occupations outside the professions.

Finally, the debate concerning the legal right of homosexual couples to marriage is one that is virtually global (Merin 2002). There are few places in which homosexuals have been granted marriage rights equal to those of heterosexuals, and family rights vary widely both within and between countries. To provide guidance to legislators in formulating marriage and family laws, demographers must develop a literature about the family practices of homosexuals. What is the average length of a homosexual relationship? How prevalent is childrearing among lesbian and gay couples? Do lesbian and gay couples predominantly adopt or raise their own children? These questions, and others, are important to address if demographers and policymakers are to understand the manner in which laws and social policies are to be constructed to address the needs of the homosexual population.

In the above and last section of this research paper, three broad areas of demographic research have been proposed requiring major conceptual and methodological advances. They represent challenges to demographers. They require demographers to not undertake fertility analyses that are based only on females, to not estimate demographic models that are based only on social variables, and to not restrict their investigations, implicitly or explicitly, to heterosexuals. According to Horton (1999), an important characteristic of “critical demography,” as opposed to “conventional demography,” is the posing of “questions that challenge the prevailing social order” (p. 365). In some ways, demographic research in the areas outlined above may well challenge existing demographic paradigms.

Also, the issues and topics presented here comprise a short and very selective list. There are certainly many other areas of research requiring the future attention of demographers.

Over the past 50 years, the field of demography has changed substantially (see Hauser and Duncan 1959c; Poston and Micklin 2005). First, the theoretical base of the field has expanded considerably in terms of the subject matter incorporated and its links to other disciplines. Demographic theories now encompass phenomena other than the standard variables reflected in the demographic equation (population size, composition, and distribution, and fertility, mortality, and migration). This is because demographic research has shown that fuller explanation of population conditions, trends, and events requires that theories and models incorporate nondemographic variables and that the effects of demographic conditions and trends extend to nearly all dimensions of human societies and their natural environments. As the substantive concerns of demographers have grown, so has their reliance on concepts, theories, and methods developed in other disciplines such as economics, political science, social psychology, and cultural anthropology. In short, the scope of the field of demography—the “demographer’s ken”—has widened considerably.

A second way in which demography has changed over the past half-century is the enormous expansion in the availability of demographic materials, including both primary and secondary data sources. The frequency, coverage, and accuracy of basic demographic data collection systems, for example, census and vital registration procedures, have increased worldwide, although there is still sizeable variation among countries and regions. Such improvements increase the likelihood that routine demographic activities such as population counts, estimates, and projections will become more accurate and, therefore, more useful for social, political, and economic planning.

Perhaps the most significant changes in the field of demography are seen in its infrastructure. Examples include a growing number of professional organizations, the expanded number and variety of outlets for distributing research findings, an enormous variety of Internet sites that provide demographic information or discussions of topics of demographic interest, and the continuing spread of efforts to use demographic information to inform and influence local, regional, national, and international practices and policies.

Throughout this research paper, we have suggested that the scope of demographic theories and research now extends throughout the social and behavioral sciences. Readers should not interpret these comments to mean that demography and population studies are any less significant for the discipline of sociology than they were decades earlier. Indeed, several features of the sociological perspective all but guarantee that demography will remain an integral component of sociological theory and research. First, a sizeable number of sociologists continue to show a primary interest in the standard demographic variables of population size, composition, and distribution and the processes that influence changes in these variables—that is, fertility, mortality, migration, and social mobility. The continued strong interest and enrollment in the Sociology of Population section in the American Sociological Association is indirect testimony to this contention. Second, much of sociology is concerned with human groups and aggregates, including such varied forms as peer and kinship groups, formal organizations, residential communities, and nation-states. Even those sociologists who focus their attention on individual conduct or personal characteristics tend more often than not to interpret these individual variables in terms of features of the group or collective context in which they are embedded. Questions about contextual effects are often raised in demographic terms, for example, various indicators of group size, composition, and/or distribution. Third, the discipline of sociology grew out of a problem-oriented concern with the quality of life in human societies, and this concern is still a vibrant force. Demographers, many of them sociologists, have continued this concern, raising questions about the effects of population size and growth on the sustainability of social and economic development, particularly in the poorer societies and regions of the world, and on mediumto long-term effects on natural resource supplies and environmental quality.

The examples presented above are intended only to whet the reader’s appetite to think more about the integral connections between sociology and demography. There is much conceptual, theoretical, and empirical territory to be explored. One conclusion, however, is clear: The study of population is a key component of twenty-first-century sociology.

Bibliography:

• Abbott, Andrew. 2001. Chaos of Disciplines. Chicago, IL: University of Chicago Press.
• Adamchak, Donald J. and Akinwumi Adebayo. 1987. “Male Fertility Attitudes: A Neglected Dimension in Nigerian Fertility Research.” Social Biology 34:57–67.
• Ahlburg, Dennis A. and Wolfgang Lutz. 1998. “Introduction: The Need to Rethink Approaches to Population Forecasts.” Population and Development Review 24(Suppl.):1–14.
• Ahlburg, Dennis A., Wolfgang Lutz, and James W. Vaupel. 1998. “Ways to Improve Population Forecasting: What Should Be Done Differently in the Future?” Population and Development Review 24(Suppl.):191–98.
• Aitken R. J., S. Irvine, J. S. Clarkson, and D. W. Richardson. 1986. “Predictive Value of In Vitro Sperm Function Tests.” Pp. 138–51 in Male Contraception: Advances and Future Prospects, edited by G. I. Zatuchni, A. Goldsmith, J. M. Spieler, and J. J. Sciarra . Philadelphia, PA: Harper & Row.
• Badgett, M. V. Lee 1995. “The Wage Effects of Sexual Orientation Discrimination.” Industrial and Labor Relations Review 48:726–39.
• Baumle, Amanda K., D’Lane Compton, and Dudley L. Poston Jr. Forthcoming. The Demography of Sexual Orientation. Albany: State University of New York Press.
• Black, Dan, Gary Gates, Seth Sanders, and Lowell Taylor. 2000. “Demographics of the Gay and Lesbian Population in the United States: Evidence from Available Systematic Data Sources.” Demography 37:138–54.
• Black, Dan, Gary Gates, Seth Sanders, and Lowell Taylor. 2002. “Why Do Gay Men Live in San Francisco?” Journal of Urban Economics 51:54–76.
• Black, Dan, Hoda R. Makar, Seth Sanders, and Lowell Taylor. 2003. “The Earnings Effects of Sexual Orientation.” Industrial and Labor Relations Review 56:449–69.
• Bledsoe, Caroline, Susanna Lerner, and Jane I. Guyer, eds. 2000. Fertility and the Male Life-Cycle in the Era of Fertility Decline. New York: Oxford University Press.
• Bogue, Donald J. 1969. Principles of Demography. New York: John Wiley.
• Brass, William. 1996. “Demographic Data Analysis in Less Developed Countries 1946–1996.” Population Studies 50(3):451–67.
• Caldwell, John C. 1996. “Demography and Social Science.” Population Studies 50:305–33.
• Caldwell, John C. 1997. “The Global Fertility Transition: The Need for a Unifying Theory.” Population and Development Review 23:803–12.
• Casterline, John B. 1995. “Biosocial Models: Can Demographers Ignore Them?” Population Research and Policy Review 14:359–71.
• Cleland, John. 1996. “Demographic Data Collection in Less Developed Countries 1946–1996.” Population Studies 50(3):433–50.
• Cleland, John and John Hobcroft. 1985. Reproductive Change in Developing Countries: Insights from the World Fertility Survey. New York: Oxford University Press.
• Cleland, J. and C. Scott. 1987. The World Fertility Survey: An Assessment. New York: Oxford University Press.
• Coale, Ansley J. and Paul Demeny. 1968. Methods of Evaluating Basic Demographic Measures from Limited and Defective Data. New York: United Nations.
• Coale, Ansely J. and James Trussell. 1996. “The Development and Use of Demographic Models.” Population Studies 50(3):469–84.
• Coleman, David. 2000. “Male Fertility Trends in Industrial Countries: Theories in Search of Some Evidence.” Pp. 29–60 in Fertility and the Male Life-Cycle in the Era of Fertility Decline, edited by C. Bledsoe, S. Lerner, and J. I. Guyer . New York: Oxford University Press.
• Coleman, David and Roger Schofield. 1986. The State of Population Theory: Forward from Malthus. Oxford, England: Basil Blackwell.
• Crimmins, Eileen M. 1993. “Demography: The Past 30 Years, the Present, and the Future.” Demography 30(4):579–91.
• Davis, Kingsley. 1948. Human Society. New York: Macmillan.
• Gates, G. J. and J. Ost. 2004. The Gay and Lesbian Atlas. Washington, DC: Urban Institute Press.
• Goodridge et al. v. Department of Public Health , 798 N.E.2d 941 (Mass. 2003).
• Greene, Margaret E. and Ann E. Biddlecom. 2000. “Absent and Problematic Men: Demographic Accounts of Male Reproductive Roles.” Population and Development Review 26:81–115.
• Gryn, Thomas A. 1997. “Internet Resources for Demographers.” Population Index 63(2):189–204.
• Gutman, Robert. 1960. “In Defense of Population Theory.” American Sociological Review 25(3):325–33.
• Guyer, Jane I. 2000. “Traditions of Studying Paternity in Social Anthropology.” Pp. 61–90 in Fertility and the Male LifeCycle in the Era of Fertility Decline, edited by C. Bledsoe, S. Lerner, and J. I. Guyer . New York: Oxford University Press.
• Hauser, Philip M. and Otis Dudley Duncan. 1959a. “The Data and Methods.” Pp. 45–75 in The Study of Population: An Inventory and Appraisal, edited by P. M. Hauser and O. D. Duncan. Chicago, IL: University of Chicago Press.
• Hauser, Philip M. and Otis Dudley Duncan. 1959b. “Demography as a Body of Knowledge.” Pp. 76–105 in The Study of Population: An Inventory and Appraisal, edited by P. M. Hauser and O. D. Duncan. Chicago, IL: University of Chicago Press.
• Hauser, Philip M. and Otis Dudley Duncan, eds. 1959c. The Study of Population: An Inventory and Appraisal. Chicago, IL: University of Chicago Press.
• Hill, Reuben, J. Mayone Stycos, and Kurt Back. 1959. The Family and Population Control: A Puerto Rican Experiment in Social Change. Chapel Hill: University of North Carolina Press.
• Hirschman, Charles. 1994. “Why Fertility Changes.” Annual Review of Sociology 20 : 203–33.
• Horton, Hayward D. 1999. “Critical Demography: The Paradigm of the Future?” Sociological Forum 14:363–67.
• Kammeyer, Kenneth C. W. and Helen Ginn. 1986. An Introduction to Population. Chicago, IL: Dorsey Press.
• Keyfitz, Nathan. 1975. “How Do We Know the Facts of Demography?” Population and Development Review 1(2):267–88.
• Keyfitz, Nathan. 1977. Applied Mathematical Demography. New York: John Wiley.
• Keyfitz, Nathan. 1981. “The Limits of Population Forecasting.” Population and Development Review 7(4):579–93.
• Kirk, Dudley. 1996. “Demographic Transition Theory.” Population Studies 50(3):361–87.
• Kiser, Clyde V. 1953. “The Indianapolis Fertility Study: An Example of Planned Observational Research.” Public Opinion Quarterly 17(4):496–510.
• Kiser, Clyde V. and Pascal K. Whelpton. 1953. “Resume of the Indianapolis Study of Social and Psychological Factors Affecting Fertility.” Population Studies 7(2):95–110.
• Klawitter, Marieka M. and Victor Flatt. 1998. “The Effects of State and Local Antidiscrimination Policies on Earnings for Gays and Lesbians.” Journal of Policy Analysis and Management 17:658–86.
• Lawrence et al. v. Texas , 123 S. Ct. 2472 (2003).
• Lee, Ronald. 2003. “The Demographic Transition: Three Centuries of Fundamental Change.” Journal of Economic Perspectives 17(4):167–90.
• Merin, Yuval. 2002. Equality for Same-Sex Couples: The Legal Recognition of Gay Partnerships in Europe and the United States. Chicago, IL: University of Chicago Press.
• Mbizvo, M. T. and Donald J. Adamchak. 1992. “Male Fertility Regulation: A Study on Acceptance among Men in Zimbabwe.” Central African Journal of Medicine 38:52–57.
• Micklin, Michael. 1969. “Traditionalism, Social Class, and Differential Fertility in Guatemala. America Latina 12:59–77.
• Micklin, Michael. 1992. “LDC Population Policies: A Challenge for Applied Demography.” Journal of Applied Sociology 9:45–63.
• Micklin, Michael and Dudley L. Poston Jr. 2005. “Prologue: The Demographer’s Ken: 50 Years of Growth and Change.” Pp. 1–15 in Handbook of Population, edited by Dudley L. Poston Jr. and Michael Micklin. New York: Springer.
• Moore, Wilbert E. 1959. “Sociology and Demography.” Pp. 832–851 in The Study of Population: An Inventory and Appraisal, edited by P. M. Hauser and O. D. Duncan . Chicago, IL: University of Chicago Press.
• Muvandi, Ityai. 1995. “Determinants of Male Fertility and Sexual Behaviour in Kenya.” Centre for African Family Studies [CAFS], CAFS Research Reports, Series No. 2.
• Nam, Charles B. 1979. “The Progress of Demography as a Scientific Discipline.” Demography 16(4):485–92.
• Poston, Dudley L., Jr. 2000. “Conceptual and Methodological Advances and Challenges in Demography.” Pp. 273–98 in The International Handbook of Sociology, edited by S. R. Quah and A. Sales. Thousand Oaks, CA: Sage.
• Poston, Dudley L., Jr.,Amanda K. Baumle, and Michael Micklin. 2005. “Epilogue: Needed Research in Demography.” Pp. 853–81 in Handbook of Population, edited by D. L. Poston Jr. and Michael Micklin. New York: Springer.
• Poston, Dudley L., Jr. and Michael Micklin, eds. 2005. Handbook of Population. New York: Springer.
• Preston, Samuel. H. 1993. “The Contours of Demography: Estimates and Projections.” Demography 30:593–606.
• Schofield, Roger and David A. Coleman. 1986. “Introduction: The State of Population Theory.” Pp. 1–13 in The State of Population Theory: Forward from Malthus, edited by D. A. Coleman and R. Schofield. New York: Basil Blackwell.
• Shi, Anqing. 2003. “The Impact of Population Pressure on Global Carbon Dioxide Emissions 1975–1996: Evidence from Pooled Cross-Country Data.” Ecological Economics 44:29–42.
• Siegel, Jacob S. 2002. Applied Demography: Applications to Business, Government, Law and Public Policy. San Diego, CA: Academic Press.
• Siegel, Jacob S. and David Swanson. 2004. The Methods and Materials of Demography. 2d ed. Boston, MA: Academic Press.
• Simmons, Tavia and Martin O’Connell. 2003. “Married Couple and Unmarried-Partner Households: 2000.” Census 2000 Special Reports. CENSR-5. Washington, DC: U.S. Bureau of the Census.
• Singh, K. and Shan S. Ratnam. 1991. “New Developments in Contraceptive Technology.” Advances in Contraception 7:137–57.
• Stycos, J. Mayone. 1955. Family and Fertility in Puerto Rico: A Study of the Lower Income Group. New York: Columbia University Press.
• Stycos, J. Mayone. 1987. “Demography as an Interdiscipline.” Sociological Forum 2:615–28.
• Udry, J. Richard. 1988. “Biological Predispositions and Social Control in Adolescent Sexual Behavior.” American Sociological Review 53:709–22.
• Udry, J. Richard. 1994. “The Nature of Gender.” Demography 31:561–73.
• Udry, J. Richard. 1995. “Policy and Ethical Implications of Biosocial Research.” Population Research and Policy Review 14:347–57.
• Udry, J. Richard. 1996. “Biosocial Models of Low-Fertility Societies.” Population and Development Review 22(Suppl.): 325–26.
• Udry, J. Richard, Naomi M. Morris, and Judith Kovenock. 1995. “Androgen Effects on Women’s Gendered Behavior.” Journal of Biosocial Science 27:359–68.
• Udry, J. Richard, L. M. Talbert, and Naomi M. Morris. 1986. “Biosocial Foundations for Adolescent Female Sexuality.” Demography 23:217–30.
• United Nations. 2001. World Population Monitoring 2001: Population, Environment and Development . New York: United Nations.
• United Nations. 2002. Demographic Yearbook, Natality Statistics. CD-ROM Special Topic, DYB-CD, No. E/F.02.XII.6. New York: United Nations.
• Vance, Rupert B. 1952. “Is Theory for Demographers?” Social Forces 31(1):9–13.
• Walther, Carol S. and Dudley L. Poston Jr. 2004. “Patterns of Gay and Lesbian Partnering in the Larger Metropolitan Areas of the United States.” Journal of Sex Research 41:201–14.
• Weeks, John R. 2005. Population: An Introduction to Concepts and Issues. 9th ed. Belmont, CA: Wadsworth.
• Weller, Bob. 1995. “Biosocial Models of Demographic Behavior: An Introduction.” Population Research and Policy Review 14:277–82.
• Westoff, Charles F., Robert Potter, and Philip Sagi. 1963. The Third Child: A Study in the Prediction of Fertility. Princeton, NJ: Princeton University Press.
• Westoff, Charles F., Robert Potter, Philip Sagi, and Elliot G. Mishler. 1961. Family Growth in Metropolitan America. Princeton, NJ: Princeton University Press.
• York, Richard, Eugene A. Rosa, and Thomas Dietz. 2003. “Footprints on the Earth: The Environmental Consequences of Modernity.” American Sociological Review 68:279–300.

ORDER HIGH QUALITY CUSTOM PAPER

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Writing Survey Questions

Perhaps the most important part of the survey process is the creation of questions that accurately measure the opinions, experiences and behaviors of the public. Accurate random sampling will be wasted if the information gathered is built on a shaky foundation of ambiguous or biased questions. Creating good measures involves both writing good questions and organizing them to form the questionnaire.

Questionnaire design is a multistage process that requires attention to many details at once. Designing the questionnaire is complicated because surveys can ask about topics in varying degrees of detail, questions can be asked in different ways, and questions asked earlier in a survey may influence how people respond to later questions. Researchers are also often interested in measuring change over time and therefore must be attentive to how opinions or behaviors have been measured in prior surveys.

Surveyors may conduct pilot tests or focus groups in the early stages of questionnaire development in order to better understand how people think about an issue or comprehend a question. Pretesting a survey is an essential step in the questionnaire design process to evaluate how people respond to the overall questionnaire and specific questions, especially when questions are being introduced for the first time.

For many years, surveyors approached questionnaire design as an art, but substantial research over the past forty years has demonstrated that there is a lot of science involved in crafting a good survey questionnaire. Here, we discuss the pitfalls and best practices of designing questionnaires.

Question development

There are several steps involved in developing a survey questionnaire. The first is identifying what topics will be covered in the survey. For Pew Research Center surveys, this involves thinking about what is happening in our nation and the world and what will be relevant to the public, policymakers and the media. We also track opinion on a variety of issues over time so we often ensure that we update these trends on a regular basis to better understand whether people’s opinions are changing.

At Pew Research Center, questionnaire development is a collaborative and iterative process where staff meet to discuss drafts of the questionnaire several times over the course of its development. We frequently test new survey questions ahead of time through qualitative research methods such as  focus groups , cognitive interviews, pretesting (often using an  online, opt-in sample ), or a combination of these approaches. Researchers use insights from this testing to refine questions before they are asked in a production survey, such as on the ATP.

Measuring change over time

Many surveyors want to track changes over time in people’s attitudes, opinions and behaviors. To measure change, questions are asked at two or more points in time. A cross-sectional design surveys different people in the same population at multiple points in time. A panel, such as the ATP, surveys the same people over time. However, it is common for the set of people in survey panels to change over time as new panelists are added and some prior panelists drop out. Many of the questions in Pew Research Center surveys have been asked in prior polls. Asking the same questions at different points in time allows us to report on changes in the overall views of the general public (or a subset of the public, such as registered voters, men or Black Americans), or what we call “trending the data”.

When measuring change over time, it is important to use the same question wording and to be sensitive to where the question is asked in the questionnaire to maintain a similar context as when the question was asked previously (see  question wording  and  question order  for further information). All of our survey reports include a topline questionnaire that provides the exact question wording and sequencing, along with results from the current survey and previous surveys in which we asked the question.

The Center’s transition from conducting U.S. surveys by live telephone interviewing to an online panel (around 2014 to 2020) complicated some opinion trends, but not others. Opinion trends that ask about sensitive topics (e.g., personal finances or attending religious services ) or that elicited volunteered answers (e.g., “neither” or “don’t know”) over the phone tended to show larger differences than other trends when shifting from phone polls to the online ATP. The Center adopted several strategies for coping with changes to data trends that may be related to this change in methodology. If there is evidence suggesting that a change in a trend stems from switching from phone to online measurement, Center reports flag that possibility for readers to try to head off confusion or erroneous conclusions.

Open- and closed-ended questions

One of the most significant decisions that can affect how people answer questions is whether the question is posed as an open-ended question, where respondents provide a response in their own words, or a closed-ended question, where they are asked to choose from a list of answer choices.

For example, in a poll conducted after the 2008 presidential election, people responded very differently to two versions of the question: “What one issue mattered most to you in deciding how you voted for president?” One was closed-ended and the other open-ended. In the closed-ended version, respondents were provided five options and could volunteer an option not on the list.

When explicitly offered the economy as a response, more than half of respondents (58%) chose this answer; only 35% of those who responded to the open-ended version volunteered the economy. Moreover, among those asked the closed-ended version, fewer than one-in-ten (8%) provided a response other than the five they were read. By contrast, fully 43% of those asked the open-ended version provided a response not listed in the closed-ended version of the question. All of the other issues were chosen at least slightly more often when explicitly offered in the closed-ended version than in the open-ended version. (Also see  “High Marks for the Campaign, a High Bar for Obama”  for more information.)

Researchers will sometimes conduct a pilot study using open-ended questions to discover which answers are most common. They will then develop closed-ended questions based off that pilot study that include the most common responses as answer choices. In this way, the questions may better reflect what the public is thinking, how they view a particular issue, or bring certain issues to light that the researchers may not have been aware of.

When asking closed-ended questions, the choice of options provided, how each option is described, the number of response options offered, and the order in which options are read can all influence how people respond. One example of the impact of how categories are defined can be found in a Pew Research Center poll conducted in January 2002. When half of the sample was asked whether it was “more important for President Bush to focus on domestic policy or foreign policy,” 52% chose domestic policy while only 34% said foreign policy. When the category “foreign policy” was narrowed to a specific aspect – “the war on terrorism” – far more people chose it; only 33% chose domestic policy while 52% chose the war on terrorism.

In most circumstances, the number of answer choices should be kept to a relatively small number – just four or perhaps five at most – especially in telephone surveys. Psychological research indicates that people have a hard time keeping more than this number of choices in mind at one time. When the question is asking about an objective fact and/or demographics, such as the religious affiliation of the respondent, more categories can be used. In fact, they are encouraged to ensure inclusivity. For example, Pew Research Center’s standard religion questions include more than 12 different categories, beginning with the most common affiliations (Protestant and Catholic). Most respondents have no trouble with this question because they can expect to see their religious group within that list in a self-administered survey.

In addition to the number and choice of response options offered, the order of answer categories can influence how people respond to closed-ended questions. Research suggests that in telephone surveys respondents more frequently choose items heard later in a list (a “recency effect”), and in self-administered surveys, they tend to choose items at the top of the list (a “primacy” effect).

Because of concerns about the effects of category order on responses to closed-ended questions, many sets of response options in Pew Research Center’s surveys are programmed to be randomized to ensure that the options are not asked in the same order for each respondent. Rotating or randomizing means that questions or items in a list are not asked in the same order to each respondent. Answers to questions are sometimes affected by questions that precede them. By presenting questions in a different order to each respondent, we ensure that each question gets asked in the same context as every other question the same number of times (e.g., first, last or any position in between). This does not eliminate the potential impact of previous questions on the current question, but it does ensure that this bias is spread randomly across all of the questions or items in the list. For instance, in the example discussed above about what issue mattered most in people’s vote, the order of the five issues in the closed-ended version of the question was randomized so that no one issue appeared early or late in the list for all respondents. Randomization of response items does not eliminate order effects, but it does ensure that this type of bias is spread randomly.

Questions with ordinal response categories – those with an underlying order (e.g., excellent, good, only fair, poor OR very favorable, mostly favorable, mostly unfavorable, very unfavorable) – are generally not randomized because the order of the categories conveys important information to help respondents answer the question. Generally, these types of scales should be presented in order so respondents can easily place their responses along the continuum, but the order can be reversed for some respondents. For example, in one of Pew Research Center’s questions about abortion, half of the sample is asked whether abortion should be “legal in all cases, legal in most cases, illegal in most cases, illegal in all cases,” while the other half of the sample is asked the same question with the response categories read in reverse order, starting with “illegal in all cases.” Again, reversing the order does not eliminate the recency effect but distributes it randomly across the population.

Question wording

The choice of words and phrases in a question is critical in expressing the meaning and intent of the question to the respondent and ensuring that all respondents interpret the question the same way. Even small wording differences can substantially affect the answers people provide.

[View more Methods 101 Videos ]

An example of a wording difference that had a significant impact on responses comes from a January 2003 Pew Research Center survey. When people were asked whether they would “favor or oppose taking military action in Iraq to end Saddam Hussein’s rule,” 68% said they favored military action while 25% said they opposed military action. However, when asked whether they would “favor or oppose taking military action in Iraq to end Saddam Hussein’s rule  even if it meant that U.S. forces might suffer thousands of casualties, ” responses were dramatically different; only 43% said they favored military action, while 48% said they opposed it. The introduction of U.S. casualties altered the context of the question and influenced whether people favored or opposed military action in Iraq.

There has been a substantial amount of research to gauge the impact of different ways of asking questions and how to minimize differences in the way respondents interpret what is being asked. The issues related to question wording are more numerous than can be treated adequately in this short space, but below are a few of the important things to consider:

First, it is important to ask questions that are clear and specific and that each respondent will be able to answer. If a question is open-ended, it should be evident to respondents that they can answer in their own words and what type of response they should provide (an issue or problem, a month, number of days, etc.). Closed-ended questions should include all reasonable responses (i.e., the list of options is exhaustive) and the response categories should not overlap (i.e., response options should be mutually exclusive). Further, it is important to discern when it is best to use forced-choice close-ended questions (often denoted with a radio button in online surveys) versus “select-all-that-apply” lists (or check-all boxes). A 2019 Center study found that forced-choice questions tend to yield more accurate responses, especially for sensitive questions.  Based on that research, the Center generally avoids using select-all-that-apply questions.

It is also important to ask only one question at a time. Questions that ask respondents to evaluate more than one concept (known as double-barreled questions) – such as “How much confidence do you have in President Obama to handle domestic and foreign policy?” – are difficult for respondents to answer and often lead to responses that are difficult to interpret. In this example, it would be more effective to ask two separate questions, one about domestic policy and another about foreign policy.

In general, questions that use simple and concrete language are more easily understood by respondents. It is especially important to consider the education level of the survey population when thinking about how easy it will be for respondents to interpret and answer a question. Double negatives (e.g., do you favor or oppose  not  allowing gays and lesbians to legally marry) or unfamiliar abbreviations or jargon (e.g., ANWR instead of Arctic National Wildlife Refuge) can result in respondent confusion and should be avoided.

Similarly, it is important to consider whether certain words may be viewed as biased or potentially offensive to some respondents, as well as the emotional reaction that some words may provoke. For example, in a 2005 Pew Research Center survey, 51% of respondents said they favored “making it legal for doctors to give terminally ill patients the means to end their lives,” but only 44% said they favored “making it legal for doctors to assist terminally ill patients in committing suicide.” Although both versions of the question are asking about the same thing, the reaction of respondents was different. In another example, respondents have reacted differently to questions using the word “welfare” as opposed to the more generic “assistance to the poor.” Several experiments have shown that there is much greater public support for expanding “assistance to the poor” than for expanding “welfare.”

We often write two versions of a question and ask half of the survey sample one version of the question and the other half the second version. Thus, we say we have two  forms  of the questionnaire. Respondents are assigned randomly to receive either form, so we can assume that the two groups of respondents are essentially identical. On questions where two versions are used, significant differences in the answers between the two forms tell us that the difference is a result of the way we worded the two versions.

One of the most common formats used in survey questions is the “agree-disagree” format. In this type of question, respondents are asked whether they agree or disagree with a particular statement. Research has shown that, compared with the better educated and better informed, less educated and less informed respondents have a greater tendency to agree with such statements. This is sometimes called an “acquiescence bias” (since some kinds of respondents are more likely to acquiesce to the assertion than are others). This behavior is even more pronounced when there’s an interviewer present, rather than when the survey is self-administered. A better practice is to offer respondents a choice between alternative statements. A Pew Research Center experiment with one of its routinely asked values questions illustrates the difference that question format can make. Not only does the forced choice format yield a very different result overall from the agree-disagree format, but the pattern of answers between respondents with more or less formal education also tends to be very different.

One other challenge in developing questionnaires is what is called “social desirability bias.” People have a natural tendency to want to be accepted and liked, and this may lead people to provide inaccurate answers to questions that deal with sensitive subjects. Research has shown that respondents understate alcohol and drug use, tax evasion and racial bias. They also may overstate church attendance, charitable contributions and the likelihood that they will vote in an election. Researchers attempt to account for this potential bias in crafting questions about these topics. For instance, when Pew Research Center surveys ask about past voting behavior, it is important to note that circumstances may have prevented the respondent from voting: “In the 2012 presidential election between Barack Obama and Mitt Romney, did things come up that kept you from voting, or did you happen to vote?” The choice of response options can also make it easier for people to be honest. For example, a question about church attendance might include three of six response options that indicate infrequent attendance. Research has also shown that social desirability bias can be greater when an interviewer is present (e.g., telephone and face-to-face surveys) than when respondents complete the survey themselves (e.g., paper and web surveys).

Lastly, because slight modifications in question wording can affect responses, identical question wording should be used when the intention is to compare results to those from earlier surveys. Similarly, because question wording and responses can vary based on the mode used to survey respondents, researchers should carefully evaluate the likely effects on trend measurements if a different survey mode will be used to assess change in opinion over time.

Question order

Once the survey questions are developed, particular attention should be paid to how they are ordered in the questionnaire. Surveyors must be attentive to how questions early in a questionnaire may have unintended effects on how respondents answer subsequent questions. Researchers have demonstrated that the order in which questions are asked can influence how people respond; earlier questions can unintentionally provide context for the questions that follow (these effects are called “order effects”).

One kind of order effect can be seen in responses to open-ended questions. Pew Research Center surveys generally ask open-ended questions about national problems, opinions about leaders and similar topics near the beginning of the questionnaire. If closed-ended questions that relate to the topic are placed before the open-ended question, respondents are much more likely to mention concepts or considerations raised in those earlier questions when responding to the open-ended question.

For closed-ended opinion questions, there are two main types of order effects: contrast effects ( where the order results in greater differences in responses), and assimilation effects (where responses are more similar as a result of their order).

An example of a contrast effect can be seen in a Pew Research Center poll conducted in October 2003, a dozen years before same-sex marriage was legalized in the U.S. That poll found that people were more likely to favor allowing gays and lesbians to enter into legal agreements that give them the same rights as married couples when this question was asked after one about whether they favored or opposed allowing gays and lesbians to marry (45% favored legal agreements when asked after the marriage question, but 37% favored legal agreements without the immediate preceding context of a question about same-sex marriage). Responses to the question about same-sex marriage, meanwhile, were not significantly affected by its placement before or after the legal agreements question.

Another experiment embedded in a December 2008 Pew Research Center poll also resulted in a contrast effect. When people were asked “All in all, are you satisfied or dissatisfied with the way things are going in this country today?” immediately after having been asked “Do you approve or disapprove of the way George W. Bush is handling his job as president?”; 88% said they were dissatisfied, compared with only 78% without the context of the prior question.

Responses to presidential approval remained relatively unchanged whether national satisfaction was asked before or after it. A similar finding occurred in December 2004 when both satisfaction and presidential approval were much higher (57% were dissatisfied when Bush approval was asked first vs. 51% when general satisfaction was asked first).

Several studies also have shown that asking a more specific question before a more general question (e.g., asking about happiness with one’s marriage before asking about one’s overall happiness) can result in a contrast effect. Although some exceptions have been found, people tend to avoid redundancy by excluding the more specific question from the general rating.

Assimilation effects occur when responses to two questions are more consistent or closer together because of their placement in the questionnaire. We found an example of an assimilation effect in a Pew Research Center poll conducted in November 2008 when we asked whether Republican leaders should work with Obama or stand up to him on important issues and whether Democratic leaders should work with Republican leaders or stand up to them on important issues. People were more likely to say that Republican leaders should work with Obama when the question was preceded by the one asking what Democratic leaders should do in working with Republican leaders (81% vs. 66%). However, when people were first asked about Republican leaders working with Obama, fewer said that Democratic leaders should work with Republican leaders (71% vs. 82%).

The order questions are asked is of particular importance when tracking trends over time. As a result, care should be taken to ensure that the context is similar each time a question is asked. Modifying the context of the question could call into question any observed changes over time (see  measuring change over time  for more information).

A questionnaire, like a conversation, should be grouped by topic and unfold in a logical order. It is often helpful to begin the survey with simple questions that respondents will find interesting and engaging. Throughout the survey, an effort should be made to keep the survey interesting and not overburden respondents with several difficult questions right after one another. Demographic questions such as income, education or age should not be asked near the beginning of a survey unless they are needed to determine eligibility for the survey or for routing respondents through particular sections of the questionnaire. Even then, it is best to precede such items with more interesting and engaging questions. One virtue of survey panels like the ATP is that demographic questions usually only need to be asked once a year, not in each survey.

U.S. Surveys

Other research methods, sign up for our weekly newsletter.

Fresh data delivered Saturday mornings

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Age & Generations
• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Terms & Conditions

Privacy Policy

Cookie Settings

Reprints, Permissions & Use Policy