ppt on sampling techniques in research methodology

Sampling techniques

This document provides an overview of sampling techniques. It defines key sampling terms like population, sample, sampling frame, and discusses the need for sampling due to constraints of time and money for a full census. The document outlines different sampling methods like simple random sampling, stratified sampling, cluster sampling and multistage sampling. It also discusses non-probability sampling techniques like convenience sampling and snowball sampling. The document emphasizes the importance of representativeness, adequacy and independence for a good sample. It concludes by noting sources of error in sampling like sampling errors and non-sampling errors. Read less

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• 1. SAMPLING TECHNIQUES DR.BHARAT PAUL
• 2. CONTENTS  Introduction  Need for sampling  Sampling Process  Essentials of Sampling  Methods of Sampling  Non Probability Sampling  Probability Sampling  Errors in Sampling  References
• 3. INTRODUCTION  Population/Universe: in statistics denotes the aggregate from which sample (items) is to be taken.  A population can be defined as including all people or items with the characteristic one wishes to understand.  Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population.
• 4. INTRODUCTION  Sampling frame is the list from which the potential respondents are drawn .  A sample is “a smaller (but hopefully representative) collection of units from a population used to determine truths about that population” (Field, 2005)
• 5. SAMPLING BREAKDOWN
• 6. SAMPLING  Sampling: the process of learning about population on the basis of sample drawn from it.  Three elements in process of sampling:  Selecting the sample  Collecting the information  Making inference about population  Statistics: values obtained from study of a sample.  Parameters: such values from study of population.
• 7. NEED FOR SAMPLING DATA (acc. to source) Primary Secondary 1. ORIGINAL IN CHARACTER 2. GENERATED IN LARGE NO. OF SURVEYS OBTAINED FROM 1. PUBLISHED SOURCES 2. UNPUBLISHED SOURCES
• 8. NEED FOR SAMPLING  When secondary data are not available for the problem under study , primary data is collected.  Two methods –  Census method or complete enumeration method  Sample method
• 9. CENSUS (Complete Enumeration Survey)  Merits  Data obtained from each and every unit of population.  Results: more representative, accurate, reliable.  Basis of various surveys.  Demerits  More effort ,money , time.  Big problem in underdeveloped countries.
• 10. ADVANTAGES OF SAMPLING  Less resources (time, money)  Less workload.  Gives results with known accuracy that can be calculated mathematically.
• 11. THEORETICAL BASIS OF SAMPLING  On the basis of sample study we can predict and generalize the behavior of mass phenomena.  There is no statistical population whose elements would vary from each other without limit.
• 12. THEORETICAL BASIS OF SAMPLING  Law of Statistical Regularity-  Sample is taken at random from a population, it is likely to possess same characteristics as that of population.  Law of inertia of large numbers-  Larger the size of sample, more accurate the results are likely to be.
• 13. SAMPLING PROCESS  Defining the population of concern.  Specifying a sampling frame, a set of items or events possible to measure.  Specifying a sampling method for selecting items or events from the frame.  Determining the sample size.  Implementing the sampling plan.  Sampling and data collection
• 14. ESSENTIALS OF SAMPLING  Representativeness- ensure by random selection  Adequacy - sample size  Independence - same chance of selection  Homogeneity - no basic difference in nature of units.
• 15. SAMPLING METHODS NON PROBABILITY PROBABILITY MIXED JUDGMENT QUOTA CONVENIENCE SNOWBALL SIMPLE RANDOM STRATIFIED RANDOM SYSTEMATIC CLUSTER MULTISTAGE MULTIPHASE LOT QUALITY ASSURANCE
• 16. NON PROBABILITY SAMPLING
• 17. JUDGMENT SAMPLING  Judgment/Purposive/Deliberate sampling.  Depends exclusively on the judgment of investigator.  Sample selected which investigator thinks to be most typical of the universe.
• 18. JUDGMENT SAMPLING  Merits  Small no. of sampling units  Study unknown traits/case sampling  Urgent public policy & business decisions  Demerits  Personal prejudice & bias  No objective way of evaluating reliability of results
• 19. JUDGMENT SAMPLING - EXAMPLE CLASS OF 20 STUDENTS Sample size for a study=8 JUDGMENT SAMPLE OF 8 STUDENTS
• 20. CONVENIENCE SAMPLING  Convenient sample units selected.  Selected neither by probability nor by judgment.  Merit – useful in pilot studies.  Demerit – results usually biased and unsatisfactory.
• 21. CONVENIENCE SAMPLING - EXAMPLE Class of 100 students 20 Students selected as per convenience
• 22. QUOTASAMPLING  Most commonly used in non probability sampling.  Quotas set up according to some specified characteristic.  Within the quota , selection depends on personal judgment.  Merit- Used in public opinion studies  Demerit – personal prejudice and bias
• 23. Quota Formation Interview 500 people Personal judgement Radio listening survey 60% housewives 25% farmers 15% children under age 15 300 125 75 500 people QUOTA SAMPLING - EXAMPLE
• 24. SNOWBALL SAMPLING  A special non probability method used when the desired sample characteristic is rare.  It may be extremely difficult or cost prohibitive to locate respondents in these situations.  Snowball sampling relies on referrals from initial subjects to generate additional subjects.
• 25. SNOWBALLSAMPLING - STEPS  Make contact with one or two cases in the population.  Ask these cases to identify further cases.  Ask these new cases to identify further new cases.  Stop when either no new cases are given or the sample is as large as is manageable.
• 26. SNOWBALL SAMPLING  Merit  access to difficult to reach populations (other methods may not yield any results).  Demerit  not representative of the population and will result in a biased sample as it is self-selecting.
• 27. PROBABILITY SAMPLING
• 28. SIMPLE RANDOM SAMPLING  Each unit has an equal opportunity of being selected.  Chance determines which items shall be included.
• 29. SIMPLE RANDOM SAMPLING  The sample is a simple random sample if any of the following is true (Chou) –  All items selected independently.  At each selection , all remaining items have same chance of being selected.  All the possible samples of a given size are equally likely to be selected.
• 30. Simple or unrestricted random sampling Lottery method Random number tables
• 31. LOTTERY METHOD -  With replacement-  Probability each item: 1/N  Without replacement –  Probability 1st draw: 1/N  Probability 2nd draw: 1/N-1
• 32. TABLE OF RANDOM NUMBERS
• 33. SIMPLE RANDOM SAMPLING  Merits  No personal bias.  Sample more representative of population.  Accuracy can be assessed as sampling errors follow principals of chance.  Demerits  Requires completely catalogued universe.  Cases too widely dispersed - more time and cost.
• 34. STRATIFIED RANDOM SAMPLING  Universe is sub divided into mutually exclusive groups.  A simple random sample is then chosen independently from each group.
• 35. STRATIFIED RANDOM SAMPLING  Issues involved in stratification  Base of stratification  Number of strata  Sample size within strata. Sample size within strata Proportional (proportion in each stratum) Disproportional (equal no. in each stratum)
• 36. Strata Formation Random sampling General (30%) SC (15%) ST (25%) OBC (30%) ROHTAK CITY 150 For sample size of 1000 STRATIFIED RANDOM SAMPLING - EXAMPLE
• 37. STRATIFIED RANDOM SAMPLING  Merits  More representative.  Greater accuracy.  Greater geographical concentration.  Demerits  Utmost care in dividing strata.  Skilled sampling supervisors.  Cost per observation may be high.
• 38. SYSTEMATIC SAMPLING  Selecting first unit at random.  Selecting additional units at evenly spaced intervals.  Complete list of population available. k=N/n k=sampling interval N=universe size n=Sample size Class of 95students : roll no. 1 to 95 Sample of 10 students k=9.5 or 10 1st student random then every 10th
• 39. SYSTEMATIC SAMPLING  Merits  Simple and convenient.  Less time consuming.  Demerits  Population with hidden periodicities.
• 40. CLUSTER SAMPLING  A sampling technique in which the entire population of interest is divided into groups, or clusters, and a random sample of these clusters is selected.  Each cluster must be mutually exclusive and together the clusters must include the entire population .  After clusters are selected, then all units within the clusters are selected. No units from non-selected clusters are included in the sample.
• 41. CLUSTER SAMPLING  In cluster sampling, the clusters are the primary sampling unit (PSU’s) and the units within the clusters are the secondary sampling units (SSU’s)
• 42. STRATIFICATION V/S CLUSTERING Stratification Clustering All strata are represented in the sample. Only a subset of clusters are in the sample. Less error compared to simple random. More error compared to simple random. More expensive to obtain stratification information before sampling. Reduces costs to sample only some areas or Organizations.
• 43. CLUSTER SAMPLING- STEPS  Identification of clusters  List all cities, towns, villages & wards of cities with their population falling in target area under study.  Calculate cumulative population & divide by 30, this gives sampling interval.  Select a random no. less than or equal to sampling interval having same no. of digits. This forms 1st cluster.  Random no.+ sampling interval = population of 2nd cluster.  Second cluster + sampling interval = 3rd cluster.  Last or 30th cluster = 29th cluster + sampling interval
• 44. • Freq c f cluster • I 2000 2000 1 • II 3000 5000 2 • III 1500 6500 • IV 4000 10500 3 • V 5000 15500 4, 5 • VI 2500 18000 6 • VII 2000 20000 7 • VIII 3000 23000 8 • IX 3500 26500 9 • X 4500 31000 10 • XI 4000 35000 11, 12 • XII 4000 39000 13 • XIII 3500 44000 14,15 • XIV 2000 46000 • XV 3000 49000 16 • XVI 3500 52500 17 • XVII 4000 56500 18,19 • XVIII 4500 61000 20 • XIX 4000 65000 21,22 • XX 4000 69000 23 • XXI 2000 71000 24 • XXII 2000 73000 • XXIII 3000 76000 25 • XXIV 3000 79000 26 • XXV 5000 84000 27,28 • XXVI 2000 86000 29 • XXVII 1000 87000 • XXVIII 1000 88000 • XXIX 1000 89000 30 • XXX 1000 90000 • 90000/30 = 3000 sampling interval CLUSTER SAMPLING
• 45. CLUSTER SAMPLING  Merits  Most economical form of sampling.  Larger sample for a similar fixed cost.  Less time for listing and implementation.  Reduce travel and other administrative costs.  Demerits  May not reflect the diversity of the community.  Standard errors of the estimates are high, compared to other sampling designs with same sample size .
• 46. MULTISTAGE SAMPLING  Sampling process carried out in various stages.  An effective strategy because it banks on multiple randomizations.  Used frequently when a complete list of all members of the population does not exist and is inappropriate.
• 47. MULTISTAGE SAMPLING
• 48. MULTISTAGE SAMPLING  Merits  Introduces flexibility in the sampling method.  Enables existing divisions and sub divisions of population to be used as units.  Large area can be covered.  Valuable in under developed areas.  Demerits  Less accurate than a sample chosen by a single stage process.
• 49. MULTIPHASE SAMPLING  Used for studies to be carried out in multiple phases.  For e.g. A cross - sectional study on nutrition may be carried out in phases Phase-1: K.A.P. study in all families Phase -2: Dietary assessment in subsample Phase-3:anthropometric examination in sub- sample of family members covered in 2nd phase
• 50. LOT QUALITYASSURANCE SAMPLING  Originated in the manufacturing industry for quality control purposes.  Manufacturers were interested in determining whether a batch, or lot, of goods met the desired specifications.  The only outcome in this type of sampling is “acceptable” or “not acceptable”
• 51. LOT QUALITYASSURANCE SAMPLING  The sample size is the number of units that are selected from each lot.  The decision value is the number of “defective” items that need to found before the lot is deemed unacceptable.  There are two types of risks  the risk of accepting a “bad” lot, referred to as Type I error  the risk of not accepting a “good” lot, referred to as Type II error.
• 52. LOT QUALITYASSURANCE SAMPLING  Information from lots can be combined to obtain the overall proportion of defects.  The population is first divided into a complete set of non-overlapping lots.  Samples are then taken from every lot, and the proportion of defective items in each lot is calculated.  The LQAS method is an example of stratified sampling, where the lots play the role of the strata.
• 53. LOT QUALITYASSURANCE SAMPLING  The advantage of the LQAS method over a traditional stratified sampling design is that the response for each lot is binary (acceptable or not), and therefore smaller sample sizes can be used.
• 54. LOT QUALITYASSURANCE SAMPLING  Can be used for evaluating a number of health programmes e.g. immnunisation coverage , knowledge of ORS, etc  Despite successful trials of LQAS in health surveys , its routine use has not been established yet.
• 55. ERRORS SAMPLING ERRORS NON SAMPLING ERRORS SAMPLE SAMPLE AND CENSUS
• 56. SAMPLING ERRORS BIASED UNBIASED DELIBERATE SELECTION SUBSTITUTION NON-RESPONSE APPEALTOVANITY (PRIDE) DIFFERENCES BETWEEN VALUE OF SAMPLE AND THAT OF POPULATION ELIMINATION OF ALL SOURCE OF BIAS INCREASE SAMPLE SIZE
• 57. NON SAMPLING ERRORS • Data specification inadequate & inconsistent with respect to objective of census. • Inaccurate or inappropriate methods of interview, observation, definitions. • Lack of trained & experienced investigators. • Errors due to non response. • Errors in data processing operations • Errors committed during presentation. MORE IN COMPLETE ENUMERATION SURVEY
• 58. REFERENCES  Methods in Biostatistics by BK Mahajan  Statistical Methods by SP Gupta  Basic & Clinical Biostatistics by Dawson and Beth.

Sampling Methods

Traci Smith

Spring 2013

Objectives:

To describe the different types of sampling.

To identify problems with a sampling technique.

How do you choose your sample?

1. Simple Random

2. Stratified

3. Systematic

5. Convenience

• Simple Random

image taken from Sullivan"Statistics informed decisions using data" 2004ed

Image taken from wikipedia commons

1. Pulling names from a hat.

2. Drawing lottery numbers

images taken from google images

1. Separate cookies by type for a contest and select the top 5 in each group

image taken from google images

Check Point

Identify the method.

1. Sophia has four concert tickets. Six of her friends would like to attend with her. Sophia can only invite three friends.

2. The baseball coach during a practice separates the players into two teams and then selects the top 7 players for an upcoming playoff game.

Cluster Sample

Testing all students from randomly selected high schools all over the country.

Systematic Sample

1. Polling every 10th person existing a local store.

Convenience Sample

image taken from google image

1. A radio personality will ask his listeners to phone in with their opinion on a certain topic.

Identify the sampling method

1. To estimate the percentage of defective Coke products, a quality control manager selects every 8th bottle from the assembly line starting with the 3rd until he reaches a sample of 120 bottles.

2. To determine customer opinion of its boarding policy, Southwest Airlines randomly selects 60 flights during a certain week and surveys all passengers on the flights.

3. A radio station asks its listeners to call in thier opinion regarding the use of guns by teachers in local schools.

• Convenience

4. A marketing firm conducts a nationwide poll by randomly selecting individuals from a list of known users of the product.

5. Students are divided into five classes: freshmen, sophomore,juniors, seniors, and graduate student. The official takes a simple random from each class and asks the members' opinions regarding student services.

Sullivan, M. (2004).Statistics Informed Decisions using Data (2nd ed.). Prentice Hall:New Jersey

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Methodology

• Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Table of contents

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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

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

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

Table of Contents

Sampling refers to the process of selecting a subset of data from a larger population or dataset in order to analyze or make inferences about the whole population.

In other words, sampling involves taking a representative sample of data from a larger group or dataset in order to gain insights or draw conclusions about the entire group.

Sampling Methods

Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research.

Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of that population, which can be time-consuming, expensive, or even impossible.

Types of Sampling Methods

Sampling can be broadly categorized into two main categories:

Probability Sampling

This type of sampling is based on the principles of random selection, and it involves selecting samples in a way that every member of the population has an equal chance of being included in the sample.. Probability sampling is commonly used in scientific research and statistical analysis, as it provides a representative sample that can be generalized to the larger population.

Type of Probability Sampling :

• Simple Random Sampling: In this method, every member of the population has an equal chance of being selected for the sample. This can be done using a random number generator or by drawing names out of a hat, for example.
• Systematic Sampling: In this method, the population is first divided into a list or sequence, and then every nth member is selected for the sample. For example, if every 10th person is selected from a list of 100 people, the sample would include 10 people.
• Stratified Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics, and then a random sample is taken from each stratum. This is often used to ensure that the sample is representative of the population as a whole.
• Cluster Sampling: In this method, the population is divided into clusters or groups, and then a random sample of clusters is selected. Then, all members of the selected clusters are included in the sample.
• Multi-Stage Sampling : This method combines two or more sampling techniques. For example, a researcher may use stratified sampling to select clusters, and then use simple random sampling to select members within each cluster.

Non-probability Sampling

This type of sampling does not rely on random selection, and it involves selecting samples in a way that does not give every member of the population an equal chance of being included in the sample. Non-probability sampling is often used in qualitative research, where the aim is not to generalize findings to a larger population, but to gain an in-depth understanding of a particular phenomenon or group. Non-probability sampling methods can be quicker and more cost-effective than probability sampling methods, but they may also be subject to bias and may not be representative of the larger population.

Types of Non-probability Sampling :

• Convenience Sampling: In this method, participants are chosen based on their availability or willingness to participate. This method is easy and convenient but may not be representative of the population.
• Purposive Sampling: In this method, participants are selected based on specific criteria, such as their expertise or knowledge on a particular topic. This method is often used in qualitative research, but may not be representative of the population.
• Snowball Sampling: In this method, participants are recruited through referrals from other participants. This method is often used when the population is hard to reach, but may not be representative of the population.
• Quota Sampling: In this method, a predetermined number of participants are selected based on specific criteria, such as age or gender. This method is often used in market research, but may not be representative of the population.
• Volunteer Sampling: In this method, participants volunteer to participate in the study. This method is often used in research where participants are motivated by personal interest or altruism, but may not be representative of the population.

Applications of Sampling Methods

Applications of Sampling Methods from different fields:

• Psychology : Sampling methods are used in psychology research to study various aspects of human behavior and mental processes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and ethnicity. Random sampling may also be used to select participants for experimental studies.
• Sociology : Sampling methods are commonly used in sociological research to study social phenomena and relationships between individuals and groups. For example, researchers may use cluster sampling to select a sample of neighborhoods to study the effects of economic inequality on health outcomes. Stratified sampling may also be used to select a sample of participants that is representative of the population based on factors such as income, education, and occupation.
• Social sciences: Sampling methods are commonly used in social sciences to study human behavior and attitudes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and income.
• Marketing : Sampling methods are used in marketing research to collect data on consumer preferences, behavior, and attitudes. For example, researchers may use random sampling to select a sample of consumers to participate in a survey about a new product.
• Healthcare : Sampling methods are used in healthcare research to study the prevalence of diseases and risk factors, and to evaluate interventions. For example, researchers may use cluster sampling to select a sample of health clinics to participate in a study of the effectiveness of a new treatment.
• Environmental science: Sampling methods are used in environmental science to collect data on environmental variables such as water quality, air pollution, and soil composition. For example, researchers may use systematic sampling to collect soil samples at regular intervals across a field.
• Education : Sampling methods are used in education research to study student learning and achievement. For example, researchers may use stratified sampling to select a sample of schools that is representative of the population based on factors such as demographics and academic performance.

Examples of Sampling Methods

Probability Sampling Methods Examples:

• Simple random sampling Example : A researcher randomly selects participants from the population using a random number generator or drawing names from a hat.
• Stratified random sampling Example : A researcher divides the population into subgroups (strata) based on a characteristic of interest (e.g. age or income) and then randomly selects participants from each subgroup.
• Systematic sampling Example : A researcher selects participants at regular intervals from a list of the population.

Non-probability Sampling Methods Examples:

• Convenience sampling Example: A researcher selects participants who are conveniently available, such as students in a particular class or visitors to a shopping mall.
• Purposive sampling Example : A researcher selects participants who meet specific criteria, such as individuals who have been diagnosed with a particular medical condition.
• Snowball sampling Example : A researcher selects participants who are referred to them by other participants, such as friends or acquaintances.

How to Conduct Sampling Methods

some general steps to conduct sampling methods:

• Define the population: Identify the population of interest and clearly define its boundaries.
• Choose the sampling method: Select an appropriate sampling method based on the research question, characteristics of the population, and available resources.
• Determine the sample size: Determine the desired sample size based on statistical considerations such as margin of error, confidence level, or power analysis.
• Create a sampling frame: Develop a list of all individuals or elements in the population from which the sample will be drawn. The sampling frame should be comprehensive, accurate, and up-to-date.
• Select the sample: Use the chosen sampling method to select the sample from the sampling frame. The sample should be selected randomly, or if using a non-random method, every effort should be made to minimize bias and ensure that the sample is representative of the population.
• Collect data: Once the sample has been selected, collect data from each member of the sample using appropriate research methods (e.g., surveys, interviews, observations).
• Analyze the data: Analyze the data collected from the sample to draw conclusions about the population of interest.

When to use Sampling Methods

Sampling methods are used in research when it is not feasible or practical to study the entire population of interest. Sampling allows researchers to study a smaller group of individuals, known as a sample, and use the findings from the sample to make inferences about the larger population.

Sampling methods are particularly useful when:

• The population of interest is too large to study in its entirety.
• The cost and time required to study the entire population are prohibitive.
• The population is geographically dispersed or difficult to access.
• The research question requires specialized or hard-to-find individuals.
• The data collected is quantitative and statistical analyses are used to draw conclusions.

Purpose of Sampling Methods

The main purpose of sampling methods in research is to obtain a representative sample of individuals or elements from a larger population of interest, in order to make inferences about the population as a whole. By studying a smaller group of individuals, known as a sample, researchers can gather information about the population that would be difficult or impossible to obtain from studying the entire population.

Sampling methods allow researchers to:

• Study a smaller, more manageable group of individuals, which is typically less time-consuming and less expensive than studying the entire population.
• Reduce the potential for data collection errors and improve the accuracy of the results by minimizing sampling bias.
• Make inferences about the larger population with a certain degree of confidence, using statistical analyses of the data collected from the sample.
• Improve the generalizability and external validity of the findings by ensuring that the sample is representative of the population of interest.

Characteristics of Sampling Methods

Here are some characteristics of sampling methods:

• Randomness : Probability sampling methods are based on random selection, meaning that every member of the population has an equal chance of being selected. This helps to minimize bias and ensure that the sample is representative of the population.
• Representativeness : The goal of sampling is to obtain a sample that is representative of the larger population of interest. This means that the sample should reflect the characteristics of the population in terms of key demographic, behavioral, or other relevant variables.
• Size : The size of the sample should be large enough to provide sufficient statistical power for the research question at hand. The sample size should also be appropriate for the chosen sampling method and the level of precision desired.
• Efficiency : Sampling methods should be efficient in terms of time, cost, and resources required. The method chosen should be feasible given the available resources and time constraints.
• Bias : Sampling methods should aim to minimize bias and ensure that the sample is representative of the population of interest. Bias can be introduced through non-random selection or non-response, and can affect the validity and generalizability of the findings.
• Precision : Sampling methods should be precise in terms of providing estimates of the population parameters of interest. Precision is influenced by sample size, sampling method, and level of variability in the population.
• Validity : The validity of the sampling method is important for ensuring that the results obtained from the sample are accurate and can be generalized to the population of interest. Validity can be affected by sampling method, sample size, and the representativeness of the sample.

Advantages of Sampling Methods

Sampling methods have several advantages, including:

• Cost-Effective : Sampling methods are often much cheaper and less time-consuming than studying an entire population. By studying only a small subset of the population, researchers can gather valuable data without incurring the costs associated with studying the entire population.
• Convenience : Sampling methods are often more convenient than studying an entire population. For example, if a researcher wants to study the eating habits of people in a city, it would be very difficult and time-consuming to study every single person in the city. By using sampling methods, the researcher can obtain data from a smaller subset of people, making the study more feasible.
• Accuracy: When done correctly, sampling methods can be very accurate. By using appropriate sampling techniques, researchers can obtain a sample that is representative of the entire population. This allows them to make accurate generalizations about the population as a whole based on the data collected from the sample.
• Time-Saving: Sampling methods can save a lot of time compared to studying the entire population. By studying a smaller sample, researchers can collect data much more quickly than they could if they studied every single person in the population.
• Less Bias : Sampling methods can reduce bias in a study. If a researcher were to study the entire population, it would be very difficult to eliminate all sources of bias. However, by using appropriate sampling techniques, researchers can reduce bias and obtain a sample that is more representative of the entire population.

Limitations of Sampling Methods

• Sampling Error : Sampling error is the difference between the sample statistic and the population parameter. It is the result of selecting a sample rather than the entire population. The larger the sample, the lower the sampling error. However, no matter how large the sample size, there will always be some degree of sampling error.
• Selection Bias: Selection bias occurs when the sample is not representative of the population. This can happen if the sample is not selected randomly or if some groups are underrepresented in the sample. Selection bias can lead to inaccurate conclusions about the population.
• Non-response Bias : Non-response bias occurs when some members of the sample do not respond to the survey or study. This can result in a biased sample if the non-respondents differ from the respondents in important ways.
• Time and Cost : While sampling can be cost-effective, it can still be expensive and time-consuming to select a sample that is representative of the population. Depending on the sampling method used, it may take a long time to obtain a sample that is large enough and representative enough to be useful.
• Limited Information : Sampling can only provide information about the variables that are measured. It may not provide information about other variables that are relevant to the research question but were not measured.
• Generalization : The extent to which the findings from a sample can be generalized to the population depends on the representativeness of the sample. If the sample is not representative of the population, it may not be possible to generalize the findings to the population as a whole.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Methods of Data Collection, Sampling Techniques and Methods in Presenting Data

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Education Standards

Radford university.

Learning Domain: Social Work

Standard: Basic Research Methodology

Lesson 10: Sampling in Qualitative Research

Lesson 11: qualitative measurement & rigor, lesson 12: qualitative design & data gathering, lesson 1: introduction to research, lesson 2: getting started with your research project, lesson 3: critical information literacy, lesson 4: paradigm, theory, and causality, lesson 5: research questions, lesson 6: ethics, lesson 7: measurement in quantitative research, lesson 8: sampling in quantitative research, lesson 9: quantitative research designs, powerpoint slides: sowk 621.01: research i: basic research methodology.

The twelve lessons for SOWK 621.01: Research I: Basic Research Methodology as previously taught by Dr. Matthew DeCarlo at Radford University. Dr. DeCarlo and his team developed a complete package of materials that includes a textbook, ancillary materials, and a student workbook as part of a VIVA Open Course Grant.

The PowerPoint slides associated with the twelve lessons of the course, SOWK 621.01: Research I: Basic Research Methodology, as previously taught by Dr. Matthew DeCarlo at Radford University. 

Sampling Business Research Methods

Aug 01, 2014

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Sampling Business Research Methods . 2. Sampling. Sampling : The process of selecting a sufficient number of elements from the population, so that results from analyzing the sample are generalizable to the population. OR

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Sampling Business Research Methods 2

Sampling • Sampling: The process of selecting a sufficient number of elements from the population, so that results from analyzing the sample are generalizable to the population. OR • The basic idea of sampling is that by selecting some of the elements in population, we draw conclusions about the entire population.

Populationrefers to the totality of people, events, things of interest or objects (which may be individuals, households, organizations, countries etc.) that the researcher wishes to investigate. E.g. All office workers in the firm compose a population of interest; all 4,000 files define a population of interest. • An elementis a single member of the population. • Element is the unit of study; it may be a person or may be something else. • E.g.: Each staff member questioned about an optimal promotional strategy is a population element. • Each advertising account analyzed is an element of an account population • Each ad is an element of a population of advertisements

Census • A census is a count of all the elements in a population; • If 4,000 files define the population, a census would obtain information from every one of them. • Sample: A subset of the population selected to investigate the properties of the population. Because populations are often extremely large, or even infinite, it is usually impossible – for cost and practical reasons – to take measurements on every element of the population. For this reason, more often, we draw a sample and generalize from the properties of the sample to the broader population. • Sampling unit:The element or set of elements that is available for selection in some stage of the sampling process. • A subject is a single member of the sample, just as an element is a single member of the population.

What is a Good Sample? • Sampling is acceptable only when it adequately reflects the population from which it is drawn; • No sample is a perfect representation of its population • The ultimate test of a sample design is how well it represents the characteristics of the population it purports to represents. • In measurement terms, the sample must be valid. • Validity of a sample depends on two considerations: • Accuracy and • Precision

Questions • You wish to study the care arrangements of at government hospitals in Islamabad and Rwp. • Find out the opinions of workers in a factory on changed working arrangements • Measuring students’ satisfaction level about teaching in the MBA/BBA/BS/MS programs • Find out the changing attitude of Pakistanis towards immigration to Australia, NZ, USA, UK.

Advantages of Sampling • Sometimes there is a need for sampling. Suppose we want to inspect the eggs, the bullets, the missiles and the tires of some firm. The study may be such that the objects are destroyed during the process of inspection. Sampling plays a key role in this process. • Sampling saves money as it is much cheaper to collect the desired information from a small sample than from the whole population. • Sampling saves a lot of time and energy as the needed data are collected and processed much faster than census information.  • Sampling makes it possible to obtain more detailed information from each unit of the sample as collecting data from a few units of the population. • Sampling has much smaller “non-response”, following up of which is much easier. • The most important advantage of sampling is that it provides a valid measure of reliability for the sample estimates. • Sample data is also used to check the accuracy of the census data.   

The Sampling Process • Major steps in sampling: • Define the population (elements, geographic boundaries, and time) • Determine the sample frame • Determine the sampling design • Determine the appropriate sample size • Execute the sampling process

Steps in sampling process • The population is defined in terms of element, units, time, etc. • Sampling unit: Maybe on a geographical basis such as state, province, district, village, etc. • Sampling frame: To prepare the source list to cover all the samples in the sampling frame that is as true a representative of the population as possible. • Size of the sample: Number of items to be selected from the universe to constitute a sample. Sampling Techniques • Probability versus nonprobability sampling • Probability sampling: elements in the population have a known and non-zero chance of being chosen

Sampling Techniques • Probability Sampling • Simple Random Sampling • Systematic Sampling • Stratified Random Sampling • Cluster Sampling • Nonprobability Sampling • Convenience Sampling • Judgment Sampling • Quota Sampling

Simple Random Sampling • Procedure • Each element has a known and equal chance of being selected • Characteristics • Highly generalizable • Easily understood • Reliable population frame necessary

Simple random sampling (all members have equal chance of being selected)

Systematic Sampling • Procedure • Each nth element, starting with random choice of an element between 1 and n • Characteristics • Easier than simple random sampling • Systematic biases when elements are not randomly listed

Systematic sampling (systematic sampling involves selecting every nth case within a defined population)

Cluster Sampling • Procedure • Divide of population in clusters • Random selection of clusters • Include all elements from selected clusters • Characteristics • Intercluster homogeneity • Intracluster heterogeneity • Easy and cost efficient • Low correspondence with reality

Cluster sampling (cluster sampling involves surveying whole clusters of the population selected through a defined random sampling strategy.)

Stratified Sampling • Procedure • Divide of population in strata • Include all strata • Random selection of elements from strata • Proportionate • Disproportionate • Characteristics • Interstrata heterogeneity • Intrastratum homogeneity • Includes all relevant subpopulations

Stratified random sampling(Dividing your population into various subgroups and then taking a simple random sample within each.)

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What Is Sample Size?

Learn what sample size is and why having the correct sample size is important in statistical research.

Sample size is the number of observations or individuals included in a study or experiment. It is the number of individuals, items, or data points selected from a larger population to represent it statistically. The sample size is a crucial consideration in research because it directly impacts the reliability and extent to which you can generalize those findings to the larger population.

A larger sample size can potentially enhance the precision of estimates, leading to a narrower margin of error . In other words, the results from a larger sample will likely be closer to the true population parameter. A larger sample size can also increase the power of a statistical test. This means that with a larger sample, you are less likely to find results that are not actually true.

However, having a sample size that is too large can cost unnecessary resources and time. A good study will be able to find the most accurate results with the least amount of subjects.

How do you determine sample size?

Determining the appropriate sample size for a study will usually involve considering the purpose of the study, population size, risk of committing an error, and available resources. When determining the appropriate sample size, you should consider factors such as the following:

Your study design

Different types of studies might require different sample sizes. For example, a study aiming to understand a rare disease might need a larger sample size to ensure it captures enough cases for analysis. Whether you choose to conduct an observational study, cohort study, case-control study, or experimental study will affect the sample size you need.

Your population

In general, with a smaller population, you will need a higher sampling ratio than in a larger population. If you are conducting a survey, you may also need to factor in your estimated response rate to ensure you are sampling enough people to get the number of responses you need. If you have a high degree of variability in your target population, you may also need to increase your sample size to increase the likelihood that your sample represents the population of interest.

Your statistical methods

You can use several statistical methods to calculate an appropriate sample size, such as power analysis. This method considers the effect size, significance level, and power to calculate the sample size. Essentially, you will need to determine what level of confidence you want to have in your results, and how much error you are willing to accept.

Your available resources

Lastly, practical considerations like time, money, and availability of subjects can affect the chosen sample size. While a larger sample size may offer more accurate results, it can also require more resources to collect and analyze.

Related terms

Margin of error

Unstructured data

Statistical modeling  

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Understanding what sample size is and its significance can help you perform many analytical tasks. The appropriate sample size is not only pivotal for the validity and reliability of the study's findings but also for ensuring that the resources used in conducting the research are efficiently utilized. To expand on your analytical skills and build a job-ready portfolio, consider the Google Data Analytics Professional Certificate on Coursera.

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A comparison of eDNA sampling methods in an estuarine environment on presence of longfin smelt (Spirinchus thaleichthys) and fish community composition

The loss of tidal wetlands in the San Francisco Bay estuary have led to declines in native fish presence. Restoration of tidal wetlands in this area has intensified, with a primary goal of increasing the number of native fishes. We compared the presence of longfin smelt in naturally accreted and beneficial dredge reuse wetlands as a measure of successful restoration. We used environmental DNA (eDNA) analyses as our metric for fish presence and fish community composition, employing two different water sampling methods for comparison (standard and high-volume). Longfin smelt were present in multiple sites, but at numbers too low for accurate comparisons across sites. Community composition varied based on the water sampling method, but the presence/absence of longfin smelt was consistent across sampling methods. As this represents a pilot study, further refinement of methodology is necessary, but the use of high-volume water sampling methods is promising.

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2024 Theses Doctoral

Statistically Efficient Methods for Computation-Aware Uncertainty Quantification and Rare-Event Optimization

He, Shengyi

The thesis covers two fundamental topics that are important across the disciplines of operations research, statistics and even more broadly, namely stochastic optimization and uncertainty quantification, with the common theme to address both statistical accuracy and computational constraints. Here, statistical accuracy encompasses the precision of estimated solutions in stochastic optimization, as well as the tightness or reliability of confidence intervals. Computational concerns arise from rare events or expensive models, necessitating efficient sampling methods or computation procedures. In the first half of this thesis, we study stochastic optimization that involves rare events, which arises in various contexts including risk-averse decision-making and training of machine learning models. Because of the presence of rare events, crude Monte Carlo methods can be prohibitively inefficient, as it takes a sample size reciprocal to the rare-event probability to obtain valid statistical information about the rare-event. To address this issue, we investigate the use of importance sampling (IS) to reduce the required sample size. IS is commonly used to handle rare events, and the idea is to sample from an alternative distribution that hits the rare event more frequently and adjusts the estimator with a likelihood ratio to retain unbiasedness. While IS has been long studied, most of its literature focuses on estimation problems and methodologies to obtain good IS in these contexts. Contrary to these studies, the first half of this thesis provides a systematic study on the efficient use of IS in stochastic optimization. In Chapter 2, we propose an adaptive procedure that converts an efficient IS for gradient estimation to an efficient IS procedure for stochastic optimization. Then, in Chapter 3, we provide an efficient IS for gradient estimation, which serves as the input for the procedure in Chapter 2. In the second half of this thesis, we study uncertainty quantification in the sense of constructing a confidence interval (CI) for target model quantities or prediction. We are interested in the setting of expensive black-box models, which means that we are confined to using a low number of model runs, and we also lack the ability to obtain auxiliary model information such as gradients. In this case, a classical method is batching, which divides data into a few batches and then constructs a CI based on the batched estimates. Another method is the recently proposed cheap bootstrap that is constructed on a few resamples in a similar manner as batching. These methods could save computation since they do not need an accurate variability estimator which requires sufficient model evaluations to obtain. Instead, they cancel out the variability when constructing pivotal statistics, and thus obtain asymptotically valid t-distribution-based CIs with only few batches or resamples. The second half of this thesis studies several theoretical aspects of these computation-aware CI construction methods. In Chapter 4, we study the statistical optimality on CI tightness among various computation-aware CIs. Then, in Chapter 5, we study the higher-order coverage errors of batching methods. Finally, Chapter 6 is a related investigation on the higher-order coverage and correction of distributionally robust optimization (DRO) as another CI construction tool, which assumes an amount of analytical information on the model but bears similarity to Chapter 5 in terms of analysis techniques.

• Operations research
• Stochastic processes--Mathematical models
• Mathematical optimization
• Bootstrap (Statistics)
• Sampling (Statistics)

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