Chapter 5 - Subjects, Participants, and Sampling

After reading Chapter 5, you should have mastered each of the following objectives.

1. Define the terms population, sample, subjects, and participants. Differentiate them from one another.
2. Explain the difference between the terms target population and sampling frame.
3. Differentiate probability sampling from non-probability sampling.
4. State the goals of probability and non-probability sampling.
5. Identify four types of probability sampling used in quantitative research. Identify the steps by which samples are selected using each of these procedures. Discuss the strengths and weaknesses of each procedure.
6. Identify the three types of non-probability sampling procedures used in quantitative research. Identify how samples are selected using each procedure (1, 2, 3). Discuss the strengths and weaknesses of each procedure.
7. State the purpose of sampling techniques used in qualitative research.
8. Identify five types of purposeful sampling techniques used in qualitative studies. Identify the unique characteristics of each technique.
9. Identify three general concerns related to the effect sampling procedures can have on a research study.
10. Identify the concerns related to the possible effects of volunteer samples, sample size, subject motivation, and sample bias can on a research study.
11. State the criteria used to evaluate the sampling procedures and samples in a research report. Evaluate the sampling procedures used in a research report using this criteria.

1. Subjects, participants, and samples
1. Subject or participant: person from whom data are collected
1. Subject: term used in a quantitative context
2. Participant: term used in a qualitative context
2. Sample: the collective group of subjects or participants from whom data are collected
2. Types of sampling procedures
1. Two types
1. Probability
1. Statistically driven sampling techniques where the probability of being selected is known
2. Purpose is to select a group of subjects representative of the larger group of subjects from which they are selected
2. Non-probability
1. Pragmatically driven sampling techniques where the probability of being selected is not known
2. Purpose is to select subjects who can be particularly informative about the research issues
2. Probability sampling
1. Method of sampling in which subjects are selected randomly from a population in such a way that the researcher knows the probability of selecting each subject
1. In a sample of 10 from a population of 100, each subject has a 10% chance of being included in the sample
2. In a sample of 50 from a population of 100, each subject has a 50% chance of being in included in the sample
2. Population: a large group of individuals to whom the results of a study can be generalized
1. Target population: the group to whom the results are intended to be generalized
2. Sampling frame (i.e., survey population or accessible population)
1. The group to whom the researcher has access and from which the actual sample will be drawn
2. Often the sampling frame and the target population are different
3. The target population could be all fourth graders in Orleans Parish; the sampling frame is fourth graders in public schools in Orleans Parish (i.e., excluding private and parochial school students due to their inaccessibility)
4. The target population could be all graduate students at the University of New Orleans; the sampling frame is all graduate students in the College of Education at UNO (i.e., excluding graduate students from all other colleges due to the lack of specific enrollment data)
3. The goal of probability sampling is to select a sample that is representative of the population from which it is selected
1. Sampling error: the difference between the "true" result and the "observed" result that can be attributed to using samples rather than populations
1. In a sample of 99 from a population of 100
1. The observed result (i.e., that determined using the sample) is likely to be very, very close to the true result (i.e., that determined using all 100 subjects in the population).
2. Sampling error is minimal.
2. In a sample of 2 from a population of 100
1. The observed result is likely to be somewhat different from the true result.
2. Sampling error is high.
2. Sampling bias: the difference between the "observed" and "true" results that is attributed to the sampling mistakes of the researcher.
1. Deliberately sampling subjects with certain attributes (e.g., positive attitudes, high self-esteem, high level of achievement, etc.)
2. Using subjects from different populations and assigning them to different treatment groups (e.g., males to an experimental treatment group and females to a traditional treatment group)
4. Types of probability sampling procedures
1. Simple random: a number is assigned to each subject in the population and a table of random numbers or a computer is used to select subjects randomly from the population
1. Research Randomizer - a web site designed to help with the selection of random samples
2. Systematic sampling: a number is assigned to each subject in the population, and every n^th member of the population is selected (e.g., 10, 20, 30, 40, etc,; 12, 22, 32, 42, etc.)
3. Stratified sampling: similar to random sampling with the exception that subjects are selected randomly from strata, or subgroups, of the population
1. Strata: homogeneous subgroups within a population (e.g., males and females; low, middle and high socio-economic status; certified and non-certified teachers working with special needs students; etc.)
2. Strata should be related to the dependent variable (e.g., socio-economic status is related to achievement and therefore is a potential stratifying variable)
3. Strata ensure adequate numbers of subjects from specific subgroups
1. Proportional stratified sample: the proportions of subjects in each strata in the population are reflected in the proportions of subjects in each strata in the sample (e.g., if the population is 60% female and 40% male, the sample consists of 60% females and 40% males)
2. Disproportional stratified sampling: the proportions of subjects in each strata in the sample are the same regardless of the proportions of subjects in the strata in the population
1. Even if the population of elementary school teachers is 90% female and 10% male, 50% of the sample is female and 50% male.
2. This mitigates concerns when only a few subjects would be included in a sample (e.g., only 2 males in a sample of 20 teachers)
4. Cluster sampling: similar to random sampling except that naturally occurring groups are randomly selected first, then subjects are randomly selected from the sampled groups
1. Useful when it is impossible to identify all of the individuals in a population
2. Typical educational clusters are districts, schools, or classrooms
3. Example - 27 of the 54 school districts were randomly selected, one secondary school in each district was randomly selected, and students randomly selected from each school were tested
5. Steps in selecting probability samples
1. Define the target population and sampling frame
2. Determine the sample size
3. Select the sampling strategy (i.e., procedure)
4. Select the sample
3. Non-probability sampling
1. Method of sampling in which the probability of selecting a subject is unknown
1. It is often not possible to use probability sampling techniques due to access, time, resource or financial constraints
2. It is often desirable to select subjects who can be particularly informative about the research issues (e.g., if the researcher is trying to understand how teachers use manipulatives, it makes sense to select teachers who do use these in their classes)
2. The goal of non-probability sampling is to identify information-rich participants
3. Three categories of non-probability sampling procedures
1. Convenience sampling: selecting a subject or group of subjects based on their availability to the researcher
1. Typical of much educational research given the constraints under which it is conducted
2. The major concern is the limited generalizability of the results from the sample to any population
3. Examples
1. Students enrolled in the researcher's classes
2. Fourth-grade students in two local, parochial schools to which the researcher has access
2. Purposive sampling: selection of particularly informative or useful subjects
1. Typically selects a few information-rich subjects who are studied in-depth
2. Also known as purposeful sampling
3. Examples
1. It is reasonable to select "expert" teachers if one is trying to understand how teachers use effective teaching strategies
2. It is reasonable to select physically fit individuals if one is trying to identify effective exercise behaviors
3. Quota sampling: non-random sampling representative of a larger population
1. Used when the researcher cannot use probability sampling procedures but does want a sample that is somewhat representative of the population
2. Similar to stratified sampling with the exception that the subjects are selected non-randomly
4. Types of sampling techniques
1. Typical case: selecting a representative participant
1. An average student
2. A typical volleyball player
2. Extreme case: selecting a unique or atypical participant
1. A failing student
2. An all-state volleyball player
3. Maximum variation: selecting participants to represent extreme cases
1. Students whose special needs are being met by the schools and those students whose needs are not being met
2. Successful and unsuccessful students in math classes
4. Snowball (i.e., network): selecting participants from recommendations of other participants
1. The recommendations of algebra teachers using math manipulatives of others who are doing the same
2. Teacher's recommendations of student leaders in a school
5. Critical case: selecting the most important participants to understand the phenomena being studied
1. Professors who received laptop computers to help incorporate technology in their teaching
2. Fifth-year athletes who received scholarships to finish their academic work
4. The use of probability and non-probability sampling
1. Quantitative studies
1. The desired use of probability sampling due to the ability to generalize the results to the larger population
2. Frequent use of non-probability techniques - particularly convenience sampling - due to access, time, resource, or financial constraints
2. Qualitative studies
1. Almost exclusive reliance on non-probability techniques - particularly purposeful sampling
3. How subjects and sampling affect results
1. How do the sampling procedures affect the results?
1. Need to identify the sampling procedure used
2. Sample error and sampling bias
3. Need to evaluate the sampling procedure in light of the research problems and conclusions
4. Considering the strengths and weaknesses of specific sampling procedures (see Table 5.1)
2. How do the characteristics of the subjects affect the usefulness and generalizability of the results?
1. Volunteer samples
1. Different characteristics between volunteers and non-volunteers can lead to different responses
1. Educational level
2. Socio-economic status
3. Need for social approval
4. Ability to socialize
5. Conformity
2. Commonly used due to their availability
2. Subject motivation
1. Specific characteristics of the sample can predispose them to respond in certain ways (e.g., only selecting teachers using holistic language strategies would likely predispose them to respond favorably to an attitudinal scale focusing on holistic language instruction)
3. Sample size
1. Does the sample represent the population?
1. A sample of 99 of 100 likely represents the population
2. A sample of 1 of 100 is unlikely to represent the population
2. General rules of thumb
1. Quantitative studies
1. 30 subjects for correlational research
2. 15 subjects in each group for experimental research
3. Approximately 250 responses for survey research
2. Qualitative studies - a sufficient number of subjects are needed to ensure that no new information is forthcoming from additional cases
3. Need to interpret results very carefully - results form studies using very large or very small samples can be misleading
1. Results indicating "no difference" or "no relationship" in studies with small samples an be problematic
2. Results of "differences" or "relationships" in studies can be problematic
4. See the NCS software for sample size
4. Criteria for evaluating subjects and sampling procedures
1. Subjects or participants should be described clearly with specific and detailed information related to demographic and other personal characteristics
2. The population should be clearly defined.
3. The sampling procedure should be clearly described.
4. The return rate should be reported and analyzed (e.g., the proportion of teachers responding to a survey, that is the number responding compared to the total number of teachers who were sent the survey).
1. Less than a 60% return rate requires a comparison of respondents to non-respondents
5. The selection of subjects should be free of bias.
6. Selection procedures should be appropriate for the problem being investigated.
7. Adequate sample sizes should be used.
8. Qualitative studies should have informative and knowledgeable subjects.

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