1. Who are the Subjects? Intro to Sampling
Typically, a relatively small group of individuals is selected to participate in a behavioral sciences research study.
Samples tend to be similar to the populations from which they are taken.
Researchers want to ensure that their samples are good representations of the populations they want to study.

2. Populations and Samples
Subject Selection: Key issue
The outcome of a study may depend on the method used to select participants.
A population is the large group of interest from which a researcher selects a sample.
Sample: the small set of individuals who participate in the study.
Goal of research ► generalize from the sample to the larger population.

3. Populations and Samples
Target population: the group defined by the researcher’s specific interests.
Accessible population: the people that the researcher can realistically involve in the study.
We must be cautious about generalizing the results of an accessible population to the target population.

4. Populations and Samples
Figure 5.2 The Relationship Among the Target Population, the Accessible Population, and the Sample

5. The Relationship between a Population and a Sample
Figure 5.1 The Relationship Between a Population and a Sample

6. Representative Samples
A sample with the same characteristics as the population
Generalizing the results from a given sample to the population
depends on the sample’s representativeness
Researcher’s judgement
how well the accessible population represents the target population.

7. Problems with Selection
Sampling Bias or Selection Bias
A biased sample’s characteristics are noticeably different from those of the population.
Some individuals in the sample may be older, or faster, or smarter, etc.
Exp: College students as a convenient sample but not generalizable to general population
- Volunteer Subjects
- Online Survey Responders
- College Student
- Patients volunteering for Clinical Trials

8. Sample Size
Law of large numbers: a large sample will probably be more representative than a small sample.
A sample size of individuals for each group or treatment condition is a good target for many studies.

9. Sample Size (2 of 2)
Figure 5.3 The Average Distance between a Sample Mean and the Population Mean as a Function of Sample Size
Note that the larger the sample, the more accurately the sample represents the population. However, representativeness increases in relation to the square root of the sample size.

10. Sampling Basics Two basic categories of sampling methods
Probability sampling or Random sampling
The exact size of the population must be known and it must be possible to list all the individuals.
Each individual in the population must have a specified probability of selection. Example: Each subject has equal chance of being selected
Random selection
Nonprobability sampling: Not Random
odds of selecting a particular individual are unknown.
Example: Administering a survey in a mall

11. Probability Sample Methods
Simple random sampling
Participants are selected from a list containing the total population.
Equality: each individual has an equal chance of selection.
Independence: choice of one individual does not influence or bias the probability of choosing another individual.

12. Simple Random Sampling
What are the two principal methods of random sampling?
Sampling with replacement
An individual selected for the sample is recorded as a sample member and then returned to the population (replaced) before the next selection.
Sampling without replacement
Removes each selected individual from the population before the next selection is made.

13. Bias with Simple Random Sampling
Chance determines SAMPLE COMPOSITION
It is possible (although usually unlikely) to obtain a very distorted sample.
Example: coin toss
Can reduce chances of distorted sample by having a robust/large sample size
Additional restrictions on random sampling techniques help avoid a nonrepresentative sample.

14. Systematic Sampling
Every nth participant is selected from a list containing the total population.
A random starting position is chosen.
Violates the principle of independence
Ensures a high degree of representativeness

15. Stratified Random Sampling
The population is divided into subgroups (strata) ► equal numbers are randomly selected from each of the subgroups.
Guarantees that each subgroup will have adequate representation
Example: choose equal #males and females
Overall sample is usually not representative of the population. (esp if population has unequal #males and females)

16. Stratified Random Sampling (2 of 2)
Figure 5.4 The Population of a Major City Shown as Different Layers, or Strata, Defined by Annual Income

17. Proportionate Stratified Random Sampling
The population is subdivided into strata.
Number of participants from each stratum is selected randomly.
The proportions in the sample correspond to the proportions in the population. (SES, Gender, Ethnicity, etc.) exp
Selection is done by each grouping according to the percentage in the general population
Exp: If 25%Males & 75%Females in Gen Pop
Selection: 1 male chosen for every 3 females
Ensures the sample will be representative of the population
Requires a lot of work and may make it difficult or impossible to compare subgroups within strata.

18. Cluster Sampling
Clusters (preexisting groups) are randomly selected from a list of all the clusters that exist within the population.
An easy method for obtaining a large, relatively random sample
Selections are entirely random or independent.
Exp: randomly choosing 5 graduate programs from the total number of graduate programs to test dissertation support intervention.
Note: Each program is selectively different from all others and generalization is a problem.

19. Combined-Strategy Sampling
Two or more sampling strategies are combined to select participants.
Optimizes the chances that a sample is representative of a widely dispersed or broad- based population
Example: Use a Cluster technique and then use stratified sampling to further choose the final set of subjectds

20. Nonprobability Sampling Methods: Not Random
Convenience sampling
Individual participants are obtained by selecting those who are available and willing.
An easy method for obtaining a sample, but a weak form of sampling
The sample is probably biased.
Select a reasonably representative sample and clearly describe the selection process to help correct the problems with this form of sampling

21. Quota Sampling Subgroups are identified to be included.
Quotas are established for individuals to be selected through convenience from each subgroup.
Exp: EQUAL NUMBER OF PATIENTS WITH MILD AND SEVERE CLINICAL DEPRESSION
NON-RANDOMLY CHOSEN
Selecting based on specific characteristics
Allows a researcher to control the composition of a convenience sample
The sample probably is biased.

22. Summary of Sampling Methods: Probability Sampling
Type of Sampling: Probability Sampling
Description
Strengths and Weaknesses
Simple random
A sample is obtained using a random process to select participants from a list containing the total population. The random process ensures that each individual has an equal and independent chance of selection.
The selection process is fair and unbiased, but there is no guarantee that the sample is representative.
Systematic
A sample is obtained by selecting every nth participant from a list containing the total population after a random start.
An easy method for obtaining an essentially random sample, but the selections are not really random or independent.
Stratified random
A sample is obtained by dividing the population into subgroups (strata) and then randomly selecting equal numbers from each of the subgroups.
Guarantees that each subgroup will have adequate representation, but the overall sample is usually not representative of the population
Proportionate stratified
A sample is obtained by subdividing the population into strata and then randomly selecting from each stratum a number of participants so that the proportions in the sample correspond to the proportions in the population.
Guarantees that the composition of the sample (in terms of the identified strata) will be perfectly representative of the composition of the population, but some strata may have limited representation in the sample.
Cluster
Instead of selecting individuals, a sample is obtained by randomly selecting clusters (preexisting groups) from a list of all the clusters that exist within the population.
An easy method for obtaining a large, relatively random sample, but the selections are not really random or independent.
Table 5.1 Summary of Sampling Methods: Probability Sampling

23. Summary of Sampling Methods: Nonprobability Sampling
Convenience
A sample is obtained by selecting individual participants who are easy to get.
An easy method for obtaining a sample, but the sample is probably biased.
Quota
A sample is obtained by identifying subgroups to be included, then establishing quotas for individuals to be selected through convenience from each subgroup.
Allows a researcher to control the composition of a convenience sample, but the sample probably is biased.
Table 5.1 Summary of Sampling Methods: Nonprobability Sampling

24. Group Work: Sampling Method
Instructions:
1. Choose one Sampling Method discussed in this lecture (you may use the chart on the previous slides)
2. Create an example of that sampling method
3. Identify strengths and weaknesses (keeping in mind concerns about sampling bias/external validity, practicality, cost in time and money)
Please turn in via as a group; subject line should have your group #