1. Introduction To Research 589(A)
Session #3

2. Population and Sample
Population: an entire group of people having one or more common characteristic.
Sample: a small subgroup of a population of interest that is representative of the larger population.

3. Population People Animals Objects Organisms Institutions Attributes
Materials
Various Other Defined Elements

4. cont. Fitness Test Scores Four Year Universities in the U.S.
Fourth Grade Teachers in Iowa
Adolescents in Battle Creek, Michigan
NCAA Division II Basketball Coaches
Health Ed. Majors at University of Georgia
Populations can be described to suit the particular research purpose.

5. cont.
Studying an entire population is impractical, if not impossible. (collecting fitness scores of every adolescent in the U.S. would be problematic in three ways: # of researchers required to conduct study; money to finance the project; exorbitant amount of time and energy the project would require.
Obtaining information that can be generalized to a population of interest = major reason for conducting research.

6. Sampling Makes a study possible.
From data collected, inferences can be made to larger population.
Five Steps:
Identify the target population.
Identify the accessible population.
Determine the desired sample size.
Select the specific sampling technique.
Implement the sampling plan.

7. cont. Sample must be representative of population.
What variables are studied in a population (attitude, behavior, self-concept, fitness, motivation) should be present in the sample drawn from that population.

8. Randomization
Important to the quality of the research for three reasons:
Helps ensure the sample is representative of the population.
Shows the researcher was unbiased as to which members of the population were selected for study.
Equalizes characteristics among groups in research studies requiring multiple groups such as control and experimental groups.
Randomization controls for internal validity.

9. Probability Sampling
Selection techniques used where the probability of selecting each participant or element is known.
Examples:
Simple Random Sampling
Stratified Random Sampling
Systematic Sampling
Cluster Sampling

10. Nonprobability Sampling
Random processes not used in the selection.
Knowing probability of selecting a given participant is impossible.
Difficult to claim the sample was representative of the population.
Limits ability of researcher to generalize results of the study.
Includes purposive sampling, convenience sampling, and quota sampling.

11. Cont.
Simple Random: every individual has an equal chance of being selected.
Stratified Random: Population is divided into subgroups, based on a characteristic that is important to control in the study, then a set number of subjects from each strata is selected.
Systematic Sampling: Drawing a sample by selecting every nth element from a list of the population.

12. cont.
Cluster Sampling: schools, classrooms, city blocks, hospitals, malls and worksites.
Limits geographical hardship.
Appropriate in situations where the researcher cannot obtain a list of the members of a population and has little knowledge of their characteristics, where the population is scattered, and in situations where it is impractical to remove individuals from a naturally occurring group.

13. cont.
A random sample is a chance sample and follows the laws of probability. Some samples do not.
Nonprobability samples are not random.
Examples are intact classes, volunteers, a typical group, or a typical person. The results of these kinds of samples frequently do not reflect accurately the traits of the population in which the researcher is interested. This may lead to faulty conclusions.

14. cont.
Purposive Sampling: the researcher knows specific characteristics exist in a segment of the population. These may be extremely critical to the results of the study. The researcher selects those subjects ‘purposefully.’ (top gymnasts, swimmers, basketball players and other athletes).
Selection process: biased

15. cont.
Convenience Sampling: researcher selects participants on the basis of being accessible. Often involves the use of volunteers and subjects in existing groups who are handy to the researcher.
Could be students enrolled in a class taught by the researcher, fellow graduate students, participants in a health promotion program that is administered by the researcher, etc.
Results are not likely generalizable.

16. Sample Size
Regardless of the size, the crucial factor is whether or not the sample is representative of the population.
50 subjects out of 100 may be representative.
50 subjects out of 1,000,000 would not be representative.
No foolproof computational formula for determining exact sample size exists.
Websites for determining Sample Size:

17. Key Factors: Determining Sample Size
1. Sampling error is related to sample size. The Larger the sample size, the smaller the sampling error and the greater likelihood that the sample is representative of the population.
2. Descriptive and correlational research typically should have larger samples than are required for experimental studies.
3. Sample size should increase as variability within the population increases. Larger samples are needed with a heterogeneous population.
4. When samples are to be divided into smaller groups to be compared, the initial sample size should be large enough that the subgroups are of adequate size to make meaningful comparisons.

18. Validity
The degree to which an instrument truly measures what it purports to measure.
Can I trust the findings of my instrument to be true?

19. cont.
Internal: How valid the findings are within, or internal, to the study. (Did the experimental treatments in the study make a difference that caused the participants to change in ability, or are the changes due to other factors?)
External: The degree to which findings in a study can be inferred or generalized to other populations, settings, or treatments.
Good internal validity is required to have good external validity, however it does not guarantee it.

20. Controlling Threats to Internal Validity
HMSTIRES (8 Internal Validity Factors)
History
Maturation
Selection
Testing
Instrumentation
Regression
Experimental Mortality
Stability
They are always present, but we attempt to minimize…

21. Sources of Error Hawthorne Effect Halo Effect
Selection (Experimenter Bias Effect)
Multiple Treatment Interference