Sampling

1. Sampling

2. Sampling Sampling provides a practical method to study a population Participants in a sample must be representative Statistic: a measured value based on sample data Parameter: a value based on a statistic inferred for a population

3. Population vs Sample Population: a group with some type of common construct selected for study Sample: small proportion of a population selected for observation and/or analysis

4. Randomness Individual events cannot be predicted with accuracy Events for a group can be identified Must be representative Must be random�equal chance of being selected for an experimental group or control group

5. Randomness Random samples may not be identical representations to the population We will have sampling error�variation from the population Sampling error is not indicative of a mistake in sampling

6. Simple random sampling Individuals or observations are chosen in a manner to which an equal and independent chance of being selected is provided Independence: refers to the fact that each observation/participant has a separate and equal chance of being selected or placed in a group

7. Random sampling Random numbers, via Internet or tables, may be utilized to create random samples Any individual or observation cannot be consciously selected.

8. Systematic sampling Used when a population can be accurately listed and provide a random sample e.g.. a phone book is used to administer a poll and every 1000th name is chosen until a sample of 200 is compiled.

9. Stratified random sample A random sample is compiled from a homogeneous population e.g.. comparing academic achievement based on SES

10. Cluster sampling A subgroup of a large population is randomly chosen e.g. In order to study freshmen in college in the State of Texas, I randomly select colleges and then randomly select the dorms

11. Nonprobability sampling Also known as a convenient sample Utilizes whatever participants are available rather than a random selection process May not reflect the population Not recommended for generalizeable conclusions

12. Purposeful sampling Used in qualitative research Goal is to produce information-rich sources Not generalizeable but may be important for transfer�the applicability and meaningfulness of the research to other contexts

13. Sample size Sampling can be time consuming and costly Samples of 30 or more are usually considered large samples and when representative are generalizeable to the population The science of the research is not necessarily improved by having a sample that is too large

14. Sample size Larger samples = decreased sampling error Considerations in sample size include Return rate Subdivisions of groups Cost Appropriate sample size

15. Experimental and Quasi-Experimental Research Quasi-experimental research differs from experimental research because values may be less exact or difficult to define Experimentation provides: Systematic and logical method for answering questions Method of hypothesis testing Method for manipulating or controlling elements

16. Experiment Compares the effects of a particular treatment with that of a different treatment (experimental group) or of no treatment (control group) Groups should be equal Experimental group(s) are exposed to influence; control group is not

17. Experiment Hawthorne Effect: the participant(s) reaction to an experimental condition is the result of the knowledge of participating in an experiment Similar responses have been exhibited when a placebo has been used in medical experiments (placebo effect)

18. Variables Independent variable: conditions or characteristics the experimenter manipulates or controls Dependent variable: conditions or characteristics that appear, disappear, or change as the independent variable changes

19. Variables Not all independent variables can be altered Treatment variables have factors that can be manipulated or randomly assigned Attribute variables have characteristics that cannot be altered by an experimenter

20. Confounding variables Aspects of a study or sample that can Influence the dependent variable Effects may be confused with the effects of the independent variable Two types of confounding variables: Intervening Extraneous Goal is to control potentially confounding variables as much as possible

21. Confounding variables Intervening variables Difficult to observe Often have to do with an individual�s feelings Extraneous variables Uncontrolled variables that may influence the results of the study