Final Study Guide Research Design

1. Final Study GuideResearch Design

2. Experimental Research

3. Experimental Research • Researchers manipulate independent variable - 2 levels • And measure the other (dependent variable) • Give treatment to participants and observe if it causes changes in behavior • Compareexperimental group (w/ treatment) with a control group (no treatment) • Can say IV caused change in the DV

4. Independent Variable • The variable whose impact you want to know • ‘Stimulus’ ‘Input’ Variable • The variable you manipulate in experimental research

5. Dependent Variable • The variable whose changes you want to know • You measure it • ‘Outcome’ ‘Response’ variable

6. Random Selection • A way to choose your sample of study • Any member of population has equal chance of being selected • Random Assignment • A way to assign participants in sample to the various treatment conditions (groups will receive different level of IV) • Any member of your sample has equal chance of being assigned in any treatment group

7. Internal Validity • Ability of your research design to adequately test your hypothesis • Showing that variation in I.V. CAUSED the variation in the D.V. in experiment • In correlational study, • Showing that changes in value of criterion variable relate solely to changes in value of predictor variable

8. Confounding • Whenever 2 or more variables combine in a way that their effects cannot be separated = confounding. • Thus, the teaching method study as designed lacks internal validity. • You don’t know if the change in the DV is from the IV or from confounding variable

9. Quasi-experimental research • Naturally occurring conditions • (IV change) • No control over variables influencing behavior (confounding variables) • Another variable that changed along with the variable of interest may have caused the observed effect • (NO random assignment)

10. Non-Experimental Research

11. Non-experimental Correlational research • Determine whether 2 or more variables are associated, • If so, to establish direction and strength of relationships • Observe variables as they are, • can’t manipulate them

12. Research design Manipulate IV Random Assignment • Experimental (Causal) x x • Quasi-experimental x • Non-experimental / • Correlational • Predictive • Descriptive

13. Causal - (Experimental) • one variable directly or indirectly influences another. • Correlational - (Non-experimental) • Changes in one variable accompany changes in another. • A relationship exists. Don’t know if either variable actually influences the other.

14. TERMS Population • Universe/entire set of people you want to draw conclusions about Sample • Subset of the population • People actually in your study Sampling error • Differences between sample & population

15. Sampling • Drawing a subgroup from a population (vs. Census)

16. Simple random Systematic random Stratified random Cluster Convenience Snowball Quota Purposive Probability vs. Non-probability Probability Sampling Non-probability Sampling Population info Available Population info Not available

17. Representativenss & Generalizability • Representativeness = Resemblance to the population characteristics • Generalizability = An ability to generalize the results of your study to the whole population • High representativeness = High generalizability • Probability sampling allows higher representativeness than non-probability

18. External Validity • Degree that results can be extended beyond the limited research setting • Generalizable • Based on sample ( rats, college students, whites, males, lab setting)

19. Non-Probability Sampling

20. Convenience Sampling • Get available people in the population • Low representativeness / generalizability

21. Quota Sampling • Predetermine the proportion of groups in the sample (e.g., male 50%, female 50%)

22. Conceptualization & Operationalization Idea Clarification Conceptualization Operationalization

23. Operationalization • From complex variable to series of simpler variables • Redefining a variable in terms of steps to measure • Conceptual definition  Operational definition • What the researcher must do to MEASURE it

24. Face validity Content validity Predictive Concurrent Convergent Discriminant Types of Measurement Validity Empirical (Criterion-related) Judgmental

25. “O = T + E” rule Observed score = True score + Eerror Observed = measured score, result True = “true”, actual, exact state Error = measurement error

26. Reliability of a Measure Degree to which a measure (score, observation) is affected by error • A reliable measure has little or no error

27. Types of Reliability • Interobserver (interrater) reliability • Test-Retest reliability • Parallel-forms reliability • Split- half

28. Inter-rater Agreement • Consistency between measurements by two or more observers • Different observers watch the same sample of behavior • Compute proportion of time both observers recorded the same behavior as happening # agreements # agreements + # disagreements (# of observations) • Training needed for observers

29. Increasing reliability • Increase number of items on your questionnaire (no 1 or 2 item measures) • Write clear, well-written items on survey • Standardize administration procedures • Treat all participants alike • Timing, procedures, instructions alike • Score survey carefully -- avoid errors

30. Valid and Reliable • A good measurement • Measures what it should measure in a consistent way

31. Reliable but Invalid • Your measurement is consistent, but not measuring what it is supposed to measure

32. Research Report Structure • Abstract • Introduction • Method • Results • Discussion • Reference