1. Review for Exam 2 Psych 231: Research Methods in Psychology

2. Review Session (Andrew & Charles) Review session Thursday Oct 26 in DeGarmo 406 @ 8:00 (PM)

3. Exam 2 Topics APA style Underlying reasons for the organization Parts of a manuscript Variables Sampling Control Experimental Designs Vocabulary Between & Within Factorial designs

4. APA style Purpose of presenting your research To get the work out there, to spur further research, replication, testing/falsifaction of your theory Why the structured format? Clairity: To ease communication of what was done Forces a minimal amount of information Provides consistent format within a discipline Allows readers to cross-reference your sources easily See Chapter 16 of your textbook

5. Parts of a research report  Title Page  Abstract  Body  Introduction & Literature review  Methods  Results  Discussion (& Conclusions)  References  Authors Notes  Footnotes  Tables  Figure Captions  Figures

6. Title Page Order of Authorship sometimes carries meaning –Title should be maximally informative while short (10 to 12 words recommended) Affiliation – where the bulk of the research was done Short title – goes in header (with page number) on each page of the manuscript Running head – will go on each page of published article, no more than 50 characters

7. Abstract short summary of entire paper 100 to 120 words the problem/issue the method the results the major conclusions

8. Body  Introduction  Background, Literature Review, Statement of purpose, Specific hypotheses  Methods (in enough detail that the reader can replicate the study)  Participants  Design  Apparatus/Materials  Procedure  Results (state the results but don’t interpret them here)  Verbal statement of results  Refer to Tables and figures  Statistical Outcomes  Discussion (interpret the results)  Relationship between purpose and results  Theoretical (or methodological) contribution  Implications

9. References Author’s name Year Title of work Publication information Journal/Book Title Issue Pages

10. Variables Characteristics of the situation Variables Levels Conceptual variables (constructs) Operationalized variables Underlying assumptions Types Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables

11. Independent variables The variables that are manipulated by the experimenter Each IV must have at least two levels Combination of all the levels of all of the IVs results in the different conditions in an experiment Methods of manipulation Straightforward manipulations Stimulus manipulation Instructional manipulation Staged manipulations Event manipulation Subject manipulations

12. Independent variables Choosing the right range Things to watch out for Demand characteristics Experimenter bias Reactivity Ceiling and floor effects

13. Dependent variables The variables that are measured by the experimenter They are “dependent” on the independent variables (if there is a relationship between the IV and DV as the hypothesis predicts). How to measure your your construct: Can the participant provide self-report? Introspection Rating scales Is the dependent variable directly observable? Choice/decision (sometimes timed) Is the dependent variable indirectly observable? Physiological measures (e.g. GSR, heart rate) Behavioral measures (e.g. speed, accuracy)

14. Dependent variables Measuring Scales of measurement Nominal Ordinal Interval Ratio Errors Validity Reliability Sampling Error Bias

15. Reliability Do you get the same score with repeated measurement? Test-restest reliability Internal consistency reliability Inter-rater reliability

16. Validity Does your measure really measure what it is supposed to measure? There are many “kinds” of validity Construct Face Internal Threats History Maturation Selection Mortality Testing External Variable representativeness Subject representativeness Setting representativeness

17. Sampling Typically we don’t test everybody Population Sample Goals: Maximize: Representativeness - to what extent do the characteristics of those in the sample reflect those in the population Reduce: Bias - a systematic difference between those in the sample and those in the population Types Probability sampling Simple random sampling Systematic sampling Cluster sampling Non-probability sampling Convenience sampling Quota sampling

18. Extraneous Variables Types Control variables Holding things constant - Controls for excessive random variability Random variables – may freely vary, to spread variability equally across all experimental conditions Randomization Confound variables Other variables, that haven’t been accounted for (manipulated, measured, randomized, controlled) that can impact changes in the dependent variable(s)

19. Experimental Control Sources of Total (T) Variability: T = NR exp + NR other +R Our goal is to reduce R and NR other so that we can detect NR exp. R NR ex p NR oth er R NR oth er That is, so we can see the changes in the DV that are due to the changes in the independent variable(s).

20. Experimental Control Methods of control Comparison Production (picking levels) Constancy/Randomization Problems Excessive random variability: Confounding Dissimulation

21. Experimental designs Some vocabulary Factors Levels Conditions Within groups Between groups Control group Single factor designs Factorial designs Main effects Interactions

22. 1 Factor - 2-level experiments Advantages: Simple, relatively easy to interpret the results Is the independent variable worth studying? If no effect, then usually don’t bother with a more complex design Sometimes two levels is all you need One theory predicts one pattern and another predicts a different pattern Disadvantages: “ True” shape of the function is hard to see Interpolation Extrapolation

23. 1 Factor - Multi-level experiments Advantages Get a better idea of the true function of the relationship Disadvantages Needs more resources (participants and/or stimuli) Requires more complex statistical analysis (analysis of variance and pair-wise comparisons)

24. Between & Within Subjects Designs Between subjects designs Each participant participates in one-and-only-one condition of the experiment. Within subjects designs all participants participate in all of the conditions of the experiment.

25. Between subjects designs Advantages: Independence of groups (levels of the IV) Harder to guess what the experiment is about without experiencing the other levels of IV exposure to different levels of the independent variable(s) cannot “contaminate” the dependent variable No order effects to worry about Counterbalancing is not required Sometimes this is a ‘must,’ because you can’t reverse the effects of prior exposure to other levels of the IV Disadvantages Individual differences between the people in the groups Non-Equivalent groups Excessive variability

26. Within subjects designs Advantages: Don’t have to worry about individual differences Same people in all the conditions Variability between groups is smaller (statistical advantage) Fewer participants are required Disadvantages Order effects: Carry-over effects Progressive error Counterbalancing is probably necessary Range effects

27. Factorial experiments Two or more factors Factors - independent variables Levels - the levels of your independent variables 2 x 4 design means two independent variables, one with 2 levels and one with 4 levels Calculate # of “conditions” by multiplying the levels, a 2x4 design has 8 different conditions Main effects - the effects of your independent variables ignoring (collapsed across) the other independent variables Interaction effects - how your independent variables affect each other Example: 2x2 design, factors A and B Interaction: At A1, B1 is bigger than B2 At A2, B1 and B2 don’t differ A1 A2 B1B2B3B4

28. 2 x 2 factorial design A1A2 B2 B1 Marginal means B1 mean B2 mean A1 meanA2 mean Main effect of B Main effect of A

29. Factorial experiments So there are lots of different potential outcomes: A = main effect of factor A B = main effect of factor B AB = interaction of A and B With 2 factors there are 8 basic possible patterns of results: 1) No effects at all 2) A only 3) B only 4) AB only 5) A & B 6) A & AB 7) B & AB 8) A & B & AB

30. Factorial Designs Advantages Interaction effects –One should always consider the interaction effects before trying to interpret the main effects – Adding factors decreases the variability –Because you’re controlling more of the variables that influence the dependent variable –This increases the statistical Power of the statistical tests – Increases generalizability of the results –Because you have a situation closer to the real world (where all sorts of variables are interacting) Disadvantages Experiments become very large, and unwieldy The statistical analyses get much more complex Interpretation of the results can get hard In particular for higher-order interactions Higher-order interactions (when you have more than two interactions, e.g., ABC).