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Review for Exam 2

Review for Exam 2

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Review for Exam 2

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

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

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

  4. 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 APA style

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

  6. 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 • Title should be maximally • informative while short • (10 to 12 words recommended) • Order of Authorship sometimes • carries meaning • Affiliation – where the bulk of • the research was done Title Page

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

  8. 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 Body

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

  10. 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 Variables

  11. 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 Independent variables

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

  13. 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) Dependent variables

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

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

  16. 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 Validity

  17. 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 Sampling

  18. 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) Extraneous Variables

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

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

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

  22. 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 1 Factor - 2-level experiments

  23. 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) 1 Factor - Multi-level experiments

  24. 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. Between & Within Subjects Designs

  25. 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 Between subjects designs

  26. 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 Within subjects designs

  27. B1 B2 B3 B4 A1 A2 • 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 Factorial experiments

  28. A1 A2 B1 mean B1 Main effect of B B2 B2 mean A1 mean A2 mean Marginal means Main effect of A 2 x 2 factorial design

  29. 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 Factorial experiments

  30. 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). Factorial Designs