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Psych 231: Research Methods in Psychology
Review for Exam 2 Psych 231: Research Methods in Psychology
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Review Session (Allison & Charles)
Review session Thursday Oct 25 in DeGarmo 5:30 Review Session (Allison & Charles)
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Exam 2 Topics APA style Variables Sampling Control
Underlying reasons for the organization Parts of a manuscript Variables Sampling Control Experimental Designs Vocabulary Single factor designs Between & Within Factorial designs Exam 2 Topics
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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 APA style
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Parts of a research report
Title Page Abstract Body Introduction & Literature review Methods Results Discussion (& Conclusions) References Authors Notes Footnotes Tables Figure Captions Figures Parts of a research report
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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
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Variables Characteristics of the situation Variables Levels Types
Conceptual variables (constructs) Underlying assumptions Operationalized variables Types Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables Variables
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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 Independent variables
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Independent variables
Choosing the right range Things to watch out for Demand characteristics Experimenter bias Reactivity Ceiling and floor effects Independent variables
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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 Is the dependent variable indirectly observable? Physiological measures (e.g. GSR, heart rate) Behavioral measures (e.g. speed, accuracy) Dependent variables
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Dependent variables Measuring Scales of measurement Errors Nominal
Ordinal Interval Ratio Errors Validity Reliability Sampling Error Bias Dependent variables
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Reliability Do you get the same score with repeated measurement?
Test-restest reliability Internal consistency reliability Inter-rater reliability Reliability
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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
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Sampling Typically we don’t test everybody Goals: Types 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
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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) Extraneous Variables
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Experimental Control Sources of Total (T) Variability:
T = NRexp + NRother +R Our goal is to reduce R and NRother so that we can detect NRexp. R NR exp other That is, so we can see the changes in the DV that are due to the changes in the independent variable(s). Experimental Control
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Experimental Control Methods of control Problems Comparison
Production (picking levels) Constancy/Randomization Problems Excessive random variability Confounding Dissimulation Experimental Control
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Experimental designs Some vocabulary Single factor designs
Factors Levels Conditions Within groups Between groups Control group Single factor designs Factorial designs Main effects Interactions Experimental designs
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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 1 Factor - 2-level experiments
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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) 1 Factor - Multi-level experiments
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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. Between & Within Subjects Designs
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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 Between subjects designs
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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 Within subjects designs
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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 B1 B2 B3 B4 Factorial experiments
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2 x 2 factorial design A1 A2 B2 B1 Marginal means B1 mean B2 mean
A1 mean A2 mean Main effect of B Main effect of A 2 x 2 factorial design
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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 Factorial experiments
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Factorial Designs Advantages Disadvantages 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
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