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

2. Review Session (Allison & Charles)
Review session Thursday Oct 25 in DeGarmo 5:30
Review Session (Allison & Charles)

3. 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

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
APA style

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
Parts of a research report

6. 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

7. 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

8. 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

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

10. 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

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

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

13. 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

14. 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

15. 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

16. 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

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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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