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

2. Exam 2 Topics Covers chapters: 4, 5, 8, 10, 11, 12, 15 APA style
Underlying reasons for the organization
Parts of a manuscript
Variables
Sampling
Control
Experimental Designs
Vocabulary
Between vs. Within
Factorial designs

3. APA style Parts (check Chapter 15 of your textbook)
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
Parts (check Chapter 15 of your textbook)

4. Parts of a research report
Title Page
Abstract
Body
References
Authors Notes
Footnotes
Tables
Figure Captions
Figures

5. Title Page 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

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

7. Body Hourglass shape Start broad Narrow focus Most focused Broaden
Background
Literature Review
Start broad
Statement of purpose
Specific hypotheses (at least at operational level)
Narrow focus
- Methods
- Results
Most focused
Discussion
Conclusions
Implications
Broaden

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 When something odd comes up, don’t guess. Look it up!
Author’s name
Year
Title of work
Publication information
Journal
Issue
pages
When something odd comes up, don’t guess. Look it up!

10. The rest
Authors Notes
Footnotes
Tables
Figure Captions
Figures

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

12. 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

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

14. 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)

15. Dependent variables Measuring Scales of measurement Errors Nominal
Ordinal
Interval
Ratio
Errors
Validity
reliability

16. 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)
Two things to watch out for:
Experimenter bias (expectancy effects)
Demand characteristics

17. Reliability & Validity
Reliability =consistency
Validity = measuring what is intended
unreliable reliable reliable invalid invalid valid

18. Reliability Test-restest reliability Internal consistency reliability
Inter-rater reliability

19. 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

20. Sampling Why? Goals: Types Don’t have the resources to 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
Stratified sampling
Non-probability sampling
Convenience sampling
Quota sampling

21. Control 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). R NR
exp
other

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

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

24. Single variable – one Factor
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

25. 1 Factor - multilevel 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)

26. Between versus 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.

27. 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

28. 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

29. 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

30. 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

31. 2 x 2 factorial design A1 A2 B2 B1 Marginal means B1 mean B2 mean
Condition
mean
A1B1
A2B1
A1B2
A2B2
Marginal means
B1 mean
B2 mean
A1 mean
A2 mean
Main effect of B
Main effect of A

32. 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).