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

Review for Exam 2

Psych 231: Research Methods in Psychology

Review Session (Andrew & Charles)

Review session Thursday Oct 26 in DeGarmo 406 @ 8:00 (PM)

Exam 2 Topics

APA style Underlying reasons for the organization

Parts of a manuscript Variables Sampling Control Experimental Designs

Vocabulary Between & Within Factorial designs

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

Parts of a research report

Title Page Abstract Body

Introduction & Literature review

Methods Results Discussion (& Conclusions)

References Authors Notes Footnotes Tables Figure Captions Figures

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

Abstract

Abstract short summary of entire paper

• 100 to 120 words • the problem/issue• the method• the results• the major conclusions

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

References

Author’s name Year Title of work Publication information• Journal/Book Title

• Issue• Pages

Shell Shock 12

References

Fussell, P. (1975). The Great War and modern memory. New

York: Oxford UP.

Marcus, J. (1989). The asylums of Antaeus: Women, war, and

madness—is there a feminist fetishism? In H. A. Veeser

(Ed.), The New Historicism (pp. 132-151). New Yo rk:

Routledge.

Mott, F. W. (1916). The effects of high explosives upon the

central nervous system. The Lancet, 55(2), 331-38.

Showalter, E. (1997). Hystories: Hysterical epidemics and modern

media. New Yor k: Columbia UP.

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

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

Choosing the right range Things to watch out for

Demand characteristics Experimenter bias Reactivity Ceiling and floor effects

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)

Dependent variables

Measuring Scales of measurement

• Nominal• Ordinal• Interval• Ratio

Errors• Validity• Reliability• Sampling Error• Bias

Reliability

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

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

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

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)

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.

RNRexp

NR

other

RNR

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

Methods of control Comparison Production (picking levels) Constancy/Randomization

Problems Excessive random variability:

Confounding Dissimulation

Experimental designs

Some vocabulary Factors Levels Conditions Within groups Between groups Control group

Single factor designs Factorial designs

• Main effects• Interactions

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 - 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)

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 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

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

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

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

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 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).