review for exam 2 psych 231: research methods in psychology
Post on 19-Dec-2015
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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).