Review for Exam 2

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Review for Exam 2

• Psych 231 Research Methods in Psychology

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Review Session (Andrew Charles)

• Review session Thursday Oct 26 in DeGarmo 406 _at_
  800 (PM)

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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

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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

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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

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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
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Abstract

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

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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 dont 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

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References

• Authors name
• Year
• Title of work
• Publication information
• Journal/Book Title
• Issue
• Pages

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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

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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

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Independent variables

• Choosing the right range
• Things to watch out for
• Demand characteristics
• Experimenter bias
• Reactivity
• Ceiling and floor effects

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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)

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Dependent variables

• Measuring
• Scales of measurement
• Nominal
• Ordinal
• Interval
• Ratio
• Errors
• Validity
• Reliability
• Sampling Error
• Bias

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Reliability

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

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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

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Sampling

• Typically we dont 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

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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 havent been accounted for
  (manipulated, measured, randomized, controlled)
  that can impact changes in the dependent
  variable(s)

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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.

• That is, so we can see the changes in the DV that
  are due to the changes in the independent
  variable(s).

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Experimental Control

• Methods of control
• Comparison
• Production (picking levels)
• Constancy/Randomization
• Problems
• Excessive random variability
• Confounding
• Dissimulation

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Experimental designs

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

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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 dont 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

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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)

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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.

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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 cant
  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

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Within subjects designs

• Advantages
• Dont 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

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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 dont differ

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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

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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 youre 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).