Psy 2200 Exam 2

  1. Correlational Research (4)
    • Examining relationships between and among variables
    • Seeing if two variables are related to each other
    • Useful for
    • - description
    • - manipulation
    • Extent to which 2 measured variables are sytematically related
  2. Why can't correlations establish causality? (2)
    • Dirction of casuality
    • -X could cause Y
    • - or Y could cause X
    • 3rd Variable Problem
    • - something else could cause both X and Y
  3. Correlational Coefficient (r) (3)
    • statistic that shows the direction of a relationship between 2 variables
    • -show the direction of a relationship: postivite/negative
    • -shows the magnitude (strength) of a relationship: relatively weak/relatively strong
  4. Positive Correlations
    • As one goes up, the other goes down
    • -go in the same direction
    • -direct relationship
  5. Negative Correlations
    • Indicated by absolute value:
    • -closer to 1 (-1) = stronger
    • -closer to 0 = weaker
    • strong relationship: variables are more related
    • weak relationship: variables are less related
  6. The statistical signficance of r is affected by what 3 factors?
    • sample size
    • -# of participants min. r for sig.
    • >>10_________.52
    • >>100________.16
    • >>1000_______.05

    • magnitude of correlation: larger r is more likely to be significant
    • -conventional rule
    • >> .10 = weak correlation
    • >> .30 = moderate correlation
    • >> .50 = stronge correlation

    alpha level: how sure we want to be
  7. 2 Aspects of Experimental Power
    • manipulation: making something happen
    • control: keeping everything the same except the manipulated variable
  8. 5 Aspects of True Experiments
    • IV is manipulated
    • -quantitatively (including high, low & no)
    • -qualitatively (e.g., rehearsal vs imagery)

    random assignment

    extraneous factors are controlled

    DV is measured

    Basic logic: if DV varies as a result of manipulation of the the IV, than the IV caused the DV to change.
  9. Internal Validity (3)
    -the basic minimum without which any experiment is uniterpretable

    -degree to which you can confidently draw a casual inference from the data (i.e., the IV caused the DV to vary)

    - degree to which your study is free from confounds
  10. Mills' 3 Requirements for Establishing Casuuality
    • Covariation: 2 things go together (change at the same time)
    • Temportal Sequence: 1 thing precedes another
    • Eliminating Confounds: other things that didn't cause it to happen
  11. Confounding Variables (confounds, nuisance variables) (2)
    any uncontrolled extraneous variable that "covaries" with the independent variable and could provide an alternative explanation of the results

    literally means mistaking one thing for another
  12. 3 Causes of Confounds
    Participant/subject variables: something about the subjects in the study (e.g., partcipants are selected base on the same trait).

    Environmental variables: something about the setting the subjects are in.

    Procedural variables: something about the procedure - the way subjects are treated.
  13. 3 Ways to Deal with Confounds
    • Be careful
    • -read as much as possible about your topic to reduce the chance of making mistakes

    • Replicate Your Findings
    • -use converging operations to study the same topic

    • Measure and Control
    • -if you can, control it from the start
    • -if you can't, measure it and control it statistically
  14. 3 Categories of Participant Confounds
    • "People are different"
    • individual differences can undermine careful, systematic scientific observation (e.g., selection bias)

    • "People change"
    • differences within people across time and situations can masquerade as meaningful research findings (e.g., history, maturation and regression toward the mean)

    • "The process of studying people changes people"
    • many studies have the uninteneded consequence of changing people's behavior in some small way (e.g., tesing effects, mortality, reaction biases and instrumentation)
  15. Selection Bias (2)
    sampling people from an unrepresentative sample

    • 2 kinds
    • -self-selection
    • -selection of samples that may differ to begin with
  16. History
    changes that occur in a very larege group of people such as a nation of culture
  17. Maturation
    specific developmental or experimental changes that occur in a particular person, or a particular cohort overtime
  18. Regression Toward the Mean
    • extreme high/low scores tend to move closer to the mean
    • -may occur when participants have been selected from a study based on extreme scores
  19. Testing Effects
    changes in test scores are due to repeated testing rather than manpulation of the IV
  20. Mortality (Attrition)
    • participants are lost from the study
    • - group differences may be due to differences in those remaining and not to the manipulation of the IV
  21. Participant Reaction Bias
    • people behave differently when they know they are being studied
  22. 3 participant expectancies
    • demand charactereistics: give hints as to what the experimenter wants
    • participant reactence: participants rebel against an experimental manipulation
    • evaluation apprehension: people are concered about being judged
  23. Experimenter Bias (aka experimenter expectancy) (2)
    errors in a study due to the predisposed notions or beliefs of the experimenter

    unintentional behaviors on the part of the experimenter
  24. Instrumentation
    • changes in the DV are produced by changes in the measurement instrument itself
    • - a bad scale (unreliable)
    • - a bad measure (rater)
    • - time of measurement
  25. 4 Ways to Minimize Confounding Variables
    • Use random assignment
    • -law of large numbers
    • -most things are normally distributed

    Pretesting: can check baseline differences and control is necessary

    Matching: ensures confounding variables are evenly distributed (e.g., gender, IQ)

    Experimental Control: keep/hold things constant
  26. Testing vs Instrumentation
    both deal with data collection

    testing sensitizes participants to data collection process

    instrumentation changes the data collection process in some way
  27. Solving Thereats to Internal Validity (3)
    • comparison/control group
    • random assignment
    • experimental control

    *need to think about threats while in the design phase of a study
  28. Manipulating the IV (3)
    operationally define the construct (concept)

    provide informed consent

    explain without revealing the hypothesis
  29. 2 Types of Manipulation
    • straightforward: no deception involved (only type used in psy 2200)
    • staged: set up "real situational/psychological state (often uses deception/confederates)
  30. Strength of Manipulation
    • Weak: run risk of not detecting small, but meaningful, differences between groups
    • Strong: maximizes differences between groups and may not reflect reality
  31. 3 Types of IVs
    • Situational: features in the environment
    • - # of people present
    • - level of lighting, volume of music

    • Task Variables: different kinds of problems to solve
    • - abstract v concrete
    • - easy v difficult

    • Instructional Variables: different ways to perform a task
    • - imagery v rehearsal
    • - argue for/against your own position
  32. Central Limit Theorem
    • the distribution of means will increasingly approximate a normal distribution as the size N of samples increases
    • -i.e., the larger the sample size the more normal the distribution will be
  33. Random Sampling/Selection (2)
    the method of selecting a sample from a population

    every memeber of the population has an equal chance of being selected for the study
  34. Random Assignment (2)
    the method of allocating participants to groups

    each participant is as likely to be assigned to one group as to another
  35. Extraneous Variables
    any thing other than the manipulated variable that may affect the outcome (aka control variables/nuisance variables)
  36. Measures are based on (3)
    • theory
    • past research
    • intuition
  37. 3 Types of Measures
    • self-report
    • behavioral (covert, overt)
    • physiological responses
  38. 2 Measurable Outcomes of an Experiment
    • outcome of the experimetnal group
    • outcome of the comparison group

    * comparison group is not necessarily a "control" group
  39. Measured outcomes depend upon...
    the behavior of the participant in response to manipulation of the IV
  40. Sensitivity of DV (2)
    must be sensitive enough to detect meaningful differences between groups

    best if graded/continous
  41. Ceiling Effect
    everyone does well when the DV is not sensitive enough
  42. Floor Effect
    everyone does poorly if the DV is too sensitive
  43. Changes in the DV
    • found by comparsion
    • -to different subjects
    • -to selves in different conditions

    The comparsion group determines/drives experimental design.
  44. Different Designs Address Different Problems
    • Between-participant (independent groups): different groups of particpants are due to different levels of the IV
    • Within-participant (repeated measures): a single group of participants is exposed to all levels of the IV.
  45. 4 treatments for control groups
    • "True" control group: no treatment
    • Placebo: give shame treatement
    • Waiting list: give no treatment right now
    • Yoked: same exact conditions except treatment
  46. Mundane Realism
    extent to which your conditions are physically similar to the "real world"

    not of primary importance but affects external validity
  47. Experimetnal Realism
    extent to which your conditions are psychologically similar to the "real world"

    of primary importance: affects internal validity
  48. 4 Types of Experimetnal Designs
    • One-group design: psuedo-experiment
    • One-group pre-test/post-test design: repeated measures/within-partcipants
    • Post-test only with non-equivalent groups: between-participants
    • Pre-test/Post-test design with nonequivalent groups: measure both, treat one, measure both again; ensures groups are similar to begin with
  49. Comparibility
    need to have comparable groups

    need to see if effect is larger than or smaller than expected
  50. complete counterbalancing (3)
    every possible sequence is used at least once

    sequences: (# of conditions) e.g., 3! = 3 x 2 x 1 = 6

    • more conditions = more sequences
    • e.g., 6! 6 x 5 x 4 x 3 x 2 x 1 = 720
  51. partial counterbalancing
    includes only some of the possible sequences

    each sequence must appear equally often

    use a balanced Latin square to choose sequences
  52. 2 Rules for balanced Latin Squares
    every condition occurs equally often in each position

    every condition precedes/follows other conditions only once
  53. Constructing a Latin Square
    • 1st row: A, B, L (last), C, L-1, D, L-2, E, L-3, etc.
    • 2nd row: increase 1 letter at each position of the 1st row (e.g., A-B, F-A)
    • Continue for each row
    • Randomly assign each condition to a letter
  54. Graphs are most useful for prediction because...
    if the relationship between 2 variables is statistically significant than the score on 1 variable can predict the score of the other
  55. 3 Cautions about Interpretations
    • don't infer casuality
    • beware of directionality problem
    • think about 3rd variables
  56. The more sohpisticated the statistical techniques...
    ...the closer we can come to making casual inferences.
  57. Partial Correlation
    • correlatation between 2 variables with influence of 1 or the other variable statistically removed.
    • If X and Y are still related after removing Z the correlation is not due to Z.
  58. One-way/Single-Factor Design
    simplest type of design

    1 IV, 2 Leveles (experimental, comparison/control)
  59. Comparison Groups (Between v Within)
    • Between-participant (independent groups): 1 group receives treatment, 1 does not.
    • Within-participant (repeated measures): group recieves treatment and also doesn't
    • -serves as own controls.
  60. 4 types of Single Factor Designs
    • independent group: between-participant design with random assignment
    • repeated mesaures: within-participant design
    • matched groups: between-participant design without random assignment
    • nonequivalent groups: not a "true" experiement
  61. Levels
    experimental group + 1 or more comparison group
  62. Increasing levels in a Single-factor design
    • 2+ levels give more information
    • (e.g., curvilinear relationships: more groups)
  63. Increasing IVs in a Single-factor design (3)
    facorial design: 2+ IV's/factors

    closer to real-world conditions

    all levels of 1 IV combined with all levels of other IVs
  64. Between-Participant Design (Indpendent Groups)
    Advantage & Disadvantages (2)
    • Advantage
    • -each participant enters the experiment untainted by previous exposure to the IV.

    • Disadvantages
    • -takes many more participants
    • -problem of equivalent groups
  65. Withn-Participant Design (Repeated Measures)
    Advantages (3) & Disadvantages (2)
    • Advantages:
    • -need fewer particpants
    • -participatints act as their own controls
    • -reduces error variance "within" conditions

    • Disadvantages
    • -more demanding to participants
    • -sequence/order effects
  66. Graphing Correlations (3)
    generally use scatterplots

    shows relationships between 2 variables

    can use only 2 datapoints but this restricts the range and may miss non-linear relationships
  67. How do graphs improve interpretation? (3)
    they help to make informed guesses about whether correlated variables might be casually related

    not definitive evidence but can support (or not) a particular casual explination

    can help predict hypotheses about possible effects of 3rd variables
  68. Factorial Design Notation
    • # of factors noted by # of numbers
    • -A x B factorial design (e.g., 2 factors A & B)
    • # of levels of each factor noted by the #s themselves
    • -3x2 factorial design
    • -2 factors: 1 has 3 lvls, 1 has 2 lvls
    • -combines to 6 conditions (2x3=6)
  69. Between-participants factorial design
    • more than 1 IV, all between-participants
    • If 1 IV is a particular variable, called a person x environment factorial design
  70. Within-participant factorial design
    • more than 1 IV, all within-participants
    • aka repeated measures factorial design
  71. Mixed Factorial Design
    • more than 1 IV, both between and within participants
    • if 1 IV is a participat variable, it's called a person x environment mixed factorial design
  72. Increasing IV's and Levels
    • 5 lvls, 3 w/2 factors, 2 w/3 factors
    • -72 conditions
    • -720 participants needed for between-participants

    • large experiments can become unwieldy
    • hard to inerpet: lots of main effects and interactions
  73. Factorial Designs Look At
    • main effects: effect of each IV in isolation
    • interactions: effect of IV's "combined"
  74. Calculating Marginal Mean
    • Marginal mean of B1 (A1B1 + A2B1/2)
    • Marginal mean of B2 (A1B2 + A2B2/2)

    If the marginal means of B1 and B2 are differnt there may be a main effect.
  75. Calculating Interaction
    • E.G.,_A1__A1
    • B1___20__40
    • B2___30__20
    • A1B1=20 A2B1=40 Difference=+20
    • A1B2=30 A2B2=20 Difference=-10

    if the differences found for B1 and B2 are different there may be an interaction.
  76. 2 Interaction Rules
    If the changes that occur in the means of one row of a factorial table are similar to changes found in the other row(s) the factors are NOT interacting.

    If the changes in one row are greater, lesser, or reversed from those in another row there is (i.e., maybe) an interaction
  77. Graphing Interactions
    • parallel lines mean no interaction
    • nature of relationship for B1 and B2 is the same across levels of A1 and A2
  78. Finding Main Effects & Interactions
    • ___________Main Effects____Interactions
    • Look at_____marginal means__cell means
    • Compare for_sig. differences___patterns of change
    • Look at_____SPSS print-out__SPSS print-out
    • Look at _____graphs________non-parallel lines
Card Set
Psy 2200 Exam 2
Research & Methods: ch 6, 9, 10, 11