R.M.

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faulkebr
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108695
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R.M.
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2011-10-31 19:58:41
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Test 2
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  1. Factors influencing product moment correlation coefficient
    • 1. Linearity
    • 2. Range of scores in sample
    • 3. Extreme scores (esp. in small sample)
  2. Range of scores in a sample should-
    match the population
  3. Truncated or restricted range of scores in sample:
    Correlation likely smaller than is in defined population
  4. Range of scores in a sample larger than defined population:
    Correlation coefficient likely larger than in the defined population
  5. Coefficient of Determination =
    r2
  6. Statistically Significant Correlation:
    • low risk of falsely concluding there is some sort of correlation in the population.
    • p<.05
  7. Factors that determine statistical significance
    • Size of sample
    • Size of correlation coefficient
  8. Advantages of Relational (correlational) Studies
    • 1. Ecological validity
    • 2. Larger range of scores
    • 3. Best method available
  9. Ecological Validity
    Extent to which results can be generalized to other situations

    (intended target situation-natural environment)
  10. Disadvantages of Relational (Correlational) Studies
    • Single relational study usually not best method for determining cause and effect
    • Open to many alternative explanations
  11. Alternative explanations to Relational (correlational) Studies-
    • Third variable problem
    • Directionality problem
  12. Third Variable Problem explanation
    An unmeasured variable changes along with the identified variable so that cannot determine which is a true cause

    • Identified and measured vs. actual variable
    • Maalox & memory loss
    • Smoking & lung cancer
    • Alcohol & risk taking
  13. Directionality Problem
    • x-->Y
    • X<--Y
    • X<-->Y
    • Sugar
    • Corporal punishment
    • bad parents
    • violent tv
  14. Variations of Relational Studies- All involve naturally occuring variables (predictor variables)
    • 1. Classic correlational study
    • 2. Form groups or conditions
  15. Classic Correlational Study
    • Simply measure available values of predictor variable and correlate with value of outcome variable
    • Extroversion and car sales
  16. Form Groups orConditions
    • Select, classify, or categorize subjects based on the value of the predictor variable and correlate, orcompare groups/conditions using a test of means (t-test, ANOVA...)
    • Subject variables: pysio./psych. Characteristics: gender, age, anxiety, conscientiousness, experiences
  17. After the fact-
    ex post facto
  18. Confounding Variable
    A variable not identified by E that systematically varies with identified variable so that the effect of the confounding variable and the effect of the identified variable can not be separated.
  19. Example of confounding variable
    • To see if blondes have more fun
    • Dr. XY compared fun surfeys in blondes from a modeling agency with those of brunettes from a temp agency.
  20. Factors to consider in evaluating Relational studies
    • 1. Cost of ignoring information
    • 2. Likelyhood that X causes Y?
  21. Cost of ignoring info.
    • If cost is high, take seriously even if evidence is notoverwhelming
    • If benefits high, and the cost low, reasonable to take advantage of infor
  22. Likelyhood that X causes Y?
    • are changes in x a cause of y?
    • Touching toads and then cancer
    • Radiation then cancer

    its a logical fallacy if this is the only info. considered (post hoc ergo)
  23. Covariation Rule
    X and Y are related
  24. Internal Validity Rule
    Other explanations for the relationship b/w x and y
  25. Most research methods:
    Attempt to demonstrate covariation(2), and eliminate 3rd or confounding variables(3) and directionality problems(1)
  26. Krauter's 4 characteristics of a true experiment:
    • 1. manipulation of X
    • 2. comparison of the effects of several levels of X on Y
    • 3. Subjects begin study equivalent on all important characteristics for each level of X (internal)
    • 4. Control over all other important variables
  27. 1. Manipulation of X:Unique advantage-
    Evidence for temporal precedence rule, but gives comparison too. 0,1,2,4,8 mg
  28. All true experiments show:
    whether there is a relationship (covariation, correlation) between the IV and DV
  29. One group post test only design or one-shot case study cant:
    • Cant pinpoint cause
    • Cant show that X and Y covary
  30. Between Subjects
    • Each subject contributes score or mean for only one level of the IV or PV and there are at least two levels.
    • Usually but not always, comparisons are made between groups of subjects
  31. Within Subjects
    Each subject contributes score or mean for all levels of the IV or PV snd there are at least two levels.
  32. Kinds of comparisos
    • 1. List each level of IV or PV
    • 2. Ask did each subject contribute data (a mean or score) for all levels (within) of variable or just one(between)
  33. Control over all other important variables-
    All subjects are treated exactly the same except for X(the IV) (external)
  34. Basic methods of controlling confounding/third variables
    • 1. Eliminate variable completely
    • 2. Hold constant or control variables across conditions (potentionally variables)
    • 3. Random assignment
  35. Problem with eliminating and holding constant variables
  36. Some variables are hard to hold constant or eliminate due to lack of knowledge
  37. Random Assignment
    S has equal and independent chance of being assigned to any group-a statistical control

    • Randomized groups design
    • Post-test only control group design

    *this is only possible if X can be manipulated
  38. Equal chance =
    Equal frequency in the long run
  39. External Validity
    Degree to which the findings can be generalized to other subjects, and to other situations (settings, levels of variables, ways of measurement)
  40. Threats to external validity
    • reactivity (measurement)
    • People in the sample that dont belong/People notin the sample that who do belong/wrong proportion of people
    • Unusual place(situation)
  41. Threats to internal validity:
    Changes or differences in the composition of the groups
    • Selection bias
    • mortality ordifferential attrition
    • Interaction of selection bias with the treatment
  42. Research participants learn about treatments given to other groups
    Diffusion, compensatory equalization, compensatory rivalry, resentful demoralization
  43. Other designs (age) with special threats
    ~Cross-Sectional Designs
    Advantages:
    • Cheap
    • Fast
  44. Other designs (age) withspecial threats:
    ~Cross sectional Designs
    Disadvantages
    • Differences among cohorts
    • Cohort = companion = accomplice
  45. Other designs (age) with special threats
    ~Longitudinal Design
    Advantages
    Eliminates cohort differences
  46. Other designs (age) with special threats
    ~Longitudinal Design
    Disadvantages
    • Very time consuming
    • Expensive
    • Lose subjects
    • Age effect may be specific to cohort you are studying
  47. Ways of dealing wit hthreats to internal validity
    • True experiments (b/w subjects designs)
    • Eliminate, hold constant/control, Random Assignment
    • Pretests (check equivalent Ss, no selection bias)
  48. Ways of dealing with threats to internal validity:
    Pretest post control group design with random assignment
    • 1. Groups equivalent
    • 2. Subjects really change
    • 3. Scale attenuation effects:
    • ceiling effects and basement (floor) effects
  49. Ways of dealing with threats to internal validity:
    Matching:
    • "hold constant" subjects
    • Reassign based on pretest or records
  50. Ways of matching
    • 1. Matching by ranking
    • --pretest or records
    • --Rank subjects best to worst
    • --divide ranked subjects into clusters according to number of groups
    • --randomly assign subjects from each cluster into the groups
    • 2. Matching by using a descriptive statistic
    • --No random assignment
    • --does not control for unknown sources of variance
  51. When matching;
    hold constant the subjects
  52. Matching by using a descriptive statistic
    • no random assignment
    • Does not control for unknown sources of variance
  53. Advantages of Within subjects designs
    • 1. exactly equivalent subjects (3rd characteristic of true experiment)
    • a.) perfect matching
    • b.)each subj. own control-compared against self, not people from another group(differences across conditions matter-not that some people better than others
    • c.)conditions not confounded with subjects (vs. BS designs; treatments ARE confounded with subjects)
  54. Advantages in within subjects designs
    2.
    3.
    • 2. Fewer subjects (more info from each)
    • -terms: levels, conditions, groups
    • 3. Statistics hve more power
  55. How to show that X is a cause of Y:
    • 1.Show that Y didnt happen until after X (temporal precedence rrule)
    • 2. Show that X and Y are related (covariation rule) The cause and effect must be correlated with each other
    • 3. Show that other explanations for the relationship between X and Y can be ruled out (internal validity rule)
    • 4. Plausible causal link between X and Y
  56. Disadvantages of within subjects deesigns
    • Not always possible to give all treatments to 1 subject
    • Strong possibilities of many threats to internal validity
  57. Sequencing effects in within subjects designs
    • 1. Order effects (maturation)
    • -order and treatment condition vary together

    • 2. Differential carry over effects(testing sort of)
    • -Performance on a condition partially dependent on specific prior treatment
  58. Carry over is differential-
    different prior treatments, different mental set for each list
  59. Counterbalancing
    influence on order effects (maturation) ex: fatigue
  60. Influence on differential carry over effects:
    alcohol
  61. Kinds of counterbalancing
    • 1. complete
    • 2. incomplete
  62. Complete counterbalancing
    • use all possible orders
    • 2 conditions: 2 X 1 = 2
    • 4 conditions: 4 X 3 X 2 X 1 = 24
    • 6 conditions: 6 X 5 X 4 X 3 X 2 X 1 =72
  63. Incomplete counterbalancing
    • 1. random order-each subject different random order
    • 2. balanced latin square
  64. Latin Squares design
    create rows that determine order each S gets each condition

    • number of rows=number of treatments (conditions)
    • number of columns=number of treatments (conditions)

    ex: left side body/alphabet, right side/ silent....

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