400a Final

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400a Final
2013-12-10 01:12:56
psych 400a stats final

all the things I need to know for analytics
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  1. SE of a parameter test
    How much we expect our parameter estimate to differ from the population estimate assuming the null hypothesis is true
  2. Tolerance
    • Measure of redundancy between the x's
    • Calculated as 1 minus the effect size of all other x's
    • Low tolerance means hard to find effect
  3. t
    Degree of deviation from beta zero when the null hypothesis is true
  4. P-value
    • Comparison of critical t value to observed t value
    • Significance
  5. Squared partial correlation
    • Comparison of one of the model c's to model a in relation to the model c
    • Use to calculate f-ratio
  6. Squared semi-partial correlation
    • Comparison of one of the model c's to model a in relation to the model c with no variables
    • Able to compare because they all have the same denominator
  7. Model c
    A model that has all of the variables except for the variable if interest
  8. Computing partial correlations from accuracy
    • Multiple r squared is a measure of accuracy
    • Less ambiguous because only 1 value (instead of multiple sse's)
  9. Improved Default
    • Add information: look at when the variable enters last AND first
    • Descriptive language: use language that presents facts without bias
    • Intellectually full: think about why it is that there is a difference between the separate tests and overall test
  10. All possible orders
    Test for significance and effect size of variables in every possible combination to look for shifting patterns of significance
  11. Hierarchical regression
    • Confirmatory analysis for which order you believe to be the model
    • Each step needs to be significant in order to continue and add the next variable
    • If any step is non-significant your hypothesis is wrong and you have to do exploratory analysis
    • Each model acts as the model c for the next step
  12. Best possible subsets
    Stupid and useless for psychology unless there is a practical application
  13. Default subsets inferences
    • Always wrong because forget about model dependency
    • As likely as chance to did the correct combination
    • Problems: based on uniqueness, ignores PRE
  14. Stepwise regression
    • Pulls out most highly correlated variable by partialling it out and then continues to find the next one
    • No better than chance
    • Problems: unique correlations, population dependent, ignores PRE
  15. All possible subsets regression
    • Evaluates every possible combination and compares
    • Considers PRE
    • Problems: model dependent, not much better than chance
  16. Hierarchical regression subsets
    • A priori sets
    • Factorial ANOVA
    • A priori control variables
  17. Factorial ANOVA
    • Two or more levels of a favored creates a set
    • The interaction between two variables creates another set of interaction variables
    • Evaluate sets simultaneously when their enter last
  18. A priori sets
    • Overall test: model A is all variables for all sets
    • Separate tests: multiple regression of all sets except the set of interest
  19. Control set
    • Control variables partialed out so that known variables (eg. gender) don't interfere with other relationships
    • Overall test: model c includes set of control variables, model a includes other variables on top of model c
    • Separate tests: look at every variable as it enters last (and first)
    • Inferences: look at differences between model a and model c
  20. Multiple regression
    • No a priori order
    • No conceptual sets
  21. Simultaneous multiple regression mimic
    • No a priori order
    • Conceptual sets
  22. Hierarchical regression
    • A priori order
    • No conceptual sets
  23. Hierarchical regression with sets
    • A priori order
    • Conceptual sets
  24. Control sets
    • A priori order
    • Mix of sets and variables