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SE of a parameter test
How much we expect our parameter estimate to differ from the population estimate assuming the null hypothesis is true

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

t
Degree of deviation from beta zero when the null hypothesis is true

Pvalue
 Comparison of critical t value to observed t value
 Significance

Squared partial correlation
 Comparison of one of the model c's to model a in relation to the model c
 Use to calculate fratio

Squared semipartial 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

Model c
A model that has all of the variables except for the variable if interest

Computing partial correlations from accuracy
 Multiple r squared is a measure of accuracy
 Less ambiguous because only 1 value (instead of multiple sse's)

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

All possible orders
Test for significance and effect size of variables in every possible combination to look for shifting patterns of significance

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 nonsignificant your hypothesis is wrong and you have to do exploratory analysis
 Each model acts as the model c for the next step

Best possible subsets
Stupid and useless for psychology unless there is a practical application

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

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

All possible subsets regression
 Evaluates every possible combination and compares
 Considers PRE
 Problems: model dependent, not much better than chance

Hierarchical regression subsets
 A priori sets
 Factorial ANOVA
 A priori control variables

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

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

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

Multiple regression
 No a priori order
 No conceptual sets

Simultaneous multiple regression mimic
 No a priori order
 Conceptual sets

Hierarchical regression
 A priori order
 No conceptual sets

Hierarchical regression with sets
 A priori order
 Conceptual sets

Control sets
 A priori order
 Mix of sets and variables

