400a Final

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

• 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

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

• Comparison of critical t value to observed t value
• Significance

• Comparison of one of the model c's to model a in relation to the model c
• Use to calculate f-ratio

• 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

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

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

• 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

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

• 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

Stupid and useless for psychology unless there is a practical application

• Always wrong because forget about model dependency
• As likely as chance to did the correct combination
• Problems: based on uniqueness, ignores PRE

• 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

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

• A priori sets
• Factorial ANOVA
• A priori control variables

• 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

• Overall test: model A is all variables for all sets
• Separate tests: multiple regression of all sets except the set of interest

• 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

• No a priori order
• No conceptual sets

• No a priori order
• Conceptual sets

• A priori order
• No conceptual sets

• A priori order
• Conceptual sets

• A priori order
• Mix of sets and variables