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Measurement and Statistics_Dasch_Date__040910

Measurement and Statistics_Dasch_Date__040910 - the...

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Regression cont'd - Forced entry regression all variables entered into model simultaneously the results obtained depend on the variables entered into the model must have good theoretical reasons for including a particular variance - stepwise regression variables are entered into the model based on mathematical reasoning (semi-partial correlation) computer selects variables in steps step 1: SPSS looks for the predictor that can explain the most variance in the outcome variable (highest semi-partial correlation) step 2: a second one is chosen from the remaining predictors the same way step 3: this process continues until all predictors are entered Multicollinearity - generally assume there isn’t - when there is a strong correlation between 2 or more predictors (.7 or greater) variance inflation factor (VIF) o greater than 10 shows a problem with multicollinearity, need to not use one of
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Unformatted text preview: the variables • tolerance (1/VIF) o .1 or less is a problem ← ← Regression in SPSS ← Analyze regression linear ← Outcome dependent ← Move independent variable you know has an effect block 1 ← Click “next” ← All other independent variables block 2 ← Click “stat” check: estimates, model fit, r-square change, collinearity diagnostics ← ←- r-square turn into percent ←- look at r-square change for variables entered into model 2 ← ←- anova table F ratios (overall fit of the model) • look at the significance ← ←-coefficients table • t statistic see if it’s significant (compare to .05) o if significant: means the variance has a significant effect on the outcome • unstandardized B slope • standardized B strength of effect ←...
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