• Shuffle
Toggle On
Toggle Off
• Alphabetize
Toggle On
Toggle Off
• Front First
Toggle On
Toggle Off
• Both Sides
Toggle On
Toggle Off
Toggle On
Toggle Off
Front

### How to study your flashcards.

Right/Left arrow keys: Navigate between flashcards.right arrow keyleft arrow key

Up/Down arrow keys: Flip the card between the front and back.down keyup key

H key: Show hint (3rd side).h key

A key: Read text to speech.a key

Play button

Play button

Progress

1/42

Click to flip

### 42 Cards in this Set

• Front
• Back
 Multiple Correlation -definition- Extends the bivariate Pearson correlation (r) to analytic circumstances involving 3+ Multiple Correlation is also known as... Multiple R Mutliple Correlation allows us to... 1) tell how much variation is ovserved w/in a selected criterion 2) And associated with the variation in socres noted within a given set of 2+ predictors Multiple correlation has the capacity to help select what? Select the "best" set of predictors available Coefficient of Mutliple Correlation R -definition- A measure of correlation between 2+ predictor variables that have been optimally weighted to yield the highest possible correlation The value of multiple R is interpreted as... An indication of the strength/magnitude of the relationship Multiple R values signify 1) Close to 0 2) +1 or -1 1) Less consequential relationships 2) Strong, influential relationships Coefficient of Mulitple determination R₂ -definition- Provides a measuire of explained variance Multiple R₂tells us... The proportion of variance in the criterion variable, expressed as %, that can be predicted, accounted for, and explained The R value can never exceed... 1.00 Examples of R and R₂ 1) R = 0.80 2) R₂ = 0.64 1) Strong correlation 2) Accounts for about 64% in variation R and R₂ considered biased estimators, but what can be done to offset it? 1) # of predictor variables should be kept small 2)R₂ should be adjusted downward Adjusted R₂ -definition- When R₂ is adjusted downward to refelct the actual number of cases and predictor variables included in the analysis 4 Basic assumptions to meet BEFORE Mutliple correlation analysis 1) Interval/Ratio are required 2)Relationships between the criterion variable and the predictors shoudl be reasonably linear 3) Data must be homoscedastic 4) Predictor variable should not correlate highly with another Homoscedasticity -definition- Equal scatter or consistent variance across a predictor variable Collinerity -definition- Predictor variables should not correlate highly with one another Multicollinearity -definiton- When 2+ predictors take up a good deal of the same explanatory space making findings inaccurate Mutliple Regression -definiton- Multivariate counterpart of the regression procedure involving a single predictor Multiple Regression allows us to predict the value of what? Predict the value of a criterion/dependent variable when we know the values of two or more predictor variables b Coefficient is also known as what? Slope or Partial Slope b Coefficient -definition- Computed for each predictor in the regression equation tells us how much of the criterion variable variation is accounted for by that predictor alone Beta weights is also known as... Beta coefficients or Partial regression Coefficients Beta coefficients -definition- Allows for the decription of the amount of variation in the criterion variable that's associated with each predictor variable Beta coefficients are determined by... Converting scores to their respective z-score 2 Critical Decisions when conducting a multiple regression analysis: 1) Which predictor variables to include 2) Specifiying the order in which these items will be entered Hierarchial Inclusion Method -definition- Draw on their knowledge of the problem Stepwise Inclusion Method -definition- Order in which th epredictor variables are entreed into the analysis is determined on statistical grounds. Variable with highest correlate is entered 1st Dicotomous variable -definition- Offers only 2 response options Binary Variable -definition- Dichotomous variable in which 0 is used to signfiy none of something and 1 the presence of something Dummy Variable -definition- New stand-in variable that is mutually exclusive, a stnadardized unit, and 0 is meaningful Factor -defintion- Independent or predictor variable In describing ANOVAs you refer to... The number of factors involved The purpose of a 2way ANOVA is... To test the signficance of differences occuring among group means Main Effect -definition- When you focus on the impact of only 1 factor Interaction Effect -defintition- Focuses on the combined effect of the 2 independent variables Sum of Squares -definition- Tells us how much variation was observed in the depression scores obtained overall, the main effects, interaction, and residual error effect Grand Mean -definition- Overall group mean Error -definition- Variance associated with individual differences occuring among subjects within the 4 groups defined by these independent variables Degress of Freedom for Error -definition- Number of cases incolced minus the number of groups associated with the independent variables of factors E.G. of degrees of freedom for error 2 x 2 Design with 40 Cases DofF for E = 36 (40-4 = 36) Mean Square -definition- Dividing the sum of squares assoiated with each source by the degrees of freedom F-statistic -definition- Dividing the mean square derived for each main and interaction effect, by the mean sqaure associated with the error term