SPSS Statistical Package for Social Sciences Multiple Regression Department of Psychology California State University Northridge www.csun.edu/plunk.

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Presentation transcript:

SPSS Statistical Package for Social Sciences Multiple Regression Department of Psychology California State University Northridge

Multiple Regression  Multiple regression predicts/explains variance in a criterion (dependent) variable from the values of the predictor (independent) variables.  Regressions also explain the strength and direction of the relationship between each IV and the DV (taking into consideration the shared variance between the IVs).

Multiple Regression  Go to “Analyze”, then “Regression”, and then “Linear”

Multiple Regression  Move “Quality of Life” into the “Dependent” box, and move “Intelligence” and “Depression” into the “Block 1 of 1” box. Then click “OK”.

Multiple Regression  Intelligence and depression accounted for significant variance in quality of life, R 2 =.28, F(2,2670) = , p <.001. The standardized beta coefficients indicated that intelligence (Beta = -.19, p <.001) and depression (Beta = -.41, p <.001) were significantly and negatively related to quality of life. Note: Another way this could have been reported is that intelligence and depression accounted for 28% of the variance in quality of life, F(2,2670) = , p <.001

Hierarchical Multiple Regression  A form of multiple regression in which the contribution toward prediction of each IV is assessed in some predetermined hierarchical order.  Researchers may put in ‘control variables’ first to determine if a set of variables account for significant variance in the DV after controlling for the ‘control variables’.  Or the researchers may have a theoretical or methodological reason to determine the order entry.  In this example, the researcher is going to assess whether intelligence and depression account for significant change in quality of life after controlling for certain demographic variables.

Hierarchical Multiple Regression  Move “Quality of Life” into the “Dependent” box, and move “gender”, “age”, and “maritalsts” into the “Block 1 of 1” box. Then click “Next”.

Hierarchical Multiple Regression  Move “Intelligence” and “Depression” into the “Block 2 of 2” box. Then click “Statistics”.

Hierarchical Multiple Regression  Select “R squared change”, then “Continue” and then “OK”

Hierarchical Multiple Regression  In the first step, the demographic variables did not account for significant variance in quality of life R 2 =.00 F(3,2658) =.12, p =.95. In step 2, intelligence and depression accounted for significant variance in quality of life, R 2 =.28, F(2,2656) = , p <.001. The standardized beta coefficients indicated that intelligence (Beta = -.20, p <.001) and depression (Beta = -.42, p <.001) were significantly and negatively related to quality of life.