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University of Saskatchewan
SOC 325
Elizabeth Quinlan

Soc 325 March 26 1 Last two days of our class will be review Chapter 14 Multiple Regression • Multivariate analysis 1. Partial correlation  Further insight into a bivariate relationship, a 3rd variable 2. Multiple regression  For the most part interval-ratio variables  Assess the effects of two or more independent variables on the dependent variable  Expand the number of independent variables but there will always only ever be one dependent variable  An extension of the linear (bivariate) regression  Multiple regression allows us to  Use more than one independent variable to predict y  Disentangle and examine the separate effects of the independent variables  We will have a couple of different kinds of B's  Assess the combined effects of the independent variables on Y  Least-squares multiple regression equation Y = a + b 1 1 b X2 2  For every independent variable you will have a different b  Where, b = the partial slope of the first independent variable on y (formula 14.4)  b = the partial slop of the second independent variable on y 2 (formula 14.5)  a =  The partial slope: shows the effects of each independent variable on Y while controlling for the effect of the other independent variables  The numerical values of b's indicate the amount of change in Y for each unite of change in one independent variable while controlling for the other  Example:  Previously we considered the relationship between the number of children (x) and husbands contribution of housework (y) for 12 dual-earner families  Here we will assess the effects of two independent variables on husbands contribution to housework: number of children (x1) and SES (measured by the years of education completed by the husband) (x2)  The partial slope for the first independent variable X1 is  A slope of .65 means that the amount of time the husband contributes to housekeeping chores increases by .65 hours per week for each additional child in the family, controlling for the effects of Socio Economic Status Soc 325 March 26 2  R y1sbands contribution to housework is positively related to number of children (.50)  R y2sbands in higher ses tend to do less housework (-.30)  R 12gher ses families tend to have fewer children (-.47)  The partial slope for the second independent variable x2 is  A slope of -.07 means that the amount of time the husbands contributes to housekeeping chores decreases by .07 hours for each child  Means calculated by SPSS  Y = 3.3  X1 = 2.7  X2 = 13.7  Rearrange formula and solve for a  A = x - b1x1 - b2x2 = 3.3 - (.65)(2.7) - (-.07)(13.7) = 3.3 - 1.8 - (-1) = 3.3 - 1.8 + 1 = 2.5  And then the multiple regression equation: y = a + b1x1 + b2x2 = 2.5 + (.65)x1 + (-.07)x2 
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