2012年10月18日星期四

Data Analysis with SPSS

Data Analysis with SPSS

when open it in SPSS and run it by clicking on the green arrow or choosing "Run" from the Macro menu. This will open an SPSS dialog window.

Example 1: Multiple mediators

For this example, we will use the hsb2 dataset with science as the dependent variable, math as the independent variable and read and write as the two mediator variables. The paths in such a model are depicted below. In our analysis, we are interested in finding these paths to calculate the direct and indirect effects of our variables.

To begin, we indicate which of our variables are the dependent, independent, and mediator variables in the dialog window.

This generates the output below.

Run MATRIX procedure: 

Dependent, Independent, and Proposed Mediator Variables: 
DV = science 
IV = math 
MEDS = read 
write 

Sample size 
200 

IV to Mediators (a paths) 
Coeff se t p 
read .7248 .0583 12.4378 .0000 
write .6247 .0566 11.0452 .0000 

Direct Effects of Mediators on DV (b paths) 
Coeff se t p 
read .3015 .0687 4.3903 .0000 
write .2065 .0708 2.9185 .0039 

Total Effect of IV on DV (c path) 
Coeff se t p 
math .6666 .0583 11.4371 .0000 

Direct Effect of IV on DV (c-prime path) 
Coeff se t p 
math .3190 .0767 4.1605 .0000 

Model Summary for DV Model 
R-sq Adj R-sq F df1 df2 p 
.4999 .4923 65.3187 3.0000 196.0000 .0000 

****************************************************************** 

NORMAL THEORY TESTS FOR INDIRECT EFFECTS 

Indirect Effects of IV on DV through Proposed Mediators (ab paths) 
Effect se Z p 
TOTAL .3476 .0596 5.8277 .0000 
read .2186 .0524 4.1692 .0000 
write .1290 .0454 2.8422 .0045 

***************************************************************** 

BOOTSTRAP RESULTS FOR INDIRECT EFFECTS 

Indirect Effects of IV on DV through Proposed Mediators (ab paths) 
Data boot Bias SE 
TOTAL .3476 .3449 -.0027 .0645 
read .2186 .2164 -.0022 .0537 
write .1290 .1285 -.0005 .0496 

Bias Corrected and Accelerated Confidence Intervals 
Lower Upper 
TOTAL .2230 .4700 
read .1125 .3245 
write .0294 .2235 

***************************************************************** 

Level of Confidence for Confidence Intervals: 
95 

Number of Bootstrap Resamples: 
1000 

------ END MATRIX -----

The results above assuming normality suggest that each of the separate indirect effects as well as the total indirect effect are significant. From the above results it is also possible to compute the ratio of indirect to direct effect (.3476/.3190 = 1.09) and the proportion of the total effect due to the indirect effect (.3476/(.3476 + .3190) = .52).

The normal theory tests for indirect effects compute the standard errors using the delta method which assumes that the estimates of the indirect effect are normally distributed. For many situations this is acceptable, but it does not work well for the indirect effects which are usually positively skewed and kurtotic. Thus the z-test and p-values for these indirect effects generally cannot be trusted. Therefore, it is recommended that bootstrap standard errors and confidence intervals be used. Additionally, if your outcome is binary, a proportion, or a percent, bootstrap estimates should be used. These can be found in the next block of output. These standard errors are slightly larger than those calculated assuming normality and the overall interpretation remains the same.

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