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# Example of Very Simple Path Analysis vi a Regression with correlation matrix input Entering a Correlation Matrix into SPSS matrix data variables rowtype ses iq am gpa tells variable names format l PDF document - DocSlides

min-jolicoeur | 2014-12-13 | General

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Example of Very Simple Path Analysis vi a Regression (with correlation matrix input) Entering a Correlation Matrix into SPSS matrix data variables = rowtype_ ses iq am gpa tells variable names / format = lower diagonal. begin data. mean 0.0 0.0 0.0 0.0 stddev 2.10 15.00 3.25 1.25 n 300 300 300 300 corr 1.00 corr .30 1.00 corr .410 .160 1.00 corr .330 .570 .500 1.00 end data.

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Getting the "First layer" multiple regression for the full model regression matrix = in(*)/ dep am/ enter ses iq. Model Summary Coefficients Getting the "Second Layer" multiple regression for the full model regression matrix = in(*)/ dep gpa/ enter ses iq am. Model Summary Coefficients Portraying the Full Path Model

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Remember you need to be very honest with yourself and with your audience about whether the reduced model is an a priori theory-driven model, or the results of inspecting a full path model (usually involves tossing the noncontributing paths, know as theory trimming ). As in all the other analyses we’ve discussed, confirmed a priori hypotheses have a special place in our hearts! regression matrix = in(*)/ dep gpa/ enter iq am. Model Summary Coefficients Portraying the Reduced or Hypothesized Path Model

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Testing the Reduced or Hypothesized Model

begin data mean 00 00 00 00 stddev 210 1500 325 125 n 300 300 300 300 corr 100 corr 30 100 corr 410 160 100 corr 330 570 500 100 end data brPage 2br Getting the First layer multiple regression for the full model regression matrix in dep am enter se ID: 23424

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Page 1

Example of Very Simple Path Analysis vi a Regression (with correlation matrix input) Entering a Correlation Matrix into SPSS matrix data variables = rowtype_ ses iq am gpa tells variable names / format = lower diagonal. begin data. mean 0.0 0.0 0.0 0.0 stddev 2.10 15.00 3.25 1.25 n 300 300 300 300 corr 1.00 corr .30 1.00 corr .410 .160 1.00 corr .330 .570 .500 1.00 end data.

Page 2

Getting the "First layer" multiple regression for the full model regression matrix = in(*)/ dep am/ enter ses iq. Model Summary Coefficients Getting the "Second Layer" multiple regression for the full model regression matrix = in(*)/ dep gpa/ enter ses iq am. Model Summary Coefficients Portraying the Full Path Model

Page 3

Remember you need to be very honest with yourself and with your audience about whether the reduced model is an a priori theory-driven model, or the results of inspecting a full path model (usually involves tossing the noncontributing paths, know as theory trimming ). As in all the other analyses we’ve discussed, confirmed a priori hypotheses have a special place in our hearts! regression matrix = in(*)/ dep gpa/ enter iq am. Model Summary Coefficients Portraying the Reduced or Hypothesized Path Model

Page 4

Testing the Reduced or Hypothesized Model

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