If it does, then it we could make better decisions about whether to admit or deny admission based on the combination of predictors than by using just ACTCOMP. The analyses SPSS The data are from a study of the relationship of GPA to personality. I saved the data to a separate file to make it easier to accessing using Rcmdr. (The original file had 900+ columns, making it somewhat unwieldy.) First, a review of the relation of GPA to ACTCOMP . . . (data are PSY 5100 MR example data.sav in the Inclass datasets folder) Model Summary Adjusted R Std. Error of the Model R R Square Square Estimate 1 .367a .135 .134 .659112 a. Predictors: (Constant), ACTCOMP Coefficientsa Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 1.403 .102 13.821 .000 ACTCOMP .073 .005 .367 16.104 .000 a. Dependent Variable: FirstReseosgpa As we would expect, ACTCOMP predicts GPA not perfectly, but significantly. – Now, a Multiple Regression, with two predictors ACTCOMP and hc. – hc is HEXACO Conscientiousness scores. The HEXACO is a popular general personality questionnaire which measures six traits Honesty/Humility, Emotionality, – eXtraversioni, Conscientiousness, and Openness to Experience. I discussed it in last week’s lecture on using r to assess convergent validity (What is nc in the above list of variables?, you might ask. It’s NEO Conscientiousness measured using the NEO personality questionnaire. I decided to use hc in this example because hc is a measure you’ll be more likely to be able to use yourself. (You have to pay to use the NEO.) Besides, as we discovered last week, nc and hc exhibit very high convergent validity. The Output Variables Entered/Removeda Variables Model Entered Variables Removed Method 1 hc, ACTCOMPb . a. Dependent Variable: FirstReseosgpa b. All requested variables entered. Model Summary Adjusted R Std. Error of the R R Square Square Estimate
If it does, then it we could make better decisions about whether to admit or deny admission based on the combination of predictors than by using just ACTCOMP. The analyses SPSS The data are from a study of the relationship of GPA to personality. I saved the data to a separate file to make it easier to accessing using Rcmdr. (The original file had 900+ columns, making it somewhat unwieldy.) First, a review of the relation of GPA to ACTCOMP . . . (data are PSY 5100 MR example data.sav in the Inclass datasets folder) Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .367a .135 .134 .659112 a. Predictors: (Constant), ACTCOMP Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.403 .102 13.821 .000 ACTCOMP .073 .005 .367 16.104 .000 a. Dependent Variable: FirstReseosgpa As we would expect, ACTCOMP predicts GPA – not perfectly, but significantly. Now, a Multiple Regression, with two predictors – ACTCOMP and hc. hc is HEXACO Conscientiousness scores. The HEXACO is a popular general personality questionnaire which measures six traits – Honesty/Humility, Emotionality, eXtraversioni, Conscientiousness, and Openness to Experience. I discussed it in last week’s lecture on using r to assess convergent validity (What is nc in the above list of variables?, you might ask. It’s NEO Conscientiousness measured using the NEO personality questionnaire. I decided to use hc in this example because hc is a measure you’ll be more likely to be able to use yourself. (You have to pay to use the NEO.) Besides, as we discovered last week, nc and hc exhibit very high convergent validity. The Output Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 hc, ACTCOMPb . a. Dependent Variable: FirstReseosgpa b. All requested variables entered. Model Summary R R Square Adjusted R Square Std. Error of the Estimate
p5100_lecture05_regresssion.docx