Logistic Regression Analysis

Logistic Regression AnalysisLacourse, Claes, and Villeneuve (2001) carried out a study to see whether a love of heavy metal could predict suicide risk. Eric Lacourse and his colleagues used questionnaires to measure several variables: suicide risk (yes or no), marital status of parents (together or divorced/separated), the extent to which the person’s mother and father were neglectful, self-estrangement/powerlessness (adolescents who have negative self-perceptions, are bored with life, etc.), social isolation (feelings of a lack of support), normlessness (beliefs that socially disapproved behaviors can be used to achieve certain goals), meaninglessness (doubting that school is relevant to gain employment) and drug use. In addition, the authors measured liking of heavy metal; they included the sub-genres of classic (Black Sabbath, Iron Maiden), thrash metal (Slayer, Metallica), death/black metal (Obituary, Burzum) and gothic (Marilyn Manson). As well as liking, they measured behavioral manifestations of worshipping these bands (e.g., hanging posters, hanging out with other metal fans) and what the authors termed ‘vicarious music listening’ (whether music was used when angry or to bring out aggressive moods). They used logistic regression to predict suicide risk from these variables for males and females separately.Upon running a logistic regression analysis, you obtain the following output: Omnibus Tests of Model Coefficients Chi-square df Sig. Step 1 Step 5.833 3 .120 Block 5.833 3 .120 Model 50.417 12 .000 Model Summary Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 1 85.116a .341 .506 a. Estimation terminated at iteration number 6 because parameter estimates changed by less than .001. Classification Tablea Observed Predicted Suicide Risk Percentage Correct Non-Suicidal Suicidal Step 1 Suicide Risk Non-Suicidal 85 6 93.4 Suicidal 13 17 56.7 Overall Percentage 84.3 a. The cut value is .500 Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 1a Age .693 .323 4.589 1 .032 1.999 Marital Status(1) -.183 .677 .073 1 .786 .832 Drug Use .317 .103 9.446 1 .002 1.373 Father Negligence .085 .048 3.127 1 .077 1.088 Social Isolation -.006 .076 .006 1 .939 .994 Meaninglessness -.067 .061 1.191 1 .275 .936 Mother Negligence -.020 .053 .136 1 .713 .981 Normlessness .191 .109 3.089 1 .079 1.211 Self-Estrangement/Powerlessness .155 .065 5.727 1 .017 1.168 Liking Metal Music .136 .092 2.184 1 .139 1.145 Vicarious Listening -.342 .196 3.033 1 .082 .710 Worshipping .159 .129 1.506 1 .220 1.172 Constant -18.828 6.314 8.891 1 .003 .000 a. Variable(s) entered on step 1: Liking Metal Music, Vicarious Listening, Worshipping.1) Does listening to heavy metal music (Variables: Liking Metal Music, Vicarious Listening, Worshipping) predict suicide risk in women?2) What factors predict suicide risk in women? Stepwise Multiple RegressionOng et al. (2011) conducted an interesting study that examined the relationship between narcissism and behavior on Facebook in 275 adolescents. They measured the Age, Gender and Grade (at school), as well as extroversion and narcissism. They also measured how often (per week) these people updated their Facebook status (FB_Status), and also how they rated their own profile picture on each of four dimensions: coolness, glamour, fashionableness and attractiveness. These ratings were summed as an indicator of how positively they perceived the profile picture they had selected for their page (FB_Profile_TOT). They hypothesized that narcissism would predict, above and beyond the other variables, the frequency of status updates, and how positive a profile picture the person chose. To test this, they conducted two hierarchical regressions: one with FB_Status as the outcome and one with FB_Profile_TOT as the outcome. In both models they entered Age, Gender and Grade in the first block, then added extroversion (NEO_FFI) in a second block, and finally narcissism (NPQC_R) in a third block. Use the provided output to answer the questions below.This regression will assess whether narcissism predicts, above and beyond the other variables, the rating of profile pictures. Block 1 Age, Gender, Grade Block 2 Extraversion Block 3 Narcissism Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Extraversion – Totalb . Enter 2 NPQC-R Totalb . Enter a. Dependent Variable: Sum of Profile picture ratings b. All requested variables entered. Model Summaryc Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change Sig. F Change 1 .355a .126 .121 3.438 .126 .000 2 .504b .254 .245 3.187 .127 .000 a. Predictors: (Constant), Extraversion – Total b. Predictors: (Constant), Extraversion – Total, NPQC-R Total c. Dependent Variable: Sum of Profile picture ratings ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 283.715 1 283.715 24.001 .000b Residual 1962.262 166 11.821 Total 2245.976 167 2 Regression 569.702 2 284.851 28.039 .000c Residual 1676.274 165 10.159 Total 2245.976 167 a. Dependent Variable: Sum of Profile picture ratings b. Predictors: (Constant), Extraversion – Total c. Predictors: (Constant), Extraversion – Total, NPQC-R Total Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.486 2.064 .720 .473 Extraversion – Total .228 .046 .355 4.899 .000 2 (Constant) .614 1.920 .320 .749 Extraversion – Total .099 .049 .155 2.013 .046 NPQC-R Total .196 .037 .409 5.306 .000 a. Dependent Variable: Sum of Profile picture ratings1. Interpret your results. How much of the variance in frequency of status updates can be explained by extraversion and narcissism?2. Does narcissism predict profile picture ratings above and beyond extraversion?3. What do the results tell us about teenagers and Facebook?