See attached
Logistic Regression Analysis
Lacourse, 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 Regression
Ong 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 ratings
1. 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?