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Nursing
Psychology
Nervous
6
11
Excited
14
9
Another way of looking at this data set would be:
6 Nervous Nursing Students
11 Nervous Psychology Students
14 Excited Nursing Students
9 Excited Psychology Students
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?

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NursingPsychologyNervous611Excited149Anoth.docx

  • 1. Nursing Psychology Nervous 6 11 Excited 14 9 Another way of looking at this data set would be: 6 Nervous Nursing Students 11 Nervous Psychology Students 14 Excited Nursing Students 9 Excited Psychology Students 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),
  • 2. 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
  • 3. 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
  • 4. 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
  • 7. .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).
  • 8. 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.
  • 9. 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
  • 10. 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
  • 11. 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
  • 12. 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?
  • 13. 2. Does narcissism predict profile picture ratings above and beyond extraversion? 3. What do the results tell us about teenagers and Facebook?