# HW 4 Part A. Problem 1.To determine what factors influence public opinion about favoring death penalty for murder: 1. Replicate the SPSS output provided below. Run Logistic regression using the...

i attached the data and article. Homework has part A and B . due 8.8 by 5

Answered 3 days AfterAug 04, 2022

## Answer To: HW 4 Part A. Problem 1.To determine what factors influence public opinion about favoring death...

Vikash Kumar answered on Aug 08 2022
HW 4 – Logistic Regression                                     2
Logistic Regression
HW 4
Part A
Problem 1
To find out what factors, change public opinion about supporting death sentence for murder:
Dependent variable – cappun (FAVOR OR OPPOSE death penalty for murder) is having a nominal scale of measurement. Coding for this variable is as follows:
0 Favo
1 Oppose
Independent variable: –
Scale of measurement
Independent variable
Label
Ratio
age
Age of the respondent
Ordinal
polv
iews
Think of self as LIBERAL or CONSERVATIVE
Nominal
reborn
Has R ever had a 'born again' experience
Nominal
Sex
Respondent’s sex
Nominal
religion
R’s religious preference
Hypothecated Model: -age
polviews
cappun
eborn
sex
eligion
SPSS Output: -
Table 1.1 Case Processing Summary
Unweighted Casesa
N
Percent
Selected Cases
Included in Analysis
553
39.1

Missing Cases
862
60.9

Total
1415
100.0
Unselected Cases
0
.0
Total
1415
100.0
a. If weight is in effect, see classification table for the total number of cases.
Table 1.1 shows the number of cases that have been included and excluded from the analysis. Out of total 1415 cases, 553 have been included for further analysis.

Table 1.2 Dependent Variable Encoding
Original Value
Internal Value
FAVOR
0
OPPOSE
1
Coding of the dependent variable have been shown in Table 1.2 and for categorical independent variables in Table 1.3. Those participants who are in favour of death penalty for murderer have been coded as 0 and in oppose to this notion have been coded as 1.
Table 1.3 Categorical Variables Codings

Frequency
Parameter coding

(1)
(2)
(3)
(4)
(5)
(6)
THINK OF SELF AS LIBERAL OR CONSERVATIVE
EXTREMELY LIBERAL
17
.000
.000
.000
.000
.000
.000

LIBERAL
51
1.000
.000
.000
.000
.000
.000

SLIGHTLY LIBERAL
59
.000
1.000
.000
.000
.000
.000

MODERATE
221
.000
.000
1.000
.000
.000
.000

SLGHTLY CONSERVATIVE
88
.000
.000
.000
1.000
.000
.000

CONSERVATIVE
91
.000
.000
.000
.000
1.000
.000

EXTRMLY CONSERVATIVE
26
.000
.000
.000
.000
.000
1.000
RS RELIGIOUS PREFERENCE
PROTESTANT
312
.000
.000
.000
.000

CATHOLIC
139
1.000
.000
.000
.000

JEWISH
6
.000
1.000
.000
.000

NONE
91
.000
.000
1.000
.000

OTHER (SPECIFY)
5
.000
.000
.000
1.000

RESPONDENTS SEX
MALE
263
.000

FEMALE
290
1.000

HAS R EVER HAD A 'BORN AGAIN' EXPERIENCE
YES
172
.000

NO
381
1.000

Block 0 assumes that there are no predictor variables in the model and just the intercept.
Block 0: Beginning Block
Table 1.4 Classification Tablea,
Observed
Predicted

FAVOR OR OPPOSE DEATH PENALTY FOR MURDER
Percentage Co
ect

FAVOR
OPPOSE

Step 0
FAVOR OR OPPOSE DEATH PENALTY FOR MURDER
FAVOR
378
0
100.0

OPPOSE
175
0
.0

Overall Percentage

68.4
a. Constant is included in the model.
b. The cut value is .500
The model with intercept term only predicts with overall percentage of 68.40.
Table 1.5 Variables in the Equation

B
S.E.
Wald
df
Sig.
Exp(B)
Step 0
Constant
-.770
.091
70.943
1
.000
.463
Block 1: Method = Ente
Block 1 has model with intercept term as well as independent variables.
Table 1.6 Omnibus Tests of Model Coefficients

Chi-square
df
Sig.
Step 1
Step
63.045
13
.000

Block
63.045
13
.000

Model
63.045
13
.000
The overall model is statistically significant, at 5% level of significance.
Table 1.7 Model Summary
Step
-2 Log likelihood
Cox & Snell R Square
Nagelkerke R Square
1
627.285a
.108
.151
a. Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found.
It is evident from the Table 1.7 that the explained variation in the dependent variable is 10.8% and 15.10% as reference with Cox and Snell and Nagelkerke respectively.
Table 1.8 Classification Tablea
Observed
Predicted

FAVOR OR OPPOSE DEATH PENALTY FOR MURDER
Percentage Co
ect

FAVOR
OPPOSE

Step 1
FAVOR OR OPPOSE DEATH PENALTY FOR MURDER
FAVOR
347
31
91.8

OPPOSE
128
47
26.9

Overall Percentage

71.2
a. The cut value is .500
With the inclusion of independent variables, the model co
ectly classifies 71.2% of the cases overall. It also represents percentage accuracy in the classification.
The sensitivity of the classification is 91.8% which tells that participants who favours the death punishment for murderer were also predicted by the model to be in...
SOLUTION.PDF