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...

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


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 following variables from the GSS 2004: cappun, age, polviews, reborn, sex, religion. Start with identifying the dependent and the independent variables. Draw your model using boxes and arrows diagram. 2. Give the full logistic regression analysis of the SPSS output. Specifically, interpret the EXP (B) coefficient (odds) for the significant results. The coding for the dependent variable is presented in the SPSS output. The coding for the other variables is available in the GSS 2004 data set. Go to Utilities – Variables – Click on any variable you need information about. Please read carefully the questions and the possible answers. The variables’ coding will help you to interpret the odds correctly. For each variable indicate the level of measurement and the codes for each category of the nominal and ordinal variables. 3. Calculate the probability of favoring death penalty for murder for a catholic man, moderate in political views and without “born again” experience. 4. Calculate the probability of favoring death penalty for murder for a non-believer woman with a “born again” experience and extremely liberal in her political views. Logistic Regression 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. Dependent Variable Encoding Original Value Internal Value oppose 0 favor 1 Pay attention to the dependent variable coding. The interpretation of the dependent variable depends on how the variable is coded! Categorical Variables Codings Frequency Parameter coding (1) (2) (3) (4) 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 Block 0: Beginning Block Classification Tablea,b Observed Predicted FAVOR OR OPPOSE DEATH PENALTY FOR MURDER oppose favor Percentage Correct Step 0 FAVOR OR OPPOSE DEATH PENALTY FOR MURDER oppose 0 175 .0 favor 0 378 100.0 Overall Percentage 68.4 a. Constant is included in the model. b. The cut value is .500 Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 0 Constant .770 .091 70.943 1 .000 2.160 Block 1: Method = Enter Omnibus Tests of Model Coefficients Chi-square df Sig. Step 1 Step 58.050 8 .000 Block 58.050 8 .000 Model 58.050 8 .000 Model Summary Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 1 632.281a .100 .140 a. Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found. Classification Tablea Observed Predicted FAVOR OR OPPOSE DEATH PENALTY FOR MURDER oppose favor Percentage Correct Step 1 FAVOR OR OPPOSE DEATH PENALTY FOR MURDER oppose 47 128 26.9 favor 35 343 90.7 Overall Percentage 70.5 a. The cut value is .500 Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 1 age -.002 .006 .182 1 .670 .998 polviews .325 .075 18.934 1 .000 1.384 reborn .704 .227 9.601 1 .002 2.022 sex -.630 .200 9.919 1 .002 .533 relig 14.505 4 .006 relig(1) -.485 .242 4.013 1 .045 .616 relig(2) -.583 .929 .394 1 .530 .558 relig(3) -1.064 .285 13.978 1 .000 .345 relig(4) 19.549 17795.489 .000 1 .999 3.089E8 Constant -.346 .658 .276 1 .599 .708 Problem 2. The Exam Practice Problem To determine what factors influence public opinion about abortion replicate the logistic regression analysis example from above using the following variables from the GSS 2006: abany, age, education, sex, religion. Start with identifying the dependent and the independent variables. Draw your model using boxes and arrows diagram. Provide coding for the nominal and ordinal variables. Provide the full logistic regression analysis. Specifically, interpret the EXP (B) coefficients (the odds) for the significant results. PART B Hugh Crean, A.D. Hightower, and Marjorie Allan (2001) “School-Based Child Care for Children of Teen parents: Evaluation of an Urban Program Designed to Keep Young Mothers in School. Educational Evaluation and Program Planning. 24: 267-275. 1. Was there any difference in the ethnic distribution of participating and non-participating mothers? Why do you think so? 2. Was there any difference in their ages when they give birth to their child? How can you prove this? 3. Why logistic regression method was used in this analysis? 4. What were the independent and dependent variables? [Indicate their level of measurement , unit of measurement and/or coding in the tables below] Variable Name Level of Measurement Unit of measurement/coding Independent/Dependent Variable 5. What were the results? Interpret the impact of each of the five independent variables on graduation using only B coefficient and p (sig). columns in Table 4 on page 272. Note: The original model is in the log odds, or logit. Therefore, the B coefficient is the effect of a one-unit change in an independent variable on the log odds of graduation. For example (just example, it is not in the table), the b coefficient for age is 0.3. This means that every additional year of age is to increase the log odds of graduation by 0.3 6. Explain the results of Table 5 on page 273. PII: S0149-7189(01)00018-0 School-based child care for children of teen parents: evaluation of an urban program designed to keep young mothers in school Hugh F. Creana,*, A.D. Hightowera, Marjorie J. Allanb aDepartment of Clinical and Social Sciences in Psychology, University of Rochester, Rochester, NY, USA bDepartment of Pediatrics, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA Abstract This study examined the effects of the school-based Early Childhood Centers for Children of Teen Parents Program. Designed to keep young mothers in school, the program provides needed support to urban young mothers including free on-site child care for their infants and toddlers, parenting classes, and referral to other service agencies. Archived school record information was collected on teen mothers who participated in the program (nˆ 81) and on a group of teen mothers who had applied for the program but did not receive services (nˆ 89). Controlling for pre-service differences, participant mothers were found to have better school attendance and deemed to be at lower overall risk than were the non-participant young mothers. Signi®cant differences were also evident in the graduation rates of these young mothersÐ 70% of the participant mothers graduated, 28% of the non-participant young mothers graduated. Logistic regression correctly classi®ed graduation/drop-out status in 76% of the cases. School attendance, mother's age at birth of the child, and participation/non-participation in the program were signi®cant predictors. Percent core courses passed and average risk scores did not signi®cantly add to prediction. Implications and future areas of study are discussed. q 2001 Elsevier Science Ltd. All rights reserved. Keywords: Teen parents; School-based child care; Educational outcomes of teen parents; Program evaluation Although recent research more critically questions the extent and causal directions associated with many of the negative outcomes associated with adolescent motherhood (Geronimus & Korenman, 1992; Hoffman, Foster & Furstenberg, 1993; Hotz, McElroy & Sanders, 1997; Luker, 1996), the consequences of teen parenting remain signi®cant. Poor, unwed teen mothers often have much to overcome to succeed. Unwed teen mothers, when compared with women of similar academic and socioeconomic back- grounds who postpone childbearing, are more likely to drop out of school (Allen & Pittman, 1986; Coley & Chase-Lans- dale, 1998; Moore, Myers, Morrison, Nord, Brown & Edmonston, 1993; Mott & Marsiglio, 1985); are less likely to ®nd stable, meaningful employment; and are more likely to rely on public assistance (Brindes & Jeremy, 1988; Duncan, 1984). Nearly seven in ten teen mothers go on welfare before their child's fourth birthday and more than 40% of young mothers who receive AFDC do so over at least a 10-year period (Allen & Pittman, 1986). Some studies indicate that teen childbearers never achieve economic parity with women who postpone childbearing until their adult years (Furstenberg, Brooks-Gunn & Chase-Lansdale, 1989; Coley & Chase-Lansdale, 1998). Later childbearers are also more likely to enter stable marriages than are those women who have children in their early teens (Furstenberg, Brooks-Gunn & Morgan, 1987; McCarthy & Menken, 1979). Nevertheless, teen mothers who can successfully manage their educational career and social relationships do drasti- cally improve the odds for themselves and their children. Initiatives focused on providing needed educational and socioemotional assistance can be effective (Furstenburg et al., 1987; Hofferth, 1987). Furstenberg et al. (1987), for instance, found that teen mothers who had received educa- tional assistance (in the form of a continuing educational program and postpartum family planning services) had better long-term outcomesÐbeing more self-suf®cient economically and having more stable and smaller families than did non-program teen mothers. Educational programs that focus on parenting skills tend to be successful in a different yet complimentary way, leading to an improved parent±child relationship and healthier overall development in the child (Clewell, Brooks-Gunn & Benasich, 1989). Related to both strategies, child care is the service most frequently requested by adolescent mothers and the service most likely to be unavailable (Flood, Greenspan & Mundorf, 1985; Furstenberg et al., 1989). Although cost Evaluation and Program Planning 24 (2001) 267±275 0149-7189/01/$ - see front matter q 2001 Elsevier Science Ltd. All rights reserved. PII: S0149-7189(01)00018-0 www.elsevier.com/locate/evalprogplan * Corresponding author. Tel.: 11-716-295-1000; fax: 11-295-4090. E-mail address: [email protected] (H.F. Crean). may be a prohibiting factor, high-quality child care has the potential for enhancing teen mothers' lives as well as bene- ®ting the development of their children (Clewell et al., 1989)
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
33 Votes
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...
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