Great Deal! Get Instant $10 FREE in Account on First Order + 10% Cashback on Every Order Order Now

Provide your answers below to each question. Do not delete the questions. Please do not use red font. Blue or green is OK. Part I (35 POINTS) The General Social Survey research department was tasked...

1 answer below »

Provide your answers below to each question. Do not delete the questions. Please do not use red font. Blue or green is OK.
Part I (35 POINTS)
The General Social Survey research department was tasked to determine the number of hours per day government employees in the USA spent on emailing. The department reported the data for a sample of 1765 government employees from GSS 2018 data on age (number of years), sex of respondent (1=males, 2=females), total family income (in constant dollars), and hours worked last week. Refer to the SPSS output below. Conduct the full regression analysis.
Regression
    Model Summary
    Model
    R
    R Square
    Adjusted R Square
    Std. E
or of the Estimate
    1
    .312a
    .097
    .092
    12.165
    a. Predictors: (Constant), Respondents sex, Age of respondent, Number of hours worked last week, Respondent income in constant dollars
    ANOVAa
    Model
    Sum of Squares
    df
    Mean Square
    F
    Sig.
    1
    Regression
     XXXXXXXXXX
    4
     XXXXXXXXXX
    16.495
    .000
    
    Residual
     XXXXXXXXXX
    611
    147.994
    
    
    
    Total
     XXXXXXXXXX
    615
    
    
    
    a. Dependent Variable: Email hours per week
    b. Predictors: (Constant), Respondents sex, Age of respondent, Number of hours worked last week, Respondent income in constant dollars
    Coefficientsa
    Model
    Unstandardized Coefficients
    Standardized Coefficients
    t
    Sig.
    
    B
    Std. E
o
    Beta
    
    
    1
    (Constant)
    -2.633
    3.044
    
    -.865
    .387
    
    Age of respondent
    -.017
    .036
    -.019
    -.483
    .629
    
    Number of hours worked last week
    .090
    .036
    .102
    2.464
    .014
    
    Respondent income in constant dollars
    8.863E-5
    .000
    .278
    6.659
    .000
    
    Respondents sex
    3.523
    1.025
    .138
    3.438
    .001
    a. Dependent Variable: Email hours per week
Hint: For the coefficient that ends in E-5, move the point 5 decimal places to the left
1. Which variables are independent variables?
2. Which variable is the dependent variable?
3. What does coefficient of determination (R2) tell you?
4. What is the intercept value? What can it indicate here?

5. Is the model overall significant?
6. Determine the multiple regression equation:
7. Discuss significant regression coefficients
8. What variable has the strongest effect on the number of hours US government employees spent emailing?
9. What is the estimated number of hours emailing per day for a woman who works 40 hours per week and who earns $50.000 per year?
Part II (35 POINTS)
Give the full logistic regression analysis of the SPSS 2014 output provided below. The coding for the dependent variable is presented in the SPSS output. Interpret and include in your regression equation only significant coefficients (use alpha= 0.05).
The question that the respondents were asked is: “Would you favor or
oppose the teaching of sex education in public schools?” (Favor= 1; Oppose=0)
Coding for variables:
polviews:
1. Extremely liberal
2. Liberal
3. Slightly Liberal
4. Moderate
5. Slightly Conservative
6. Conservative
7. Extremely Conservative
pillok: Birth Control to Teenagers 14-16
XXXXXXXXXXQuestion asked: Do you strongly agree, agree, disagree, or strongly disagree that it is acceptable to make birth control devices available to teenagers, age 14-16?
1. Strongly Agree
2. Agree
3. Disagree
4. Strongly disagree
educ: XXXXXXXXXXHighest year of school completed
Logistic Regression
    Case Processing Summary
    Unweighted Casesa
    N
    Percent
    Selected Cases
    Included in Analysis
    1579
    62.2
    
    Missing Cases
    959
    37.8
    
    Total
    2538
    100.0
    Unselected Cases
    0
    .0
    Total
    2538
    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
    Favo
    1
    Categorical Variables Codings
    
    Frequency
    Parameter coding
    
    
    (1)
    (2)
    (3)
    BIRTH CONTROL TO TEENAGERS 14-16
    STRONGLY AGREE
    403
    1.000
    .000
    .000
    
    AGREE
    518
    .000
    1.000
    .000
    
    DISAGREE
    389
    .000
    .000
    1.000
    
    STRONGLY DISAGREE
    269
    .000
    .000
    .000
Block 0: Beginning Block
    Classification Tablea,
    
    Observed
    Predicted
    
    
    SEX EDUCATION IN PUBLIC SCHOOLS RECODED
    Percentage Co
ect
    
    
    Oppose
    Favo
    
    Step 0
    SEX EDUCATION IN PUBLIC SCHOOLS RECODED
    Oppose
    0
    141
    .0
    
    
    Favo
    0
    1438
    100.0
    
    Overall Percentage
    
    
    91.1
    a. Constant is included in the model.
    b. The cut value is .500
Block 1: Method = Ente
    Omnibus Tests of Model Coefficients
    
    Chi-square
    df
    Sig.
    Step 1
    Step
    161.537
    6
    .000
    
    Block
    161.537
    6
    .000
    
    Model
    161.537
    6
    .000
    Model Summary
    Step
    -2 Log likelihood
    Cox & Snell R Square
    Nagelkerke R Square
    1
    788.732a
    .097
    .215
    a. Estimation terminated at iteration number 7 because parameter estimates changed by less than .001.
    Classification Tablea
    
    Observed
    Predicted
    
    
    SEX EDUCATION IN PUBLIC SCHOOLS RECODED
    Percentage Co
ect
    
    
    Oppose
    Favo
    
    Step 1
    SEX EDUCATION IN PUBLIC SCHOOLS RECODED
    Oppose
    7
    134
    5.0
    
    
    Favo
    9
    1429
    99.4
    
    Overall Percentage
    
    
    90.9
    a. The cut value is .500
    Variables in the Equation
    
    B
    S.E.
    Wald
    df
    Sig.
    Exp(B)
    Step 1a
    AGE OF RESPONDENT
    -.020
    .006
    13.774
    1
    .000
    .980
    
    HIGHEST YEAR OF SCHOOL COMPLETED
    .100
    .031
    10.271
    1
    .001
    1.105
    
    THINK OF SELF AS LIBERAL OR CONSERVATIVE
    -.447
    .074
    36.855
    1
    .000
    .639
    
    BIRTH CONTROL TO TEENAGERS 14-16
    
    
    45.412
    3
    .000
    
    
    BIRTH CONTROL TO TEENAGERS XXXXXXXXXX)
    1.940
    .364
    28.347
    1
    .000
    6.957
    
    BIRTH CONTROL TO TEENAGERS XXXXXXXXXX)
    1.402
    .261
    28.755
    1
    .000
    4.064
    
    BIRTH CONTROL TO TEENAGERS XXXXXXXXXX)
    .800
    .227
    12.437
    1
    .000
    2.226
    
    Constant
    3.177
    .646
    24.168
    1
    .000
    23.966
    a. Variable(s) entered on step 1: AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, THINK OF SELF AS LIBERAL OR CONSERVATIVE, BIRTH CONTROL TO TEENAGERS 14-16.
1. What variables are independent variables?
2. What variable is the dependent variable?
3. Determine the logistic regression equation
4. Determine which independent variables are statistically significant and interpret the regression (B) coefficients
    Variable Name
    
    Level of significance
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
5. Interpret the Exp (B) coefficients
    Variable Name
    
    Level of significance
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
6. Provide overall conclusion
Part III (30 POINTS)
Review the article: Anna Ya Ni and Stuart Bretschneider. “The Decision to Contract Out: A study of Contracting for E-Government Services in State Governments.” Public Administration Review 64, 3: XXXXXXXXXXIt is available on Module 9 in the Readings folder. Write you review keeping the following questions in mind:

1. What is the policy analysis question?
2. What is the unit of analysis (identify a case in this study)?
3. What method of analysis is used in this research?
4. Draw the model using boxes and a
ows diagram.
5. Indicate the dependent and independent variables used in this model with their level of measurement and unit of measurement; fill in the table provided below. You can add or delete rows from the table.
    Unit of Analysis:
    Variable Name
    Level of Measurement
    Unit of measurement/coding
    Independent/Dependent Variable
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
6. Explain the logistic regression results. Use the table format and the information you have inputted in question 5.
    Hypothesis Give a number to hypothesis. (H1 etc.) It is conventional way to indicate hypotheses.
    Logistic Regression results
    
    
    
    
    
    
    
    
    
    
    
    
7. How type of service influences the outsourcing decision?
Extra credit (10 points)
Using your regression equation from part II, calculate the probability of favoring teaching of sex education in public schools for a 40-year-old person with some college education (15 years), liberal in political views. That person strongly agrees that birth control pills should be available to teenagers.
6

Th e Decision to Contract Out: A Study of Contracting for E-Government Services in State Governments
The Decision to Contract Out 531
Government contracting, especially for information
technology products and services, has accelerated in recent
years in the United States. Drawing on the insights of
privatization studies, the authors examine the economic
and political rationales underpinning government
decisions to contract out e-government services. Th is
article tests the extent to which economic and political
ationality infl uence governments’ contracting decisions
using data from multiple sources: a survey conducted by
National Association of State Chief Information Offi cers,
a survey by the National Association of State Procure-
ment Offi cers, the Council of State Legislatures, and
macro-level state data from the U.S. Census Bureau.
Important factors aff ecting the state-level contracting
decision are population size, market size, the competitive-
ness of the bidding process, the professional management
of contracts, the partisan composition of legislatures, and
political competition. Political rationales appear to play a
major role in state contracting decisions. Some arguments
associated with markets and economic rationality are
clearly politically motivated.
During the last two decades, as privatization has gained political cu
ency, contracts with private sector organizations have increased
dramatically in bipartisan governments. Privatization
advocates believe that contracting out is an eff ective
tool for government to reduce costs, increase effi -
ciency, improve services, and encourage innovations
( Gore 1993; Kettl 1993; Osborne and Gaebler 1992;
Salamon 1989; Savas 1987 ).
Along with the growth of contracting out, interest in
electronic government (e-government) has also grown
for many of the same reasons. A substantial number
of e-government initiatives have made heavy use of
contracting out ( Gant, Gant, and Johnson 2002 ).
Th e popularity of contracting out services such as data
processing, Web site hosting, training, and project
management has spread across all levels of government.
It is believed that contracting helps governments over-
come fi nancial diffi culties in accessing the esoteric
expertise and professional management skills of private
fi rms to develop e-government applications ( Brown
and Brudney 1998; Chen and Pe
y 2002 ).
Growing interest in privatization has fueled many
Answered 5 days After Aug 18, 2022

Solution

Vishali answered on Aug 23 2022
83 Votes
Provide your answers below to each question. Do not delete the questions. Please do not use red font. Blue or green is OK.
Part I (35 POINTS)
The General Social Survey research department was tasked to determine the number of hours per day government employees in the USA spent on emailing. The department reported the data for a sample of 1765 government employees from GSS 2018 data on age (number of years), sex of respondent (1=males, 2=females), total family income (in constant dollars), and hours worked last week. Refer to the SPSS output below. Conduct the full regression analysis.
Regression
    Model Summary
    Model
    R
    R Square
    Adjusted R Square
    Std. E
or of the Estimate
    1
    .312a
    .097
    .092
    12.165
    a. Predictors: (Constant), Respondents sex, Age of respondent, Number of hours worked last week, Respondent income in constant dollars
    ANOVAa
    Model
    Sum of Squares
    df
    Mean Square
    F
    Sig.
    1
    Regression
    9764.913
    4
    2441.228
    16.495
    .000
    
    Residual
    90424.483
    611
    147.994
    
    
    
    Total
    100189.396
    615
    
    
    
    a. Dependent Variable: Email hours per week
    b. Predictors: (Constant), Respondents sex, Age of respondent, Number of hours worked last week, Respondent income in constant dollars
    Coefficientsa
    Model
    Unstandardized Coefficients
    Standardized Coefficients
    t
    Sig.
    
    B
    Std. E
o
    Beta
    
    
    1
    (Constant)
    -2.633
    3.044
    
    -.865
    .387
    
    Age of respondent
    -.017
    .036
    -.019
    -.483
    .629
    
    Number of hours worked last week
    .090
    .036
    .102
    2.464
    .014
    
    Respondent income in constant dollars
    8.863E-5
    .000
    .278
    6.659
    .000
    
    Respondents sex
    3.523
    1.025
    .138
    3.438
    .001
    a. Dependent Variable: Email hours per week
Hint: For the coefficient that ends in E-5, move the point 5 decimal places to the left
1. Which variables are independent variables?
Ans-
· Age of respondent
· Number of hours worked last week
· Respondent income in constant dollars
· Respondents sex
2. Which variable is the dependent variable?
Email hours per week
3. What does coefficient of determination (R2) tell you?
R2 measures explanatory power of model. It reflects model accuracy in sense of how much is explanatory power of explanatory(independent) variable. Value of R2 commonly describes how well sample regression line fits observed data. Here, value of R2 is 0.097 which means explanatory variables explains only 9.7% variability in our model.

4. What is the intercept value? What can it indicate here?
Intercept here is -2.633. Here, its p value = 0.387 which means intercept here is non-significant. It indicates that result of the analysis will be zero if all other variables are zero

5. Is the model overall significant?
From Anova table, we can see value of F statistic= 16.495 and p value is 0.001. Model is overall significant at 1%, 5%, 10% level of significance.
6. Determine the multiple regression equation:
Email hours per week= -0.017*Age of respondent + 0.090*Number of hours worked last week + 0.000088*Respondent income in constant dollars + 3.523* Respondents sex - 2.633
Significant regression equation:
Email hours per week= 0.090*Number of hours worked last week + 0.000088*Respondent income in constant dollars + 3.523* Respondents sex
7. Discuss significant regression coefficients.
When null hypothesis (H0) is rejected, question comes which regression coefficients are significant. In Multiple regression model, there may be some regression coefficients which may are non-significant i.e. their contribution to predict y is almost negligible and hence they should not be retained in the final model. Significant regression coefficients are those whose p value is less than 0.05 if level of significance chosen is 5%.
Here, Number of hours worked last week, Respondent income in constant dollars, Respondents sex are significant at 5% level of significance.
8. What variable has the strongest effect on the number of hours US government employees spent emailing?
Respondent income in constant dollars has the strongest effect on the number of hours US government employees spent emailing
9. What is the estimated number of hours emailing per day for a woman who works 40 hours per week and who earns $50.000 per year?
Put Respondents sex=2, Number of hours worked last week=40, Respondent income in constant dollars= 50.000
Reduced Regression Equation at 5% level of significance:
Email hours per week= 0.090*Number of hours worked last week + 0.000088*Respondent income in constant dollars + 3.523* Respondents sex
= -0.90*40 + 0.000088*50.000 + 3.523*2
=43.0504
Hence, the estimated number of hours emailing per day for a woman who works 40 hours per week and who earns $50.000 per year is 43(approx.).
Part II (35 POINTS)
Give the full logistic regression analysis of the SPSS 2014 output provided below. The coding for the dependent variable is presented in the SPSS output. Interpret and include in your regression equation only significant coefficients (use alpha= 0.05).
The question that the respondents were asked is: “Would you favor or
oppose the teaching of sex education in public schools?” (Favor= 1; Oppose=0)
Coding for variables:
polviews:
1. Extremely liberal
2. Liberal
3. Slightly Liberal
4. Moderate
5. Slightly Conservative
6. Conservative
7. Extremely Conservative
pillok: Birth Control to Teenagers 14-16
Question asked: Do you strongly agree, agree, disagree, or strongly disagree that it is acceptable to make birth control devices available to teenagers, age 14-16?
1. Strongly Agree
2. Agree
3. Disagree
4. Strongly disagree
educ: Highest year of school completed
Logistic Regression
    Case Processing Summary
    Unweighted Casesa
    N
    Percent
    Selected Cases
    Included in Analysis
    1579
    62.2
    
    Missing Cases
    959
    37.8
    
    Total
    2538
    100.0
    Unselected Cases
    0
    .0
    Total
    2538
    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
    Favo
    1
    Categorical Variables Codings
    
    Frequency
    Parameter coding
    
    
    (1)
    (2)
    (3)
    BIRTH CONTROL TO TEENAGERS 14-16
    STRONGLY...
SOLUTION.PDF

Answer To This Question Is Available To Download

Related Questions & Answers

More Questions »

Submit New Assignment

Copy and Paste Your Assignment Here