1. Instructor's Notes # 2 2. Healey 10 th edition Chapter 15 start reading from page 412. Healey...

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1. Instructor's Notes # 2
2. Healey 10 th edition Chapter 15 start reading from page 412. Healey 11 th edition Chapter 15 start reading from page 431.
A. Using SPSS p XXXXXXXXXXth edition); XXXXXXXXXXth edition)
Follow the directions in the book but skip the co
elations table and its interpretation.
In step 3, use the following variables: Dependent Variable: Traffic Fatalities 2009 per 100 million miles driven
Independent variables: People per square mile 2010, and Pct XXXXXXXXXX
Provide full regression analysis. The SPSS output for the regression analysis is provided below. Reproduce these four tables using SPSS and paste them in your homework document.
       You can use the analysis the book provides for your help. However, for the full regression analysis you should do the following:
· Estimate (multiple) regression equation
· Interpret the intercept.
· Identify and explain which independent variables appear to have a significant effect on the dependent variable.
· For significant independent variables interpret the regression coefficients b (slopes).
· Interpret the beta weights: which variables appear to be the most important predictors of the dependent variable?
· R Square (what percentage of the variation in the dependent variable could you explain? What percentage did you fail to explain?)
· Is your model overall significant?
      For interpretation example, use handout Multiple Regression Interpretation and Heating Cost Example available in Module 2 folder.

    Descriptive Statistics

    Mean
    Std. Deviation
    N
    Traffic Fatalities 2009 per 100 million miles driven
    1.2220
    .33460
    50
    Pct XXXXXXXXXX
    13.2920
    1.66401
    50
    People per square mile 2010
     XXXXXXXXXX
     XXXXXXXXXX
    50

    Model Summary
    Model
    R
    R Square
    Adjusted R Square
    Std. E
or of the Estimate
    1
    .531a
    .282
    .252
    .28948
    a. Predictors: (Constant), People per square mile 2010, Pct XXXXXXXXXX

    ANOVAa
    Model
    Sum of Squares
    df
    Mean Square
    F
    Sig.
    1
    Regression
    1.547
    2
    .774
    9.233
    .000

    Residual
    3.938
    47
    .084



    Total
    5.486
    49



    a. Dependent Variable: Traffic Fatalities 2009 per 100 million miles driven
    b. Predictors: (Constant), People per square mile 2010, Pct XXXXXXXXXX

    Coefficientsa
    Model
    Unstandardized Coefficients
    Standardized Coefficients
    t
    Sig.

    B
    Std. E
o
    Beta


    1
    (Constant)
    .688
    .334

    2.059
    .045

    Pct XXXXXXXXXX
    .050
    .025
    .248
    1.977
    .054

    People per square mile 2010
    -.001
    .000
    -.514
    -4.097
    .000
    a. Dependent Variable: Traffic Fatalities 2009 per 100 million miles driven

Part B.  For this week’s assignment we will use Texas Education data from 1,044 public school districts in Texas. This data set is especially useful for illustrating how regression analyses in the field of education policy analysis are performed using statistical software packages. For example, teacher turnover has become a serious management challenge for public school administrators and government officials. The turnover of qualified teachers negatively affects the quality of education and increases operation costs. The EDUCATION dataset contains variables related to student performance, district finances, and teacher and staff characteristics. The dataset is saved as the SPSS data file. Two of the homework problems require that you access this dataset and perform regression analysis. Each problem describes the variables that should be used in the analysis but does not provide a detailed description of each variable. Brief descriptions of each variable can be found in the dataset by going to the “View” menu in SPSS and selecting “Variables.” The variable descriptions are provided in the code book posted in the Data file also.
For example, use handout Multiple Regression Interpretation and Heating Cost example provided in Module 2 folder.
Refer to the EDUCATION data set available in the Data folder. Copy the data set to your computer first and then open it. The Code book for the data is also available in the Data folder.
Exercise 1.
Use the following set of independent variables – SALTEACH, REVPUB, CLASS, and PECD – to explain teacher turnover rates (TETURN).
Do the full regression analysis. This means that you must:

· Estimate (multiple) regression equation
· Interpret the intercept.
· Which independent variables appear to have a significant effect on the dependent variable? Why?
· For significant independent variables interpret the regression coefficients b (slopes).
· Interpret the beta weights: which variables appear to be the most important predictors of the teacher turnover rates?
· R Square (what percentage of the variation in the dependent variable could you explain? What percentage did you fail to explain?)
· Is your model overall significant?
Exercise 2.
Use the following set of independent variables – PAFR, PHISP, CLASS, and TETURN – to explain overall student pass rates (PASSALL).
Do the full regression analysis. This means that you must:
· Estimate regression equation
· Interpret the intercept
· Determine which independent variables appear to have a significant effect on the dependent variable? Why?
· Interpret the regression coefficients b (slopes) only for significant independent variables.
· Interpret the beta weights: which variables appear to be the most important predictors of the dependent variable in your model?
· R Square (what percentage of the variation in the dependent variable could you explain? What percentage did you fail to explain?)
· Is your model overall significant?
· Based on your interpretation of R square for the model, does it seem that relevant explanatory variables might be missing from the model? Explain.
· Add ATTEND to the existing set of the independent variables and generate a second regression.
· Interpret the slopes, intercept, and R2 for the model.
· Has the addition of a new independent variable improved the explanatory power of the model? Explain.
Part C: Article review: Moynihan, Donald P. and Noel Landuyt (2009). “How do Public Organizations Learn? Bridging Structural and Cultural Divides.”  Public Administration Review. 69(6): XXXXXXXXXX.
Submit the answers to the questions on the Moynihan and Landuyt article. Please read the article at least twice before answering the questions.
Answer the following questions:

1. What is the research question?
2. What sample is used?
3. What is the number of respondents used in this analysis?
4. What quantitative method was used to answer the research question?
5. What are independent and dependent variables?
6. How are these variables measured?
7. Specify the regression equation using the results from Table 1 (with all theoretical and control variables).
8. Give your interpretation of the regression coefficients of the five theoretical variables (effects).
9. Explain why we can use the regression coefficients to interpret the relative strength of these effects (no need for the Betas)?
10. Give your interpretation of the regression coefficients of all control variables.
11. What are the practical implications of the research results?
12. What are the advantages and the disadvantages of using qualitative versus quantitative research methods for studying organizational learning?

states-data-3bahxzeh.sav assignment-717-docx-dahvtzga.docx texaseducationdata1-1-0ph1acmj.sav

Ajay · Accepted Answer

6
How Do Public Organizations Learn? Bridging Cultural and Structural Perspectives
Answer the following questions:
 
1. What is the research question?
How do public organization learn? 
2. What sample is used?
The data were obtained from a 2004 survey of Texas State agencies, the Survey of Organizational Excellence. A total of 62,628 employees were surveyed in 53 different state agencies, resulting in 34,668 usable responses.
3. What is the number of respondents used in this analysis?
A total of 62,628 respondents were used for this analysis.
4. What quantitative method was used to answer the research question?
Case study method is used to answer the research question.
5. What are independent and dependent variables?
Dependent variables – organizational learning 
Independent variable – Adequacy of resources, effective information systems, mission orientation, decision flexibility, learning forums
6. How are these variables measured?
By recording close ended individual responses 
7. Specify the regression equation using the results from Table 1 (with all theoretical and control variables).
Y = alpha + a1x1 +b2x2 +b3x3 +b4x4 +b5x5
8. Give your interpretation of the regression coefficients of the five theoretical variables (effects).
All the regression coefficients are positively associated with Organizational Learning Controlling for Agency-Level
Effects. information systems is 0.147, mission orientation is 0.13, decision flexibility is 0.07, learning forums is 0.41, and adequacy of resources is 0.088.
9. Explain why we can use the regression coefficients to interpret the relative strength of these effects (no need for the Betas)?
Statisticians consider regression coefficients to be an unstandardized effect size because they indicate the strength of the relationship between variables using values that retain the natural units of the dependent variable. Effect sizes help you understand how important the findings are in a practical sense.
10. Give your interpretation of the regression coefficients of all control variables.
While most of the variables are statistically significant, none approaches the explanatory power  of any of the theoretical variables tested. We find that age is positively related to perceptions of learning, but length of state service is not. This finding is consistent with other results showing that older public sector employees tend to have positive job attitudes, but that those who have been in the same organization or position for a long time (controlling for age) tend to be less engaged (Moynihan & Landuyt, 2009). The impact of tenure may cause employees to negatively modify their expectations toward organizational activity, so that they are more likely to be critical of and less involved in organizational learning eff orts. We fi nd that supervisors, females, and minorities are more likely to perceive evidence of learning, although the impact of supervisory and minority status is relatively small and signifi cant only at the .01 level. Interestingly, level of education is negatively related to perceptions of organizational learning. Th is may be because those with more educational experience have higher standards for what constitutes learning. A complementary explanation is that those with greater education begin with a higher level of knowledge, and so their organizational experiences are less likely to engender new knowledge. 
11. What are the practical implications of the research results?
Let’s begin by pulling back a bit. Research aims to broaden knowledge in the physical, biological, and social spheres. This discovery of new knowledge can be either in the form of new concepts or the advancement of existing knowledge, leading to a new understanding or innovation. The findings of a study need to be appraised in the context of the field, as well as its practical utility. In other words, readers and other researchers are interested in knowing what your findings mean for your field, perhaps even other fields, as well as policymaking (if applicable). The implications of a study are typically described in the Discussion section or 
the Conclusion section (in journals where the latter is a separate section from the former), where the study limitations and road ahead are mentioned.
12.

1. Instructor's Notes # 2 2. Healey 10 th edition Chapter 15 start reading from page 412. Healey 11 th edition Chapter 15 start reading from page 431. A. Using SPSS p XXXXXXXXXXth edition);...

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