Statistical Analysis Process

In this assignment, you are assessing the impact of the management principles, management process and organizational behavior modification on the individual performance of the employee.

As before, we are looking for strategies that will yield even greater individual performance. You will all eventually become managers charged with improving the performance of your employees. Classical management theory says that you should make certain that you are following the principles of management (PRI), have implemented the management process (PRO), and are reinforcing performance through an organizational behavior modification (OBM) program.

The research question is: "Will implementation of the classical management practices (PRI, PRO, and OBM) lead to improved individual performance (IP)?" The theoretical model is presented below. Note that there are five hypotheses. Follow the steps in the statistical analysis process to determine evaluate the hypotheses and answer the research question.

Management Basics Theoretical Model

Management

Principles

H5: (+)

H3: (+)

Individual

Performance

H4: (+)

H2: (+)

H1: (+)

Org. Behavior

Modification

Management

Process

1. Research Question - State the research question (from the introductory material above).
2. Hypotheses - State the five hypotheses (one for each arrow in the model above).
3. Identify measurement scales and collect data – The data has already been collected. Load the Assignment_8_SAP_Management_Basics.sav data file into SPSS.
4. Assess the measurement scales for validity (content, discriminant, convergent) and reliability. Take a look at the scales in Appendix A and assess them from the standpoint of content validity. Do they exhibit sufficient content validity? How do you know?
5. Now, assess for discriminant and convergent validity by running a confirmatory factor analysis using the following command sequence [analyze, dimension reduction, factor, move all of the items (both the MO and OP items) to the variables block, click on extraction, select "maximum likelihood", continue, rotation, select "varimax", continue, okay] .
   You should note some real problems here. Scales don’t separate like they should and items don’t converge like they should. You will get seven factors instead of the expected four factors. I just want you to know that things don’t always go smoothly . There are some things that could be done to improve the situation like removing bad items but that is a little beyond where we are right now.
   Do the measurement scales exhibit sufficient convergent and discriminant validity? Explain why not.
6. Assess the scales for reliability using the following command sequence [analyze, scale reliability, move the measurement items for PRI to the items box, okay]. Now do the same thing for all of the PRO items, then for all the OBM items, and then for all the IP items.
7. Compute summary variables (PRI, PRO, OBM, and IP). Do this even though you are not happy with the convergent and discriminant validity results.
8. Conduct statistical analyses.

1. Descriptive Statistics – assess for normality
2. Correlation – assess the type, strength, and significance of the relationships among the study variables.
3. Regression – test for multicolinearity (see note below), interpret the R ² value, identify the significance level of the regression coefficients.
   Note on Multicolinearity - I want you to do something new here. I want you to assess for mulitcolinearity. When multicolinearity is present in multiple regression, it means that the independent variables are too highly correlated and may be measuring the same thing rather than distinctly different things. If multicolinearity is present, the regression coefficients cannot be interpreted.
   To test for multicolinearity, make the following change to the regression command sequence: [analyze, regression, linear, move PRI, PRO, and OBM to the independent variables box and move IP to the dependent variables box, click on the "statistics" box, click on the "colinearity diagnostics" feature, continue, OK]. Now, look in the coefficients table and find the VIF column. VIF stands for Variance Inflation Factor. If the VIF values are greater than or equal to 10, multicolinearity is a problem and the regression coefficients can’t be interpreted. If the VIF values are less 10, multicolinearity is not a problem.
4. Path Analysis – fill in the beta values and R ² values in the path model.
   

   Management

   Principles

1. R ² =

   Beta =

   Beta =

   Beta =

   R ² =

   R ² =

   Individual

   Performance

   Beta =

   Beta =

   Org. Behavior

   Modification

   Management

   Process

9. Interpret results – Which of the hypothesized links are positive and significant? Compare the betas between PRI, PRO, OBM and IP. Which of the three has the biggest impact on IP? Make sure to say that there are some concerns related to scale assessment.

10. Draw a conclusion and make a recommendation – this is the answer to the research question accompanied by a recommendation for managers based on the results of the study.

Price: $29.0

Solution: The downloadable solution consists of 16 pages, 1300 words and 60 charts.
Deliverable: Word Document