# Project #35314 - Statics II

Sections 1 and 2 have served to prepare you for the understanding of advanced statistical techniques. This section covers the following analytical strategies (if it becomes difficult to keep all the techniques you are learning straight, refer to the last page of your text ï¿½ there is a great table that can help you out):

ANCOVA. The Analysis of Covariance technique is a life-saver when you are comparing means between defined groups and have an additional variable (or variables) that you would like to ï¿½controlï¿½ for. An example might be: Are mean productivity scores for three groups of work teams different when you control for length of time on the job? Or: Are depression scores for young, middle, and older adults different after controlling for health, gender, and social support?

Factorial ANOVA. When you have more than one predictor variable, a Factorial ANOVA design might be just what you are looking for. These techniques include two-way repeated-measures ANOVA, two-way mixed ANOVA, three-way independent ANOVA, and so on. For example: Perhaps you are going to design a social support study for people suffering from chronic pain. Your study includes two treatment groups and control group. Further, you have every reason to believe (based on past research and theory) that men and women will respond differently to the treatment groups. A factorial design can handle such complexities.

Repeated-Measures. If you are examining multiple groups but the same people belong to each group, you will use a repeated-measures design. For example, instead of randomly assigning people to either treatment A or treatment B, if you choose to have all participants in both treatments (of course you would need to consider carry-over effects, practice, and counter balancing, etc.) then you have a repeated-measures design. There are some great advantages to repeated-measures design (key among them: the ability to reduce the statistical impact of individual differences).

MANOVA. With the tests you have learned thus far, we have been constrained by one requirement of one outcome variables. A MANOVA allows for a design in which you have groups being compared on multiple outcome variables; for example, if you are interested in comparing men and women and their psychological health. You may have a number of measures that assess the construct of psychological health: depression, life satisfaction, and well-being. A MANOVA allows you to make this comparison with one elegant analysis.

Non-Parametric Tests. Now that you have learned a number of parametric techniques, what do you do if your data do not meet parametric assumptions? Non-parametric tests can help and include: Wilcoxon rank-sum test, Mann-Whitney tests, Kruskal-Wallis test for independent conditions and Freidmanï¿½s ANOVA for related conditions.

Assignment 7   Non-Parametric Tests

While you have learned a number of parametric statistical techniques, you are also aware that if the assumptions related to the tests are violated, then the tests are not valid. Because many phenomena examined in business are not normally distributed, it is critically important to understand the role of non-parametric tests. It is possible you will need to use one or more of the methods covered in this activity in your dissertation.

Activity Resources
• Field, A. (2013): Chapter 6
Self-Tests
• Smart Alex's Quizzes
SPSS Data Sets
• Activity4.sav
Optional Resources
• Interactive Multiple Choice Questions
• Flashcards
To Prepare this weekï¿½s Activity
• Activity 4a.sav (file you created in Activity 4 , Part A)
• Activity 4b.sav (file you created in Activity 4, Part B)
• Activity 4c.sav (file you created in Activity 4, Part C)
Read Chapter 6 in the text. It will be to your advantage to have SPSS open on your computer as you work through the chapter. While you are reading consider your area of research interest and when you have seen non-parametric methods applied. How might you use these analytical strategies in your dissertation research?

Complete the Self-Tests in the chapter. Answers are available: http://www.sagepub.com/field4e/study/selftest.htm.

Complete Smart Alexï¿½s Quizzes. Answers are available at:
http://www.sagepub.com/field4e/study/smartalex.htm.

Optional Preparation for this weekï¿½s Activity
After completing the above activities, if you feel you need additional instruction on the concepts covered, please choose any of the following activities that will assist you in mastering the core concepts.
Main Task: Application ï¿½ Non-Parametric Tests
You will submit one Word document for this activity. In the first part your activity document, provide short answers to the following questions (250 words or less).

Part A. Questions about non-parametric procedures
1. What are the most common reasons you would select a non-parametric test over the parametric alternative?

2. Discuss the issue of statistical power in non-parametric tests (as compared to their parametric counterparts). Which type tends to be more powerful? Why?

3. For each of the following parametric tests, identify the appropriate non-parametric counterpart:

a. Dependent t test

b. Independent samples t test

c. Repeated measures ANOVA (one-variable)

d. One-way ANOVA (independent)

e. Pearson Correlation

Part B. SPSS Activity
In this part of the Activity you will perform the non-parametric version of the tests you used in Week 4. In each case, assume that you opted to use the non-parametric equivalent rather than the parametric test. Using the data files from earlier activities, complete the following tests and paste your results into a Word document:

1. Week 4 Activity 6, Part A: non-parametric version of the dependent t test
2. Week 4 Activity6, Part B: non-parametric version of the independent t test
3. Week 4 Activity6, Part C: non-parametric version of the single factor ANOVA

Submit your document in the Course Work area below the Activity screen.

Learning Outcomes: 5, 6, 7, 9, 10, 11
Assignment Outcomes
Apply appropriate statistical tests based on level of measurement.
Determine the appropriate use of inferential statistical analysis.
Correlate how population, sampling, and statistical power are related to inferential analysis.
Compare and contrast parametric and non-parametric data analysis in order to apply the correct statistical procedure.
Demonstrate proficiency in the use of SPSS.
Demonstrate proficiency in reporting statistical output in APA format.

### Course Work

 Subject Business Due By (Pacific Time) 07/20/2014 12:00 am
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