Section 3: Advanced Statistical Techniques

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.

Required Reading:

Please refer to each Activity for required readings within Activity Resources.

Assignment 8 Signature Assignment: MANOVA and Course Reflection

This final week introduces you to a very common technique ï¿½ MANOVA. MANOVA is simply an extension of an ANOVA and allows for the comparison of multiple outcome variables (again, a very common situation in business research - instead of having to perform a series of analyses, one MANOVA can do it all for you!).

Activity Resources

Download the following SPSS Data Set.

Complete the Self-Tests in the chapter. Answers are available at: 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 -- MANOVA

You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into Word.

Part A. SPSS Activity

In this exercise, you are playing the role of a researcher that is testing new medication designed to improve cholesterol levels. When examining cholesterol in clinical settings, we look at two numbers: low-density lipoprotein (LDL) and high-density lipoprotein (HDL). You may have heard these called ï¿½goodï¿½ (HDL) and ï¿½badï¿½ (LDL) cholesterol. For LDL, lower numbers are better (below 100 is considered optimal). For HDL, 60 or higher is optimal.

In this experiment, you will be testing three different versions of the new medication. In data file ï¿½Activity 8.savï¿½ you will find the following variables: group (0=control, 1=Drug A, 2=Drug B, 3=Drug C), LDL, and HDL (cholesterol numbers of participants after 12 weeks).

Using a MANOVA, try to ascertain which version of the drug (A, B or C) shows the most promise. Perform the following analyses and paste the SPSS output into your Word document.

1. Exploratory Data Analysis.

2. Perform a MANOVA. Using the ï¿½Activity 8.savï¿½ data set, perform a MANOVA. ï¿½Groupï¿½ is your fixed factor and LDL and HDL are your dependent variables. Be sure to include simple contrasts to distinguish between the drugs (group variable). In the same analysis, include descriptive statistics and parameter estimates. Finally, be certain to inform SPSS that you want a post-hoc test to help you determine which drug works best.

Part B. Reflection

Reflect on your experience throughout the course and how you will use your new statistical skill set in the dissertation phase of your degree program. Include a brief assessment of what you have learned. In 2-3 paragraphs, cover the following:

Learning Outcomes: 3, 4, 5, 6, 10, 11

Activity Resources

- Field, A. (2013): Chapter 16

- Smart Alex's Quizzes

- Activity8.sav

- Interactive Multiple Choice Questions
- Flashcards

Download the following SPSS Data Set.

- Activity8.sav

Complete the Self-Tests in the chapter. Answers are available at: 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 -- MANOVA

You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into Word.

Part A. SPSS Activity

In this exercise, you are playing the role of a researcher that is testing new medication designed to improve cholesterol levels. When examining cholesterol in clinical settings, we look at two numbers: low-density lipoprotein (LDL) and high-density lipoprotein (HDL). You may have heard these called ï¿½goodï¿½ (HDL) and ï¿½badï¿½ (LDL) cholesterol. For LDL, lower numbers are better (below 100 is considered optimal). For HDL, 60 or higher is optimal.

In this experiment, you will be testing three different versions of the new medication. In data file ï¿½Activity 8.savï¿½ you will find the following variables: group (0=control, 1=Drug A, 2=Drug B, 3=Drug C), LDL, and HDL (cholesterol numbers of participants after 12 weeks).

Using a MANOVA, try to ascertain which version of the drug (A, B or C) shows the most promise. Perform the following analyses and paste the SPSS output into your Word document.

1. Exploratory Data Analysis.

a. Perform exploratory data analysis on the relevant variables in the dataset. When possible, include appropriate graphs to help illustrate the dataset.

b. Compose a one to two paragraph write up of the data.

c. Create an APA style table that presents descriptive statistics for the sample.

2. Perform a MANOVA. Using the ï¿½Activity 8.savï¿½ data set, perform a MANOVA. ï¿½Groupï¿½ is your fixed factor and LDL and HDL are your dependent variables. Be sure to include simple contrasts to distinguish between the drugs (group variable). In the same analysis, include descriptive statistics and parameter estimates. Finally, be certain to inform SPSS that you want a post-hoc test to help you determine which drug works best.

a. Is there any statistically significant difference in how the drugs perform? If so, explain the effect. Use the post hoc tests as needed.

b. Write up the results using APA style and interpret them.

Part B. Reflection

Reflect on your experience throughout the course and how you will use your new statistical skill set in the dissertation phase of your degree program. Include a brief assessment of what you have learned. In 2-3 paragraphs, cover the following:

- What were the three most important concepts you learned?
- How will the material in this course help you in your dissertation work?
- What would you like to have seen covered that wasnï¿½t or what would you have liked more practice with?

Learning Outcomes: 3, 4, 5, 6, 10, 11

Assignment Outcomes

Calculate and interpret descriptive statistical analysis.

Create and interpret visual displays of data.

Apply appropriate statistical tests based on level of measurement.

Determine the appropriate use of inferential statistical analysis.

Demonstrate proficiency in the use of SPSS.

Demonstrate proficiency in reporting statistical output in APA format.

Create and interpret visual displays of data.

Apply appropriate statistical tests based on level of measurement.

Determine the appropriate use of inferential statistical analysis.

Demonstrate proficiency in the use of SPSS.

Demonstrate proficiency in reporting statistical output in APA format.

Subject | Business |

Due By (Pacific Time) | 07/27/2014 12:00 am |

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