Project #35312 - Statics II

Learner Course ID: 358116

Activity 5

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 � 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 �control� 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 Freidman�s ANOVA for related conditions.


Required Reading:
Please refer to each Activity for required readings within Activity Resources.

Assignment 5   ANCOVA & Factorial ANOVA
In Week 4 you learned how to conduct tests that determine if there are differences between mean scores for groups. However, many research questions require more complex designs that include the ability to control for confounding variables and/or include multiple independent variables. For example: You are interested in outcomes for three different therapeutic techniques and want to control for severity of illness at the outset. Or: You want to examine attitudes towards a new federal law and believe that political affiliation and gender are relevant factors to consider. 

This week you will learn these advanced techniques.

Activity Resources
  • Field, A. (2013): Chapters 12, 13
Self-Tests
  • Smart Alex's Quizzes
SPSS Data Sets
  • Activity5.sav
Optional Resources
  • Interactive Multiple Choice Questions
  • Flashcards
To Prepare for this week�s Activity
Download the following SPSS Data Set. 
  • Activity 5.sav
Read Chapters 12 and 13 in the text. It will be to your advantage to have SPSS open on your computer as you work through these chapters. While you are reading consider your area of research interest and when you have seen these more advanced ANOVA models applied. How might you use these analytical strategies in your dissertation research?

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

Complete Smart Alex�s Quizzes. Answers are available at: 
http://www.sagepub.com/field4e/study/smartalex.htm. 

Optional Preparation for this week�s 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 � ANCOVA and Factorial ANOVA
You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into Word. 

This Activity consists of two parts. In the first part, you will utilize an existing dataset to compute a factorial ANOVA. All SPSS output should be pasted into your Word document. In the second part, you will be asked to create a hypothetical ANCOVA output table for variables related to your area of research interest. 

Part A. SPSS Activity
The �Activity 5.sav� file contains a dataset of a researcher interested in finding the best way to educate elementary age children in mathematics. In particular, she believes that 5th grade girls do better in small class sizes while boys excel in larger classes. Through the school district, she has arranged a pilot program in which some classroom sizes are reduced prior to the state-wide mathematics competency assessment. In the dataset, you will find the following variables:
Participant: unique identifier
Gender: Male (M) or Female (F)
Classroom
Small (1) � no more than 10 children
Medium (2) � between 11 and 19 children
Large (3) � 20 or more children
Score: final score on the statewide competency assessment.

To complete this Activity
1. Exploratory Data Analysis.

a. Perform exploratory data analysis on all variables in the data set. Realizing that you have six groups, be sure that your exploratory analysis is broken down by group. 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. Factorial ANOVA. Perform a factorial ANOVA using the �Activity 5.sav� data set. 

a. Is there a main effect of gender? If so, explain the effect. Use post hoc tests when necessary or explain why they are not required in this specific case.

b. Is there a main effect of classroom size? If so, explain the effect. Use post hoc tests when necessary or explain why they are not required in this specific case.

c. Is there an interaction between your two variables? If so, using post hoc tests, describe these differences.

d. Is there support for the researcher�s hypothesis that girls would do better than boys in classrooms with fewer students? Explain your answer.

e. Write up the results in APA style and interpret them. Be sure that you discuss both main effects and the presence/absence of an interaction between the two.


Part B. Applying Analytical Strategies to an Area of Research Interest
3. Briefly restate your research area of interest.

Analysis of Covariance. Using your area of interest, identify one independent and two dependent variables, such that the dependent variables would likely be covariates. Now, assume you conducted an ANCOVA that shows both the independent variable as well as the covariate significantly predicts the dependent variable. Create a mock ANCOVA output table (see SPSS Output 11.3 in your text for an example) that supports the relationship shown above. Report your mock finding APA style.

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

Learning Outcomes: 2, 3, 4, 5, 6, 10, 11
Assignment Outcomes
Develop appropriate null and alternative hypotheses given a research question.
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.

Course Work

Course: BTM8107-8 Syllabus: 31479
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