# Statistics in Experimental Design (ANOVAs)

**Statistics in Experimental Design (ANOVAs)**

**Statistics in Experimental Design (ANOVAs)**

## Objectives:

The course concentrates on the analysis of data obtained from experiments in the behavioral sciences, especially psychology. Major emphasis is placed on analysis of variance techniques going from simple single-factor independent group designs to multiple-factor designs involving both between- and within- subject manipulations. General rules are stressed.

After completing this course, students will be able to:

Think critically about the application of experimental and quasi-experimental designs in psychological research

Use computers to conduct analyses and interpret their output

Understand and apply appropriate statistical analyses for different types of experimental designs

Apply data analysis skills to research in different areas of psychology

### Text:

Keppel, G., & Wickens, T. D. (2004).

*Design and Analysis: A Researcher's Handbook*(4th ed.). Upper Saddle River, NJ: Pearson Prentice Hall. (available at Norris Center bookstore).Other assigned articles or handouts available through this web site or in class

### Class Meetings:

Tuesdays and Thursdays: 9:30 - 10:50 AM

Harris - Room 205

### Requirements:

An objective midterm exam near the middle of the term (October 21)

An objective final exam (during the university final exam period)

Weekly homework sets (Posted and due each Thursday starting the second week of class)

### Grading:

Homework - 40%

Midterm Exam - 30%

Final Exam - 30%

**Note:**

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Course Outline:

Week

Topic

Course Notes

Reading

Problem Sets and Data

Week 1

Introduction to the course

Experimental method

randomization

independence

null hypotheses

Chapter 1

N/A

Week 2

null hypotheses

partialing variance

F-distributions

Comparing F distributions with other distributions

Comparison of ANOVA with other methods

Chapter 2

Chapter 3

Problem Set #1

SPSS Data #1

Week 3

Assumptions made by the ANOVA

What to do when the assumptions don't hold

Effect Size

Power

Meaning of positive and negative results

Tuesday

Data Set for 10/5

Output from data set for 10/5

Thursday

Chapter 7

Tukey's Ladder of Powers Handout

Tabachanick and Fidell recommendations

Chapter 8

Wilkinson Article

Cohen Article

Problem Set #2

Week 4

Anaytic, Planned Comparisons

Trend Analysis

Tuesday

Thursday

Chapter 4

Chapter 5

Problem Set #3

Week 5

Simultaneous Comparisons

**Midterm Examination - Thursday**

**Tuesday**

Exam Solutions

Chapter 6

N/A

N/A

Week 6

Introduction to Factorial Design

Two Factor Designs

Main Effect Calculations

More on Main Effects

Simple Effects

Tuesday

Thursday

Chapter 10

Chapter 11

Chapter 12

Problem Set #4

Week 7

More on Interactions and Calculations

General Linear Model

Unequal Sample Sizes

Tuesday

Thursday

Chapter 13

Chapter 14

Problem Set #5

Week 8

Analysis of Covariance

Single Factor Within Subjects Design

Tuesday

Thursday

Chapter 15

Chapter 16

Problem Set #6

Week 9

Further Within Subjects Topics

Two-Factor Within-Subjects Designs

Tuesday

Thursday

Chapter 17

Chapter 18

Problem Set #7 - Due Tuesday, 11/30

Week 10

Mixed Design: Overall Analysis

No Class Thursday - Thanksgiving

Tuesday

Chapter 19

N/A

Week 11

Mixed Design: Analytical Analyses

Catch Up / Review / Further Topics

Tuesday

Thursday

Chapter 20

Chapter 26 (?)

N/A

Final Exam

**Monday, December 6, 2004****12:00 - 2:00 P.M.**

### References:

Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159.

Tukey, J. W. (1977). Exploratory Data Analysis. Reading, MA: Addison-Wesley Publishing Company.

Tabachnick, B. G., & Fidell, L. S. (1996). Using Multivariate Statistics. (3rd ed.). New York: Harper Collins.

Wilkinson, L., & APA Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594-604.

### Work Mentioned in Class:

Cohen, J. (1962) The statistical power of abnormal-social psychological research: a review. Journal of Abnormal and Social Psychology 69, 145-153.

Cohen, J. (1988) Statistical Power Analysis for the Behavioral Sciences. (2nd ed). Mahwah, NJ: Lawrence Erlbaum.

### Some Other Useful Resources with Information on Statistics in Experimental Design:

Abelson, R. P. (1995). Statistics as Principled Argument. New Jersey: Lawrence Erlbaum Associates.

Hays, W. L. (1994). Statistics (5th ed.). Fort Worth, TX: Harcourt Brace College Publishers.

Howell, D. C. (2001). Statistical Methods for Psychology (5th ed.). Belmont, CA: Duxbury Press.

Kirk, R. E. (1995). Experimental Design: Procedures for the Behavioral Sciences. (3rd ed.). Pacific Grove, CA: Brooks/Cole.

Kuehl, R. O. (2000). Design of Experiments: Statistical Principles of Research Design and Analysis (2nd ed.). Pacific Grove, CA: Duxbury Press.

Maxwell, S. E., & Delaney, H. D. (2004). Designing Experiments and Analyzing Data: A Model Comparison Perspective. (2nd ed.). Mahwah, NJ: Erlbaum.

Neter, G., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear statistical models (4th ed.). Boston, MA: McGraw-Hill.

Pedhauzer, E. J., & Pedhauzer Schmelkin, L. (1991). Measurement, Design, and Analysis: An Integrated Approach. Hillsdale, NJ: Erlbaum.

Rosenthal, R., & Rosnow, R. L. (1991). Essentials of Behavioral Research: Methods and Data Analysis (2nd ed.). Boston, MA: McGraw-Hill.

Schinka, J. A., & Velicer, W. F. (2003). Handbook of Psychology - Volume 2: Research Methods in Psychology. Hoboken, NJ: John Wiley.

Winer, B. J., Brown, D. R., & Michels, K. M. (1991). Statistical Principles in Experimental Design (3rd ed.). Boston, MA: McGraw-Hill.