ILLINOIS STATE UNIVERSITY

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EAF 511:  RESEARCH METHODOLOGY AND STATISTICS IN EDUCATION III

            SUMMER 2006

Instructor: Dr. John K. Rugutt

Place of work: 323 DeGarmo

Phone: (309) 438-2051

Office Hours: By appointment (Email preferable).

Class Meets: Friday [5:00pm-9:00pm] and Saturday [10:00am-2:00pm] --Quad Cities       

                    Dates: [ 5-19,20; 6-2,3; 6-9,10; 6-16,17; 6-23,24; 6-30, 7-1]

Email: jkrugut@ilstu.edu

Click here for a pdf version of the course syllabus

| Contact  | Content Outline | Course Objective | Class Format | Texts and Software | Student Tasks | Performance Evaluation Methods | Course Delivery System |

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                                                 Topical/Content Outline...Subject to Change

The instructor reserves the right to make changes to the course syllabus as necessary. 

It is the student's responsibility to keep up with changes to the syllabus

Note:   * Quantitative Research Methods for Professionals (W. Paul, Vogt)

            ** Multiple Regression in Behavioral Research: Explanation and Prediction (Elazar, J. Pedhazur)

            *** Instructor’s discussions

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1   Research Methodology and Statistics in Education III

1.1 Course Objectives

This is a Ph.D/Ed.D graduate-level introduction to multivariate data analysis. My goal will be to provide students with statistical

tools of how to use the most common statistical techniques, how to make them work for them, how to read and understand papers

that use these techniques.  We will not cover proofs but the course will emphasize the application of multivariate statistical techniques. 

Topics reviewed include factorial ANOVA, repeated measures ANOVA, analysis of covariance (ANCOVA), multivariate analysis of

variance (MANOVA), multivariate analysis of covariance (MANCOVA), simple and multiple regression, discriminant function analysis (DFA),

Principal components, and factor analysis.  Before getting to the multivariate material however, we need to finish the spillover from EAF 510.

Upon completion of this course, students will be able to:

            a.       Identify a variety of research designs (mostly quantitative paradigms).                           

b.      Understand the concepts of internal and external validity and be able to use SPSS to generate internal consistency reliability coefficients.

c.       Identify a researchable problem and conduct research on it incorporating a variety of quantitative research designs.

d.      Use a research library and other public sources of data and research.

e.       Demonstrates ones’ ability to critically analyze and interpret published research (through literature

      reviews and/or article critiques).

f.        Understand the issues related to the concepts of instrument validity and reliability and use SPSS to run factor analysis and reliability analyses.

g.       Correctly calculate and interpret basic descriptive statistics, and intermediate inferential statistics (factorial ANOVA, repeated ANOVA, ANCOVA).

h.      Understand the logic of statistical inference and hypothesis testing for intermediate and advanced (multivariate) statistics.

i.        Be able to correctly use a statistical software package to run multivariate data analyses and interpret the output.

j.        Write according to American Psychological Association (APA) guidelines.

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1.2 Class Format

The format of the course will be a combination of lectures, seminar, and computer time. Each topic that we cover will have a

combination of lecturing by me, to give you the necessary background for the topic, statistical computer exercises so that we can

learn how to interpret output, and a discussion period where students discuss their understanding of the assigned readings and

then talk about practical applications.

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1.3 Texts and Software

Required texts are:

(EJP) Pedhazur, E. J. (1997). Multiple Regression in Behavioral Research: Explanation and

Prediction (3rd ed). Wadsworth-Thompson Learning. ISBN: 0-03-072831-2.

(Vogt) Vogt, W. P. (2007):  Quantitative Research Methods for Professionals. Allyn & Bacon.

ISBN: 0-205-35913-2

Recommended texts:

Dictionary of Statistics and Methodology (3^rd ed.) Thousand Oaks, CA: Sage.

Applied Multivariate Statistics for the Social Sciences by James P. Stevens

Using Multivariate Statistics by Barbara G. Tabachnik and Linda S. Fidell
Applied Multivariate Statistical Analysis by Richard A. Johnson and Dean W. Wichern

Multivariate Data Analysis with Readings by Joseph F. Hair, Rolph E. Anderson, Ronald L.

            Tatham & William C. Black

Primary software: SPSS (Statistical Package for the Social Sciences). We will use the Windows version as much as possible.

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1.4 Prerequisites

A strong background in data analysis and use of SPSS for data analysis is essential. Successful experience analyzing data is required. 

A willingness to tackle new problems and use of computer statistical programs is also needed.

1.5 Required Student Tasks

Course Requirements and Required Student Tasks:

1.                Participate in all class activities, complete all assigned readings, and be prepared to discuss them in class;

2.               Complete the assignments by the due dates;

3.               Complete a final paper and deliver a presentation of the research project.

1.       Class Participation/Attendance.  Attendance and active participation in class is very important and will be part of your grade. 

Note that work on data analysis using computers will be primarily an in-class activity, so attendance is particularly crucial.  Class

participation and attendance will also involve class discussion of the assigned readings.  Being sick will not count as an absence. 

You will receive a maximum of 5 points for class participation and attendance.

1.       Assignments/Mini-projects.  Each student will complete a series of five assignments/projects that together reinforce the major topics and concepts covered in the course. Students will use their own data or data provided by the instructor to complete the class projects. More details about the assignments/projects and due dates will be posted on the assignment link within the WebCT courseware. 

1.6 Student Performance Evaluation Methods

The following point allocation will be used to determine final grades for the class:

            1.         ANOVA (Factorial/ANCOVA)                    20 points

            2.         MANOVA/MANCOVA                               20 points

            3.         Factor Analysis                                               20 points        

            4.         Regression Analysis                                        20 points

            5.         Discriminant Analysis                                     20 points

Assignments.  Assignments/mini projects are worth 20 points a piece for a maximum of 100 points.  Handing in a well thought out and well written assignment on the due date is worth 20 points.  Assignments turned in late will receive half-credit of 10 points if well done.  Students who do not hand in assignments will not receive credit toward their final semester grade. All the five projects will follow a standard format with the following components completed: Introduction, problem statement, research questions, method, output and interpretation of results, and presentation of results.  

Letter grades will be assigned in accordance with the following scheme:

            Points                 Letter Grade

            90-100             A (Exceptional Performance)

            80-89               B (Above Average Performance)
            70-79               C (Average Performance)
            60-69               D (Below Average Performance)
            0-59                 F (Failing)

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1.7 Delivery System

This course will be presented using a variety of delivery systems:  The class will combine lecture, seminar/discussion (in-class and through online),

statistical computing and student presentation.

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