EAF 510: RESEARCH METHODOLOGY AND STATISTICS IN EDUCATION II

FALL 2007

Instructor: John K. Rugutt, Ph.D.

Place of work: 323 DeGarmo

Phone: (309) 438-2051

Office Hours: By appointment (Email preferable).

Class Meets: Monday 5:30-8:20pm, Room: DEG 463

Click here (word/pdf) for a word/pdf version of the course syllabus

Department Name	Educational Administration and Foundations
Course Number	EAF 510
Course Title	Research Methodology and Statistics in Education II
Catalog Description	Logic of statistical inference; introductory study of analysis of variance and multiple regression, with a computer oriented approach. Prerequisite: EAF 508/509 or equivalent.
Course Overview	This is the third part of a five-semester course that covers a wide range of statistical methods and their applications. Similar to the first and second course, instead of concentrating on how to enter numbers in formulas, emphasis is on understanding concepts and processes behind statistical procedures. The purpose of this course is to introduce students to intermediate statistical techniques for analyzing educational data. Various univariate and multivariate procedures will be discussed. The emphasis of the course will be on practical applications of statistical techniques. The course will concentrate more on statistical inference involving t-test, simple and factorial ANOVA, bivariate correlation/regression analysis, multiple linear regression, repeated measures ANOVA and Chi-square using conceptual definitions, without access to formulas. Ability to use SPSS for data analysis is also expected.

Topics

| Student Performance and Evaluation | Delivery System |ISU Policies |

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

Week	Date	Topic	Assignment	Chapter
1	08/20	Introduction and Review		SRM, Ch1-4
2	08/27	Introduction to t-statistic Hypothesis tests--two independent samples Research Design, Measurement, and Analysis Variables and relationships among them	Student_1 Student_2 Student_3	SRM-Ch7-8 H-Ch11 Vogt, Ch1, 2 Other**
3	09/03	Labor Day Holiday	No Class	----
4	09/10	Hypothesis tests with related samples Introduction to Analysis of Variance Uses of descriptive statistics Survey and Sampling	Assignment #1 Student_4 Student_5 Student_6	SRM-Ch9-10 Vogt, Ch4-5 Other
5	09/17	Multiple Comparison Procedures Two-factor Analysis of Variance Statistical Inference		Other Vogt, Ch8
6	09/24	Two-factor Analysis of Variance Experiments and Random Assignment	Student_7 Student_8	Other Vogt, Ch6
7	10/01	Repeated Measures Analysis of Variance Standard Deviation and Correlation	Assignment #2 Student_9	Other Vogt, Ch2
8	10/08	Correlation Analysis Simple Linear Regression Regression Analysis Reviewing, Critiquing, and Synthesizing Research	Student_10	SRM, Ch13 Vogt, Ch10, 17 Other
9	10/15	Catch-up 1		----
10	10/22	Simple Linear Regression Multiple Linear Regression Back to Regression	Student_11 Student_12	SRM, Ch13 Vogt, Ch9 Other
11	10/29	Simple Linear Regression Multiple Linear Regression Back to Regression		SRM, Ch13-14 Vogt, Ch9
12	11/05	Ordinal and Nominal Procedures (Non-Parametric Statistics: Chi-square Distributions Methods for Categorical Variables: Contingency Tables	Student_13	SRM, Ch12 Vogt, Ch11 (pp. 191-197)
13	11/12	Ordinal and Nominal Procedures (Non-Parametric Statistics: Chi-square Distributions	Project Presentation	SRM, Ch12 Vogt, Ch11 (pp. 191-197)
14	11/19	Thanksgiving Holiday	No Class	----
15	11/26	Research Project Day
16	12/03	Catch-up 2
17	12/10	Research Project Completion	Final Project Due

Note: * Statistics for the Social Sciences (R. Mark Sirkin)

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

*** Summary notes provided by the instructor

1 Research Methodology and Statistics In Education II

1.1 Course Objectives

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.

c. Identify a researchable problem and conduct research on it.

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.

g. Correctly calculate and interpret basic descriptive and inferential statistics.

h. Understand the logic of statistical inference and hypothesis testing.

i. Be able to calculate and interpret inferential statistics on z, t, F, r, χ²(chi-square statistics).

j. Write according to APA guidelines.

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, lab exercises so that we can learn how to interpret output, and a discussion period where we all read papers that apply the topic and then talk about its practical application. Each graduate student will lead one discussion section on one of our core topics.

Preparation for discussion on selected topic will involve completing the following steps.

Use Milner Library electronic databases to find one paper that applies (topic assigned for each week, for instance multiple regression). Find something related to your field of interest.
Post an electronic copy of the article/paper on the WebCT discussion forum for that topic (for instance, if we are discussing regression, post your regression article to the regression discussion group).
Let each one of you try to find different papers to read.
On the discussion day (for instance, during the week multiple regression is due for discussion in class), come prepared to present a synopsis of the paper. If you don't fully understand what the author did, then that would be a good time to ask.
If you are the discussion leader (s) for this topic (e.g., multiple regression), then be prepared to give an overview on the topic and to lead the discussion. In that case, you should fully understand (conceptually) how the technique was used before the class discussion.

1.3 Texts and Software

Required texts are:

(SRM) R. Mark Sirkin (2005). Statistics for the Social Sciences (3^rd ed). Sage Publications, Inc.

ISBN: 9781412905466

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

ISBN: 0-205-35913-2

Recommended Texts:

Green, S. B., & Salkind, N. J. (2004): Using SPSS for Windows and Macintosh: Analyzing and Understanding Data (4th). Pearson Education, Inc. ISBN: 0-13-146597

American Psychological Association. (2000). Publication manual of the American Psychological Association (5th ed.). Washington, DC: American Psychological Association. ISBN 13: 978-1-55798-791-4

Gravetter, F. J. & Forzano, Lori-Ann, B. (2006). Research Methods for the Behavioral Sciences. Belmont, CA: Thomson Wadsworth. (ISBN: 0-534-55811-9)

Hinkle, D. E., Wiersma, W., Jurs, S. G. (2003). Applied statistics for the behavioral sciences (fifth ed). Haughton Mifflin. ISBN: 0-618-12405-5

Additional required readings and assignments will be available through WebCT.

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

as much as possible. The examples I will offer in class and the lab computer exercises will be computed

in SPSS. SPSS online tutorials are available on http://people.coe.ilstu.edu/jkrugut/SPSS/

1.4 Required Student Tasks

Course Requirements and Required Student Tasks:

Participate in all class activities, complete all assigned readings, and be prepared to discuss them in class;
Complete the assignments (lab projects and graded exercises) by the due dates;
Complete a final paper and deliver presentation of the paper.

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. Being sick will not count as an absence. You

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

2. Assignments:

A. Research Project: Each student will complete two major assignments that together describe a process for writing a research paper. The final research project, that will have a data analysis component, needs to tie together the two assignments in a coherent fashion and go further to reflect a “big picture” view of the issue (your research idea). Details of the assignments (1 & 2) are provided on the assignment link within the WebCT courseware.

Assignments 1-2 are worth 5 points a piece for a maximum of 10 points. Handing in a well thought out and well written assignment on the due date is worth 5 points. Assignments turned in late will receive half-credit of 2.5 points if well done. A high-quality final paper submitted on time will receive 15 points. Final papers turned in one day late will receive a maximum of 10 points and final papers turned in more than one day late will receive half-credit, or a maximum of 7.5 points. A well-done final presentation of your results will receive 5 points. Students who do not present their results in will not receive credit for the presentation.

B. Graded Exercises: After a topic is covered, an online (WebCT) graded exercise will be completed. Topics that will have graded exercises are: 1) One-sample t-test; 2) an independent -measures t-test; 3) t-test for related samples; 4) one-way analysis of variance; 5) repeated-measures analysis of variance; 6) two-factor analysis of variance (independent measures); 7) correlation analysis; 8) Regression analysis; and 9) nonparametric tests (chi-square).

C. Lab Projects: Students will conduct and summarize a study: 1) that uses SPSS data preparation and management techniques; 2) of differences in mean (t-tests and ANOVAs); 3) that uses correlation analysis; 4) that uses regression analysis; and 5) of association among nominal variables (chi-square tests). Any issue may be addressed in the study, but it must be based on data provided by the instructor and related to your research project.

1.5 Student Performance Evaluation Methods

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

1. Class participation/attendance 5 points

2. Assignments 1 & 2 10 points

3. Lab Projects (1, 2, 3, 4, & 5) 20 points

4. Final Project 15 points

5. Presentation of Final Project 5 points

6. Graded Exercises (1 thru 9) 45 points

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)

1.6 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.

1.7 ISU Policies

ISU Regulations state:

"Written or other work a student submits in a course must be the product of his/her own efforts: plagiarism, cheating, or other forms of academic dishonesty are prohibited." Cases of suspected copying, cheating, or plagiarism are referred to Student Dispute Office for a University hearing.

Any student who needs to arrange a reasonable accommodation for a documented disability should contact Disability Concerns at 350 Fell Hall, 438-5853 (voice), 438-8620 (TDD).