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Ed 794 Syllabus

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									                                          SYLLABUS
                                        Education 793
                 Introduction to Quantitative Methods in Educational Research

Instructors:             Eric Dey              Janel Sutkus             Julia Parkinson
Email @umich.edu         dey                   jsutkus                  juliap
                         SEB 2117              SEB 2108F                SEB 1302

Office hours:            Monday by             Thursday                 Tuesday
                         appointment;          10-12 noon               2 – 4pm
                         email to arrange

Meetings:       Wednesday, 1 - 4pm, SEB 2225
                Sutkus lab: Thursday, 5-7pm, Angell Hall, Computing Classroom A
                Parkinson lab: Friday, 10am-noon, Angell Hall, Computing Classroom A

Home page: http://www.umich.edu/~ed793/
CTools site: Resources available through the standard CTools web portal
             (https://ctools.umich.edu/portal/) or directly at
             http://ed793.notlong.com
Email list:  ed793.class@umich.edu


Introduction

Education 793 is a first course in undertaking quantitative research in the field of
education, and a foundation for other methods courses in education and the social
sciences. (A second course in this series, Education 795, is an extension of this course
that focuses on more advanced methods.) By the end of this course you should have a
fair idea of how to begin to make sense out of a body of quantitative data by applying
standard computer-based data analysis methods and statistical reasoning. There will be
some math involved in this course, but our primary focus will be on developing:

       1) An understanding of quantitative research methods,
       2) Expertise in using various software tools useful in conducting educational
          research, and
       3) Skill in communicating research results to others (in written form and
          through presentations).

The course is organized into two major sections. The first section is designed to develop
an understanding of basic educational research concepts, including methods designed
to describe or summarize quantitative data -- descriptive statistics. Inferential statistics -
- techniques designed to help us draw inferences about populations based on data from
samples -- are the focus of the second. All of these statistical ideas are embedded in case


                                                                             Fall, 2001 — Page 1
studies drawn from various subject areas relevant to education. For each case study, we
will consider the issues motivating the research, the key research questions, and reports
of findings. We will then undertake analyses the data using the techniques described
above and, based on our work, we will critically evaluate the validity of inferences
previously drawn. Thus, the course will consider all statistical choices and inferences in
the context of the broader logic of educational inquiry with the aim of strengthening
our understanding of that logic as well as of the statistical methods.

Through the course and laboratory exercises students will develop a working
knowledge of SPSS statistical software. Some of our analyses -- especially those
undertaken early in the course -- may be done by hand in order to get a better
understanding of statistical techniques being presented. Given this, high school algebra
-- remembered or relearned -- will be needed to get the most out of the course.

Like most social science research endeavors, educational research is primarily a group
activity. As such, there will be an emphasis on group work throughout the course. In
addition, students are encouraged to communicate, ask and answer questions, and
engage in class discussions via electronic mail (messages sent to ed793.class@umich.edu
are automatically distributed to all class members and archived on our CTools site).


Media

Lecture and lab notes are available online through the CTools web page, and also as a
coursepack available through Dollar Bill’s Copying. You will find it most convenient if
you dedicate a standard 3-ring binder to collect and organize the lecture materials.

The text for this course is strongly recommended: Statistical Reasoning for the
Behavioral Sciences (3rd Edition), by Richard J. Shavelson, published by Pearson, Allyn,
& Bacon (1996). This text is available at local bookstores as well as online through
Amazon.com (which also carries used copies). The majority of reading for the class
will consist of publicly available on-line resources as well as materials made available
through our CTools site. The primary on-line resources we will be using are HyperStat:

                          http://davidmlane.com/hyperstat/

and the StatSoft Electronic Textbook:

                 http://www.statsoftinc.com/textbook/stathome.html

All readings need to be completed before class on the assigned date, and should be
brought to class for reference.




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Some practical details

The core of this course is a series of weekly research and writing assignments. By
completing these assignments, students will develop and demonstrate the knowledge
and skills needed to undertake quantitative data analysis. Keeping up-to-date with
these readings and assignments is crucial: You must understand the previous material
in order to follow what comes next. Exercises are due at the start of lab the week after
they are assigned; they will be read and returned the following week. Late assignments
will be accepted, but they will be graded down substantially (25 percent reduction for
any late submission; 50 percent reduction for any assignments more than one week
late). There will also be a mid-term examination.

For use during class and examination periods (and for your homework), you should
have access to a battery-powered scientific calculator (unless, of course, you particularly
enjoy doing tedious arithmetic computations by hand). In addition to simple arithmetic
functions, the calculator should have the square-root function and a simple memory
feature. You may find additional features -- such as basic statistical functions and
programmability -- useful, but these are not strictly necessary for the purpose of this
course.

Basic instruction in SPSS in both Windows and Unix environments will be provided.
You may find it useful to purchase a flash drive (key disk) to hold copies of your
electronic working files.




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Assessment and evaluation

It is often the case that students enter introductory research methods courses with
educational and professional backgrounds that have differentially prepared them for
such courses. Given this, I would like to have all students complete a short examination
on the first day of class. Please note: This examination will not count toward the final
grade. The purpose of the pretest examination is to assess the preparation level of the
class so that I can prepare the most appropriate materials for subsequent course
meetings.

For the purpose of assigning grades, student performance will be evaluated according
to the following scheme. Note that all of the written products must be prepared
according to the standards in the American Psychological Association (APA)
publication manual (5th edition).

   1. Lab assignments – 40% of course grade

      There will be a series of four lab assignments in the course. As described above,
      these exercises will be due at the beginning of class the week after they are
      assigned; they will be read and returned the following week. Handwritten
      exercises will not be accepted and late assignments will be graded down
      substantially.

   2. Research Report -- 30% of course grade

      In addition to the lab assignments, there will be a somewhat larger research
      project due near the end of the course. This larger project will require you to
      analyze data to answer a substantive research question. The format of this project
      will be that of research notes that commonly appear in scholarly journals. An
      extensive literature review is not required, although you should probably
      present at least two or three background paragraphs on the problem before
      jumping right into your results.

   3. Examination -- 20% of course grade

      There will be one mid-term examination in this course. Details on the format of
      the exams will follow.

   4. Class and lab participation – 10% of course grade




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Course schedule

Background assignments for the opening segment of the course are provided below,
and should be completed prior to the meeting of the class. In addition, tentative dates
for the major course assignments are provided, with detailed reading assignments to
follow once the class finds its rhythm (so we can better plan the balance of the term):

                                 Tentative course schedule


Date         Topics                        Readings                      Lab Activity
Sep. 7       Course overview and           Class notes: Set 1            Introduction to SPSS
             procedures

             Introduction to the
             language and logic of
             quantitative social science
             inquiry


Sep. 14      Case 1: Competing             Class notes: Set 2            Describe univariate
             explanations of social                                      distributions of key
             stratification                Raudenbush and Kasim          variables in NALS
                                           (1998) *
             The logic of survey
             research and the National     Jaeger (1998) *
             Adult Literacy Survey
                                           Shavelson: 1, 3, 4, & 5
             Univariate distributions
                                           Hyperstat: Describing
                                           Univariate Data

Sep. 21      Bivariate distributions       Class notes: Set 3            Graphical displays and
                                                                         summaries of key
                                           Shavelson: 6, 7               bivariate associations in
                                                                         NALS
                                           Zeisel: Say it With
                                           Figures *

                                           StatSoft: Basic Statistics/
                                           Correlation;
                                           Crosstabulation and
                                           Stub-and-banner Tables




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Date      Topics                        Readings                   Lab Activity

Sep. 28   Case 2: College as a          Class notes: Set 4         Descriptive statistics
          gendered experience                                      assignment due
                                        Smith, Morrison, &
          Introduction to the           Wolf (1994) *              Conduct sampling
          Cooperative Institutional                                experiment
          Research Program (CIRP)       Shavelson: 5 (115-136),
          data                          8 (211-225), 9

          Key ideas in probability:     Statsoft: Distribution
                                        Fitting/Normal
          - Probability                 Distribution
          - Probability distributions
          - The normal distribution     Hyperstat: Normal
          - The Central limit           Distribution
          theorem

Oct. 5    Key ideas in statistical      Class notes: Set 5         Compute tests and
          inference:                                               confidence intervals
                                        Shavelson: 10, 12          using CIRP
          - Hypothesis testing
          - Confidence intervals        Statsoft: Basic
          - Inference about means       Statistics/t-tests

                                        Hyperstat: Statistical
                                        Equality of Two or More
                                        Populations


Oct. 12   Testing bivariate             Class notes: Set 6         Test bivariate
          associations                                             associations in CIRP
                                        Shavelson: 12, 13, 19

                                        Statsoft: ANOVA

                                        Hyperstat: ANOVA,
                                        Chisquare


Oct. 19   Decision, error and power     Class notes: Set 7         Inferential statistics
                                                                   assignment due
                                        Shavelson: 11 (311-328)

                                        Statsoft: Power Analysis



                                                                         Dey: Ed 793 — Page 6
Date             Topics                             Readings                         Lab Activity

Oct. 26          Midterm exam week


Nov. 2           Correlation analyses,              Class notes: Set 8
                 linear regression
                                                    Shavelson: 6, 7

                                                    Statsoft: Linear
                                                    regression


Nov. 9           Linear regression                  Class notes: Set 8               Linear regression


Nov. 16          Linear regression review           Class notes: Set 9               Linear regression
                 Multiple regression                                                 assignment due
                                                    Shavelson: 18
                                                                                     Multiple regression
                                                    Statsoft: General Linear
                                                    Models


Nov. 23          No Class -- Thanksgiving


Nov. 30          Introduction to ANOVA /            Class notes: Set 10              Multiple regression
                 ANCOVA                                                              assignment due
                                                    Shavelson: 13, 17
                                                                                     One-way ANOVA

Dec. 7           Catch-up and review

                 Discuss final projects


Dec. 14          Note: All course materials         due by 4pm, Wednesday
                 submitted for evaluation           Dec 14th
All of the On-line sources can be found on Ctools in the On-Line Resources section of our Ctools page.
*Document is available as a PDF on Ctools.




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Lab Location
Both lab sections will be held in the Angell Hall Classroom A computing site, which is located at
444-A Mason Hall, within the Angell Hall Courtyard (Fishbowl) at 419 S. State. It is part of the
Angell Hall Computing Site that is run by Campus Computing Sites group. UM Parking is
available in the Thompson Street Structure, Thayer Street Structure, or on North University and
S. State.




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