EDMS 651
Applied Multiple Regression Analysis

Summer Session I -- 2013
Tuesday and Thursday, 9:30am-12:40pm
1107 Benjamin Building
Instructor : Jeffrey Harring
Office: 1230E Benjamin Building
Office Hours: Thursday 1-2pm or by appointment

Course Links:
General Course Information
Lecture, Homework & Exam Schedule
Related Course Information

General Course Information

Course Description

Multiple regression is a highly general, and therefore very flexible, data analytic framework in which to examine phenomena naturally occurring in the behavioral sciences (i.e., motivation, anxiety, aggression, achievement, and so on). At its core, multiple regression analysis is used to model the relation between a single dependent variable and factors of interest -- the independent variables. In this course we will examine quantitative as well as qualitative dependent variables, continuous and categorical independent variables. The course builds on topics which were introduced in EDMS 645 and 646 including: descriptive statistics and graphs, basic sampling and hypothesis testing, two-group mean comparison, simple analysis of variance (ANOVA), multiple comparison procedures, factorial ANOVA designs and repeated measures. Note that students who have not had the necessary prerequisite coursework should not take EDMS 651. On the flip side, if you have taken a regression course before, then undoubtedly some of the material covered in this course will be repetitive. You should know this going into the course and not feel disappointed by the overlap of material or pace of the course.

Explicit in the course title is the word “applied,” meaning that course material will be presented to facilitate your conceptual understanding of fundamental statistical methods typically employed in educational and psychological research settings. This does not mean that underlying statistical and mathematical theory will not be presented; it just won’t be the focal point of the course. Technical aspects of analyses will be presented and stressed as the material warrants.


Suggested Textbook

There is no required textbook for this course. The paperback book

is a suggested supplement to other course materials, but is not required. It is the companion to the book, We will use a combination of my notes and online resources as the primary tools for the course. My notes are detailed and will give a decent overview of the topics we'll cover this semester. I will leave three textbooks on reserve at McKeldin Library for your use throughout the semester. They are listed below. To see if these books are currently in use please follow these instructions:
  1. Log into ELMS
  2. Select the course
  3. Click Course Tools
  4. Click Course Reserves
This will take you to the reserves system where you will see the list of books. You can then click each one and view a) the call number (which is how McKeldin shelves them; and what you will need when you come to the desk) and b) the Availability link that goes out to the catalog so you can see if that particular book is checked out or not at that moment.

Lecture Notes

As the course progesses, I will post lecture notes to the course website. Links to these notes are in the lecture, homework & exam schedule. Lectures are saved as Portable-Document-Format (.pdf) files. Free Adobe pdf-file viewers for a variety of computing platforms (Windows, Macintosh, and Linux/Unix systems) are available from Adobe and can be configured to operate from most web browsers (including MS Internet Explorer and Firefox). Prior to coming to class each week, students are to print out and bring with them the lecture notes for that week’s class. Materials should be posted by the morning on the day of class.


This course is not graded on a curve. Your homework and exams will be combined using a weighted average grading scheme with the corresponding weights given below. Final letter grades will then be assigned based on the given scale.

Assessment Weight Overall Percentage Letter Grade
Homework 50% 100% - 92% A
Quizzes 25% 91% - 87% A-
Take Home Exam 25% 86% - 84% B+
83% - 80% B
79% - 77% B-
76% - 74% C+
73% - 70% C
69% - 67% C-
66% - 60% D
59% - Below F

Your course grade will be based only on the above assessments. There will be no extra credit opportunities. Please do not ask for exceptions.

Other Important Information

Accommodations for Emergencies

In the event that the University closes on the day of class (for instance, a huge snow storm rips through the campus), we will obviously have no class. However, if the University does not shut down and there is a threat of inclement weather, etc., we will still have class unless you hear from me otherwise. With that said, please check your email and/or the course website sometime during the day of class for any last minute postings of announcements regarding the course. If you need to be gone from class, out of common courtesy I would appreciate if you would send me an email to let me know. All students are expected to take the exams and/or submit assignments on the specified dates and no make-up exams are planned (see section on make-up exams below). You must contact me before an exam if you are going to be absent or you will receive a zero for that assessment.

Academic Accommodations

In compliance with and in the spirit of the Americans with Disabilities Act (ADA), I want to work with you if you have a documented disability that is relevant to successfully completing your work in this course. If you need academic accommodation by virtue of a documented disability, please contact me as soon as possible to discuss your needs. Students with documented needs for such accommodations must meet the same achievement standards required of all other students, although the exact way in which achievement is demonstrated may be altered. All requests for academic accommodations should be made as early as possible in the semester. For further information concerning disability accommodations, please contact Dr. Jo Ann Hutchinson at the Disability Support Service – (301) 314-7682.

Academic Integrity

The University of Maryland, College Park, has a nationally recognized “Code of Academic Integrity,” administered by the Student Honor Council. This Code sets standards for academic integrity at Maryland for all undergraduate and graduate students. As a student you are responsible to uphold these standards for this course. It is imperative that you are aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the code of Academic Integrity or the Student Honor Council, please visit Student Honor Council for details.


It is important that the student synthesize pertinent information from any readings and class lectures when writing up homework assignments. Synthesis does not occur when large blocks of text are copied from a textbook, or online resource, or my course notes and used to answer questions. It is understood that the student will have to use some verbatim phrases and definitions from these sources however. This is not considered a case of scholastic misconduct. For example, a textbook may have a sentence reading “The mean of the IQ distribution is 101.” If your software output indicates that the mean is 101 and you are asked to provide the mean of the IQ distribution, it is perfectly lawful for you to write “The mean of the IQ distribution is 101.” What must be avoided is extensive verbatim copying of information from the previously mentioned sources when answering the longer questions on the assignments.

Make-Up Examinations

The University policy states: “An instructor is not under obligation to offer a substitute assignment or to give a student a make-up assessment unless the failure to perform was due to an excused absence, that is, due to illness (of the student or a dependent), religious observance (where the nature of the observance prevents the student from being present during the class period), participation in university activities at the request of university authorities, or compelling circumstances beyond the student’s control. Students claiming excused absence must apply in writing and furnish documentary support for their assertion that absence resulted from one of these causes.”

Course Evaluations

Your participation in the evaluation of courses through CourseEvalUM is a responsibility you hold as a student member of our academic community. Your feedback is confidential and important to the improvement of teaching and learning at the University as well as to the tenure and promotion process. CourseEvalUM will be open for you to complete your evaluations for spring 2013 semester courses starting on or around May 1. You can go directly to the website (www.courseevalum.umd.edu) to complete your evaluations. By completing all of your evaluations each semester, you will have the privilege of accessing the summary reports for thousands of courses online at Testudo. More information is at: https://www.irpa.umd.edu/Assessment/CourseEval/stdt_faq.shtml.

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There will be several assignments spaced evenly throughout the semester, each designed to give students an opportunity to apply and practice concepts learned in class. It is expected that students will be using statistical software of their choosing (i.e., R, SPSS, SAS or some comparable software), for their homework where computer work is required. In working the assignments, you are expected to pull together the material from lecture, textbooks and online resources, and any supplemental notes where applicable. Late homework assignments will not be accepted. Also, homework cannot be submitted via email but submitted during class time unless directed otherwise. Graded assignments will generally be returned the following week after they are submitted. Students are encouraged to work together in computing and discussing the homework problems, and submit one assignment as a group. Of course, you can also work independently if you wish. Group size is limited to 4. You may wish to keep a photocopy or at the very least save assignments electronically for your own protection. Assignments are due upon entering the class on the specified due date.

As the focus of the class is on the practical application of regression analytic methods, many of the problems will involve using statistical software to carry out analyses on real data sets. For assignments students should cut and paste relevant portions of the computer output into the appropriate places in your homework to show how you arrived at your solution. Students should not write, “See p.86 of the attached computer printout to see where I got my answer.” Assignments should be well-organized and free from spelling, grammatical, and punctuation errors. Finally, all assignments must be word-processed. You will need to use Microsoft Equation Editor 3.0 or similar software to incorporate any mathematical notation and symbols into your documents. This package is located in Microsoft Word and can be accessed within the document: INSERT > OBJECT > Microsoft Equation 3.0. There is also other software that you can purchase that adds functionality to Equation Editor called Math Type. It costs around $60 dollars. Of course, there are other available mathematical notation software packages (e.g., Latex), but none which interfaces with Microsoft Word as well.

I do expect that your homework will conform as closely as possible to APA style presentation of tables, graphics, and references. One of the goals of this class is to be able to write-up statistical results as if it were going into a journal article or your thesis. To that end, I will post templates of how to write-up statistical results at the beginning of the semester. For APA style reference, enlist your APA manual (6th ed.) and/or go to the website of Douglas Degelman for manuscripts following APA style. For an example of a substantive article written in APA style, click: here.

Another wonderful resource (a "cannot do without" guide) is an edited volume by Greg Hancock and Ralph Mueller: Hancock, G. R., & Mueller, R. O. (Eds.). (2010). The reviewer's guide to quantitative methods in the social sciences. New York: Taylor & Francis.

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Other Assessments

There will be quizzes given throughout the session (dates TBA) and a final take home exam. Both of which, unlike the homework, will be done by yourself. More information on these will be discussed during the first class meeting.

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You will need Adobe Acrobat Reader to read and print most of the course materials. This program is free and comes already installed on most new computers. You may also want to install Java on your computer. This will allow you to open any web applets that we will use in the course. You will also need access to a statistical package such as SPSS, R, SAS, or some other software that will allow running general linear and generalized linear models. As this course is not about learning how to use software, you may use any software that you are familiar with. Both R and SPSS are available to use in the Benjamin building basement computer lab (0230). The R software is free and easy to install on your own computer. It is currently maintained by the R Core development team – a hard working, very competent, international group of volunteer developers. You can download R at the home page of the R project. Due to its worldwide popularity, this main website will direct you to choose another, local “mirror” site from which to actually download the software. UMD students can go to UCLA to download R, but quite frankly, it doesn’t really matter which site you choose. Once at the mirror site, find the “Download and Install R” window. You will be prompted to identify which operating system you are using and choose the corresponding link for Windows, Linux, and Macintosh versions of the software. For Windows, follow the link to the base distribution, then download to your desktop the R-3.0.0-Win32.exe file or R-3.0.0-Win64.exe (depending on the "bit" you have on your machine), or a file with a similar name (since R is updated regularly, there may be a more current version of the software available). Once the file is copied to your computer, double click the file’s icon and the installation process will start. The default configuration is just fine. A nice user interface with R is RStudio, which can be downloaded from RStudio. This interface makes installing packages, graphing, and programming a bit easier.

Examples in class will come from R, although we will attempt to provide some parallel analyses with SPSS. Some, if not most of you, may choose to use SPSS. There are a couple of options for using SPSS or other software…

Besides the basement computer lab, other on-campus computing possibilities can be found by going to the Office of Information Technology Where-to-go webpage and clicking on the software list button. This will direct you to a page that lists all the WAM (Workstation at Maryland) computer labs with the available loaded software.

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Lecture, Homework & Exam Schedule

In the table below you will find a list of course topics by date, suggested readings (books on reserve in McKeldin Library) corresponding to the lecture notes, downloadable lecture notes, & homework. You will be directed to the data files and code used for in-class examples as well as homework by clicking on data files.

Lecture Notes
Readings & Software Code
Online Statistical Resources
May 28
  • General introdcution
  • Correlation
May 30
  • Correlation Continued
  • Bivariate Regression
  • Applets to Study Correlation:
Corr-1   Corr-2
June 4
  • Bivariate Regression Continued
  • Multiple Regression
Reg-1   Reg-2

June 6
  • Multiple Regression Continued
  • Predictors
  • Karl Wuensch's Statistical Help Homepage
June 11
  • Predictors in MRA Continued
June 13
  • Predictors in MRA Continued
June 18
  • Mediation & MRA Diagnostics
June 20
  • Mediation, MRA Diagnostics
June 25
  • Diagnostics, Logistic Regression
June 27
  • Logistic Regression
July 2
  • Other GzLMs, Extensions of MRA
July 4
  • Extensions in MRA
  • MRA-Extension-Slides
  • The book on missing data: Enders, C. K. (2010). Applied missing data analysis. New York: The Guilford Press.
No Class July 4
No Class July 4
No Class July 4
No Class July 4
No Class July 4
No Class July 4

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Data Files

Below are data sets used for in-class examples as well as the data you will need to complete the homework assignments. Online resources for pareticular software packages (e.g., SPSS or R) can be found in the "Statistical Software" column.

Supplementary Resources
Cyril-Burt.csv    Stress.csv    LakeMary.csv
Fac-Salary.csv    Fuel90.csv    World95.csv    Latino.csv
School.csv    Bank.csv    Prestige.csv
Maternal-Care.csv    Encoding.csv    BigMac.csv
Admission.csv    Baumann.csv    Duncan.csv    Challenger.csv
HSB.csv    Moore.csv    Ornstein.csv    Field-Goal.csv    Car-Sales.csv

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Related Course Information

Reference Textbooks

Students may find the following texts helpful resources that provide more in-depth mathematical treatment of course topics or alternative perspectives.
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Last modified January 5 2013 by Jeff Harring (harring@umd.edu)