EDMS 651 : General Linear Models II
Spring 2016

Mondays 4:15-7:00pm
1107 Benjamin Building
Instructor : Jeff Harring
EDMS : Webpage
Office : 1230E Benjamin Building
Office Hours : By appointment

Teaching Assistant : Yating Zheng
Office : 0114 Benjamin Building
Office Hours : Mondays 3:00-4:00pm and Wednesdays 2:00-3:00pm




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

General Course Information


Course Description

General and generalized linear models are highly general and therefore very flexible data analytic frameworks in which to examine phenomena naturally occurring in the behavioral sciences (i.e., motivation, anxiety, aggression, achievement, and so on). At its core is multiple regression analysis that is used to model the relations between a dependent variable and factors of interest, the independent variables. This semester we will examine quantitative as well as qualitative dependent variables, continuous and categorical independent variables, and uncorrelated errors. This course builds on topics which were introduced in EDMS 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 and ANCOVA. Note that students who have not had the necessary prerequisite coursework should not take EDMS 651.

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.


Textbooks

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,

If you are brave and want to use R for this course, please find “an R book” for multiple regression so that you can have a reference besides my course notes. Speaking of which, 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 also like the following textbook for its detailed discussions of interesting issues related to multiple regression.


Course Delivery & Lecture Notes

I will be using my own course website to deliver materials for the course, including class notes, data sets, ancillary materials, extra readings, etc. You can access the course website at 651 Website. You will find navigation buttons to access course materials. You will be asked to provide your Username (Directory ID) and a Password. If you have difficulty logging in there, please contact me as soon as possible. Prior to coming to class each week, students are to print out and bring with them the lecture notes and software script files for that week’s class. Please follow the syllabus for topics and dates. Materials should be posted by the morning on the day of class.


Grading

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
Exam 1 25% 91% - 87% A-
Exam 2 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 hurricane 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.


Religious Observances

The University of Maryland policy on religious observances states that students not be penalized in any way for participation in religious observances. Students shall be allowed, whenever possible, to make up academic assignments that are missed due to such absences. However, the student must contact the instructor before the absence with a written notification of the projected absence, and arrangements will be made for make-up work or examinations.


Academic Accommodations

If you have a registered disability that will require accommodation, please see the instructor so necessary arrangements can be made. If you have a disability and have not yet registered with the University, please contact Disability Support Services in the Shoemaker Building (301.314.7682, or 301.405.7683 TTD) as soon as possible.


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.


Plagarism

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

As a member of our academic community, students have a number of important responsibilities. One of these responsibilities is to submit course evaluations each term though CourseEvalUM in order to help faculty and administrators improve teaching and learning at Maryland. All information submitted to CourseEvalUM is confidential. Campus will notify you when CourseEvalUM is open for you to complete your evaluations for fall semester courses. Please 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 online, at Testudo, the evaluation reports for the thousands of courses for which 70% or more students submitted their evaluations.

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Homework

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, Stata 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. Maximum group size is limited to 4. If you decide to work as a group it is understood that each member of the group will receive the same grade on that particular assignment. 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. I want you to spend time thinking about how to respond to questions; formulate a cogent, logical line of thinking, and present your findings in a professional manner. To this end, all assignments must be word-processed. You will need to use Microsoft Equation Editor 3.0, MathType or LaTex to incorporate any mathematical notation and symbols into your documents. DO NOT USE the insert equation feature in Word!

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 Purdue Owl Website.


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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Exams

There will be two exams—one toward the middle of the semester and one at the end. The content of the first exam will cover topics presented in class up to that point. I have not yet decided on the format of the exams (whether in-class or take-home), but will give you plenty of forewarning about this. If the exam is in-class, then it will be closed book and closed class note; however, students may prepare and use up to two 8.5”x11” two-sided pages of notes. Students should bring a calculator to the exam; and note the sharing of calculators between students will not be allowed. Unlike the homework, the exams will be completed by you alone, without the aid of discussing the questions and solutions with other classmates, students outside the class or faculty. Students are on their honor to do exams completely independently; any student found doing otherwise will be subject to the maximum University penalties.

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Computing

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 (EDU 0230). The R software is free and easy to install on your own computer and is available for three different operating platforms: Windows, MAC, and Linux. 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-3.2.3-Win32.exe file or R-3.2.3-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. Downloading R Studio also downloads the latest version of R automatically. I will use this program some during the lectures. Students are encouraged to bring their laptops to class and work on examples in real time as we go over them during the lectures.

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Lecture, Homework & Exam Schedule

In the table below you will find a list of course topics by date, suggested readings corresponding to the lecture notes, downloadable lecture notes, slides for class & 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.

>
Date
Topic
Lecture Notes
Readings & Software Code
Online Statistical Resources
Homework
1-25
BLIZZARD
OF
2016
NO
CLASS
2-1
  • General introdcution

  • Multiple regression review, parameter interpretation, model comparisons
Guest Lecturer:
Dr. Laura Stapleton
2-8
  • Assumption checking and regression diagnostics
2-15
  • Categorical predictors, moderation, mediation, and path analysis
2-22
  • Categorical predictors, moderation, mediation, and path analysis continued

  • OLS, GLS, multivariate normal distribution, properties of the normal model, maximum likelihood
2-29
  • OLS, GLS, multivariate normal distribution, properties of the normal model, maximum likelihood continued

  • Information-theoretic approach to model selection and multimodel inference
3-7
Exam 1
Exam 1
Exam 1
Exam 1
Exam 1
3-14
Spring
Break
Spring
Break
Spring
Break
Spring
Break
Spring
Break
3-21
  • Missing data in regression analyses
3-28
  • Introduction to generalized linear models

  • Logistic regression
4-4
  • Logistic regression continued

  • Multinomial logistic regression
4-11
  • Multinomial logistic regression continued

  • Poisson regression, zero-inflated Poisson regression, negative binomial regression, overdispersion
4-18
  • Generalized linear models wrap-up
4-25
  • Bootstrapping regression models
5-2
  • Effect sizes, power/sample size calulations, applicationsto large scale datasets
5-9
Exam 2
Exam 2
Exam 2
Exam 2
Exam 2

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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.

Data
Supplementary Resources
Stress.csv    LakeMary.csv
School.csv    Prestige.csv
Ornstein.csv
Duncan.csv     Duncan.sav


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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)