General Course Information

EDMS 646 is the second course of the EDMS graduate level statistics sequence. The course builds on topics which were introduced in EDMS 645. An understanding of all of the following is assumed: (i) normal distributions, (ii) z-tests, (iii) Student's t-distribution, (iv) t-tests, (v) bivariate correlation and simple linear regression, and (vi) Chi-square tests of goodness-of-fit and independence. I am also assuming familiarity with SPSS/Windows. The class is 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.

Course material will be presented to facilitate your conceptual understanding of fundamental statistical methods typically employed in educational and psychological research settings. The use of statistical software will be emphasized throughout the course. The class is applied, meaning that we will concentrate on conceptual issues important in applied research. 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. It is assumed that the student has at least a working knowledge of high school algebra.

Lomax, R.G. (2007). Statistical Concepts: A Second Course, (3rd edition). Mahwah, NJ: Lawrence Erlbaum Associates.

Agresti, A., & Finlay, B. (1997). Statistical Methods for the Social Sciences (3rd edition). Upper Saddle River, NJ: Prentice Hall. (Novice to intermediate)

Howell, D.C. (2002). Statistical Methods for Psychology (5th edition). Pacific Grove, CA: Duxbury Press. (Intermediate)

Glass, G.V. & Hopkins, K.D. (1984). Statistical methods in education and psychology (2nd edition). Englewood Cliffs, NJ: Prentice-Hall. (Moderately advanced)

Hays, W. (1999). Statistics (5th edition). New York: Hot, Rinehart, & Winston. (Advanced)

Neter, J., Kutner, M.H., Nachtsheim, C.J., & Wasserman, W. (2004). Applied linear statistical models (5th edition). Chicago: Irwin. (Advanced)

As the course progesses, I will post lecture notes to the web site for the course. 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 day, students are to print out and bring with them the lecture notes for that day's class. Materials should be posted by the morning on the day of class. If they are not, please don’t fret. I will make copies of the notes and bring them to class for you. Please note: because of the accelerated schedule in the summer a packet of notes could last all week.

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 (there will be no rounding).

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

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.

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. William Scales at the Disability Support Service – (301) 314-7682.

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 http://www.studenthonorcouncil.umd.edu/code.html for details.

It is important that the student synthesize pertinent information from the readings and class lectures when writing up homework assignments. Synthesis does not occur when large blocks of text are copied from the textbook or my notes and used to answer questions. It is understood that the student will have to use some verbatim phrases and definitions from the textbook or notes. 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 SPSS 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 textbook or my notes when answering the longer questions on the assignments.

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.”

Homework

There will be several assignments spaced evenly throughout the summer, each designed to give students an opportunity to apply and practice concepts learned in class. It is expected that students will be using SPSS for their homework where computer work is required. In working the assignments, you are expected to pull together the material from lecture, the text, and the supplemental notes where applicable. Late homework assignments will not be accepted. Also, homework cannot be submitted via email. Graded assignments will generally be returned the following day in class after they are submitted. Students are encouraged to work in groups on homework and to turn in a single homework with the names of the group members (maximum of 4 students per group). It should be understood that all members of a group receive the same score on homework completed together. 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 statistical methods, many of the problems will involve using statistical software to carry out analyses on real data sets. In the 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 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.

Exams

There will be two in-class exams (dates to be determined) at approximately one-third and two-thirds of the way through the class. The content of each exam will cover topics presented in class up to that point. Both exams will be closed book and closed class note; however, students may prepare and use two 8.5”x11” two-sided pages of notes. Each student should bring a calculator to the exam.

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 may use in the course. ou will also need access to a statistical package such as SPSS, R, or SAS. You may use any software that you are familiar with, but the course will focus on SPSS with some R examples. 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-2.6.0-Win32.exe file, 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.

There are three possible courses of action for using SPSS…

Option 1: Buy nothing. Use a campus lab to run SPSS for this class.

Option 2: Buy a version of the Student/Ware package, which include a manual and should get you through EDMS 651. Be warned though that this version is limited in both the amount of data that can be stored and analyzed as well as the program is missing a couple of key statistical modules (i.e., Repeated Measures). In general, I do not recommend it.

Option 3: Buy a version of the Grad Pack, which historically costs about $225 and manuals must be purchased separately. This is for the student who plans to take more quantitative classes beyond EDMS 651, and/or who plans to conduct serious quantitative research (e.g., a dissertation).

Lecture, Homework & Exam Schedule

In the table below you will find a list of course topics by date, suggested readings (Lomax plus potential other readings) 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.

Data Files

Below are data sets used for in-class examples as well as the data you will need to complete the homework assignments. Related articles or textbook references for topics in the course and extensions can be found in the "Related Topics" column.

Related Course Information

You may want to install Java on your computer. This will allow you to open any web applets that we may use in the course, particularly applets needed to complete the homework assignments.

Computing in the course could be done with numerous other statistical software packages. One such software package, R, is a free implementation of the S statistical computing language and environment. S has become more or less the standard language for statistical computing, at least among statisticians. You can download R at the home page of the R project. R is made available for several operating systems including OS X (Macintosh), Linux/Unix and Windows.

Free online tutorials for different statistical software packages are available at University of California Los Angeles

GPower is free power analysis software that will perform power analyses for all of the statistical tests we will examine this semester. GPower has the ability to draw publication quality graphs relating different aspects of the analysis (e.g., sample size and power).

To learn about SPSS graphics and data analytic tools use SPSS Video Tutorials.

Last modified 22 October 2007 by Jeff Harring (harring@umd.edu)