Psychology
543
Research Design and Analysis http://www.uic.edu/classes/psych/psych543/ 2:30  4:50 Tuesdays  3:00  4:50 Thursdays 2019 Behavioral Sciences Building 
Instructor:
R. Chris Fraley
Office: 1050 "A" BSB
fraley@uic.edu  http://www.uic.edu/~fraley
TA: Josh Hemmerich
joshh@uic.edu  http://tigger.uic.edu/~joshh/
Course
Description
The content of the course will be organized into three sections. We will begin by discussing ways in which mathematics and statistics can be used to quantify psychological phenomena. We'll review some important ideas regarding psychological measurement, ways of quantifying the properties of common statistical distributions, and ways of quantifying relationships between variables. Next, we'll discuss the role of mathematics and statistics in theory development. One of the points I'll emphasize repeatedly throughout the course is that statistics is not the same thing as "data analysis." Statistics and mathematics should be incorporated into the theoretical stage of the scientific process, not just the data analysis stage. We'll discuss some ways of doing this, focusing primarily on the use of linear equations to model the assumed causal relationships between variables. We'll review methods for constructing these models, and various techniques for estimating their parameters. Finally, we'll discuss ways in which mathematics and statistics can be used to evaluate scientific models. We'll focus on three sources of model misfit: (a) errors in measurement, (b) sampling errors, and (c) fundamental problems in the model per se. For each of these issues, we'll review techniques that are used to handle or minimize these problems (e.g., estimating reliability of scores, designing research in order to minimize the standard error of estimates). Finally, we'll review a set of tools commonly used in psychology, null hypothesis significance tests, designed to handle the problem of sampling error. You will need to be aware of "significance tests" because they are widely used. However, these classic techniques are hotly debated among quantitative psychologists in contemporary psychology, and you will need to be aware of their limitations. We'll discuss both their uses and misuses, and focus on alternative methods for dealing with the problem of sampling error (e.g., parameter estimates, effect sizes, confidence intervals).
Historically, Psych 543 is known as the "ANOVA course" in our department because it is typically taught with a strong emphasis on experimental design. As someone who does not conduct a lot of randomized experiments, I tend to emphasize the modeling of psychological phenomena the way they occur "naturally" (i.e., in their full confounded glory). We will briefly review ANOVA, but you need to know in advance that ANOVA will only constitute a small piece of this course. If you plan to focus on experimental research in your graduate career, I encourage you to take an additional class in ANOVA during your second year.
I have six major objectives for this course:
The first is to provide you with a deep, intuitive understanding of some fundamental concepts in statistics, as they apply to the study of psychological phenomena. We obviously will not have the time (or interest) to review every kind of statistical technique that is used by psychologists. Therefore, I plan to make sure you have a strong handle on the basic mathematical and statistical concepts that are common to many applications.
My second objective is to make sure you can use SPSS to conduct some common statistical analyses. Every two weeks, you will be given a new data set and a set of analytic objectives. You will be required to analyze those data in SPSS, and, as explained below, write both a methods and results section. Midway through the course, I will only provide you with the data; you will need to determine on your own how to analyze the data and summarize them appropriately.
Third, in an attempt to help you "think outside the box", I intend to teach you how to program statistical routines in the SPlus programming environment. Unless you know a little bit about statistical programming, you will never have the freedom to think creatively about mathematics and statistics, and you'll be constrained to run only the analyses that are available in popular statistical packages, such as SPSS. Every other week you will have to complete a programming exercise for SPlus (e.g., simulating sampling distributions, bootstrapping confidence intervals). Early in the semester I'll spend time explicitly teaching you how to use SPlus, and I'll walk you through some of the assignments. Later in the semester I'll simply give you a problem and expect you to solve it on your own by using SPlus.
Fourth, I want you to become experienced in thinking critically about research design. Unfortunately, many studies that are conducted in psychology are not designed carefully enough to answer the basic questions they set out to answer. By providing you with examples to study and critiqueand by encouraging you to think critically about your own research ideasI hope to provide you with skills that will benefit your research in the long run.
My fifth goal is to make you aware of some basic controversies in the application of statistical tools to psychological science. Students often learn about statistics as if they are learning a set of recipes for data analysis, or worse, a set of facts. Many of the most commonly used statistical tools in psychology have been hotly contested since their inception. In order to be an educated consumer and user of statistical techniques, you need to understand these debates and exactly what certain tools are and are not capable of achieving.
Finally, I plan to help you learn how to write professional methods and results sections. Every two weeks you will be asked to write a methods and results section for a hypothetical set of data. I will grade these fairly rigorously, attending not only to the basic organization of your ideas, but the quality and clarity with which you convey them. Although my primary goal is to help you become great a scientist, my secondary goal is to help you become a great writer.
Textbooks
Hays, W. L. (1994). Statistics (5th ed.). Fort Worth: Harcourt College Publishers.
This text is currently being revised, and the new edition will not be available
for another year or two. In the meantime, used copies of the most recent edition
are available via many outlets online (e.g., www.amazon.com's used book sellers).
I encourage you to order a copy and read it at your own pace. I will not be
assigning specific chapters from this text, but you will need a fairly comprehensive
statistics text on your reference shelf if you plan to pursue a PhD in psychological
science. [link
to Hays text on amazon.com]
Salsburg, D. (2001). The lady tasting tea: How statistics revolutionized
science in the twentieth century. NY: W. H. Freeman and co.
This book provides a wonderful overview of the history of statistics, with special
attention to the major players and the key controversies. Why am I assigning
this book? Statistics courses are often taught in a passionfree, recipelike
manner. This way of characterizing the development of modern statistics overlooks
the fact that statisticians have beenand arequite divided over some of the
concepts that psychologists accept as "facts." This book will help
make you aware of these debates, and, I hope, facilitate your ability to think
critically about "statistical conventions." [link
to Salsburg book on amazon.com]
Krause, A., & Olson, M. (2002). The basics of SPlus. NY: SpringerVerlag.
This book is recommended (not required) if you would like a useful text on programming
in SPlus. [link
to KrauseOlson book on amazon.com]
Grading
Grades will be based on your class participation, the quality of your assignments
(SPlus exercises, SPSS exercises, and Methods/Results exercises), quizzes,
and exams.
Students
Name


adabel@uic.edu


ebal@psych.uic.edu 

zlakda1@uic.edu 

aarati@uic.edu 

srk@uic.edu 

ben.jee@utoronto.ca 

kbaile3@uic.edu 

aliza@uic.edu


drusch1@uic.edu 

evatt@uic.edu 
Updates
Assignments, datasets, notes, and updates will be listed here throughout the
semester. Please treat this webpage as your primary syllabus.
August
29, 2002
September
12, 2002 September
12, 2002
If you are using the room during one of these times, and if you're the last one to leave, please be sure the lights are out and that the door is closed. October
2, 2002 October
9, 2002 
Lecture
Overheads
Overheads and (some) notes from class discussions will be available here. You
may click on the lecture title to view an HTML version of the overheads. Alternatively,
you may rightclick on the PowerPoint option and select "save target"
to save the original PowerPoint files.
Lecture
1  August 29, 2002 Lecture
3  September 5, 2002 Lecture
4  September 9, 2002 Lecture
5 September 12, 2002 Lecture
6 September 17, 2002
September
26, 2002 October
1, 2002 October
8, 2002 October
10, 2002 October
15, 2002 October
22, 2002 October
29, 2002 October
31, 2002 November
5, 2002 November
7, 2002 November
12, 2002 November
14, 2002 November
19, 2002 November
21, 2002 November
26, 2002 homework due on Dec 3 2002 regarding power analyses for correlations, ttests, and ANOVA December
3, 2002 December
5, 2002 Final
Exam  Due Dec 13th 