This course is designed to be an intensive investigation into statistical analyses commonly used in Psychology and other social-behavioral sciences. Topics include Factorial ANOVA, Repeated Measures, Multiple Regression, Trend Analysis, Non-Parametric procedures, and the General Linear Model (GLM).
In addition, the student will be exposed to various analytic philosophies. The course will be computer intensive (using SPSS). Students are expected to be familiar with basic statistical issues, though they will be reviewed at the beginning of the class.
The main goals of this course are to: (a) expose the student to advanced statistical techniques, (b) make the student proficient in the techniques, (c) give the student the expertise to “think” about appropriate statistical techniques for the problems they will face in and out of academic settings, and (d) give the student exposure to different analytic strategies and philosophies. This course is associated with select student-learning objectives (linked to Program Goals), including writing skills, critical analysis of research, research skills, applying knowledge to real world problems/applications, and working with others.
This course is analysis and writing intensive. Students usually spend approximately 5-10 hours per week on homework assignments beyond class time.
Tuesday Class Syllabus
Thursday Class Syllabus
PLEASE NOTE: The example data here is taken from the Thursday stats section of my statistics class during the Fall semester of 2012. The actual data in these examples may differ slightly from the data used in your class. Similarly, the syntax matches the data used in their class -- you may need to adjust the name of the variable slightly to match the names used in your class per se. If you were in the Thursday stats class from fall of 2012, you're in luck -- this is exactly what you saw in class.
ALSO, please note that prior to Fall of 2012, Argosy always started the school year on a Monday, so classes that meet twice during the week starts on Thursday. This means that an academic week on this calendar runs from Thursday to Wednesday. That means that for the week of Thanksgiving, it reads "No Class." However, there is no class for the Thursday class, but there is class for the Tuesday class, and they are still finishing up their week.
This class was originally taught using the SPSS software package. However, modifications have been made and all examples have been/will be re-worked using both R and SAS. I hope that in the future I'll also add Stata, though I no longer have access to it; I hope to have it again in the fall of 2013.
Some Useful Links
A link to some old statistics resources I once posted; I no longer have access to this page and cannot verify that all links work or change broken ones any more.
SPSS's web page
SPSS trial version for download
David Howell's Statistical Methods Webpage
Andy Fields' Discovering Statistics (aka www.statisticshell.com) Webpage
R, a commonly used, free statistical software package/language
SAS, a commonly used statistical software package (alas, not free)
Stata, a commonly used statistical software package (alas, not free)
NEW: In my examples of R below, I always use syntax. However, some people don't like syntax. It's possible to make R behave like SPSS. If you JUST can't be bothered by syntax, here's how to make R pretty much just like SPSS (this even includes how to work with an SPSS data set in R when you don't have SPSS but just have an SPSS file). A link to a document by John Fox entitled "Getting Started with the R Commander" is also useful.
Kelley & Preacher (2012). On effect size. (pdf)
Cohen (1992). A power primer. (pdf)
Paperclip Example Data from Class (no syntax, all we did was means and standard deviations, which you know how to do)
Height/Weight Data from Class
SPSS Syntax for Height/Weight Example from Class
Howell's Data for Figure 2.1/Table 2.2 in SPSS Format
R syntax for self-contained example of t-tests
R syntax that accesses SPSS data set and then runs t-test examples
SAS syntax for self-contained example of t-tests (coming soon)
SAS syntax that accesses SPSS data set and then runs t-test examples (coming soon)
Multiple Comparison Procedure Data
Multiple Comparison Procedure Syntax (includes LMATRIX for orthogonal contrasts)
Toothacher's book on multiple comparison procedures
UCLA's webpage on the planned comparisons portion of Geoff Keppel's ANOVA course (MANOVA was not covered in class, but GLM was)
Homework #5 Data
Licht (2004) Chapter (pdf)
Correlation and Simple Regression Data
Correlation and Simple Regression Syntax
A web page about extrapolation in regression
Some Halloween humor
Multiple Regression Data
Multiple Regression Syntax
Some notes on regression and correlation
An example of an APA style regression table
A piece on cautions related to using stepwise regression
Bem (2011) article mentioned in class on psi phenomenon/ESP
French's (2012) post about trying to publish replications
Ioannidis (2004) article on published findings being wrong
Homework #8 Data
Moderation Example Handout
Cohen, Cohen, West, and Aiken data for moderation/simple slopes example
Cohen, Cohen, West, and Aiken syntax to run moderation and simple slopes analysis
Baron & Kenny (1986) Article (pdf)
Holmbeck (2002) Article (pdf)
Homework #9 Data
Mediation Example Handout
Mediation Example Data
Mediation Example Syntax
A mediation power point slide show
The In-N-Out Burger website
Chi-Squared and Logistic Regression Handout (we didn't cover logistic regression -- see EXTRAS)
Chi-Squared and Logistic Regression Data to accompany handout
Article to accompany Week #10 Homework (the source of your homework)
Article on why it's a bad idea to turn a continuous variable into a categorical one
Homework #10 Data
Because we didn't get to Logistic Regression in class, I have this great chapter
Here's data from the Titanic on who lives or dies. This data is more complete than that used in the chapter, so the numbers won't match the chapter exactly. However, the variables are the same and you can try the models in the chapter for fun!
General Linear Models Lecture (coming soon)
Cohen (1968) General Linear Model Article (pdf)
Exam Review - Bring your questions to ask (I won't post what you ask here, but that's part of today's plan)
Key to Homework #10 (you won't get it back before the exam)
Final Exam Take Home
Final Exam Take Home Data
Final Exam In Class