Jason
Newsom's
USP 654 Data Analysis II

Description
This
course takes an applied approach to statistical analysis and research
methodology and is the second in a twocourse sequence. The goal is to
provide students with statistical background, conceptual understanding,
technical writing skills, computer application, and the ability to apply
these skills to realistic data analysis problems and research designs. Topics
include simple regression and correlation, multiple regression, and logistic
regression. The laboratory (USP 654L) must be taken concurrently. Recommended
prerequisite: USP 634 Data Analysis or an equivalent course approved by the
instructor and prior experience with statistical software. All download files pdf unless otherwise noted. 

Fall
2015 Syllabus
Click here
for the class syllabus, reading list, and my contact information.

Supplemental
Readings
Click here to
download readings in a zip file (9.1mb).

Current
Homework
Get a copy of the current homework.
Download homework
articles.

Homework
Data Sets
Get the data you need here.

Handouts
and Overheads
(sorry,
not available until it has been covered in class, not all overheads included)
Correlation
Example: SPSS and R
Levels of
Measurement and Choosing the Correct Statistical Test
tTests, Chisquares, Phi, Correlations: Its all the same
stuff
Overhead:
Simple Regression Variance Partitioning
Simple
Regression Example: SPSS and R, Extra
R Code
Overhead:
Venn Diagram for Multiple Regression
Multiple
Regression Example: SPSS and R
Hierarchical
Regression Example: SPSS and R
Hierarchical
Table Example I Like
Overhead: Partial
and Semipartial Correlation
Partial and Semipartial Correlation Example
Coding of
Categorical Predictors and ANCOVA
Creating Coding
Variables for Four Categories
Equivalence
of ANOVA and Regression: SPSS and R, Extra R
Code
Darlington
Suppression Example
Summary of Regression
Diagnostics and Cutoffs
SPSS Regression
Diagnostics Example (with tweaked data)
Regression
Diagnostic Examples with R
Remedies for
Assumption Violations and Multicollinearity
Curvilinear
Regression Example: SPSS and R
Regression
Moderation Examples: SPSS and R
Mediation
Examples: SPSS and R
Chisquare
Example: SPSS and R
Simple
Logistic Regression Using Continuous Predictor: SPSS and R
Computing the Odds Ratio
from Cell Frequencies
Simple
Logistic Regression with a Dichotomous Predictor: SPSS and R
Multiple
Logistic Regression and Model Fit
Multiple
Logistic Regression Example: SPSS and R
Link Functions and the
Generalized Linear Model
Regression Models
for Ordinal Dependent Variables
Ordinal
Logit and Probit Examples: SPSS and R
Regression Models for
Count Data

Computer
Lab
Handouts
and data, lab instructor: Jamaal Green
Stats
Notes
Over 20 "Web
lectures" on introductory graduate statistics

Links
Jason's list of
statistics links
Jason's SPSS Macros for
Interactions and Simple effects
UCLA's statistical
computing site on SPSS
UCLA's
statistical computing site on regression with SPSS
Dave MacKinnon's
site on mediation
David Kenny's site (click on
"mediation")
Automatic calculator for calculating
significance of indirect effects
(compliments of Preacher & Leonardelli at KU and U of Toronto)
Oscar
TorresReyna's Regression with R tutorial
Brief
summary of some R regression related functions
Bootstrap
Macros for calculating significance of indirect effects
(compliments of Hayes and Preacher at Ohio State & KU)
Carl Falk's
mediation page (with R code links)
R
for SAS and SPSS Users by Rob Muenchen