This is a seminar course designed to provide students with an intensive experience in the multivariate analysis of social science data. Computing for the course will be primarily through SPSS for Windows and AMOS. The WebStats site running SPSS under Unix may be useful at various portions of the course, but AMOS running under Windows will be necessary for SEM. Computing will be available free of charge for student use in the social science multi-media facility at FE36. Alternatively, site licenses for SPSS are available to students through the Software License Office located on the ground floor of Robarts library. In addition the SPSS Grad Pack is a good choice. Please do not purchase the student version of SPSS available through the bookstore as it is inadequate for our needs. 

Data for instruction and assignments will be drawn from a variety of source which generally will be freely available for student use. Anyone with particular research interests may, with permission, use their own data or other data acquired through the University of Toronto Data Library Service or other archives.

Students wishing to review basic data analysis techniques may wish to consult Alan Agresti and Barbara Finlay, Statistical Methods for the Social Sciences, 4th edition (Pearson-Prentice Hall, 2009). Also very useful is Carol S. Aneshensel, Theory-Based Data Analysis for the Social Sciences. Thousand Oaks, California: Sage, 2002. 

Numerous introductions to SPSS are available including David De Vaus, Analyzing Social Science Data (Sage 2002) or Darren George and Paul Mallery's, SPSS for Windows: Step by Step: A Simple Guide and Reference (Toronto: Allyn and Bacon) or, more briefly, Stephen Sweet's, Data Analysis with SPSS, (Allyn and Bacon).

Required readings are drawn from:

Rex B. Kline, Principles and Practice of Structural Equation Modeling 2nd edition (NY:Guilford, 2005). 

Other texts TBA

Recommended Introductions to SEM:

Barbara M Byrne, Structural Equation Modeling with AMOS: Basic Concepts, Applications and Programming, (Erlbaum, 2001).

Niels J. Blunch, Introduction to Stuctural Equation Modelling, Thousand Oaks, California: Sage, 2009.

David Kaplan, Structural Equation Modelling:Foundations and Extensions, 2nd editon, Thousand Oaks, California: Sage, 2009.

Ed Ringdon's SEM Page

Recommended General Multivariate Texts:

Orley Ashenfelter et al. Statustucs and Econometrics:Methods and Applications. NY: wileyDuncan Cramer, Advanced Quantitative Analysis (NY: Open University Press-McGraw Hill, 2006).

Barbara Tabachnick and Linda Fidell, Using Multivariate Statistics, 5th Edition, Toronto:Pearson, 2007.

Lawrence S Myers et al Applied Multivariate Research., Thousand Oaks, California: Sage, 2006..

Statsoft Inc., Electronic Statistics Textbook Tulsa, (Oklahoma: Statsoft). 

David Stockburger's Multivariate Site

G. David Garson's Statnotes

Schedule of  First Term ClassesRequired readings for each week are indicated by an asterisk. All others are recommended.

September 10

Introduction

September 17

Reviewing Basic Statistics

Garson's entries on Nominal Association, Ordinal Association, Chi Square and the Elaboration Paradigm

Elaboration Chart

Alan Agresti and Barbara Finlay, Statistical Methods for the Social Sciences, 4th edition (NJ: Prentice Hall, 2009,1997), Chap. 8

In addition please consult the various codebooks on the Research Methods Website:

September 24

Reviewing Regression & Measurement .

*Rex Kline, Principles and Practice of Structural Equation Modeling, Chapter 2

Garson's entries on Regression, Reliability and Factor Analysis

Michael Lewis-Beck, Data Analysis: An Introduction (Thousand Oaks, Calif: Sage, 1994) Chapters 3, 4 & 5.

Alan Agresti & Barbara Finlay, Statistical Methods for the Social Sciences, 4th edition (Prentice Hall, 2009, 1997), Chapters 1, 528-532..

Paul Kline, An Easy Guide To Factor Analysis (NY: Routledge, 1993).

Ross E. Traub Reliability for the Social Sciences, ( Sage, 1994).

October 1   

Core Techniques of SEM

*Rex Kline, Principles and Practice of Structural Equation Modeling, Chaps 1&4

*J. Fletcher & P. Howe, "A Structural Model of Support for the Supreme Court of Canada"

Alan Agresti & Barbara Finlay, Statistical Methods for the Social Sciences, 4th edition (Prentice Hall, 2009, 1997), Chapters 1, 528-532.

Alan Bryman and Duncan Cramer, Quantitative Data Analysis with SPSS for Windows (Routledge, 2001) 

October 8

Data Cleaning and Screening

*Rex Kline, Principles and Practice of Structural Equation Modeling, Chapter 3

Darren George and Paul Mallery, SPSS for Windows Step by Step: A Simple Guide and Reference, (Toronto.: Allyn and Bacon, 2001). Chapter 28 and review Chapters 4, 7 & 16.

B.G. Tabachnick and L.S. Fidell, Using Multivariate Statistics 4th ed. (Allyn & Bacon, 2000), Chapter 4.

October 15

Models with Observed Variables 

*Rex Kline, Principles and Practice of Structural Equation Modeling  Chapter 5

October 22

Model Fit, Model Building and Trimming

*Rex Kline, Principles and Practice of Structural Equation Modeling  Chapter 6

October 29

Models with Latent Variables 

*Rex Kline, Principles and Practice of Structural Equation Modeling,Chapter 7 .

November 5 

Hybrid Models 

 *Rex Kline, Principles and Practice of Structural Equation Modeling , Chapter 8

November 12

Nonrecursive Models

*Rex Kline, Principles and Practice of Structural Equation Modeling, Chapter 9

November 19

Mean Models

*Rex Kline, Principles and Practice of Structural Equation Modeling  Chapter 10

K. Bollen and S. Long (eds.), Testing Structural Equation Models, (Newbury Park: Sage, 1993).

Leslie A. Hayduk, LISREL Issues, Debates, and Strategies (Baltimore: Johns Hopkins, 1996).

James Jaccard and Choi K. Wan, Lisrel Approaches to Interaction Effects in Multiple Regression, (Sage, 1996).

David Kaplan, Structural Equation Modeling: Foundations and Extensions, (Sage, 2000)

E. Kevin Kelloway, Using Lisrel for Structural Equation Models: A Researcher's Guide, (Thousand Oaks, California, 1998).

Judea Pearl, Causality: Models, Reasoning, and Inference (Cambridge, 2000).

Ralph O. Meuller, Basic Principles of Structural Equation Modeling: an Introduction to LISREL and EQS, (NY: Springer, 1995)

George A. Marcoulides and Randall E. Schumacker, eds. Advanced Structural Equation Modeling: Issues and Techniques (Mahwah, New Jersey: Erlbaum, 1996).

Randall E. Schumacker and George A. Marcoulides, eds. Interaction and Nonlinear Effects in Structural Equation Modeling (Erlbaum, 1998).

Structual Equation Modeling (Erlbaum)

http://www.gsu.edu/~mkteer/semnet.html

Jason Newsom's reference list

November 26

Multiple Samples

*Rex Kline, Principles and Practice of Structural Equation Modeling  Chapter 11

December 3

Looking Back & Ahead

*Rex Kline, Principles and Practice of Structural Equation Modeling  Chapters 12 & 13

January 7

Cluster Analysis I

Jan 14
Cluster Analysis II

Jan 21
Cluster Analysis III

Jan28
Binomial Logistic Regression I

Fred Pample Logistic Regression:A Primer (Sage: 2000)

Feb 4
Binomial Logistic Regression II

Feb 11 Binomial Logistic Regression III (Interaction Terms)

Feb18
Reading Week

Feb 24
Multinomial Logistic

Mar 4
Ordinal Regression or PLUM Polytonomous

Universal Model)

Mar 11
Ordinal SEM

Mar 18
Ordinal SEM II

Mar 25
No class

Apr 1
Logit Loglinear Models I

Apr 8
Logit Loglinear Models II