Longitudinal Models (HPSC 513)

This course is an elective in the clinical psychology Ph.D. program.  

The class was offered during the 2015/2016 school year.  I used SPSS and Mplus; while background through and including multivariate statistics (i.e., Statistics II at RFUMS), no knowledge of statistics packages is assumed (some students have experience with LISREL, whereas others worked with Mplus).  The plan is to offer this course every other spring.

The course focuses on longitudinal modeling.  Modeling will be done from a structural equation modeling perspective (SEM), multilevel modeling (MLM) and generalized estimating equation (GEE) approaches, as well as to techniques with which people are more familiar (i.e., t-tests, regressions, analyses of variance [ANOVAs]). 

E-mail me your thoughts!

Spring, 2016

Little, T. D. (2013). Longitudinal structural equation modeling. New York: The Guilford Press.

Articles:

Enders, C. K. (2001).  A primer on maximum likelihood algorithms available for use with missing data.  Structural Equation Modeling, 8(1), 128-141. (pdf)

Graham, J. W. (2009). Missing data analysis:  Making it work in the real world. Annual Review of Psychology, 60, 549-576(pdf)

McArdle, J. J., & Hamagami, F. (2001). Latent difference score structural equation models for linear dynamic analysis. In L. M. Collins & A. G. Sayer (eds.). New methods for the analysis of change. (pp. 139-175). Washington, DC, US: American Psychological Association. (pdf)

Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediationPsychological Methods15, 209-233.

My multilevel structural equation modeling resources site

Assignments:

Homework          25%
Exam:                 25%
Presentation        25%
Final Paper           25%

Calendar

 Week     Day (Tentative) Topic Reading Example Assignment
 Week 1Monday 
February 29
 Introduction

Review of Structural Equation Modeling

Latent Difference Score Models and paired samples t-tests and missing data, oh my!

Missinng data (SPSS vs. Mplus)
Syllabus

Enders, C. K. (2001).  A primer on maximum likelihood algorithms available for use with missing data.  Structural Equation Modeling, 8(1), 128-141. (pdf)

Graham, J. W. (2009). Missing data analysis:  Making it work in the real world. Annual Review of Psychology, 60, 549-576. (pdf)

McArdle, J. J., & Hamagami, F. (2001). Latent difference score structural equation models for linear dynamic analysis. In L. M. Collins & A. G. Sayer (eds.). New methods for the analysis of change. (pp. 139-175). Washington, DC, US: American Psychological Association. (pdf)

Little, Ch. 1
Data File for Day 1 (SPSS)
Data File for Day 1 (Mplus)

Input

Output
Other
    Info Sheet from Syllabus

    (due next class)
    Week 2
      Data File for Day 2 (SPSS)

    Data File for Day 2 (Mplus)

    Input
    A

    Output
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    Other
    A
     
    Week 3   Data File for Day 3 (SPSS)

    Data File for Day 3 (Mplus)

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    Output
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    Other
    A
     
    Week 4    Data File for Day 4 (SPSS)

    Data File for Day 4 (Mplus)

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    A
     
    Week 5   Data File for Day 5 (SPSS)

    Data File for Day 5 (Mplus)

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    A

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    Other
    A
     
    Week 6    Data File for Day 6 (SPSS)

    Data File for Day 6 (Mplus)

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    Week 7    Data File for Day 7 (SPSS)

    Data File for Day 7 (Mplus)

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    Week 8    Data File for Day 8 (SPSS)

    Data File for Day 8 (Mplus)

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    Week 9Monday, April 25Final Exam Assigned

     Data File for Day 9 (SPSS)

    Data File for Day 9 (Mplus)

    Input
    A

    Output
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    Week 10Monday, May 2Final Exam Due

    Survival Analysis

    MEPSUM

    Structured Curves



     Data File for Day 10 (SPSS)

    Data File for Day 10 (Mplus)

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    A

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     Week 11   Monday, May 9Presentations - Day 1   
     Week 12         Monday, MY 16        Presentations - Day 2

    Go Over Final Examinations

    Potluck

       


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