Title: Mplus Bootcamp: An Introduction Covering Basic to Intermediate Functions
Level: Basic
Presenters:
Abby Lynn Braitman, Ph.D., Old Dominion University
Abstract:
Mplus is data analysis software focusing on latent variable modeling but includes a wide array of
analysis capabilities beyond structural equation modeling (SEM). Because of its ability to analyze
complex data over a wide range of conditions, Mplus is increasingly popular as a statistical toolbox with
clinical psychology researchers. At the same time, Mplus is almost entirely driven by command-line
syntax, which may be a hurdle for users unfamiliar with code-based analysis. This AMASS will focus on
familiarizing attendees with Mplus, including an overview of its function and characteristics, as well as
basic and intermediate commands. Topics covered will include path analysis, SEM, mediation,
bootstrapping, latent growth modeling, and mixture modeling (i.e., latent class/profile analysis). We will
review both input and output as well as troubleshooting options. It is assumed that participants are
familiar with the theory behind each analysis covered (regression, SEM, latent growth modeling, etc.) as
the focus of the presentation is on how to use Mplus to accomplish these analyses. Having a copy of
Mplus is not required for participation, and those who might be considering Mplus as data analysis
software are encouraged to attend.
You Will Learn:
1. How to execute several analyses using Mplus (e.g., path analysis, SEM, mediation, latent growth
models, mixture modeling).
2. How to activate specific coding options (e.g., bootstrapping, technical output).
3. How to read and interpret Mplus output.
Recommended Readings:
Muthén, L.K., & Muthén, B.O. (1998–2012). Mplus User’s Guide (7th ed.). Los Angeles, CA: Muthén &
Muthén. [PDF freely available via Mplus website: https://www.statmodel.com/]
Title: Introduction to Hierarchical Linear Models for Longitudinal Data
Level: Basic
Presenters:
Robert Gallop, Ph.D., West Chester University
Abstract:
Longitudinal data is extremely common in clinical research, whether from intervention studies or
observational research. Hierarchical linear models (HLM; also called multilevel or mixed effects models)
are powerful regression-based models that have a number of advantages for longitudinal data. This
AMASS will introduce the basics of HLM for longitudinal data analysis in clinical research, with particular
focus on psychotherapy research. In addition, this presentation will cover some of the complexities that
can arise with clinical data and how best to address them, including (a) impact of missing data in the
HLM, (b) determination of the complexity of the covariance structure and its implication on model
interpretation, (c) issues with centering, (d) model diagnostics for HLM, (e) model formation including
implementation dependent on the treatment of time and distribution of the outcome, and (f) model
estimation. Data from psychotherapy research settings will also be discussed as examples. Finally, this
session will offer some caveats and advice for recognizing various complexities and proper procession to
ensure accurate implementation of the HLM and the interpretation of the results. Analysis examples will
be presented using SPSS, though resources for other software packages will be provided.
You Will Learn:
1. How to estimate and interpret results from HLM analyses of longitudinal psychotherapy data.
2. How to assess for and fit more complex models for change, such as quadratic, cubic, and piecewise.
3. How to write SPSS commands for HLM via examples including sample syntax and screen captures of
pull-down menus.
Recommended Readings:
Gallop, R., & Tasca, G.A. (2009). Multilevel modeling of longitudinal data for psychotherapy researchers:
II. The complexities. Psychotherapy Research, 19, 435-452.
Hedeker, D., & Gibbons, R.D. (2006). Longitudinal data analysis. Hoboken, NJ: Wiley.
Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical linear models (2nd ed.). Thousand Oaks, CA: Sage.
Tasca, G.A., & Gallop, R. (2009). Multilevel modeling of longitudinal data for psychotherapy researchers:
I. The basics. Psychotherapy Research, 19, 429-437.