Resources · Curran-Bauer Analytics


We seek to provide resources that researchers can use to gain greater knowledge of quantitative methods and to facilitate various types of statistical analysis. See below for currently available resources.

CBA Office Hour Videos

In an ongoing series of short informal videos, we answer frequently asked questions about statistical methods. Topics range from introductions to modeling techniques (e.g., "What is growth curve modeling?") to comparisons between techniques (e.g., "What's the difference between finite mixture models and cluster analysis?") and questions about the application of these techniques in practice (e.g., "How many clusters do I need to fit a multilevel model?"). We also often include links to other resources for those wishing to learn more about each topic.
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Syntax for Computing Random Effect Estimates in SPSS

histoMany programs can be used to fit multilevel models. For instance, in our multilevel modeling summer workshop, we demonstrate three programs: SAS, SPSS, and Stata. Unlike SAS, Stata, and many other programs, however, SPSS does not currently offer the option to output estimates of the random effects. Obtaining estimates of the random effects can be useful for a variety of purposes, for instance when producing plots or evaluating model assumptions. To overcome this limitation of SPSS, we have developed documentation and syntax for computing random effects in SPSS based upon output that is easily obtained from the MIXED command.
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Workshop Materials: Introduction to Growth Curve Modeling

SRA We are pleased to make available a number of materials related to a workshop titled Introduction to Growth Curve Modeling: An Overview and Recommendations for Practice that Curran-Bauer Analytics conducted at the biennial convention of the Society for Research on Adolescence on April 3rd, 2016. We have posted a copy of the slides from the workshop as well as example data and syntax files for fitting mulitilevel growth models in SAS, SPSS, and Stata and latent curve models in Stata and Mplus. We hope these resources will be of use to those who are beginning to consider the use of growth models in their own research.
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