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Methodologists’ view of the so-called “reproducibility crisis”

January 5, 2016

There is increasing attention paid to the so-called “reproducibility crisis” in psychology. There have been a number of high-profile reports of failures to reproduce many classic findings in psychology. Although this is of course a critically important topic within the social sciences, a recent article by Samantha Anderson and Scott Maxwell in Psychological Methods argues that we must take a broader perspective on reproducibility than simply replicating the single outcome of a given experiment. In “There’s More Than One Way to Conduct a Replication Study: Beyond Statistical Significance” Anderson and Maxwell propose additional “replication goals” that should be considered in the planning of a study. We recommend this paper if you are interested in learning about a broader perspective on replication within the behavioral sciences.

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Summer Workshop Schedule

December 18, 2015

We’re pleased to announce our summer workshop schedule:

  • May 23-27: Structural Equation Modeling
  • June 6-10: Longitudinal Structural Equation Modeling
  • June 13-17: Cluster Analysis and Mixture Modeling
  • June 20-24: Multilevel Modeling
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    New Workshops at Curran-Bauer Analytics

    December 15, 2015

    We’re pleased to announce the addition of two new workshops to our summer line up: Longitudinal Structural Equation Modeling and Cluster Analysis and Mixture Modeling

    Longitudinal Structural Equation Modeling is an extension of our prior three-day course on latent curve modeling. In response to requests from participants, we’ve expanded this course into a full five-day workshop. In addition to covering introductory and advanced topics in latent curve modeling, we now also include material on longitudinal measurement modeling, autoregressive cross-lagged panel models, and latent change score analysis, providing a complete treatment of longitudinal modeling approaches within the SEM framework.

    Cluster Analysis and Mixture Modeling is an entirely new five-day workshop focused on the application and interpretation of statistical techniques designed to identify subgroups within a heterogeneous population. This course is being developed in partnership with and will be co-taught by Doug Steinley, a professor of quantitative psychology at the University of Missouri who has published extensively on these topics and is the current editor of Journal of Classification. Doug is a remarkable writer and speaker, and we are excited about this joint offering.

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    Inferring Cause and Effect in Time Series

    December 15, 2015

    One of the greatest challenges in social science research is to validly identify cause-and-effect relations. A recent interactive graphic in the New York Times highlights a prospective, quasi-experimental approach to linking increased gun sales to specific social and political events.

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    Within- and Between-Group Effects

    December 9, 2015

    An important concept to understand with multilevel data is the distinction between between-group and within-group effects. For instance, larger animal species (elephants) tend to live longer than smaller species (ducks); this is the between-group effect of size on life expectancy. Within a species, however, larger individuals (big ducks) tend to live less long than smaller individuals (small ducks); this is the within-group effect of size on life expectancy. Recently, Shankar Vedantan described another example on NPR: people report being happier making $50 for a project if their co-workers are making $40 versus making $60 for the same project if their co-workers are making $70. See the full story.

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