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Latent Class Analysis in Prevention Science

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Defining Prevention Science

Part of the book series: Advances in Prevention Science ((Adv. Prevention Science))

Abstract

Often prevention researchers have an innate sense that important and noticable difference exist in the popoulatoin that they are studying. In fact, it is usually of utmost importance to identify and understand the meaningful, yet often subtle, differences that might exist. Latent class analysis (LCA) has a rich history as an analytical tool for many disciplines and provides a unique opportunity for prevention scientists to better understand those nuanced differences—that is, heterogeneity—in a population. A better understanding of the population is likely to lead to better treatment outcomes, such as more specifically tailored interventions. Further, the intervention or program may have different effects for different parts of a targeted population; thus, a one-size-fits-all approach to studying intervention effects will not properly describe the ways in which the intervention made a difference. In this way, LCA offers the opportunity to allow for treatment effects to be estimated separately for different subgroups of interest—that is, to investigate potential mechanisms by which certain effects are produced.

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Notes

  1. 1.

    In Mplus, posterior probabilities can be saved to an external data file using the “SAVEDATA” command and specifying “save = cprobabilities.”

  2. 2.

    The authors would like to thank Drs. Erin Dowdy, Jill Sharkey, Erika Felix, and Mike Furlong for access to these data.

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Authors and Affiliations

  1. Department of Education, Gevirtz Graduate School of Education, University of California, Santa Barbara, CA, 93106-9490, USA

    Karen Nylund-Gibson

  2. Department of Education, Gevirtz Graduate School of Education, University of California, Santa Barbara, CA, USA

    Shelley R. Hart

Authors
  1. Karen Nylund-Gibson
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  2. Shelley R. Hart
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Correspondence to Karen Nylund-Gibson .

Editor information

Editors and Affiliations

  1. Ontario, Ohio, USA

    Zili Sloboda

  2. JBS International, Inc., Rockville, USA

    Hanno Petras

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Nylund-Gibson, K., Hart, S.R. (2014). Latent Class Analysis in Prevention Science. In: Sloboda, Z., Petras, H. (eds) Defining Prevention Science. Advances in Prevention Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7424-2_21

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  • DOI: https://doi.org/10.1007/978-1-4899-7424-2_21

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