TY - JOUR
T1 - Commentary on latent class, latent profile, and latent transition analysis for characterizing individual differences in learning
AU - Bray, Bethany C.
AU - Dziak, John J.
N1 - Funding Information:
This commentary was supported by an award from the National Institute on Drug Abuse (P50-DA039838 to L. M. Collins). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse or the National Institutes of Health. The authors declare no conflicts of interest.
Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/8
Y1 - 2018/8
N2 - The collection of articles in this special issue focus on latent variable mixture models including latent class analysis (LCA), latent profile analysis (LPA), and latent transition analysis (LTA). These are all methods for summarizing observed variables by postulating an underlying categorical latent variable representing a type or status; in the case of LTA, the status of an individual may change over time and the pathways of change are of interest. As the introductory article by Hickendorff, Edelsbrunner, McMullen, Schneider, and Trezise points out, these methods are useful when theory suggests that a learning or problem-solving process can occur in distinct modes or phases. They can also be useful when it is desirable to give qualitative descriptions of individuals’ approaches to a task based on their responses across several variables rather than just simple numerical scores. The articles in this special issue use latent variable mixture models in creative and insightful ways, demonstrating their versatility and practicality. However, some challenges remain for researchers using these methods. A number of exciting future directions remain for quantitative methodologists and applied researchers to work together to address new questions in learning and individual differences research. Latent variable mixture modeling will continue to be a powerful tool learning researchers can use to address the critical, sophisticated, theoretically based research questions facing the field.
AB - The collection of articles in this special issue focus on latent variable mixture models including latent class analysis (LCA), latent profile analysis (LPA), and latent transition analysis (LTA). These are all methods for summarizing observed variables by postulating an underlying categorical latent variable representing a type or status; in the case of LTA, the status of an individual may change over time and the pathways of change are of interest. As the introductory article by Hickendorff, Edelsbrunner, McMullen, Schneider, and Trezise points out, these methods are useful when theory suggests that a learning or problem-solving process can occur in distinct modes or phases. They can also be useful when it is desirable to give qualitative descriptions of individuals’ approaches to a task based on their responses across several variables rather than just simple numerical scores. The articles in this special issue use latent variable mixture models in creative and insightful ways, demonstrating their versatility and practicality. However, some challenges remain for researchers using these methods. A number of exciting future directions remain for quantitative methodologists and applied researchers to work together to address new questions in learning and individual differences research. Latent variable mixture modeling will continue to be a powerful tool learning researchers can use to address the critical, sophisticated, theoretically based research questions facing the field.
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U2 - 10.1016/j.lindif.2018.06.001
DO - 10.1016/j.lindif.2018.06.001
M3 - Article
AN - SCOPUS:85048431017
SN - 1041-6080
VL - 66
SP - 105
EP - 110
JO - Learning and Individual Differences
JF - Learning and Individual Differences
ER -