Abstract
Person-centered methodologies generally refer to those that take unobserved heterogeneity of populations into account. The use of person-centered methodologies has proliferated, which is likely due to a number of factors, such as methodological advances coupled with increased personal computing power and ease of software use. Using latent class analysis and its extension for longitudinal data, [latent transition analysis (LTA)], multiple underlying, homogeneous subgroups can be inferred from a set of categorical and/or continuous observed variables within a large heterogeneous data set. Such analyses allow researchers to statistically treat members of different subgroups separately, which may provide researchers with more power to detect effects of interest and closer alignment between statistical modeling and one’s guiding theory. For many educational and psychological settings, the hierarchical structure of organizational data must also be taken into account; for example, students (i.e., level-1 units) are nested within teacher/schools (i.e., level-2 units). Finally, multilevel LTA can be used to estimate the number of latent classes in each structured unit and the potential movement, or transitions, participants make between latent classes across time. The transitions/stability between latent classes across time can be treated as the outcome in and of itself, or the transitions/stability can be used as a correlate or predictor of some other, distal outcome. The purpose of the paper is to discuss multilevel LTA, provide considerations for its use, and demonstrate variance decomposition, which requires numerous steps. The variance decomposition steps are presented didactically along with a worked example based on analysis from the Social Rating Scale of ECLS-K.
Highlights
Efforts to classify individual cases into homogeneous groups have long been used in order to better understand complex sets of information
When using latent class analysis and its extension for longitudinal data, [latent transition analysis (LTA)], multiple underlying, homogeneous subgroups can be inferred from a set of categorical and/or continuous observed variables within a large heterogeneous data set
Latent class 1 is characterized by students who had lower ratings on the three positive constructs (i.e., Approaches to Learning (AtL), SC, and Interpersonal Skills (IPS)) and higher scores on the constructs reflecting problem behaviors (i.e., Externalizing Problem Behaviors (EPB) and Internalizing Problem Behaviors (IPB))
Summary
Efforts to classify individual cases into homogeneous groups have long been used in order to better understand complex sets of information. Rather than treat all individuals as if they originated from a single underlying population, as is true with variable-centered methodologies, person-centered methodologies allow for multiple subpopulations to underlie a set of data The challenge with these methods is identifying the correct number (i.e., frequency) of subpopulations, or classes, and the parameters (i.e., form). When using latent class analysis and its extension for longitudinal data, [latent transition analysis (LTA)], multiple underlying, homogeneous subgroups can be inferred from a set of categorical and/or continuous observed variables within a large heterogeneous data set Such analyses allow researchers to statistically treat members of different subgroups separately, which may provide researchers with more power to detect effects of interest and closer alignment between statistical modeling and one’s guiding theory. An optimal solution will have high classification probabilities for each latent class, illustrating that the classes are distinct
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