For some time, there have been differing recommendations about how and when to include covariates in the mixture model building process. Some have advocated the inclusion of covariates after ...enumeration, whereas others recommend including them early on in the modeling process. These conflicting recommendations have led to inconsistent practices and unease in trusting modeling results. In an attempt to resolve this discord, we conducted a Monte Carlo simulation to examine the impact of covariate exclusion and misspecification of covariate effects on the enumeration process. We considered population and analysis models with both direct and indirect paths from the covariates to the latent class indicators. As expected, misspecified covariate effects most commonly led to the overextraction of classes. Findings suggest that the number of classes could be reliably determined using the unconditional latent class model, thus our recommendation is that class enumeration be done prior to the inclusion of covariates.
Putting the “Person” in the Center Woo, Sang Eun; Jebb, Andrew T.; Tay, Louis ...
Organizational research methods,
10/2018, Letnik:
21, Številka:
4
Journal Article
Recenzirano
This article provides a review and synthesis of person-centered analytic (i.e., clustering) methods in organizational psychology with the aim of (a) placing them into an organizing framework to ...facilitate analysis and interpretation and (b) constructing a set of practical recommendations to guide future person-centered research. To do so, we first clarify the terminological and conceptual issues that still cloud person-centered approaches. Next, we organize the diverse kinds of person-centered analyses into two major statistical approaches, algorithmic and latent-variable approaches. We then present a literature review that quantifies how these two approaches have been used within our field, identifying trends over time and typical study characteristics. Out of this review, we construct a unifying taxonomy of the five ways in which clusters are differentiated: (1) construct-based patterns, (2) response-style patterns, (3) predictive relations, (4) growth trajectories, and (5) measurement models. We also provide a set of practical guidelines for researchers and highlight a few remaining questions and/or areas in which future work is needed for further advancing person-centered methodologies.
Individual-level longitudinal data on biological, behavioural, and social dimensions are becoming increasingly available. Typically, these data are analysed using mixed effects models, with the ...result summarised in terms of an average trajectory plus measures of the individual variations around this average. However, public health investigations would benefit from finer modelling of these individual variations which identify not just one average trajectory, but several typical trajectories. If evidence of heterogeneity in the development of these variables is found, the role played by temporally preceding (explanatory) variables as well as the potential impact of differential trajectories may have on later outcomes is often of interest. A wide choice of methods for uncovering typical trajectories and relating them to precursors and later outcomes exists. However, despite their increasing use, no practical overview of these methods targeted at epidemiological applications exists. Hence we provide: (a) a review of the three most commonly used methods for the identification of latent trajectories (growth mixture models, latent class growth analysis, and longitudinal latent class analysis); and (b) recommendations for the identification and interpretation of these trajectories and of their relationship with other variables. For illustration, we use longitudinal data on childhood body mass index and parental reports of fussy eating, collected in the Avon Longitudinal Study of Parents and Children.
poLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both models ...can be called using a single simple command line. The basic latent class model is a finite mixture model in which the component distributions are assumed to be multi-way cross-classification tables with all variables mutually independent. The latent class regression model further enables the researcher to estimate the effects of covariates on predicting latent class membership. poLCA uses expectation-maximization and Newton-Raphson algorithms to find maximum likelihood estimates of the model parameters.
Abstract
The use of
longitudinal finite mixture models such as group-based trajectory modeling has seen a sharp increase during the last few decades in the medical literature. However, these methods ...have been criticized, especially because of the data-driven modeling process, which involves statistical decision-making. In this paper, we propose an approach that uses the bootstrap to sample observations with replacement from the original data to validate the number of groups identified and to quantify the uncertainty in the number of groups. The method allows investigation of the statistical validity and uncertainty of the groups identified in the original data by checking to see whether the same solution is also found across the bootstrap samples. In a simulation study, we examined whether the bootstrap-estimated variability in the number of groups reflected the replicationwise variability. We evaluated the ability of 3 commonly used adequacy criteria (average posterior probability, odds of correct classification, and relative entropy) to identify uncertainty in the number of groups. Finally, we illustrate the proposed approach using data from the Quebec Integrated Chronic Disease Surveillance System to identify longitudinal medication patterns between 2015 and 2018 in older adults with diabetes.
In this article, we consider the broad applicability of latent class analysis (LCA) and related approaches to advance research on child development. First, we describe the role of person‐centered ...methods such as LCA in developmental research, and review prior applications of LCA to the study of development and related areas of research. Then we present practical considerations when applying LCA in developmental research, including model selection and statistical power. Finally, we introduce several recent methodological innovations in LCA, including causal inference in LCA, predicting a distal outcome from LC membership, and LC moderation (in which LCA quantifies multidimensional moderators of effects in observational and experimental studies), and we discuss their potential to advance developmental science. We conclude with suggestions for ongoing developmental research using LCA.
Statistical latent class models are widely used in social and psychological researches, yet it is often difficult to establish the identifiability of the model parameters. In this paper, we consider ...the identifiability issue of a family of restricted latent class models, where the restriction structures are needed to reflect pre-specified assumptions on the related assessment. We establish the identifiability results in the strict sense and specify which types of restriction structure would give the identifiability of the model parameters. The results not only guarantee the validity of many of the popularly used models, but also provide a guideline for the related experimental design, where in the current applications the design is usually experience based and identifiability is not guaranteed. Theoretically, we develop a new technique to establish the identifiability result, which may be extended to other restricted latent class models.
PURPOSEFew studies have captured the multidimensionality of pregnancy intentions for adolescents on a national level, particularly missing the perspectives of male adolescents. Therefore, this study ...aimed to identify and describe pregnancy intention profiles among U.S. adolescents. METHODSLatent class analysis was conducted using data from two cycles of the National Survey of Family Growth (2015-2017 and 2017-2019) among U.S. adolescents 15-19 years old (N = 3,812). Stratified by sex, six National Survey of Family Growth indicators around desires, feeling, timing, and social acceptability were included. Multinomial logistic regression was used to identify the correlates of class membership. RESULTSThree latent classes of pregnancy intention were identified for each sex, which were distinguished by immediate and future desires, feelings, timing, and social acceptability. For both females and males, Delayed Pro-pregnancy (53% vs. 82%) and Near Pro-pregnancy (28% vs. 8%) were identified. Ambivalent-pregnancy (14%) and Anti-pregnancy (10%) were specific to females and males, respectively. Near Pro-pregnancy females and Anti-pregnancy males were more likely to be sexually active, older, of Hispanic descent, report receiving public assistance, and have a teen mother than adolescents classified as Delayed Pro-pregnancy. Females with a pregnancy history were more likely to be classified as Ambivalent than Delayed Pro-pregnancy. DISCUSSIONWhile most adolescents intend to delay or avoid childbearing, there are subsets of adolescents whose pregnancy intentions are in favor of early childbearing, which is often dismissed in adolescent sexual and reproductive health. Current efforts can use these distinct pregnancy intention classes to tailor sexual and reproductive health services specifically for diverse adolescent populations.
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