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.
Including auxiliary variables such as antecedent and consequent variables in mixture models provides valuable insight in understanding the population heterogeneity embodied by a latent class ...variable. The model building process regarding how to include predictors/correlates and outcomes of the latent class variables into mixture models is an area of active research. As such, new methods of including these variables continue to emerge and best practices for the application of these methods in real data settings (including simple guidelines for choosing amongst them) are still not well established. This paper focuses on one type of auxiliary variable-distal outcomes-providing an overview of the methods currently available for estimating the effects of latent class membership on subsequent distal outcomes. We illustrate the recommended methods in the software packages Mplus and Latent Gold using a latent class model to capture population heterogeneity in students' mathematics attitudes, linking latent class membership to two distal outcomes.
In the aftermath of the COVID-19 pandemic, teleworking is still widely adopted even though it is no longer a mandatory work arrangement. It is important to understand the motives of those who ...continue to telework, as diverse motives may be associated with heterogeneous travel patterns. In this study, we apply a latent class choice model to a 2021 sample of current and expected future teleworkers, and identify five latent segments based on their dominant telework-related motives, namely travel-dominant (comprising 25% of the weighted sample), flexibility-dominant (24%), career-dominant (22%), workplace-discouraged (20%), and family-dominant (9%). Specifically, travel-dominant, workplace-discouraged, and family-dominant teleworkers may use teleworking to solve particular issues, while flexibility-dominant teleworkers may simply be drawn to some of the assorted merits of teleworking (perhaps even to its “option value”). Career-dominant teleworkers tend to treat teleworking as an occasional work arrangement. We also generate detailed segment-specific profiles to better understand each type of teleworker. The segment-specific outcome models explore factors that influence the expected post-COVID teleworking frequency. We find that factors such as gender, education, and job characteristics have heterogeneous impacts on teleworkers with different dominant motives. Interestingly, results suggest gendered family-support roles even among teleworkers with apparently similarly family-oriented motivations. Overall, this study provides useful insights based on classifying teleworkers by different telework-related motives and exploring the heterogeneous impacts of other variables on teleworking frequency. The results will lay a foundation for future studies to explore teleworking impacts on post-COVID travel behavior changes.
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.
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.
PDF
