Latent class analysis (LCA) is a statistical method used to identify unobserved subgroups in a population with a chosen set of indicators. Given the increasing popularity of LCA, our aim is to equip psychological researchers with the theoretical and statistical fundamentals that we believe will facilitate the application of LCA models in practice. In this article, we provide answers to 10 frequently asked questions about LCA. The questions included in this article were fielded from our experience consulting with applied researchers interested in using LCA. The major topics include a general introduction in the LCA; an overview of class enumeration (e.g., deciding on the number of classes), including commonly used statistical fit indices; substantive interpretation of LCA solutions; estimation of covariates and distal outcome relations to the latent class variable; data requirements for LCA; software choices and considerations; distinctions and similarities among LCA and related latent variable models; and extensions of the LCA model. To illustrate the modeling ideas described in this article, we present an applied example using LCA. Specifically, we use LCA to model individual differences in positive youth development among college students and analyze demographic characteristics as covariates and a distal outcome of overall life satisfaction. We also include key references that direct readers to more detailed and technical discussions of these topics for which we provide an applied and introductory overview. We conclude by mentioning future developments in research and practice, including advanced cross-sectional and longitudinal extensions of LCA. What is the significance of this article for the general public?In this article, we answer 10 frequently asked questions about the technical and applied underpinnings of latent class analysis (LCA), a statistical approach to understanding unobservable within-group differences in a population. Our goal is to provide readers with an introductory and conceptual understanding of LCA to inform appropriate application and interpretation of these models in research practice.

The 3-step method for estimating the effects of auxiliary variables (i.e., covariates and distal outcome) in mixture modeling provides a useful way to specify complex mixture models. One of the benefits of this method is that the measurement parameters of the mixture model are not influenced by the auxiliary variable(s). In addition, it allows for models that involve multiple latent class variables to be specified without each part of the model influencing the others. This article describes a unique latent transition analysis model where the measurement models are a latent class analysis model and a growth mixture model. We highlight the application of this model to study kindergarten readiness profiles and link it to elementary students' reading trajectories. Mplus syntax for the 3-step specification is provided.

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As frontline education providers, teachers have encountered many challenges since the outbreak of the COVID-19 pandemic. To better understand teacher well-being during this crisis and inform practices to support them, this study employed an online survey with a mixed-methods approach to assess teacher well-being and the support they need to work effectively. A sample of 151 elementary school teachers in the United States was recruited in summer 2020 to complete an online survey through emails and social media outlets. Participants were asked to provide retrospective reports of their experiences teaching in spring 2020 after schools closed due to COVID-19. The majority of participants reported feeling emotionally exhausted and high levels of task stress and job ambiguity. Consistent with hypotheses, path analysis testing a model informed by the job demand-resources framework indicated that task stress and job ambiguity were robustly related to teacher well-being. Moreover, three job resources (i.e., teaching efficacy, school connectedness, and teaching autonomy) were related to job satisfaction. A moderation finding revealed that teachers who reported high teaching efficacy felt emotionally exhausted when they were unclear of their job duties. Thematic analysis of responses to an open-ended question found that teachers would feel supported if provided resources to develop competence in distance learning, workplace emotional support, and flexibility during COVID-19. The findings identified a critical need to allocate more attention and resources to support teacher psychological health by strengthening emotional support, autonomy, and teaching efficacy. Impact and ImplicationsNearly half of the surveyed teachers experienced a high level of stress during the first few months of teaching during school closures due to COVID-19. The findings describe how key job demands and job resources influence elementary school teachers' well-being in the pandemic. In particular, school psychologists and administrators may better support teachers' well-being during the COVID-19 pandemic by reducing teachers' workload, developing clear job expectations, and promoting online teaching competence.