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.

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The COVID-19 crisis and the subsequent social distancing measures have imposed numerous challenges on school psychologists balancing public health and students' right to a free and appropriate education. This article addresses one of the most contentious issues for school psychologists during the pandemic: how to ethically conduct valid psychoeducational assessments without placing anyone's health at risk. Legal guidance and regulations pertaining to special education evaluations during the pandemic are first delineated, followed by a discussion of the feasibility of tele-assessment from ethical, legal, and implementational perspectives. Lastly, based on the epidemiological knowledge of COVID-19 transmission and practices implemented in other countries, a protocol for special education assessment is introduced that aims at assisting school psychologists to conduct necessary assessments in face of the ongoing pandemic. IMPACT STATEMENTWith limitations in current online assessment options, lack of certainty with regard to a vaccine solution, and the mounting number of assessments that will befall school psychologists, this article offers an assessment protocol in light of what is currently known about the epidemiological nature of COVID-19.

Latent transition analysis (LTA), also referred to as latent Markov modeling, is an extension of latent class/profile analysis (LCA/LPA) used to model the interrelations of multiple latent class variables. LTA methods have become increasingly accessible and in-turn are being utilized in applied research. The current article provides an introduction to LTA by answering 10 questions commonly asked by applied researchers. Topics discussed include: (1) an overview of LTA; (2) a comparison of LTA to other longitudinal models; (3) software used to run LTA; (4) sample size suggestions; (5) modeling steps in LTA; (6) measurement invariance; (7) the inclusion of auxiliary variables; (8) interpreting results of an LTA; (9) the nature of data (e.g., longitudinal, cross-sectional); and (10) extensions of LTA. An applied example of LTA is included to help understand how to build an LTA and interpret results. Finally, the article suggests future areas of research for LTA. This article provides an overview of LTA, highlighting key decisions researchers need to make to navigate and implement an LTA analysis from start to finish.