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Research on how COVID-19 school closures impacted the learning of young children is still sparse, and the broader implications of these findings have yet to be fully explored. In this study, we examine the impacts of COVID-19 school closures on language and literacy development in a state-funded pre-K program in a Florida school district. Using COVID closures as a natural experiment, we additionally explore the implications of these findings for the literature on the efficacy and dosage of state-funded pre-K programs. We used propensity score matching to compare children from the COVID cohort (n = 1,211) to children from a pre-COVID cohort (n = 1,167). Results revealed no significant difference on the Florida Kindergarten Readiness Screener (FLKRS) assessment between the two cohorts at kindergarten entry. The COVID cohort had significantly higher scores on each of the individual subdomains of the FLKRS than the pre-COVID cohort, with especially large positive effect sizes for constrained literacy skills. Results indicate that COVID-19 closures did not have significant negative impacts on pre-K children's language and literacy skills at kindergarten entry. Findings further suggest that state-funded pre-K programs with comparatively less funding and lower quality ratings may not provide adequate support for children's language and literacy outcomes.

Latent profile analysis (LPA) identifies heterogeneous subgroups based on continuous indicators that represent different dimensions. It is a common practice to measure each dimension using items, create composite or factor scores for each dimension, and use these scores as indicators of profiles in LPA. In this case, measurement models for dimensions are not included and potential noninvariance across latent profiles is not modeled in LPA. This simulation study examined the robustness of LPA in terms of class enumeration and parameter recovery when the noninvariance was unmodeled by using composite or factor scores as profile indicators. Results showed that correct class enumeration rates of LPA were relatively high with small degree of noninvariance, large class separation, large sample size, and equal proportions. Severe bias in profile indicator mean difference was observed with intercept and loading noninvariance, respectively. Implications for applied researchers are discussed.

Multilevel latent class analysis (MLCA) has been increasingly used to investigate unobserved population heterogeneity while taking into account data dependency. Nonparametric MLCA has gained much popularity due to the advantage of classifying both individuals and clusters into latent classes. This study demonstrated the need to relax the assumption in specifying the nonparametric MLCA: item response probabilities varied only across level-1 latent classes, but not level-2 latent classes. An empirical demonstration with data from the Trends in International Mathematics and Science Study (TIMSS) 2011 showed that item response probabilities could vary across both level-1 and level-2 latent classes. This relaxed MLCA yielded better model fit and provided more nuanced understanding of the heterogeneous response patterns. Monte Carlo simulation was conducted to evaluate class enumeration and assignment accuracy of the relaxed MLCA. Based on the simulation results, we recommended the use of AIC in class enumeration and highlighted the benefits of having larger cluster size.

A simulation study was conducted to examine the efficacy of nine frequently-used HOV tests, including Levene's tests with squared residuals and with absolute residuals, Brown and Forsythe (BF) test, Bootstrap BF test, O'Brien test, Z-variance test, Box-Scheffé (BS) test, Bartlett test, and Pseudo jackknife test under comprehensive simulation conditions. The results indicate that O'Brien, Levene's with squared deviations, BS, BF, and Bootstrap BF tests had adequate Type I error control, especially for O'Brien test and Levene's test with squared deviations. Under conditions of adequately controlling for Type I error, BF and Bootstrap BF tests had consistently higher power estimates across simulation conditions. Practical recommendations of guidelines for HOV test selection in factorial ANOVA designs are presented in the paper.

Although collecting data from multiple informants is highly recommended, methods to model the congruence and incongruence between informants are limited. Bauer and colleagues suggested the trifactor model that decomposes the variances into common factor, informant perspective factors, and item-specific factors. This study extends their work to the trifactor mixture model that combines the trifactor model and the mixture model. This combined approach allows researchers to investigate the common and unique perspectives of multiple informants on targets using latent factors and simultaneously take into account potential heterogeneity of targets using latent classes. We demonstrate this model using student self-rated and teacher-rated academic behaviors (N = 24,094). Model specification and testing procedures are explicated in detail. Methodological and practical issues in conducting the trifactor mixture analysis are discussed.

Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed.

Using data from multiple informants has long been considered best practice in education. However, multiple informants often disagree on similar constructs, complicating decision-making. Polynomial regression and response-surface analysis (PRA) is often used to test the congruence effect between multiple informants on an outcome. However, PRA assumes the congruence effect on the outcome is homogeneous across individuals. To investigate unknown heterogeneity in the congruence effect, we introduce polynomial regression mixture modeling (PRMix). We demonstrate how PRMix enhances congruence research by allowing researchers to investigate individual differences in the congruence effect. The differential congruence effects between latent classes are illustrated with response-surface plots. We provide practical suggestions for using PRMix and discuss future research directions in methodology. [ABSTRACT FROM AUTHOR]