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Purpose: The purpose of current study was to evaluate change in hours of Applied Behavior Analysis (ABA) therapy utilization for autistic children during the year prior to the COVID-19 pandemic, the first three months of the pandemic (crisis phase), and the following 9 months of the pandemic (mitigation phase). Additionally, this study aimed to evaluate if change in therapy utilization differed based on child race, ethnicity, and primary payer of services. Finally, we aimed to identify potential mechanisms of ABA therapy disruption by interpreting findings using an extended version of Donabedian's structure-process-outcome model. Methods: Retrospective clinical data on client demographics and therapy utilization (n = 283) were collected from ABA clinics in California and analyzed with four piecewise growth multi-level models. Results: We found that therapy utilization dropped during the first three months of the pandemic (-10.65 h/month; p < 0.001) and increased during the following 9 months (2.39 h/month; p < 0.001). Moderator analyses revelated that Asian, Non-Latinx and school-district funded children had significantly different trajectories of change in therapy utilization compared to white, non-Latinx participants and private insurance funded participants, respectively. Conclusion: Findings suggest that utilization of ABA therapy was disrupted for a full year following the onset of the COVID-19 pandemic and that child race/ethnicity and primary payer influenced the degree to which autistic children were impacted by service disruption. These findings have implications for autistic children who lost therapy access during key developmental periods and for the ABA care delivery system.

As student engagement in science learning continues to garner significant attention from educational researchers, practitioners, and policymakers, the importance of understanding person and context-related factors that impact student engagement is increasingly recognized. In this mixed methods study, middle school students' (N = 1,848) engagement was examined in relation to opportunities to participate in science learning (OtP in science) from 29 schools in the United States. Drawing on the ecological systems and opportunity-propensity frameworks, multilevel structural equation modeling was applied to test the relationship between OtP in science and student engagement, accounting for student (gender, grade, ethnicity) and school (socioeconomic status) factors. Results showed that at the student level, OtP in science was a statistically significant predictor of engagement, and this relationship varied by school. In addition, school SES predicted school-average OtP in science, and there was a statistically significant cross-level interaction for school SES, in that the relationship between OtP in science and engagement was stronger for schools with higher SES. In the qualitative phase, 6 student focus group interviews were analyzed to develop explanatory accounts for the quantitative results. Collaborative hands-on activities, peer-to-peer discourse, and positive teacher and student relationships were identified as social processes that facilitated students' propensity to engage in the science learning opportunities. Factors related to school SES that explain the link between OtP in science and student engagement are also described. Taken together, our findings provide a more complete picture of how students' engagement in science relates to person and context-related factors and hold theoretical and practical implications for the study of engagement in diverse classrooms.

Previous studies have detailed the consequence of ignoring a level of clustering in multilevel models with straightly hierarchical structures and have proposed methods to adjust for the fixed effect standard errors (SEs). However, in behavioral and social science research, there are usually two or more crossed clustering levels, such as when students are cross-classified by schools and neighborhoods, yet it is not uncommon that researchers focus only on one level of clustering. Using the generalized least squares framework, in this study, we derive the bias in the fixed effect SE estimators when one crossed random effect is omitted. We then showed, using data from the Scotland Neighborhood Study, how one can correct for the SEs and obtain corrected statistical inference when a misspecified two-level model was used in a primary study, which is useful when evaluating observational studies or cluster randomized trials that ignored a crossed random effects or when conducting meta-analyses. In addition, our analytic results provide theoretical insights on how one can quantify imbalance with cross-classified data by the strength of association between the two-crossed random effects in a contingency table and how the degree of imbalance relates to the correction factor for the fixed effect SEs.

Path models with observed composites based on multiple items (e.g., mean or sum score of the items) are commonly used to test interaction effects. Under this practice, researchers generally assume that the observed composites are measured without errors. In this study, we reviewed and evaluated two alternative methods within the structural equation modeling (SEM) framework, namely, the reliability-adjusted product indicator (RAPI) method and the latent moderated structural equations (LMS) method, which can both flexibly take into account measurement errors. Results showed that both these methods generally produced unbiased estimates of the interaction effects. On the other hand, the path model--without considering measurement errors--led to substantial bias and a low confidence interval coverage rate of nonzero interaction effects. Other findings and implications for future studies are discussed.

Although letter naming fluency (LNF) and letter sound fluency (LSF) measures are widely available to educators for assessing early literacy skills of kindergarten children, better understanding of the contributions of these skills to reading development can help improve the interpretation of LNF and LSF data for instructional decisions. This study investigated the interrelations of growth in LNF and LSF across the kindergarten year and their unique roles in predicting later reading fluency. Piecewise parallel-process growth models indicated that although LNF and LSF were highly correlated at kindergarten entry, fall LNF status was positively predictive of LSF growth across the fall. Bidirectional effects were present, as initial LSF was also a positive predictor of LNF growth across the fall; however, its effects were not as strong as those of initial LNF on LSF growth. More importantly, both initial status and growth in LNF and LSF were uniquely predictive of first-grade reading fluency, indicating the independent effects of each on subsequent text reading skills. Indirect effects were also observed for kindergarten LNF and LSF growth on reading fluency in second and third grades. Implications for kindergarten assessment and instruction are discussed.

Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models (which differ in the location of the clustering effect). With a 3 (design effect) × 5 (cluster size) × 4 (number of clusters) Monte Carlo simulation study we found that the rule should not be applied when researchers: (a) are interested in the effects of higher-level predictors, or (b) have a cluster size less than 10. Implications of the findings and limitations of the study are discussed.

Multilevel modeling techniques are becoming more popular in handling data with multilevel structure in educational and behavioral research. Recently, researchers have paid more attention to cross-classified data structure that naturally arises in educational settings. However, unlike traditional single-level research, methodological studies about multilevel effect size have been rare and those that have recently appeared had an emphasis on strictly hierarchical data structure. This article extends the work on multilevel standardized mean differences from strictly hierarchical structure to both fully and partially cross-classified structures. Analytically derived formulae for calculating effect sizes and the corresponding sampling variances (or standard errors) are presented, verified by simulation results, and illustrated with real data examples. Implications for primary research studies and meta-analyses are discussed.

The authors review the "Advanced Clinical Solutions for WAIS-IV and WMS-IV". The "Advanced Clinical Solutions (ACS) for the Wechsler Adult Intelligence Scale-Fourth Edition" (WAIS-IV; Wechsler, 2008) and the "Wechsler Memory Scale-Fourth Edition" (WMS-IV; Wechsler, 2009) was published by Pearson in 2009. It is a clinical tool for extending the assessment of individuals' cognitive functioning. Generally, the ACS provides supplemental information for the results of the WAIS-IV and WMS-IV; it includes six components that are relatively independent, namely, additional scores, effort measures, demographically adjusted norms, reliable change scores, test of premorbid functioning, and social cognition subtests. These new elements were specially designed for forensic evaluations, readministrations, and neuropsychological evaluations. The age range for the ACS varies for different elements, but for most tests it is 16 to 90, as consistent with the WAIS-IV and WMS-IV. A technician or graduate assistant with appropriate graduate-level training can administer and score the tests under supervision, but as stressed in the Administration and Scoring Manual results should only be interpreted by professionals with extensive training in assessment. In addition, to use the ACS for assessing neuropsychological functioning, examiners must have adequate background in neuropsychological assessment. The components of the ACS for WAIS-IV and WMS-IV are described.