Applied developmental scientists are often interested in how school contexts-critical proximal environments that shape students' developmental pathways-relate to long-term outcomes. However, school contexts are dynamic, and student mobility is common, creating challenges for researchers attempting to model developmental processes within changing school environments. Traditional multilevel models assume that each student is nested within a single, stable school context, an assumption that is violated when students attend multiple schools over time. Multiple membership multilevel modeling is an approach that can be used to account for students' experience of multiple school contexts, yet it has not been widely applied in practice. In this manuscript, we demonstrate multiple membership modeling and compare it to single membership multilevel modeling approaches, highlighting the differences that emerge. We provide implications for selecting modeling strategies that align with the research question, developmental theory, and the dynamic nature of school contexts.

Synthetic data hold strong potential to increase access to administrative data systems while protecting privacy for individuals. This paper details the approach taken to evaluate the synthesis of Maryland’s State Longitudinal Data System (SLDS) using fully synthetic Classification and Regression Tree (CART) models. Results demonstrate low disclosure risk (near zero) and high research utility, validated through robust evaluations. Practical insights and best practices from this case provide valuable lessons for other organisations seeking balanced synthetic data solutions for administrative data. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

Reducing prejudice in childhood requires changing group norms that often perpetuate prejudicial attitudes and in-group bias. Research has shown that intergroup contact is one of the most effective means to reduce prejudice. Yet little research has examined whether intergroup contact in the form of class discussions that challenge negative group norms might promote the desire to play with diverse peers. This study tested whether a classroom intervention program, Developing Inclusive Youth, which included experiences of direct and indirect contact, would increase children's desire for contact with diverse peers and reduce reported experiences of social exclusion. A multisite randomized control trial was implemented with 983 students (502 females; 58.5% White; Mage = 9.64 years) and 48 teachers in 48 third-, fourth-, and fifth-grade classrooms across six schools. Overall, students in the program displayed more positive expectations about play with diverse peers and fewer experiences with social exclusion. Classroom discussions involved challenging group norms that perpetuate same-group preferences. Children's grade moderated their desire for contact with peers from some but not all social groups. This type of program may be an effective means for increasing positive, inclusive group norms in childhood, as this is a time in development when attitudes and preferences for peer friendships are forming. Increasing positive intergroup norms in the classroom creates academic learning environments that promote healthy child development.

Attrition is a critical concern for evaluating the rigor of prevention studies, and the current study provides rates of attrition for subgroups of students and schools who are often sampled for prevention science. This is the first study to provide practical guidance for expected rates of attrition using population-level statewide data; findings indicated that researchers using K-12 school-based samples should plan for attrition rates as high as 27% during middle school and 54% during elementary school. However, researchers should consider the grade levels initially sampled, the length of follow-up, and the specific student characteristics and schools available for sampling. Postsecondary attrition ranged from 45% for bachelor's degree seekers to 73% for associate degree seekers. This practical guidance can help researchers to proactively plan for attrition in the study design phase, limiting bias and increasing the validity of prevention studies.

The Developing Inclusive Youth program is a classroom-based, individually administered video tool that depicts peer-based social and racial exclusion, combined with teacher-led discussions. A multisite randomized control trial was implemented with 983 participants (502 females; 58.5% White, 41.5% Ethnic/racial minority; M age  = 9.64 years) in 48 third-, fourth-, and fifth-grade classrooms across six schools. Children in the program were more likely to view interracial and same-race peer exclusion as wrong, associate positive traits with peers of different racial, ethnic, and gender backgrounds, and report play with peers from diverse backgrounds than were children in the control group. Many approaches are necessary to achieve antiracism in schools. This intervention is one component of this goal for developmental science.

We examined the effects of a professional development (PD) with coaching model designed to improve literacy and co-taught instruction for students with and without disabilities in co-taught content-area middle school classes. Eleven co-teaching pairs in nine schools were randomly assigned to the Content Area Literacy Instruction (CALI) condition (n = 7 pairs) or a business-as-usual comparison condition (n = 4 pairs). All 22 teachers individually completed researcher developed pre- and posttests of teacher knowledge and skills and perceived effectiveness of their personal ability and their co-teachers' ability. At pre- and posttest, students (n = 212) completed three measures of reading comprehension. CALI teachers demonstrated significantly higher scores than comparison teachers at posttest on a measure of knowledge and skills, perceived personal effectiveness, and perceived co-teachers' effectiveness. All CALI co-teaching pairs demonstrated high levels of fidelity. Students in the CALI classrooms demonstrated significant gains on an essential aspect of a researcher-developed measure of reading comprehension. However, the treatment effect was non-significant for the two standardized measures of reading comprehension. Results provide initial support for a model in improving teacher instructional outcomes and student academic outcomes.

Abstract The Developing Inclusive Youth program is a classroom‐based, individually administered video tool that depicts peer‐based social and racial exclusion, combined with teacher‐led discussions. A multisite randomized control trial was implemented with 983 participants (502 females; 58.5% White, 41.5% Ethnic/racial minority; M age = 9.64 years) in 48 third‐, fourth‐, and fifth‐grade classrooms across six schools. Children in the program were more likely to view interracial and same‐race peer exclusion as wrong, associate positive traits with peers of different racial, ethnic, and gender backgrounds, and report play with peers from diverse backgrounds than were children in the control group. Many approaches are necessary to achieve antiracism in schools. This intervention is one component of this goal for developmental science.

Given concerns about the reading achievement of Dual Language Learners (DLLs) in comparison to English Monolinguals (EMs), this study examined individual difference variables contributing to English reading comprehension growth in Spanish-speaking DLLs and their EM counterparts in Grades 1–4. The participants, who included 578 DLLs and 412 EMs, were primarily from low-income backgrounds. They were assessed in the fall and spring of one school year on decoding, vocabulary, and oral language comprehension (established predictors of reading comprehension for DLLs and EMs); higher order strategic processes, executive functions, and reading engagement (understudied predictors for DLLs); and reading comprehension. Among the key findings were that each of the three understudied predictors was associated with reading comprehension growth over the school year, over and above the contributions of the established predictors, in both language groups. Additionally, higher order strategic processes partially mediated the relations of executive functioning in the fall with reading comprehension in the spring for both DLLs and EMs. Theoretical and practical implications of the findings for understanding and strengthening the reading achievement of all students are considered.

Previous studies offer mixed evidence regarding whether a unified model of reading comprehension predictors applies to Dual Language Learners (DLLs) and English Speakers (ESs), or whether distinctive models across language groups are empirically supported. The present study adds another dimension to this body of work by examining multiple reading engagement and motivation predictors alongside cognitive predictors of reading comprehension. The participants—188 DLLs and 166 ESs in the fourth and fifth grades—completed measures of word identification, linguistic comprehension, cognitive strategy use, internal motivation, and extrinsic motivation, and their teachers rated their reading engagement. Language status did not moderate the relations of any predictors with either concurrent reading comprehension performance or growth of reading comprehension across the school year, supporting a unified model of reading comprehension for DLLs and ESs. Word identification and linguistic comprehension showed the strongest relations with concurrent reading comprehension and growth. While the role of reading engagement was less prominent, it was demonstrated to be a plausible partial mediator of the relation of word identification with concurrent reading comprehension.

Many national and international educational data collection programs offer researchers opportunities to investigate contextual effects related to student performance. In those programs, schools are often used in the first-stage sampling process and students are randomly drawn from selected schools. However, the incidental dependence of students within classrooms, which are not part of the sampling design, may violate assumptions of statistical models, but this nesting also offers the opportunity for educational researchers to evaluate contextual effects. In this manuscript, we utilize the Early Childhood Longitudinal Study-Kindergarten dataset to demonstrate impacts of incidental dependence using a two-level model and a three-level model. We then illustrate, through a simulation, that both models can yield unbiased parameter estimates. However, two-level models tend to provide underestimated standard errors for fixed effects at the incidental level, and variance components of the random effect at the incidental level are divided into the flanking levels when it is ignored. In addition, another method of modeling nested data, using generalized estimating equations, was also compared with the model-based methods.

Respondent attrition is a common problem in national longitudinal panel surveys. To make full use of the data, weights are provided to account for attrition. Weight adjustments are based on sampling design information and data from the base year; information from subsequent waves is typically not utilized. Alternative methods to address bias from nonresponse are full information maximum likelihood (FIML) or multiple imputation (MI). The effects on bias of growth parameter estimates from using these methods are compared via a simulation study. The results indicate that caution needs to be taken when utilizing panel weights when there is missing data, and to consider methods like FIML and MI, which are not as susceptible to the omission of important auxiliary variables.

There is demand among policy-makers for the use of state education longitudinal data systems, yet laws and policies regulating data disclosure limit access to such data, and security concerns and risks remain high. Well-developed synthetic datasets that statistically mimic the relations among the variables in the data from which they were derived, but which contain no records that represent actual persons, present a viable solution to these laws, policies, concerns, and risks. We present a case study in the development of a synthetic data system and highlight potential applications of synthetic data. We begin with an overview of synthetic data, what it is, how it has been utilized thus far, and the potential benefits and concerns in its application to education data systems. We then describe our federally-funded project, proposing the steps required to synthesize a statewide longitudinal data system covering high school, postsecondary, and workforce data. Last, for use as a template for other agencies considering synthetic data, we review the challenges we have confronted in the development of our synthetic data system for research and policy evaluation purposes.

The inclusion of individuals with intellectual disability (ID) in typical settings is increasing. To promote success in these settings, educators must support the reading comprehension of individuals with ID. Therefore, we conducted a synthesis of the extant research on reading comprehension interventions for individuals in the largest category of ID-mild ID-in Grades 4 through 12 and postsecondary programs. We review the methodological and intervention features of eight group-design studies and six single-case design studies published between January 2001 and December 2018. Findings from the 14 studies revealed inconsistent effects of single-component and multicomponent interventions on expository and narrative reading comprehension. However, medium to large positive effects were typically found from interventions using peer-mediated instruction and explicit strategy instruction. More rigorous research investigating the effects of reading comprehension interventions for individuals with mild ID using standardized measures is warranted. Practical implications and recommendations for future research are discussed.

When researchers model multilevel data, often a shared construct of interest is measured by individual-level observations, for example, students’ responses regarding how engaging their instructor’s teaching style is. In such cases, the construct of interest, “engaging teaching,” is shared at the cluster level across individuals, yet rarely are these shared constructs modeled as such. To address this gap, we discuss multilevel confirmatory factor analysis models that have been applied to item-level data obtained from multiple raters within given clusters, focusing particularly on measuring a characteristic at the cluster level. After discussing the parameters in each potential model, we make recommendations as to the appropriate modeling approach and the steps to be taken for model assessment given a set of data and hypothesized construct of interest. In particular, we encourage applied researchers not to use a model without constraints across the within-cluster level and the between-cluster level because such models assume that the average amount of the individual-level construct in a cluster does not differ across clusters. To illustrate this issue, we present simulation results and evaluate a series of models using empirical data from the Trends in International Mathematics and Science Study.

The use of surveys in social science research is abundant and may appear straightforward, but can involve a complex set of procedures. Total survey error refers to all of the errors that could occur in researchers' attempts to gain valid information about a population with the use of surveys. These errors have been placed into five categories: coverage error, nonresponse error, editing and processing errors, measurement error, and sampling errors (Groves, 1989). Coverage error refers to the failure to provide some members of the population the chance to be selected into the sample, nonresponse error refers to the failure to obtain responses from all members of the selected sample, editing and process errors refer to the failure to capture data accurately from the respondents, measurement error represents the failure of the observed response to reflect the true opinion of the sample member, and sampling error refers to the fact that sample statistics are not expected to exactly reflect population parameters.

Propensity score (PS) analysis aims to reduce bias in treatment effect estimates obtained from observational studies, which may occur due to non-random differences between treated and untreated groups with respect to covariates related to the outcome. We demonstrate how to use structural equation modeling (SEM) for PS analysis to remove selection bias due to latent covariates and estimate treatment effects on latent outcomes. Following the discussion of the design and analysis stages of PS analysis with SEM, an example is presented which uses the Mplus software to analyze data from the 1999 School and Staffing Survey (SASS) and 2000 Teacher Follow-up Survey (TFS) to estimate the effects teacher's participation in a network of teachers on the teacher's perception of workload manageability.

This research empirically evaluates data sets from the National Center for Education Statistics (NCES) for design effects of ignoring the sampling design in weighted two-level analyses. Currently, researchers may ignore the sampling design beyond the levels that they model which might result in incorrect inferences regarding hypotheses due to biased standard error estimates; the degree of bias depends on the informativeness of any ignored stratification and clustering in the sampling design. Some multilevel software packages accommodate first-stage sampling design information for two-level models but not all. For five example public release data sets from the NCES, design effects of ignoring the sampling design in unconditional and conditional two-level models are presented for 15 dependent variables selected based on a review of published research using these five data sets. Empirical findings suggest that there are minor effects of ignoring the additional sampling design and no differences in inference would be made had the first-stage sampling design been ignored. Strategically, researchers without access to multilevel software that can accommodate the sampling might consider including stratification variables as independent variables at level 2 of their model.

The Reviewer’s Guide to Quantitative Methods in the Social Sciences is designed for evaluators of research manuscripts and proposals in the social and behavioral sciences, and beyond. Its thirty-one uniquely structured chapters cover both traditional and emerging methods of quantitative data analysis, which neither junior nor veteran reviewers can be expected to know in detail. The book updates readers on each technique’s key principles, appropriate usage, underlying assumptions, and limitations. It thereby assists reviewers to offer constructive commentary on works they evaluate, and also serves as an indispensable author’s reference for preparing sound research manuscripts and proposals. Key features include: The chapters cover virtually all of the popular classic and emerging quantitative techniques, thus helping reviewers to evaluate a manuscript’s methodological approach and its data analysis. In addition, the volume serves as an indispensable reference tool for those designing their own research. For ease of use, all chapters follow the same structure: the opening page of each chapter defines and explains the purpose of that statistical method the next one or two pages provide a table listing various criteria that should be considered when evaluating and applying that methodological approach to data analysis the remainder of each chapter contains numbered sections corresponding to the numbered criteria listed in the opening table. Each section explains the role and importance of that particular criterion. Chapters are written by methodological and applied scholars who are expert in the particular quantitative method being reviewed.