Investigation of Poor Academic Achievement in Children with Duchenne Muscular Dystrophy

Duchenne Muscular Dystrophy (DMD) is a neurogenetic developmental disorder that presents with progressive muscular weakness. It is caused by a mutation in a gene that results in the absence of specific products that normally localize to muscle cells and the central nervous system (CNS). The majority of affected individuals have IQs within the normal range, generally with lower verbal than performance IQ scores. Prior work has demonstrated selective deficits on tests of verbal span and immediate memory. For the current study, 26 boys with DMD (and normal intellectual function) and their unaffected siblings were evaluated. Paired comparisons demonstrated that the children with DMD had significantly poorer academic achievement scores than their siblings, even though their vocabulary levels and home and educational environments were comparable. Children with DMD also had more behavioral concerns, physical disabilities, and poorer verbal memory spans. Linear regression indicated that behavioral concerns, executive function, and physical disability did not contribute substantially to academic performance, whereas performance on verbal span did. DMD presents with a selective developmental aberration in verbal span that has wide‐ranging consequences on learning skills.     

Establishing a Community of Inquiry through Hybrid Courses in Clinical Social Work Education

Utilizing the conceptual framework of Garrison, Anderson, and Archer for critical inquiry, this paper outlines the importance of the community of inquiry (COI) model and how it may inform online social work education. Integrating the COI model, we discuss how online learning in the classroom with a hybrid approach has been used to facilitate critical reflection and discourse. To illustrate this approach, we provide two case examples from clinical courses taught in an MSW program.     

A Theoretical Model for Preparing Incarcerated Youth for Careers in an Increasingly Technological World: Computational Logic as a Vehicle for Improving Human Decision-making Skills

Most youth in juvenile delinquent systems end up incarcerated due in part to poor decision-making skills. This study describes a theoretical framework undergirding the relationships across delinquency, learning climate, decision-making, and computational logic. This framework will inform a team of educators, criminologists, and psychologists, and their peers in computer science and mathematics, instructional software design, and content experts as they build and test an innovative instructional model for youth in secure care settings. The goal of the system is to improve human decision-making skills, while preparing youth for further education potentially leading to careers in STEM fields.     

Placement disruption and negative placement outcomes among adolescents in long-term foster care: the role of behavior problems

OBJECTIVE: This study examined risk of placement disruption and negative placement outcomes (e.g., residential treatment and incarceration) among adolescents placed in traditional family foster care for a year or longer. A foster parent's report of externalizing behavior problems was expected to be a stronger predictor of disruption and negative outcomes than a caseworker's report. Additionally, the association between behavior problems and placement disruption was expected to be mediated by the youth's degree of belonging and integration in the foster home. METHOD: The caseworkers and foster parents of 179 randomly selected 12-13-year-old adolescents placed in traditional foster care were interviewed by telephone. Interviews included standardized measures of externalizing behavioral problems and several other variables that have been previously associated with placement movement. Disruption from the youth's foster home at the time of the interview was prospectively tracked for 5 years. RESULTS: Over half of the youth experienced a disruption of their placement. Contrary to expectations, behavior problems as reported by caseworkers, but not foster parents, were predictive of placement disruption. However, the foster parent's report of behavior problems predicted risk of negative outcome after a period of 5 years. As hypothesized, integration in the foster home was highly predictive of placement stability and mediated the association between behavior problems and risk of disruption. CONCLUSIONS: Results suggest that integration in the foster home might be an important dimension of placement adaptation that should be considered during service planning for foster youth in long-term foster care. In addition, using standardized measures of behavior with both foster parents and caseworkers might be necessary to assess both long-term risk of negative outcomes and more immediate risk of placement disruption.     

Diagnosing Global Case Influence on MAR Versus MNAR Model Comparisons

When missingness is suspected to be not at random (MNAR) in longitudinal studies, researchers sometimes compare the fit of a target model that assumes missingness at random (here termed a MAR model) and a model that accommodates a hypothesized MNAR missingness mechanism (here termed a MNAR model). It is well known that such comparisons are only interpretable conditional on the validity of the chosen MNAR model’s assumptions about the missingness mechanism. For that reason, researchers often perform a sensitivity analysis comparing the MAR model to not one, but several, plausible alternative MNAR models. In the social sciences, it is not widely known that such model comparisons can be particularly sensitive to case influence, such that conclusions drawn could depend on a single case. This article describes two convenient diagnostics suited for detecting case influence on MAR–MNAR model comparisons. Both diagnostics require much less computational burden than global influence diagnostics that have been used in other disciplines for MNAR sensitivity analyses. We illustrate the interpretation and implementation of these diagnostics with simulated and empirical latent growth modeling examples. It is hoped that this article increases awareness of the potential for case influence on MAR–MNAR model comparisons and how it could be detected in longitudinal social science applications.     

Fitting Nonlinear Latent Growth Curve Models With Individually Varying Time Points

Individual growth trajectories of psychological phenomena are often theorized to be nonlinear. Additionally, individuals’ measurement schedules might be unique. In a structural equation framework, latent growth curve model (LGM) applications typically have either (a) modeled nonlinearity assuming some degree of balance in measurement schedules, or (b) accommodated truly individually varying time points, assuming linear growth. This article describes how to fit 4 popular nonlinear LGMs (polynomial, shape-factor, piecewise, and structured latent curve) with truly individually varying time points, via a definition variable approach. The extension is straightforward for certain nonlinear LGMs (e.g., polynomial and structured latent curve) but in the case of shape-factor LGMs requires a reexpression of the model, and in the case of piecewise LGMs requires introduction of a general framework for imparting piecewise structure, along with tools for its automation. All 4 nonlinear LGMs with individually varying time scores are demonstrated using an empirical example on infant weight, and software syntax is provided. The discussion highlights some advantages of modeling nonlinear growth within structural equation versus multilevel frameworks, when time scores individually vary.     

Teaching Subtraction and Multiplication with Regrouping Using the Concrete‐Representational‐Abstract Sequence and Strategic Instruction Model

Based on Common Core Standards (2010), mathematics interventions should emphasize conceptual understanding of numbers and operations as well as fluency. For students at risk for failure, the concrete‐representational‐abstract (CRA) sequence and the Strategic Instruction Model (SIM) have been shown effective in teaching computation with an emphasis on conceptual understanding. No studies have investigated the effects of CRA and SIM for teaching multiplication with regrouping. Therefore, the purpose of this study was to replicate and extend the literature, teaching subtraction and multiplication with regrouping using CRA and SIM. Three students receiving tier three mathematics interventions participated. A multiple‐probe across behaviors design was used to show a functional relation. All of the students demonstrated increases in fluency across all regrouping tasks.