Search

Results from research indicate writing is a critical skill linked to several academic outcomes. To promote improvements in writing quantity and quality, intervention might target increasing students' academic engagement during time designated to practice writing. The purpose of this study was to implement an evidence-based classwide behavioral intervention, the Good Behavior Game (GBG), during daily writing practice time in two classrooms. Participants (n = 45) included students in a Grade 1 and Grade 2 class enrolled in an elementary school in a large suburb in the northeast U.S. Findings based on visual analysis and multilevel modeling indicate that students, on average, wrote more words (quantity) and more correct writing sequences (quality) when the GBG was played versus when it was not. Implications include the need for replication studies to extend findings and explore how school psychologists might consider the use of behavioral interventions to promote improved engagement and academic output in the classroom. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The limited research available on writing in Grade 1 led to the development and implementation of an intervention for students who were performing below expectations. Ten students participated in a...

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.

High school students in accelerated classes face heightened school-related stressors but have access to few specialized supports. This article describes the development and initial application of the Motivation, Assessment, and Planning (MAP) intervention, developed to meet the needs of freshmen in Advanced Placement (AP) and International Baccalaureate (IB) curricula. MAP is grounded in motivational interviewing and designed to evoke behavior change following one to two individual meetings with a coach. This study used a multimethod, multisource approach to evaluate the applicability and acceptability of MAP as a Tier 2 support for 9th-grade students completing AP/IB coursework. Quantitative and qualitative acceptability data from 49 AP/IB students, 7 coaches, and 3 potential end-users of MAP were examined. Results indicate MAP was perceived as an acceptable intervention for addressing the social–emotional needs of high-achieving students taking AP/IB classes. Findings and directions for further research of MAP are discussed.

This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates produced in the correct and the misspecified models were compared under varying conditions of cluster number, cluster size, intraclass correlation, and the magnitude of the interaction effect in the population model. Results showed that the two main effects were overestimated by approximately half of the size of the interaction effect, and the between-level factor mean was underestimated. None of comparative fit index, Tucker–Lewis index, root mean square error of approximation, and standardized root mean square residual was sensitive to the omission of the interaction effect. The sensitivity of information criteria varied depending majorly on the magnitude of the omitted interaction, as well as the location of the interaction (i.e., at the between level, within level, or cross level). Implications and recommendations based on the findings were discussed.

Multiple baseline (MB) designs are becoming more prevalent in educational and behavioral research, and as they do, there is growing interest in combining effect size estimates across studies. To further refine the meta-analytic methods of estimating the effect, this study developed and compared eight alternative methods of estimating intervention effects from a set of MB studies. The methods differed in the assumptions made and varied in whether they relied on within- or between-series comparisons, modeled raw data or effect sizes, and did or did not standardize. Small sample functioning was examined through two simulation studies, which showed that when data were consistent with assumptions the bias was consistently less than 5% of the effect size for each method, whereas root mean squared error varied substantially across methods. When assumptions were violated, substantial biases were found. Implications and limitations are discussed.

In this paper, we provide a critique focused on the What Works Clearinghouse (WWC) Standards for Single-Case Research Design (Standards 4.1). Specifically, we (a) recommend the use of visual-analysis to verify a single-case intervention study's design standards and to examine the study's operational issues, (b) identify limitations of the design-comparable effect-size measure and discuss related statistical matters, (c) review the applicability and practicality of Standards 4.1 to single-case designs (SCDs), and (d) recommend inclusion of content pertaining to diversity, equity, and inclusion in future standards. Within the historical context of the WWC Pilot Standards for Single-Case Design (1.0), we suggest that Standards 4.1 may best serve as standards for meta-analyses of SCDs but will need to make clear distinctions among the various types of SCD studies that are included in any research synthesis. In this regard, we argue for transparency in SCD studies that meet design standards and those that do not meet design standards in any meta-analysis emanating from the WWC. The intent of these recommendations is to advance the science of SCD research both in research synthesis and in promoting evidence-based practices.

Single-case researchers often implement multiple-baseline designs as their preferred methodology for intervention evaluations. Recent writings and empirical investigations have argued in favor of incorporating various forms of randomization into such designs for the purpose of elevating the intervention study's internal validity and scientific credibility. In this article, we consider a variety of randomized multiple-baseline designs and associated randomization statistical tests, along with their potential strengths and limitations. In what amounts to a practical guide, we refer school psychology researchers to these versatile randomization procedures for planning and executing their intervention studies.

© 2019, © 2019 Taylor & Francis Group, LLC. Recently, researchers have used multilevel models for estimating intervention effects in single-case experiments that include replications across participants (e.g., multiple baseline designs) or for combining results across multiple single-case studies. Researchers estimating these multilevel models have primarily relied on restricted maximum likelihood (REML) techniques, but Bayesian approaches have also been suggested. The purpose of this Monte Carlo simulation study was to examine the impact of estimation method (REML versus Bayesian with noninformative priors) on the estimation of treatment effects (relative bias, root mean square error) and on the inferences about those effects (interval coverage) for autocorrelated multiple-baseline data. Simulated conditions varied with regard to the number of participants, series length, and distribution of the variance within and across participants. REML and Bayesian estimation led to estimates of the fixed effects that showed little to no bias but that differentially impacted the inferences about the fixed effects and the estimates of the variances. Implications for applied researchers and methodologists are discussed. ispartof: JOURNAL OF EXPERIMENTAL EDUCATION vol:88 issue:4 pages:698-710 status: published

In multilevel multiple-indicator multiple-cause (MIMIC) models, covariates can interact at the within level, at the between level, or across levels. This study examines the performance of multilevel MIMIC models in estimating and detecting the interaction effect of two covariates through a simulation and provides an empirical demonstration of modeling the interaction in multilevel MIMIC models. The design factors include the location of the interaction effect (i.e., between, within, or across levels), cluster number, cluster size, intraclass correlation (ICC) level, magnitude of the interaction effect, and cross-level measurement invariance status. Type I error, power, relative bias, and root mean square of error of the interaction effects are examined. The results showed that multilevel MIMIC models performed well in detecting the interaction effect at the within or across levels. However, when the interaction effect was at the between level, the performance of multilevel MIMIC models depended on the magnitude of the interaction effect, ICC, and sample size, especially cluster number. Overall, cross-level measurement noninvariance did not make a notable impact on the estimation of interaction in the structural part of multilevel MIMIC models when factor loadings were allowed to be different across levels.

The increasing diversity in classrooms throughout the U.S. calls for teachers who are culturally competent and have quality professional development in working with dual language learners (DLLs) in order to ensure children from diverse groups are receiving equitable and high-quality education. This study focuses on teacher characteristics that play a significant role in the development of young children’s bilingual abilities with regard to language, literacy and math. We examined the school readiness skills of 217 Latino DLLs attending Head Start taking into account specific teacher characteristics that may impact the development of readiness skills. Different teacher characteristics have different effects on the developmental outcomes. Amount of training had a positive significant effect on several children’s outcomes in English and in Spanish. The years of experience a teacher had working in classrooms with DLLs had a positive effect on children’s English spelling skills. Teachers’ cultural competency rating had a positive effect on receptive language and literacy in Spanish. Findings and implications are discussed.

Research Findings: Naturalistic instruction is the intentional use of strategies embedded within typical activities of the environment to create learning opportunities for children. We used multilevel modeling to analyze data from this single-case design study that focused on improving teachers’ use of naturalistic language strategies and children’s communication through professional development with bug-in-ear coaching. Our results showed that professional development was positively associated with children’s communication during and after professional development. During generalization sessions in different types of activities, teachers’ use of the naturalistic language strategies was positively associated with children’s functional communication. We also found that children were more likely to communicate during child-led activities. Practice or Policy: Associations among professional development, teachers’ use of evidence-based strategies, and children’s outcomes have yet to be fully understoo...

Research has shown that students in Advanced Placement (AP) classes and International Baccalaureate (IB) programs experience higher levels of stress compared to students in general education classes. Elevated stress can serve as a risk factor for students’ academic and mental health problems. Given the documented stress of these students, additional investigations are needed to more fully understand how students experience these curricula and the factors associated with positive student outcomes. Thus, we set out to identify factors associated with success among AP/IB students, with an emphasis on exploring potentially malleable factors that could be targeted with existing or newly developed interventions. Data were collected via self-report measures and school records from 2,379 students (Grades 9-12) enrolled in AP or IB in 20 school programs in one state. We examined the relationships among 34 predictors (e.g., stressors, coping styles, student engagement, family factors, school factors, and demographic features) of success. Success was represented by five outcomes in two domains: mental health (life satisfaction, psychopathology, school burnout) and academic (GPA, AP/IB exam scores). Better outcomes in both domains were associated with higher levels of achievement motivation and cognitive engagement, as well as lower levels of parent–child conflict, stress from major life events, and use of avoidance coping strategies. Higher levels of affective engagement, use of approach coping, and authoritative parenting were robust predictors of positive mental health outcomes and unrelated (in multivariate analyses) to academic outcomes. Findings have implications for subsequent development of intervention efforts targeting factors associated with student success.

Wording effects associated with positively and negatively worded items have been found in many scales. Such effects may threaten construct validity and introduce systematic bias in the interpretation of results. A variety of models have been applied to address wording effects, such as the correlated uniqueness model and the correlated traits and correlated methods model. This study presents the multilevel bifactor approach to handling wording effects of mixed-format scales used in a multilevel context. The Students Confident in Mathematics scale is used to illustrate this approach. Results from comparing a series of models showed that positive and negative wording effects were present at both the within and the between levels. When the wording effects were ignored, the within-level predictive validity of the Students Confident in Mathematics scale was close to that under the multilevel bifactor model. However, at the between level, a lower validity coefficient was observed when ignoring the wording effects. Implications for applied researchers are discussed.

A number of randomization statistical procedures have been developed to analyze the results from single-case multiple-baseline intervention investigations. In a previous simulation study, comparisons of the various procedures revealed distinct differences among them in their ability to detect immediate abrupt intervention effects of moderate size, with some procedures (typically those with randomized intervention start points) exhibiting power that was both respectable and superior to other procedures (typically those with single fixed intervention start points). In Investigation 1 of the present follow-up simulation study, we found that when the same randomization-test procedures were applied to either delayed abrupt or immediate gradual intervention effects: (1) the powers of all of the procedures were severely diminished; and (2) in contrast to the previous study's results, the single fixed intervention start-point procedures generally outperformed those with randomized intervention start points. In Investigation 2 we additionally demonstrated that if researchers are able to successfully anticipate the specific alternative effect types, it is possible for them to formulate adjusted versions of the original randomization-test procedures that can recapture substantial proportions of the lost powers.

Previous research found bias in the estimate of the overall fixed effects and variance components using multilevel meta-analyses of standardized single-case data. Therefore, we evaluate two...

Most interventions intended to improve subjective well-being, termed positive psychology interventions (PPIs), have neglected to include relevant stakeholders in youth's lives and have not included booster sessions intended to maintain gains in subjective well-being. The current study investigated the impact of a multitarget, multicomponent (i.e., students plus parents), small group PPI on students' mental health (subjective well-being as well as symptoms of internalizing and externalizing forms of psychopathology) at postintervention and approximately two months follow-up. Forty-two seventh-grade students were randomly assigned either to immediately receive the PPI or to a wait-list control group. At postintervention, students who participated in the PPI evidenced significant gains in all indicators of subjective well-being (life satisfaction, positive affect, and negative affect), and there was a trend for practically meaningful reductions in internalizing and externalizing problems relative to...

Mental health is increasingly viewed as a complete state of being, consisting of the absence of psychopathology and the presence of positive factors such as subjective well-being (SWB). This cross-sectional study analyzed multimethod and multisource data for 500 high school students (ages 14–18 years, M = 15.27 years, SD = 1.0 years) to examine how mental health, defined in a dual-factor model, relates to adjustment. Relevant outcomes within self-determination theory include academic adjustment, social adjustment, identity development, and physical health. A dual-factor model was supported through identification of four groups: complete mental health (62.2% of sample), vulnerable (11.4%), symptomatic but content (11.4%), and troubled (15%). Results extend the importance of high SWB to optimal functioning during middle adolescence, as students with complete mental health (high SWB, low psychopathology) reported better outcomes than vulnerable students (low SWB despite low psychopathology) in terms...

What causes the literacy gap and can schools compensate for it? The authors investigated 3 drivers of the gap: preliteracy knowledge, schooling, and the summer vacation. Longitudinal literacy data over 5 time points were collected on 126 five-year-olds attending higher or lower socioeconomic status (SES) schools during their first 15 months of school. There were several noteworthy findings: (a) gaps in preliteracy knowledge at school entry favor higher SES schools, (b) preliteracy knowledge predicted later progress over and above SES and gender, (c) during the school year there was a widening of the gap between higher SES schools and lower SES schools in reading and spelling skills, and (d) children attending lower SES schools exhibited losses during summer whereas children attending higher SES schools nearly always gained. Contrary to previous studies, the present results indicated that when there are concentrations of children from higher and lower SES in schools located in the children's respec...

The impact of misspecifying covariance matrices at the second and the third levels of the three-level model on inferences regarding the overall treatment effect and (co)variances estimates is evaluated by means of a simulation study and an empirical illustration is given. The results indicate that ignoring an existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased and well estimated either when covariance is modeled or ignored. If the research interest lies in the between-study variance estimate, including at least 30 studies is warranted to get unbiased and precise estimates. Modeling covariance does not result in less biased and more precise between-study variance estimates as the between-study covariance estimate is extremely biased. As a consequence, single-case researchers can use either the model ignoring or modeling covariance when the research interest lies in the overall treatment effect estimate and/or the between-case variance. In addition, when the research interest lies on the between-case covariance, the model including covariance results in unbiased between-case variance estimates. The three-level model appears to be less appropriate to estimate the between-study variance if less than 30 studies are included. peerreview_statement: The publishing and review policy for this title is described in its Aims & Scope. aims_and_scope_url: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=vjxe20 ispartof: Journal of Experimental Education vol:84 issue:3 pages:473-509 status: published

The purpose of this study is to illustrate the multilevel meta-analysis of results from single-subject experimental designs of different types, including AB phase designs, multiple-baseline designs, ABAB reversal designs, and alternating treatment designs. Current methodological work on the meta-analysis of single-subject experimental designs often focuses on combining simple AB phase designs or multiple-baseline designs. We discuss the estimation of the average intervention effect estimate across different types of single-subject experimental designs using several multilevel meta-analytic models. We illustrate the different models using a reanalysis of a meta-analysis of single-subject experimental designs (Heyvaert, Saenen, Maes, & Onghena, in press). The intervention effect estimates using univariate 3-level models differ from those obtained using a multivariate 3-level model that takes the dependence between effect sizes into account. Because different results are obtained and the multivariate model has multiple advantages, including more information and smaller standard errors, we recommend researchers to use the multivariate multilevel model to meta-analyze studies that utilize different single-subject designs.

In this investigation, we examined the effects of a differentiated reading approach on fourth grade students’ reading comprehension and attitudes toward reading. Eight Title I schools within one urban district were randomly assigned to treatment (Schoolwide Enrichment Model–Reading [SEM-R]) or control (district reading curriculum) conditions. Treatment teachers implemented SEM-R as a supplement to the district curriculum for 1 academic year, whereas control teachers used the district curriculum. Based on multilevel analyses of students’ posttest reading comprehension scores on the Iowa Test of Basic Skills ( n = 358) and attitudes toward reading as measured by the Elementary Reading Attitudes Survey ( n = 429), no statistically significant differences in students’ attitudes toward reading were found, but SEM-R students had significantly higher scores on the comprehension posttest compared with the students in the control schools.

In special education, multilevel models of single-case research have been used as a method of estimating treatment effects over time and across individuals. Although multilevel models can accurately summarize the effect, it is known that if the model is misspecified, inferences about the effects can be biased. Concern with the potential for model misspecification motivates our method for evaluating multilevel models of single-case data. This method is based on the visual analysis of graphs that have the model-implied individual trajectories superimposed on plots of the raw data. Through the reanalysis of a published study, we show how this visual analysis approach can identify model misspecifications and motivate the consideration of alternative model specifications that lead to better fit.

In two studies ( ns = 312 and 1,149) with 9- to 12-grade students in pre–International Baccalaureate (IB) and IB Diploma programs, we evaluated the reliability, factor structure, measurement invariance, and criterion-related validity of the scores from the School Attitude Assessment Survey–Revised (SAAS-R). Reliabilities of the five SAAS-R subscale scores were good (αs > .80) for pre-IB (Grades 9-10) and IB students (Grades 11-12). Study 1 model fit indices for the five-factor SAAS-R model from confirmatory factor analyses showed greater misfit than those previously reported by McCoach and Siegle. In contrast, Study 2 fit indices for the five-factor model with pre-IB and IB students were similar to values reported by McCoach and Siegle. Tests of measurement invariance in Study 2 using multigroup confirmatory factor analysis identified three items within the Motivation/Self-Regulation subscale that differed in their item intercepts (i.e., uniform differential item functioning) with pre-IB students endorsing these items more strongly compared with IB students. Based on these results along with evidence of criterion-related validity as reflected in the moderate statistical relations between the SAAS-R subscales and students’ GPAs, the SAAS-R shows promise as a research tool that can be used to examine the psychological factors associated with pre-IB and IB students’ academic achievement.

High school students in accelerated curricula face stressors beyond typical adolescent developmental challenges. The Student Rating of Environmental Stressors Scale (StRESS) is a self-report measure of environmental stressors appropriate for students in Advanced Placement (AP) and International Baccalaureate (IB) courses. We developed the StRESS in parallel with a new measure of coping designed for this same population. Items were derived from sentiments expressed during focus groups and individual interviews with 177 students, 72 parents, and 47 teachers. Multiple iterations of expert review and item analyses focusing on conceptual clarity and comprehensiveness resulted in 75 items reflecting stressors across domains, including school, home, and peers. High school students in AP or IB ( N = 727) completed the 75-item inventory. Exploratory factor analyses and additional item review indicated a five-factor solution with 32 items. Cronbach’s alpha reliabilities ranged from .67 to .88. Five additional items representing a composite of Major Life Events also were included. The five factors and Major Life Events composite had test–retest reliabilities greater than .70. These scores were related to multiple conceptualizations of stress, as well as academic outcomes (GPA and attendance) and mental health (life satisfaction and anxiety), thus supporting the construct validity of the StRESS scores. Further support for the five-factor structure of the StRESS was provided by results of a confirmatory factor analysis (standardized root mean square residual = .051, root mean square error of approximation = .048, comparative fit index = .900) with a separate sample of 2,193 AP and IB students.

Successful coping with academic demands is important given the inverse relationship between stress and positive adjustment in adolescents. The Coping With Academic Demands Scale (CADS) is a new measure of coping appropriate for students pursuing advanced high school curricula, specifically Advanced Placement (AP) classes and the International Baccalaureate (IB) program. We developed the CADS in parallel with a new measure of stress designed for this same population. We generated an initial item pool using multiple sources including focus groups and individual interviews with 177 students, 72 teachers, and 47 parents. Multiple iterations of expert review and item analyses resulted in 120 items, which were completed by 727 high school students in six schools (312 IB, 415 not in IB but taking at least one AP class). Exploratory factor analyses and additional item review indicated a 16-factor solution with 58 items. Cronbach’s alpha reliabilities for the factors ranged from .53 to .90, with 11 factors exceeding .70. All 16 factors had test–retest reliabilities greater than .70. Support for the construct validity of the CADS scores was provided using a nomological network, which specified relationships between the CADS and broader dimensions of school-related coping dimensions (task, avoidance, and emotion-oriented), as well as indicators of achievement (grade point averages) and mental health (life satisfaction). An additional seven items that were not part of the 16-factor CADS, but which were identified as relevant in different phases of instrument development, are provided as a resource for researchers.

The authors examined long-term outcomes for children who experienced delayed entry to kindergarten or kindergarten retention. They used a cohort of 6,841 students to compare these groups to each other and typically progressing peers. First, the authors compared the groups on demographic and early childhood variables. For the long-term school-based outcomes, they used propensity score analysis to address pretreatment differences between groups and examined outcomes by free or reduced-price versus paid lunch status. Results showed that the retained group experienced greater early risk than the delayed entry and typically progressing groups and poorer long-term outcomes even when controlling for pre-existing differences. Other than placement in special education, few differences emerged between the delayed entry and typically progressing groups. Implications of the study for progression decisions are discussed.

A multilevel logistic model for estimating a nonlinear trajectory in a multiple-baseline design is introduced. The model is applied to data from a real multiple-baseline design study to demonstrate interpretation of relevant parameters. A simple change-in-levels (ΔLevels) model and a model involving a quadratic function (Quadratic) for the nonlinear intervention phase data were also estimated. In addition, a simulation study was conducted to assess Markov chain Monte Carlo estimation of the logistic model and compare its trajectory recovery with use of the ΔLevels and Quadratic models. While most of the logistic model's parameter values were recovered well, trajectory recovery was very reasonable using the simpler Quadratic model. Results are discussed along with recommendations for practitioners and directions for future research.

This study investigates the impact of The Personal Strengths Program (PSP) on seven college students with learning disabilities and/or attention deficit hyperactivity disorder (LD/ADHD) using a multiple baseline design. Students with LD/ADHD experience increased challenges in school settings and decreased post-secondary outcomes when compared with their peers without disabilities. Research has indicated that students who are able to transition into post-secondary settings successfully often have higher levels of self-determination than those who do not transition as successfully. PSP is grounded in effective teaching practices for students with LD/ADHD, self-determination, and positive psychology. It utilizes guided cognitive strategy instruction to assist students in identifying and using their strengths to achieve their goals related to their academic classes. Results were mixed, but do suggest there is some effect from participating in PSP on self-determination levels. Implications for students with LD/ADHD and researchers conducting intervention studies are discussed.

A multilevel meta-analysis can combine the results of several single-subject experimental design studies. However, the estimated effects are biased if the effect sizes are standardized and the number of measurement occasions is small. In this study, the authors investigated 4 approaches to correct for this bias. First, the standardized effect sizes are adjusted using Hedges’ small sample bias correction. Next, the within-subject standard deviation is estimated by a 2-level model per study or by using a regression model with the subjects identified using dummy predictor variables. The effect sizes are corrected using an iterative raw data parametric bootstrap procedure. The results indicate that the first and last approach succeed in reducing the bias of the fixed effects estimates. Given the difference in complexity, we recommend the first approach.

Parent education and support has been identified as a potential resource to help improve outcomes for youth who have emotional disturbances (ED). The current study builds on promising results from Parent Connectors (PCs) (Kutash et al. in Adm Policy Ment Health Ment Health Serv Res 38:412–427, 2011), a peer-to-peer program aimed at increasing the engagement of families in the education and treatment of their children who have ED. A total of 128 parents of students identified as having ED and enrolled in special education programs in 23 middle schools or special centers were randomly assigned to a treatment (had a PC) or comparison group. For parents who had a PC, results indicated positive program effects including increased perceived benefit of engagement, more engagement in their child’s services, and a more positive response to social norms. Students received more mental health services, were enrolled more days in school, and were suspended fewer times and for fewer days. The moderating effects of parent strain and child emotional functioning were examined, and implications for practice are discussed.

Aim Cannabinoid receptor type 1 (CB1) antagonists have been developed for the treatment of obesity and associated risk factors. Surinabant is a high affinity CB1 antagonist in vitro. The aim of this study was to assess the magnitude of inhibition by surinabant of CNS effects and heart rate induced by Δ9-tetrahydrocannabinol (THC) in humans. Methods This was a double-blind, placebo-controlled, randomized, four period six sequence crossover study. Thirty healthy young male occasional cannabis users (

Purpose To develop a population pharmacokinetic (PK) model for cabazitaxel in patients with advanced solid tumors and examine the influence of demographic and baseline parameters. Methods One hundred and seventy patients who received cabazitaxel (10–30 mg/m2, 1-h IV infusion) every 7 or 21 days in five Phase I–III studies were analyzed by non-linear mixed-effect modeling (NONMEM VI). Model evaluation comprised non-parametric bootstrap and visual predictive checks. Results Cabazitaxel PK was best described by a linear three-compartment model with: first-order elimination; interindividual variability on clearance (CL), central volume of distribution (V1), and all intercompartmental rate constants except K21; interoccasion variability in CL and V1; proportional residual error of 27.8 %. Cabazitaxel CL was related to body surface area (BSA) and tumor type (breast cancer; finding confounded by study). Typical CL for a non-breast cancer patient with a BSA of 1.84 m2 was 48.5 L/h, with V1 26.0 L, steady-state volume of distribution 4,870 L and alpha, beta, and gamma half-lives of 4.4 min, 1.6, and 95 h, respectively. Sex, height, weight, age, Caucasian race, renal/hepatic function, and cytochrome P450 inducer use did not significantly further explain the PK of cabazitaxel. Bootstrap and posterior predictive checks confirmed the adequacy of the model. Conclusions Cabazitaxel PK appears unaffected by most baseline patient factors, and the influence of BSA on CL is addressed in practice by BSA-dependent doses. This analysis suggests consistent cabazitaxel PK and exposure across most solid tumor types, although the potential influence of breast cancer on CL requires further confirmation. Electronic supplementary material The online version of this article (doi:10.1007/s00280-012-2058-9) contains supplementary material, which is available to authorized users.

Using data from the Special Education Elementary Longitudinal Study, we describe parent involvement and support activities in schools educating a nationally representative sample of students with emotional disturbances (EDs). We describe the extent to which programs aimed at encouraging family involvement are offered by schools and received by families and their relationship to child, family, and school characteristics. Student-level information was obtained from students, teachers, and parents, whereas school-level information was obtained from school administrators. Although schools attended by 71% of the students reported offering at least one type of family involvement activity, teachers reported only 17% of families of children with ED received a parent support service. Compared with families who did not receive support services, those who did were more involved in their school, and their children received more services in the community and had higher achievement levels. The need and potential methods for increasing parent engagement are discussed.

The purpose of the current study is to contribute to the knowledge base on the use of family education and support (FES) services by examining the longitudinal trajectories of FES receipt and multiple domains of child and family functioning. Using an extant data set of more than 9,000 youth and their caregivers, results indicate that families who received FES on entry into services had greater caregiver strain, and their children experienced greater emotional challenges than families who did not receive FES services. Furthermore, for families who received FES, the longitudinal results revealed an immediate effect of seeking additional services, decreasing caregiver strain 6 months after receipt of FES services, and improving child emotional functioning 6 to 18 months after initial receipt of FES services. The complex, lagged effects in the results are discussed in the context of the theorized cyclical course of family stress as exemplified by the Double ABCX model of adjustment and adaptation. Implications for future research of FES services are discussed, especially the need to develop a functional logic model and an operational definition of FES and its components.

Building on previous arguments for why educational researchers should not provide effect-size estimates in the face of statistically nonsignificant outcomes (Robinson & Levin, 1997), Onwuegbuzie and Levin (2005) proposed a 3-step statistical approach for assessing group differences when multiple outcome measures are individually analyzed within the same study. One suggested Step 3 strategy was to conduct a binomial (or “sign”) test of the number of between-group outcome mean differences that are in the same direction. However, because multiple measures within a study typically are correlated, the binomial test's independence assumption will be violated. In the present investigation, the authors (a) performed a Monte Carlo simulation study to assess the Type I error behavior of the binomial test under varying degrees of independence-assumption violations, resulting in a table of adjusted critical values; and (b) illustrated use of this table by applying its adjusted critical values to a real research example.

This longitudinal investigation examined the utility of subjective well-being (SWB) and psychopathology in predicting subsequent academic achievement and in-school behavior in 300 middle school students. Initial SWB predicted students’ grade point averages (GPAs) 1 year later, initial internalizing psychopathology predicted absences 1 year later, and initial externalizing psychopathology predicted grades, absences, and discipline problems 1 year later. Students’ grades and attendance across time varied as a function of mental health group yielded from a dual factor model. Specifically, students in the troubled mental health group declined at a significantly faster rate on GPAs than youth without psychopathology. In contrast, students in the symptomatic but content group were not significantly different from peers with low psychopathology. At Time 2, the best attendance, grades, and math skills were found among students who had both average/high SWB and low psychopathology 1 year earlier, supporting the lo...

The increasing length of time that students take to attain the doctorate (TTD) is of concern in higher education. Separate focus groups with students and eight faculty members were used to investigate factors perceived to influence TTD in Education. The percentage of participants citing a theme (theme frequency) and the percentage of statements referring to a particular theme (theme intensity) were used to describe the strength of the associations found. Analysis of students and faculty responses suggests that structure, defined as the nature and arrangement of program tasks and resources, is strongly associated with TTD whereas communication, the way program expectations and requirements are communicated, has a moderate association. Students’ responses indicate that the nature of the dissertation committee formed (committee) and the desire to work and attain goals despite obstacles encountered (motivation) are each strongly associated with TTD whereas faculty responses indicate these associations are moderate and minimal, respectively. Faculty responses indicate that the amount and quality of academic preparation received (preparation), and the nature of academic guidance, mentoring and supervision received (advising) have at least a moderate association with TTD whereas students’ responses suggest the association is at most moderate. Whereas faculty comments suggest that whether a student enrolls part-time or full-time (enrollment) is moderately associated with TTD, enrollment did not emerge as a factor among students. Recommendations for practice and further research are provided.

Traditional comparisons of test score means identify group differences in broad academic areas, but fail to provide substantive description of how the groups differ on the specific cognitive attributes required for success in the academic area. The rule space method (RSM) allows for group comparisons at the cognitive attribute level, which consists of the cognitive knowledge, skills, and abilities an individual can employ to solve a problem. In the current study, we extend RSM group comparison methods to include comparisons of the attribute characteristic curves (ACCs) and provide a method for estimating and plotting the ACCs using SAS. We further investigated Taiwanese mathematics performance on TIMSS-1999 by comparing cognitive attributes between students of different achievement levels as well as between male and female students. The results indicated the highest and lowest achieving students differed most on mastery probabilities for Algebra (C3), Open-ended items (S10), and Rule application in algebr...

This study analyzed the reporting of multilevel modeling applications of a sample of 99 articles from 13 peer-reviewed journals in education and the social sciences. A checklist, derived from the methodological literature on multilevel modeling and focusing on the issues of model development and specification, data considerations, estimation, and inference, was used to analyze the articles. The most common applications were two-level models where individuals were nested within contexts. Most studies were non-experimental and used nonprobability samples. The amount of data at each level varied widely across studies, as did the number of models examined. Analyses of reporting practices indicated some clear problems, with many articles not reporting enough information for a reader to critique the reported analyses. For example, in many articles, one could not determine how many models were estimated, what covariance structure was assumed, what type of centering if any was used, whether the data were consistent with assumptions, whether outliers were present, or how the models were estimated. Guidelines for researchers reporting multilevel analyses are provided.

Approximately 20%-25% of the pediatric population will likely develop a sleep disorder sometime during childhood or adolescence. Studies have shown that untreated sleep disorders can negatively affect cognitive abilities, and aca- demic and behavior performance. The Sleep Disorders Inventory for Students (SDIS) is a screening instrument designed to be used by school psychologists to determine if problems related to sleep may be affecting a student's educational performance. The SDIS was validated on 821 students in the southeastern United States and seven sleep centers nationally. This study presents the development of both forms of the SDIS, the SDIS—Children's Form and the SDIS—Adolescent Form, as well as the empirical data to support the reliability of the scores and the validity of the inferences. Practical implications for the use of the SDIS are discussed, and suggestions for future research related to the screening of pediatric sleep disorders are presented.

A concrete example is used to illustrate maximum likelihood estimation of a structural equation model with two unknown parameters. The fitting function is found for the example, as are the vector of first-order partial derivatives, the matrix of second-order partial derivatives, and the estimates obtained from each iteration of the Newton-Raphson algorithm. The goal is to provide a concrete illustration to help those learning structural equation modeling bridge the gap between the verbal descriptions of estimation procedures and the mathematical definition of these procedures provided in the technical literature.

Monte Carlo methods were used to examine techniques for constructing confidence intervals around multivariate effect sizes. Using interval inversion and bootstrapping methods, confidence intervals were constructed around the standard estimate of Mahalanobis distance ( D2), two bias-adjusted estimates of D2, and Huberty’s I. Interval coverage and width were examined across conditions by adjusting sample size, number of variables, population effect size, population distribution shape, and the covariance structure. The accuracy and precision of the intervals varied considerably across methods and conditions; however, the interval inversion approach appears to be promising for D2, whereas the percentile bootstrap approach is recommended for the other effect size measures. The results imply that it is possible to obtain fairly accurate coverage estimates for multivariate effect sizes. However, interval width estimates tended to be large and uninformative, suggesting that future efforts might focus on investigating design factors that facilitate more precise estimates of multivariate effect sizes.

The authors present a method that ensures control over the Type I error rate for those who visually analyze the data from response-guided multiple-baseline designs. The method can be seen as a modification of visual analysis methods to incorporate a mechanism to control Type I errors or as a modification of randomization test methods to allow response-guided experimentation and visual analysis. The approach uses random assignment of participants to intervention times and a data analyst who is blind to which participants enter treatment at which points in time. The authors provide an example to illustrate the method and discuss the conditions necessary to ensure Type I error control.

The authors used Monte Carlo methods to examine the Type I error rates for randomization tests applied to single-case data arising from ABAB designs involving random, systematic, or response-guided assignment of interventions. Six randomization tests were examined (permuting blocks of 1, 2, 3, or 5 observations, and randomly selecting intervention triplets so that each phase has at least 3 or 5 observations). When the design included randomization, the Type I error rate was controlled. When the design was systematic or guided by the absolute value of the slope, the tests permuting blocks tended to be liberal with positive autocorrelation, whereas those based on the random selection of intervention triplets tended to be conservative across levels of autocorrelation.

This study examined out-of-school suspensions (OSS) in a large, ethnically diverse school district using both quantitative and qualitative procedures. Pearson product moment correlations and semi-partial correlations were used to identify those school-level variables that showed the strongest relationships to the duplicated OSS rate among elementary schools (n = 97) and secondary schools (n = 45). Additionally, interviews were conducted with administrators and student support personnel from the 24 schools in the district with the highest suspension rates and 24 demographically matched schools with significantly lower suspension rates. The majority of these schools served a high percentage of children from low socioeconomic backgrounds. Although the correlational analyses indicated that student demographic variables (e.g., percentage of White students, percentage of Black students, percentage of students receiving free or reduced price lunch) were strongly related to a school's suspension rate, the school comparisons showed that not all schools serving a high percentage of children placed at risk have high suspension rates. Implications of the findings for school discipline reform are discussed. © 2002 Wiley Periodicals, Inc.

This research focuses on the evolving area of cyberviolence and draws on a pioneering study to discuss benefits and risks of online interaction among adolescent girls. This new area of inquiry introduces educators to the social and cultural communities of the Internet, a virtual venue with unique perspectives on power, identity, and gender for children and youth. This research focuses on the evolving area of cyberviolence and draws on a pioneering study to discuss benefits and risks of online interaction among adolescent girls. This new area of inquiry introduces educators to the social and cultural communities of the Internet, a virtual venue with unique perspectives on power, identity, and gender for children and youth.