Understanding the dynamic interinfluences of implementation processes: An illustration by multiple case studies

Many dropout prevention programs have been developed and validated in the past decades. Yet, little is known about the contextual factors influencing the implementation of these programs. Implementation processes, such as school principal leadership and governmental funding, have been identified for their influence on program implementation, but the mutual or dynamic influence of these processes is yet to be understood. This study examines the processes involved in the implementation of Check & Connect (C&C), a well-established targeted dropout prevention program validated in several countries that aims at promoting the development of a significant relationship between at-risk students and a mentor as a way to prevent school dropout.Semi-structured interviews were conducted with 12 mentors and coordinators directly involved in C&C implementation. Analyses were conducted in two subsequent steps: thematic analyses first helped identify implementation processes described by respondents, and then synthetic case studies allowed us to build the implementation stories of distinct sites. This last step was conducted using the Planned Change Framework.This framework helped us to understand the complex dynamics of implementation processes in each site, which were associated with previously identified program outcomes, beyond implementation fidelity. Implications for future implementations of evidence-based programs in the school setting are discussed.     

基于教育社会分层视角的农村学生社会流动 与教育选择

随着教育的发展和社会分化的加剧,教育的社会分层功能与农村学生的社会流动日益受到人们关注.当前,农村学生在社会流动与社会地位获得的过程中,由于受到以城市教育为中心的发展战略及重点学校制度的行政约束、高等教育分层和高等教育大众化对文凭筛选功能的影响、高考和就业场域潜规则的干预和农村社会文化价值体系的制约,教育所铺架的向上流动的阶梯出现了明显的短路,农村学生不管在高等教育数量获得还是高等教育质量获得方面,均处于相对劣势;农村大学生也没有从根本上改变自己的命运,他们的阶层地位仍然较低,非正规就业居多、职业地位低、收入低、被限制在边缘行业,结果导致部分农村学生弃考和辍学.     

Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data

Summary.  A common objective in longitudinal studies is the joint modelling of a longitudinal response with a time-to-event outcome. Random effects are typically used in the joint modelling framework to explain the interrelationships between these two processes. However, estimation in the presence of random effects involves intractable integrals requiring numerical integration. We propose a new computational approach for fitting such models that is based on the Laplace method for integrals that makes the consideration of high dimensional random-effects structures feasible. Contrary to the standard Laplace approximation, our method requires much fewer repeated measurements per individual to produce reliable results.     

Dropping out of high school: Effects of close and distant friendships

Prior research highlights the role of friends in influencing whether a student completes high school. Students who drop out tend to have fewer friends, as well as friends who are less oriented toward school success. We distinguish between close and distant friendships by developing a theoretical framework which predicts close and distant friends likely have distinct effects on dropping out. Close friendships provide valuable emotional support, and forging numerous close friendships at school should decrease one’s risk of dropping out. In contrast, the characteristics of distant friends help shape students’ social identities and beliefs about “what’s normative.” Our analyses of the Add Health data set confirm our expectations. Students with more close friendships are less likely to drop out, but close friends’ characteristics are unrelated to dropping out. Distant relationships (as measured by affect and regularity of interaction) with friends who have a high risk of dropping out significantly increase a student’s own risk of dropping out.     

Neighborhood distress and school dropout: the variable significance of community context

Although a substantial body of recent research has examined the impact of neighborhood socioeconomic distress on youth socioeconomic attainment and urban social dislocations, few studies have determined under what conditions, and for what types of adolescents, neighborhood characteristics matter most. Drawing on theories of collective socialization, social capital, and social control, we develop hypotheses regarding the conditional nature of neighborhood effects on the risk of dropping out of high school, and we then test these hypotheses by estimating event history models based on data from the 1968–1993 waves of the Panel Study of Income Dynamics. We find that, among African Americans, the detrimental impact of neighborhood socioeconomic distress on school dropout has increased significantly over the past quarter-century, a probable repercussion of the increasing geographic concentration of urban poverty. The negative effect of neighborhood distress on high school completion is particularly pronounced among black adolescents from single-parent households and among white adolescents from low-income families, results broadly consistent with Wilson’s claim that exposure to neighborhood poverty reinforces the damaging consequences of individual disadvantage. Supporting the social capital perspective, among both black and white adolescents the deleterious impact of neighborhood distress on school dropout is stronger for recent in-movers than for long-term residents. The impact of neighborhood disadvantage also varies significantly by gender for both racial groups and, among whites, is stronger for younger than older adolescents. We conclude with a discussion of the implications of these findings for theories of neighborhood effects.     

Childhood maltreatment and motivation for treatment in girls in compulsory residential care

The first objective of the current study was to examine the relationship between childhood maltreatment, trauma-related symptoms and motivation for treatment in girls in compulsory residential treatment facilities. The second objective was to examine the extent to which various forms of childhood maltreatment, trauma-related symptoms and motivation for treatment predicted (time to) dropout from these facilities. Participants were 154 adolescent girls recruited from three residential treatment settings in The Netherlands. Multiple linear regression analysis revealed that age and ethnicity were associated with motivation for treatment. Furthermore, emotional abuse contributed to motivation for treatment. In addition, internalizing symptoms (e.g., anxiety and depression) significantly predicted level of distress; symptoms of dissociation predicted doubt about treatment. Logistic regression analyses with multiple imputation and competing risk regression analyses revealed no significant predictors for (time to) dropout. The findings suggest that clinicians and therapists should focus on experiences of emotional abuse, traumatic symptoms and treatment motivation in girls in compulsory residential care settings.     

Modeling dropouts by conditional distribution, a copula-based approach

In this paper the concept of copulas is implemented into the methodology for solving the imputation problem in correlated incomplete data. We use the Gaussian copula as alternative to the joint distribution for modeling the conditional distribution, conditioned by the observed values of measurements. The general formula for imputation and its application for compound symmetry correlation structure are given.     

Duration analysis in longitudinal studies with intermittent observation times and losses to followup

We consider the analysis of spell durations observed in event history studies where members of the study panel are seen intermittently. Challenges for analysis arise because losses to followup are frequently related to previous event history, and spells typically overlap more than one observation period. We provide methods of estimation based on inverse probability of censoring weighting for parametric and semiparametric Cox regression models. Selection of panel members through a complex survey design is also addressed, and the methods are illustrated in an analysis of jobless spell durations based on data from the Statistics Canada Survey of Labour and Income Dynamics. The Canadian Journal of Statistics 40: 1–21; 2012 © 2012 Statistical Society of Canada     

An ECM Estimation Approach for Analyzing Multivariate Skew-Normal Data with Dropout

In this article, an ECM algorithm is developed to obtain the maximum likelihood estimates of parameters where multivariate skew-normal distribution is used for analyzing longitudinal skewed normal regression data with dropout. A simulation study is performed to investigate the performance of the presented algorithm. Also, the methodology is illustrated through two applications and the results of proposed methodology are compared with ECM under multivariate normal assumption using AIC and BIC criteria. Standard errors of parameter estimates are obtained by asymptotic observed information matrix.     

An R package for model fitting,model selection and the simulation for longitudinal data with dropout missingness

Abstract

Missing data arise frequently in clinical and epidemiological fields, in particular in longitudinal studies. This paper describes the core features of an R package wgeesel, which implements marginal model fitting (i.e., weighted generalized estimating equations, WGEE; doubly robust GEE) for longitudinal data with dropouts under the assumption of missing at random. More importantly, this package comprehensively provide existing information criteria for WGEE model selection on marginal mean or correlation structures. Also, it can serve as a valuable tool for simulating longitudinal data with missing outcomes. Lastly, a real data example and simulations are presented to illustrate and validate our package.