Individual Mobility across Clusters: The Impact of Ignoring Cross-Classified Data Structures in Discrete-Time Survival Analysis.

A multilevel-discrete time survival model may be appropriate for purely hierarchical data, but when data are non-purely hierarchical due to individual mobility across clusters, a cross-classified discrete time survival model may be necessary. The purpose of this research was to investigate the performance of a cross-classified discrete-time survival model and assess the impact of ignoring a cross-classified data structure on the model parameters of a conventional discrete-time survival model and a multilevel discrete-time survival model. A Monte Carlo simulation was used to examine the performance of three discrete-time survival models when individuals are mobile across clusters. Simulation factors included the value of the between-clusters variance, number of clusters, within-cluster sample size, Weibull scale parameter, and mobility rate. The results suggest that substantial relative parameter bias, unacceptable coverage of the 95% confidence intervals, and severely biased standard errors are possible for all model parameters when a discrete-time survival model is used that ignores the cross-classified data structure. The findings presented in this study are useful for methodologists and practitioners in educational research, public health, and other social sciences where discrete-time survival analysis is a common methodological technique for analyzing event-history data.

Effect of Repeated Exposure to the Visual Environment on Young Children's Attention.

Prior research suggests that visual features of the classroom environment (e.g., charts and posters) are potential sources of distraction hindering children's ability to maintain attention to instructional activities and reducing learning gains in a laboratory classroom. However, prior research only examined short-term exposure to elements of classroom décor, and it remains unknown whether children habituate to the visual environment with repeated exposure. In study 1, we explored experimentally the possibility that children may habituate to the visual environment if the visual displays are static. We measured kindergarten children's patterns of attention allocation in a decorated classroom environment over a 2-week period and compared the percentage of time children spent off-task to a baseline condition in which the classroom environment was streamlined (i.e., charts, posters, and manipulatives were removed). The findings indicate that with more prolonged exposure to a static visual environment, partial habitation effects were observed: Attention to the environment declined at the end of the exposure period compared to the beginning of the study; however, the environment remained a significant source of off-task behavior even after 2 weeks of exposure. In study 2, we extend this work by conducting a longitudinal observation of six primary classrooms in which we measured children's patterns of attention allocation in real classrooms for 15 weeks to investigate whether increasing familiarity with the classroom décor would influence attention toward the visual environment. No evidence of habituation was observed in genuine classrooms in study 2. Potential implications for classroom design and future directions are discussed.

The impacts of ignoring individual mobility across clusters in estimating a piecewise growth model.

A three-level piecewise growth model (3L-PGM) can be used to break up nonlinear growth into multiple components, providing the opportunity to examine potential sources of variation in individual and contextual growth within different segments of the model. The conventional 3L-PGM assumes that the data are strictly hierarchical in nature, where measurement occasions (level 1) are nested within individuals (level 2) who are members of a single cluster (level 3). However, in longitudinal research, it is sometimes difficult for data structures to remain purely clustered during a study, such as when some students change classrooms or schools over time. One resulting data structure in this situation is known as a multiple membership structure, where some lower-level units are members of more than one higher-level unit. The new multiple membership PGM (MM-PGM) extends the 3L-PGM to handle multiple membership data structures frequently found in the social sciences. This study sought to examine the consequences of ignoring individual mobility across clusters when estimating a 3L-PGM in comparison to estimating a MM-PGM. MM-PGM estimates were less biased (especially in the cluster-level coefficient estimates), although we found substantial bias in cluster-level variance components across some conditions for both models.

A new way for handling mobility in longitudinal data.

In the social sciences, applied researchers often face a statistical dilemma when multilevel data is structured such that lower-level units are not purely clustered within higher-level units. To aid applied researchers in appropriately analyzing such data structures, this study proposes a multiple membership growth curve model (MM-GCM). The MM-GCM offers some advantages to other similar modeling approaches, including greater flexibility in modeling the intercept at the time-point most desired for interpretation. A real longitudinal dataset from the field of education with a multiple membership structure, where some students changed schools over time, was used to demonstrate the application of the MM-GCM. Baseline and conditional MM-GCMs are presented, and parameter estimates were compared with two other common approaches to handling such data structures - the final school-GCM that ignores mobile students by only modeling the final school attended and the delete-GCM that deletes mobile students. Additionally, a simulation study was conducted to further assess the impact of ignoring mobility on parameter estimates. The results indicate that ignoring mobility results in substantial bias in model estimates, especially for cluster-level coefficients and variance components.

An Investigation of Exposure Control Methods With Variable-Length CAT Using the Partial Credit Model.

The purpose of this simulation study was to investigate the effect of several different item exposure control procedures in computerized adaptive testing (CAT) with variable-length stopping rules using the partial credit model. Previous simulation studies on CAT exposure control methods with polytomous items rarely considered variable-length tests. The four exposure control techniques examined were the randomesque with a group of three items, randomesque with a group of six items, progressive-restricted standard error (PR-SE), and no exposure control. The two variable-length stopping rules included were the SE and predicted standard error reduction (PSER), along with three item pools of varied sizes (43, 86, and 172 items). Descriptive statistics on number of nonconvergent cases, measurement precision, testing burden, item overlap, item exposure, and pool utilization were calculated. Results revealed that the PSER stopping rule administered fewer items on average while maintaining measurement precision similar to the SE stopping rule across the different item pool sizes and exposure controls. The PR-SE exposure control procedure surpassed the randomesque methods by further reducing test overlap, maintaining maximum exposure rates at the target rate or lower, and utilizing all items from the pool with a minimal increase in number of items administered and nonconvergent cases.

Understanding Open Access Data Using Visuals: Integrating Prospective Studies of Children's Responses to Natural Disasters.

BACKGROUND: As access to open data is increasing, researchers gain the opportunity to build integrated datasets and to conduct more powerful statistical analyses. However, using open access data presents challenges for researchers in understanding the data. Visuals allow researchers to address these challenges by facilitating a greater understanding of the information available. OBJECTIVES: This paper illustrates how visuals can address the challenges that researchers face when using open access data, such as: (1) becoming familiar with the data, (2) identifying patterns and trends within the data, and (3) determining how to integrate data from multiple studies. METHOD: This paper uses data from an integrative data analysis study that combined data from prospective studies of children's responses to four natural disasters: Hurricane Andrew, Hurricane Charley, Hurricane Katrina, and Hurricane Ike. The integrated dataset assessed hurricane exposure, posttraumatic stress symptoms, anxiety, social support, and life events among 1707 participants (53.61% female). The children's ages ranged from 7 to 16 years (M = 9.61, SD = 1.60). RESULTS: Visuals serve as an effective method for understanding new and unfamiliar datasets. CONCLUSIONS: In response to the growth of open access data, researchers must develop the skills necessary to create informative visuals. Most research-based graduate programs do not require programming-based courses for graduation. More opportunities for training in programming languages need to be offered so that future researchers are better prepared to understand new data. This paper discusses implications of current graduate course requirements and standard journal practices on how researchers visualize data.

Residual Normality Assumption and the Estimation of Multiple Membership Random Effects Models.

While conventional hierarchical linear modeling is applicable to purely hierarchical data, a multiple membership random effects model (MMrem) is appropriate for nonpurely nested data wherein some lower-level units manifest mobility across higher-level units. Although a few recent studies have investigated the influence of cluster-level residual nonnormality on hierarchical linear modeling estimation for purely hierarchical data, no research has examined the statistical performance of an MMrem given residual non-normality. The purpose of the present study was to extend prior research on the influence of residual non-normality from purely nested data structures to multiple membership data structures. Employing a Monte Carlo simulation study, this research inquiry examined two-level MMrem parameter estimate biases and inferential errors. Simulation factors included the level-two residual distribution, sample sizes, intracluster correlation coefficient, and mobility rate. Results showed that estimates of fixed effect parameters and the level-one variance component were robust to level-two residual non-normality. The level-two variance component, however, was sensitive to level-two residual non-normality and sample size. Coverage rates of the 95% credible intervals deviated from the nominal value assumed when level-two residuals were non-normal. These findings can be useful in the application of an MMrem to account for the contextual effects of multiple higher-level units.

Factor Structure of the Community Reintegration of Service-Members (CRIS) in Veterans with Blast-Related Mild Traumatic Brain Injury.

Veterans with blast-related mild traumatic brain injury (mTBI) report difficulty engaging in life roles, also referred to as participation. Current measures are either global or lack comprehensive coverage of life roles and have not been validated in Veterans with mTBI. The Community Reintegration of Service-members instrument (CRIS) is a promising measure that was specifically developed for Veterans using a well-formulated conceptual framework and Rasch analysis. However, the CRIS has not been validated in Veterans with mTBI. Two data sets were combined for 191 Veterans with blast-related mTBI to conduct a confirmatory factor analysis of the CRIS. High residual and low loading items (33) were removed to improve the model fit. The remaining items demonstrated high correlation (0.87-0.89) between subscales and high test re-test (0.85 to 0.95). Mean scores were better for Veterans without Post Traumatic Stress Disorder (PTSD) or depression compared to Veterans with PTSD or depression. The refined CRIS offers a valid comprehensive measure of participation for Veterans with blast-related mTBI. Future directions include examining aspects of participation that may not be covered by the CRIS for Veterans with mTBI..

Estimation of a Latent Variable Regression Growth Curve Model for Individuals Cross-Classified by Clusters.

The cross-classified multiple membership latent variable regression (CCMM-LVR) model is a recent extension to the three-level latent variable regression (HM3-LVR) model which can be utilized for longitudinal data that contains individuals who changed clusters over time (for instance, student mobility across schools). The HM3-LVR model can include the initial status on growth effect as varying across those clusters and allows testing of more flexible hypotheses about the influence of initial status on growth and of factors that might impact that relationship, but only in the presence of pure clustering of participants within higher-level units. This Monte Carlo study was conducted to evaluate model estimation under a variety of conditions and to measure the impact of ignoring cross-classified data when estimating the incorrectly specified HM3-LVR model in a scenario in which true values for parameters are known. Furthermore, results from a real-data analysis were used to inform the design of the simulation. Overall, it would be recommended for researchers to utilize the CCMM-LVR model over the HM3-LVR model when individuals are cross-classified, and to use a bare minimum of more than 100 clustering units in order to avoid overestimation of the level-3 variance component estimates.

Predicting dropout using student- and school-level factors: An ecological perspective.

High school dropout has been associated with negative outcomes, including increased rates of unemployment, incarceration, and mortality. Dropout rates vary significantly depending on individual and environmental factors. The purpose of our study was to use an ecological perspective to concurrently explore student- and school-level predictors associated with dropout for the purpose of better understanding how to prevent it. We used the Education Longitudinal Study of 2002 dataset. Participants included 14,106 sophomores across 684 public and private schools. We identified variables of interest based on previous research on dropout and implemented hierarchical generalized linear modeling. In the final model, significant student-level predictors included academic achievement, retention, sex, family socioeconomic status (SES), and extracurricular involvement. Significant school-level predictors included school SES and school size. Race/ethnicity, special education status, born in the United States, English as first language, school urbanicity, and school region did not significantly predict dropout after controlling for the aforementioned predictors. Implications for prevention and intervention efforts within a multitiered intervention model are discussed. (PsycINFO Database Record

A comparison of stopping rules for computerized adaptive screening measures using the rating scale model.

The current study evaluates three stopping rules for computerized adaptive testing (CAT): the predicted standard error reduction (PSER), the fixed-length, and the minimum SE using Andrich's rating scale model with a survey to identify at-risk students. PSER attempts to reduce the number of items administered and increase measurement precision of the trait. Several variables are manipulated, such as trait distribution and item pool size, in order to evaluate how these conditions interact and potentially help improve the correct classification of students. The findings indicate that the PSER stopping rule may be preferred when wanting to correctly diagnose or classify students at-risk and at the same time alleviate test burden for those taking screening measures based on the rating scale model with smaller item pools.