The Misspecification of the Covariance Structures in Multilevel Models for Single-Case Data: A Monte Carlo Simulation Study

The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest lies in the between-study variance estimate, including at least 30 studies is warranted. Modeling covariance does not result in less biased between-study variance estimates as the between-study covariance estimate is biased. When the research interest lies in the between-case covariance, the model including covariance results in unbiased between-case variance estimates. The three-level model appears to be less appropriate for estimating between-study variance if fewer than 30 studies are included.     

Adolescents’ Friendships,Academic Achievement,and Risk Behaviors: Same-Behavior and Cross-Behavior Selection and Influence Processes

This study examined to what extent adolescents’ and their friends’ risk behaviors (i.e., delinquency and alcohol use) hinder or promote their academic achievement (grade point average [GPA]), and vice versa. Longitudinal data were used (N = 1,219 seventh- to ninth-grade adolescents; M_age = 13.69). Results showed that risk behaviors negatively affected adolescents’ GPA, whereas GPA protected against engaging in risk behaviors. Moreover, adolescents tended to select friends who have similar behaviors and friends’ behaviors became more similar over time (same-behavior selection and influence). Furthermore, although same-behavior effects seemed to dominate, evidence was found for some cross-behavior selection effects and a tendency in seventh grade for cross-behavior influence effects. Concluding, it is important to investigate the interplay between different behaviors with longitudinal social network analysis.     

Bias Corrections for Standardized Effect Size Estimates Used With Single-Subject Experimental Designs

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.     

HistoViewer: An interactive e‐learning platform facilitating group and peer group learning

Understanding tissue architecture and the morphological characteristics of cells is a central prerequisite to comprehending the basis of physiological tissue function in healthy individuals and relating this to disease states. Traditionally, medical curricula include courses where students examine glass slides of cytological or tissue samples under a light microscope. However, it is challenging to implement group and peer group learning in these courses and to give students sufficient time to study specimens. An increasing number of medical schools have thus started to implement digital slide viewers, so‐called virtual microscopes, in histology and histopathology. These websites are mostly based on standard commercial software and offer limited adaptation to the special needs of first‐year students. An e‐learning platform has therefore been developed for use in cytology and histology courses. This virtual microscopy tool is coupled to a central database in which students can label and store the positions of individual structures for later repetition. As learning in pairs and peer groups has been shown to provide a high learning outcome, identified structures can be shared and discussed with students' peers or faculty via a built‐in communication module. This website has the possibility of opening an arbitrary number of frames which all can actively be moved and changed in magnification to enable the comparison of specimens and thus encourage a more global understanding of related tissues. HistoViewer is thus suggested as an e‐learning tool combining several modern teaching concepts. © 2013 American Association of Anatomists.     

Employee Retention: Organisational and Personal Perspectives

In this continuously changing contemporary economy, companies have to be able to anticipate technological innovations and to compete with other companies worldwide. This need makes important a company’s ability to evolve through its employees’ learning and through continuous development. Securing and retaining skilled employees plays an important role in this process, because employees’ knowledge and skills are central to companies’ ability to be economically competitive. Given that employee retention is very important for the functioning and competitiveness of a company, this study focuses on the organisational and personal factors that influence employee retention. A special interest is taken in employees’ learning, because this is seen as a retention supporting activity. A questionnaire was administered to 349 employees, and 11 employees were interviewed. The interviews are used to illustrate and contextualise the quantitative results. The results show a large positive contribution of appreciation and stimulation of the employee to employee retention. This result is consistent with findings of earlier research. However, the retention benefits arising from personal development offer new possibilities when attempting to enhance employee retention. This study also showed that individual differences influence employee retention. Leadership skills and seniority have a positive relationship with employee retention and the level of readiness and initiative regarding learning are negatively related to retention.     

Testing the Intervention Effect in Single-Case Experiments: A Monte Carlo Simulation Study

This article reports on a Monte Carlo simulation study, evaluating two approaches for testing the intervention effect in replicated randomized AB designs: two-level hierarchical linear modeling (HLM) and using the additive method to combine randomization test p values (RTcombiP). Four factors were manipulated: mean intervention effect, number of cases included in a study, number of measurement occasions for each case, and between-case variance. Under the simulated conditions, Type I error rate was under control at the nominal 5% level for both HLM and RTcombiP. Furthermore, for both procedures, a larger number of combined cases resulted in higher statistical power, with many realistic conditions reaching statistical power of 80% or higher. Smaller values for the between-case variance resulted in higher power for HLM. A larger number of data points resulted in higher power for RTcombiP.     

Approaches for Specifying the Level-1 Error Structure When Synthesizing Single-Case Data

Multilevel modeling has been utilized for combining single-case experimental design (SCED) data assuming simple level-1 error structures. The purpose of this study is to compare various multilevel analysis approaches for handling potential complexity in the level-1 error structure within SCED data, including approaches assuming simple and complex error structures (heterogeneous, autocorrelation, and both) and those using fit indices to select between alternative error structures. A Monte Carlo study was conducted to empirically validate the suggested multilevel modeling approaches. Results indicate that each approach leads to fixed effect estimates with little to no bias and that inferences for fixed effects were frequently accurate, particularly when a simple homogeneous level-1 error structure or a first-order autoregressive structure was assumed and the inferences were based on the Kenward-Roger method. Practical implications and recommendations are discussed.