Does a contextualized theory of planned behavior explain why teenagers stay in virtual worlds?

Academics and business practitioners are intrigued by the factors that foster continued user engagement in virtual worlds (VWs). It is unclear how, and to what extent, existing theories can explain their continued use. As young people constitute the majority of VW users, understanding the reasons for their continued use intentions is important. Taking the distinctive features of VW services into account, such as the visibility of users’ actions to other users, we contextualize the Theory of Planned Behavior and investigate the role of teenagers’ attitudes, social influences and perceived behavioral control as the determinants of their continued intention to use VW. We then apply a set of constructs from prior Information Systems (IS) literature to decompose these constructs. By analyzing primary data collected from 923 users of Habbo Hotel, a leading virtual world for teenagers, this study confirms the role of users’ intrinsic motivation, interpersonal influence and self-efficacy as key constituents of sustained user engagement in VWs. In addition, we demonstrate that decomposing these three established constructs can capture many of the key contextual characteristics of VWs. The study contributes to the literature by showing that IS theories and constructs are appropriate for the VW setting and teenage users.     

Effects and side effects of inspections and accountability in education: an overview of empirical studies

This paper presents an overview of studies into effects and side effects of control mechanisms in education. We focus on effects and side effects of inspection visits and public performance indicators. A first conclusion is that the studies do not provide us with a clear answer to the question of whether inspections have positive causal effects on the quality of schools. Results of studies of publications on public performance indicators are more unambiguous. They lead us to the conclusion that although principals and teachers believe performance indicators are important, parents and pupils take very little notice of these indicators when choosing schools. A third conclusion concerns the occurrence of side effects of school inspections and other control mechanisms in education. Several of the studies discussed clearly refer to the existence of these side effects, such as ‘window dressing’ and other types of ‘gaming’.     

Testing Measurement Invariance in the Target Rotated Multigroup Exploratory Factor Model

We propose a method to investigate measurement invariance in the multigroup exploratory factor model, subject to target rotation. We consider both oblique and orthogonal target rotation. This method has clear advantages over other approaches, such as the use of congruence measures. We demonstrate that the model can be implemented readily in the freely available Mx program. We present the results of 2 illustrative analyses, one based on artificial data, and the other on real data relating to personality in male and female psychology students.     

GraphoGame SI: the development of a technology-enhanced literacy learning tool for Standard Indonesian

Early intervention programs are generally considered the most efficient and beneficial approach to providing support to struggling beginning readers. This paper discusses the theoretical background, development, and design of, as well as the first results obtained with, GraphoGame for Standard Indonesian, a technology-enhanced learning environment that trains the basic skills of reading by high but playful exposure to grapheme-phoneme coupling. The results of the pilot study assessing the usability of the program in 69 first-graders show that the more the students with low pre-test phonological skills were exposed to the game, the better their post-test performance on reading and decoding fluency became. Although large-scale, randomized controlled studies are needed to confirm the effectiveness of GraphoGame SI, these promising first results may offer a stepping stone for the development of additional language versions and future systematic comparisons of the method’s results in different languages and populations.     

Detection of macroenzymes: establishing upper reference limits for eight enzymes after polyethylene glycol precipitation

IntroductionThe presence of macroenzymes in blood can cause diagnostic confusion. Therefore, confirming the presence of macroenzymes is important to reduce unnecessary (non-)invasive investigations. Polyethylene glycol (PEG) precipitation is a simple and fast first-line method for the detection of macroenzymes. However, there is no consensus on the upper reference limit for the PEG-precipitable activity (%PPA) of monomeric enzymes. The aim of this study was to verify a PEG precipitation protocol for the detection of macroenzymes in our laboratory by establishing upper reference limits (URLs) and determining imprecision for eight enzymes after PEG precipitation. In addition, we aimed to clinically verify the URLs using samples containing macroenzymes as identified by electrophoresis.Materials and methodsPer enzyme, at least 40 leftover blood samples from adult patients with either normal or increased enzyme activities were diluted 1:1 with 25% PEG 6000 and 1:1 with 0.9% NaCl. Mixtures were incubated for 10 min at 37°C and centrifuged. Supernatant enzyme activity was measured on Cobas c702 and the %PPA was calculated.ResultsThe following URLs were obtained: 26% PPA for amylase, 29% PPA for alkaline phosphatase (ALP), 61% PPA for alanine aminotransferase, 48% PPA for aspartate aminotransferase, 24% PPA for creatine kinase (CK), 55% PPA for gamma-glutamyltransferase, 65% PPA for lactate dehydrogenase, and 56% PPA for lipase. The within-lab imprecision was < 15%. Regarding the clinical verification, the two historical samples with proven macroCK showed a %PPA of 69% and 43%, respectively, and a sample with proven macroALP had a %PPA of 52%.ConclusionIn this study, URLs for monomeric enzyme activities after PEG precipitation for eight different enzymes were established. The URLs are suitable for clinical use, but are only partially in line with other studies. Therefore, our data highlight the importance of establishing laboratory-specific upper reference limits for %PPA to allow a correct interpretation.     

Simulation study of item bias detection with restricted factor analysis

Restricted factor analysis (RFA) can be used to detect item bias (also called differential item functioning). In the RFA method of item bias detection, the common factor model serves as an item response model, but group membership is also included in the model. Two simulation studies are reported, both showing that the RFA method detects bias in 7‐point scale items very well, especially when the sample size is large, the mean trait difference between groups is small, the group sizes are equal, and the amount of bias is large. The first study further shows that the RFA method detects bias in dichotomous items at least as well as an established method based on the one‐parameter logistic item response model. The second study concerns various procedures to evaluate the significance of two‐item bias indices provided by the RFA method. The results indicate that the RFA method performs best when it is used in an iterative procedure.     

Measurement Bias in Multilevel Data

Measurement bias can be detected using structural equation modeling (SEM), by testing measurement invariance with multigroup factor analysis (Jöreskog, 1971;Meredith, 1993;Sörbom, 1974) MIMIC modeling (Muthén, 1989) or restricted factor analysis (Oort, 1992,1998). In educational research, data often have a nested, multilevel structure, for example when data are collected from children in classrooms. Multilevel structures might complicate measurement bias research. In 2-level data, the potentially “biasing trait” or “violator” can be a Level 1 variable (e.g., pupil sex), or a Level 2 variable (e.g., teacher sex). One can also test measurement invariance with respect to the clustering variable (e.g., classroom). This article provides a stepwise approach for the detection of measurement bias with respect to these 3 types of violators. This approach works from Level 1 upward, so the final model accounts for all bias and substantive findings at both levels. The 5 proposed steps are illustrated with data of teacher–child relationships.     

A Test for Cluster Bias: Detecting Violations of Measurement Invariance Across Clusters in Multilevel Data

We present a test for cluster bias, which can be used to detect violations of measurement invariance across clusters in 2-level data. We show how measurement invariance assumptions across clusters imply measurement invariance across levels in a 2-level factor model. Cluster bias is investigated by testing whether the within-level factor loadings are equal to the between-level factor loadings, and whether the between-level residual variances are zero. The test is illustrated with an example from school research. In a simulation study, we show that the cluster bias test has sufficient power, and the proportions of false positives are close to the chosen levels of significance.