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In this article, I argue that Islamic authority is being made between online and offline environments used by young Muslims, between religious experts and ‘laymen’. Youngsters try to find their way and mosques try to direct and inform Muslims about and through online sources and strengthen their online presence. This article analyses four online discussions between young Dutch Muslims of Moroccan descent on the forum Marokko.nl. These discussions about Islamic ideologies, imams and mosques show how the participants try to convince each other of their points of view. I intend to contribute to the ongoing debates on the relationship between religion and cyberspace, and particularly on how the construct of religious authority is mediated and negotiated among youngsters by zooming into and analysing some excerpts from their online discussions.

Dyadic network data occur when each member in a group provides data about each other member in the group (e.g., how much they like each other person). Such data have a complex nesting structure, such that bivariate responses (e.g., Person A’s liking of B and vice versa) are dependent upon out-going and in-coming random effects that are correlated within individuals. Dyadic network models for such data include the social relations model for normal data and the p2 and j2 models for dichotomous data, but we have seen no application or generalization to accommodate a rarely discussed type of variable from this framework: variables that are constant within a dyad. Dyad-constant variables could include background variables such as whether a dyad is same or opposite sex or how many years two friends have known each other, which require no special modification to use as predictors (Jorgensen et al., Soc Netw 54:26–40, 2018). But they could also be outcomes, such as the difference in a married couple’s relationship satisfaction or the similarity in symptoms of a (set of) psychological disorder(s). We explore how such dyad-constant outcomes can be modeled, demonstrating on a data set from a clinic for patients with eating disorders.

Interrater reliability (IRR) is often estimated by intraclass correlation coefficients (ICCs). Using Markov chain Monte Carlo (MCMC) estimation of Bayesian hierarchical models to estimate ICCs has several benefits over traditional approaches such as analysis of variance or maximum likelihood estimation. However, estimation of ICCs with small sample sizes and variance parameters close to zero, which are typical conditions in studies for which the IRR should be estimated, remains problematic in this MCMC approach. The estimation of the variance components that are used to estimate ICCs can heavily depend on the hyperprior distributions specified for these random-effect parameters. In this study, we explore the effect of a uniform and half-t hyperprior distribution on bias, coverage, and efficiency of the random-effect parameters and ICCs. The results indicated that a half-t distribution outperforms a uniform distribution but that slightly increasing the number of raters in a study is more influential than the choice of hyperprior distributions. We discuss implications and directions for future research.

We report the results of an academic survey into the theoretical and methodological foundations, common assumptions, and the current state of the field of consciousness research. The survey consisted of 22 questions and was distributed on two different occasions of the annual meeting of the Association of the Scientific Study of Consciousness (ASSC, 2018 and 2019). We examined responses from 166 consciousness researchers with different backgrounds (e.g., philosophy, neuroscience, psychology, computer science) and at various stages of their careers (e.g., junior/senior faculty, graduate/undergraduate students). The results reveal that there remains considerable discussion and debate between the surveyed researchers about the definition of consciousness and the way it should be studied. To highlight a few observations, a majority of respondents believe that machines could have consciousness, that consciousness is a gradual phenomenon in the animal kingdom and that unconscious processing is extensive, encompassing both low-level as well as high-level cognitive functions. Further, we show which theories of consciousness are currently considered most promising by respondents and how supposedly different theories cluster together, which dependent measures are considered best to index the presence or absence of consciousness, and which neural measures are thought to be the most likely signatures of consciousness. These findings provide us with a snapshot of the current views of researchers in the field and may therefore help prioritise research and theoretical approaches to foster progress.

Mokken’s scalability coefficients take values on the interval (−∞, 1]. The sampling distribution of scalability coefficients is skewed near the boundary, so Wald-based confidence intervals and significance tests may be biased. We introduce a transformation of the scalability coefficients and their standard errors, which can be used to construct range-preserving confidence intervals and significance tests. We demonstrated that for scalability coefficients away from the boundary, the properties of this range-preserving method are similar to the properties of the Wald-based method, but the range-preserving method outperforms the Wald-based method when the coefficient is close to unity. The range-preserving method can be applied to all types of scalability coefficients. Its implementation in software is discussed.

This proceedings volume highlights the latest research and developments in psychometrics and statistics. It represents selected and peer-reviewed presentations given at the 85th Annual International Meeting of the Psychometric Society (IMPS), held virtually on July 13-17, 2020. The IMPS is one of the largest international meetings on quantitative measurement in education, psychology and the social sciences. It draws approximately 500 participants from around the world, featuring paper and poster presentations, symposiums, workshops, keynotes, and invited presentations. Leading experts and promising young researchers have written the included chapters. The chapters address a wide variety of topics including but not limited to item response theory, adaptive testing, Bayesian estimation, propensity scores, and cognitive diagnostic models. This volume is the 9th in a series of recent works to cover research presented at the IMPS.

This proceedings volume highlights the latest research and developments in psychometrics and statistics. It represents selected and peer reviewed presentations given at the 84th Annual International Meeting of the Psychometric Society (IMPS), organized by Pontificia Universidad Católica de Chile and held in Santiago, Chile during July 15th to 19th, 2019. The IMPS is one of the largest international meetings on quantitative measurement in education, psychology and the social sciences. It draws approximately 500 participants from around the world, featuring paper and poster presentations, symposiums, workshops, keynotes, and invited presentations. Leading experts and promising young researchers have written the included chapters. The chapters address a large variety of topics including but not limited to item response theory, multistage adaptive testing, and cognitive diagnostic models. This volume is the 8th in a series of recent volumes to cover research presented at the IMPS.

When sample sizes are too small to support multiple-group models, an alternative method to evaluate measurement invariance is restricted factor analysis (RFA), which is statistically equivalent to the more common multiple-indicator multiple-cause (MIMIC) model. Although these methods traditionally were capable of detecting only uniform measurement bias, RFA can be extended with latent moderated structural equations (LMS) to assess nonuniform measurement bias. As LMS is implemented in limited structural equation modeling (SEM) computer programs (e.g., Mplus), we propose the use of the product indicator (PI) method in RFA models, which is available in any SEM software. Using simulated data, we illustrate how to apply this method to test for measurement bias, and we compare the conclusions with those reached using LMS in Mplus. Both methods obtain comparable results, indicating that the PI method is a viable alternative to LMS for researchers without access to SEM software featuring LMS.

The purpose of the present research was to introduce and evaluate random permutation testing applied to measurement invariance testing with ordered-categorical data. The random permutation test builds a reference distribution from the observed data that is used to calculate a p value for the observed (Δ)χ2 statistic. The reference distribution is built by repeatedly shuffling the grouping variable and then saving the Δχ2 statistic between the two models fitted to the resulting data. The present research consisted of two Monte Carlo simulations. The first simulation was designed to evaluate random permutation testing across a variety of conditions with scalar invariance testing in comparison to an existing analytical solution: the robust mean- and variance-adjusted Δχ2 test. The second simulation was designed to evaluate the random permutation test applied to testing configural invariance by evaluating overall model fit (the χ2 fit statistic). Simulation results and suggestions for the use of the random permutation test are provided.

We simulated Bayesian CFA models to investigate the power of PPP to detect model misspecification by manipulating sample size, strongly and weakly informative priors for nontarget parameters, degree of misspecification, and whether data were generated and analyzed as normal or ordinal. Rejection rates indicate that PPP lacks power to reject an inappropriate model unless priors are unrealistically restrictive (essentially equivalent to fixing nontarget parameters to zero) and both sample size and misspecification are quite large. We suggest researchers evaluate global fit without priors for nontarget parameters, then search for neglected parameters if PPP indicates poor fit.

One course of intravenous immunoglobulins (IVIg) of 2 g/kg is standard treatment in Guillain-Barré syndrome (GBS) patients unable to walk independently. Despite treatment some patients recover poorly, in part related to rapid consumption of IVIg, indicating that they may benefit from a second course of IVIg. The aim of the study is to determine whether a second course of IVIg, administered 1 week after start of the first course in patients with GBS and predicted poor outcome improves functional outcome on the GBS disability scale after 4 weeks. Secondary outcome measures include adverse events (AEs), Medical Research Council sumscore and GBS disability score after 8, 12, and 26 weeks, length of hospital and ICU admission, mortality, and changes in serum IgG levels. GBS patients of 12 years and older with a poor prognosis, based on the modified Erasmus GBS outcome score (mEGOS) at 1 week after start of the first IVIg course are eligible for randomization in this double-blind, placebo-controlled (IVIg or albumin) clinical trial. This study will determine if a second course of IVIg administered in the acute phase of the disease is safe, feasible, and effective in patients with GBS and a poor prognosis. This Dutch trial is registered prospectively as NTR 2224 in the Netherlands National Trial Register (NTR) which is the Primary Registry in the WHO Registry Network for the Netherlands.

The objective of this paper is to present a tournament scheme where the winner is determined as a function of pairwise competition between the players. We show that this tournament can be described by a time homogeneous discrete absorbing Markov chain, and derive the appropriate expressions for the probabilities and expectations of the tournament progression.

This chapter presents tools to facilitate specific tests on three sources of nonnormality in subtest scores or latent response variates: nonnormality of the latent trait, heteroscedasticity of the manifest variable residuals, and level dependency of the factor loadings. It outlines factor model and discusses existing approaches to test for each of the three sources of nonnormality. The chapter also outlines a unified approach in which the different sources of nonnormality are tested simultaneously. It also discusses examples in which the models are applied to intelligence subtest scores, and to affective analyzing item scores. It analyzes ordinal item level data from a personality questionnaire. The chapter utilizes specific functional forms for the relation between the latent trait on the one side and the residuals variances and the factor loadings on the other side. The develpoment of nonparametric approaches could ideally be combined into a single model with existing nonparametric approaches to modeling the latent trait distribution.

For four data sets of different measurement levels, we computed 20 coefficients that estimate interrater reliability. The results show that the coefficients provide very different numerical values when applied to the same data. We discuss possible explanations for the differences among coefficients and suggest further research that is needed to clarify which coefficient a researcher should use to estimate interrater reliability.