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Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evaluated on cross-sectional, time-series, or panel data. These networks constitute an established methodology to visualise and conceptualise the interactions and relative importance of nodes/indicators, providing an important complement to other approaches such as factor analysis. However, limiting the representation to pairwise relationships can neglect potentially critical information shared by groups of three or more variables (higher-order statistical interdependencies). To overcome this important limitation, here we propose an information-theoretic framework to assess these interdependencies and consequently to use hypergraphs as representations in psychometrics. As edges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer account on the interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs can highlight meaningful redundant and synergistic interactions on either simulated or state-of-the-art, re-analysed psychometric datasets. Overall, our framework extends current network approaches while leading to new ways of assessing the data that differ at their core from other methods, enriching the psychometrics toolbox, and opening promising avenues for future investigation. [ABSTRACT FROM AUTHOR]

We propose and demonstrate a new two-stage maximum likelihood estimator for parameters of a social relations structural equation model (SR-SEM) using estimated summary statistics ( Σ ^ ) as data, as well as uncertainty about Σ ^ to obtain robust inferential statistics. The SR-SEM is a generalization of a traditional SEM for round-robin data, which have a dyadic network structure (i.e., each group member responds to or interacts with each other member). Our two-stage estimator is developed using similar logic as previous two-stage estimators for SEM, developed for application to multilevel data and multiple imputations of missing data. We demonstrate out estimator on a publicly available data set from a 2018 publication about social mimicry. We employ Markov chain Monte Carlo estimation of Σ ^ in Stage 1, implemented using the R package rstan. In Stage 2, the posterior mean estimates of Σ ^ are used as input data to estimate SEM parameters with the R package lavaan. The posterior covariance matrix of estimated Σ ^ is also calculated so that lavaan can use it to calculate robust standard errors and test statistics. Results are compared to full-information maximum likelihood (FIML) estimation of SR-SEM parameters using the R package srm. We discuss how differences between estimators highlight the need for future research to establish best practices under realistic conditions (e.g., how to specify empirical Bayes priors in Stage 1), as well as extensions that would make 2-stage estimation particularly advantageous over single-stage FIML. [ABSTRACT FROM AUTHOR]

In confirmatory factor analysis (CFA), model parameters are usually estimated by iteratively minimizing the Maximum Likelihood (ML) fit function. In optimal circumstances, the ML estimator yields the desirable statistical properties of asymptotic unbiasedness, efficiency, normality, and consistency. In practice, however, real-life data tend to be far from optimal, making the algorithm prone to convergence failure, inadmissible solutions, and bias. In this study, we revisited some old, yet largely neglected non-iterative alternatives and compared their performance to more recently proposed procedures in an extensive simulation study. We conclude that closed-form expressions may serve as viable alternatives for ML, with the Multiple Group Method – the oldest method under consideration – showing favorable results across all settings. [ABSTRACT FROM AUTHOR]

When faced with a binary or count outcome, informative hypotheses can be tested in the generalized linear model using the distance statistic as well as modified versions of the Wald, the Score and the likelihood-ratio test (LRT). In contrast to classical null hypothesis testing, informative hypotheses allow to directly examine the direction or the order of the regression coefficients. Since knowledge about the practical performance of informative test statistics is missing in the theoretically oriented literature, we aim at closing this gap using simulation studies in the context of logistic and Poisson regression. We examine the effect of the number of constraints as well as the sample size on type I error rates when the hypothesis of interest can be expressed as a linear function of the regression parameters. The LRT shows the best performance in general, followed by the Score test. Furthermore, both the sample size and especially the number of constraints impact the type I error rates considerably more in logistic compared to Poisson regression. We provide an empirical data example together with R code that can be easily adapted by applied researchers. Moreover, we discuss informative hypothesis testing about effects of interest, which are a non-linear function of the regression parameters. We demonstrate this by means of a second empirical data example. [ABSTRACT FROM AUTHOR]

In Structural Equation Modeling (SEM), the measurement part and the structural part are typically estimated simultaneously via an iterative Maximum Likelihood (ML) procedure. In this study, we compare performance of the standard procedure to the Structural After Measurement (SAM) approach, where the structural part is separated from the measurement part. One appealing feature of the latter multi-step procedure is that it extends the scope of possible estimators, as now also non-iterative methods from factor-analytic literature can be used to estimate the measurement models. In our simulations, the SAM approach outperformed vanilla SEM in small to moderate samples (i.e., no convergence issues, no inadmissible solutions, smaller MSE values). Notably, this held regardless of the estimator used for the measurement part, with negligible differences between iterative and non-iterative estimators. This may call into question the added value of advanced iterative algorithms over closed-form expressions (which generally require less computational time and resources). [ABSTRACT FROM AUTHOR]

Structural equation modeling is prone to a variety of problems when the sample size is small. One solution that attempts to solve the (non-convergence) problem of small sample SEM is found in shrinkage estimation, where a weighted average between the sample variance-covariance matrix (S) and a highly structured shrinkage target (T) is calculated to construct an adjusted sample variance-covariance matrix ( S a ), which is then used as input for the analysis. Different target candidates have already been put forward in the literature, but in this paper, we propose using a model-based target matrix specifically tailored for structural equation models. Two simulation studies demonstrate the benefit of using a model-based target over other candidate targets in terms of convergence rate, bias, and MSE, but also emphasize the importance of constructing an optimal shrinkage intensity. [ABSTRACT FROM AUTHOR]

Factor score regression (FSR) is widely used as a convenient alternative to traditional structural equation modeling (SEM) for assessing structural relations between latent variables. But when latent variables are simply replaced by factor scores, biases in the structural parameter estimates often have to be corrected, due to the measurement error in the factor scores. The method of Croon (MOC) is a well-known bias correction technique. However, its standard implementation can render poor quality estimates in small samples (e.g. less than 100). This article aims to develop a small sample correction (SSC) that integrates two different modifications to the standard MOC. We conducted a simulation study to compare the empirical performance of (a) standard SEM, (b) the standard MOC, (c) naive FSR, and (d) the MOC with the proposed SSC. In addition, we assessed the robustness of the performance of the SSC in various models with a different number of predictors and indicators. The results showed that the MOC with the proposed SSC yielded smaller mean squared errors than SEM and the standard MOC in small samples and performed similarly to naive FSR. However, naive FSR yielded more biased estimates than the proposed MOC with SSC, by failing to account for measurement error in the factor scores. [ABSTRACT FROM AUTHOR]

Pairwise maximum likelihood (PML) estimation is a promising method for multilevel models with discrete responses. Multilevel models take into account that units within a cluster tend to be more alike than units from different clusters. The pairwise likelihood is then obtained as the product of bivariate likelihoods for all within‐cluster pairs of units and items. In this study, we investigate the PML estimation method with computationally intensive multilevel random intercept and random slope structural equation models (SEM) in discrete data. In pursuing this, we first reconsidered the general 'wide format' (WF) approach for SEM models and then extend the WF approach with random slopes. In a small simulation study we the determine accuracy and efficiency of the PML estimation method by varying the sample size (250, 500, 1000, 2000), response scales (two‐point, four‐point), and data‐generating model (mediation model with three random slopes, factor model with one and two random slopes). Overall, results show that the PML estimation method is capable of estimating computationally intensive random intercept and random slopes multilevel models in the SEM framework with discrete data and many (six or more) latent variables with satisfactory accuracy and efficiency. However, the condition with 250 clusters combined with a two‐point response scale shows more bias. [ABSTRACT FROM AUTHOR]

In their article, Yuan and Deng argue that a structural parameter under partial least squares structural equation modeling (PLS-SEM) is zero if and only if the same structural parameter is zero under covariance-based structural equation modeling (CB-SEM). Yuan and Deng then conclude that statistical tests on individual structural parameters assessing the null hypothesis of no effect can achieve the same purpose in CB-SEM and PLS-SEM. Our response to their article highlights that the relationship they find between PLS-SEM and CB-SEM structural parameters is not universally valid, and that consequently, tests on individual parameters in CB-SEM and PLS-SEM generally do not fulfill the same purpose. [ABSTRACT FROM AUTHOR]

We show that separable nonlinear least squares (SNLLS) estimation is applicable to all linear structural equation models (SEMs) that can be specified in RAM notation. SNLLS is an estimation technique that has successfully been applied to a wide range of models, for example neural networks and dynamic systems, often leading to improvements in convergence and computation time. It is applicable to models of a special form, where a subset of parameters enters the objective linearly. Recently, Kreiberg et al. (Struct Equ Model Multidiscip J 28(5):725–739, 2021. https://doi.org/10.1080/10705511.2020.1835484) have shown that this is also the case for factor analysis models. We generalize this result to all linear SEMs. To that end, we show that undirected effects (variances and covariances) and mean parameters enter the objective linearly, and therefore, in the least squares estimation of structural equation models, only the directed effects have to be obtained iteratively. For model classes without unknown directed effects, SNLLS can be used to analytically compute least squares estimates. To provide deeper insight into the nature of this result, we employ trek rules that link graphical representations of structural equation models to their covariance parametrization. We further give an efficient expression for the gradient, which is crucial to make a fast implementation possible. Results from our simulation indicate that SNLLS leads to improved convergence rates and a reduced number of iterations. [ABSTRACT FROM AUTHOR]

Late elementary education constitutes a critical period in the development of reading comprehension strategies, a key competence in today's society. However, to date, appropriate measurements to map late elementary students' reading strategies are lacking. In this respect, the present article first describes the development and validation of the 26-item reading comprehension strategies questionnaire (RCSQ). To this aim, exploratory (sample 1: n = 1585 students) and confirmatory (sample 2: n = 1585 students) factor analyses were conducted. These analyses resulted in the RCSQ, consisting of five subscales: (1) overt cognitive reading strategies, (2) covert cognitive reading strategies, (3) monitoring, and (4) evaluating. For non-native and bilingual students, a fifth subscale 'using home language in view of comprehending texts' emerged. Second, multilevel analyses were performed to explore individual differences in late elementary students' reading comprehension strategy use. Implications for practice and future research are discussed. [ABSTRACT FROM AUTHOR]

The increasing mainstream adoption of immersive virtual reality (iVR) in education has triggered research about key variables explaining acceptance of iVR by teachers. In this study we adopted the UTAUT2 acceptance model as a theoretical framework enriched with the variable personal innovativeness. 379 Flemish secondary education teachers watched a video about iVR learning experiences, after which an online survey concerning their perceptions was administered. General linear modeling was performed to test the hypotheses. Results indicate performance expectancy, social influence, facilitating conditions, hedonic motivation and personal innovativeness to be significantly associated with behavioural intention to use. No moderating effect of age, gender or experience was observed. The results account for 54% of the variance in behavioural intention to use. The findings help to understand which factors are key in the acceptance of mobile iVR by secondary education teachers and might help defining successful iVR implementation strategies. [ABSTRACT FROM AUTHOR]

Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We compare the performance of the recently developed EGA with various traditional factor retention criteria. We use both continuous and binary data, as evidence regarding the accuracy of such criteria in the latter case is scarce. Simulation results, based on scenarios resulting from varying sample size, communalities from major factors, interfactor correlations, skewness, and correlation measure, show that EGA outperforms the traditional factor retention criteria considered in most cases in terms of bias and accuracy. In addition, we show that factor retention decisions for binary data are preferably made using Pearson, instead of tetrachoric, correlations, which is contradictory to popular belief. [ABSTRACT FROM AUTHOR]

Maximum likelihood estimation of generalized linear mixed models (GLMMs) is difficult due to marginalization of the random effects. Derivative computations of a fitted GLMM's likelihood are also difficult, especially because the derivatives are not by-products of popular estimation algorithms. In this paper, we first describe theoretical results related to GLMM derivatives along with a quadrature method to efficiently compute the derivatives, focusing on fitted lme4 models with a single clustering variable. We describe how psychometric results related to item response models are helpful for obtaining the derivatives, as well as for verifying the derivatives' accuracies. We then provide a tutorial on the many possible uses of these derivatives, including robust standard errors, score tests of fixed effect parameters, and likelihood ratio tests of non-nested models. The derivative computation methods and applications described in the paper are all available in easily obtained R packages. [ABSTRACT FROM AUTHOR]

Structural Equation Models (SEMs) are typically estimated via Maximum Likelihood. Grounded in large sample theory, estimates are prone to finite sample bias. Although Restricted Maximum Likelihood (REML) can alleviate this bias, its applicability is constrained to SEMs that are mathematically equivalent to mixed effect models. Via Monte Carlo simulations, we explored whether resampling based corrections could serve as viable, more broadly applicable alternatives. Results indicate that Bootstrap and Jackknife corrections effectively attenuate small sample bias, at the expected expense of an increase in variability. Similar conclusions are drawn with respect to a more recently proposed analytic approach by Ozenne et al., which was included for comparison. For all corrective methods, caution is advised when dealing with non-normal data and/or low reliability. [ABSTRACT FROM AUTHOR]

The most frustrating outcome of an SEM analysis is nonconvergence. Nonconvergence typically happens when the sample size is small ( N < 100) or very small ( N < 50). To minimize the frequency of nonconvergence, this paper proposes a solution called bounded estimation. The idea is to use data-driven lower and upper bounds for a subset of the model parameters during estimation. In this paper, we provide a rationale to compute these bounds, and we study the effect of different approaches to employ these bounds on the frequency of nonconvergence. A simulation study shows that bounded estimation dramatically decreases the frequency of nonconvergence in both correctly and misspecified models, without any (negative) effects on the quality of the point estimates for the unbounded parameters. [ABSTRACT FROM AUTHOR]

This article provides an overview of different computational options for inference following normal theory maximum likelihood (ML) estimation in structural equation modeling (SEM) with incomplete normal and nonnormal data. Complete data are covered as a special case. These computational options include whether the information matrix is observed or expected, whether the observed information matrix is estimated numerically or using an analytic asymptotic approximation, and whether the information matrix and the outer product matrix of the score vector are evaluated at the saturated or at the structured estimates. A variety of different standard errors and robust test statistics become possible by varying these options. We review the asymptotic properties of these computational variations, and we show how to obtain them using lavaan in R. We hope that this article will encourage methodologists to study the impact of the available computational options on the performance of standard errors and test statistics in SEM. [ABSTRACT FROM AUTHOR]

Growth curve modeling is commonly used in psychological, educational, and social science research. The mainstream estimators for growth curve modeling are based on normal theory, but real data are unlikely to be exactly normally distributed. To improve estimation and inference with non-normal data, various estimators have been proposed. Among these estimators, the asymptotically distribution free (ADF) estimator does not need to rely on any distribution assumption but it is not efficient with small and modest sample sizes. We propose a distributionally weighted least squares D L S estimator in the growth curve modeling framework. D L S combines normal theory based and A D F based generalized least squares estimation to balance the information from the data and the normality assumption. Computer simulation results suggest that model-implied covariance-based D L S ( D L S M ) generally provides more accurate and efficient estimates than the examined alternative methods regardless of the distribution. In addition, the relative biases of standard error estimates and the Type I error rates of the Satorra–Bentler test statistic ( T S B ) in D L S M were competitive with the classical methods including maximum likelihood and generalized least squares estimation. We illustrate how to implement D L S M and select the optimal tuning parameter by a bootstrap procedure in a real data example. [ABSTRACT FROM AUTHOR]

Using the EffectLiteR framework, researchers can test classical null hypotheses about effects of interest via Wald and F-tests, while taking into account the stochastic nature of group sizes. This paper aims at extending EffectLiteR to test informative hypotheses, assuming for example that the average effect of a new treatment is greater than the average effect of an old treatment, which in turn is greater than zero. We present a simulated data example to show two methodological novelties. First, we illustrate how to use the Fbar- and generalized linear Wald test to assess informative hypotheses. While the classical test did not reach significance, the informative test correctly rejected the null hypothesis, indicating the need to take into account the order of the treatment groups. Second, we demonstrate how to account for stochastic group sizes in informative hypotheses using the generalized non-linear Wald statistic. The paper concludes with a short data example. [ABSTRACT FROM AUTHOR]

We propose an extension of the Wilcoxon–Mann–Whitney test to compare two groups when the outcome variable is latent. We empirically demonstrate that the test can have superior power properties relative to tests based on Structural Equation Modeling for a variety of settings. In addition, several other advantages of the Wilcoxon–Mann–Whitney test are retained such as robustness to outliers and good small sample performance. We demonstrate the proposed methodology on a case study. [ABSTRACT FROM AUTHOR]

We propose a two-step procedure to estimate structural equation models (SEMs). In a first step, the latent variable is replaced by its conditional expectation given the observed data. This conditional expectation is estimated using a James–Stein type shrinkage estimator. The second step consists of regressing the dependent variables on this shrinkage estimator. In addition to linear SEMs, we also derive shrinkage estimators to estimate polynomials. We empirically demonstrate the feasibility of the proposed method via simulation and contrast the proposed estimator with ML and MIIV estimators under a limited number of simulation scenarios. We illustrate the method on a case study. [ABSTRACT FROM AUTHOR]

This Teacher's Corner paper introduces Bayesian evaluation of informative hypotheses for structural equation models, using the free open-source R packages bain, for Bayesian informative hypothesis testing, and lavaan, a widely used SEM package. The introduction provides a brief non-technical explanation of informative hypotheses, the statistical underpinnings of Bayesian hypothesis evaluation, and the bain algorithm. Three tutorial examples demonstrate informative hypothesis evaluation in the context of common types of structural equation models: 1) confirmatory factor analysis, 2) latent variable regression, and 3) multiple group analysis. We discuss hypothesis formulation, the interpretation of Bayes factors and posterior model probabilities, and sensitivity analysis. [ABSTRACT FROM AUTHOR]

Multilevel SEM is an increasingly popular technique to analyze data that are both hierarchical and contain latent variables. The parameters are usually jointly estimated using a maximum likelihood estimator (MLE). This has the disadvantage that a large sample size is needed and misspecifications in one part of the model may influence the whole model. We propose an alternative stepwise estimation method, which is an extension of the Croon method for factor score regression. In this article, we extend this method to the multilevel setting. A simulation study was used to compare this new estimation method to the standard MLE. The Croon method outperformed MLE with regard to convergence rate, bias, MSE, and coverage, in particular when models contained a structural misspecification. In conclusion, the Croon method seems to be a promising alternative to MLE. [ABSTRACT FROM AUTHOR]

This study introduces the statistical theory of using the Standardized Root Mean Squared Error (SRMR) to test close fit in ordinal factor analysis. We also compare the accuracy of confidence intervals (CIs) and tests of close fit based on the SRMR with those obtained based on the Root Mean Squared Error of Approximation (RMSEA). The current (biased) implementation for the RMSEA never rejects that a model fits closely when data are binary and almost invariably rejects the model in large samples if data consist of five categories. The unbiased RMSEA produces better rejection rates, but it is only accurate enough when the number of variables is small and the degree of misfit is small. In contrast, across all simulated conditions, the tests of close fit based on the SRMR yield acceptable type I error rates. SRMR tests of close fit are also more powerful than those using the unbiased RMSEA. [ABSTRACT FROM AUTHOR]

Factor score regression (FSR) is a popular alternative for structural equation modeling. Naively applying FSR induces bias for the estimators of the regression coefficients. Croon proposed a method to correct for this bias. Next to estimating effects without bias, interest often lies in inference of regression coefficients or in the fit of the model. In this article, we propose fit indices for FSR that can be used to inspect the model fit. We also introduce a model comparison test based on one of these newly proposed fit indices that can be used for inference of the estimators on the regression coefficients. In a simulation study we compare FSR with Croon's corrections and structural equation modeling in terms of bias of the regression coefficients, Type I error rate and power. [ABSTRACT FROM AUTHOR]

In the presence of omitted variables or similar validity threats, regression estimates are biased. Unbiased estimates (the causal effects) can be obtained in large samples by fitting instead the Instrumental Variables Regression (IVR) model. The IVR model can be estimated using structural equation modeling (SEM) software or using Econometric estimators such as two-stage least squares (2SLS). We describe 2SLS using SEM terminology, and report a simulation study in which we generated data according to a regression model in the presence of omitted variables and fitted (a) a regression model using ordinary least squares, (b) an IVR model using maximum likelihood (ML) as implemented in SEM software, and (c) an IVR model using 2SLS. Coverage rates of the causal effect using regression methods are always unacceptably low (often 0). When using the IVR model, accurate coverage is obtained across all conditions when N = 500. Even when the IVR model is misspecified, better coverage than regression is generally obtained. Differences between 2SLS and ML are small and favor 2SLS in small samples (N ≤ 100). [ABSTRACT FROM AUTHOR]

This paper presents a new statistical method and accompanying software for the evaluation of order constrained hypotheses in structural equation models (SEM). The method is based on a large sample approximation of the Bayes factor using a prior with a data-based correlational structure. An efficient algorithm is written into an R package to ensure fast computation. The package, referred to as Bain, is easy to use for applied researchers. Two classical examples from the SEM literature are used to illustrate the methodology and software. [ABSTRACT FROM AUTHOR]

Correlational measures are probably the most spread statistical tools in psychological research. They are used by researchers to investigate, for example, relations between self-report measures usually collected using paper-pencil or online questionnaires. Like many other statistical analysis, also correlational measures can be seriously affected by specific sources of bias which constitute serious threats to the final observed results. In this contribution, we will focus on the impact of the fake data threat on the interpretation of statistical results for two well-know correlational measures (the Pearson product-moment correlation and the Spearman rank-order correlation). By using the Sample Generation by Replacement (SGR) approach, we analyze uncertainty in inferences based on possible fake data and evaluate the implications of fake data for correlational results. A population-level analysis and a Monte Carlo simulation are performed to study different modulations of faking on bivariate discrete variables with finite supports and varying sample sizes. We show that by using our paradigm it is always possible, under specific faking conditions, to increase (resp. decrease) the original correlation between two discrete variables in a predictable and systematic manner. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore whether the relationship between teachers’ person–organisation (P-O) fit, job satisfaction and intention to move to another school varies depending on the way a bundle of human resource (HR) practices is configured in education. Drawing on strategic human resource management literature, a strategic and human resource orientation is considered to provide insight into the configuration of HR practices. A mixed methods design was used to collect both qualitative and quantitative data from principals and teachers in 56 Flemish primary schools. The qualitative data, which provides insight into principals’ configuration of HR practices, were converted into numerical scores and analysed, together with the quantitative teacher data, using multiple group path analysis. The key findings show that the relation between P-O fit and intention to move operates differently depending on the HR orientation of the teacher’s principal but not on their strategic orientation. This finding supports the need for closer attention to be given to the investment of new teachers and the impact this has on the direct and indirect effect of teachers’ P-O fit on teachers’ intention to move. [ABSTRACT FROM AUTHOR]

We compare the accuracy of confidence intervals (CIs) and tests of close fit based on the root mean square error of approximation (RMSEA) with those based on the standardized root mean square residual (SRMR). Investigations used normal and nonnormal data with models ranging from p = 10 to 60 observed variables. CIs and tests of close fit based on the SRMR are generally accurate across all conditions (even at p = 60 with nonnormal data). In contrast, CIs and tests of close fit based on the RMSEA are only accurate in small models. In larger models (p ≥ 30), they incorrectly suggest that models do not fit closely, particularly if sample size is less than 500. [ABSTRACT FROM AUTHOR]

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Teacher turnover is an international issue of continuing concern in education. While different antecedents of teachers’ turnover intention have been identified, teachers’ fit within the school (i.e. person-organisation [P–O] fit) in the context of turnover intention is still an emerging field of research. Building on the unfolding model of voluntary turnover, this study investigates the moderating role of teachers’ perceived employability on teachers’ P–O fit–job satisfaction–turnover intention relationship. Using survey data from 997 teachers across 74 schools, we conducted moderated mediation analysis. The analysis revealed different results for the hypothesised relationships depending on the specific dimension of turnover (i.e. intention to leave the profession or intention to move to another school). While P–O fit is directly related to the intention to move to another school, no evidence was found for a direct relation between P–O fit and the intention to leave the profession. Moreover, the results showed that the relation between P–O fit and the intention to leave/intention to move is mediated by job satisfaction. Yet, only for intention to leave, a small but significant moderating effect of perceived employability was found. These findings provide insight into the psychological processes of teachers wanting to leave the teaching profession or move to another school. Limitations, suggestions for further research and implications are also discussed in this article. [ABSTRACT FROM AUTHOR]

In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and with structural equation modeling (SEM) by using analytic calculations and two Monte Carlo simulation studies to examine their finite sample characteristics. Several performance criteria are used, such as the bias using the unstandardized and standardized parameterization, efficiency, mean square error, standard error bias, type I error rate, and power. The results show that the bias correcting method, with the newly developed standard error, is the only suitable alternative for SEM. While it has a higher standard error bias than SEM, it has a comparable bias, efficiency, mean square error, power, and type I error rate. [ABSTRACT FROM AUTHOR]

White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia. [ABSTRACT FROM AUTHOR]

We present a framework for estimating average and conditional effects of a discrete treatment variable on a continuous outcome variable, conditioning on categorical and continuous covariates. Using the new approach, termed theEffectLiteRapproach, researchers can consider conditional treatment effects given values of all covariates in the analysis and various aggregates of these conditional treatment effects such as average effects, effects on the treated, or aggregated conditional effects given values of a subset of covariates. Building on structural equation modeling, key advantages of the new approach are (1) It allows for latent covariates and outcome variables; (2) it permits (higher order) interactions between the treatment variable and categorical and (latent) continuous covariates; and (3) covariates can be treated as stochastic or fixed. The approach is illustrated by an example, and open source software EffectLiteR is provided, which makes a detailed analysis of effects conveniently accessible for applied researchers. [ABSTRACT FROM PUBLISHER]

Lebel and Paunonen (2011) highlight that despite their importance and popularity in both theoretical and applied research, many implicit measures continue to be plagued by a persistent and troublesome issue--low reliability. In their paper, they offer a conceptual analysis of the relationship between reliability, power and replicability, and then provide a series of recommendations for researchers interested in using implicit measures in an experimental setting. At the core of their account is the idea that reliability can be equated with statistical power, such that "lower levels of reliability are associated with decreasing probabilities of detecting a statistically significant effect, given one exists in the population" (p. 573). They also take the additional step of equating reliability and replicability. In our commentary, we draw attention to the fact that there is no direct, fixed or one-to-one relation between reliability and power or replicability. More specifically, we argue that when adopting an experimental (rather than a correlational) approach, researchers strive to minimize inter-individual variation, which has a direct impact on sample based reliability estimates. We evaluate the strengths and weaknesses of the LeBel and Paunonen's recommendations and refine them where appropriate. [ABSTRACT FROM AUTHOR]

For more than 30 years, the Interpersonal Reactivity Index (IRI) has been used to measure the multidimensional aspects of empathy. But the 28-item, 4-factor model of Davis (1980 ) is currently contested because of methodological issues and for theoretical reasons. Confirmatory (CFA) and exploratory factor analyses (EFA) were applied in two French-speaking Belgian student samples (1,244 participants in the first and 729 in the second study) to test this model and to propose a shortened version. A non-optimal fit was found with respect to the CFI value (Study 1). By splitting the student group into two random subsamples, EFA and then CFA were used to propose a 15-item, 4-factor model with good fit indices. A CFA on the second student group (Study 2) replicated this model. Results are discussed considering the influence of social desirability response bias, an absence of strong invariance across sex and the usefulness of self-report scales to measure empathy. [ABSTRACT FROM AUTHOR]

LeBel and Paunonen (2011) highlight that despite their importance and popularity in both theoretical and applied research, many implicit measures continue to be plagued by a persistent and troublesome issue - low reliability. In their paper, they offer a conceptual analysis of the relationship between reliability, power and replicability, and then provide a series of recommendations for researchers interested in using implicit measures in an experimental setting. At the core of their account is the idea that reliability can be equated with statistical power, such that "lower levels of reliability are associated with decreasing probabilities of detecting a statistically significant effect, given one exists in the population" (p.573). They also take the additional step of equating reliability and replicability. In our commentary, we draw attention to the fact that there is no direct, fixed or one-to-one relation between reliability and power or replicability. In our commentary, we draw attention to the fact that there is no direct, fixed or one-to-one relation between reliability and power or replicability. More specifically, we argue that when adopting an experimental (rather than a correlational) approach, researchers strive to minimize inter-individual variation, which has a direct impact on sample based reliability estimates. We evaluate the strengths and weaknesses of the LeBel and Paunonen's recommendations and refine them where appropriate. [ABSTRACT FROM AUTHOR]

The influence of socio-economic background factors, such as poverty, on the risk of children to be disproportionately represented and placed in residential care has increasingly been the subject of international research. This article reports on the findings of a research project that focused on the relationship between poverty and child welfare and protection (CWP) interventions in Flanders (the Dutch-speaking part of Belgium). Using logistic regression models (N = 33,423), we examined which specific socio-economic risk factors in the total population of children enhances the risk of CWP interventions. The results show that all included socio-economic variables, except one, show an increased risk of CWP interventions. The results also reveal that a rather dominant social, cultural and historically rooted construction of middle-class family life seems to be an important ground for interventions. Based on these findings, it is argued that the current debate on bias might mask implicit assumptions within CWP decision making. [ABSTRACT FROM AUTHOR]

Background: In problem-based learning, a tutor, the quality of the problems and group functioning play a central role in stimulating student learning. This study is conducted in a hybrid medical curriculum where problem-based learning is one of the pedagogical approaches. The aim of this study was to examine which tutor tasks are the most important during the tutorial sessions and thus should be promoted in hybrid (and in maybe all) problem-based learning curricula in higher education. Methods: A student (N = 333) questionnaire was used to obtain data about the problem-based learning process, combined with the achievement score of the students on a multiple-choice exam. Structural equation modeling was used to test the fit of different models (two existing models and a new simplified model) representing the factors of interest and their relationships, in order to determine which tutor characteristics are the most important in the present study. Results: A new simplified model is presented, which demonstrates that stimulation of active and self-directed learning by tutors enhances the perceived case quality and the perceived group functioning. There was no significant effect between the stimulation of collaborative learning and perceived group functioning. In addition, group functioning was not a significant predictor for achievement. Conclusions: We found that stimulating active and self-directed learning are perceived as tutors' most important tasks with regard to perceived case quality and group functioning. It is necessary to train and teach tutors how they can stimulate active and self-directed learning by students. [ABSTRACT FROM AUTHOR]

In the psychology and educational science programs at Ghent University, only 36.1 % of the new incoming students in 2011 and 2012 passed all exams. Despite availability of information, many students underestimate the scientific character of social science programs. Statistics courses are a major obstacle in this matter. Not all enrolling students master the basic mathematical skills needed to pass statistics courses. Therefore, we propose a test that measures these skills. Our aim is to examine the predictive validity of the test with regard to the statistics course and also as to overall academic success. The results indicate that a test of very basic mathematics skills helps identify at-risk students at and before the start of the academic year. The practical implications of these results are discussed. The test aids the efficient use of means for remedial interventions and supports future students in choosing a higher education program that suits their potential. [ABSTRACT FROM AUTHOR]

Although numerous people grow up speaking more than one language, the impact of bilingualism on brain developing neuroanatomy is still poorly understood. This study aimed to determine whether the changes in the mean fractional-anisotropy (MFA) of language pathways are different between bilingual and monolingual children. Simultaneous-bilinguals, sequential-bilinguals and monolingual, male and female 10–13 years old children participated in this longitudinal study over a period of two years. We used diffusion tensor tractography to obtain mean fractional-anisotropy values of four language related pathways and one control bundle: 1-left-inferior-occipitofrontal fasciculus/lIFOF, 2-left-arcuate fasciculus/lAF/lSLF, 3-bundle arising from the anterior part of corpus-callosum and projecting to orbital lobe/AC-OL, 4-fibres emerging from anterior-midbody of corpus-callosum (CC) to motor cortices/AMB-PMC, 5- right-inferior-occipitofrontal fasciculus rIFOF as the control pathway unrelated to language. These values and their rate of change were compared between 3 groups. FA-values did not change significantly over two years for lAF/lSLF and AC-OL. Sequential-bilinguals had the highest degree of change in the MFA value of lIFOF, and AMB-PMC did not present significant group differences. The comparison of MFA of lIFOF yielded a significantly higher FA-value in simultaneous bilinguals compared to monolinguals. These findings acknowledge the existing difference of the development of the semantic processing specific pathway between children with different semantic processing procedure. These also support the hypothesis that age of second language acquisition affects the maturation and myelination of some language specific white-matter pathways. [ABSTRACT FROM AUTHOR]

Researchers in the social and behavioral sciences often have clear expectations about the order/direction of the parameters in their statistical model. For example, a researcher might expect that regression coefficient β1 is larger than β2 and β3. The corresponding hypothesis is H: β1 > {β2, β3} and this is known as an (order) constrained hypothesis. A major advantage of testing such a hypothesis is that power can be gained and inherently a smaller sample size is needed. This article discusses this gain in sample size reduction, when an increasing number of constraints is included into the hypothesis. The main goal is to present sample-size tables for constrained hypotheses. A sample-size table contains the necessary sample-size at a pre-specified power (say, 0.80) for an increasing number of constraints. To obtain sample-size tables, two Monte Carlo simulations were performed, one for ANOVA and one for multiple regression. Three results are salient. First, in an ANOVA the needed sample-size decreases with 30-50% when complete ordering of the parameters is taken into account. Second, small deviations from the imposed order have only a minor impact on the power. Third, at the maximum number of constraints, the linear regression results are comparable with the ANOVA results. However, in the case of fewer constraints, ordering the parameters (e.g., β1 > β2) results in a higher power than assigning a positive or a negative sign to the parameters (e.g., β1 > 0). [ABSTRACT FROM AUTHOR]

A relationship between motor and mathematical skills has been shown by previous research. However, the question of whether subtypes can be differentiated within developmental coordination disorder (DCD) and/or mathematical learning disability (MLD) remains unresolved. In a sample of children with and without DCD and/or MLD, a data-driven model-based clustering was used to identify subgroups of individuals with relatively homogeneous profiles on measures associated with motor and mathematical skills. One subgroup of children with motor problems was found based on motor variables. Based on mathematical variables, two clinical clusters were found: a subtype with number fact retrieval problems and a subtype with procedural calculation problems. Clustering with motor and mathematical skills revealed two clinical clusters: a cluster with number fact retrieval as well as procedural calculation problems and below average motor and visual-motor integration skills. A second cluster of children had only procedural calculation and visual-motor problems. Our results raise questions about the usefulness of placing children who have below average mathematical skills into a single diagnostic category. Furthermore, we inform ongoing debates about the overlap between DCD and MLD, as below average motor skills were found in both MLD subgroups, although a different motor profile is linked to a different mathematical profile. [ABSTRACT FROM PUBLISHER]

Principal component analysis (PCA) and cluster analysis are used frequently to derive dietary patterns. Decisions on how many patterns to extract are primarily based on subjective criteria, whereas different solutions vary in their food-group composition and perhaps association with disease outcome. Literature on reliability of dietary patterns is scarce, and previous studies validated only 1 preselected solution. Therefore, we assessed reliability of different pattern solutions ranging from 2 to 6 patterns, derived from the aforementioned methods. A validated food frequency questionnaire was administered at baseline (1993-1997) to 39,678 participants in the European Prospective Investigation into Cancer and Nutrition-The Netherlands (EPIC-NL) cohort. Food items were grouped into 31 food groups for dietary pattern analysis. The cohort was randomly divided into 2 halves, and dietary pattern solutions derived in 1 sample through PCA were replicated through confirmatory factor analysis in sample 2. For cluster analysis, cluster stability and split-half reproducibility were assessed for various solutions. With PCA, we found the 3-component solution to be best replicated, although all solutions contained ≤ 1 poorly confirmed component. No quantitative criterion was in agreement with the results. Associations with disease outcome (coronary heart disease) differed between the component solutions. For all cluster solutions, stability was excellent and deviations between samples was negligible, indicating good reproducibility. All quantitative criteria identified the 2-cluster solution as optimal. Associations with disease outcome were comparable for different cluster solutions. In conclusion, reliability of obtained dietary patterns differed considerably for different solutions using PCA, whereas cluster analysis derived generally stable, reproducible clusters across different solutions. Quantitative criteria for determining the number of patterns to retain were valuable for cluster analysis but not for PCA. Associations with disease risk were influenced by the number of patterns that are retained, especially when using PCA. Therefore, studies on associations between dietary patterns and disease risk should report reasons to choose the number of retained patterns. [ABSTRACT FROM AUTHOR]

Simulation studies that validate statistical techniques for fMRI data are challenging due to the complexity of the data. Therefore, it is not surprising that no common data generating process is available (i.e. several models can be found to model BOLD activation and noise). Based on a literature search, a database of simulation studies was compiled. The information in this database was analysed and critically evaluated focusing on the parameters in the simulation design, the adopted model to generate fMRI data, and on how the simulation studies are reported. Our literature analysis demonstrates that many fMRI simulation studies do not report a thorough experimental design and almost consistently ignore crucial knowledge on how fMRI data are acquired. Advice is provided on how the quality of fMRI simulation studies can be improved. [ABSTRACT FROM AUTHOR]

Estimation of the direct effect of an exposure on an outcome requires adjustment for confounders of the exposure–outcome and mediator–outcome relationships. When some of the latter confounders have been affected by the exposure, then standard regression adjustment is prone to possibly severe bias. The use of inverse probability weighting under so-called marginal structural models has recently been suggested as a solution in the psychological literature. In this article, we show how progress can alternatively be made via G-estimation. We show that this estimation method can be easily embedded within the structural equation modeling framework and could in particular be used for estimating direct effects in the presence of latent variables. Moreover, by avoiding inverse probability weighting, it accommodates the typical problem of unstable weights in the alternative estimation approaches based on marginal structural models. We illustrate the approach both by simulations and by the analysis of a longitudinal study in individiduals who ended a romantic relationship. In this example we explore whether the effect of attachment anxiety during the relationship on mental distress 2 years after the breakup is mediated by rumination or not. [ABSTRACT FROM PUBLISHER]

Signal-to-noise ratio, the ratio between signal and noise, is a quantity that has been well established for MRI data but is still subject of ongoing debate and confusion when it comes to fMRI data. fMRI data are characterised by small activation fluctuations in a background of noise. Depending on how the signal of interest and the noise are identified, signal-to-noise ratio for fMRI data is reported by using many different definitions. Since each definition comes with a different scale, interpreting and comparing signal-to-noise ratio values for fMRI data can be a very challenging job. In this paper, we provide an overview of existing definitions. Further, the relationship with activation detection power is investigated. Reference tables and conversion formulae are provided to facilitate comparability between fMRI studies. [ABSTRACT FROM AUTHOR]

Although spatial smoothing of fMRI data can serve multiple purposes, increasing the sensitivity of activation detection is probably its greatest benefit. However, this increased detection power comes with a loss of specificity when non-adaptive smoothing (i.e. the standard in most software packages) is used. Simulation studies and analysis of experimental data was performed using the packages neuRosim and fmri. In these studies, we systematically investigated the effect of spatial smoothing on the power and number of false positives in two particular cases that are often encountered in fMRI research: (1) Single condition activation detection for regions that differ in size, and (2) multiple condition activation detection for neighbouring regions. Our results demonstrate that adaptive smoothing is superior in both cases because less false positives are introduced by the spatial smoothing process compared to standard Gaussian smoothing or FDR inference of unsmoothed data. [ABSTRACT FROM AUTHOR]