Search

Research indicates the need to further examine the dimensions of reading motivation. A clear theoretical basis is necessary for conceptualizing reading motivation and considering contextual differences therein. The present study develops and validates the SRQ-Reading Motivation, a questionnaire measuring recreational and academic reading motivation based on self-determination theory. The study clarifies the relation among reading motivation, reading self-concept, reading behavior (i.e., engagement and frequency), and reading performance (i.e., comprehension). Participants included 1,260 Flemish fifth-grade students and their 67 teachers. Exploratory and confirmatory factor analyses indicated that both recreational and academic reading motivation comprise 2 factors: autonomous and controlled motivation. This factor structure was found to be invariant across boys and girls. Comparisons of the SRQ-Reading Motivation with subscales of the Motivation for Reading Questionnaire provide evidence for the construct validity of the instrument. Structural equation modeling confirmed that recreational autonomous reading motivation is associated with more positive reading behavior and better performance. In the academic setting, only the equivalent relationship between autonomous reading motivation and leisure-time reading frequency could be corroborated. In this respect, the results confirm the independent contribution of recreational autonomous reading motivation and reading self-concept to reading behavior and performance. No significant indirect relationship between reading motivation and reading comprehension through reading frequency or reading engagement was found. The theoretical and practical significance of the present study is discussed. [ABSTRACT FROM AUTHOR]

This article aims to search for a specific female, psychiatric profile based on a large European sample of substance dependent clients (828) entering therapeutic communities. First, all six areas of functioning of the EuropASI were included, using the composite scores to search for gender differences. Next, the ''psychiatric'' status section was selected for further study. A binary logistic regression was performed with gender as the dependent variable, and nine individual psychiatric items, country, and age as predictors. According to this model a number of problem variables could be identified as being reported more often by women than by men in therapeutic community treatment. Women are more likely to report serious depression, problems in understanding, concentrating or remembering, being prescribed medication, and serious thoughts about suicide; they have also attempted suicide more often than men. Women find treatment for these psychological problems more important than their male counterparts. They also have a more severe history of abuse. Women in therapeutic communities may need specific treatment interventions for their more severe psychiatric needs. [Copyright &y& Elsevier]

Behavioral scientists often examine the relations between two or more latent variables (e.g., how emotions relate to life satisfaction), and structural equation modeling (SEM) is the state-of-the-art for doing so. When comparing these "structural relations" among many groups, they likely differ across the groups. However, it is equally likely that some groups share the same relations so that clusters of groups emerge. Latent variables are measured indirectly by questionnaires and, for validly comparing their relations among groups, the measurement of the latent variables should be invariant across the groups (i.e., measurement invariance). However, across many groups, often at least some measurement parameters differ. Restricting these measurement parameters to be invariant, when they are not, causes the structural relations to be estimated incorrectly and invalidates their comparison. We propose mixture multigroup SEM (MMG-SEM) to gather groups with equivalent structural relations in clusters while accounting for the reality of measurement noninvariance. Specifically, MMG-SEM obtains a clustering of groups focused on the structural relations by making them cluster-specific, while capturing measurement noninvariances with group-specific measurement parameters. In this way, MMG-SEM ensures that the clustering is valid and unaffected by differences in measurement. This article proposes an estimation procedure built around the R package "lavaan" and evaluates MMG-SEM's performance through two simulation studies. The results demonstrate that MMG-SEM successfully recovers the group-clustering as well as the cluster-specific relations and the partially group-specific measurement parameters. To illustrate its empirical value, we apply MMG-SEM to cross-cultural data on the relations between experienced emotions and life satisfaction. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

In structural equation modeling (SEM), the measurement and structural parts of the model are usually estimated simultaneously. In this article, we revisit the long-standing idea that we should first estimate the measurement part, and then estimate the structural part. We call this the "structural-after-measurement" (SAM) approach to SEM. We describe a formal framework for the SAM approach under settings where the latent variables and their indicators are continuous. We review earlier SAM methods and establish how they are specific instances of the SAM framework. Decoupled estimation for the measurement and structural parts using SAM possesses three key advantages over simultaneous estimation in standard SEM. First, estimates are more robust against local model misspecifications. Second, estimation routines are less vulnerable to convergence issues in small samples. Third, estimates exhibit smaller finite sample biases under correctly specified models. We propose two variants of the SAM approach. "Local" SAM expresses the mean vector and variance-covariance matrix of the latent variables as a function of the observed summary statistics and the parameters of the measurement model. "Global" SAM holds the parameters of the measurement part fixed while estimating the parameters of the structural part. Our framework includes two-step corrected standard errors, and permits computing both local and global fit measures. Nonetheless, the SAM approach is an estimation strategy, and should not be regarded as a model-building tool. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

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., (© 2023 The Authors. British Journal of Mathematical and Statistical Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

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., (© 2022. The Author(s).)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Bogaert, Merchie, Rosseel and Van Keer.)

Within the framework of constrained statistical inference, we can test informative hypotheses, in which, for example, regression coefficients are constrained to have a certain direction or be in a specific order. A large amount of frequentist informative test statistics exist that each come with different versions, strengths and weaknesses. This paper gives an overview about these statistics, including the Wald, the LRT, the Score, the F ¯ - and the D -statistic. Simulation studies are presented that clarify their performance in terms of type I and type II error rates under different conditions. Based on the results, it is recommended to use the Wald and F ¯ -test rather than the LRT and Score test as the former need less computing time. Furthermore, it is favorable to use the degrees of freedom corrected rather than the naive mean squared error when calculating the test statistics as well as using the F ¯ - rather than the χ ¯ 2 -distribution when calculating the p -values., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Keck, Mayer and Rosseel.)

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., Competing Interests: Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2021.)

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., (© 2022. The Author(s) under exclusive licence to The Psychometric Society.)

The ideal experiment in physics must be conceptual, cunning, and conclusive. Adoption of these same standards in psychology has led to experiments that are uninformative and frivolous. We explain why we believe that psychology is fundamentally incompatible with hypothesis-driven theoretical science and conclude that this erodes the logic behind recent proposals to improve psychological research, such as stricter statistical standards, preregistration, and replication. The failure of psychology is not that it is somehow insufficiently scientific but rather that it makes inordinate use of methods that are a mismatch for the aspirations of researchers in the field, at the expense of valuable empirical research.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Dehaene, De Neve and Rosseel.)

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.

Evaluation of model fit is critically important for every structural equation model (SEM), and sophisticated methods have been developed for this task. Among them are the χ² goodness-of-fit test, decomposition of the χ², derived measures like the popular root mean square error of approximation (RMSEA) or comparative fit index (CFI), or inspection of residuals or modification indices. Many of these methods provide a global approach to model fit evaluation: A single index is computed that quantifies the fit of the entire SEM to the data. In contrast, graphical criteria like d-separation or trek-separation allow derivation of implications that can be used for local fit evaluation, an approach that is hardly ever applied. We provide an overview of local fit evaluation from the viewpoint of SEM practitioners. In the presence of model misfit, local fit evaluation can potentially help in pinpointing where the problem with the model lies. For models that do fit the data, local tests can identify the parts of the model that are corroborated by the data. Local tests can also be conducted before a model is fitted at all, and they can be used even for models that are globally underidentified. We discuss appropriate statistical local tests, and provide applied examples. We also present novel software in R that automates this type of local fit evaluation. (PsycINFO Database Record, ((c) 2018 APA, all rights reserved).)

Unlabelled: Instruments to assess chronic pain acceptance have been developed and used. However, whether and to what extent the content of the items reflects acceptance remain uninvestigated. A content analysis of 13 instruments that aim to measure acceptance of chronic pain was performed. A coding scheme was used that consisted of 3 categories representing the key components of acceptance, that is, disengagement from pain control, pain willingness, and engagement in activities other than pain control. The coding scheme consisted of 5 additional categories in order to code items that do not represent acceptance, that is, controlling pain, pain costs, pain benefits, unclear, and no fit. Two coders rated to what extent the items of acceptance instruments belonged to one or more of these categories. Results indicated that acceptance categories were not equally represented in the acceptance instruments. Of note, some instruments had many items in the category controlling pain. Further analyses revealed that the meaning of acceptance differs among different instruments and among different versions of the same instrument. This study illustrates the importance of content validity when developing and evaluating self-report instruments., Perspective: This article investigated the content validity of questionnaires designed to measure acceptance in individuals with chronic pain. Knowledge about the content of the instruments will provide further insight into the features of acceptance and how to measure them., (Copyright © 2015 American Pain Society. Published by Elsevier Inc. All rights reserved.)

The goal of this research is to test the hypothesis that a category is not necessarily represented by all observed exemplars, but by a reduced subset of these exemplars. To test this hypothesis, we made use of a study reported by Nosofsky, Clark, and Shin (1989), and replicated their Experiment 1 in order to gather individual-participant data. Both a full exemplar model and a reduced exemplar model were fit to the data. In general, the fits of the reduced exemplar model were superior to those of the full exemplar model. The results suggest that only a subset of exemplars may be sufficient for category representation.

Background: Alexithymia is a disturbance in regulating affective states. Clinical observations suggest that alexithymic patients relate to others in a specific way. This paper explores whether specific relationship or transference patterns are typical of alexithymia., Sampling and Methods: Relationship patterns were assessed by means of the Core Conflictual Relationship Theme method, standard categories version. This method examines transference patterns and was applied to clinical interview data collected from a sample (n = 31) of mental health outpatients. Alexithymia was assessed by means of a score on the 20-item Toronto Alexithymia Scale corrected for the degree of depression (measured by the Beck Depression Inventory-II). Data were analysed by means of the leaps and bounds regression algorithm for selecting optimal subsets of indicators and by bootstrapping to determine 95% confidence intervals., Results: First, we observed that alexithymia can be meaningfully explained by typical wishes, typical subjective perceptions of how others respond and one's own typical responses to others. This result indicates that the more marked a patient's alexithymic traits are, the more probable it is that specific transference themes come to the fore. Second, a set of three core indicators of trait alexithymia was mapped: little concern about being good to others, a strong perception of others as cooperative, and weak levels of reacting to others and to conflict by means of somatic symptoms., Conclusion: Alexithymia is related to a double interpersonal indifference: not much is expected from others, nor is there a personal urge to fulfill the expectations of others. Moreover, in alexithymia somatic symptoms proved to be non-reactive to interpersonal situations. Implications for diagnosis and treatment are highlighted. Limitations of our study are that alexithymia was only assessed with a self-report measure and that conclusions are based only upon data from a heterogeneous mental health sample.

Objectives: We study the pattern of interpersonal alliances typical of depressive symptoms by means of the Core Conflictual Relationship Theme (CCRT) method. In an exploratory manner we examine which CCRT-coded wishes, perceived responses from the other and associated responses of the self predict the magnitude of a score on the Beck Depression Inventory-II., Methods: Interview data from 31 mental health patients were coded (CCRT expanded standard categories system). Relations between the CCRT categories and the dependent variable were examined by combining 1000-fold forward stepwise regression analyses, and multiple regression analyses in which confidence intervals were estimated with the bootstrap technique., Results: Three CCRT categories that jointly explain more than half of the variance were selected: diminished levels of the wish to be understood, and elevated levels of disliking others and of feeling helpless., Conclusions: The results and implications for psychotherapy are discussed within a psychoanalytic context. Suggestions for further research are indicated.