Dimensionality assessment in the presence of wording effects: A network psychometric and factorial approach.

This study proposes a procedure for substantive dimensionality estimation in the presence of wording effects, the inconsistent response to regular and reversed self-report items. The procedure developed consists of subtracting an approximate estimate of the wording effects variance from the sample correlation matrix and then estimating the substantive dimensionality on the residual correlation matrix. This is achieved by estimating a random intercept factor with unit loadings for all the regular and unrecoded reversed items. The accuracy of the procedure was evaluated through an extensive simulation study that manipulated nine relevant variables and employed the exploratory graph analysis (EGA) and parallel analysis (PA) retention methods. The results indicated that combining the proposed procedure with EGA or PA achieved high accuracy in estimating the substantive latent dimensionality, but that EGA was superior. Additionally, the present findings shed light on the complex ways that wording effects impact the dimensionality estimates when the response bias in the data is ignored. A tutorial on substantive dimensionality estimation with the R package EGAnet is offered, as well as practical guidelines for applied researchers.

Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation.

Identifying the correct number of factors in multivariate data is fundamental to psychological measurement. Factor analysis has a long tradition in the field, but it has been challenged recently by exploratory graph analysis (EGA), an approach based on network psychometrics. EGA first estimates a network and then applies the Walktrap community detection algorithm. Simulation studies have demonstrated that EGA has comparable or better accuracy for recovering the same number of communities as there are factors in the simulated data than factor analytic methods. Despite EGA's effectiveness, there has yet to be an investigation into whether other sparsity induction methods or community detection algorithms could achieve equivalent or better performance. Furthermore, unidimensional structures are fundamental to psychological measurement yet they have been sparsely studied in simulations using community detection algorithms. In the present study, we performed a Monte Carlo simulation using the zero-order correlation matrix, GLASSO, and two variants of a non-regularized partial correlation sparsity induction methods with several community detection algorithms. We examined the performance of these method-algorithm combinations in both continuous and polytomous data across a variety of conditions. The results indicate that the Fast-greedy, Louvain, and Walktrap algorithms paired with the GLASSO method were consistently among the most accurate and least-biased overall.

An interdisciplinary intervention program to prevent early childhood caries in the Dominican Republic.

Objective: The principal aim of this randomized clinical trial (RCT) was to test the effectiveness in the prevention of Early Childhood Caries (ECC) through an educational intervention program with the use of a printed guide for pediatricians and parents both designed by pediatric dentists. Materials and methods: After ethical approval, the first step was to design the educational guides, which were based on the information obtained from a focus group with pediatricians (n = 3), phone interviews with mothers to toddlers' (n = 7), and the best evidence available about children's oral health. For the RCT, 309 parents with their 10-12 months old children were randomly allocated to either the intervention or the control group. Parents in the intervention group received oral health education from the pediatricians supported by the printed guides. Parents in both groups received an oral health kit with a toothbrush and toothpaste at the first visit as well as at each 6-month follow-up visit. After 18 months the children were evaluated using ICDAS criteria. Results: At baseline, data were available from 309 children (49.8% girls). The mean age of the children was of 10.8 months (SD = 0.8) and 69.3% had not had their teeth brushed with toothpaste. After 18 months, a total of 28 (22%) children in the intervention group and 44 (24%) in the control group were clinically examined. Regarding the number of tooth surfaces with caries lesions, the children in the intervention group had a mean of 6.50 (SD = 6.58) surfaces, while the children in the control group had a mean of 5.43 (SD = 4.74) surfaces with caries lesions. This difference was not significant (p = 0.460). Conclusion: The RCT showed no effectiveness in caries-progression control. Despite this result, this study managed to identify barriers that do not allow pediatricians from offering parents adequate oral health recommendations. With this learning, it is possible to work on collaborative programs with pediatricians that over time likely will increase dental health by controlling for ECC.

The efficacy of combined arginine and probiotics as an add-on to 1450 ppm fluoride toothpaste to prevent and control dental caries in children - A randomized controlled trial.

OBJECTIVES: To determine how daily consumption of a lozenge combining arginine and two probiotic strains affects the Relative Risk Reduction (RRR) in children regarding dental caries transitions and lesion activity at tooth surface level during 10-12 months. METHODS: A total of 21,888 tooth surfaces in 288 children were examined. The intervention group (n = 141) received a lozenge containing 2% arginine, Lacticaseibacillus rhamnosus, LGG® (DSM33156), and Lactobacillus paracasei subsp. paracasei, L. CASEI 431® (DSM33451). The placebo group (n = 147) received a placebo lozenge. Both groups received 1,450 ppm F- toothpaste. Primary canines, molars, and first permanent molars were examined clinically (ICDAS0-6) and radiographically (R0-6) at baseline and follow-up. Sealed, filled, and missing surfaces were also included. Caries activity was computed as a sum of each caries lesion's location, color, texture, cavitation, and gingival bleeding. RRRs were computed with cluster effect on surface level. ICH-GCP was followed, including external monitoring. RESULTS: A total of 19,950 surfaces were analyzed after excluding 1,938 tooth surfaces. No statistically significant differences were found between the groups. The RRRs showed less caries progression (13.6%, p = 0.20), more regression (0.3%, p = 0.44), and fewer active caries lesions (15.3%, p = 0.15) in the intervention group. CONCLUSION: Daily consumption of a lozenge combining arginine and probiotics for 10-12 months given to 5-9-years-old children characterized being with low caries risk demonstrated a marked, though not statistically significant RRR for caries progression, regression, and number of active lesions in the intervention group compared to the placebo-group. CLINICALTRIALS: gov (NCT03928587). CLINICAL SIGNIFICANCE: Since all the RRRs were in favor of the intervention group and the PF of combined arginine and probiotics is high (81.6%) compared to fluoride toothpaste (24.9%) and arginine-fluoride toothpaste alone (19.6%) the combined pre-and probiotics approach may be a future additional tool regarding caries prevention and control.

Dimensionality assessment in bifactor structures with multiple general factors: A network psychometrics approach.

The accuracy of factor retention methods for structures with one or more general factors, like the ones typically encountered in fields like intelligence, personality, and psychopathology, has often been overlooked in dimensionality research. To address this issue, we compared the performance of several factor retention methods in this context, including a network psychometrics approach developed in this study. For estimating the number of group factors, these methods were the Kaiser criterion, empirical Kaiser criterion, parallel analysis with principal components (PAPCA) or principal axis, and exploratory graph analysis with Louvain clustering (EGALV). We then estimated the number of general factors using the factor scores of the first-order solution suggested by the best two methods, yielding a "second-order" version of PAPCA (PAPCA-FS) and EGALV (EGALV-FS). Additionally, we examined the direct multilevel solution provided by EGALV. All the methods were evaluated in an extensive simulation manipulating nine variables of interest, including population error. The results indicated that EGALV and PAPCA displayed the best overall performance in retrieving the true number of group factors, the former being more sensitive to high cross-loadings, and the latter to weak group factors and small samples. Regarding the estimation of the number of general factors, both PAPCA-FS and EGALV-FS showed a close to perfect accuracy across all the conditions, while EGALV was inaccurate. The methods based on EGA were robust to the conditions most likely to be encountered in practice. Therefore, we highlight the particular usefulness of EGALV (group factors) and EGALV-FS (general factors) for assessing bifactor structures with multiple general factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Unique Variable Analysis: A Network Psychometrics Method to Detect Local Dependence.

The local independence assumption states that variables are unrelated after conditioning on a latent variable. Common problems that arise from violations of this assumption include model misspecification, biased model parameters, and inaccurate estimates of internal structure. These problems are not limited to latent variable models but also apply to network psychometrics. This paper proposes a novel network psychometric approach to detect locally dependent pairs of variables using network modeling and a graph theory measure called weighted topological overlap (wTO). Using simulation, this approach is compared to contemporary local dependence detection methods such as exploratory structural equation modeling with standardized expected parameter change and a recently developed approach using partial correlations and a resampling procedure. Different approaches to determine local dependence using statistical significance and cutoff values are also compared. Continuous, polytomous (5-point Likert scale), and dichotomous (binary) data were generated with skew across a variety of conditions. Our results indicate that cutoff values work better than significance approaches. Overall, the network psychometrics approaches using wTO with graphical least absolute shrinkage and selector operator with extended Bayesian information criterion and wTO with Bayesian Gaussian graphical model were the best performing local dependence detection methods overall.

Exploratory Bi-factor Analysis with Multiple General Factors.

Exploratory bi-factor analysis (EBFA) is a very popular approach to estimate models where specific factors are concomitant to a single, general dimension. However, the models typically encountered in fields like personality, intelligence, and psychopathology involve more than one general factor. To address this circumstance, we developed an algorithm (GSLiD) based on partially specified targets to perform exploratory bi-factor analysis with multiple general factors (EBFA-MGF). In EBFA-MGF, researchers do not need to conduct independent bi-factor analyses anymore because several bi-factor models are estimated simultaneously in an exploratory manner, guarding against biased estimates and model misspecification errors due to unexpected cross-loadings and factor correlations. The results from an exhaustive Monte Carlo simulation manipulating nine variables of interest suggested that GSLiD outperforms the Schmid-Leiman approximation and is robust to challenging conditions involving cross-loadings and pure items of the general factors. Thereby, we supply an R package (bifactor) to make EBFA-MGF readily available for substantive research. Finally, we use GSLiD to assess the hierarchical structure of a reduced version of the Personality Inventory for DSM-5 Short Form (PID-5-SF).

Focusing on cybersickness: pervasiveness, latent trajectories, susceptibility, and effects on the virtual reality experience.

Although virtual reality (VR) usage has become widespread in the last decade, its adoption has been hampered by experiences of user discomfort known as cybersickness. The present study, in line with the "2020 cybersickness R&D agenda", sought to provide a broad examination of the cybersickness phenomenon, assessing its pervasiveness, latent trajectories, impacts on the VR experience, and predictor variables. The study was composed of 92 participants living in the Dominican Republic with ages ranging from 18 to 52 years (M = 26.22), who experienced a 10-min VR immersion in two environments designed for psychotherapy. The results indicated that cybersickness was pervasive, with 65.2% of the participants experiencing it, and 23.9% severely. Additionally, the latent trajectories of cybersickness were positive and curvilinear, with large heterogeneity across individuals. Cybersickness also had a substantive negative impact on the user experience and the intentions to adopt the VR technology. Finally, motion sickness susceptibility, cognitive stress, and recent headaches uniquely predicted greater severity of cybersickness, while age was negatively related. These combined results highlight the critical role that cybersickness plays on the VR experience and underscore the importance of finding solutions to the problems, such as technological advancements or special usage protocols for the more susceptible individuals. Supplementary Information: The online version contains supplementary material available at 10.1007/s10055-022-00636-4.

Construyendo test adaptativos de elección forzosa “On The Fly” para la medición de la personalidad / Building adaptive forced choice tests “On The Fly” for personality measurement

Los nuevos desarrollos metodológicos y tecnológicos de la última década permiten resolver, o al menos atenuar, los problemas psicométricos de los test de elección forzosa (EF) para la medición de la personalidad. En estas pruebas, a la persona evaluada se le muestran bloques de dos o más frases de parecida deseabilidad social, entre las que debe elegir aquella que le represente mejor. De esta manera, los test de EF buscan reducir los sesgos de respuesta en pruebas de autoinforme. No obstante, su uso no está exento de riesgos y complicaciones si no se elaboran adecuadamente. Afortunadamente, los nuevos modelos psicométricos permiten modelar las respuestas en este tipo de test, así como optimizar su construcción. Más aún, permiten la construcción de Test Adaptativos Informatizados de EF (TAI-EF) “on-the-fiy”, en los que cada bloque se construye en el mismo momento de aplicación, emparejando óptimamente las frases de un banco previamente calibrado.(AU)

The new methodological and technological developments of the last decade make it possible to resolve or, at least, attenuate the psychometric problems of forced-choice (FC) tests for the measurement of personality. In these tests, the person being tested is shown blocks of two or more sentences of similar social desirability, from which he or she must choose which one best represents him or her. Thus, FC tests aim to reduce response bias in self-report questionnaires. However, their use is not without risks and complications if they are not created properly. Fortunately, new psychometric models make it possible to model responses in this type of test and to optimize their construction. Moreover, they allow the construction of “on the fly” computerized adaptive FC tests (CAT-FC), in which each item is constructed on the spot, optimally matching sentences from a previously calibrated bank.(AU)

COVID-19 pandemic and health worker stress: The mediating effect of emotional regulation.

BACKGROUND/INTRODUCTION: Psychological and physical well-being of health personnel has been significantly affected by COVID-19. Work overload and continuous exposure to positive COVID-19 cases have caused them fatigue, stress, anxiety, insomnia and other detriments. This research aims: 1) to analyze whether the use of cognitive reevaluation and emotional suppression strategies decreases and increases, respectively, stress levels of health personnel; 2) to quantify the impact of contact with patients with COVID-19 on stress levels of medical staff. METHOD: Emotion regulation strategies (cognitive reevaluation and emotional expression) and stress levels were evaluated in 155 Dominican physicians who were treating people infected with COVID-19 at the moment of the study (67.9% women and 32.1% men; mean age = 34.89; SD = 9.26). In addition, a questionnaire created by the researchers quantified the impact that contact with those infected had on their stress levels. RESULTS: Contact with patients with COVID-19 predicts increased use of emotion suppression strategies, although is not associated with the use of cognitive reevaluation. These findings lead to an even greater increase in stress on health care providers. CONCLUSIONS: Contextual contingencies demand immediate responses and may not allow health personnel to use cognitive re-evaluation strategies, leaning more towards emotion suppression. However, findings regarding high levels of stress require the implementation of intervention programs focused on the promotion of more functional emotion regulation strategies. Such programs may reduce current stress and prevent post-traumatic symptoms.

Using Constrained Factor Mixture Analysis to Validate Mixed-Worded Psychological Scales: The Case of the Rosenberg Self-Esteem Scale in the Dominican Republic.

A common method to collect information in the behavioral and health sciences is the self-report. However, the validity of self-reports is frequently threatened by response biases, particularly those associated with inconsistent responses to positively and negatively worded items of the same dimension, known as wording effects. Modeling strategies based on confirmatory factor analysis have traditionally been used to account for this response bias, but they have recently become under scrutiny due to their incorrect assumption of population homogeneity, inability to recover uncontaminated person scores or preserve structural validities, and their inherent ambiguity. Recently, two constrained factor mixture analysis (FMA) models have been proposed by Arias et al. (2020) and Steinmann et al. (2021) that can be used to identify and screen inconsistent response profiles. While these methods have shown promise, tests of their performance have been limited and they have not been directly compared. Thus the objective of the current study was to assess and compare their performance with data from the Dominican Republic of the Rosenberg Self-Esteem Scale (N = 632). Additionally, as this scale had not yet been studied for this population, another objective was to show how using constrained FMAs could help in the validation of mixed-worded scales. The results indicated that removing the inconsistent respondents identified by both FMAs (≈8%) reduced the amount of wording effects in the database. However, whereas the Steinmann et al. method only cleaned the data partially, the Arias et al. (2020) method was able to remove the great majority of the wording effects variance. Based on the screened data with the Arias et al. method, we evaluated the psychometric properties of the RSES for the Dominican population, and the results indicated that the scores had good validity and reliability properties. Given these findings, we recommend that researchers incorporate constrained FMAs into their toolbox and consider using them to screen out inconsistent respondents to mixed-worded scales.

Modeling Wording Effects Does Not Help in Recovering Uncontaminated Person Scores: A Systematic Evaluation With Random Intercept Item Factor Analysis.

The item wording (or keying) effect consists of logically inconsistent answers to positively and negatively worded items that tap into similar (but polarly opposite) content. Previous research has shown that this effect can be successfully modeled through the random intercept item factor analysis (RIIFA) model, as evidenced by the improvements in the model fit in comparison to models that only contain substantive factors. However, little is known regarding the capability of this model in recovering the uncontaminated person scores. To address this issue, the study analyzes the performance of the RIIFA approach across three types of wording effects proposed in the literature: carelessness, item verification difficulty, and acquiescence. In the context of unidimensional substantive models, four independent variables were manipulated, using Monte Carlo methods: type of wording effect, amount of wording effect, sample size, and test length. The results corroborated previous findings by showing that the RIIFA models were consistently able to account for the variance in the data, attaining an excellent fit regardless of the amount of bias. Conversely, the models without the RIIFA factor produced increasingly a poorer fit with greater amounts of wording effects. Surprisingly, however, the RIIFA models were not able to better estimate the uncontaminated person scores for any type of wording effect in comparison to the substantive unidimensional models. The simulation results were then corroborated with an empirical dataset, examining the relationship between learning strategies and personality with grade point average in undergraduate studies. The apparently paradoxical findings regarding the model fit and the recovery of the person scores are explained, considering the properties of the factor models examined.

Design and Validation of Augmented Reality Stimuli for the Treatment of Cleaning Obsessive-Compulsive Disorder.

Fear to contamination is an easy-to-provoke, intense, hard-to-control, and extraordinarily persistent fear. A worsening of preexisting psychiatric disorders was observed during the COVID-19 (coronavirus disease 2019) outbreak, and several studies suggest that those with obsessive-compulsive disorder (OCD) may be more affected than any other group of people. In the face of worsening OCD symptoms, there is a need for mental health professionals to provide the support needed not only to treat patients who still report symptoms, but also to improve relapse prevention. In this line, it is recommended to improve alternative strategies such as online consultations and digital psychiatry. The aim of this study is to develop augmented reality (AR) stimuli that are clinically relevant for patients with cleaning OCD and assess their efficiency to obtain emotionally significant responses. Four AR stimuli were developed: a plastic bag full of garbage, a piece of bread with mold, a dirty sports shoe, and a piece of rotten meat. All stimuli were shown to a clinical group (17 patients with cleaning OCD) and a control group (11 patients without OCD). Relevant results were the design of the AR stimuli. These stimuli were validated with the statistical difference in perceived anxiety in the meat stimuli between the clinical and control groups. Nevertheless, when looking at effect sizes, all stimuli present effect sizes from small (plastic bag) to large (meat), with both shoe and bread between small and medium effect sizes. These results are a valuable support for the clinical use of these AR stimuli in the treatment of cleaning OCD.

A modern network approach to revisiting the Positive and Negative Affective Schedule (PANAS) construct validity.

INTRODUCTION: The factor structure of the Positive and Negative Affective Schedule (PANAS) is still a topic of debate. There are several reasons why using Exploratory Graph Analysis (EGA) for scale validation is advantageous and can help understand and resolve conflicting results in the factor analytic literature. OBJECTIVE: The main objective of the present study was to advance the knowledge regarding the factor structure underlying the PANAS scores by utilizing the different functionalities of the EGA method. EGA was used to (1) estimate the dimensionality of the PANAS scores, (2) establish the stability of the dimensionality estimate and of the item assignments into the dimensions, and (3) assess the impact of potential redundancies across item pairs on the dimensionality and structure of the PANAS scores. METHOD: This assessment was carried out across two studies that included two large samples of participants. RESULTS AND CONCLUSION: In sum, the results are consistent with a two-factor oblique structure.

Entropy Fit Indices: New Fit Measures for Assessing the Structure and Dimensionality of Multiple Latent Variables.

The accurate identification of the content and number of latent factors underlying multivariate data is an important endeavor in many areas of Psychology and related fields. Recently, a new dimensionality assessment technique based on network psychometrics was proposed (Exploratory Graph Analysis, EGA), but a measure to check the fit of the dimensionality structure to the data estimated via EGA is still lacking. Although traditional factor-analytic fit measures are widespread, recent research has identified limitations for their effectiveness in categorical variables. Here, we propose three new fit measures (termed entropy fit indices) that combines information theory, quantum information theory and structural analysis: Entropy Fit Index (EFI), EFI with Von Neumman Entropy (EFI.vn) and Total EFI.vn (TEFI.vn). The first can be estimated in complete datasets using Shannon entropy, while EFI.vn and TEFI.vn can be estimated in correlation matrices using quantum information metrics. We show, through several simulations, that TEFI.vn, EFI.vn and EFI are as accurate or more accurate than traditional fit measures when identifying the number of simulated latent factors. However, in conditions where more factors are extracted than the number of factors simulated, only TEFI.vn presents a very high accuracy. In addition, we provide an applied example that demonstrates how the new fit measures can be used with a real-world dataset, using exploratory graph analysis.

Bi-factor Exploratory Structural Equation Modeling Done Right: Using the SLiDapp Application / Aplicando Bi-factor ESEM: Uso de la Aplicación SLiDapp

BACKGROUND: Due to its flexibility and statistical properties, bi-factor Exploratory Structural Equation Modeling (bi-factor ESEM) has become an often-recommended tool in psychometrics. Unfortunately, most recent methods for approximating these structures, such as the SLiD algorithm, are not available in the leading software for performing ESEM (i.e., Mplus). To resolve this issue, we present a novel, user-friendly Shiny application for integrating the SLiD algorithm in bi-factor ESEM estimation in Mplus. Thus, a two-stage framework for conducting SLiD-based bi-factor ESEM in Mplus was developed. METHOD: This approach was presented in a step-by-step guide for applied researchers, showing the utility of the developed SLiDApp application. Using data from the Open-Source Psychometrics Project (N = 2495), we conducted a bi-factor ESEM exploration of the Generic Conspiracist Beliefs Scale. We studied whether bi-factor modelling was appropriate and if both general and group factors were related to each personality trait. RESULTS: The application of the SLiD algorithm provided unique information regarding this factor structure and its ESEM structural parameters. CONCLUSIONS: The results illustrated the usefulness and validity of SLiD-based bi-factor ESEM, and how the proposed Shiny app could make it eaiser for applied researchers to use these methods

ANTECEDENTES: los modelos bi-factoriales de ecuaciones estructurales exploratorias (bi-factor ESEM) se han convertido en una herramienta clave en psicometría. Desafortunadamente, las últimas alternativas para su estimación no se encuentran disponibles en el software principal usado para su aproximación (i.e., Mplus). Para solucionar este problema se presenta una aplicación Shiny (SLiDApp) que permite integrar los resultados del algoritmo SLiD en un modelo bi-factor ESEM estimado en Mplus. Para ello, se diseñó una estrategia de dos pasos para aproximar estos modelos. MÉTODO: este enfoque se ilustró a través de una guía paso por paso de cómo usar la aplicación diseñada y el análisis de un modelo bi-factor ESEM basado en SLiD de la Escala de Creencias Conspirativas Genéricas usando datos del Open-Source Psychometrics Project (N = 2495). Se analizó la relación de los factores generales y de grupo con los cinco factores de personalidad. RESULTADOS: los resultados mostraron cómo el algoritmo SLiD proveía de información única acerca de la estructura factorial y los parámetros estructurales. CONCLUSIONES: este estudio demostró la utilidad tanto de los modelos bi-factoriales ESEM basados en SLiD cómo de la app propuesta. Se espera así que esta aplicación facilite el uso de dichos métodos por parte de investigadores aplicados

Rubber Dam Isolation Survey (RDIS) for adhesive restorative treatments.

INTRODUCTION: Rubber dam isolation is considered as an essential component of modern adhesive dentistry. However, dental students do not always use it due to several barriers they face on their clinical practice. The aim of this study was to design and validate a questionnaire based on the COM-B model to measure students' implementation of rubber dam isolation in restorative treatments with adhesive materials. MATERIALS AND METHODS: A 7-item questionnaire was developed based on the COM-B model, with questions measuring the Capability, Opportunity (Relevance and Resources), and Motivation to perform rubber dam isolation (Behaviour). Content validation of the questionnaire was conducted by experts in aesthetic/restorative dentistry that assessed the clarity, coherence and relevance of the questions. The final survey was administered to a dental student population from three large private universities in the Dominican Republic. Descriptive analysis, t tests, polychoric correlations and a path analysis were carried out to establish the validity of the instrument. RESULTS: A total of 382 students from three universities completed the questionnaire. According to the COM-B path model, the significant predictors of the implementation of rubber dam isolation were Capability and Motivation for University A, Motivation and Opportunity-Resources for University B, and Opportunity-Relevance and Capability for University C. CONCLUSIONS: The RDIS is a very short, easy to administer and valid questionnaire that can be applied by the universities to determine where they need to focus their interventions to achieve better rubber dam isolation implementation by their students.

Class enumeration false positive in skew-t family of continuous growth mixture models.

Growth Mixture Modeling (GMM) has gained great popularity in the last decades as a methodology for longitudinal data analysis. The usual assumption of normally distributed repeated measures has been shown as problematic in real-life data applications. Namely, performing normal GMM on data that is even slightly skewed can lead to an over selection of the number of latent classes. In order to ameliorate this unwanted result, GMM based on the skew t family of continuous distributions has been proposed. This family of distributions includes the normal, skew normal, t, and skew t. This simulation study aims to determine the efficiency of selecting the "true" number of latent groups in GMM based on the skew t family of continuous distributions, using fit indices and likelihood ratio tests. Results show that the skew t GMM was the only model considered that showed fit indices and LRT false positive rates under the 0.05 cutoff value across sample sizes and for normal, and skewed and kurtic data. Simulation results are corroborated by a real educational data application example. These findings favor the development of practical guides of the benefits and risks of using the GMM based on this family of distributions.

Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial.

Exploratory graph analysis (EGA) is a new technique that was recently proposed within the framework of network psychometrics to estimate the number of factors underlying multivariate data. Unlike other methods, EGA produces a visual guide-network plot-that not only indicates the number of dimensions to retain, but also which items cluster together and their level of association. Although previous studies have found EGA to be superior to traditional methods, they are limited in the conditions considered. These issues are addressed through an extensive simulation study that incorporates a wide range of plausible structures that may be found in practice, including continuous and dichotomous data, and unidimensional and multidimensional structures. Additionally, two new EGA techniques are presented: one that extends EGA to also deal with unidimensional structures, and the other based on the triangulated maximally filtered graph approach (EGAtmfg). Both EGA techniques are compared with 5 widely used factor analytic techniques. Overall, EGA and EGAtmfg are found to perform as well as the most accurate traditional method, parallel analysis, and to produce the best large-sample properties of all the methods evaluated. To facilitate the use and application of EGA, we present a straightforward R tutorial on how to apply and interpret EGA, using scores from a well-known psychological instrument: the Marlowe-Crowne Social Desirability Scale. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Is Small Still Beautiful for the Strengths and Difficulties Questionnaire? Novel Findings Using Exploratory Structural Equation Modeling.

During the present decade a large body of research has employed confirmatory factor analysis (CFA) to evaluate the factor structure of the Strengths and Difficulties Questionnaire (SDQ) across multiple languages and cultures. However, because CFA can produce strongly biased estimations when the population cross-loadings differ meaningfully from zero, it may not be the most appropriate framework to model the SDQ responses. With this in mind, the current study sought to assess the factorial structure of the SDQ using the more flexible exploratory structural equation modeling approach. Using a large-scale Spanish sample composed of 67,253 youths aged between 10 and 18 years (M = 14.16, SD = 1.07), the results showed that CFA provided a severely biased and overly optimistic assessment of the underlying structure of the SDQ. In contrast, exploratory structural equation modeling revealed a generally weak factorial structure, including questionable indicators with large cross-loadings, multiple error correlations, and significant wording variance. A subsequent Monte Carlo study showed that sample sizes greater than 4,000 would be needed to adequately recover the SDQ loading structure. The findings from this study prevent recommending the SDQ as a screening tool and suggest caution when interpreting previous results in the literature based on CFA modeling.