From innovation to sustainable transformation: a multidimensional approach

Minor cross-loadings on non-targeted factors are often found in psychological or other instruments. Forcing them to zero in confirmatory factor analyses (CFA) leads to biased estimates and distorted structures. Alternatively, exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM) have been proposed. In this research, we compared the performance of the traditional independent-clusters-confirmatory-factor-analysis (ICM-CFA), the nonstandard CFA, ESEM with the Geomin- or Target-rotations, and BSEMs with different cross-loading priors (correct; small- or large-variance priors with zero mean) using simulated data with cross-loadings. Four factors were considered: the number of factors, the size of factor correlations, the cross-loading mean, and the loading variance. Results indicated that ICM-CFA performed the worst. ESEMs were generally superior to CFAs but inferior to BSEM with correct priors that provided the precise estimation. BSEM with large- or small-variance priors performed similarly while the prior mean for cross-loadings was more important than the prior variance.

Cross-loadings are common in multidimensional instruments; however, they cannot be appropriately addressed in conventional structural equation modeling (SEM) owing to the assumption of zero cross-loadings in standard confirmatory factor analysis (CFA). Although it has been proposed that exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM) can address this issue more flexibly, their performance in structural parameter estimation has not been adequately compared. This study uses simulated data to evaluate and compare SEM, ESEM, and BSEM in estimating structural models under different manipulation conditions (i.e., sample size, target loading, cross-loading, and path coefficient). The results demonstrated that the performances of these approaches were similar in the case of zero cross-loadings. SEM performed worse as cross-loadings increased, and the performance of BSEM significantly depended on the accuracy of the priors for cross-loadings. ESEM was inferior to BSEM with correctly specified prior means for cross-loadings in most evaluation measures and exhibits unstable performance in conditions with small target loadings. Recommended strategies for selecting an appropriate modeling approach are discussed based on our findings.

The number of available factor analytic techniques has been increasing in the last decades. However, the lack of clear guidelines and exhaustive comparison studies between the techniques might hinder that these valuable methodological advances make their way to applied research. The present paper evaluates the performance of confirmatory factor analysis (CFA), CFA with sequential model modification using modification indices and the Saris procedure, exploratory factor analysis (EFA) with different rotation procedures (Geomin, target, and objectively refined target matrix), Bayesian structural equation modeling (BSEM), and a new set of procedures that, after fitting an unrestrictive model (i.e., EFA, BSEM), identify and retain only the relevant loadings to provide a parsimonious CFA solution (ECFA, BCFA). By means of an exhaustive Monte Carlo simulation study and a real data illustration, it is shown that CFA and BSEM are overly stiff and, consequently, do not appropriately recover the structure of slightly misspecified models. EFA usually provides the most accurate parameter estimates, although the rotation procedure choice is of major importance, especially depending on whether the latent factors are correlated or not. Finally, ECFA might be a sound option whenever an a priori structure cannot be hypothesized and the latent factors are correlated. Moreover, it is shown that the pattern of the results of a factor analytic technique can be somehow predicted based on its positioning in the confirmatory-exploratory continuum. Applied recommendations are given for the selection of the most appropriate technique under different representative scenarios by means of a detailed flowchart. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p factor) in classifying, researching, diagnosing, and treating psychiatric disorders depends (among other issues) on the extent to which comorbidity is symptom-general rather than staying largely within the confines of narrower transdiagnostic factors such as internalizing and externalizing. In this study, we compared three methods of estimating p factor strength. We compared omega hierarchical and explained common variance calculated from confirmatory factor analysis (CFA) bifactor models with maximum likelihood (ML) estimation, from exploratory structural equation modeling/exploratory factor analysis models with a bifactor rotation, and from Bayesian structural equation modeling (BSEM) bifactor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings might be the preferred option. However, CFA with ML also performed well provided secondary loadings were modeled. We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n = 1,286) and a university counseling sample (n = 359).

The aim of this study was to reexamine the dimensionality of the widely used 9-item Utrecht Work Engagement Scale using the maximum likelihood (ML) approach and Bayesian structural equation modeling (BSEM) approach. Three measurement models (1-factor, 3-factor, and bi-factor models) were evaluated in two split samples of 1,112 health-care workers using confirmatory factor analysis and BSEM, which specified small-variance informative priors for cross-loadings and residual covariances. Model fit and comparisons were evaluated by posterior predictive p-value (PPP), deviance information criterion, and Bayesian information criterion (BIC). None of the three ML-based models showed an adequate fit to the data. The use of informative priors for cross-loadings did not improve the PPP for the models. The 1-factor BSEM model with approximately zero residual covariances displayed a good fit (PPP>0.10) to both samples and a substantially lower BIC than its 3-factor and bi-factor counterparts. The BSEM results demonstrate empirical support for the 1-factor model as a parsimonious and reasonable representation of work engagement.

The interpretation of the Wechsler Intelligence Scale for Children--Fourth Edition (WISC-IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell-Horn-Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all cross-loadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8-12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC-IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores.

This paper explores the utilisation of Bayesian structural equation modelling (BSEM) in psychology, highlighting its advantages over frequentist methods for handling complex models and small sample sizes. Basic concepts and fundamental issues relevant to BSEM are introduced, such as prior setting, model convergence, and model fit evaluation and so on. The paper also provides illustrative examples of commonly employed BSEMs, including confirmatory factor analysis (CFA) models, mediation models and multigroup CFA models, accompanied by empirical data and computer codes to facilitate implementation. Our goal is to provide researchers with novel ideas for empirical research and equip them to overcome challenges inherent to traditional methods. As BSEM continues to gain traction in various fields, we anticipate its development will feature improved methods, techniques and reporting standards.

The Statistics Anxiety Rating Scale (STARS) is a 51-item scale commonly used to measure college students’ anxiety regarding statistics. To date, however, limited empirical research exists that examines statistics anxiety among ethnically diverse or first-generation graduate students. We examined the factor structure and reliability of STARS scores in a diverse sample of students enrolled in graduate courses at a Minority-Serving Institution (n = 194). To provide guidance on assessing dimensionality in small college samples, we compared the performance of best-practice factor analysis techniques: confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). We found modest support for the original six-factor structure using CFA, but ESEM and BSEM analyses suggested that a four-factor model best captures the dimensions of the STARS instrument within the context of graduate-level statistics courses. To enhance scale efficiency and reduce respondent fatigue, we also tested and found support for a reduced 25-item version of the four-factor STARS scale. The four-factor STARS scale produced constructs representing task and process anxiety, social support avoidance, perceived lack of utility, and mathematical self-efficacy. These findings extend the validity and reliability evidence of the STARS inventory to include diverse graduate student populations. Accordingly, our findings contribute to the advancement of data science education and provide recommendations for measuring statistics anxiety at the graduate level and for assessing construct validity of psychometric instruments in small or hard-to-survey populations.

PurposeThis paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis (CFA), structural equation modelling (SEM), mediation and moderation analysis, with the intention that the novice researchers will apply this method in their research. The paper made an attempt in discussing various complex mathematical concepts such as Markov Chain Monte Carlo, Bayes factor, Bayesian information criterion and deviance information criterion (DIC), etc. in a lucid manner.Design/methodology/approachData collected from 172 pharmaceutical sales representatives were used. The study will help the management researchers to perform Bayesian CFA, Bayesian SEM, Bayesian moderation analysis and Bayesian mediation analysis using SPSS AMOS software.FindingsThe interpretation of the results of Bayesian CFA, Bayesian SEM and Bayesian mediation analysis were discussed.Practical implicationsThe management scholars are non-statisticians and are not much aware of the benefits offered by Bayesian methods. Hitherto, the management scholars use predominantly traditional SEM in validating their models empirically, and this study will give an exposure to “Bayesian statistics” that has practical advantages.Originality/valueThis is one paper, which discusses the following four concepts: Bayesian method of CFA, SEM, mediation and moderation analysis.

Construct validity of the pictorial scale of Perceived Movement Skill Competence

The aim of this article is to present the concept of analysis aimed at evaluating the innovative capacity of enterprises. The starting point for its estimation is the innovative potential of the surveyed enterprises, while reference is made to the innovation system of enterprises. The aim of the re- search outlined in this way required the development of the concept of diag- nostic analysis aimed at evaluating the innovative potential and capacity of an enterprise. An important research tool is the analysis of the relationship between the resources and the capacity, which shows the cause-and-effect relationship between these categories. The following arguments have been assumed in the presented concept: 1) innovative capacity is both a function and a criterion for the evaluation of the innovation system of an enterprise, as well as for the projection of the possibility of expanding innovative activities; 2) innovative capacity can be seen in parts as well as in an aggregate structure for a given enterprise or the entire industry; 3) the value of innovative capacity (innovation potential, innovation system) can be classified at the following levels of quality: low, medium and high, using the method of categorization. The research proce- dure has four steps: 1) determination of the object and scope of analysis; 2) formulation of dimensions (criteria) of the innovation potential; 3) measur- ing the quality level of innovative capacity; 4) verification. For illustrative purposes, an example of the innovation capacity of enterprises was shown. The research included 316 enterprises from the Malopolska Region.

The latent structure of the Hospital Anxiety and Depression Scale (HADS) has caused inconsistent results in the literature. The HADS is frequently analyzed via maximum likelihood confirmatory factor analysis (ML-CFA). However, the overly restrictive assumption of exact zero cross-loadings and residual correlations in ML-CFA can lead to poor model fits and distorted factor structures. This study applied Bayesian structural equation modeling (BSEM) to evaluate the latent structure of the HADS. Three a priori models, the two-factor, three-factor, and bifactor models, were investigated in a Chinese community sample (N=312) and clinical sample (N=198) using ML-CFA and BSEM. BSEM specified approximate zero cross-loadings and residual correlations through the use of zero-mean, small-variance informative priors. The model comparison was based on the Bayesian information criterion (BIC). Using ML-CFA, none of the three models provided an adequate fit for either sample. The BSEM two-factor model with approximate zero cross-loadings and residual correlations fitted both samples well with the lowest BIC of the three models and displayed a simple and parsimonious factor-loading pattern. The study demonstrated that the two-factor structure fitted the HADS well, suggesting its usefulness in assessing the symptoms of anxiety and depression in clinical practice. BSEM is a sophisticated and flexible statistical technique that better reflects substantive theories and locates the source of model misfit. Future use of BSEM is recommended to evaluate the latent structure of other psychological instruments.

Purpose: There is little consensus in the literature regarding a) what it means for a school leader to lead with data, and b) how to measure data-informed leadership in a reliable and valid way. This study examines the psychometric properties of an operational measure intended to assess the extent to which a school leader is a ‘data-informed school leader. The measurement invariance, reliabilities and construct and predictive validities of the Data-Informed School Leadership Survey (DISL Survey) are assessed using various psychometric statistical techniques. Methods: Using data collected from 155 teachers from 7 public schools in a southern state, the following psychometric statistics used to address our purpose: the Many-Facet Rasch (MFR) Model, Bayesian second-order Confirmatory Factor Analysis (CFA), Bayesian Structural Equation Modeling- Multiple Indicators, Multiple Causes analysis (Bayesian SEM-MIMIC), and reliability analysis. Findings: Results show an adequate fit from all MFR, Bayesian CFA, and MIMIC models and a high reliability (Cronbach α = .98). The DISL Survey instrument exhibits sound psychometric properties. Results likewise confirm the value of using MFR modeling and Bayesian methods to examine the psychometric properties of DISL Survey as a means of improving educational leadership measures. Implications for Research and Practice: Data from this study confirm the validity and reliability of the Data-Informed School Leadership Survey (DISL Survey) as an instrument to assess the strengths and weaknesses of Data-Informed School Leadership (DISL) and as a means for providing feedback for improving such leadership. Heretofore a measure for assessing this leadership was non-existent.

Background: Risk-taking has an influential role in accident occurrence or prevention. Hence, having a valid tool to evaluate workers for risk-taking is necessary. Risk-taking has been intensely studied in the field of psychology and neuroscience, leading to the development of several questionnaires and software. Objectives: In the present study, a new questionnaire was developed and validated based on the conceptual model to assess risk-taking behavior in people working in operator control rooms in industrial settings. Methods: Questions were selected from well-known psychological scales based on a conceptual model, followed by approving its face validity and conducting exploratory factor analysis (EFA). Afterward, some models were computed with confirmatory factor analysis (CFA) and Bayesian structural equation modeling (BSEM). The questionnaire was completed by 208 males who worked in the control room of some industries in Markazi province, Iran, in 2021. Test-retest reliability was done by 42 participants at a three-month interval. Results: One factor and 12 items were suggested by EFA. The model was accepted based on the results of Operator Control Room Risk-taking Questionnaire (ORTQ) goodness of fit CFA (χ2/df = 1.89, comparative fit index (CFI) = 0.90, root mean square error of approximation (RMSEA) = 0.09, and P < 0.001). The correlation coefficient (P < 0.001) between risk-taking and impulsivity was 0.65. However, it was 0.69 between venturesomeness and risk-taking and 0.58 between impulsivity and venturesomeness. Cronbach's alpha was 0.89 for ORTQ test-retest reliability. Operator Control Room Risk-taking Questionnaire was confirmed to have a better leave-one-out cross-validation information criterion (LOOIC) based on BSEM rather than Bayesian confirmatory factor analysis (BCFA). Conclusions: Operator Control Room Risk-taking Questionnaire is a valid and reliable questionnaire that can be used as a screening tool for risk-taking traits in workers before enrolment.

Orientation: The rigid application of conventional confirmatory factor analysis (CFA) techniques, the overreliance on global model fit indices and the dismissal of the chi-square statistic appear to have an adverse impact on the research of psychological ownership measures.Research purpose: The purpose of this study was to explicate the South African Psychological Ownership Questionnaire’s (SAPOS’s) CFA model fit using the Bayesian structural equation modelling (BSEM) technique.Motivation for the study: The need to conduct this study derived from a renewed awareness of the incorrect use of the chi-square statistic and global fit indices of CFA in social sciences research.Research approach/design and method: The SAPOS measurement model fit was explicated on two study samples consisting, respectively, of 712 and 254 respondents who worked in various organisations in South Africa. A Bayesian approach to CFA was used to evaluate if local model misspecifications were substantive and justified the rejection of the SAPOS model.Main findings: The findings suggested that a rejection of the SAPOS measurement model based on the results of the chi-square statistic and global fit indices would be unrealistic and unfounded in terms of substantive test theory.Practical/managerial implications: BSEM appeared to be a valuable diagnostic tool to pinpoint and evaluate local CFA model misspecifications and their effect on a measurement model.Contribution/value-add: This study showed the importance of considering local misspecifications rather than only relying the chi-square statistic and global fit indices when evaluating model fit.