Detecting non-content-based response styles in survey data: An application of mixture factor analysis.

It is common for some participants in self-report surveys to be careless, inattentive, or lacking in effort. Data quality can be severely compromised by responses that are not based on item content (non-content-based [nCB] responses), leading to strong biases in the results of data analysis and misinterpretation of individual scores. In this study, we propose a specification of factor mixture analysis (FMA) to detect nCB responses. We investigated the usefulness and effectiveness of the FMA model in detecting nCB responses using both simulated data (Study 1) and real data (Study 2). In the first study, FMA showed reasonably robust sensitivity (.60 to .86) and excellent specificity (.96 to .99) on mixed-worded scales, suggesting that FMA had superior properties as a screening tool under different sample conditions. However, FMA performance was poor on scales composed of only positive items because of the difficulty in distinguishing acquiescent patterns from valid responses representing high levels of the trait. In Study 2 (real data), FMA detected a minority of cases (6.5%) with highly anomalous response patterns. Removing these cases resulted in a large increase in the fit of the unidimensional model and a substantial reduction in spurious multidimensionality.

How do dental practitioners, educators and students diagnose and manage caries risk and caries lesions? A COM-B analysis.

OBJECTIVE: The International Caries Classification and Management System (ICCMS™), a comprehensive, evidence-informed, best clinical practice system, comprises a 4D cycle: 1D-Determine risk; 2D-Detect and assess lesions; 3D-Decide on a personalized care plan; and 4D-Do preventive and tooth-preserving care. The aim of this study was to establish how Colombian dental practitioners, educators and students diagnose and manage caries risk and caries lesions using the COM-B model and the ICCMS™ system. METHODS: A total of 1094 participants (practitioners: n = 277; educators: n = 212; students: n = 605) completed a previously validated 79-item questionnaire which explores, based on the COM-B model, the practitioners' self-reported caries diagnosis and management behaviours. Descriptive statistics, Welch's ANOVAs and multiple linear regressions were computed. RESULTS: All groups generally performed the behaviours within the 4-D categories 'Most of the time' to 'Always' (students: 4.06 ± 0.95; educators: 3.94 ± 0.98; practitioners: 3.86 ± 1.01). The most frequently performed diagnosis behaviours (1D/2D) were for practitioners assessing initial/moderate lesions (4.09 ± 1.01) and for educators and students cleaning teeth before lesion assessment (4.41 ± 0.80 and 4.38 ± 0.77 respectively). The least frequently performed decision/management (3D/4D) behaviour was non-operative care for moderate-caries lesions (when applicable) (practitioners: 2.64 ± 1.23; educators: 2.68 ± 1.17; students: 3.22 ± 1.41). Opportunity (Resources and Relevance) was the best COM-B predictor for diagnostic behaviours, whereas capability and opportunity (Relevance) were the strongest predictors for management behaviours. CONCLUSION: Colombian practitioners, educators and students diagnose and manage caries risk and caries lesions implementing best practice with a high to very high frequency.

On Omega Hierarchical Estimation: A Comparison of Exploratory Bi-Factor Analysis Algorithms.

As general factor modeling continues to grow in popularity, researchers have become interested in assessing how reliable general factor scores are. Even though omega hierarchical estimation has been suggested as a useful tool in this context, little is known about how to approximate it using modern bi-factor exploratory factor analysis methods. This study is the first to compare how omega hierarchical estimates were recovered by six alternative algorithms: Bi-quartimin, bi-geomin, Schmid-Leiman (SL), empirical iterative empirical target rotation based on an initial SL solution (SLiD), direct SL (DSL), and direct bi-factor (DBF). The algorithms were tested in three Monte-Carlo simulations including bi-factor and second-order structures and presenting complexities such as cross-loadings or pure indicators of the general factor and structures without a general factor. Results showed that SLiD provided the best approximation to omega hierarchical under most conditions. Overall, neither SL, bi-quartimin, nor bi-geomin produced an overall satisfactory recovery of omega hierarchical. Lastly, the performance of DSL and DBF depended upon the average discrepancy between the loadings of the general and the group factors. The re-analysis of eight classical datasets further illustrated how algorithm selection could influence judgments regarding omega hierarchical.

Bi-factor Exploratory Structural Equation Modeling Done Right: Using the SLiDapp Application.

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.

Searching for G: A New Evaluation of SPM-LS Dimensionality.

There has been increased interest in assessing the quality and usefulness of short versions of the Raven's Progressive Matrices. A recent proposal, composed of the last twelve matrices of the Standard Progressive Matrices (SPM-LS), has been depicted as a valid measure of g. Nonetheless, the results provided in the initial validation questioned the assumption of essential unidimensionality for SPM-LS scores. We tested this hypothesis through two different statistical techniques. Firstly, we applied exploratory graph analysis to assess SPM-LS dimensionality. Secondly, exploratory bi-factor modelling was employed to understand the extent that potential specific factors represent significant sources of variance after a general factor has been considered. Results evidenced that if modelled appropriately, SPM-LS scores are essentially unidimensional, and that constitute a reliable measure of g. However, an additional specific factor was systematically identified for the last six items of the test. The implications of such findings for future work on the SPM-LS are discussed.

Calibrating a new item pool to adaptively assess the Big Five.

BACKGROUND: Even though the Five Factor Model (FFM) has been the dominant paradigm in personality research for the past two decades, very few studies have measured the FFM adaptively. Thus, the purpose of this research was the building of a new item pool to develop a computerized adaptive test (CAT) for personality assessment. METHOD: A pool of 480 items that measured the FFM facets was developed and applied to 826 participants. Facets were calibrated separately and item selection was performed being mindful of the preservation of unidimensionality of each facet. Then, a post-hoc simulation study was carried out to test the performance of separate CATs to measure the facets. RESULTS: The final item pool was composed of 360 items with good psychometric properties. Findings reveal that a CAT administration of four items per facet (total length of 120 items) provides accurate facets scores, while maintaining the factor structure of the FFM. CONCLUSIONS: An item pool with good psychometric properties was obtained and a CAT simulation study demonstrated that the FFM facets could be measured with precision using a third of the items in the pool.

Iteration of Partially Specified Target Matrices: Application to the Bi-Factor Case.

The current study proposes a new bi-factor rotation method, Schmid-Leiman with iterative target rotation (SLi), based on the iteration of partially specified target matrices and an initial target constructed from a Schmid-Leiman (SL) orthogonalization. SLi was expected to ameliorate some of the limitations of the previously presented SL bi-factor rotations, SL and SL with target rotation (SLt), when the factor structure either includes cross-loadings, near-zero loadings, or both. A Monte Carlo simulation was carried out to test the performance of SLi, SL, SLt, and the two analytic bi-factor rotations, bi-quartimin and bi-geomin. The results revealed that SLi accurately recovered the bi-factor structures across the majority of the conditions, and generally outperformed the other rotation methods. SLi provided the biggest improvements over SL and SLt when the bi-factor structures contained cross-loadings and pure indicators of the general factor. Additionally, SLi was superior to bi-quartimin and bi-geomin, which performed inconsistently across the types of factor structures evaluated. No method produced a good recovery of the bi-factor structures when small samples (N = 200) were combined with low factor loadings (0.30-0.50) in the specific factors. Thus, it is recommended that larger samples of at least 500 observations be obtained.

The impact of ambiguous response categories on the factor structure of the GHQ-12.

Previous research has suggested multiple factor structures for the 12-item General Health Questionnaire (GHQ-12), with contradictory evidence arising across different studies on the validity of these models. In the present research, it was hypothesized that these inconsistent findings were due to the interaction of 3 main methodological factors: ambiguous response categories in the negative items, multiple scoring schemes, and inappropriate estimation methods. Using confirmatory factor analysis with appropriate estimation methods and scores obtained from a large (n = 27,674) representative Spanish sample, we tested this hypothesis by evaluating the fit and predictive validities of 4 GHQ-12 factor models-unidimensional, Hankins' (2008a) response bias model, Andrich and Van Schoubroeck's (1989) 2-factor model, and Graetz's (1991) 3-factor model-across 3 scoring methods: standard, corrected, and Likert. In addition, the impact of method effects on the reliability of the global GHQ-12 scores was also evaluated. The combined results of this study support the view that the GHQ-12 is a unidimensional measure that contains spurious multidimensionality under certain scoring schemes (corrected and Likert) as a result of ambiguous response categories in the negative items. Therefore, it is suggested that the items be scored using the standard method and that only a global score be derived from the instrument.