scholarly journals Exploratory Bifactor Measurement Models in Vocational Behavior Research

2020 ◽  
Author(s):  
Casey Giordano
Keyword(s):  
Performance Feedback ◽  
Bifactor Model ◽  
Vocational Psychology ◽  
Measurement Models ◽  
Vocational Behavior ◽  
Bifactor Models ◽  
Developmental Performance

We provide an overview of and guidance for applying exploratory bifactor models to vocational research. First, we describe bifactor models and highlight their potential and actual applications in vocational psychology. Second, we review the theoretical bases of bifactor models and offer methodological guidance to correctly implement and interpret these models in practice. Third, we estimate a bifactor model in two vocational datasets to illustrate the concepts reviewed in this manuscript. The resulting models highlight novel insights in careers research (e.g., developmental performance feedback and personality [conscientiousness] modeling) that are made possible by leveraging bifactor measurement models. Overall, this manuscript provides a useful introduction to bifactor models to facilitate vocational behavior scholars and practitioners in thoughtfully producing and consuming bifactor models in their own research.

scholarly journals Bifactor Models for Predicting Criteria by General and Specific Factors: Problems of Nonidentifiability and Alternative Solutions

2018 ◽  
Vol 6 (3) ◽  
pp. 42 ◽  
Author(s):  
Michael Eid ◽  
Stefan Krumm ◽  
Tobias Koch ◽  
Julian Schulze
Keyword(s):  
Factor Models ◽  
Formal Proof ◽  
Bifactor Model ◽  
First Order ◽  
Applied Model ◽  
Measurement Models ◽  
Bifactor Models ◽  
Specific Factors ◽  
Order Factor ◽  
Criterion Variables

The bifactor model is a widely applied model to analyze general and specific abilities. Extensions of bifactor models additionally include criterion variables. In such extended bifactor models, the general and specific factors can be correlated with criterion variables. Moreover, the influence of general and specific factors on criterion variables can be scrutinized in latent multiple regression models that are built on bifactor measurement models. This study employs an extended bifactor model to predict mathematics and English grades by three facets of intelligence (number series, verbal analogies, and unfolding). We show that, if the observed variables do not differ in their loadings, extended bifactor models are not identified and not applicable. Moreover, we reveal that standard errors of regression weights in extended bifactor models can be very large and, thus, lead to invalid conclusions. A formal proof of the nonidentification is presented. Subsequently, we suggest alternative approaches for predicting criterion variables by general and specific factors. In particular, we illustrate how (1) composite ability factors can be defined in extended first-order factor models and (2) how bifactor(S-1) models can be applied. The differences between first-order factor models and bifactor(S-1) models for predicting criterion variables are discussed in detail and illustrated with the empirical example.

scholarly journals Empirical Underidentification with the Bifactor Model: A Case Study

2017 ◽  
Vol 78 (5) ◽  
pp. 717-736 ◽  
Author(s):  
Samuel Green ◽  
Yanyun Yang
Keyword(s):  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Bifactor Model ◽  
Data Sets ◽  
Bifactor Models

Bifactor models are commonly used to assess whether psychological and educational constructs underlie a set of measures. We consider empirical underidentification problems that are encountered when fitting particular types of bifactor models to certain types of data sets. The objective of the article was fourfold: (a) to allow readers to gain a better general understanding of issues surrounding empirical identification, (b) to offer insights into empirical underidentification with bifactor models, (c) to inform methodologists who explore bifactor models about empirical underidentification with these models, and (d) to propose strategies for structural equation model users to deal with underidentification problems that can emerge when applying bifactor models.

The Test of Gross Motor Development—Third Edition: A Bifactor Model, Dimensionality, and Measurement Invariance

2021 ◽  
pp. 1-16
Author(s):  
Sedigheh Salami ◽  
Paulo Felipe Ribeiro Bandeira ◽  
Cristiano Mauro Assis Gomes ◽  
Parvaneh Shamsipour Dehkordi
Keyword(s):  
Measurement Invariance ◽  
Motor Development ◽  
Goodness Of Fit ◽  
Factor Model ◽  
Bifactor Model ◽  
General Factor ◽  
Convenience Sample ◽  
Gross Motor ◽  
Measurement Models ◽  
Gross Motor Development

Aim: To examine the latent structure of the Test of Gross Motor Development—Third Edition (TGMD-3) with a bifactor modeling approach. In addition, the study examines the dimensionality and model-based reliability of general and specific contributions of the test’s subscales and measurement invariance of the TGMD-3. Methods: A convenience sample of (N = 496; Mage = 7.23 ± 2.03 years; 53.8% female) typically developed children participated in this study. Three alternative measurement models were tested: (a) a unidimensional model, (b) a correlated two-factor model, and (c) a bifactor model. Results: The totality of results, including item loadings, goodness-of-fit indexes, and reliability estimates, all supported the bifactor model and strong evidence of a general factor, namely gross motor competence. Additionally, the reliability of subscale scores was poor, and it is thus contended that scoring, reporting, and interpreting of the subscales scores are probably not justifiable. Conclusions: This study shows the advantages of using bifactor approach to examine the TGMD-3 factor structure and suggests that the two traditionally hypothesized factors are better understood as “grouping” factors rather than as representative of latent constructs. In addition, our findings demonstrate that the bifactor model appears invariant for sex.

scholarly journals Exploratory bifactor measurement models in vocational behavior research

2020 ◽  
Vol 120 ◽  
pp. 103430 ◽  
Author(s):  
Casey Giordano ◽  
Deniz S. Ones ◽  
Niels G. Waller ◽  
Kevin C. Stanek
Keyword(s):  
Measurement Models ◽  
Vocational Behavior

Application of bifactor models in criminal psychology research: a guide to researchers

2015 ◽  
Vol 5 (2) ◽  
pp. 65-74 ◽  
Author(s):  
Philip Hyland
Keyword(s):  
Design Methodology ◽  
Criminal Psychology ◽  
Bifactor Model ◽  
Psychology Research ◽  
Content Type ◽  
Analytical Technique ◽  
Psychological Literature ◽  
Factor Analytic ◽  
Bifactor Models ◽  
Research Questions

Purpose – The purpose of this paper is to introduce the reader to the nature of confirmatory bifactor modelling. Confirmatory bifactor modelling is a factor analytic procedure that allows researchers to model unidimensionality and multidimensionality simultaneously. This method has important applications in the field of criminal psychology. Design/methodology/approach – This paper begins by introducing the topic of factor analysis and explains how confirmatory bifactor modelling is similar yet distinct to the more familiar factor analytical procedures in the psychological literature. Findings – Through practical examples this paper explains the value of this analytical technique to researchers in criminal psychology. Examples from the existing criminal psychological literature are used to illustrate the way in which bifactor analysis allows important theoretical questions to be addressed. Originality/value – This paper highlights the strengths and limitations associated with traditional “restricted” confirmatory bifactor models and introduces the notion of the “unrestricted” bifactor model. The unrestricted bifactor model allows greater flexibility for addressing interesting research questions. The paper concludes by providing the reader with an annotated Mplus syntax file for how to perform confirmatory bifactor modelling.

Evaluating the Hierarchical Structure of ADHD Symptoms and Invariance Across Age and Gender

Assessment ◽  
2017 ◽  
Vol 26 (3) ◽  
pp. 508-523 ◽  
Author(s):  
Alexandra Sturm ◽  
James T. McCracken ◽  
Li Cai
Keyword(s):  
Clinical Sample ◽  
Bifactor Model ◽  
Adhd Symptoms ◽  
Item Functioning ◽  
Specific Dimension ◽  
Bifactor Models ◽  
The Hierarchical Structure ◽  
Parsimonious Models ◽  
And Gender ◽  
Correlated Factors

The bifactor model of attention-deficit/hyperactivity disorder (ADHD) has been extensively explored, yet the tendency of the bifactor model to overfit data necessitates investigation of alternative, more parsimonious models, such as a modified bifactor structure. The present study used item response theory to compare unidimensional, correlated factors, bifactor, and modified bifactor models of ADHD symptoms in a clinical sample of youth ( N = 1,612) and examined differential item functioning (DIF) by age (<11 and ≥11 years) and gender. Results suggested that two restricted bifactor models showed superior fit compared with alternative models, and support strong general and inattention dimensions, with unreliable hyperactivity and impulsivity dimensions. No DIF was found across gender or age. The present study confirms that the general dimension (i.e., inhibition) and one specific dimension (i.e., sustained attention) represent distinct variability in ADHD symptoms that may improve prediction of symptom persistence, treatment response, or functional outcomes.

scholarly journals 18-Item Version of the Short Gambling Harm Screen (SGHS-18): Validation of Screen for Assessing Gambling-Related Harm among Finnish Population

2021 ◽  
Vol 18 (21) ◽  
pp. 11552
Author(s):  
Tiina Latvala ◽  
Matthew Browne ◽  
Matthew Rockloff ◽  
Anne H. Salonen
Keyword(s):  
Convergent Validity ◽  
Factor Model ◽  
Bifactor Model ◽  
Mental Health Issue ◽  
General Factor ◽  
Web Based ◽  
Gambling Harm ◽  
Measurement Models ◽  
Coefficient Omega ◽  
Gambling Harms

Background and aims: It is common for gambling research to focus on problem and disordered gambling. Less is known about the prevalence of gambling-related harms among people in the general population. This study aimed to develop and validate the 18-item version of the Short Gambling Harms Screen (SGHS-18). Methods: Population-representative web-based and postal surveys were conducted in the three geographical areas of Finland (n = 7186, aged 18 or older). Reliability and internal structure of SGHS-18 was assessed using coefficient omega and via confirmatory factor analysis (CFA). Four measurement models of SGHS-18 were compared: one-factor, six-factor, a second-ordered factor model and a bifactor model (M4). Results: The analysis revealed that only the bifactor model had adequate fit for SGHS-18 (CFI = 0.953, TLI = 0.930, GFI = 0.974, RMSEA = 0.047, SRMR = 0.027). The general factor explained most of the common variance compared to specific factors. Coefficient omega hierarchical value for global gambling harm factor (0.80) was high, which suggested that SGHS-18 assessed the combination of general harm constructs sufficiently. The correlation with the Problem and Pathological Gambling Measures (PPGM) was 0.44, potentially reflecting that gambling harms are closely—although not perfectly—aligned with the mental health issue of problem gambling. SGHS-18 scores were substantially higher for participants who gambled more often, who spent more money or who had gambling problems, demonstrating convergent validity for the screen. Discussion: The SGHS-18 comprehensively measures the domains of gambling harm, while demonstrating desirable properties of internal consistency, and criterion and convergent validity.

Evaluating Bifactor Models of Psychopathology Using Model-Based Reliability Indices

2019 ◽  
Author(s):  
Matthew Constantinou ◽  
Peter Fonagy
Keyword(s):  
Bifactor Model ◽  
Common Variance ◽  
Reliability Indices ◽  
Bifactor Models ◽  
Study Characteristics ◽  
Specific Factors ◽  
P Factor ◽  
Explained Common Variance ◽  
Internalizing And Externalizing ◽  
Variance Explained

There is has been a rapid increase in quantitative researchers applying the bifactor model to psychopathology data. The bifactor model, which typically includes a general p factor and internalizing and externalizing residual factors, consistently demonstrates superior model fit to competing models, including the correlated factors model, which typically includes internalizing and externalizing factors. However, the bifactor model’s superior fit might stem from its tendency to overfit noise and flexibly fit most datasets. An alternative approach to evaluating bifactor models that does not rely on fit statistics is model-based reliability assessment. Reliability indices, including omega/omega hierarchical, explained common variance, and percent uncontaminated correlations can be used to determine the viability of the general and specific psychopathology factors and the extent that the underlying data structure and its measurement is multidimensional. In this methodological review, we identified 49 studies published between 2009 and 2019 that applied the bifactor model to at least two separate symptom domains and calculated reliability indices from the standardized factor loading matrices. We also predicted variation in the p factor’s strength, indexed by the explained common variance, from study characteristics. We found that psychopathology measures tend to be multidimensional, with 57% of the variance explained by the p factor and the remaining variance explained by specific factors. By contrast, most of the variance in observed total scores (74%) was explained by the p factor, while relatively little of the variance in in observed subscale scores (37%) was explained by specific factors beyond the p factor. Finally, 62% of the variability in the p factor’s strength could be predicted by study characteristics, most notably the informant (in a simultaneous regression model), but also age, percent uncontaminated correlations, and the number of items (in separate regression models). We conclude that the latent structure of psychopathology is multidimensional, but its measurement is governed by a single dimension, the strength of which is predicted by study characteristics, particularly the informant.

THE TEST OF GROSS MOTOR DEVELOPMENT-THIRD EDITION: A BIFACTOR MODEL, DIMENSIONALITY, AND MEASUREMENT INVARIANCE

2020 ◽  
Author(s):  
Sedigheh Salami ◽  
Paulo Felipe Ribeiro Bandeira ◽  
Cristiano Mauro Assis Gomes ◽  
Parvaneh Shamsipour Dehkordi
Keyword(s):  
Measurement Invariance ◽  
Motor Development ◽  
Goodness Of Fit ◽  
Factor Model ◽  
Bifactor Model ◽  
Gross Motor ◽  
Measurement Models ◽  
Gross Motor Development ◽  
Bifactor Modeling ◽  
Iranian Children

Aim: To examine the latent structure of the Test of Gross Motor Development, 3rd Edition (TGMD-3) with a bifactor modeling approach. Furthermore, the study examines the dimensionality, model-based reliability of general and specific contributions of the test's subscales and measurement invariance of the TGMD-3. Methods: Using a sample of 496 Iranian children (M age = 7.23±2.03 years; 53.8 female) from the five main geographic regions of Tehran city, three alternative measurement models were tested: (a) a unidimensional model, (b) a correlated 2-factor model, (c) a bifactor model. Results: The totality of results including item loadings, goodness-of-fit indexes and reliability estimates all supported the bifactor model and strong evidence of general fundamental movement factor. Additionally, the reliability of subscale scores was poor, it is thus contended that scoring, reporting and interpreting of the subscales scores are probably not justifiable. Suggesting that the 2 traditionally hypothesized factors are better understood as “grouping” factors rather than as representative of latent constructs. Furthermore, the bifactor model appears invariant for gender. Conclusion: This study is the first to address the bifactor model and new insights regarding the application and interpretation of the test battery most widely used with children.

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