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

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.

Download Full-text

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

Journal of Motor Learning and Development ◽

10.1123/jmld.2020-0069 ◽

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.

Download Full-text

Examining the Impostor-Profile—Is There a General Impostor Characteristic?

Frontiers in Psychology ◽

10.3389/fpsyg.2021.720072 ◽

2021 ◽

Vol 12 ◽

Author(s):

Fabio Ibrahim ◽

Johann-Christoph Münscher ◽

Philipp Yorck Herzberg

Keyword(s):

Convergent Validity ◽

Factor Model ◽

Second Order ◽

Model Fit ◽

Impostor Phenomenon ◽

General Factor ◽

Order Model ◽

First Order ◽

Order Factor ◽

Best Fit

The Impostor-Profile (IPP) is a six-dimensional questionnaire measuring the Impostor Phenomenon facets. This study aims to test (a) the appropriateness of a total score, (b) measurement invariance (MI) between gender, (c) the reliability of the IPP, and (d) the convergent validity of the IPP subscales. The sample consisted of N = 482 individuals (64% female). To identify whether the scales of the IPP form a total score, we compared four models: (1) six correlating subscales, (2) a general factor model, (3) a second-order model with one second-order factor and six first-order factors, and (4) a bifactorial model with six group factors. The bifactorial model obtained the best fit. This supports the assumption of a total impostor score. The inspection of structural validity between gender subgroups showed configural, metric, and partial scalar MI. Factor mean comparisons supported the assumption that females and males differ in latent means of the Impostor Phenomenon expressions. The omega coefficients showed sufficient reliability (≥0.71), except for the subscale Need for Sympathy. Overall, the findings of the bifactor model fit and construct validity support the assumption that the measurement through total expression is meaningful in addition to the theoretically formulated multidimensionality of the Impostor Phenomenon.

Download Full-text

Psychometric properties of a new scale for measuring academic positive psychological capital

10.36253/978-88-5518-461-8.06 ◽

2021 ◽

pp. 25-30

Author(s):

Pasquale Anselmi ◽

Daiana Colledani ◽

Luigi Fabbris ◽

Egidio Robusto ◽

Manuela Scioni

Keyword(s):

Psychological Capital ◽

Factor Model ◽

Bifactor Model ◽

Specific Factor ◽

General Factor ◽

Positive Psychological Capital ◽

Four Dimensions ◽

Explained Common Variance ◽

University Of Padua ◽

Academic Results

Positive psychological capital (PsyCap) is the name given to a set of psychological dimensions (hope, resilience, self-efficacy, and optimism) that may support students in their effort to achieve better academic results and even improve the employability of graduates. These dimensions could help students to achieve better academic results and impact fresh graduates’ ability to stand the labour market in times of crisis. A scale, called Academic PsyCap, was specifically developed to evaluate the four PsyCap dimensions among students and fresh graduates. To deeply investigate the structural validity of the scale, three alternative models (one-factor model, correlated four-factor model, bifactor model) were run on the responses provided by about 1,600 fresh graduates at the University of Padua. The results indicated that the bifactor model fit the data better than the other two models. In this model, all items significantly loaded on both their own domain specific factor and on the general factor. The values of Percentage of Uncontaminated Correlations (PUC), Explained Common Variance (ECV), and Hierarchical Omega suggested that multidimensionality in the scale was not severe enough to disqualify the use of a total PsyCap score. The scale was found to be invariant across gender and academic degree (bachelor’s and master’s degree). Internal consistency indices were satisfactory for the four dimensions and the total scale.

Download Full-text

Higher-Order and Bifactor Models of the Drinking Motives Questionnaire

Assessment ◽

10.1177/1073191115603503 ◽

2016 ◽

Vol 24 (2) ◽

pp. 222-231 ◽

Cited By ~ 9

Author(s):

Andrew Lac ◽

Candice D. Donaldson

Keyword(s):

Structural Equation ◽

Negative Reinforcement ◽

Factor Model ◽

Equation Model ◽

Higher Order ◽

Bifactor Model ◽

Drinking Motives ◽

General Factor ◽

Fit Indices ◽

Confirmatory Factor Analyses

The Drinking Motives Questionnaire, previously postulated and documented to exhibit a measurement structure of four correlated factors (social, enhancement, conformity, and coping), is a widely administered assessment of reasons for consuming alcohol. In the current study ( N = 552), confirmatory factor analyses tested the plausibility of several theoretically relevant factor structures. Fit indices corroborated the original four-factor model, and also supported a higher-order factor model involving a superordinate motives factor that explicated four subordinate factors. A bifactor model that permitted items to double load on valence type (positive or negative reinforcement) and source type (external or internal) generated mixed results, suggesting that this 2 × 2 motivation paradigm was not entirely tenable. Optimal fit was obtained for a bifactor model depicting a general factor and four specific factors of motives. Latent factors derived from this structure exhibited criterion validity in predicting frequency and quantity of alcohol usage in a structural equation model. Findings are interpreted in the context of theoretical implications of the instrument, alternative factor structures of drinking motives, and assessment applications.

Download Full-text

Measuring Positive Mental Health in the Postpartum Period: The Bifactor Structure of the Mental Health Continuum–Short Form in Portuguese Women

Assessment ◽

10.1177/1073191120910247 ◽

2020 ◽

pp. 107319112091024 ◽

Cited By ~ 2

Author(s):

Fabiana Monteiro ◽

Ana Fonseca ◽

Marco Pereira ◽

Maria Cristina Canavarro

Keyword(s):

Mental Health ◽

Factor Model ◽

Short Form ◽

High Reliability ◽

Reliability And Validity ◽

Total Sample ◽

Bifactor Model ◽

Positive Mental Health ◽

General Factor ◽

Explained Common Variance

This study aimed to investigate the factor structure of the Mental Health Continuum–Short Form (MHC-SF) in the postpartum context using a single-factor model, a correlated three-factor model, and a bifactor model. The reliability and validity of the MHC-SF were also examined. The total sample consisted of 882 postpartum Portuguese women. Confirmatory factor analysis showed that the bifactor model yielded a significantly better fit to the data than the other models. The unidimensionality strength indices (explained common variance = .76, percentage of uncontaminated correlations = .69) and the ω H values supported the general factor of positive mental health, which accounted for 91.5% of the reliable variance in the total score. Additionally, the MHC-SF showed high reliability (ω = .96), and its total and subscale scores were significantly correlated with other measures related to mental health. The results of this study suggest a strong general factor of positive mental health and support the use of its total score in this context.

Download Full-text

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

10.31234/osf.io/urcmw ◽

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.

Download Full-text

Comparing Job Applicants to Non–Applicants Using An Item–Level Bifactor Model on the Hexaco Personality Inventory

European Journal of Personality ◽

10.1002/per.2120 ◽

2017 ◽

Vol 31 (6) ◽

pp. 669-684 ◽

Cited By ~ 18

Author(s):

Jeromy Anglim ◽

Gavin Morse ◽

Reinout E. De Vries ◽

Carolyn MacCann ◽

Andrew Marty ◽

...

Keyword(s):

Social Desirability ◽

Factor Model ◽

Higher Order ◽

Personality Inventory ◽

Bifactor Model ◽

General Factor ◽

Personality Psychology ◽

Job Applicant ◽

Item Level ◽

Improved Model

The present study evaluated the ability of item–level bifactor models (a) to provide an alternative explanation to current theories of higher order factors of personality and (b) to explain socially desirable responding in both job applicant and non–applicant contexts. Participants (46% male; mean age = 42 years, SD = 11) completed the 200–item HEXACO Personality Inventory–Revised either as part of a job application ( n = 1613) or as part of low–stakes research ( n = 1613). A comprehensive set of invariance tests were performed. Applicants scored higher than non–applicants on honesty–humility ( d = 0.86), extraversion ( d = 0.73), agreeableness ( d = 1.06), and conscientiousness ( d = 0.77). The bifactor model provided improved model fit relative to a standard correlated factor model, and loadings on the evaluative factor of the bifactor model were highly correlated with other indicators of item social desirability. The bifactor model explained approximately two–thirds of the differences between applicants and non–applicants. Results suggest that rather than being a higher order construct, the general factor of personality may be caused by an item–level evaluative process. Results highlight the importance of modelling data at the item–level. Implications for conceptualizing social desirability, higher order structures in personality, test development, and job applicant faking are discussed. Copyright © 2017 European Association of Personality Psychology

Download Full-text

Three recommendations based on a comparison of the reliability and validity of the predominant models used in research on the empirical structure of psychopathology

10.31219/osf.io/fhp2r ◽

2020 ◽

Author(s):

Miriam K. Forbes ◽

Ashley Lauren Greene ◽

Holly Levin-Aspenson ◽

Ashley L. Watts ◽

Michael Hallquist ◽

...

Keyword(s):

Factor Model ◽

Reliability And Validity ◽

The United States ◽

Higher Order ◽

Bifactor Model ◽

Model Fit ◽

General Factor ◽

Single Factor ◽

Over Time ◽

Single Factor Model

The present study compared the primary models used in research on the structure of psychopathology (i.e., correlated factor, higher-order, and bifactor models) in terms of structural validity (model fit and factor reliability), longitudinal measurement invariance, concurrent and prospective predictive validity in relation to important outcomes, and longitudinal consistency in individuals’ factor score profiles. Two simpler operationalizations of a general factor of psychopathology were also examined—a single-factor model and a count of diagnoses. Models were estimated based on structured clinical interview diagnoses in two longitudinal waves of nationally representative data from the United States (n = 43,093 and n = 34,653). Models that included narrower factors (fear, distress, and externalizing) were needed to capture the observed multidimensionality of the data. In the correlated factor and higher-order models these narrower factors were reliable, largely invariant over time, had consistent associations with indicators of adaptive functioning, and had moderate stability within individuals over time. By contrast, the fear and distress specific factors in the bifactor model did not show good reliability or validity throughout the analyses. Notably, the general factor of psychopathology (p-factor) performed similarly well across tests of reliability and validity regardless of whether the higher-order or bifactor model was used; the simplest (single-factor) model was also comparable across most tests, with the exception of model fit. Given the limitations of categorical diagnoses, it will be important to repeat these analyses using dimensional measures. We conclude that when aiming to understand the structure and correlates of psychopathology it is important to: 1) look beyond model fit indices to choose between different models; 2) examine the reliability of latent variables directly; and 3) be cautious when isolating and interpreting the unique effects of specific psychopathology factors, regardless of which model is used.

Download Full-text

Relational Uncertainty Within Relational Turbulence Theory: The Bifactor Exploratory Structural Equation Model

Journal of Communication ◽

10.1093/joc/jqab009 ◽

2021 ◽

Author(s):

Alan K Goodboy ◽

San Bolkan ◽

, Kellie Brisini ◽

Denise Haunani Solomon

Keyword(s):

Structural Equation Model ◽

Structural Equation ◽

Factor Model ◽

Equation Model ◽

Turbulence Theory ◽

General Factor ◽

Relational Uncertainty ◽

Measurement Models ◽

Relational Turbulence ◽

Confirmatory Factor

Abstract Relational uncertainty consists of self, partner, and relationship uncertainty, which are core parameters in relational turbulence theory (RTT). Advances in latent variable modeling allow researchers to examine the multidimensional construct as a bifactor model, including a general factor of relational uncertainty and residualized factors of self, partner, and relationship uncertainty. This advance is theoretically consequential because RTT maintains the importance of distinctions among the facets of relational uncertainty, even while empirical evidence demonstrates considerable overlap among them. In two data sets (college sample, N = 513; married sample, N = 354), competing measurement models specified Relational Uncertainty Scale items as a unidimensional confirmatory factor model, 3-factor independent clusters confirmatory factor model, 3-factor exploratory structural equation model (ESEM), bifactor confirmatory factor model, and bifactor-ESEM. The bifactor-ESEM provided the best fit in both samples, and bifactor statistics clarified how variance from the scale is partitioned to yield essential unidimensionality for the general factor of relational uncertainty, controlling for its residualized factors. Tests of a latent predictive model using a bifactor specification of relational uncertainty were consistent with RTT. Implications for testing communication theory using bifactor-ESEM are discussed in light of these findings.

Download Full-text

Comparing Job Applicants to Non-Applicants Using an Item-Level Bifactor Model on the HEXACO Personality Inventory

10.31219/osf.io/8j54g ◽

2017 ◽

Author(s):

Jeromy Anglim ◽

Gavin Morse ◽

Reinout E. de Vries ◽

Carolyn MacCann ◽

Andrew Marty

Keyword(s):

Social Desirability ◽

Factor Model ◽

Higher Order ◽

Personality Inventory ◽

Bifactor Model ◽

Order Structure ◽

General Factor ◽

Job Applicant ◽

Item Level ◽

Improved Model

The present study evaluated the ability of item-level bifactor models (a) to provide an alternative explanation to current theories of higher-order factors of personality, and (b) to explain socially desirable responding in both job applicant and non-applicant contexts. Participants (46% male; mean age=42 years, SD=11) completed the 200- item HEXACO Personality Inventory-Revised (HEXACO PI-R) either as part of a job application (n = 1613) or as part of low-stakes research (n = 1613). A comprehensive set of invariance test were performed. Applicants scored higher than non-applicants on honesty- humility (d = 0.86), extraversion (d = 0.73), agreeableness (d = 1.06), and conscientiousness (d = 0.77). The bifactor model provided improved model fit relative to a standard correlated factor model, and loadings on the evaluative factor of the bifactor model were highly correlated with other indicators of item social desirability. The bifactor model explained approximately two-thirds of the differences between applicants and non- applicants. Results suggest that rather than being a higher-order construct, the general factor of personality may be caused by an item- level evaluative process. Results highlight the importance of modelling data at the item-level. Implications for conceptualizing social desirability, higher-order structure in personality, test development, and job applicant faking are discussed.

Download Full-text