A Bifactor Model of Trait Trust, Distrust, and Suspicion

2021 ◽  
Author(s):  
Gene M. Alarcon ◽  
August Capiola ◽  
Sarah A. Jessup ◽  
Tyler J. Ryan ◽  
Anthony M. Gibson
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
General Factor ◽  
Best Fitting ◽  
Competing Models ◽  
Latent Traits ◽  
Relationship Of ◽  
Dispositional Trust ◽  
The Relationship ◽  
Fitting Model

Abstract. We explored competing models using bifactor item response theory (IRT) analyses to determine the relationship between trait measures of trust, distrust, and suspicion. The model with a general factor for all three scales fits the data best. We explored the relationship of the emergent general factor by correlating it with two latent traits: Agreeableness and the Trust facet of Agreeableness. The exploratory findings showed evidence that the general factor from the best-fitting model was practically identical to the Trust facet of Agreeableness. We concluded that trait trust, distrust, and suspicion reside on a continuum represented by the general factor, which is dispositional trust.

Revisiting the Structure of the Short Dark Triad

Assessment ◽  
2017 ◽  
Vol 26 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Björn N. Persson ◽  
Petri J. Kajonius ◽  
Danilo Garcia
Keyword(s):  
Dark Triad ◽  
Specific Factor ◽  
General Factor ◽  
Confirmatory Factor Analyses ◽  
Factor Loadings ◽  
The Past ◽  
S Factor ◽  
Confirmatory Factor ◽  
Best Fitting ◽  
Fitting Model

In the past decade, extensive interest has been directed toward the Dark Triad (i.e., Machiavellianism, narcissism, and psychopathy), popularly assessed by the Short Dark Triad (SD3). Nevertheless, relatively little research has been conducted on the SD3’s factor structure. We investigated the SD3’s psychometric properties in three studies with three independent samples, using exploratory and confirmatory factor analyses ( N1 = 1,487; N2 = 17,740; N3 = 496). In all three studies, Machiavellianism and psychopathy items displayed large general factor loadings, and narcissism larger specific factor loadings. In subsequent studies, two- and three-factor models fitted the data similarly, with the best fitting model being a bifactor model with items from Machiavellianism and psychopathy modelled as one specific factor, and narcissism as a second specific factor. On this basis, we suggest that the SD3 does not seem to capture the different mental processes theorized to underlie the similar behaviors generated by Machiavellianism and psychopathy. Additionally, we recommend the use of a single SD3 composite score, and not subscale scores, as subscales contain small amounts of reliable variance beyond the general factor.

scholarly journals Item Response Theory Analyses of Barkley’s Adult Attention-Deficit/Hyperactivity Disorder Rating Scales

2020 ◽  
Vol 35 (7) ◽  
pp. 1094-1108
Author(s):  
Morgan E Nitta ◽  
Brooke E Magnus ◽  
Paul S Marshall ◽  
James B Hoelzle
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Adult Adhd ◽  
Rating Scales ◽  
Rating Scale ◽  
Self Report ◽  
Clinical Population ◽  
Response Theory ◽  
Item Level ◽  
Latent Traits

Abstract There are many challenges associated with assessment and diagnosis of ADHD in adulthood. Utilizing the graded response model (GRM) from item response theory (IRT), a comprehensive item-level analysis of adult ADHD rating scales in a clinical population was conducted with Barkley's Adult ADHD Rating Scale-IV, Self-Report of Current Symptoms (CSS), a self-report diagnostic checklist and a similar self-report measure quantifying retrospective report of childhood symptoms, Barkley's Adult ADHD Rating Scale-IV, Self-Report of Childhood Symptoms (BAARS-C). Differences in item functioning were also considered after identifying and excluding individuals with suspect effort. Items associated with symptoms of inattention (IA) and hyperactivity/impulsivity (H/I) are endorsed differently across the lifespan, and these data suggest that they vary in their relationship to the theoretical constructs of IA and H/I. Screening for sufficient effort did not meaningfully change item level functioning. The application IRT to direct item-to-symptom measures allows for a unique psychometric assessment of how the current DSM-5 symptoms represent latent traits of IA and H/I. Meeting a symptom threshold of five or more symptoms may be misleading. Closer attention given to specific symptoms in the context of the clinical interview and reported difficulties across domains may lead to more informed diagnosis.

Bayesian Nonparametric Monotone Regression of Dynamic Latent Traits in Item Response Theory Models

2019 ◽  
Vol 45 (3) ◽  
pp. 274-296
Author(s):  
Yang Liu ◽  
Xiaojing Wang
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Nonlinear Effects ◽  
Real Data ◽  
Monotone Regression ◽  
Response Theory ◽  
Growth Trend ◽  
Nonparametric Prior ◽  
Latent Traits ◽  
Item Response Theory Models

Parametric methods, such as autoregressive models or latent growth modeling, are usually inflexible to model the dependence and nonlinear effects among the changes of latent traits whenever the time gap is irregular and the recorded time points are individually varying. Often in practice, the growth trend of latent traits is subject to certain monotone and smooth conditions. To incorporate such conditions and to alleviate the strong parametric assumption on regressing latent trajectories, a flexible nonparametric prior has been introduced to model the dynamic changes of latent traits for item response theory models over the study period. Suitable Bayesian computation schemes are developed for such analysis of the longitudinal and dichotomous item responses. Simulation studies and a real data example from educational testing have been used to illustrate our proposed methods.

scholarly journals Spectral Indices: In-Season Dry Mass and Yield Relationship of Flue-Cured Tobacco under Different Planting Dates and Fertiliser Levels

ISRN Agronomy ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Ezekia Svotwa ◽  
J. Anxious Masuka ◽  
Barbara Maasdorp ◽  
Amon Murwira
Keyword(s):  
Spectral Characteristics ◽  
Dry Mass ◽  
Coefficient Of Determination ◽  
Planting Dates ◽  
Randomized Design ◽  
Crop Development ◽  
Best Fitting ◽  
Whole Plants ◽  
Relationship Of ◽  
The Relationship

This experiment investigated the relationship between tobacco canopy spectral characteristics and tobacco biomass. A completely randomized design, with plantings on the 15th of September, October, November, and December, each with 9 variety × fertiliser management treatments, was used. Starting from 6 weeks after planting, reflectance measurements were taken from one row, using a multispectral radiometer. Individual plants from the other 3 rows were also measured, and the above ground whole plants were harvested and dried for reflectance/dry mass regression analysis. The central row was harvested, cured, and weighed. Both the maximum NDVI and mass at untying declined with later planting and so was the mass-NDVI coefficient of determination. The best fitting curves for the yield-NDVI correlations were quadratic. September reflectance values from the October crop reflectance were statistically similar (P>0.05), while those for the November and the December crops were significantly different (P<0.05) from the former two. Mass at untying and NDVI showed a quadratic relationship in all the three tested varieties. The optimum stage for collecting spectral data for tobacco yield estimation was the 8–12 weeks after planting. The results could be useful in accurate monitoring of crop development patterns for yield forecasting purposes.

scholarly journals A comprehensive analysis of the factor structure of the Beck Depression Inventory-II in a sample of outpatients with adjustment disorder and depressive episode

2017 ◽  
Vol 35 (1) ◽  
pp. 53-61 ◽  
Author(s):  
E. McElroy ◽  
P. Casey ◽  
G. Adamson ◽  
P. Filippopoulos ◽  
M. Shevlin
Keyword(s):  
Beck Depression Inventory ◽  
Factor Structure ◽  
Depressive Episode ◽  
Adjustment Disorder ◽  
Scale Scores ◽  
Best Fitting ◽  
Competing Models ◽  
Multidimensional Models ◽  
Unidimensional Scale ◽  
Fitting Model

ObjectivesDespite being commonly used in research and clinical practice, the evidence regarding the factor structure of the Beck Depression Inventory-II (BDI-II) remains equivocal and this has implications on how the scale scores should be aggregated. Researchers continue to debate whether the BDI-II is best viewed as a unidimensional scale, or whether specific subscales have utility. The present study sought to test a comprehensive range of competing factor analytic models of the BDI-II, including traditional non-hierarchical multidimensional models and confirmatory bifactor models.MethodParticipants (n=370) were clinical outpatients diagnosed with either depressive episode or adjustment disorder. Confirmatory factor analysis and confirmatory bifactor modelling were used to test 15 competing models. The unidimensionality of the best fitting model was assessed using three strength indices (explained common variance, percentage of uncontaminated correlations and ω hierarchical).ResultsOverall, bifactor solutions provided superior fit than both unidimensional and non-hierarchical multidimensional models. The best fitting model consisted of a general depression factor and three specific factors: cognitive, somatic and affective. High factor loadings and strength indices for the general depression factor supported the view that the BDI-II measures a single latent construct.ConclusionsThe BDI-II should primarily be viewed as a unidimensional scale, and should be scored as such. Although it is not recommended that scores on individual subscales are used in isolation, they may prove useful in clinical assessment and/or treatment planning if used in conjunction with total scores.

scholarly journals Assessment, Testing and Comparison of Statistical Models using R

2021 ◽  
Author(s):  
Daniel Lüdecke ◽  
Mattan S. Ben-Shachar ◽  
Indrajeet Patil ◽  
Philip Waggoner ◽  
Dominique Makowski
Keyword(s):  
Regression Models ◽  
Model Fit ◽  
Fit Indices ◽  
Diagnostic Plots ◽  
Fit Statistics ◽  
Best Fitting ◽  
Competing Models ◽  
Crucial Part ◽  
Fitting Model ◽  
Consistent Approach

A crucial part of statistical analysis is evaluating a model's quality and fit, or performance. During analysis, especially with regression models, investigating the fit of models to data also often involves selecting the best fitting model amongst many competing models. Upon investigation, fit indices should also be reported both visually and numerically to bring readers in on the investigative effort. While functions to build and produce diagnostic plots or to compute fit statistics exist, these are located across many packages, which results in a lack of a unique and consistent approach to assess the performance of many types of models. The result is a difficult-to-navigate, unorganized ecosystem of individual packages with different syntax, making it onerous for researchers to locate and use fit indices relevant for their unique purposes. The performance package in R fills this gap by offering researchers a suite of intuitive functions with consistent syntax for computing, building, and presenting regression model fit statistics and visualizations.

scholarly journals RESIDUAL ANALYSIS IN RASCH POISSON COUNTS MODELS

2021 ◽  
Vol 39 (1) ◽  
pp. 206
Author(s):  
Naiara Caroline Aparecido dos SANTOS ◽  
Jorge Luiz BAZÁN
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Bayesian Approach ◽  
Residual Analysis ◽  
Model Parameters ◽  
Irt Model ◽  
Latent Traits ◽  
Over Time ◽  
Dichotomous Item ◽  
Poisson Counts

A Rasch Poisson counts (RPC) model is described to identify individual latent traits and facilities of the items of tests that model the error (or success) count in several tasks over time, instead of modeling the correct responses to items in a test as in the dichotomous item response theory (IRT) model. These types of tests can be more informative than traditional tests. To estimate the model parameters, we consider a Bayesian approach using the integrated nested Laplace approximation (INLA). We develop residual analysis to assess model t by introducing randomized quantile residuals for items. The data used to illustrate the method comes from 228 people who took a selective attention test. The test has 20 blocks (items), with a time limit of 15 seconds for each block. The results of the residual analysis of the RPC were promising and indicated that the studied attention data are not well tted by the RPC model.

Estimating Skewed Latent Traits Within Item-Response Theory

2006 ◽  
Author(s):  
Daniel A. Sass ◽  
Cindy M. Walker ◽  
Thomas A. Schmitt
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Response Theory ◽  
Latent Traits

scholarly journals An Introduction to Item Response Theory and Rasch Analysis: Application Using the Eating Assessment Tool (EAT-10)

2017 ◽  
Vol 19 (1) ◽  
pp. 91-102 ◽  
Author(s):  
Jacob Kean ◽  
Erica F. Bisson ◽  
Darrel S. Brodke ◽  
Joshua Biber ◽  
Paul H. Gross
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Rasch Analysis ◽  
Assessment Tool ◽  
Oropharyngeal Dysphagia ◽  
Response Theory ◽  
Worked Example ◽  
Health Related ◽  
Analysis Application ◽  
Latent Traits

Item response theory has its origins in educational measurement and is now commonly applied in health-related measurement of latent traits, such as function and symptoms. This application is due, in large part, to gains in the precision of measurement attributable to item response theory and corresponding decreases in response burden, study costs, and study duration. The purpose of this paper is twofold: introduce basic concepts of item response theory and demonstrate this analytic approach in a worked example, a Rasch model (1PL) analysis of the Eating Assessment Tool (EAT-10), a commonly used measure for oropharyngeal dysphagia. The results of the analysis were largely concordant with previous studies of the EAT-10 and illustrate for brain impairment clinicians and researchers how IRT analysis can yield greater precision of measurement.

scholarly journals Multidimensional Computerized Adaptive Testing Using Non-Compensatory Item Response Theory Models

2018 ◽  
Vol 43 (6) ◽  
pp. 464-480
Author(s):  
Chia-Ling Hsu ◽  
Wen-Chung Wang
Keyword(s):  
Item Response Theory ◽  
Mutual Information ◽  
Item Response ◽  
Fisher Information ◽  
Computerized Adaptive Testing ◽  
Test Reliability ◽  
Information Method ◽  
Leibler Information ◽  
Termination Criterion ◽  
Latent Traits

Current use of multidimensional computerized adaptive testing (MCAT) has been developed in conjunction with compensatory multidimensional item response theory (MIRT) models rather than with non-compensatory ones. In recognition of the usefulness of MCAT and the complications associated with non-compensatory data, this study aimed to develop MCAT algorithms using non-compensatory MIRT models and to evaluate their performance. For the purpose of the study, three item selection methods were adapted and compared, namely, the Fisher information method, the mutual information method, and the Kullback–Leibler information method. The results of a series of simulations showed that the Fisher information and mutual information methods performed similarly, and both outperformed the Kullback–Leibler information method. In addition, it was found that the more stringent the termination criterion and the higher the correlation between the latent traits, the higher the resulting measurement precision and test reliability. Test reliability was very similar across the dimensions, regardless of the correlation between the latent traits and termination criterion. On average, the difficulties of the administered items were found to be at a lower level than the examinees’ abilities, which shed light on item bank construction for non-compensatory items.

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