Item selection via Bayesian IRT models

2014 ◽  
Vol 34 (3) ◽  
pp. 487-503 ◽  
Author(s):  
Serena Arima
Keyword(s):  
Item Selection ◽  
Irt Models ◽  
Bayesian Irt

Bayesian IRT Models (MML Estimation)

2016 ◽  
pp. 261-282 ◽  
Author(s):  
Margaret Wu ◽  
Hak Ping Tam ◽  
Tsung-Hau Jen
Keyword(s):  
Irt Models ◽  
Bayesian Irt

scholarly journals Bayesian Irt Models Incorporating General and Specific Abilities

2009 ◽  
Vol 36 (1) ◽  
pp. 27-48 ◽  
Author(s):  
Yanyan Sheng ◽  
Christopher K. Wikle
Keyword(s):  
Irt Models ◽  
Bayesian Irt

Following the Ants

2017 ◽  
Vol 33 (6) ◽  
pp. 409-421 ◽  
Author(s):  
Anne B. Janssen ◽  
Martin Schultze ◽  
Adrian Grötsch
Keyword(s):  
Short Form ◽  
Supervisor Support ◽  
Proactive Personality ◽  
Work Behavior ◽  
Model Fit ◽  
Item Selection ◽  
Short Version ◽  
Innovative Work Behavior ◽  
Innovative Work ◽  
Two Measures

Abstract. Employees’ innovative work is a facet of proactive work behavior that is of increasing interest to industrial and organizational psychologists. As proactive personality and supervisor support are key predictors of innovative work behavior, reliable, and valid employee ratings of these two constructs are crucial for organizations’ planning of personnel development measures. However, the time for assessments is often limited. The present study therefore aimed at constructing reliable short scales of two measures of proactive personality and supervisor support. For this purpose, we compared an innovative approach of item selection, namely Ant Colony Optimization (ACO; Leite, Huang, & Marcoulides, 2008 ) and classical item selection procedures. For proactive personality, the two item selection approaches provided similar results. Both five-item short forms showed a satisfactory reliability and a small, however negligible loss of criterion validity. For a two-dimensional supervisor support scale, ACO found a reliable and valid short form. Psychometric properties of the short version were in accordance with those of the parent form. A manual supervisor support short form revealed a rather poor model fit and a serious loss of validity. We discuss benefits and shortcomings of ACO compared to classical item selection approaches and recommendations for the application of ACO.

New Evidence on the Construct Validity of the ASI-3 and the Dimensional Conceptualization of Trait Anxiety Sensitivity From IRT Modeling

2017 ◽  
Vol 33 (3) ◽  
pp. 181-189 ◽  
Author(s):  
Christoph J. Kemper ◽  
Michael Hock
Keyword(s):  
Construct Validity ◽  
Anxiety Sensitivity ◽  
Latent Class ◽  
Dimensional Structure ◽  
Sensitivity Index ◽  
Response Scale ◽  
Established Risk Factor ◽  
Irt Models ◽  
Level Data ◽  
Extreme Response

Abstract. Anxiety Sensitivity (AS) denotes the tendency to fear anxiety-related sensations. Trait AS is an established risk factor for anxiety pathology. The Anxiety Sensitivity Index-3 (ASI-3) is a widely used measure of AS and its three most robust dimensions with well-established construct validity. At present, the dimensional conceptualization of AS, and thus, the construct validity of the ASI-3 is challenged. A latent class structure with two distinct and qualitatively different forms, an adaptive form (normative AS) and a maladaptive form (AS taxon, predisposing for anxiety pathology) was postulated. Item Response Theory (IRT) models were applied to item-level data of the ASI-3 in an attempt to replicate previous findings in a large nonclinical sample (N = 2,603) and to examine possible interpretations for the latent discontinuity observed. Two latent classes with a pattern of distinct responses to ASI-3 items were found. However, classes were indicative of participant’s differential use of the response scale (midpoint and extreme response style) rather than differing in AS content (adaptive and maladaptive AS forms). A dimensional structure of AS and the construct validity of the ASI-3 was supported.

Identifying Measurement Invariant Item Sets in Cross-Cultural Settings Using an Automated Item Selection Procedure

Methodology ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 177-188 ◽  
Author(s):  
Martin Schultze ◽  
Michael Eid
Keyword(s):  
Measurement Invariance ◽  
Optimal Solution ◽  
Selection Procedure ◽  
Cross Cultural ◽  
Item Selection ◽  
Ant System ◽  
Item Quality ◽  
Ant System Algorithm ◽  
Selection For ◽  
Selection Of

Abstract. In the construction of scales intended for the use in cross-cultural studies, the selection of items needs to be guided not only by traditional criteria of item quality, but has to take information about the measurement invariance of the scale into account. We present an approach to automated item selection which depicts the process as a combinatorial optimization problem and aims at finding a scale which fulfils predefined target criteria – such as measurement invariance across cultures. The search for an optimal solution is performed using an adaptation of the [Formula: see text] Ant System algorithm. The approach is illustrated using an application to item selection for a personality scale assuming measurement invariance across multiple countries.

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