Does an Overall Job Crafting Dimension Exist?

2021 ◽  
pp. 1-4
Author(s):  
Leonidas A. Zampetakis
Keyword(s):  
Item Response ◽  
Multidimensional Item Response Theory ◽  
Job Crafting ◽  
General Level ◽  
Multidimensional Item Response ◽  
Greek Version ◽  
Multidimensional Construct ◽  
Irt Model ◽  
Irt Models ◽  
Correlated Factors

Abstract. Job crafting is a multidimensional construct that can be conceptualized both at the general level and at the daily level. Several researchers have used aggregated scores across the dimensions of job crafting, to represent an overall job crafting construct. The purpose of the research presented herein is to investigate the factor structure of the general and daily versions of the job crafting scale developed by Petrou et al., (2012) (PJCS), using parametric multidimensional Item Response Theory (IRT) models. A sample of 675 employees working on different occupational sectors completed the Greek version of the scales. Results are in line with theoretical underpinnings and suggest that, although a bifactor IRT model offers an adequate fit, a correlated factors IRT model is more appropriate for both versions of the PJCS. Results caution against using aggregated scores across the dimensions of PJCS for both the general and daily versions.

scholarly journals A Gibbs Sampler for the Multidimensional Item Response Model

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Yanyan Sheng ◽  
Todd C. Headrick
Keyword(s):  
Item Response ◽  
Latent Trait ◽  
Multidimensional Item Response Theory ◽  
Relative Importance ◽  
Response Model ◽  
Item Response Model ◽  
Mcmc Algorithm ◽  
Multidimensional Item Response ◽  
Irt Models ◽  
Latent Traits

Current procedures for estimating compensatory multidimensional item response theory (MIRT) models using Markov chain Monte Carlo (MCMC) techniques are inadequate in that they do not directly model the interrelationship between latent traits. This limits the implementation of the model in various applications and further prevents the development of other types of IRT models that offer advantages not realized in existing models. In view of this, an MCMC algorithm is proposed for MIRT models so that the actual latent structure is directly modeled. It is demonstrated that the algorithm performs well in modeling parameters as well as intertrait correlations and that the MIRT model can be used to explore the relative importance of a latent trait in answering each test item.

scholarly journals Software Review of flexMIRT Version 3.5

2017 ◽  
Vol 42 (3) ◽  
pp. 240-255 ◽  
Author(s):  
S. Austin Cavanaugh ◽  
Ciji A. Heiser ◽  
Karen B. Hoeve ◽  
Eren Halil Ozberk ◽  
Elizabeth A. Patton ◽  
...  
Keyword(s):  
Item Response ◽  
Software Package ◽  
Multidimensional Item Response Theory ◽  
Simulation Studies ◽  
Classification Models ◽  
Multidimensional Item Response ◽  
Diagnostic Classification Models ◽  
Irt Models ◽  
Good Product ◽  
Almost All

flexMIRT is a versatile program for unidimensional and multidimensional item response theory (IRT) calibrations, scoring analyses, and model-based simulations. With an adaptable syntax that allows for various combinations of model specifications, estimation constraints, and estimation choices, flexMIRT can handle almost all of the most popular IRT models for dichotomous and polytomous data. The software package also supports diagnostic classification models and multigroup and multilevel analyses. This review evaluates the software from a user’s perspective as well as some of its calibration, scoring, and simulation capabilities. Two simulation studies are included: one demonstrates some basic simulation capabilities and the other provides some direct comparisons with BILOG-MG. The review suggests that flexMIRT is a very good product that is only likely to get better as new features and suggestions for improvement are implemented.

A Bifactor Multidimensional Item Response Theory Model for Differential Item Functioning Analysis on Testlet-Based Items

2011 ◽  
Vol 35 (8) ◽  
pp. 604-622 ◽  
Author(s):  
Hirotaka Fukuhara ◽  
Akihito Kamata
Keyword(s):  
Item Response Theory ◽  
Differential Item Functioning ◽  
Item Response ◽  
Estimation Method ◽  
Multidimensional Item Response Theory ◽  
Multidimensional Item Response ◽  
Response Theory ◽  
Data Set ◽  
Detection Rates ◽  
Item Functioning

A differential item functioning (DIF) detection method for testlet-based data was proposed and evaluated in this study. The proposed DIF model is an extension of a bifactor multidimensional item response theory (MIRT) model for testlets. Unlike traditional item response theory (IRT) DIF models, the proposed model takes testlet effects into account, thus estimating DIF magnitude appropriately when a test is composed of testlets. A fully Bayesian estimation method was adopted for parameter estimation. The recovery of parameters was evaluated for the proposed DIF model. Simulation results revealed that the proposed bifactor MIRT DIF model produced better estimates of DIF magnitude and higher DIF detection rates than the traditional IRT DIF model for all simulation conditions. A real data analysis was also conducted by applying the proposed DIF model to a statewide reading assessment data set.

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