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 A Multilevel Multidimensional Finite Mixture Item Response Model to Cluster Respondents and Countries

2020 ◽  
pp. 1-12
Author(s):  
Martin Kanovský ◽  
Júlia Halamová ◽  
David C. Zuroff ◽  
Nicholas A. Troop ◽  
Paul Gilbert ◽  
...  
Keyword(s):  
Item Response ◽  
Latent Class ◽  
Latent Trait ◽  
Three Dimensional ◽  
Finite Mixture ◽  
Response Model ◽  
Item Response Model ◽  
Irt Models ◽  
Dimensional Measure ◽  
Different Levels

Abstract. The aim of this study was to test the multilevel multidimensional finite mixture item response model of the Forms of Self-Criticising/Attacking and Self-Reassuring Scale (FSCRS) to cluster respondents and countries from 13 samples ( N = 7,714) and from 12 countries. The practical goal was to learn how many discrete classes there are on the level of individuals (i.e., how many cut-offs are to be used) and countries (i.e., the magnitude of similarities and dissimilarities among them). We employed the multilevel multidimensional finite mixture approach which is based on an extended class of multidimensional latent class Item Response Theory (IRT) models. Individuals and countries are partitioned into discrete latent classes with different levels of self-criticism and self-reassurance, taking into account at the same time the multidimensional structure of the construct. This approach was applied to the analysis of the relationships between observed characteristics and latent trait at different levels (individuals and countries), and across different dimensions using the three-dimensional measure of the FSCRS. Results showed that respondents’ scores were dependent on unobserved (latent class) individual and country membership, the multidimensional structure of the instrument, and justified the use of a multilevel multidimensional finite mixture item response model in the comparative psychological assessment of individuals and countries. Latent class analysis of the FSCRS showed that individual participants and countries could be divided into discrete classes. Along with the previous findings that the FSCRS is psychometrically robust we can recommend using the FSCRS for measuring self-criticism.

scholarly journals Quantifying Liver Stagnation Spleen Deficiency Pattern for Diarrhea Predominate Irritable Bowel Syndromes Using Multidimensional Analysis Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Zhongyu Huang ◽  
Zhengkun Hou ◽  
Xianhua Liu ◽  
Fengbin Liu ◽  
Yuefeng Wu
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Item Response ◽  
Multidimensional Analysis ◽  
Rational Approach ◽  
Response Model ◽  
Item Response Model ◽  
Multidimensional Item Response ◽  
Spleen Deficiency ◽  
Analysis Methods

Objective. This study aims to offer a new approach for quantifying severity of traditional Chinese medicine pattern with multidimensional analysis methods. Methods. A scale and theoretical models were constructed based on the definition of liver stagnation spleen deficiency pattern. Clinical data of 344 IBS-D patients from a cross-sectional study was used for feature validation of the model. Confirmatory factor analysis was used for evaluating the models. Also, multidimensional item response model was used for assessing multidimensional psychometric properties of the scale. Results. Detecting two latent traits, the Cronbach’s alpha of the 9-item scale was 0.745. Multidimensional model was evaluated with significant goodness of fit indices while the unidimensional model was rejected. The multidimensional item response model showed all the items had adequate discrimination. Parameters presented adequate explanation regarding mental syndromes having high factor loading on the liver stagnation factor and abdominal discomfort syndromes highly related to the spleen deficiency factor. Test information function showed that scale demonstrated the highest discrimination power among patients with moderate to high level of severity. Conclusions. The application of the multidimensional analysis methods on the basis of theoretical model construction provides a useful and rational approach for quantifying the severity of traditional Chinese medicine patterns.

DSM criteria for major depression: evaluating symptom patterns using latent-trait item response models

2004 ◽  
Vol 35 (4) ◽  
pp. 475-487 ◽  
Author(s):  
STEVEN H. AGGEN ◽  
MICHAEL C. NEALE ◽  
KENNETH S. KENDLER
Keyword(s):  
Major Depression ◽  
Item Response ◽  
Latent Trait ◽  
Risk Measurement ◽  
Measurement Properties ◽  
Model Parameters ◽  
Response Model ◽  
Item Response Model ◽  
Response Models ◽  
Item Response Models

Background. Expert committees of clinicians have chosen diagnostic criteria for psychiatric disorders with little guidance from measurement theory or modern psychometric methods. The DSM-III-R criteria for major depression (MD) are examined to determine the degree to which latent trait item response models can extract additional useful information.Method. The dimensionality and measurement properties of the 9 DSM-III-R criteria plus duration are evaluated using dichotomous factor analysis and the Rasch and 2 parameter logistic item response models. Quantitative liability scales are compared with a binary DSM-III-R diagnostic algorithm variable to determine the ramifications of using each approach.Results. Factor and item response model results indicated the 10 MD criteria defined a reasonably coherent unidimensional scale of liability. However, person risk measurement was not optimal. Criteria thresholds were unevenly spaced leaving scale regions poorly measured. Criteria varied in discriminating levels of risk. Compared to a binary MD diagnosis, item response model (IRM) liability scales performed far better in (i) elucidating the relationship between MD symptoms and liability, (ii) predicting the personality trait of neuroticism and future depressive episodes and (iii) more precisely estimating heritability parameters.Conclusions. Criteria for MD largely defined a single dimension of disease liability although the quality of person risk measurement was less clear. The quantitative item response scales were statistically superior in predicting relevant outcomes and estimating twin model parameters. Item response models that treat symptoms as ordered indicators of risk rather than as counts towards a diagnostic threshold more fully exploit the information available in symptom endorsement data patterns.

scholarly journals About the Equivalence of the Latent D-Scoring Model and the Two-Parameter Logistic Item Response Model

2021 ◽  
Author(s):  
Alexander Robitzsch
Keyword(s):  
Item Response ◽  
Logistic Model ◽  
Latent Trait ◽  
Response Model ◽  
Item Response Model ◽  
Numerical Illustration ◽  
Scoring Model ◽  
Estimation Techniques ◽  
Pisa Data ◽  
Two Parameter

This article shows that the recently proposed latent D-scoring model of Dimitrov is statistically equivalent to the two-parameter logistic item response model. An analytical derivation and a numerical illustration are employed for demonstrating this finding. Hence, estimation techniques for the two-parameter logistic model can be used for estimating the latent D-scoring model. In an empirical example using PISA data, differences of country ranks are investigated when using different metrics for the latent trait. In the example, the choice of the latent trait metric matters for the ranking of countries. Finally, it is argued that an item response model with bounded latent trait values like the latent D-scoring model might have advantages for reporting results in terms of interpretation.

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 Parameter Recovery in Multidimensional Item Response Theory Models Under Complexity and Nonnormality

2017 ◽  
Vol 41 (7) ◽  
pp. 530-544 ◽  
Author(s):  
Dubravka Svetina ◽  
Arturo Valdivia ◽  
Stephanie Underhill ◽  
Shenghai Dai ◽  
Xiaolin Wang
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Computerized Adaptive Testing ◽  
Latent Trait ◽  
Item Parameter ◽  
Multidimensional Item Response ◽  
Response Theory ◽  
Parameter Recovery ◽  
Assessment Development ◽  
Latent Traits

Information about the psychometric properties of items can be highly useful in assessment development, for example, in item response theory (IRT) applications and computerized adaptive testing. Although literature on parameter recovery in unidimensional IRT abounds, less is known about parameter recovery in multidimensional IRT (MIRT), notably when tests exhibit complex structures or when latent traits are nonnormal. The current simulation study focuses on investigation of the effects of complex item structures and the shape of examinees’ latent trait distributions on item parameter recovery in compensatory MIRT models for dichotomous items. Outcome variables included bias and root mean square error. Results indicated that when latent traits were skewed, item parameter recovery was generally adversely impacted. In addition, the presence of complexity contributed to decreases in the precision of parameter recovery, particularly for discrimination parameters along one dimension when at least one latent trait was generated as skewed.

Sign in / Sign up

Export Citation Format

Share Document