francesco bartolucci

Dimensionality of the latent structure and item selection via

latent class multidimensional IRT models

FRANCESCO BARTOLUCCI

Outline

• Introduction• Data set• Statistic Methodology• Strategy of Analysis• Application to the Dataset• Conclusion

Introduction

• Dimensionality issue of health conditions: Subjects show a degenerative health status to a specific pathology, but have overall good health status.

• Assume the population is divided into a certain number of latent classes.

• Address the issue of item selection.

The ULISSE Dataset

• A network on health care services for older people

• Longitudinal survey• Filled out by the nursing assistant• Since 2004 through the repeated

administration every 6months• 79 items– 1: presence of a specific health problem– 0: its absence

Model

• Latent class

• Multidimensional 2PL

– Constraint:

Latent class model

Model

• log-likelihood

• number of free parameter– LC:

– 2PL:

– Difference between them:

Estimate

• Expectation-Maximization (EM)• E-step: conditional expected value

• M-step: maximizing the log-likelihood where is replaced.

Strategy of Analysis

• Selection of the number of latent class• Validation of the multidimensional 2PL model• Assessment of the number of dimensions• Reduction of the number of items

Step 1. Selection of the number of latent classes

• BIC: – LC or 2PL

• #par: penalization term– Number of classes increasing, #par rising

•

• AIC tends to overestimate the number of classes

Step 2. Validation of the Multidimensional 2PL model

• Compare the LC and 2PL model by BIC.• For validate the structure of the

questionnaire.• LC, which is completely unconstrained, allows

each item to measure a separate dimension.• If 2PL proves preferable in BIC, the evidence

of item structure is found.

Step 3. Assessment of the Number of Dimensions

• Chi-square, df=k-2

– is the probability under the s dimensions– is the probability under the s-1 dimensions

• When sample size is large, the criterion is too severe, it may lead to overestimating the number of dimensions.

• Adopt BIC

Step 4. Reduction of the Number of Items

• Discrimination index between 0 and 1 (constraint)

• Minimum number of items is 5 retained for each dimension.

• However, indices are not comparable across dimensions, so latent trait standardized for each dimension is required.


•

• Standardized ability: • Transform the items parameter:

• normalized Garma:


•

• Item reduction changes the classification of the subjects.

• – Posterior on the full set items, and then on the

subset.– Use the same parameter obtained with the initial

set.

Application to the ULISSE Dataset

• Selection of the Number of Latent Classes


• Validation of the 2PL Model– 2PL: BIC=68,653.32 <– LC: BIC=69845.39

• 2PL proves preferable• The structure is also validated, and the

assumption (each section measures each dimension) is supported.


• Assessment of the Number of Dimensions

Application to the ULISSE Dataset• The initial number of dimension (8) may be

excessive.


• The latent classes can be interpretation of different degrees of impairment of health status.


• investigate the stability of 5 dimension, compare between s=5 and s=4 model.– Cross –validated log-liklihood.

Cross-validated log-liklihood

• 2 Randomly chosen partitions of equal size– Training data– Test data

• Training: s=4 Test: s=4Training: s=5 Test: s=5

• BIC: s=5 is a proper solution

Reduction of the Number of Items

Conclusion

• More general structure• Missing responses• Polytomous items

Question

• why not studying the number of latent classes and dimensionality simultaneously?

• MNSQ item-fit statistic used to reducing items could be tried in this process.

• Simulation studies should be conducted to confirm its efficiency and accuracy of the proposed approach.

francesco bartolucci

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