scholarly journals Multiple-true-false questions reveal more thoroughly the complexity of student thinking than multiple-choice questions: a Bayesian item response model comparison

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Chad E. Brassil ◽  
Brian A. Couch
Keyword(s):  
Item Response ◽  
Model Comparison ◽  
Multiple Choice ◽  
Student Thinking ◽  
Response Model ◽  
Item Response Model ◽  
Multiple Choice Questions ◽  
True False

scholarly journals Random Item Response Model Approaches to Evaluating Item Format Effects

2018 ◽  
Vol 8 (3) ◽  
pp. 98
Author(s):  
Yongsang Lee ◽  
Inyong Park
Keyword(s):  
Item Response ◽  
Multiple Choice ◽  
Test Model ◽  
Science Assessment ◽  
Response Model ◽  
Item Response Model ◽  
Item Format ◽  
Data Set ◽  
Format Effects ◽  
Pisa 2006

The PISA 2006 science assessment is composed of open response, multiple-choice, and constructed multiple choice items. The current study introduced the random item response models to investigate the item format effects on item difficulties, and these models include the linear logistic test model with raThe PISA 2006 science assessment is composed of open response, multiple-choice, and constructed multiple choice items. The current study introduced the random item response models to investigate the item format effects on item difficulties, and these models include the linear logistic test model with random item effects (i.e., the LLTM-R) and the hierarchical item response model (i.e., the hierarchical IRM). In this study these models were applied to the PISA 2006 science data set to explore the relationship between items' format and their difficulties. The empirical analysis results in the PISA 2006 science assessment first find that the LLTM-R and the hierachical IRM provides equivalent item difficulty estimates compared with those from the Rasch model and the LLTM, and also clearly show that the item difficulties are substantially affected by item formats. This result implies that item difficulties may be different to each other depending on the item format although they deal with the same content.ndom item effects (i.e., the LLTM-R) and the hierarchical item response model (i.e., the hierarchical IRM). In this study these models were applied to the PISA 2006 science data set to explore the relationship between items' format and their difficulties. The empirical analysis results in the PISA 2006 science assessment first find that the LLTM-R and the hierachical IRM provides equivalent item difficulty estimates compared with those from the Rasch model and the LLTM, and also clearly show that the item difficulties are substantially affected by item formats. This result implies that item difficulties may be different to each other depending on the item format although they deal with the same content.

scholarly journals A Zero-Inflated Box-Cox Normal Unipolar Item Response Model for Measuring Constructs of Psychopathology

2018 ◽  
Vol 42 (7) ◽  
pp. 571-589 ◽  
Author(s):  
Brooke E. Magnus ◽  
Yang Liu
Keyword(s):  
Item Response ◽  
Latent Variable ◽  
Model Comparison ◽  
Latent Class ◽  
Normal Population ◽  
Empirical Support ◽  
Variable Density ◽  
Parameter Estimates ◽  
Response Model ◽  
Item Response Model

This research introduces a latent class item response theory (IRT) approach for modeling item response data from zero-inflated, positively skewed, and arguably unipolar constructs of psychopathology. As motivating data, the authors use 4,925 responses to the Patient Health Questionnaire (PHQ-9), a nine Likert-type item depression screener that inquires about a variety of depressive symptoms. First, Lucke’s log-logistic unipolar item response model is extended to accommodate polytomous responses. Then, a nontrivial proportion of individuals who do not endorse any of the symptoms are accounted for by including a nonpathological class that represents those who may be absent on or at some floor level of the latent variable that is being measured by the PHQ-9. To enhance flexibility, a Box-Cox normal distribution is used to empirically determine a transformation parameter that can help characterize the degree of skewness in the latent variable density. A model comparison approach is used to test the necessity of the features of the proposed model. Results suggest that (a) the Box-Cox normal transformation provides empirical support for using a log-normal population density, and (b) model fit substantially improves when a nonpathological latent class is included. The parameter estimates from the latent class IRT model are used to interpret the psychometric properties of the PHQ-9, and a method of computing IRT scale scores that reflect unipolar constructs is described, focusing on how these scores may be used in clinical contexts.

A Mixture Item Response Model for Multiple-Choice Data

2001 ◽  
Vol 26 (4) ◽  
pp. 381-409 ◽  
Author(s):  
Daniel M. Bolt ◽  
Allan S. Cohen ◽  
James A. Wollack
Keyword(s):  
Item Response ◽  
Multiple Choice ◽  
Test Construction ◽  
Item Parameter ◽  
Monte Carlo Algorithm ◽  
Choice Test ◽  
Model Parameters ◽  
Response Model ◽  
Item Response Model ◽  
Choice Data

A mixture item response model is proposed for investigating individual differences in the selection of response categories in multiple-choice items. The model accounts for local dependence among response categories by assuming that examinees belong to discrete latent classes that have different propensities towards those responses. Varying response category propensities are captured by allowing the category intercept parameters in a nominal response model ( Bock, 1972 ) to assume different values across classes. A Markov Chain Monte Carlo algorithm for the estimation of model parameters and classification of examinees is described. A real-data example illustrates how the model can be used to distinguish examinees that are disproportionately attracted to different types of distractors in a test of English usage. A simulation study evaluates item parameter recovery and classification accuracy in a hypothetical multiple-choice test designed to be diagnostic. Implications for test construction and the use of multiple-choice tests to perform cognitive diagnoses of item response patterns are discussed.

scholarly journals Extensions of the skew-normal ogive item response model

2014 ◽  
Vol 28 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Jorge Luis Bazán ◽  
Márcia D. Branco ◽  
Heleno Bolfarine
Keyword(s):  
Item Response ◽  
Response Model ◽  
Item Response Model ◽  
Skew Normal

Are Teachers' Unions Really to Blame? Collective Bargaining Agreements and Their Relationships with District Resource Allocation and Student Performance in California

2011 ◽  
Vol 6 (3) ◽  
pp. 354-398 ◽  
Author(s):  
Katharine O. Strunk
Keyword(s):  
Resource Allocation ◽  
Collective Bargaining ◽  
Student Performance ◽  
Item Response ◽  
Response Model ◽  
Item Response Model ◽  
Teachers Unions ◽  
Achievement Growth ◽  
Unique Measure ◽  
Level Student

Increased spending and decreased student performance have been attributed in part to teachers' unions and to the collective bargaining agreements (CBAs) they negotiate with school boards. However, only recently have researchers begun to examine impacts of specific aspects of CBAs on student and district outcomes. This article uses a unique measure of contract restrictiveness generated through the use of a partial independence item response model to examine the relationships between CBA strength and district spending on multiple areas and district-level student performance in California. I find that districts with more restrictive contracts have higher spending overall, but that this spending appears not to be driven by greater compensation for teachers but by greater expenditures on administrators' compensation and instruction-related spending. Although districts with stronger CBAs spend more overall and on these categories, they spend less on books and supplies and on school board–related expenditures. In addition, I find that contract restrictiveness is associated with lower average student performance, although not with decreased achievement growth.

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