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Media convergence is considered an effective way to alleviate the decline of traditional media audiences. Although media convergence has nearly swept through the world’s foremost traditional media, studies reflecting its effects on individuals at the micro level are lacking. Therefore, this study established a multidimensional perceived media convergence scale to measure the actual perception of media convergence from the audience’s perspective. To achieve this, a comprehensive field survey was conducted in a county in southwestern China, yielding a sample of 966 participants. Study 1 used exploratory factor analysis and a multidimensional graded response model (MGRM) to optimize the item pool. Study 2 then tested the reliability and validity of the scale using confirmatory factor analysis and a MGRM. Moreover, the entire sample was tested and analyzed for the measurement accuracy of this scale. The final instrument contains 24 items in six dimensions: communication power, influence power, guiding power, credibility power, political propaganda power, and service power. This study demonstrated the robustness and scalability of the scale. Based on this scale, future research can provide a deep understanding of which groups of people perceive media convergence in what way and conduct causal inference or mechanism analysis in conjunction with the audience’s behaviors.

In the context of both cognitive and affective tests, items are usually designed to involve more than two responses, for which polytomous models are applicable. The purpose of this paper is to propose a highly effective Pólya-Gamma Gibbs sampling algorithm based on auxiliary variables to estimate the multidimensional graded response model that has been widely used in psychological, educational, and health-related assessment. The strategy is based on the Pólya Gamma family of distributions which provides a closed-form posterior distribution for logistic-based models. With the introduction of the two latent variables, the full conditional distributions are tractable, and consequently the Gibbs sampling is easy to implement. Nice features including empirical performance of the proposed methodology are demonstrated by simulation studies. Finally, two empirical data sets were analysed to demonstrate the efficiency and utility of the proposed method. [ABSTRACT FROM AUTHOR]

As a branch of statistical latent variable modeling, multidimensional item response theory (MIRT) plays an important role in psychometrics. Multidimensional graded response model (MGRM) is a key model for the development of multidimensional computerized adaptive testing (MCAT) with graded-response data and multiple traits. This paper explores how to automatically identify the item-trait patterns of replenished items based on the MGRM in MCAT. The problem is solved by developing an exploratory pattern recognition method for graded-response items based on the least absolute shrinkage and selection operator (LASSO), which is named LPRM-GR and facilitates the subsequent parameter estimation of replenished items and helps maintaining the effectiveness of item replenishment in MCAT. In conjunction with the proposed approach, the regular BIC and weighted BIC are applied, respectively, to select the optimal item-trait patterns. Simulation for evaluating the LPRM-GR in pattern recognition accuracy of replenished items and the corresponding item estimation accuracy is conducted under multiple conditions across different numbers with respect to dimensionality, response-category numbers, latent trait correlation, stopping rules, and item selection criteria. Results show that the proposed method with the two types of BIC both have good performance in pattern recognition for item replenishment in the two- to four-dimensional MCAT with the MGRM, for which the weighted BIC is generally superior to the regular BIC. The proposed method has relatively high accuracy and efficiency in identifying the patterns of graded-response items, and has the advantages of easy implementation and practical feasibility. [ABSTRACT FROM AUTHOR]

Achievement tests are used to characterize the proficiency of higher-education students. Item response theory (IRT) models are applied to these tests to estimate the ability of students (as latent variable in the model). In order for quality IRT parameters to be estimated, especially ability parameters, it is important that the appropriate number of dimensions is identified. Through a case study, based on a statistics exam for students in higher education, we show how dimensions and other model parameters can be chosen in a real situation. Our model choice is based both on empirical and on background knowledge of the test. We show that dimensionality influences the estimates of the item-parameters, especially the discrimination parameter which provides information about the quality of the item. We perform a simulation study to generalize our conclusions. Both the simulation study and the case study show that multidimensional models have the advantage to better discriminate between examinees. We conclude from the simulation study that it is safer to use a multidimensional model compared to a unidimensional if it is unknown which model is the correct one. [ABSTRACT FROM AUTHOR]

As a branch of statistical latent variable modeling, multidimensional item response theory (MIRT) plays an important role in psychometrics. Multidimensional graded response model (MGRM) is a key model for the development of multidimensional computerized adaptive testing (MCAT) with graded-response data and multiple traits. This paper explores how to automatically identify the item-trait patterns of replenished items based on the MGRM in MCAT. The problem is solved by developing an exploratory pattern recognition method for graded-response items based on the least absolute shrinkage and selection operator (LASSO), which is named LPRM-GR and facilitates the subsequent parameter estimation of replenished items and helps maintaining the effectiveness of item replenishment in MCAT. In conjunction with the proposed approach, the regular BIC and weighted BIC are applied, respectively, to select the optimal item-trait patterns. Simulation for evaluating the LPRM-GR in pattern recognition accuracy of replenished items and the corresponding item estimation accuracy is conducted under multiple conditions across different numbers with respect to dimensionality, response-category numbers, latent trait correlation, stopping rules, and item selection criteria. Results show that the proposed method with the two types of BIC both have good performance in pattern recognition for item replenishment in the two- to four-dimensional MCAT with the MGRM, for which the weighted BIC is generally superior to the regular BIC. The proposed method has relatively high accuracy and efficiency in identifying the patterns of graded-response items, and has the advantages of easy implementation and practical feasibility.

This study explored calibrating a large item bank for use in multidimensional health measurement with computerized adaptive testing, using both item responses and response time (RT) information. The Activity Measure for Post-Acute Care is a patient-reported outcomes measure comprised of three correlated scales (Applied Cognition, Daily Activities, and Mobility). All items from each scale are Likert type, so that a respondent chooses a response from an ordered set of four response options. The most appropriate item response theory model for analyzing and scoring these items is the multidimensional graded response model (MGRM). During the field testing of the items, an interviewer read each item to a patient and recorded, on a tablet computer, the patient's responses and the software recorded RTs. Due to the large item bank with over 300 items, data collection was conducted in four batches with a common set of anchor items to link the scale. van der Linden's (2007) hierarchical modeling framework was adopted. Several models, with or without interviewer as a covariate and with or without interaction between interviewer and items, were compared for each batch of data. It was found that the model with the interaction between interviewer and item, when the interaction effect was constrained to be proportional, fit the data best. Therefore, the final hierarchical model with a lognormal model for RT and the MGRM for response data was fitted to all batches of data via a concurrent calibration. Evaluation of parameter estimates revealed that (1) adding response time information did not affect the item parameter estimates and their standard errors significantly; (2) adding response time information helped reduce the standard error of patients' multidimensional latent trait estimates, but adding interviewer as a covariate did not result in further improvement. Implications of the findings for follow up adaptive test delivery design are discussed.

This study explored calibrating a large item bank for use in multidimensional health measurement with computerized adaptive testing, using both item responses and response time (RT) information. The Activity Measure for Post-Acute Care is a patient-reported outcomes measure comprised of three correlated scales (Applied Cognition, Daily Activities, and Mobility). All items from each scale are Likert type, so that a respondent chooses a response from an ordered set of four response options. The most appropriate item response theory model for analyzing and scoring these items is the multidimensional graded response model (MGRM). During the field testing of the items, an interviewer read each item to a patient and recorded, on a tablet computer, the patient's responses and the software recorded RTs. Due to the large item bank with over 300 items, data collection was conducted in four batches with a common set of anchor items to link the scale. van der Linden's (2007) hierarchical modeling framework was adopted. Several models, with or without interviewer as a covariate and with or without interaction between interviewer and items, were compared for each batch of data. It was found that the model with the interaction between interviewer and item, when the interaction effect was constrained to be proportional, fit the data best. Therefore, the final hierarchical model with a lognormal model for RT and the MGRM for response data was fitted to all batches of data via a concurrent calibration. Evaluation of parameter estimates revealed that (1) adding response time information did not affect the item parameter estimates and their standard errors significantly; (2) adding response time information helped reduce the standard error of patients' multidimensional latent trait estimates, but adding interviewer as a covariate did not result in further improvement. Implications of the findings for follow up adaptive test delivery design are discussed. [ABSTRACT FROM AUTHOR]

Multidimensional computerized adaptive testing (MCAT) has been developed over the past decades, and most of them can only deal with dichotomously scored items. However, polytomously scored items have been broadly used in a variety of tests for their advantages of providing more information and testing complicated abilities and skills. The purpose of this study is to discuss the item selection algorithms used in MCAT with polytomously scored items (PMCAT). Several promising item selection algorithms used in MCAT are extended to PMCAT, and two new item selection methods are proposed to improve the existing selection strategies. Two simulation studies are conducted to demonstrate the feasibility of the extended and proposed methods. The simulation results show that most of the extended item selection methods for PMCAT are feasible and the new proposed item selection methods perform well. Combined with the security of the pool, when two dimensions are considered (Study 1), the proposed modified continuous entropy method (MCEM) is the ideal of all in that it gains the lowest item exposure rate and has a relatively high accuracy. As for high dimensions (Study 2), results show that mutual information (MUI) and MCEM keep relatively high estimation accuracy, and the item exposure rates decrease as the correlation increases. [ABSTRACT FROM AUTHOR]

Objectives: To propose a multidimensional item response theory (MIRT) scoring system for the Short Form 12 (SF-12) with good psychometric properties in terms of fit and reliability. Study Design and Settings: Two models, indicating physical (PCS) and mental component summary (MCS) dimensions, were fitted to SF-12 data from the European Study of the Epidemiology of Mental Disorders, a representative sample from European adult general population (n521,425; response rate561.2%). Goodness of fit, information, reliability, and agreement of individual scores were compared with the classical SF-12 and RAND-12 algorithms. Results: The bidimensional response process (BRP) model, where all items are indicators of both dimensions, yielded the best fit (root mean square error of approximation50.057, comparative fit index50.95, and TuckereLewis index50.94), and highly agreed with PCS and MCS scores from the SF-12 (intraclass correlation coefficients of 0.92 and 0.88, respectively) and RAND-12 (0.88 and 0.95). Regarding reliability, the BRP yielded 0.75 and 0.77 (PCS and MCS, respectively), greater than SF-12 (0.65 and 0.66) and RAND-12 (0.65 and 0.67). As indicated by scale linking, MIRT scores can be interpreted similarly to the classical scores. Conclusion: The MIRT models showed a clear construct structure for the PCS and MCS dimensions, defined by functional and role limitation content. Results support the use of SF-12 MIRT-based scores as a valid and reliable option to assess health status. © 2013 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Maximum likelihood and Bayesian ability estimation in multidimensional item response models can lead to paradoxical results as proven by Hooker, Finkelman, and Schwartzman (Psychometrika 74(3): 419-442, ): Changing a correct response on one item into an incorrect response may produce a higher ability estimate in one dimension. Furthermore, the conditions under which this paradox arises are very general, and may in fact be fulfilled by many of the multidimensional scales currently in use. This paper tries to emphasize and extend the generality of the results of Hooker et al. by (1) considering the paradox in a generalized class of IRT models, (2) giving a weaker sufficient condition for the occurrence of the paradox with relations to an important concept of statistical association, and by (3) providing some additional specific results for linearly compensatory models with special emphasis on the factor analysis model. [ABSTRACT FROM AUTHOR]