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1.
Mokken scale analysis (MSA) is a probabilistic‐nonparametric approach to item response theory (IRT) that can be used to evaluate fundamental measurement properties with less strict assumptions than parametric IRT models. This instructional module provides an introduction to MSA as a probabilistic‐nonparametric framework in which to explore measurement quality, with an emphasis on its application in the context of educational assessment. The module describes both dichotomous and polytomous formulations of the MSA model. Examples of the application of MSA to educational assessment are provided using data from a multiple‐choice physical science assessment and a rater‐mediated writing assessment.  相似文献   

2.
A rapidly expanding arena for item response theory (IRT) is in attitudinal and health‐outcomes survey applications, often with polytomous items. In particular, there is interest in computer adaptive testing (CAT). Meeting model assumptions is necessary to realize the benefits of IRT in this setting, however. Although initial investigations of local item dependence have been studied both for polytomous items in fixed‐form settings and for dichotomous items in CAT settings, there have been no publications applying local item dependence detection methodology to polytomous items in CAT despite its central importance to these applications. The current research uses a simulation study to investigate the extension of widely used pairwise statistics, Yen's Q3 Statistic and Pearson's Statistic X2, in this context. The simulation design and results are contextualized throughout with a real item bank of this type from the Patient‐Reported Outcomes Measurement Information System (PROMIS).  相似文献   

3.
Traditional methods for examining differential item functioning (DIF) in polytomously scored test items yield a single item‐level index of DIF and thus provide no information concerning which score levels are implicated in the DIF effect. To address this limitation of DIF methodology, the framework of differential step functioning (DSF) has recently been proposed, whereby measurement invariance is examined within each step underlying the polytomous response variable. The examination of DSF can provide valuable information concerning the nature of the DIF effect (i.e., is the DIF an item‐level effect or an effect isolated to specific score levels), the location of the DIF effect (i.e., precisely which score levels are manifesting the DIF effect), and the potential causes of a DIF effect (i.e., what properties of the item stem or task are potentially biasing). This article presents a didactic overview of the DSF framework and provides specific guidance and recommendations on how DSF can be used to enhance the examination of DIF in polytomous items. An example with real testing data is presented to illustrate the comprehensive information provided by a DSF analysis.  相似文献   

4.
In this digital ITEMS module, Dr. Brian Leventhal and Dr. Allison Ames provide an overview of Monte Carlo simulation studies (MCSS) in item response theory (IRT). MCSS are utilized for a variety of reasons, one of the most compelling being that they can be used when analytic solutions are impractical or nonexistent because they allow researchers to specify and manipulate an array of parameter values and experimental conditions (e.g., sample size, test length, and test characteristics). Dr. Leventhal and Dr. Ames review the conceptual foundation of MCSS in IRT and walk through the processes of simulating total scores as well as item responses using the two-parameter logistic, graded response, and bifactor models. They provide guidance for how to implement MCSS using other item response models and best practices for efficient syntax and executing an MCSS. The digital module contains sample SAS code, diagnostic quiz questions, activities, curated resources, and a glossary.  相似文献   

5.
Bock, Muraki, and Pfeiffenberger (1988) proposed a dichotomous item response theory (IRT) model for the detection of differential item functioning (DIF), and they estimated the IRT parameters and the means and standard deviations of the multiple latent trait distributions. This IRT DIF detection method is extended to the partial credit model (Masters, 1982; Muraki, 1993) and presented as one of the multiple-group IRT models. Uniform and non-uniform DIF items and heterogeneous latent trait distributions were used to generate polytomous responses of multiple groups. The DIF method was applied to this simulated data using a stepwise procedure. The standardized DIF measures for slope and item location parameters successfully detected the non-uniform and uniform DIF items as well as recovered the means and standard deviations of the latent trait distributions.This stepwise DIF analysis based on the multiple-group partial credit model was then applied to the National Assessment of Educational Progress (NAEP) writing trend data.  相似文献   

6.
Orlando and Thissen's S‐X 2 item fit index has performed better than traditional item fit statistics such as Yen's Q1 and McKinley and Mill's G2 for dichotomous item response theory (IRT) models. This study extends the utility of S‐X 2 to polytomous IRT models, including the generalized partial credit model, partial credit model, and rating scale model. The performance of the generalized S‐X 2 in assessing item model fit was studied in terms of empirical Type I error rates and power and compared to G2. The results suggest that the generalized S‐X 2 is promising for polytomous items in educational and psychological testing programs.  相似文献   

7.
Using Muraki's (1992) generalized partial credit IRT model, polytomous items (responses to which can be scored as ordered categories) from the 1991 field test of the NAEP Reading Assessment were calibrated simultaneously with multiple-choice and short open-ended items. Expected information of each type of item was computed. On average, four-category polytomous items yielded 2.1 to 3.1 times as much IRT information as dichotomous items. These results provide limited support for the ad hoc rule of weighting k-category polytomous items the same as k - 1 dichotomous items for computing total scores. Polytomous items provided the most information about examinees of moderately high proficiency; the information function peaked at 1.0 to 1.5, and the population distribution mean was 0. When scored dichotomously, information in polytomous items sharply decreased, but they still provided more expected information than did the other response formats. For reference, a derivation of the information function for the generalized partial credit model is included.  相似文献   

8.
Drawing valid inferences from item response theory (IRT) models is contingent upon a good fit of the data to the model. Violations of model‐data fit have numerous consequences, limiting the usefulness and applicability of the model. This instructional module provides an overview of methods used for evaluating the fit of IRT models. Upon completing this module, the reader will have an understanding of traditional and Bayesian approaches for evaluating model‐data fit of IRT models, the relative advantages of each approach, and the software available to implement each method.  相似文献   

9.
In observed‐score equipercentile equating, the goal is to make scores on two scales or tests measuring the same construct comparable by matching the percentiles of the respective score distributions. If the tests consist of different items with multiple categories for each item, a suitable model for the responses is a polytomous item response theory (IRT) model. The parameters from such a model can be utilized to derive the score probabilities for the tests and these score probabilities may then be used in observed‐score equating. In this study, the asymptotic standard errors of observed‐score equating using score probability vectors from polytomous IRT models are derived using the delta method. The results are applied to the equivalent groups design and the nonequivalent groups design with either chain equating or poststratification equating within the framework of kernel equating. The derivations are presented in a general form and specific formulas for the graded response model and the generalized partial credit model are provided. The asymptotic standard errors are accurate under several simulation conditions relating to sample size, distributional misspecification and, for the nonequivalent groups design, anchor test length.  相似文献   

10.
In this article, procedures are described for estimating single-administration classification consistency and accuracy indices for complex assessments using item response theory (IRT). This IRT approach was applied to real test data comprising dichotomous and polytomous items. Several different IRT model combinations were considered. Comparisons were also made between the IRT approach and two non-IRT approaches including the Livingston-Lewis and compound multinomial procedures. Results for various IRT model combinations were not substantially different. The estimated classification consistency and accuracy indices for the non-IRT procedures were almost always lower than those for the IRT procedures.  相似文献   

11.
IRT下题量与被试量对参数估计模拟返真性能的影响   总被引：1，自引：0，他引：1  
在项目反应理论下的题库建设时,进行纸笔测验测试时需要多少被试量、题量,试题的参数估计能够达到较为精确估计?本文使用蒙特卡洛模拟方法模拟测验情境,对此问题进行探讨。分析题量的变化和被试量的变化对a、b参数估计的模拟返真性能的影响。1)从被试量角度来看,在两级、多级记分试题模拟测验情境下,随着被试量逐渐增大,项目参数估计值模拟返真指标均方误差逐渐减小。2)从题量角度来看,在两级记分试题模拟情境下,均方误差曲线在题量为25题左右时有一个拐点,即当题量小于25题时,随着题量增加时RMSE减小幅度较大,而当题量大于25题时,这时再增加题量,RMSE减小幅度很小。在多级记分试题模拟情境下,均方误差曲线在题量为15题左右时有一个拐点,即当题量小于15题时,随着题量增加, RMSE逐渐减小,当题量大于15题时,随着题量增加,RMSE逐渐增大。  相似文献   

12.
Item response theory (IRT) methods are generally used to create score scales for large-scale tests. Research has shown that IRT scales are stable across groups and over time. Most studies have focused on items that are dichotomously scored. Now Rasch and other IRT models are used to create scales for tests that include polytomously scored items. When tests are equated across forms, researchers check for the stability of common items before including them in equating procedures. Stability is usually examined in relation to polytomous items' central “location” on the scale without taking into account the stability of the different item scores (step difficulties). We examined the stability of score scales over a 3–5-year period, considering both stability of location values and stability of step difficulties for common item equating. We also investigated possible changes in the scale measured by the tests and systematic scale drift that might not be evident in year-to-year equating. Results across grades and content areas suggest that equating results are comparable whether or not the stability of step difficulties is taken into account. Results also suggest that there may be systematic scale drift that is not visible using year-to-year common item equating.  相似文献   

13.
Single‐best answers to multiple‐choice items are commonly dichotomized into correct and incorrect responses, and modeled using either a dichotomous item response theory (IRT) model or a polytomous one if differences among all response options are to be retained. The current study presents an alternative IRT‐based modeling approach to multiple‐choice items administered with the procedure of elimination testing, which asks test‐takers to eliminate all the response options they consider to be incorrect. The partial credit model is derived for the obtained responses. By extracting more information pertaining to test‐takers’ partial knowledge on the items, the proposed approach has the advantage of providing more accurate estimation of the latent ability. In addition, it may shed some light on the possible answering processes of test‐takers on the items. As an illustration, the proposed approach is applied to a classroom examination of an undergraduate course in engineering science.  相似文献   

14.
Data from a large-scale performance assessment ( N = 105,731) were analyzed with five differential item functioning (DIF) detection methods for polytomous items to examine the congruence among the DIF detection methods. Two different versions of the item response theory (IRT) model-based likelihood ratio test, the logistic regression likelihood ratio test, the Mantel test, and the generalized Mantel–Haenszel test were compared. Results indicated some agreement among the five DIF detection methods. Because statistical power is a function of the sample size, the DIF detection results from extremely large data sets are not practically useful. As alternatives to the DIF detection methods, four IRT model-based indices of standardized impact and four observed-score indices of standardized impact for polytomous items were obtained and compared with the R 2 measures of logistic regression.  相似文献   

15.
Sometimes, test‐takers may not be able to attempt all items to the best of their ability (with full effort) due to personal factors (e.g., low motivation) or testing conditions (e.g., time limit), resulting in poor performances on certain items, especially those located toward the end of a test. Standard item response theory (IRT) models fail to consider such testing behaviors. In this study, a new class of mixture IRT models was developed to account for such testing behavior in dichotomous and polytomous items, by assuming test‐takers were composed of multiple latent classes and by adding a decrement parameter to each latent class to describe performance decline. Parameter recovery, effect of model misspecification, and robustness of the linearity assumption in performance decline were evaluated using simulations. It was found that the parameters in the new models were recovered fairly well by using the freeware WinBUGS; the failure to account for such behavior by fitting standard IRT models resulted in overestimation of difficulty parameters on items located toward the end of the test and overestimation of test reliability; and the linearity assumption in performance decline was rather robust. An empirical example is provided to illustrate the applications and the implications of the new class of models.  相似文献   

16.
项目反应理论（Item Response Theory,IRT）是现代教育心理测量领域中最有影响的一种测量理论,它的一个明确目标是扩展模型的种类以至于能够处理实际测试中任何形式的反应数据。在已有的各种模型研究中,对于多级评分项目,只考虑到项目区分度和难度。但在实际测验中,此类项目还可能存在猜测度。本研究基于Samejima等级反应模型,将项目猜测度融合到多级评分模型中,提出了三参数等级反应模型（Three-parameter Graded Response Model,3PL-GRM）。由于忽略多级反应项目的猜测度会使得该项目的信息量虚假升高,本研究还进一步将3PL—GRM的信息函数应用到试卷质量分析中。  相似文献   

17.
The nature of anatomy education has changed substantially in recent decades, though the traditional multiple‐choice written examination remains the cornerstone of assessing students' knowledge. This study sought to measure the quality of a clinical anatomy multiple‐choice final examination using item response theory (IRT) models. One hundred seventy‐six students took a multiple‐choice clinical anatomy examination. One‐ and two‐parameter IRT models (difficulty and discrimination parameters) were used to assess item quality. The two‐parameter IRT model demonstrated a wide range in item difficulty, with a median of ?1.0 and range from ?2.0 to 0.0 (25th to 75th percentile). Similar results were seen for discrimination (median 0.6; range 0.4–0.8). The test information curve achieved maximum discrimination for an ability level one standard deviation below the average. There were 15 items with standardized loading less than 0.3, which was due to several factors: two items had two correct responses, one was not well constructed, two were too easy, and the others revealed a lack of detailed knowledge by students. The test used in this study was more effective in discriminating students of lower ability than those of higher ability. Overall, the quality of the examination in clinical anatomy was confirmed by the IRT models. Anat Sci Educ 3:17–24, 2010. © 2009 American Association of Anatomists.  相似文献   

18.
In this digital ITEMS module, Dr. Roy Levy describes Bayesian approaches to psychometric modeling. He discusses how Bayesian inference is a mechanism for reasoning in a probability-modeling framework and is well-suited to core problems in educational measurement: reasoning from student performances on an assessment to make inferences about their capabilities more broadly conceived, as well as fitting models to characterize the psychometric properties of tasks. The approach is first developed in the context of estimating a mean and variance of a normal distribution before turning to the context of unidimensional item response theory (IRT) models for dichotomously scored data. Dr. Levy illustrates the process of fitting Bayesian models using the JAGS software facilitated through the R statistical environment. The module is designed to be relevant for students, researchers, and data scientists in various disciplines such as education, psychology, sociology, political science, business, health, and other social sciences. It contains audio-narrated slides, diagnostic quiz questions, and data-based activities with video solutions as well as curated resources and a glossary.  相似文献   

19.
In this article, I address two competing conceptions of differential item functioning (DIF) in polytomously scored items. The first conception, referred to as net DIF, concerns between-group differences in the conditional expected value of the polytomous response variable. The second conception, referred to as global DIF, concerns the conditional dependence of group membership and the polytomous response variable. The distinction between net and global DIF is important because different DIF evaluation methods are appropriate for net and global DIF; no currently available method is universally the best for detecting both net and global DIF. Net and global DIF definitions are presented under two different, yet compatible, modeling frameworks: a traditional item response theory (IRT) framework, and a differential step functioning (DSF) framework. The theoretical relationship between the IRT and DSF frameworks is presented. Available methods for evaluating net and global DIF are described, and an applied example of net and global DIF is presented.  相似文献   

20.
In test development, item response theory (IRT) is a method to determine the amount of information that each item (i.e., item information function) and combination of items (i.e., test information function) provide in the estimation of an examinee's ability. Studies investigating the effects of item parameter estimation errors over a range of ability have demonstrated an overestimation of information when the most discriminating items are selected (i.e., item selection based on maximum information). In the present study, the authors examined the influence of item parameter estimation errors across 3 item selection methods—maximum no target, maximum target, and theta maximum—using the 2- and 3-parameter logistic IRT models. Tests created with the maximum no target and maximum target item selection procedures consistently overestimated the test information function. Conversely, tests created using the theta maximum item selection procedure yielded more consistent estimates of the test information function and, at times, underestimated the test information function. Implications for test development are discussed.  相似文献   

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