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1.
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   

2.
This article summarizes the continuous latent trait IRT approach to skills diagnosis as particularized by a representative variety of continuous latent trait models using item response functions (IRFs). First, several basic IRT-based continuous latent trait approaches are presented in some detail. Then a brief summary of estimation, model checking, and assessment scoring aspects are discussed. Finally, the University of California at Berkeley multidimensional Rasch-model-grounded SEPUP middle school science-focused embedded assessment project is briefly described as one significant illustrative application.  相似文献   

3.
Item response theory (IRT) procedures have been used extensively to study normal latent trait distributions and have been shown to perform well; however, less is known concerning the performance of IRT with non-normal latent trait distributions. This study investigated the degree of latent trait estimation error under normal and non-normal conditions using four latent trait estimation procedures and also evaluated whether the test composition, in terms of item difficulty level, reduces estimation error. Most importantly, both true and estimated item parameters were examined to disentangle the effects of latent trait estimation error from item parameter estimation error. Results revealed that non-normal latent trait distributions produced a considerably larger degree of latent trait estimation error than normal data. Estimated item parameters tended to have comparable precision to true item parameters, thus suggesting that increased latent trait estimation error results from latent trait estimation rather than item parameter estimation.  相似文献   

4.
Many large‐scale assessments are designed to yield two or more scores for an individual by administering multiple sections measuring different but related skills. Multidimensional tests, or more specifically, simple structured tests, such as these rely on multiple multiple‐choice and/or constructed responses sections of items to generate multiple scores. In the current article, we propose an extension of the hierarchical rater model (HRM) to be applied with simple structured tests with constructed response items. In addition to modeling the appropriate trait structure, the multidimensional HRM (M‐HRM) presented here also accounts for rater severity bias and rater variability or inconsistency. We introduce the model formulation, test parameter recovery with a focus on latent traits, and compare the M‐HRM to other scoring approaches (unidimensional HRMs and a traditional multidimensional item response theory model) using simulated and empirical data. Results show more precise scores under the M‐HRM, with a major improvement in scores when incorporating rater effects versus ignoring them in the traditional multidimensional item response theory model.  相似文献   

5.
Rating scale items have been widely used in educational and psychological tests. These items require people to make subjective judgments, and these subjective judgments usually involve randomness. To account for this randomness, Wang, Wilson, and Shih proposed the random‐effect rating scale model in which the threshold parameters are treated as random effects rather than fixed effects. In the present study, the Wang et al. model was further extended to incorporate slope parameters and embed the new model within the framework of multilevel nonlinear mixed‐effect models. This was done so that (1) no efforts are needed to derive parameter estimation procedures, and (2) existing computer programs can be applied directly. A brief simulation study was conducted to ascertain parameter recovery using the SAS NLMIXED procedure. An empirical example regarding students’ interest in learning science is presented to demonstrate the implications and applications of the new model.  相似文献   

6.
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   

7.
This article considers potential problems that can arise in estimating a unidimensional item response theory (IRT) model when some test items are multidimensional (i.e., show a complex factorial structure). More specifically, this study examines (1) the consequences of model misfit on IRT item parameter estimates due to unintended minor item‐level multidimensionality, and (2) whether a Projection IRT model can provide a useful remedy. A real‐data example is used to illustrate the problem and also is used as a base model for a simulation study. The results suggest that ignoring item‐level multidimensionality might lead to inflated item discrimination parameter estimates when the proportion of multidimensional test items to unidimensional test items is as low as 1:5. The Projection IRT model appears to be a useful tool for updating unidimensional item parameter estimates of multidimensional test items for a purified unidimensional interpretation.  相似文献   

8.
This study proposes a structured constructs model (SCM) to examine measurement in the context of a multidimensional learning progression (LP). The LP is assumed to have features that go beyond a typical multidimentional IRT model, in that there are hypothesized to be certain cross‐dimensional linkages that correspond to requirements between the levels of the different dimensions. The new model builds on multidimensional item response theory models and change‐point analysis to add cut‐score and discontinuity parameters that embody these substantive requirements. This modeling strategy allows us to place the examinees in the appropriate LP level and simultaneously to model the hypothesized requirement relations. Results from a simulation study indicate that the proposed change‐point SCM recovers the generating parameters well. When the hypothesized requirement relations are ignored, the model fit tends to become worse, and the model parameters appear to be more biased. Moreover, the proposed model can be used to find validity evidence to support or disprove initial theoretical hypothesized links in the LP through empirical data. We illustrate the technique with data from an assessment system designed to measure student progress in a middle‐school statistics and modeling curriculum.  相似文献   

9.
Large‐scale assessments such as the Programme for International Student Assessment (PISA) have field trials where new survey features are tested for utility in the main survey. Because of resource constraints, there is a trade‐off between how much of the sample can be used to test new survey features and how much can be used for the initial item response theory (IRT) scaling. Utilizing real assessment data of the PISA 2015 Science assessment, this article demonstrates that using fixed item parameter calibration (FIPC) in the field trial yields stable item parameter estimates in the initial IRT scaling for samples as small as n = 250 per country. Moreover, the results indicate that for the recovery of the county‐specific latent trait distributions, the estimates of the trend items (i.e., the information introduced into the calibration) are crucial. Thus, concerning the country‐level sample size of n = 1,950 currently used in the PISA field trial, FIPC is useful for increasing the number of survey features that can be examined during the field trial without the need to increase the total sample size. This enables international large‐scale assessments such as PISA to keep up with state‐of‐the‐art developments regarding assessment frameworks, psychometric models, and delivery platform capabilities.  相似文献   

10.
As a global measure of precision, item response theory (IRT) estimated reliability is derived for four coefficients (Cronbach's α, Feldt‐Raju, stratified α, and marginal reliability). Models with different underlying assumptions concerning test‐part similarity are discussed. A detailed computational example is presented for the targeted coefficients. A comparison of the IRT model‐derived coefficients is made and the impact of varying ability distributions is evaluated. The advantages of IRT‐derived reliability coefficients for problems such as automated test form assembly and vertical scaling are discussed.  相似文献   

11.
潘浩 《考试研究》2014,(2):59-63
早期的单维IRT模型忽视了测验多维性的可能,而多维IRT模型对各维度的划分不够明确,不能很好地反映各维度能力的内涵。高阶IRT模型承认测验的多维性,以分测验划分维度,同时又将多个维度的能力统一到一个高阶的能力中,能够在了解被试各维度的能力同时,为被试提供整体的能力估计,它能更好地反映实际,并且适应大规模测验的需求。  相似文献   

12.
Unidimensionality and local independence are two common assumptions of item response theory. The former implies that all items measure a common latent trait, while the latter implies that responses are independent, conditional on respondents’ location on the latent trait. Yet, few tests are truly unidimensional. Unmodeled dimensions may result in test items displaying dependencies, which can lead to misestimated parameters and inflated reliability estimates. In this article, we investigate the dimensionality of interim mathematics tests and evaluate the extent to which modeling minor dimensions in the data change model parameter estimates. We found evidence of minor dimensions, but parameter estimates across models were similar. Our results indicate that minor dimensions outside the primary trait have negligible consequences on parameter estimates. This finding was observed despite the ratio of multidimensional to unidimensional items being above previously recommended thresholds.  相似文献   

13.
Cognitive diagnosis models (CDMs) have been developed to evaluate the mastery status of individuals with respect to a set of defined attributes or skills that are measured through testing. When individuals are repeatedly administered a cognitive diagnosis test, a new class of multilevel CDMs is required to assess the changes in their attributes and simultaneously estimate the model parameters from the different measurements. In this study, the most general CDM of the generalized deterministic input, noisy “and” gate (G‐DINA) model was extended to a multilevel higher order CDM by embedding a multilevel structure into higher order latent traits. A series of simulations based on diverse factors was conducted to assess the quality of the parameter estimation. The results demonstrate that the model parameters can be recovered fairly well and attribute mastery can be precisely estimated if the sample size is large and the test is sufficiently long. The range of the location parameters had opposing effects on the recovery of the item and person parameters. Ignoring the multilevel structure in the data by fitting a single‐level G‐DINA model decreased the attribute classification accuracy and the precision of latent trait estimation. The number of measurement occasions had a substantial impact on latent trait estimation. Satisfactory model and person parameter recoveries could be achieved even when assumptions of the measurement invariance of the model parameters over time were violated. A longitudinal basic ability assessment is outlined to demonstrate the application of the new models.  相似文献   

14.
Two new methods have been proposed to determine unexpected sum scores on sub-tests (testlets) both for paper-and-pencil tests and computer adaptive tests. A method based on a conservative bound using the hypergeometric distribution, denoted p, was compared with a method where the probability for each score combination was calculated using a highest density region (HDR). Furthermore, these methods were compared with the standardized log-likelihood statistic with and without a correction for the estimated latent trait value (denoted as l*z and lz, respectively). Data were simulated on the basis of the one-parameter logistic model, and both parametric and non-parametric logistic regression was used to obtain estimates of the latent trait. Results showed that it is important to take the trait level into account when comparing subtest scores. In a nonparametric item response theory (IRT) context, on adapted version of the HDR method was a powerful alterative to p. In a parametric IRT context, results showed that l*z had the highest power when the data were simulated conditionally on the estimated latent trait level.  相似文献   

15.
马洪超 《考试研究》2012,(1):61-66,85
参数估计是项目反应理论应用、发展的前提。本研究针对六种不同的HSK考生样本,分别使用三种软件,采用不同的参数估计方法对考生能力值进行估计,结果表明能力值估计结果与考生潜在能力分布有关系。当潜在能力分布趋向正态分布时,能力值的估计的误差较小。此外,不同软件的参数估计方法的能力值估计结果均有差异。  相似文献   

16.
Contamination of responses due to extreme and midpoint response style can confound the interpretation of scores, threatening the validity of inferences made from survey responses. This study incorporated person-level covariates in the multidimensional item response tree model to explain heterogeneity in response style. We include an empirical example and two simulation studies to support the use and interpretation of the model: parameter recovery using Markov chain Monte Carlo (MCMC) estimation and performance of the model under conditions with and without response styles present. Item intercepts mean bias and root mean square error were small at all sample sizes. Item discrimination mean bias and root mean square error were also small but tended to be smaller when covariates were unrelated to, or had a weak relationship with, the latent traits. Item and regression parameters are estimated with sufficient accuracy when sample sizes are greater than approximately 1,000 and MCMC estimation with the Gibbs sampler is used. The empirical example uses the National Longitudinal Study of Adolescent to Adult Health’s sexual knowledge scale. Meaningful predictors associated with high levels of extreme response latent trait included being non-White, being male, and having high levels of parental support and relationships. Meaningful predictors associated with high levels of the midpoint response latent trait included having low levels of parental support and relationships. Item-level covariates indicate the response style pseudo-items were less easy to endorse for self-oriented items, whereas the trait of interest pseudo-items were easier to endorse for self-oriented items.  相似文献   

17.
This study introduces three growth modeling techniques: latent growth modeling (LGM), hierarchical linear modeling (HLM), and longitudinal profile analysis via multidimensional scaling (LPAMS). It compares the multilevel growth parameter estimates and potential predictor effects obtained using LGM, HLM, and LPAMS. The purpose of this multilevel growth analysis is to alert applied researchers to selected analytical issues that are required for consideration in decisions to apply one of these three approaches to longitudinal academic achievement studies. The results indicated that there were no significant distinctions on either mean growth parameter estimates or on the effects of potential predictors to growth factors at both the student and school levels. However, the study also produced equivocal findings on the statistical testing of variance and covariance growth parameter estimates. Other practical issues pertaining to the three growth modeling methods are also discussed.  相似文献   

18.
In this article, linear item response theory (IRT) observed‐score equating is compared under a generalized kernel equating framework with Levine observed‐score equating for nonequivalent groups with anchor test design. Interestingly, these two equating methods are closely related despite being based on different methodologies. Specifically, when using data from IRT models, linear IRT observed‐score equating is virtually identical to Levine observed‐score equating. This leads to the conclusion that poststratification equating based on true anchor scores can be viewed as the curvilinear Levine observed‐score equating.  相似文献   

19.
Rater‐mediated assessments are a common methodology for measuring persons, investigating rater behavior, and/or defining latent constructs. The purpose of this article is to provide a pedagogical framework for examining rater variability in the context of rater‐mediated assessments using three distinct models. The first model is the observation model, which includes ecological/environmental considerations for the evaluation system. The second model is the measurement model, which includes the transformation of observed, rater response data to linear measures using a measurement model with specific requirements of rater‐invariant measurement in order to examine raters’ construct‐relevant variability stemming from the evaluative system. The third model is the interaction model, which includes an interaction parameter to allow for the investigation into raters’ systematic, construct‐irrelevant variability stemming from the evaluative system. Implications for measurement outcomes and validity are discussed.  相似文献   

20.
The aim of this article is to introduce the R package semds for structural equation multidimensional scaling. This methodology combines multidimensional scaling with latent variable features from structural equation modeling and is applicable to asymmetric and three-way input dissimilarity data. This key idea of this approach is that the input data are assumed to be imperfect measurements of a latent symmetric dissimilarity matrix. The parameter estimation is performed via an alternating least squares multidimensional scaling procedure that minimizes the stress. The latent dissimilarities are estimated as factor scores within a structural equation modeling framework. Applications shown in the article involve data associated with the banking crisis and data from avalanche research. The models fitted with the semds package are compared to related methods from multidimensional scaling. The R code to reproduce all the computations is provided in the supplementary materials.  相似文献   

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