Stability of Rasch Scales Over Time

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.     

Evaluating the Effects of Multidimensionality on IRT True-Score Equating

The performance of the item response theory (IRT) true-score equating method is examined under conditions of test multidimensionality. It is argued that a primary concern in applying unidimensional equating methods when multidimensionality is present is the potential decrease in equity (Lord, 1980) attributable to the fact that examinees of different ability are expected to obtain the same test scores. In contrast to equating studies based on real test data, the use of simulation in equating research not only permits assessment of these effects but also enables investigation of hypothetical equating conditions in which multidimensionality can be suspected to be especially problematic for test equating. In this article, I investigate whether the IRT true-score equating method, which explicitly assumes the item response matrix is unidimensional, is more adversely affected by the presence of multidimensionality than 2 conventional equating methods-linear and equipercentile equating-using several recently proposed equity-based criteria (Thomasson, 1993). Results from 2 simulation studies suggest that the IRT method performs at least as well as the conventional methods when the correlation between dimensions is high (³ 0.7) and may be only slightly inferior to the equipercentile method when the correlation is moderate to low (£ 0.5).     

Equating Minimum-Competency Tests: Comparisons of Methods

The 1986 scores from Florida's Statewide Student Assessment Test, Part II (SSAT-II), a minimum-competency test required for high school graduation in Florida, were placed on the scale of the 1984 scores from that test using five different equating procedures. For the highest scoring 84 % of the students, four of the five methods yielded results within 1.5 raw-score points of each other. They would be essentially equally satisfactory in this situation, in which the tests were made parallel item by item in difficulty and content and the groups of examinees were population cohorts separated by only 2 years. Also, the results from six different lengths of anchor items were compared. Anchors of 25, 20, 15, or 10 randomly selected items provided equatings as effective as 30 items using the concurrent IRT equating method, but an anchor of 5 randomly selected items did not     

Comparison of Item Preequating and Random Groups Equating Using IRT and Equipercentile Methods

An item-preequating design and a random groups design were used to equate forms of the American College Testing (ACT) Assessment Mathematics Test. Equipercentile and 3-parameter logistic model item-response theory (IRT) procedures were used for both designs. Both pretest methods produced inadequate equating results, and the IRT item preequating method resulted in more equating error than had no equating been conducted. Although neither of the item preequating methods performed well, the results from the equipercentile preequating method were more consistent with those from the random groups method than were the results from the IRT item pretest method. Item context and position effects were likely responsible, at least in part, for the inadequate results for item preequating. Such effects need to be either controlled or modeled, and the design further researched before the item preequating design can be recommended for operational use.     

Asymptotic Standard Errors of Observed‐Score Equating With Polytomous IRT Models

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.     

The Accuracy and Consistency of a Series of IRT True Score Equatings

This study investigates a sequence of item response theory (IRT) true score equatings based on various scale transformation approaches and evaluates equating accuracy and consistency over time. The results show that the biases and sample variances for the IRT true score equating (both direct and indirect) are quite small (except for the mean/sigma method). The biases and sample variances for the equating functions based on the characteristic curve methods and concurrent calibrations for adjacent forms are smaller than the biases and variances for the equating functions based on the moment methods. In addition, the IRT true score equating is also compared to the chained equipercentile equating, and we observe that the sample variances for the chained equipercentile equating are much smaller than the variances for the IRT true score equating with an exception at the low scores.     

THE EFFECTS OF VIOLATIONS OF UNIDIMENSIONALITY ON THE ESTIMATION OF ITEM AND ABILITY PARAMETERS AND ON ITEM RESPONSE THEORY EQUATING OF THE GRE VERBAL SCALE

One of the major assumptions of item response theory (IRT)models is that performance on a set of items is unidimensional, that is, the probability of successful performance by examinees on a set of items can be modeled by a mathematical model that has only one ability parameter. In practice, this strong assumption is likely to be violated. An important pragmatic question to consider is: What are the consequences of these violations? In this research, evidence is provided of violations of unidimensionality on the verbal scale of the GRE Aptitude Test, and the impact of these violations on IRT equating is examined. Previous factor analytic research on the GRE Aptitude Test suggested that two verbal dimensions, discrete verbal (analogies, antonyms, and sentence completions)and reading comprehension, existed. Consequently, the present research involved two separate calibrations (homogeneous) of discrete verbal items and reading comprehension items as well as a single calibration (heterogeneous) of all verbal item types. Thus, each verbal item was calibrated twice and each examinee obtained three ability estimates: reading comprehension, discrete verbal, and all verbal. The comparability of ability estimates based on homogeneous calibrations (reading comprehension or discrete verbal) to each other and to the all-verbal ability estimates was examined. The effects of homogeneity of item calibration pool on estimates of item discrimination were also examined. Then the comparability of IRT equatings based on homogeneous and heterogeneous calibrations was assessed. The effects of calibration homogeneity on ability parameter estimates and discrimination parameter estimates are consistent with the existence of two highly correlated verbal dimensions. IRT equating results indicate that although violations of unidimensionality may have an impact on equating, the effect may not be substantial.     

How to Equate Tests With Little or No Data

Standard procedures for equating tests, including those based on item response theory (IRT), require item responses from large numbers of examinees. Such data may not be forthcoming for reasons theoretical, political, or practical. Information about items' operating characteristics may be available from other sources, however, such as content and format specifications, expert opinion, or psychological theories about the skills and strategies required to solve them. This article shows how, in the IRT framework, collateral information about items can be exploited to augment or even replace examinee responses when linking or equating new tests to established scales. The procedures are illustrated with data from the Pre-Professional Skills Test (PPST).     

Evaluating Equating Accuracy and Assumptions for Groups That Differ in Performance

Accurate equating results are essential when comparing examinee scores across exam forms. Previous research indicates that equating results may not be accurate when group differences are large. This study compared the equating results of frequency estimation, chained equipercentile, item response theory (IRT) true‐score, and IRT observed‐score equating methods. Using mixed‐format test data, equating results were evaluated for group differences ranging from 0 to .75 standard deviations. As group differences increased, equating results became increasingly biased and dissimilar across equating methods. Results suggest that the size of group differences, the likelihood that equating assumptions are violated, and the equating error associated with an equating method should be taken into consideration when choosing an equating method.     

An Extension of IRT-Based Equating to the Dichotomous Testlet Response Theory Model

ABSTRACT

Current procedures for equating number-correct scores using traditional item response theory (IRT) methods assume local independence. However, when tests are constructed using testlets, one concern is the violation of the local item independence assumption. The testlet response theory (TRT) model is one way to accommodate local item dependence. This study proposes methods to extend IRT true score and observed score equating methods to the dichotomous TRT model. We also examine the impact of local item dependence on equating number-correct scores when a traditional IRT model is applied. Results of the study indicate that when local item dependence is at a low level, using the three-parameter logistic model does not substantially affect number-correct equating. However, when local item dependence is at a moderate or high level, using the three-parameter logistic model generates larger equating bias and standard errors of equating compared to the TRT model. However, observed score equating is more robust to the violation of the local item independence assumption than is true score equating.     

Applications of the Analytically Derived Asymptotic Standard Errors of Item Response Theory Item Parameter Estimates

The analytically derived asymptotic standard errors (SEs) of maximum likelihood (ML) item estimates can be approximated by a mathematical function without examinees' responses to test items, and the empirically determined SEs of marginal maximum likelihood estimation (MMLE)/Bayesian item estimates can be obtained when the same set of items is repeatedly estimated from the simulation (or resampling) test data. The latter method will result in rather stable and accurate SE estimates as the number of replications increases, but requires cumbersome and time-consuming calculations. Instead of using the empirically determined method, the adequacy of using the analytical-based method in predicting the SEs for item parameter estimates was examined by comparing results produced from both approaches. The results indicated that the SEs yielded from both approaches were, in most cases, very similar, especially when they were applied to a generalized partial credit model. This finding encourages test practitioners and researchers to apply the analytically asymptotic SEs of item estimates to the context of item-linking studies, as well as to the method of quantifying the SEs of equating scores for the item response theory (IRT) true-score method. Three-dimensional graphical presentation for the analytical SEs of item estimates as the bivariate function of item difficulty together with item discrimination was also provided for a better understanding of several frequently used IRT models.     

A Method for Maintaining Scale Stability in the Presence of Test Speededness

Administering tests under time constraints may result in poorly estimated item parameters, particularly for items at the end of the test (Douglas, Kim, Habing, & Gao, 1998; Oshima, 1994). Bolt, Cohen, and Wollack (2002) developed an item response theory mixture model to identify a latent group of examinees for whom a test is overly speeded, and found that item parameter estimates for end-of-test items in the nonspeeded group were similar to estimates for those same items when administered earlier in the test. In this study, we used the Bolt et al. (2002) method to study the effect of removing speeded examinees on the stability of a score scale over an II-year period. Results indicated that using only the nonspeeded examinees for equating and estimating item parameters provided a more unidimensional scale, smaller effects of item parameter drift (including fewer drifting items), and less scale drift (i.e., bias) and variability (i.e., root mean squared errors) when compared to the total group of examinees.     

Stepwise Analysis of Differential Item Functioning Based on Multiple-Group Partial Credit Model

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.     

Unidimensional Interpretations for Multidimensional Test Items

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.     

IRT Equating Methods

The purpose of this instructional module is to provide the basis for understanding the process of score equating through the use of item response theory (IRT). A context is provided for addressing the merits of IRT equating methods. The mechanics of IRT equating and the need to place parameter estimates from separate calibration runs on the same scale are discussed. Some procedures for placing parameter estimates on a common scale are presented. In addition, IRT true-score equating is discussed in some detail. A discussion of the practical advantages derived from IRT equating is offered at the end of the module.     

Multidimensional Linking for Tests with Mixed Item Types

Numerous assessments contain a mixture of multiple choice (MC) and constructed response (CR) item types and many have been found to measure more than one trait. Thus, there is a need for multidimensional dichotomous and polytomous item response theory (IRT) modeling solutions, including multidimensional linking software. For example, multidimensional item response theory (MIRT) may have a promising future in subscale score proficiency estimation, leading toward a more diagnostic orientation, which requires the linking of these subscale scores across different forms and populations. Several multidimensional linking studies can be found in the literature; however, none have used a combination of MC and CR item types. Thus, this research explores multidimensional linking accuracy for tests composed of both MC and CR items using a matching test characteristic/response function approach. The two-dimensional simulation study presented here used real data-derived parameters from a large-scale statewide assessment with two subscale scores for diagnostic profiling purposes, under varying conditions of anchor set lengths (6, 8, 16, 32, 60), across 10 population distributions, with a mixture of simple versus complex structured items, using a sample size of 3,000. It was found that for a well chosen anchor set, the parameters recovered well after equating across all populations, even for anchor sets composed of as few as six items.     

A new approach to test score equating using item response theory with fixed C-parameters

Because parameter estimates from different calibration runs under the IRT model are linearly related, a linear equation can convert IRT parameter estimates onto another scale metric without changing the probability of a correct response (Kolen & Brennan, 1995, 2004). This study was designed to explore a new approach to finding a linear equation by fixing C-parameters for anchor items in IRT equating. A rationale for fixing C-parameters for anchor items in IRT equating can be established from the fact that the C-parameters are not affected by any linear transformation. This new approach can avoid the difficulty in getting accurate C-parameters for anchor items embedded in the application of the IRT model. Based upon our findings in this study, we would recommend using the new approach to fix C-parameters for anchor items in IRT equating. This work was supported by a Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research     

The Effects of Test Length and Sample Size on the Reliability and Equating of Tests Composed of Constructed-Response Items

Examined in this study were the effects of test length and sample size on the alternate forms reliability and the equating of simulated mathematics tests composed of constructed-response items scaled using the 2-parameter partial credit model. Test length was defined in terms of the number of both items and score points per item. Tests with 2, 4, 8, 12, and 20 items were generated, and these items had 2, 4, and 6 score points. Sample sizes of 200, 500, and 1,000 were considered. Precise item parameter estimates were not found when 200 cases were used to scale the items. To obtain acceptable reliabilities and accurate equated scores, the findings suggested that tests should have at least eight 6-point items or at least 12 items with 4 or more score points per item.     

A Comparison Between Linear IRT Observed‐Score Equating and Levine Observed‐Score Equating Under the Generalized Kernel Equating Framework

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.     

测验等值是开发中考评价功能之必需

中考是各地区规模较大和有影响力的高利害性考试,只有建立科学完善的考试评价系统才能充分发挥中考对地区初中教学多方面的服务作用,而建立完善考试评价系统的必备程序是等值。IRT等值的步骤包括估计项目参数、进行IRT量表转换以及制作分数转换表。