Improving the Bandwidth Selection in Kernel Equating

We investigate the current bandwidth selection methods in kernel equating and propose a method based on Silverman's rule of thumb for selecting the bandwidth parameters. In kernel equating, the bandwidth parameters have previously been obtained by minimizing a penalty function. This minimization process has been criticized by practitioners for being too complex and that it does not offer sufficient smoothing in certain cases. In addition, the bandwidth parameters have been treated as constants in the derivation of the standard error of equating even when they were selected by considering the observed data. Here, the bandwidth selection is simplified, and modified standard errors of equating (SEEs) that reflect the bandwidth selection method are derived. The method is illustrated with real data examples and simulated data.     

Local Observed‐Score Kernel Equating

Three local observed‐score kernel equating methods that integrate methods from the local equating and kernel equating frameworks are proposed. The new methods were compared with their earlier counterparts with respect to such measures as bias—as defined by Lord's criterion of equity—and percent relative error. The local kernel item response theory observed‐score equating method, which can be used for any of the common equating designs, had a small amount of bias, a low percent relative error, and a relatively low kernel standard error of equating, even when the accuracy of the test was reduced. The local kernel equating methods for the nonequivalent groups with anchor test generally had low bias and were quite stable against changes in the accuracy or length of the anchor test. Although all proposed methods showed small percent relative errors, the local kernel equating methods for the nonequivalent groups with anchor test design had somewhat larger standard error of equating than their kernel method counterparts.     

Local Linear Observed‐Score Equating

Two methods of local linear observed‐score equating for use with anchor‐test and single‐group designs are introduced. In an empirical study, the two methods were compared with the current traditional linear methods for observed‐score equating. As a criterion, the bias in the equated scores relative to true equating based on Lord's (1980) definition of equity was used. The local method for the anchor‐test design yielded minimum bias, even for considerable variation of the relative difficulties of the two test forms and the length of the anchor test. Among the traditional methods, the method of chain equating performed best. The local method for single‐group designs yielded equated scores with bias comparable to the traditional methods. This method, however, appears to be of theoretical interest because it forces us to rethink the relationship between score equating and regression.     

Smoothing Methods for Estimating Test Score Distributions

Frequency distributions of test scores may appear irregular and, as estimates of a population distribution, contain a substantial amount of sampling error. Techniques for smoothing score distributions are available that have the capacity to improve estimation. In this article, estimation/smoothing methods that are flexible enough to fit a wide variety of test score distributions are reviewed. The methods are a kernel method, a strong true–score model–based method, and a method that uses polynomial log–linear models. The use of these methods is then reviewed, and applications of the methods are presented that include describing and comparing test score distributions, estimating norms, and estimating equipercentile equivalents in test score equating. Suggestions for further research are also provided.     

A New Statistic for Selecting the Smoothing Parameter for Polynomial Loglinear Equating Under the Random Groups Design

Smoothing is designed to yield smoother equating results that can reduce random equating error without introducing very much systematic error. The main objective of this study is to propose a new statistic and to compare its performance to the performance of the Akaike information criterion and likelihood ratio chi-square difference statistics in selecting the smoothing parameter for polynomial loglinear equating under the random groups design. These model selection statistics were compared for four sample sizes (500, 1,000, 2,000, and 3,000) and eight simulated equating conditions, including both conditions where equating is not needed and conditions where equating is needed. The results suggest that all model selection statistics tend to improve the equating accuracy by reducing the total equating error. The new statistic tended to have less overall error than the other two methods.     

Equating With Miditests Using IRT

The equating performance of two internal anchor test structures—miditests and minitests—is studied for four IRT equating methods using simulated data. Originally proposed by Sinharay and Holland, miditests are anchors that have the same mean difficulty as the overall test but less variance in item difficulties. Four popular IRT equating methods were tested, and both the means and SDs of the true ability of the group to be equated were varied. We evaluate equating accuracy marginally and conditional on true ability. Our results suggest miditests perform about as well as traditional minitests for most conditions. Findings are discussed in terms of comparability to the typical minitest design and the trade‐off between accuracy and flexibility in test construction.     

Evaluation of Two New Smoothing Methods in Equating: The Cubic B-Spline Presmoothing Method and the Direct Presmoothing Method

This article considers two new smoothing methods in equipercentile equating , the cubic B-spline presmoothing method and the direct presmoothing method. Using a simulation study , these two methods are compared with established methods , the beta-4 method , the polynomial loglinear method , and the cubic spline postsmoothing method , under three sample sizes (300 , 1,000 , and 3,000) and for three test content areas (ITBS Maps and Diagrams , ITBS Reference and Materials , and ITBS Capitalization). Ten thousand random samples were simulated from population distributions , and the standard error , bias , and RMSE statistics were calculated. The cubic B-spline presmoothing method performed well in reducing total error of equating , whereas the direct presmoothing method appeared to need some modification for it to be as accurate as other smoothing methods.     

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.     

Practical Application of a Synthetic Linking Function on Small-Sample Equating

The synthetic function is a weighted average of the identity (the linking function for forms that are known to be completely parallel) and a traditional equating method. The purpose of the present study was to investigate the benefits of the synthetic function on small-sample equating using various real data sets gathered from different administrations of tests from a licensure testing program. We investigated the chained linear, Tucker, Levine, and mean equating methods, along with the identity and the synthetic functions with small samples (N = 19 to 70). The synthetic function did not perform as well as did other linear equating methods because test forms differed markedly in difficulty; thus, the use of the identity function produced substantial bias. The effectiveness of the synthetic function depended on the forms' similarity in difficulty.     

一种间接求解约束优化问题的遗传算法

传统的遗传算法在求解带约束的数值优化问题时,主要采用罚函数法。文章针对罚函数法在实际应用中的不足,提出了一种将约束优化问题转化为双目标优化问题,然后使用双目标遗传算法进行求解的方法。仿真结果表明该方法是一种有效的约束问题寻优方法。     

A Comparison of the Kernel Equating Method with Traditional Equating Methods Using SAT® Data

This study applied kernel equating (KE) in two scenarios: equating to a very similar population and equating to a very different population, referred to as a distant population, using SAT^® data. The KE results were compared to the results obtained from analogous traditional equating methods in both scenarios. The results indicate that KE results are comparable to the results of other methods. Further, the results show that when the two populations taking the two tests are similar on the anchor score distributions, different equating methods yield the same or very similar results, even though they have different assumptions.     

A New Approach to Comparing Several Equating Methods in the Context of the NEAT Design

The nonequivalent groups with anchor test (NEAT) design involves missing data that are missing by design. Three equating methods that can be used with a NEAT design are the frequency estimation equipercentile equating method, the chain equipercentile equating method, and the item-response-theory observed-score-equating method. We suggest an approach to perform a fair comparison of the three methods. The approach is then applied to compare the three equating methods using three data sets from operational tests. For each data set, we examine how the three equating methods perform when the missing data satisfy the assumptions made by only one of these equating methods. The chain equipercentile equating method is somewhat more satisfactory overall than the other methods.     

Selection Strategies for Univariate Loglinear Smoothing Models and Their Effect on Equating Function Accuracy

In this study, we compared 12 statistical strategies proposed for selecting loglinear models for smoothing univariate test score distributions and for enhancing the stability of equipercentile equating functions. The major focus was on evaluating the effects of the selection strategies on equating function accuracy. Selection strategies' influence on the estimation of cumulative test score distributions was also assessed. The results of this simulation study differentiate the selection strategies and define the situations where their use has the most important implications for equating function accuracy. The recommended strategy for estimating test score distributions and for equating is AIC minimization.     

An Approach to Evaluating the Missing Data Assumptions of the Chain and Post-stratification Equating Methods for the NEAT Design

Two important types of observed score equating (OSE) methods for the non-equivalent groups with Anchor Test (NEAT) design are chain equating (CE) and post-stratification equating (PSE). CE and PSE reflect two distinctly different ways of using the information provided by the anchor test for computing OSE functions. Both types of methods include linear and nonlinear equating functions. In practical situations, it is known that the PSE and CE methods will give different results when the two groups of examinees differ on the anchor test. However, given that both types of methods are justified as OSE methods by making different assumptions about the missing data in the NEAT design, it is difficult to conclude which, if either, of the two is more correct in a particular situation. This study compares the predictions of the PSE and CE assumptions for the missing data using a special data set for which the usually missing data are available. Our results indicate that in an equating setting where the linking function is decidedly non-linear and CE and PSE ought to be different, both sets of predictions are quite similar but those for CE are slightly more accurate .     

Several Variations of Simple-Structure MIRT Equating

The current study proposed several variants of simple-structure multidimensional item response theory equating procedures. Four distinct sets of data were used to demonstrate feasibility of proposed equating methods for two different equating designs: a random groups design and a common-item nonequivalent groups design. Findings indicated some notable differences between the multidimensional and unidimensional approaches when data exhibited evidence for multidimensionality. In addition, some of the proposed methods were successful in providing equating results for both section-level and composite-level scores, which has not been achieved by most of the existing methodologies. The traditional method of using a set of quadrature points and weights for equating turned out to be computationally intensive, particularly for the data with higher dimensions. The study suggested an alternative way of using the Monte-Carlo approach for such data. This study also proposed a simple-structure true-score equating procedure that does not rely on a multivariate observed-score distribution.     

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.     

Statistical Assessment of Estimated Transformations in Observed‐Score Equating

Equating methods make use of an appropriate transformation function to map the scores of one test form into the scale of another so that scores are comparable and can be used interchangeably. The equating literature shows that the ways of judging the success of an equating (i.e., the score transformation) might differ depending on the adopted framework. Rather than targeting different parts of the equating process and aiming to evaluate the process from different aspects, this article views the equating transformation as a standard statistical estimator and discusses how this estimator should be assessed in an equating framework. For the kernel equating framework, a numerical illustration shows the potentials of viewing the equating transformation as a statistical estimator as opposed to assessing it using equating‐specific criteria. A discussion on how this approach can be used to compare other equating estimators from different frameworks is also included.     

Small‐Sample Equating Using a Synthetic Linking Function

This study addressed the sampling error and linking bias that occur with small samples in a nonequivalent groups anchor test design. We proposed a linking method called the synthetic function, which is a weighted average of the identity function and a traditional equating function (in this case, the chained linear equating function). Specifically, we compared the synthetic, identity, and chained linear functions for various‐sized samples from two types of national assessments. One design used a highly reliable test and an external anchor, and the other used a relatively low‐reliability test and an internal anchor. The results from each of these methods were compared to the criterion equating function derived from the total samples with respect to linking bias and error. The study indicated that the synthetic functions might be a better choice than the chained linear equating method when samples are not large and, as a result, unrepresentative.     

Evaluating Equating Results: Percent Relative Error for Chained Kernel Equating

This article presents a method for evaluating equating results. Within the kernel equating framework, the percent relative error (PRE) for chained equipercentile equating was computed under the nonequivalent groups with anchor test (NEAT) design. The method was applied to two data sets to obtain the PRE, which can be used to measure equating effectiveness. The study compared the PRE results for chained and poststratification equating. The results indicated that the chained method transformed the new form score distribution to the reference form scale more effectively than the poststratification method. In addition, the study found that in chained equating, the population weight had impact on score distributions over the target population but not on the equating and PRE results.     

The Effect of Repeaters on Equating

Test equating might be affected by including in the equating analyses examinees who have taken the test previously. This study evaluated the effect of including such repeaters on Medical College Admission Test (MCAT) equating using a population invariance approach. Three-parameter logistic (3-PL) item response theory (IRT) true score and traditional equipercentile equating methods were used under the random groups equating design. This study also examined whether or not population sensitivity of equating by repeater status varies depending on other background variables (gender and ethnicity). The results indicated that there was some evidence of repeaters' effect on equating with varying amounts of such effect by gender.