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1.
Item response models are finding increasing use in achievement and aptitude test development. Item response theory (IRT) test development involves the selection of test items based on a consideration of their item information functions. But a problem arises because item information functions are determined by their item parameter estimates, which contain error. When the "best" items are selected on the basis of their statistical characteristics, there is a tendency to capitalize on chance due to errors in the item parameter estimates. The resulting test, therefore, falls short of the test that was desired or expected. The purposes of this article are (a) to highlight the problem of item parameter estimation errors in the test development process, (b) to demonstrate the seriousness of the problem with several simulated data sets, and (c) to offer a conservative solution for addressing the problem in IRT-based test development.  相似文献   

2.
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   

3.
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   

4.
In structural equation modeling software, either limited-information (bivariate proportions) or full-information item parameter estimation routines could be used for the 2-parameter item response theory (IRT) model. Limited-information methods assume the continuous variable underlying an item response is normally distributed. For skewed and platykurtic latent variable distributions, 3 methods were compared in Mplus: limited information, full information integrating over a normal distribution, and full information integrating over the known underlying distribution. Interfactor correlation estimates were similar for all 3 estimation methods. For the platykurtic distribution, estimation method made little difference for the item parameter estimates. When the latent variable was negatively skewed, for the most discriminating easy or difficult items, limited-information estimates of both parameters were considerably biased. Full-information estimates obtained by marginalizing over a normal distribution were somewhat biased. Full-information estimates obtained by integrating over the true latent distribution were essentially unbiased. For the a parameters, standard errors were larger for the limited-information estimates when the bias was positive but smaller when the bias was negative. For the d parameters, standard errors were larger for the limited-information estimates of the easiest, most discriminating items. Otherwise, they were generally similar for the limited- and full-information estimates. Sample size did not substantially impact the differences between the estimation methods; limited information did not gain an advantage for smaller samples.  相似文献   

5.
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.  相似文献   

6.
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   

7.
本文研究的是不同的测试方法-单项选择和信息转移-是否会在阅读理解考试中产生测试方法效应的问题.除对学生的考试成绩(分数)进行分析外,本研究还进一步对试题的难度值进行了分析,而本研究中试题难度是通过项目反应理论(Item Response Theory)计算得到的.结果显示不同测试方法的确会影响题目难度及考生的考试表现,就试题难度而言信息转移比单项选择更难.  相似文献   

8.
Two item selection algorithms were compared in simulated linear and adaptive tests of cognitive ability. One algorithm selected items that maximally differentiated between examinees. The other used item response theory (IRT) to select items having maximum information for each examinee. Normally distributed populations of 1,000 cases were simulated, using test lengths of 4, 5, 6, and 7 items. Overall, adaptive tests based on maximum information provided the most information over the widest range of ability values and, in general, differentiated among examinees slightly better than the other tests. Although the maximum differentiation technique may be adequate in some circumstances, adaptive tests based on maximum information are clearly superior.  相似文献   

9.
Using factor analysis, we conducted an assessment of multidimensionality for 6 forms of the Law School Admission Test (LSAT) and found 2 subgroups of items or factors for each of the 6 forms. The main conclusion of the factor analysis component of this study was that the LSAT appears to measure 2 different reasoning abilities: inductive and deductive. The technique of N. J. Dorans & N. M. Kingston (1985) was used to examine the effect of dimensionality on equating. We began by calibrating (with item response theory [IRT] methods) all items on a form to obtain Set I of estimated IRT item parameters. Next, the test was divided into 2 homogeneous subgroups of items, each having been determined to represent a different ability (i.e., inductive or deductive reasoning). The items within these subgroups were then recalibrated separately to obtain item parameter estimates, and then combined into Set II. The estimated item parameters and true-score equating tables for Sets I and II corresponded closely.  相似文献   

10.
The usefulness of item response theory (IRT) models depends, in large part, on the accuracy of item and person parameter estimates. For the standard 3 parameter logistic model, for example, these parameters include the item parameters of difficulty, discrimination, and pseudo-chance, as well as the person ability parameter. Several factors impact traditional marginal maximum likelihood (ML) estimation of IRT model parameters, including sample size, with smaller samples generally being associated with lower parameter estimation accuracy, and inflated standard errors for the estimates. Given this deleterious impact of small samples on IRT model performance, use of these techniques with low-incidence populations, where it might prove to be particularly useful, estimation becomes difficult, especially with more complex models. Recently, a Pairwise estimation method for Rasch model parameters has been suggested for use with missing data, and may also hold promise for parameter estimation with small samples. This simulation study compared item difficulty parameter estimation accuracy of ML with the Pairwise approach to ascertain the benefits of this latter method. The results support the use of the Pairwise method with small samples, particularly for obtaining item location estimates.  相似文献   

11.
Item analysis is an integral part of operational test development and is typically conducted within two popular statistical frameworks: classical test theory (CTT) and item response theory (IRT). In this digital ITEMS module, Hanwook Yoo and Ronald K. Hambleton provide an accessible overview of operational item analysis approaches within these frameworks. They review the different stages of test development and associated item analyses to identify poorly performing items and effective item selection. Moreover, they walk through the computational and interpretational steps for CTT‐ and IRT‐based evaluation statistics using simulated data examples and review various graphical displays such as distractor response curves, item characteristic curves, and item information curves. The digital module contains sample data, Excel sheets with various templates and examples, diagnostic quiz questions, data‐based activities, curated resources, and a glossary.  相似文献   

12.
Automatic item generation (AIG)—a means of leveraging technology to create large quantities of items—requires a minimum number of items to offset the sizable upfront investment (i.e., model development and technology deployment) in order to achieve cost savings. In this cost–benefit analysis, we estimated the cost of each step of AIG and manual item writing and applied cost—benefit formulas to calculate the number of items that would have to be produced before the upfront costs of AIG outweigh manual item writing costs in the context of K‐12 mathematics items. Results indicated that AIG is more cost‐effective than manual item writing when developing, at a minimum, 173 to 247 items within one fine‐grained content area (e.g., fourth‐ through seventh‐grade area of figures). The article concludes with a discussion of implications for test developers and the nonmonetary tradeoffs involved in AIG.  相似文献   

13.
During computerized adaptive testing (CAT), items are selected continuously according to the test-taker's estimated ability. The traditional method of attaining the highest efficiency in ability estimation is to select items of maximum Fisher information at the currently estimated ability. Test security has become a problem because high-discrimination items are more likely to be selected and become overexposed. So, there seems to be a tradeoff between high efficiency in ability estimations and balanced usage of items. This series of four studies with simulated data addressed the dilemma by focusing on the notion of whether more or less discriminating items should be used first in CAT. The first study demonstrated that the common maximum information method with Sympson and Hetter (1985) control resulted in the use of more discriminating items first. The remaining studies showed that using items in the reverse order (i.e., less discriminating items first), as described in Chang and Ying's (1999) stratified method had potential advantages: (a) a more balanced item usage and (b) a relatively stable resultant item pool structure with easy and inexpensive management. This stratified method may have ability-estimation efficiency better than or close to that of other methods, particularly for operational item pools when retired items cannot be totally replenished with similar highly discriminating items. It is argued that the judicious selection of items, as in the stratified method, is a more active control of item exposure, which can successfully even out the usage of all items.  相似文献   

14.
The validity of inferences based on achievement test scores is dependent on the amount of effort that examinees put forth while taking the test. With low-stakes tests, for which this problem is particularly prevalent, there is a consequent need for psychometric models that can take into account differing levels of examinee effort. This article introduces the effort-moderated IRT model, which incorporates item response time into proficiency estimation and item parameter estimation. In two studies of the effort-moderated model when rapid guessing (i.e., reflecting low examinee effort) was present, one based on real data and the other on simulated data, the effort-moderated model performed better than the standard 3PL model. Specifically, it was found that the effort-moderated model (a) showed better model fit, (b) yielded more accurate item parameter estimates, (c) more accurately estimated test information, and (d) yielded proficiency estimates with higher convergent validity.  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.
As low-stakes testing contexts increase, low test-taking effort may serve as a serious validity threat. One common solution to this problem is to identify noneffortful responses and treat them as missing during parameter estimation via the effort-moderated item response theory (EM-IRT) model. Although this model has been shown to outperform traditional IRT models (e.g., two-parameter logistic [2PL]) in parameter estimation under simulated conditions, prior research has failed to examine its performance under violations to the model’s assumptions. Therefore, the objective of this simulation study was to examine item and mean ability parameter recovery when violating the assumptions that noneffortful responding occurs randomly (Assumption 1) and is unrelated to the underlying ability of examinees (Assumption 2). Results demonstrated that, across conditions, the EM-IRT model provided robust item parameter estimates to violations of Assumption 1. However, bias values greater than 0.20 SDs were observed for the EM-IRT model when violating Assumption 2; nonetheless, these values were still lower than the 2PL model. In terms of mean ability estimates, model results indicated equal performance between the EM-IRT and 2PL models across conditions. Across both models, mean ability estimates were found to be biased by more than 0.25 SDs when violating Assumption 2. However, our accompanying empirical study suggested that this biasing occurred under extreme conditions that may not be present in some operational settings. Overall, these results suggest that the EM-IRT model provides superior item and equal mean ability parameter estimates in the presence of model violations under realistic conditions when compared with the 2PL model.  相似文献   

17.
The psychometric literature provides little empirical evaluation of examinee test data to assess essential psychometric properties of innovative items. In this study, examinee responses to conventional (e.g., multiple choice) and innovative item formats in a computer-based testing program were analyzed for IRT information with the three-parameter and graded response models. The innovative item types considered in this study provided more information across all levels of ability than multiple-choice items. In addition, accurate timing data captured via computer administration were analyzed to consider the relative efficiency of the multiple choice and innovative item types. As with previous research, multiple-choice items provide more information per unit time. Implications for balancing policy, psychometric, and pragmatic factors in selecting item formats are also discussed.  相似文献   

18.
This article illustrates five different methods for estimating Angoff cut scores using item response theory (IRT) models. These include maximum likelihood (ML), expected a priori (EAP), modal a priori (MAP), and weighted maximum likelihood (WML) estimators, as well as the most commonly used approach based on translating ratings through the test characteristic curve (i.e., the IRT true‐score (TS) estimator). The five methods are compared using a simulation study and a real data example. Results indicated that the application of different methods can sometimes lead to different estimated cut scores, and that there can be some key differences in impact data when using the IRT TS estimator compared to other methods. It is suggested that one should carefully think about their choice of methods to estimate ability and cut scores because different methods have distinct features and properties. An important consideration in the application of Bayesian methods relates to the choice of the prior and the potential bias that priors may introduce into estimates.  相似文献   

19.
The current study investigated how item formats and their inherent affordances influence test‐takers’ cognition under uncertainty. Adult participants solved content‐equivalent math items in multiple‐selection multiple‐choice and four alternative grid formats. The results indicated that participants’ affirmative response tendency (i.e., judge the given information as True) was affected by the presence of a grid, type of grid options, and their visual layouts. The item formats further affected the test scores obtained from the alternatives keyed True and the alternatives keyed False, and their psychometric properties. The current results suggest that the affordances rendered by item design can lead to markedly different test‐taker behaviors and can potentially influence test outcomes. They emphasize that a better understanding of the cognitive implications of item formats could potentially facilitate item design decisions for large‐scale educational assessments.  相似文献   

20.
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.  相似文献   

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