首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 421 毫秒
1.
This study investigates the effect of multidimensionality on extraction of latent classes in mixture Rasch models. In this study, two‐dimensional data were generated under varying conditions. The two‐dimensional data sets were analyzed with one‐ to five‐class mixture Rasch models. Results of the simulation study indicate the mixture Rasch model tended to extract more latent classes than the number of dimensions simulated, particularly when the multidimensional structure of the data was more complex. In addition, the number of extracted latent classes decreased as the dimensions were more highly correlated regardless of multidimensional structure. An analysis of the empirical multidimensional data also shows that the number of latent classes extracted by the mixture Rasch model is larger than the number of dimensions measured by the test.  相似文献   

2.
In the presence of test speededness, the parameter estimates of item response theory models can be poorly estimated due to conditional dependencies among items, particularly for end‐of‐test items (i.e., speeded items). This article conducted a systematic comparison of five‐item calibration procedures—a two‐parameter logistic (2PL) model, a one‐dimensional mixture model, a two‐step strategy (a combination of the one‐dimensional mixture and the 2PL), a two‐dimensional mixture model, and a hybrid model‐–by examining how sample size, percentage of speeded examinees, percentage of missing responses, and way of scoring missing responses (incorrect vs. omitted) affect the item parameter estimation in speeded tests. For nonspeeded items, all five procedures showed similar results in recovering item parameters. For speeded items, the one‐dimensional mixture model, the two‐step strategy, and the two‐dimensional mixture model provided largely similar results and performed better than the 2PL model and the hybrid model in calibrating slope parameters. However, those three procedures performed similarly to the hybrid model in estimating intercept parameters. As expected, the 2PL model did not appear to be as accurate as the other models in recovering item parameters, especially when there were large numbers of examinees showing speededness and a high percentage of missing responses with incorrect scoring. Real data analysis further described the similarities and differences between the five procedures.  相似文献   

3.
Abstract

Multilevel Rasch models are increasingly used to estimate the relationships between test scores and student and school factors. Response data were generated to follow one-, two-, and three-parameter logistic (1PL, 2PL, 3PL) models, but the Rasch model was used to estimate the latent regression parameters. When the response functions followed 2PL or 3PL models, the proportion of variance explained in test scores by the simulated student or school predictors was estimated accurately with a Rasch model. Proportion of variance within and between schools was also estimated accurately. The regression coefficients were misestimated unless they were rescaled out of logit units. However, item-level parameters, such as DIF effects, were biased when the Rasch model was violated, similar to single-level models.  相似文献   

4.
The purpose of this study was to investigate whether simulated differential motivation between the stakes for operational tests and anchor items produces an invalid linking result if the Rasch model is used to link the operational tests. This was done for an external anchor design and a variation of a pretest design. The study also investigated whether a constrained mixture Rasch model could identify latent classes in such a way that one latent class represented high‐stakes responding while the other represented low‐stakes responding. The results indicated that for an external anchor design, the Rasch linking result was only biased when the motivation level differed between the subpopulations to which the anchor items were administered. However, the mixture Rasch model did not identify the classes representing low‐stakes and high‐stakes responding. When a pretest design was used to link the operational tests by means of a Rasch model, the linking result was found to be biased in each condition. Bias increased as percentage of students showing low‐stakes responding to the anchor items increased. The mixture Rasch model only identified the classes representing low‐stakes and high‐stakes responding under a limited number of conditions.  相似文献   

5.
Compared to unidimensional item response models (IRMs), cognitive diagnostic models (CDMs) based on latent classes represent examinees' knowledge and item requirements using discrete structures. This study systematically examines the viability of retrofitting CDMs to IRM‐based data with a linear attribute structure. The study utilizes a procedure to make the IRM and CDM frameworks comparable and investigates how estimation accuracy is affected by test diagnosticity and the match between the true and fitted models. The study shows that comparable results can be obtained when highly diagnostic IRM data are retrofitted with CDM, and vice versa, retrofitting CDMs to IRM‐based data in some conditions can result in considerable examinee misclassification, and model fit indices provide limited indication of the accuracy of item parameter estimation and attribute classification.  相似文献   

6.
Functional form misfit is frequently a concern in item response theory (IRT), although the practical implications of misfit are often difficult to evaluate. In this article, we illustrate how seemingly negligible amounts of functional form misfit, when systematic, can be associated with significant distortions of the score metric in vertical scaling contexts. Our analysis uses two‐ and three‐parameter versions of Samejima's logistic positive exponent model (LPE) as a data generating model. Consistent with prior work, we find LPEs generally provide a better comparative fit to real item response data than traditional IRT models (2PL, 3PL). Further, our simulation results illustrate how 2PL‐ or 3PL‐based vertical scaling in the presence of LPE‐induced misspecification leads to an artificial growth deceleration across grades, consistent with that commonly seen in vertical scaling studies. The results raise further concerns about the use of standard IRT models in measuring growth, even apart from the frequently cited concerns of construct shift/multidimensionality across grades.  相似文献   

7.
In one study, parameters were estimated for constructed-response (CR) items in 8 tests from 4 operational testing programs using the l-parameter and 2- parameter partial credit (IPPC and 2PPC) models. Where multiple-choice (MC) items were present, these models were combined with the 1-parameter and 3-parameter logistic (IPL and 3PL) models, respectively. We found that item fit was better when the 2PPC model was used alone or with the 3PL model. Also, the slopes of the CR and MC items were found to differ substantially. In a second study, item parameter estimates produced using the IPL-IPPC and 3PL-2PPC model combinations were evaluated for fit to simulated data generated using true parameters known to fit one model combination or ttle other. The results suggested that the more flexible 3PL-2PPC model combination would produce better item fit than the IPL-1PPC combination.  相似文献   

8.
Abstract

Factor mixture models are designed for the analysis of multivariate data obtained from a population consisting of distinct latent classes. A common factor model is assumed to hold within each of the latent classes. Factor mixture modeling involves obtaining estimates of the model parameters, and may also be used to assign subjects to their most likely latent class. This simulation study investigates aspects of model performance such as parameter coverage and correct class membership assignment and focuses on covariate effects, model size, and class-specific versus class-invariant parameters. When fitting true models, parameter coverage is good for most parameters even for the smallest class separation investigated in this study (0.5 SD between 2 classes). The same holds for convergence rates. Correct class assignment is unsatisfactory for the small class separation without covariates, but improves dramatically with increasing separation, covariate effects, or both. Model performance is not influenced by the differences in model size investigated here. Class-specific parameters may improve some aspects of model performance but negatively affect other aspects.  相似文献   

9.
When tests are administered under fixed time constraints, test performances can be affected by speededness. Among other consequences, speededness can result in inaccurate parameter estimates in item response theory (IRT) models, especially for items located near the end of tests (Oshima, 1994). This article presents an IRT strategy for reducing contamination in item difficulty estimates due to speededness. Ordinal constraints are applied to a mixture Rasch model (Rost, 1990) so as to distinguish two latent classes of examinees: (a) a "speeded" class, comprised of examinees that had insufficient time to adequately answer end-of-test items, and (b) a "nonspeeded" class, comprised of examinees that had sufficient time to answer all items. The parameter estimates obtained for end-of-test items in the nonspeeded class are shown to more accurately approximate their difficulties when the items are administered at earlier locations on a different form of the test. A mixture model can also be used to estimate the class memberships of individual examinees. In this way, it can be determined whether membership in the speeded class is associated with other student characteristics. Results are reported for gender and ethnicity.  相似文献   

10.
Both the statistical and interpretational shortcomings of the three‐parameter logistic (3PL) model in accommodating guessing effects on multiple‐choice items are well documented. We consider the use of a residual heteroscedasticity (RH) model as an alternative, and compare its performance to the 3PL with real test data sets and through simulation analyses. Our results suggest advantages to the RH approach, including closer fit to real data, more interpretable parameter estimates, and greater psychological plausibility.  相似文献   

11.
The purpose of the current study is to examine the performance of four information criteria (Akaike's information criterion [AIC], corrected AIC [AICC] Bayesian information criterion [BIC], sample-size adjusted BIC [SABIC]) for detecting the correct number of latent classes in the mixture Rasch model through simulations. The simulation study manipulated various class-distinction features (percentages of class-variant items, magnitudes, and patterns of item difficulty differences) and mixing proportions, assuming that a mixture Rasch model with two latent classes was the true model. Unlike previous studies that showed BIC's superiority to other indices, our findings from this study suggested that the four information criteria had differential performance depending on the percentage of class-variant items and the magnitude and pattern of item difficulty differences under a two-class structure. Furthermore, the present study revealed that AICC and SABIC generally performed as good as or better than their counterparts, AIC and BIC, respectively, for the class-class structure with a sample of 3,000.  相似文献   

12.
In a previous simulation study of methods for assessing differential item functioning (DIF) in computer-adaptive tests (Zwick, Thayer, & Wingersky, 1993, 1994), modified versions of the Mantel-Haenszel and standardization methods were found to perform well. In that study, data were generated using the 3-parameter logistic (3PL) model and this same model was assumed in obtaining item parameter estimates. In the current study, the 3PL data were used but the Rasch model was assumed in obtaining the item parameter estimates, which determined the information table used for item selection. Although the obtained DIF statistics were highly correlated with the generating DIF values, they tended to be smaller in magnitude than in the 3PL analysis, resulting in a lower probability of DIF detection. This reduced sensitivity appeared to be related to a degradation in the accuracy of matching. Expected true scores from the Rasch-based computer-adaptive test tended to be biased downward, particularly for lower-ability examinees  相似文献   

13.
Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study population. This article presents the results of a simulation study that examines the performance of likelihood-based tests and the traditionally used Information Criterion (ICs) used for determining the number of classes in mixture modeling. We look at the performance of these tests and indexes for 3 types of mixture models: latent class analysis (LCA), a factor mixture model (FMA), and a growth mixture models (GMM). We evaluate the ability of the tests and indexes to correctly identify the number of classes at three different sample sizes (n = 200, 500, 1,000). Whereas the Bayesian Information Criterion performed the best of the ICs, the bootstrap likelihood ratio test proved to be a very consistent indicator of classes across all of the models considered.  相似文献   

14.
The applications of item response theory (IRT) models assume local item independence and that examinees are independent of each other. When a representative sample for psychometric analysis is selected using a cluster sampling method in a testlet‐based assessment, both local item dependence and local person dependence are likely to be induced. This study proposed a four‐level IRT model to simultaneously account for dual local dependence due to item clustering and person clustering. Model parameter estimation was explored using the Markov Chain Monte Carlo method. Model parameter recovery was evaluated in a simulation study in comparison with three other related models: the Rasch model, the Rasch testlet model, and the three‐level Rasch model for person clustering. In general, the proposed model recovered the item difficulty and person ability parameters with the least total error. The bias in both item and person parameter estimation was not affected but the standard error (SE) was affected. In some simulation conditions, the difference in classification accuracy between models could go up to 11%. The illustration using the real data generally supported model performance observed in the simulation study.  相似文献   

15.
A mixed‐effects item response theory (IRT) model is presented as a logical extension of the generalized linear mixed‐effects modeling approach to formulating explanatory IRT models. Fixed and random coefficients in the extended model are estimated using a Metropolis‐Hastings Robbins‐Monro (MH‐RM) stochastic imputation algorithm to accommodate for increased dimensionality due to modeling multiple design‐ and trait‐based random effects. As a consequence of using this algorithm, more flexible explanatory IRT models, such as the multidimensional four‐parameter logistic model, are easily organized and efficiently estimated for unidimensional and multidimensional tests. Rasch versions of the linear latent trait and latent regression model, along with their extensions, are presented and discussed, Monte Carlo simulations are conducted to determine the efficiency of parameter recovery of the MH‐RM algorithm, and an empirical example using the extended mixed‐effects IRT model is presented.  相似文献   

16.
Growth mixture models combine latent growth curve models and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. Analyses based on these models are becoming quite common in social and behavioral science research because of recent advances in computing, the availability of specialized statistical programs, and the ease of programming. In this article, we show how mixture models can be fit to examine the presence of multiple latent classes by algorithmically grouping or clustering individuals who follow the same estimated growth trajectory based on an evaluation of individual case residuals. The approach is illustrated using empirical longitudinal data along with an easy to use computerized implementation.  相似文献   

17.
The Progressive Matrices items require varying degrees of analytical reasoning. Individuals high on the underlying trait measured by the Raven should score high on the test. Latent trait models applied to data of the Raven form provide a useful methodology for examining the tenability of the above hypothesis. In this study the Rasch latent model was applied to investigate the fit of observed performance on Raven items to what was expected by the model for individuals at six different levels of the underlying scale. For the most part the model showed a good fit to the test data. The findings were similar to previous empirical work that has investigated the behavior of Rasch test scores. In three instances, however, the item fit statistic was relatively large. A closer study of the “misfitting” items revealed two items were of extreme difficulty, which is likely to contribute to the misfit. The study raises issues about the use of the Rasch model in instances of small samples. Other issues related to the interpretation of the Rasch model to Raven-type data are discussed.  相似文献   

18.
Latent class analysis is an analytic technique often used in educational and psychological research to identify meaningful groups of individuals within a larger heterogeneous population based on a set of variables. This technique is flexible, encompassing not only a static set of variables but also longitudinal data in the form of growth mixture modeling, as well as the application to complex multilevel sampling designs. The goal of this study was to investigate—through a Monte Carlo simulation study—the performance of several methods for parameterizing multilevel latent class analysis. Of particular interest was the comparison of several such models to adequately fit Level 1 (individual) data, given a correct specification of the number of latent classes at both levels (Level 1 and Level 2). Results include the parameter estimation accuracy as well as the quality of classification at Level 1.  相似文献   

19.
We propose a maximum likelihood framework for estimating finite mixtures of multivariate regression and simultaneous equation models with multiple endogenous variables. The proposed “semi‐parametric” approach posits that the sample of endogenous observations arises from a finite mixture of components (or latent‐classes) of unknown proportions with multiple structural relations implied by the specified model for each latent‐class. We devise an Expectation‐Maximization algorithm in a maximum likelihood framework to simultaneously estimate the class proportions, the class‐specific structural parameters, and posterior probabilities of membership of each observation into each latent‐class. The appropriate number of classes can be chosen using various information‐theoretic heuristics. A data set entailing cross‐sectional observations for a diverse sample of businesses is used to illustrate the proposed approach.  相似文献   

20.
Growth mixture modeling (GMM) is a useful statistical method for longitudinal studies because it includes features of both latent growth modeling (LGM) and finite mixture modeling. This Monte Carlo simulation study explored the impact of ignoring 3 types of time series processes (i.e., AR(1), MA(1), and ARMA(1,1)) in GMM and manipulated the separation of the latent classes, the strength of the time series process, and whether the errors conformed to the time series process in 1 or 2 latent classes. The results showed that omitting time series processes resulted in more serious bias in parameter estimation as the distance between classes increased. However, when the class distances were small, ignoring time series processes contributed to the selection of the correct number of classes. When the GMM models correctly specified the time series process, only models with an AR(1) time series process produced unbiased parameter estimates in most conditions. It was also found that among design factors manipulated, the distance between classes prominently affected the identification of the number of classes and parameter estimation.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号