共查询到20条相似文献，搜索用时 250 毫秒
1.
In this study we compared five item selection procedures using three ability estimation methods in the context of a mixed-format adaptive test based on the generalized partial credit model. The item selection procedures used were maximum posterior weighted information, maximum expected information, maximum posterior weighted Kullback-Leibler information, and maximum expected posterior weighted Kullback-Leibler information procedures. The ability estimation methods investigated were maximum likelihood estimation (MLE), weighted likelihood estimation (WLE), and expected a posteriori (EAP). Results suggested that all item selection procedures, regardless of the information functions on which they were based, performed equally well across ability estimation methods. The principal conclusions drawn about the ability estimation methods are that MLE is a practical choice and WLE should be considered when there is a mismatch between pool information and the population ability distribution. EAP can serve as a viable alternative when an appropriate prior ability distribution is specified. Several implications of the findings for applied measurement are discussed. 相似文献
2.
对正交频分复用(OFDM)系统中的两种导频辅助信道估计方法，即最大似然估计(MLE)和贝叶斯最小均方误差估计(MMSEE)，在估计误差性能方面的特性进行了详尽的比较研究。理论分析及计算机仿真试验表明，当信道噪比较低时，MMSEE有比MLE更好的估计误差性能，但有比MLE复杂得多的计算复杂度。而当信道信噪比较高或插入导引符号序列数目足够多时，MMSEE与MLE误差估计性能基本一致。 相似文献
3.
Error indices (bias, standard error of estimation, and root mean squared error) obtained on different measurement scales under different test-termination rules in computerized adaptive testing (CAT) were examined. Four ability estimation methods (maximum likelihood estimation, weighted likelihood estimation, expected a posterior, and maximum a posterior), three measurement scales (θ, number-correct score, and ACT score), and three test-termination rules (fixed length, fixed standard error, and target information) were studied for a real and a generated item pool. The findings indicated that the amount and direction of bias, standard error of estimation, and root mean squared error obtained under different ability estimation methods were influenced both by scale transformations and by test-termination rules in a CAT environment. The implications of these effects for testing programs are discussed. 相似文献
4.
Deborah L. Bandalos 《Structural equation modeling》2013,20(1):102-116
Robust maximum likelihood (ML) and categorical diagonally weighted least squares (cat-DWLS) estimation have both been proposed for use with categorized and nonnormally distributed data. This study compares results from the 2 methods in terms of parameter estimate and standard error bias, power, and Type I error control, with unadjusted ML and WLS estimation methods included for purposes of comparison. Conditions manipulated include model misspecification, level of asymmetry, level and categorization, sample size, and type and size of the model. Results indicate that cat-DWLS estimation method results in the least parameter estimate and standard error bias under the majority of conditions studied. Cat-DWLS parameter estimates and standard errors were generally the least affected by model misspecification of the estimation methods studied. Robust ML also performed well, yielding relatively unbiased parameter estimates and standard errors. However, both cat-DWLS and robust ML resulted in low power under conditions of high data asymmetry, small sample sizes, and mild model misspecification. For more optimal conditions, power for these estimators was adequate. 相似文献
5.
Adam E. Wyse 《Educational Measurement》2017,36(4):16-27
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
6.
When a response pattern does not fit a selected measurement model, one may resort to robust ability estimation. Two popular robust methods are biweight and Huber weight. So far, research on these methods has been quite limited. This article proposes the maximum a posteriori biweight (BMAP) and Huber weight (HMAP) estimation methods. These methods use the Bayesian prior distribution to compensate for information lost due to aberrant responses. They may also be more resistant to the detrimental effects of downweighting the nonaberrant responses. The effectiveness of BMAP and HMAP was evaluated through a Monte Carlo simulation. Results show that both methods, especially BMAP, are more effective than the original biweight and Huber weight in correcting mild forms of aberrant behavior. 相似文献
7.
The precision of the group-level IRT model applied to school ability estimation is described, assuming use of Bayesian estimation with precision represented by the standard deviation of the posterior distribution. Similarities and differences between the school-level model and the familiar individual-level IRT model are considered. School size and between-school variability, two factors not relevant at the student level, are dominant determinants of school-level precision. Under the multiple-matrix sampling design required for the school-level IRT, the number of items associated with a scale does not influence the precision at the school level. Also, the effects of school ability and item quality on school-level precision are often relatively weak. It was found that the use of Bayesian estimation could result in a systematic distortion of the true ranking of schools based on ability because of an estimation bias which is a function of school size. 相似文献
8.
Robustness of the School-Level IRT Model 总被引：1，自引：0，他引：1
Richard L. Tate 《Journal of Educational Measurement》1995,32(2):145-162
The robustness of the school-level item response theoretic (IRT) model to violations of distributional assumptions was studied in a computer simulation. Estimated precision of "expected a posteriori" (EAP) estimates of the mean school ability from BILOG 3 was compared with actual precision, varying school size, intraclass correlation, school ability, number of forms comprising the test, and item parameters. Under conditions where the school-level precision might be possibly acceptable for real school comparisons, the EAP estimates of school ability were robust over a wide range of violations and conditions, with the estimated precision being either consistent with the actual precision or somewhat conservative. Some lack of robustness was found, however, under conditions where the precision was inherently poor and the test would presumably not be used for serious school comparisons. 相似文献
9.
This article compares maximum likelihood and Bayesian estimation of the correlated trait–correlated method (CT–CM) confirmatory factor model for multitrait–multimethod (MTMM) data. In particular, Bayesian estimation with minimally informative prior distributions—that is, prior distributions that prescribe equal probability across the known mathematical range of a parameter—are investigated as a source of information to aid convergence. Results from a simulation study indicate that Bayesian estimation with minimally informative priors produces admissible solutions more often maximum likelihood estimation (100.00% for Bayesian estimation, 49.82% for maximum likelihood). Extra convergence does not come at the cost of parameter accuracy; Bayesian parameter estimates showed comparable bias and better efficiency compared to maximum likelihood estimates. The results are echoed via 2 empirical examples. Hence, Bayesian estimation with minimally informative priors outperforms enables admissible solutions of the CT–CM model for MTMM data. 相似文献
10.
Conventionally, moderated mediation analysis is conducted through adding relevant interaction terms into a mediation model of interest. In this study, we illustrate how to conduct moderated mediation analysis by directly modeling the relation between the indirect effect components including a and b and the moderators, to permit easier specification and interpretation of moderated mediation. With this idea, we introduce a general moderated mediation model that can be used to model many different moderated mediation scenarios including the scenarios described in Preacher, Rucker, and Hayes (2007). Then we discuss how to estimate and test the conditional indirect effects and to test whether a mediation effect is moderated using Bayesian approaches. How to implement the estimation in both BUGS and Mplus is also discussed. Performance of Bayesian methods is evaluated and compared to that of frequentist methods including maximum likelihood (ML) with 1st-order and 2nd-order delta method standard errors and mL with bootstrap (percentile or bias-corrected confidence intervals) via a simulation study. The results show that Bayesian methods with diffuse (vague) priors implemented in both BUGS and Mplus yielded unbiased estimates, higher power than the ML methods with delta method standard errors, and the ML method with bootstrap percentile confidence intervals, and comparable power to the ML method with bootstrap bias-corrected confidence intervals. We also illustrate the application of these methods with the real data example used in Preacher et al. (2007). Advantages and limitations of applying Bayesian methods to moderated mediation analysis are also discussed. 相似文献
11.
In psychological research, available data are often insufficient to estimate item factor analysis (IFA) models using traditional estimation methods, such as maximum likelihood (ML) or limited information estimators. Bayesian estimation with common-sense, moderately informative priors can greatly improve efficiency of parameter estimates and stabilize estimation. There are a variety of methods available to evaluate model fit in a Bayesian framework; however, past work investigating Bayesian model fit assessment for IFA models has assumed flat priors, which have no advantage over ML in limited data settings. In this paper, we evaluated the impact of moderately informative priors on ability to detect model misfit for several candidate indices: posterior predictive checks based on the observed score distribution, leave-one-out cross-validation, and widely available information criterion (WAIC). We found that although Bayesian estimation with moderately informative priors is an excellent aid for estimating challenging IFA models, methods for testing model fit in these circumstances are inadequate. 相似文献
12.
Multilevel Structural equation models are most often estimated from a frequentist framework via maximum likelihood. However, as shown in this article, frequentist results are not always accurate. Alternatively, one can apply a Bayesian approach using Markov chain Monte Carlo estimation methods. This simulation study compared estimation quality using Bayesian and frequentist approaches in the context of a multilevel latent covariate model. Continuous and dichotomous variables were examined because it is not yet known how different types of outcomes—most notably categorical—affect parameter recovery in this modeling context. Within the Bayesian estimation framework, the impact of diffuse, weakly informative, and informative prior distributions were compared. Findings indicated that Bayesian estimation may be used to overcome convergence problems and improve parameter estimate bias. Results highlight the differences in estimation quality between dichotomous and continuous variable models and the importance of prior distribution choice for cluster-level random effects. 相似文献
13.
This Monte Carlo simulation study compares methods to estimate the effects of programs with multiple versions when assignment of individuals to program version is not random. These methods use generalized propensity scores, which are predicted probabilities of receiving a particular level of the treatment conditional on covariates, to remove selection bias. The results indicate that inverse probability of treatment weighting (IPTW) removes the most bias, followed by optimal full matching (OFM), and marginal mean weighting through stratification (MMWTS). The study also compared standard error estimation with Taylor series linearization, bootstrapping and the jackknife across propensity score methods. With IPTW, these standard error estimation methods performed adequately, but standard errors estimates were biased in most conditions with OFM and MMWTS. 相似文献
14.
《Structural equation modeling》2013,20(2):232-244
The choice of constraints used to identify a simple factor model can affect the shape of the likelihood. Specifically, under some nonzero constraints, standard errors may be inestimable even at the maximum likelihood estimate (MLE). For a broader class of nonzero constraints, symmetric normal approximations to the modal region may not be appropriate. A simple graphical technique to gain insight into the relative location of equivalent modes is introduced. Implications for estimation and inference in factor models, and latent trait models more generally, are discussed. 相似文献
15.
This study demonstrated the equivalence between the Rasch testlet model and the three‐level one‐parameter testlet model and explored the Markov Chain Monte Carlo (MCMC) method for model parameter estimation in WINBUGS. The estimation accuracy from the MCMC method was compared with those from the marginalized maximum likelihood estimation (MMLE) with the expectation‐maximization algorithm in ConQuest and the sixth‐order Laplace approximation estimation in HLM6. The results indicated that the estimation methods had significant effects on the bias of the testlet variance and ability variance estimation, the random error in the ability parameter estimation, and the bias in the item difficulty parameter estimation. The Laplace method best recovered the testlet variance while the MMLE best recovered the ability variance. The Laplace method resulted in the smallest random error in the ability parameter estimation while the MCMC method produced the smallest bias in item parameter estimates. Analyses of three real tests generally supported the findings from the simulation and indicated that the estimates for item difficulty and ability parameters were highly correlated across estimation methods. 相似文献
16.
Research in covariance structure analysis suggests that nonnormal data will invalidate chi‐square tests and produce erroneous standard errors. However, much remains unknown about the extent to and the conditions under which highly skewed and kurtotic data can affect the parameter estimates, standard errors, and fit indices. Using actual kurtotic and skewed data and varying sample sizes and estimation methods, we found that (a) normal theory maximum likelihood (ML) and generalized least squares estimators were fairly consistent and almost identical, (b) standard errors tended to underestimate the true variation of the estimators, but the problem was not very serious for large samples (n = 1,000) and conservative (99%) confidence intervals, and (c) the adjusted chi‐square tests seemed to yield acceptable results with appropriate sample sizes. 相似文献
17.
Walter P. Vispoel Thomas R. Rocklin Tianyou Wang Timothy Bleiler 《Journal of Educational Measurement》1999,36(2):141-157
Part of the controversy about allowing examinees to review and change answers to previous items on computerized adaptive tests (CATs) centers on a strategy for obtaining positively biased ability estimates attributed to Wainer (1993) in which examinees intentionally answer items incorrectly before review and to the best of their abilities upon review. Our results, based on both simulated and live testing data, showed that there were instances in which the Wainer strategy yielded inflated ability estimates as well as instances in which it yielded deflated ability estimates. The success of the strategy in inflating ability estimates depended on the ability estimation method used (maximum likelihood versus Bayesian), the examinee's true ability level, the standard error of the ability estimate, the examinee's ability to implement the strategy, and the type of decision made from the ability estimate. We discuss approaches to dealing with the Wainer strategy in operational CAT settings. 相似文献
18.
Mark J. Gierl Dianne Henderson Michael Jodoin Don Klinger 《Journal of Experimental Education》2013,81(3):261-279
In test development, item response theory (IRT) is a method to determine the amount of information that each item (i.e., item information function) and combination of items (i.e., test information function) provide in the estimation of an examinee's ability. Studies investigating the effects of item parameter estimation errors over a range of ability have demonstrated an overestimation of information when the most discriminating items are selected (i.e., item selection based on maximum information). In the present study, the authors examined the influence of item parameter estimation errors across 3 item selection methods—maximum no target, maximum target, and theta maximum—using the 2- and 3-parameter logistic IRT models. Tests created with the maximum no target and maximum target item selection procedures consistently overestimated the test information function. Conversely, tests created using the theta maximum item selection procedure yielded more consistent estimates of the test information function and, at times, underestimated the test information function. Implications for test development are discussed. 相似文献
19.
采用带有随机微分方程的非线性混合效应模型对群体药物代谢动力学数据建模,通过在状态方程中引入随机项,将常微分方程扩展到随机微分方程.和常微分方程相比,随机微分方程可解决群体药物代谢动力学模型中相关残差问题.利用贝叶斯估计对非线性混合效应随机微分方程模型参数进行估计,给出群体参数及个体参数的精确后验分布,将Gibbs和Metropolis-Hastings算法相结合,给出参数估计值.通过计算机模拟和实例分析验证了方法的可靠性,结果表明利用非线性混合效应随机微分方程模型及贝叶斯估计方法分析群体药物代谢动力学数据是可行的. 相似文献
20.
A Monte Carlo approach was used to examine bias in the estimation of indirect effects and their associated standard errors. In the simulation design, (a) sample size, (b) the level of nonnormality characterizing the data, (c) the population values of the model parameters, and (d) the type of estimator were systematically varied. Estimates of model parameters were generally unaffected by either nonnormality or small sample size. Under severely nonnormal conditions, normal theory maximum likelihood estimates of the standard error of the mediated effect exhibited less bias (approximately 10% to 20% too small) compared to the standard errors of the structural regression coefficients (20% to 45% too small). Asymptotically distribution free standard errors of both the mediated effect and the structural parameters were substantially affected by sample size, but not nonnormality. Robust standard errors consistently yielded the most accurate estimates of sampling variability. 相似文献