共查询到20条相似文献，搜索用时 15 毫秒
1.
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
2.
Models of change typically assume longitudinal measurement invariance. Key constructs are often measured by ordered-categorical indicators (e.g., Likert scale items). If tests based on such indicators do not support longitudinal measurement invariance, it would be useful to gauge the practical significance of the detected non-invariance. The authors focus on the commonly used second-order latent growth curve model, proposing a sensitivity analysis that compares the growth parameter estimates from a model assuming the highest achieved level of measurement invariance to those from a model assuming a higher, incorrect level of measurement invariance as a measure of practical significance. A simulation study investigated the practical significance of non-invariance in different locations (loadings, thresholds, uniquenesses) in second-order latent linear growth models. The mean linear slope was affected by non-invariance in the loadings and thresholds, the intercept variance was affected by non-invariance in the uniquenesses, and the linear slope variance and intercept–slope covariance were affected by non-invariance in all three locations. 相似文献
3.
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
4.
Mediation is one concept that has shaped numerous theories. The list of problems associated with mediation models, however, has been growing. Mediation models based on cross-sectional data can produce unexpected estimates, so much so that making longitudinal or causal inferences is inadvisable. Even longitudinal mediation models have faults, as parameter estimates produced by these models are specific to the lag between observations, leading to much debate over appropriate lag selection. Using continuous time models (CTMs) rather than commonly employed discrete time models, one can estimate lag-independent parameters. We demonstrate methodology that allows for continuous time mediation analyses, with attention to concepts such as indirect and direct effects, partial mediation, the effect of lag, and the lags at which relations become maximal. A simulation compares common longitudinal mediation methods with CTMs. Reanalysis of a published covariance matrix demonstrates that CTMs can be fit to data used in longitudinal mediation studies. 相似文献
5.
Ordinal variables are common in many empirical investigations in the social and behavioral sciences. Researchers often apply the maximum likelihood method to fit structural equation models to ordinal data. This assumes that the observed measures have normal distributions, which is not the case when the variables are ordinal. A better approach is to use polychoric correlations and fit the models using methods such as unweighted least squares (ULS), maximum likelihood (ML), weighted least squares (WLS), or diagonally weighted least squares (DWLS). In this simulation evaluation we study the behavior of these methods in combination with polychoric correlations when the models are misspecified. We also study the effect of model size and number of categories on the parameter estimates, their standard errors, and the common chi-square measures of fit when the models are both correct and misspecified. When used routinely, these methods give consistent parameter estimates but ULS, ML, and DWLS give incorrect standard errors. Correct standard errors can be obtained for these methods by robustification using an estimate of the asymptotic covariance matrix W of the polychoric correlations. When used in this way the methods are here called RULS, RML, and RDWLS. 相似文献
6.
Trevor Kerry 《International Journal of Research & Method in Education》2013,36(1):103-111
The capacity of Bayesian methods in estimating complex statistical models is undeniable. Bayesian data analysis is seen as having a range of advantages, such as an intuitive probabilistic interpretation of the parameters of interest, the efficient incorporation of prior information to empirical data analysis, model averaging and model selection. As a simplified demonstration, we illustrate (1) how Bayesians test and compare two non‐nested growth curve models using Bayesian estimation with non‐informative prior; (2) how Bayesians model and handle missing outcomes in the context of missing values; and (3) how Bayesians incorporate data‐based evidence from a previous data set, construct informative priors and treat them as extra information while conducting an up‐to‐date analogy analysis. 相似文献
7.
讨论了一类基于T-S模糊模型的非线性系统,根据满意控制思想提出一种条件约束下的状态反馈控制设计方法。即在H∞控制基础上,加入圆形极点指标和状态协方差指标约束。进而研究了圆形极点约束和状态协方差约束下,系统被控输出对扰动输入的H∞抑制界优化问题,并且将H∞优化、圆形极点和状态协方差指标约束的状态反馈控制器设计归结为求一组线性矩阵不等式(LMI)的可行解问题,通过求解LMI得到满足要求的控制器参数。仿真结果表明该方法可行有效。 相似文献
8.
Tacksoo Shin 《Asia Pacific Education Review》2012,13(1):65-76
This study introduced various nonlinear growth models, including the quadratic conventional polynomial model, the fractional
polynomial model, the Sigmoid model, the growth model with negative exponential functions, the multidimensional scaling technique,
and the unstructured growth curve model. It investigated which growth models effectively describe student growth in math and
reading using four-wave longitudinal achievement data. The objective of the study is to provide valuable information to researchers
especially when they consider applying one of the nonlinear models to longitudinal studies. The results showed that the quadratic
conventional polynomial model fit the data best. However, this model seemed to overfit the data and made statistical inference
problematic concerning parameter estimates. Alternative nonlinear models with fewer parameters adequately fit the data and
yielded consistent significance testing results under extreme multicollinearity. It indicates that the alternative models
denoting somewhat simpler models would be selected over the conventional polynomial model with more fixed parameters. Other
practical issues pertaining to these growth models are also discussed. 相似文献
9.
Recently, advancements in Bayesian structural equation modeling (SEM), particularly software developments, have allowed researchers to more easily employ it in data analysis. With the potential for greater use, come opportunities to apply Bayesian SEM in a wider array of situations, including for small sample size problems. Effective use of Bayseian estimation hinges on selection of appropriate prior distributions for model parameters. Researchers have suggested that informative priors may be useful with small samples, presuming that the mean of the prior is accurate with respect to the population mean. The purpose of this simulation study was to examine model parameter estimation for the Multiple Indicator Multiple Cause model when an informative prior distribution had an incorrect mean. Results demonstrated that the use of incorrect informative priors with somewhat larger variance than is typical, yields more accurate parameter estimates than do naïve priors, or maximum likelihood estimation. Implications for practice are discussed. 相似文献
10.
This article introduces developmentalists to methods for estimating individual developmental functions from longitudinal data in a multilevel analysis. Quantitative growth curve models for estimating the developmental functions from various types of longitudinal data are discussed in the context of both an investigator's assumptions about individual development on the attribute and the design characteristics of the prospective study. General linear and inherently nonlinear models that estimate population, individual, and prototypic growth curves are illustrated and contrasted when they are fit to speech development data. 相似文献
11.
We compared six common methods in estimating the 2-1-1 (level-2 independent, level-1 mediator, level-1 dependent) multilevel mediation model with a random slope. They were the Bayesian with informative priors, the Bayesian with non-informative priors, the Monte-Carlo, the distribution of the product, the bias-corrected, and the bias-uncorrected parametric percentile residual bootstrap. The Bayesian method with informative priors was superior in relative mean square error (RMSE), power, interval width, and interval imbalance. The prior variance and prior mean were also varied and examined. Decreasing the prior variance increased the power, reduced RMSE and interval width when the prior mean was the true value, but decreasing the prior variance reduced the power when the prior mean was set incorrectly. The influence of misspecification of prior information of the b coefficient on multilevel mediation analysis was greater than that on coefficient a. An illustrate example with the Bayesian multilevel mediation was provided. 相似文献
12.
Peter M. Bentler 《Structural equation modeling》2018,25(6):961-964
Basic growth curve models parameterize the mean and covariance structure of a set of repeated measures by latent factors that represent the polynomial influences of time. In practice it may be hard to choose the number of factors, i.e., the order of the polynomial. Simple calculations are proposed to estimate this order. 相似文献
13.
For linear switched system with both parameter uncertainties and time delay,a delay-dependent sufficient condition for the existence of a new robust H∞ feedback controller was formulated in nonlinear matrix inequalities solvable by an LMI-based iterative algorithm.Compared with the conventional state-feedback controller,the proposed controller can achieve better robust control performance since the delayed state is utilized as additional feedback information and the parameters of the proposed controllers are changed synchronously with the dynamical characteristic of the system.This design method was also extended to the case where only delayed state is available for the controller.The example of balancing an inverted pendulum on a cart demonstrates the effectiveness and applicability of the proposed design methods. 相似文献
14.
《教育实用测度》2013,26(4):351-368
Through a large-scale simulation study, this article compares item parameter estimates obtained by the marginal maximum likelihood estimation (MMLE) and marginal Bayes modal estimation (MBME) procedures in the 3-parameter logistic model. The impact of different prior specifications on the MBME estimates is also investigated using carefully selected prior distributions. The results indicate that, in general, the MBME provides more accurate item parameter estimates than the MMLE procedure. The impact of different priors on the Bayesian estimates is modest when the examinee sample size is not extremely small. 相似文献
15.
非线性模型中无信息方差和协方差分量Bayes估计 总被引：1，自引：1，他引：0
闫同新 《福建工程学院学报》2009,7(4)
采用Bayes方法从无先验信息出发,得到了非线性模型中方差和协方差分量的估计(包含相关系数),最后通过实例解算,结果表明:非线性模型中方差和协方差分量的估计,与ρ的理论值-0.5偏差不大,当没有先验信息时,该方法是可行的. 相似文献
16.
Joshua N. Pritikin Lance M. Rappaport Michael C. Neale 《Structural equation modeling》2017,24(3):395-401
The precision of estimates in many statistical models can be expressed by a confidence interval (CI). CIs based on standard errors (SEs) are common in practice, but likelihood-based CIs are worth consideration. In comparison to SEs, likelihood-based CIs are typically more difficult to estimate, but are more robust to model (re)parameterization. In latent variable models, some parameters might take on values outside of their interpretable range. Therefore, it is desirable to place a bound to keep the parameter interpretable. For likelihood-based CI, a correction is needed when a parameter is bounded. The correction is known (Wu & Neale, 2012), but is difficult to implement in practice. A novel automatic implementation that is simple for an applied researcher to use is introduced. A simulation study demonstrates the accuracy of the correction using a latent growth curve model and the method is illustrated with a multilevel confirmatory factor analysis. 相似文献
17.
《教育实用测度》2013,26(2):199-210
When the item response theory (IRT) model uses the marginal maximum likelihood estimation, person parameters are usually treated as random parameters following a certain distribution as a prior distribution to estimate the structural parameters in the model. For example, both PARSCALE (Muraki &; Bock, 1999) and BILOG 3 (Mislevy &; Bock, 1990) use a standard normal distribution as a default person prior. When the fixed-item linking method is used with an IRT program having a fixed-person prior distribution, it biases person ability growth downward or upward depending on the direction of the growth due to the misspecification of the prior. This study demonstrated by simulation how much biasing impact there is on person ability growth from the use of the fixed prior distribution in fixed-item linking for mixed-format test data. In addition, the study demonstrated how to recover growth through an iterative prior update calibration procedure. This shows that fixed-item linking is still a viable linking method for a fixed-person prior IRT calibration. 相似文献
18.
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
19.
Sarah Depaoli 《Structural equation modeling》2013,20(2):239-252
Within Bayesian estimation, prior distributions are placed on model parameters and these distributions can take on many different levels of informativeness. Although much of the research conducted within this estimation framework uses what are called diffuse (or noninformative) priors, there are certain models and modeling circumstances where it is more optimal to use what are referred to as informative priors. This study focuses on the latter situation and examines the effects of inaccurate informative priors on the growth parameters within the context of growth mixture modeling. Overall, results indicated that growth mixture modeling is relatively robust to the use of inaccurate mean hyperparameters for the growth parameters, as long as the variance hyperparameters are somewhat large. 相似文献
20.
The cohort growth model (CGM) is a method for estimating the parameters of a latent growth model (LGM) based on cross-sectional data. The CGM models the interindividual differences in the growth rate, and it models how subjects’ growth rate is related to their initial status. We derive model identification for the CGM and illustrate, in a simulation study, that the CGM provides unbiased parameter estimates in most simulation conditions. Based on empirical data we compare the estimates of the CGM with the estimates of the LGM. The results were comparable for both models. Although the estimates of the (co)-variances were different, the estimates of both models led to similar conclusions on the developmental change. Finally, we discuss the advantages and limitations of the CGM, and we provide recommendations for its use in empirical research. 相似文献