共查询到20条相似文献，搜索用时 812 毫秒
1.
方差分量模型的随机效应的协方差为单位阵时<线性模型引论>已进行研究.把随机效应的协方差推广为正定阵进行研究.用最小范数二次无偏估计法给出方差分量的估计. 相似文献
2.
Ehri Ryu 《Structural equation modeling》2013,20(4):617-630
This article examined the role of centering in estimating interaction effects in multilevel structural equation models. Interactions are typically represented by product term of 2 variables that are hypothesized to interact. In multilevel structural equation modeling (MSEM), the product term involving Level 1 variables is decomposed into within-cluster and between-cluster random components. The choice of centering affects the decomposition of the product term, and therefore affects the sample variance and covariance associated with the product term used in the maximum likelihood fitting function. The simulation study showed that for an interaction between a Level 1 variable and a Level 2 variable, the product term of uncentered variables or the product term of grand mean centered variables produced unbiased estimates in both Level 1 and Level 2 models. The product term of cluster mean centered variables produced biased estimates in the Level 1 model. For an interaction between 2 Level 1 variables, the product term of cluster mean centered variables produced unbiased estimates in the Level 1 model, whereas the product term of grand mean centered variables produced unbiased estimates for the Level 1 model. Recommendations for researchers who wish to estimate interactions in MSEM are provided. 相似文献
3.
刘谢进 《楚雄师范学院学报》2006,21(12):47-49
在给定的权回归模型下,讨论了最小二乘估计、最优加权最小二乘估计和线性无偏最小方差估计的性能比较,得出了在随机误差方差矩阵可逆条件下,可算出最优加权最小二乘估计与线性无偏最小方差估计误差方差阵的差表达式,并在一定条件下,两者趋于一致。 相似文献
4.
Jason A. Schoeneberger 《Journal of Experimental Education》2016,84(2):373-397
The design of research studies utilizing binary multilevel models must necessarily incorporate knowledge of multiple factors, including estimation method, variance component size, or number of predictors, in addition to sample sizes. This Monte Carlo study examined the performance of random effect binary outcome multilevel models under varying methods of estimation, level-1 and level-2 sample size, outcome prevalence, variance component sizes, and number of predictors using SAS software. Mean estimates of statistical power were influenced primarily by sample sizes at both levels. In addition, confidence interval coverage and width and the likelihood of nonpositive definite random effect covariance matrices were impacted by variance component size and estimation method. The interactions of these and other factors with various model performance outcomes are explored. 相似文献
5.
Leslie A. Hayduk 《Structural equation modeling》2013,20(4):658-675
In models containing reciprocal effects, or longer causal loops, the usual effect estimates assume that any effect touching a loop initiates an infinite cycling of effects around that loop. The real world, in contrast, might permit only finite feedback cycles. I use a simple hypothetical model to demonstrate that if the world permits only a few effect cycles, many coefficient estimates are substantially biased. If the world permits additional partial-cycle use in addition to full cyclings around the causal loop, some of the effect estimates are proper, and a full set of proper effect estimates can be recovered by hand calculations involving the model total effects. If the world permits no additional partial-cycle use, it might not be possible to recover proper estimates from the usual output. It is not the equations representing the causal model, but rather the calculations of the covariance implications of the model, that change with limited cycling possibilities. Unfortunately, the features required to permit direct estimation of limited-cycle effects are not under user control in common structural equation programs, so estimation and detailed investigation of models with finite cycling of effects around feedback loops awaits new programming. To obtain unbiased estimates with limited causal cyclings, the researcher must continue to strive to specify the proper effect locations but must also attend to the number of full and partial causal cyclings permitted by the world. Determining the appropriate number of cycles is not a matter to be delegated to a statistician; it is something the researcher must attend to as a matter of substantive theory, methodology, and model interpretation. 相似文献
6.
《Structural equation modeling》2013,20(4):475-502
Over the past decade and a half, methodologists working with structural equation modeling (SEM) have developed approaches for accommodating multilevel data. These approaches are particularly helpful when modeling data that come from complex sampling designs. However, most data sets that are associated with complex sampling designs also include observation weights, and methods to incorporate these sampling weights into multilevel SEM analyses have not been addressed. This article investigates the use of different weighting techniques and finds, through a simulation study, that the use of an effective sample size weight provides unbiased estimates of key parameters and their sampling variances. Also, a popular normalization technique of scaling weights to reflect the actual sample size is shown to produce negatively biased sampling variance estimates, as well as negatively biased within-group variance parameter estimates in the small group size case. 相似文献
7.
The importance of reporting explained variance (sometimes referred to as magnitude of effects) in ANOVA designs is discussed in this paper. Explained variance is an estimate of the strength of the relationship between treatment (or other factors such as sex, grade level, etc.) and dependent variables of interest to the researcher(s). Three methods that can be used to obtain estimates of explained variance in ANOVA designs are described and applied to 16 studies that were reported in recent volumes of this journal. The results show that, while in most studies the treatment accounts for a relatively small proportion of the variance in dependent variable scores., in., some studies the magnitude of the treatment effect is respectable. The authors recommend that researchers in science education report explained variance in addition to the commonly reported tests of significance, since the latter are inadequate as the sole basis for making decisions about the practical importance of factors of interest to science education researchers. 相似文献
8.
Mieke Heyvaert Mariola Moeyaert Paul Verkempynck Wim Van den Noortgate Marlies Vervloet Maaike Ugille 《Journal of Experimental Education》2017,85(2):175-196
This article reports on a Monte Carlo simulation study, evaluating two approaches for testing the intervention effect in replicated randomized AB designs: two-level hierarchical linear modeling (HLM) and using the additive method to combine randomization test p values (RTcombiP). Four factors were manipulated: mean intervention effect, number of cases included in a study, number of measurement occasions for each case, and between-case variance. Under the simulated conditions, Type I error rate was under control at the nominal 5% level for both HLM and RTcombiP. Furthermore, for both procedures, a larger number of combined cases resulted in higher statistical power, with many realistic conditions reaching statistical power of 80% or higher. Smaller values for the between-case variance resulted in higher power for HLM. A larger number of data points resulted in higher power for RTcombiP. 相似文献
9.
首先给出一阶移动平均型式的自相关及其扰动项的均值、方差、协方差,并给出扰动项的协方差矩阵,Ω证明Ω是正定矩阵;然后由此推得回归模型Y=Xβ+μ中β的LS估计值■,给出了■的均值、方差,最后给出了σ2的无偏估计量■2及在正态分布的场合下■与■2的分布。 相似文献
10.
Ben Kelcey Jessaca Spybrook Geoffrey Phelps Nathan Jones Jiaqi Zhang 《Journal of Experimental Education》2017,85(3):389-410
We develop a theoretical and empirical basis for the design of teacher professional development studies. We build on previous work by (a) developing estimates of intraclass correlation coefficients for teacher outcomes using two- and three-level data structures, (b) developing estimates of the variance explained by covariates, and (c) modifying the conventional optimal design framework to include differential covariate costs so as to capture the point at which the cost of collecting a covariate overtakes the reduction in variance it supplies. We illustrate the use of these estimates to explore the absolute and relative sensitivity of multilevel designs in teacher professional development studies. The results from these analyses are intended to guide researchers in making more-informed decisions about the tradeoffs and considerations involved in selecting study designs for assessing the impacts of professional development programs. 相似文献
11.
Growth curve modeling provides a general framework for analyzing longitudinal data from social, behavioral, and educational sciences. Bayesian methods have been used to estimate growth curve models, in which priors need to be specified for unknown parameters. For the covariance parameter matrix, the inverse Wishart prior is most commonly used due to its proper and conjugate properties. However, many researchers have pointed out that the inverse Wishart prior might not work as expected. The purpose of this study is to investigate the influence of the inverse Wishart prior and compare it with a class of separation-strategy priors on the parameter estimates of growth curve models. In this article, we illustrate the use of different types of priors with 2 real data analyses, and then conduct simulation studies to evaluate and compare these priors in estimating both linear and nonlinear growth curve models. For the linear model, the simulation study shows that both the inverse Wishart and the separation-strategy priors work well for the fixed effects parameters. For the Level 1 residual variance estimate, the separation-strategy prior performs better than the inverse Wishart prior. For the covariance matrix, the results are mixed. Overall, the inverse Wishart prior is suggested if the population correlation coefficient and at least 1 of the 2 marginal variances are large. Otherwise, the separation-strategy prior is preferred. For the nonlinear growth curve model, the separation-strategy priors work better than the inverse Wishart prior. 相似文献
12.
Appropriate model specification is fundamental to unbiased parameter estimates and accurate model interpretations in structural equation modeling. Thus detecting potential model misspecification has drawn the attention of many researchers. This simulation study evaluates the efficacy of the Bayesian approach (the posterior predictive checking, or PPC procedure) under multilevel bifactor model misspecification (i.e., ignoring a specific factor at the within level). The impact of model misspecification on structural coefficients was also examined in terms of bias and power. Results showed that the PPC procedure performed better in detecting multilevel bifactor model misspecification, when the misspecification became more severe and sample size was larger. Structural coefficients were increasingly negatively biased at the within level, as model misspecification became more severe. Model misspecification at the within level affected the between-level structural coefficient estimates more when data dependency was lower and the number of clusters was smaller. Implications for researchers are discussed. 相似文献
13.
Most studies of persistence behavior use path analysis or ordinary least squares regression to estimate unknown coefficients. However, estimates produced by these techniques are biased if selectivity bias contaminates choices made by individuals in the data sample. We explain this problem, argue that it is present in data samples used in persistence studies, and discuss an alternative estimation technique that controls for it. The methodology and the differences in the interpretation of coefficient estimates are illustrated with a data sample of individual students at a single university. 相似文献
14.
Herbert W. Marsh 《Structural equation modeling》2013,20(1):22-36
The use of sample covariance matrices constructed with pairwise deletion for data missing completely at random (SPW) is addressed in a simulation study based on 3 sample sizes (n = 200, 500, 1,000) and 5 levels of missing data (%miss = 0, 1, 10, 25, and 50). Parameter estimates were unbiased, parameter variability was largely explicable in terms of the number of nonmissing cases, and no sample covariance matrices were nonpositive definite except when %miss was 50 and the sample size was 200. However, nominal χ2 test statistics (and, thus, fit indices based on χ2s) were substantially biased by %miss and its interaction with N. Corrected χ2s based on the minimum, mean, and maximum number of nonmissing cases per measured variables and cases per covariance term (NPC) reduced but did not eliminate the bias. Empirically derived power functions did substantially better but may not generalize to other situations. Whereas the minimum NPC (the default in the SPSS version of LISREL) is probably better than most simple alternatives in many applications, the problem of how to assess fit for models fit to SPWS has no simple solution; caution is recommended, and there is need for further research with more suitable methods for this problem. 相似文献
15.
16.
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
17.
王勤 《金华职业技术学院学报》2004,4(2):48-50,52
研究了相关情况下第i个顺序统计量及其标准化变量的期望、方差、协方差以及在各阶矩上的变换作用，将Gauss—Markov定理推广至一般相关的不完全样本的情形，并用最小二乘法对顺序统计量的参数作出了最佳线性无偏估计，推导出在样本值之间不相关或相关条件下计算估计量方差的公式。 相似文献
18.
When data for multiple outcomes are collected in a multilevel design, researchers can select a univariate or multivariate analysis to examine group-mean differences. When correlated outcomes are incomplete, a multivariate multilevel model (MVMM) may provide greater power than univariate multilevel models (MLMs). For a two-group multilevel design with two correlated outcomes, a simulation study was conducted to compare the performance of MVMM to MLMs. The results showed that MVMM and MLM performed similarly when data were complete or missing completely at random. However, when outcome data were missing at random, MVMM continued to provide unbiased estimates, whereas MLM produced grossly biased estimates and severely inflated Type I error rates. As such, this study provides further support for using MVMM rather than univariate analyses, particularly when outcome data are incomplete. 相似文献
19.
均匀分布分位数的区间估计 总被引：1，自引：0，他引：1
龙兵 《江苏广播电视大学学报》2009,20(3):55-58,68
给出了均匀分布的参数和P分位数的无偏估计,讨论了P分位数的常用区间估计及最短区间估计;同时,给出了假设检验的方法.最后,根据具体数据计算出了这两种区间估计及其区间估计的长度,并把所得结果进行了比较,得出了相应的结论. 相似文献
20.
The purpose of this study was to examine the impact of misspecifying a growth mixture model (GMM) by assuming that Level-1 residual variances are constant across classes, when they do, in fact, vary in each subpopulation. Misspecification produced bias in the within-class growth trajectories and variance components, and estimates were substantially less precise than those obtained from a correctly specified GMM. Bias and precision became worse as the ratio of the largest to smallest Level-1 residual variances increased, class proportions became more disparate, and the number of class-specific residual variances in the population increased. Although the Level-1 residuals are typically of little substantive interest, these results suggest that researchers should carefully estimate and report these parameters in published GMM applications. 相似文献