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1.
介绍了现场实验中“不等同对照组”问题对实验处理效应分析和推论准确性的影响,提出以“不等同对照组准实验设计”模式和协方差分析(ANCOVA)模型解决教育实验中的“不等同对照组”问题。ANCOVA模型使用一个或多个前测作为实验处理变量的“协变量”,可以校正由于“选择—成熟”因素带来的实验前各组之间固有的差异,提高实验结果推论的效度。 相似文献
2.
Gwowen Shieh 《Journal of Experimental Education》2015,83(2):203-217
Analysis of variance is one of the most frequently used statistical analyses in the behavioral, educational, and social sciences, and special attention has been paid to the selection and use of an appropriate effect size measure of association in analysis of variance. This article presents the sample size procedures for precise interval estimation of eta-squared and partial eta-squared in fixed-effects analysis of variance designs. The desired precision of a confidence interval is assessed with respect to (a) the control of expected width and (b) the tolerance probability of interval width within a designated value. In addition, sample size calculations for standardized contrasts of treatment effects and corresponding partial strength of association effect sizes are also considered. 相似文献
3.
《Journal of research on educational effectiveness》2013,6(2):280-302
AbstractBayesian alternatives to frequentist propensity score approaches have recently been proposed. However, few studies have investigated their covariate balancing properties. This article compares a recently developed two-step Bayesian propensity score approach to the frequentist approach with respect to covariate balance. The effects of different priors on covariate balance are evaluated and the differences between frequentist and Bayesian covariate balance are discussed. Results of the case study reveal that both the Bayesian and frequentist propensity score approaches achieve good covariate balance. The frequentist propensity score approach performs slightly better on covariate balance for stratification and weighting methods, whereas the two-step Bayesian approach offers slightly better covariate balance in the optimal full matching method. Results of a comprehensive simulation study reveal that accuracy and precision of prior information on propensity score model parameters do not greatly influence balance performance. Results of the simulation study also show that overall, the optimal full matching method provides the best covariate balance and treatment effect estimates compared to the stratification and weighting methods. A unique feature of covariate balance within Bayesian propensity score analysis is that we can obtain a distribution of balance indices in addition to the point estimates so that the variation in balance indices can be naturally captured to assist in covariate balance checking. 相似文献
4.
《Structural equation modeling》2013,20(4):487-513
We consider a general type of model for analyzing ordinal variables with covariate effects and 2 approaches for analyzing data for such models, the item response theory (IRT) approach and the PRELIS-LISREL (PLA) approach. We compare these 2 approaches on the basis of 2 examples, 1 involving only covariate effects directly on the ordinal variables and 1 involving covariate effects on the latent variables in addition. 相似文献
5.
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
6.
Exploratory structural equation modeling (ESEM) is an approach for analysis of latent variables using exploratory factor analysis to evaluate the measurement model. This study compared ESEM with two dominant approaches for multiple regression with latent variables, structural equation modeling (SEM) and manifest regression analysis (MRA). Main findings included: (1) ESEM in general provided the least biased estimation of the regression coefficients; SEM was more biased than MRA given large cross-factor loadings. (2) MRA produced the most precise estimation, followed by ESEM and then SEM. (3) SEM was the least powerful in the significance tests; statistical power was lower for ESEM than MRA with relatively small target-factor loadings, but higher for ESEM than MRA with relatively large target-factor loadings. (4) ESEM showed difficulties in convergence and occasionally created an inflated type I error rate under some conditions. ESEM is recommended when non-ignorable cross-factor loadings exist. 相似文献
7.
The study, using a Monte Carlo technique, was designed to investigate the effect of the differences in covariate means among the treatment groups on the significance level and the power of the F-test of the analysis of covariance. The results show that the covariate group means differences have little effect on the significance level if the covariate is highly correlated with the criterion variable. However, if the correlation is .4 or less, larger sample sizes are required. The effect on the power is more sensitive for smaller experiments. The larger the differences among covariate group means, the lower the actual power becomes compared to the approximate theoretical power. 相似文献
8.
广义Pareto分布(Generalized Pareto Distribution,简称GPD)是统计推断中重要的一个分布,其目前在诸多领域得到广泛的应用.GPD的参数估计方法有多种,但各种方法及估计效果一般都受到形状参数k的限制,总结几种常用的参数估计方法,如:矩估计(the method of moments,简记MOM)、最小二乘估计(the least squares estimation,简记LSE)、基于分位数估计(the elemental percentile method,简记EPM)、近似广义最小二乘估计(AGLSE)等,通过模拟研究,得出不存在一致最优的参数估计方法.而在k较大时,LSE在GPD参数估计中模拟效果较为理想,特别当k1/2时,AGLSE对k的估计精度较高. 相似文献
9.
Models to assess mediation in the pretest–posttest control group design are understudied in the behavioral sciences even though it is the design of choice for evaluating experimental manipulations. The article provides analytical comparisons of the four most commonly used models to estimate the mediated effect in this design: analysis of covariance (ANCOVA), difference score, residualized change score, and cross-sectional model. Each of these models is fitted using a latent change score specification and a simulation study assessed bias, Type I error, power, and confidence interval coverage of the four models. All but the ANCOVA model make stringent assumptions about the stability and cross-lagged relations of the mediator and outcome that might not be plausible in real-world applications. When these assumptions do not hold, Type I error and statistical power results suggest that only the ANCOVA model has good performance. The four models are applied to an empirical example. 相似文献
10.
Randomized controlled trials in educational research tend to be small. Small trials can have large, chance, imbalances in important covariates. For studies with sample sizes greater than 50, chance imbalances can be corrected using analysis of covariance; for small trials, however, statistical power is maximized if the trial is balanced and analysis of covariance is used in the analysis. The aim of the present study was to discuss methods of improving covariate balance in trial design and to demonstrate the method of minimization. Using an exemplar of a cluster or class‐randomized trial with 29 classes, we employed minimization to achieve covariate balance. Minimization achieved good balance on four prognostic variables. Many trialists in education use restricted forms of allocation, including pairing or stratified randomization. These approaches have disadvantages. Another approach rarely used in educational research is minimization. Minimization uses a simple arithmetic algorithm to produce balanced groups across a number of important covariates and should be more widely used in educational and psychological research. 相似文献
11.
The authors compared the Type I error rate and the power to detect differences in slopes and additive treatment effects of analysis of covariance (ANCOVA) and randomized block (RB) designs with a Monte Carlo simulation. For testing differences in slopes, 3 methods were compared: the test of slopes from ANCOVA, the omnibus Block × Treatment interaction, and the linear component of the Block × Treatment interaction of RB. In the test for adjusted means, 2 variations of both ANCOVA and RB were used. The power of the omnibus test of the interaction decreased dramatically as the number of blocks used increased and was always considerably smaller than the specific test of differences in slopes found in ANCOVA. Tests for means when there were concomitant differences in slopes showed that only ANCOVA uniformly controlled Type I error under all configurations of design variables. The most powerful option in almost all simulations for tests of both slopes and means was ANCOVA. 相似文献
12.
The analysis of interaction among latent variables has received much attention. This article introduces a Bayesian approach to analyze a general structural equation model that accommodates the general nonlinear terms of latent variables and covariates. This approach produces a Bayesian estimate that has the same statistical optimal properties as a maximum likelihood estimate. Other advantages over the traditional approaches are discussed. More important, we demonstrate through examples how to use the freely available software WinBUGS to obtain Bayesian results for estimation and model comparison. Simulation studies are conducted to assess the empirical performances of the approach for situations with various sample sizes and prior inputs. 相似文献
13.
Kristin E. Porter Sean F. Reardon Fatih Unlu Howard S. Bloom Joseph R. Cimpian 《Journal of research on educational effectiveness》2017,10(1):138-167
A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the “surface” method, the “frontier” method, the “binding-score” method, and the “fuzzy instrumental variables” method. This article uses a series of simulations to evaluate the relative performance of each of these four methods under a variety of different data-generating models. Focusing on a two-rating RDD (2RRDD), we compare the methods in terms of their bias, precision, and mean squared error when implemented as they most likely would be in practice—using optimal bandwidth selection. We also apply the lessons learned from the simulations to a real-world example that uses data from a study of an English learner reclassification policy. Overall, this article makes valuable contributions to the literature on MRRDDs in that it makes concrete recommendations for choosing among MRRDD estimation methods, for implementing any chosen method using local linear regression, and for providing accurate statistical inferences. 相似文献
14.
Confidence Intervals for Weighted Composite Scores Under the Compound Binomial Error Model 
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下载免费PDF全文 Reporting confidence intervals with test scores helps test users make important decisions about examinees by providing information about the precision of test scores. Although a variety of estimation procedures based on the binomial error model are available for computing intervals for test scores, these procedures assume that items are randomly drawn from a undifferentiated universe of items, and therefore might not be suitable for tests developed according to a table of specifications. To address this issue, four interval estimation procedures that use category subscores for the computation of confidence intervals are presented in this article. All four estimation procedures assume that subscores instead of test scores follow a binomial distribution (i.e., compound binomial error model). The relative performance of the four compound binomial–based interval estimation procedures is compared to each other and to the better known normal approximation and Wilson score procedures based on the binomial error model. 相似文献
15.
李云飞 《内江师范学院学报》2012,27(6):15-17
针对不完全样本观测数据,讨论了一类均匀分布总体参数的区间估计问题．利用样本中位数给出了构造置信区间的一个新枢轴量,推导出了枢轴量的概率密度函数表达式,并且在大样本场合,讨论了总体参数的近似置信区间．该方法不仅适用于不完全数据场合,而且还适用于样本中可能存在异常数据的情形,具有稳健性． 相似文献
16.
Scott B. Watson 《科学教学研究杂志》1991,28(2):141-146
The purpose of this study was to examine the effects of cooperative learning and Group Educational Modules (GEM) on the achievement of high school biology students. GEM materials are self-instructional packets designed for use with groups of biology students. Cooperative learning is a classroom learning environment in which students work in small, mixed-ability groups toward a common goal. A 2 × 2 factorial design was used in this study. The independent variables considered included (1) participation of students in the GEM project, and (2) use of cooperative learning techniques including heterogeneous grouping and group incentives. The dependent variables for all treatment groups were scores on the instrument developed for this study. A total of 11 teachers with 36 classes and 715 students were included in this study. All teachers involved covered the same general subject matter during the study period. An analysis of covariance (ANCOVA) was used as the data analysis procedure. Significant differences were found in the achievement of students using GEM materials and those using traditional instructional approaches. The use of cooperative learning also produced significant differences when compared to traditional classroom structures. 相似文献
17.
杨明 《河北能源职业技术学院学报》2003,3(2):69-71,86
由于地质体和地质过程相当复杂，其相应的数学模型也较复杂，模型的求解只能采用数值法近似得到，结果误差大，精度低。要想得到较高的精度，需大大地增加运算量。本文介绍一种新的数值解析解，是基于有限元方法，通过变化单元的划分，消除系统误差，来推出高精度的解。此法方便易行，使用于各种复杂的数学模型，尤其适用于精度较高的计算问题。文中给出一个渗流算例，结果令人满意。 相似文献
18.
Undergraduate students in dyads (N = 72) were randomly and equally assigned to four groups, namely three teaching groups (General, Infusion, and Immersion) and the control group. Students were initially administered the California Critical Thinking Skills Test (CCTST). After instruction, each dyad's critical-thinking performance on an ill-defined problem was tested. A one-way ANCOVA, with the mean CCTST score of each dyad as covariate, indicated that the covariate and the teaching method were significant. Post hoc comparisons showed that the Infusion and the Immersion groups outperformed only the control group. Other quantitative and qualitative analyses revealed that students assigned to the different teaching groups exhibited diverse understandings of critical thinking. 相似文献
19.
精准营销可以帮助企业节约营销成本、提升营销效果,基于大数据的消费者行为分析也是大数据领域的一个热点研究。为此,基于运营商大数据对汽车用户精准营销算法进行研究,提出基于专家经验与统计学方法的精准营销算法。首先对用户上网日志数据进行加工,得到用户行为标签,然后根据专家经验与统计学公式计算用户购车意向得分,输出潜在购车客户信息。通过在某运营商真实环境下进行实验,验证了算法的可行性与有效性。实验结果表明,面向运营商大数据的汽车用户精准营销算法成功率可达到5.98%,相比现有推荐算法效率明显提升。 相似文献
20.
《Journal of research on educational effectiveness》2013,6(4):556-569
ABSTRACTAn appropriate estimate of statistical power is critical for the design of intervention studies. Although the inclusion of a pretest covariate in the test of the primary outcome can increase statistical power, samples selected on the basis of pretest performance may demonstrate range restriction on the selection measure and other correlated measures. This can result in attenuated pretest–posttest correlations, reducing the variance explained by the pretest covariate. We investigated the implications of two potential range restriction scenarios: direct truncation on a selection measure and indirect range restriction on correlated measures. Empirical and simulated data indicated that direct range restriction on the pretest covariate greatly reduced statistical power and necessitated sample size increases of 82%–155% (dependent on selection criteria) to achieve equivalent statistical power to parameters with unrestricted samples. However, measures demonstrating indirect range restriction required much smaller sample size increases (32%–71%) under equivalent scenarios. Additional analyses manipulated the correlations between measures and pretest–posttest correlations to guide planning experiments. Results highlight the need to differentiate between selection measures and potential covariates and to investigate range restriction as a factor impacting statistical power. 相似文献