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Herber, Stefanie P.; Kalinowski, Michael – Education Economics, 2019
We estimate the percentage of eligible students who do not take up their federal need-based student financial aid entitlements in a microsimulation model for the German Socio-Economic Panel Study 2002--2013. We find that about 40% of the eligible low-income students do not take up their entitlements. Non-take-up is inversely and rather…
Descriptors: Foreign Countries, Student Financial Aid, Low Income, Eligibility
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Olvera Astivia, Oscar L.; Zumbo, Bruno D. – Educational and Psychological Measurement, 2015
To further understand the properties of data-generation algorithms for multivariate, nonnormal data, two Monte Carlo simulation studies comparing the Vale and Maurelli method and the Headrick fifth-order polynomial method were implemented. Combinations of skewness and kurtosis found in four published articles were run and attention was…
Descriptors: Data, Simulation, Monte Carlo Methods, Comparative Analysis
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Tipton, Elizabeth; Pustejovsky, James E. – Journal of Educational and Behavioral Statistics, 2015
Meta-analyses often include studies that report multiple effect sizes based on a common pool of subjects or that report effect sizes from several samples that were treated with very similar research protocols. The inclusion of such studies introduces dependence among the effect size estimates. When the number of studies is large, robust variance…
Descriptors: Meta Analysis, Effect Size, Computation, Robustness (Statistics)
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Moraveji, Behjat; Jafarian, Koorosh – International Journal of Education and Literacy Studies, 2014
The aim of this paper is to provide an introduction of new imputation algorithms for estimating missing values from official statistics in larger data sets of data pre-processing, or outliers. The goal is to propose a new algorithm called IRMI (iterative robust model-based imputation). This algorithm is able to deal with all challenges like…
Descriptors: Mathematics, Computation, Robustness (Statistics), Regression (Statistics)
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Chang, Chi – Society for Research on Educational Effectiveness, 2015
It is known that interventions are hard to assign randomly to subjects in social psychological studies, because randomized control is difficult to implement strictly and precisely. Thus, in nonexperimental studies and observational studies, controlling the impact of covariates on the dependent variables and addressing the robustness of the…
Descriptors: Job Satisfaction, Intervention, Sample Size, Weighted Scores
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van Smeden, Maarten; Hessen, David J. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
In this article, a 2-way multigroup common factor model (MG-CFM) is presented. The MG-CFM can be used to estimate interaction effects between 2 grouping variables on 1 or more hypothesized latent variables. For testing the significance of such interactions, a likelihood ratio test is presented. In a simulation study, the robustness of the…
Descriptors: Multivariate Analysis, Robustness (Statistics), Sample Size, Statistical Analysis
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Cools, Wilfried; De Fraine, Bieke; Van den Noortgate, Wim; Onghena, Patrick – School Effectiveness and School Improvement, 2009
In educational effectiveness research, multilevel data analyses are often used because research units (most frequently, pupils or teachers) are studied that are nested in groups (schools and classes). This hierarchical data structure complicates designing the study because the structure has to be taken into account when approximating the accuracy…
Descriptors: Effective Schools Research, Program Effectiveness, School Effectiveness, Simulation
Fouladi, Rachel T. – 1998
A variety of approaches have been suggested by which to assess the equality of population mean vectors under conditions of population covariance matrix homogeneity and heterogeneity. The nonrobustness of commonly used multivariate tests of means to population covariance matrix heterogeneity has been long documented. However, most studies have…
Descriptors: Correlation, Monte Carlo Methods, Multivariate Analysis, Robustness (Statistics)
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Coombs, William T.; Algina, James – Journal of Educational and Behavioral Statistics, 1996
Type I error rates for the Johansen test were estimated using simulated data for a variety of conditions. Results indicate that Type I error rates for the Johansen test depend heavily on the number of groups and the ratio of the smallest sample size to the number of dependent variables. Sample size guidelines are presented. (SLD)
Descriptors: Group Membership, Hypothesis Testing, Multivariate Analysis, Robustness (Statistics)
Beasley, T. Mark; Sheehan, Janet K. – 1994
C. L. Olson (1976, 1979) suggests the Pillai-Bartlett trace (V) as an omnibus multivariate analysis of variance (MANOVA) test statistic for its superior robustness to heterogeneous variances. J. Stevens (1979, 1980) contends that the robustness of V, Wilk's lambda (W) and the Hotelling-Lawley trace (T) are similar, and that their power functions…
Descriptors: Analysis of Covariance, Comparative Analysis, Matrices, Monte Carlo Methods