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Gagnon-Bartsch, J. A.; Sales, A. C.; Wu, E.; Botelho, A. F.; Erickson, J. A.; Miratrix, L. W.; Heffernan, N. T. – Grantee Submission, 2019
Randomized controlled trials (RCTs) admit unconfounded design-based inference--randomization largely justifies the assumptions underlying statistical effect estimates--but often have limited sample sizes. However, researchers may have access to big observational data on covariates and outcomes from RCT non-participants. For example, data from A/B…
Descriptors: Randomized Controlled Trials, Educational Research, Prediction, Algorithms
Finch, W. Holmes – Journal of Experimental Education, 2016
Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…
Descriptors: Multivariate Analysis, Educational Research, Error of Measurement, Research Problems
McNeish, Daniel – Review of Educational Research, 2017
In education research, small samples are common because of financial limitations, logistical challenges, or exploratory studies. With small samples, statistical principles on which researchers rely do not hold, leading to trust issues with model estimates and possible replication issues when scaling up. Researchers are generally aware of such…
Descriptors: Models, Statistical Analysis, Sampling, Sample Size
Televantou, Ioulia; Marsh, Herbert W.; Kyriakides, Leonidas; Nagengast, Benjamin; Fletcher, John; Malmberg, Lars-Erik – School Effectiveness and School Improvement, 2015
The main objective of this study was to quantify the impact of failing to account for measurement error on school compositional effects. Multilevel structural equation models were incorporated to control for measurement error and/or sampling error. Study 1, a large sample of English primary students in Years 1 and 4, revealed a significantly…
Descriptors: Hierarchical Linear Modeling, Statistical Bias, Error of Measurement, Educational Research
Huang, Francis L. – Practical Assessment, Research & Evaluation, 2014
Clustered data (e.g., students within schools) are often analyzed in educational research where data are naturally nested. As a consequence, multilevel modeling (MLM) has commonly been used to study the contextual or group-level (e.g., school) effects on individual outcomes. The current study investigates the use of an alternative procedure to…
Descriptors: Hierarchical Linear Modeling, Regression (Statistics), Educational Research, Sampling
Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
Morin, Alexandre J. S.; Marsh, Herbert W.; Nagengast, Benjamin; Scalas, L. Francesca – Journal of Experimental Education, 2014
Many classroom climate studies suffer from 2 critical problems: They (a) treat climate as a student-level (L1) variable in single-level analyses instead of a classroom-level (L2) construct in multilevel analyses; and (b) rely on manifest-variable models rather than on latent-variable models that control measurement error at L1 and L2, and sampling…
Descriptors: Classroom Environment, Hierarchical Linear Modeling, Structural Equation Models, Grade 5
Marsh, Herbert W.; Ludtke, Oliver; Nagengast, Benjamin; Trautwein, Ulrich; Morin, Alexandre J. S.; Abduljabbar, Adel S.; Koller, Olaf – Educational Psychologist, 2012
Classroom context and climate are inherently classroom-level (L2) constructs, but applied researchers sometimes--inappropriately--represent them by student-level (L1) responses in single-level models rather than more appropriate multilevel models. Here we focus on important conceptual issues (distinctions between climate and contextual variables;…
Descriptors: Foreign Countries, Classroom Environment, Educational Research, Research Design
Olsen, Robert B.; Unlu, Fatih; Price, Cristofer; Jaciw, Andrew P. – National Center for Education Evaluation and Regional Assistance, 2011
This report examines the differences in impact estimates and standard errors that arise when these are derived using state achievement tests only (as pre-tests and post-tests), study-administered tests only, or some combination of state- and study-administered tests. State tests may yield different evaluation results relative to a test that is…
Descriptors: Achievement Tests, Standardized Tests, State Standards, Reading Achievement
Thompson, Bruce – 1995
Stepwise methods are frequently employed in educational and psychological research, both to select useful subsets of variables and to evaluate the order of importance of variables. Three problems with stepwise applications are explored in some detail. First, computer packages use incorrect degrees of freedom in their stepwise computations,…
Descriptors: Educational Research, Error of Measurement, Heuristics, Psychological Testing
Wang, Lin; Fan, Xitao – 1997
Standard statistical methods are used to analyze data that is assumed to be collected using a simple random sampling scheme. These methods, however, tend to underestimate variance when the data is collected with a cluster design, which is often found in educational survey research. The purposes of this paper are to demonstrate how a cluster design…
Descriptors: Cluster Analysis, Educational Research, Error of Measurement, Estimation (Mathematics)
Adams, Raymond J. – Studies in Educational Evaluation, 2005
Test reliability is a concept central to classical test theory and it is commonly stated as a requirement that a test attain a certain level of reliability before it be considered of sufficient quality for practical use. This article discusses the role of reliability in item response theory, and in particular the role of reliability in contexts…
Descriptors: Test Reliability, Error of Measurement, Item Sampling, Item Response Theory
Penfield, Douglas A. – 1972
Thirty-four papers on educational statistics which were presented at the 1971 AERA Conference are summarized. Six major interest areas are covered: (a) general information; (b) non-parametric methods; (c) errors of measurement and correlation techniques; (d) regression theory; (e) univariate and multivariate analysis; (f) factor analysis. (MS)
Descriptors: Analysis of Variance, Bayesian Statistics, Behavioral Science Research, Computers

Wilson, Mark – Journal of Educational Statistics, 1989
An empirical sampling approach was used to assess the accuracy of a Taylor approximation for the estimation of sampling errors. The sampling errors were in the statistics involved in estimating a path model based on medium-sized samples gathered using five sample designs commonly used in educational research. (TJH)
Descriptors: Educational Research, Error of Measurement, Estimation (Mathematics), Mathematical Models
Moore, James D., Jr. – 1996
The serious problems associated with the use of stepwise methods are well documented. Various authors have leveled scathing criticisms against the use of stepwise techniques, yet it is not uncommon to find these methods continually employed in educational and psychological research. The three main problems with stepwise techniques are: (1)…
Descriptors: Computer Software, Discriminant Analysis, Educational Research, Error of Measurement
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