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Yang, Ji Seung; Cai, Li – Journal of Educational and Behavioral Statistics, 2014
The main purpose of this study is to improve estimation efficiency in obtaining maximum marginal likelihood estimates of contextual effects in the framework of nonlinear multilevel latent variable model by adopting the Metropolis-Hastings Robbins-Monro algorithm (MH-RM). Results indicate that the MH-RM algorithm can produce estimates and standard…
Descriptors: Computation, Hierarchical Linear Modeling, Mathematics, Context Effect
Phillips, Gary W. – Applied Measurement in Education, 2015
This article proposes that sampling design effects have potentially huge unrecognized impacts on the results reported by large-scale district and state assessments in the United States. When design effects are unrecognized and unaccounted for they lead to underestimating the sampling error in item and test statistics. Underestimating the sampling…
Descriptors: State Programs, Sampling, Research Design, Error of Measurement
Wu, Margaret – Educational Measurement: Issues and Practice, 2010
In large-scale assessments, such as state-wide testing programs, national sample-based assessments, and international comparative studies, there are many steps involved in the measurement and reporting of student achievement. There are always sources of inaccuracies in each of the steps. It is of interest to identify the source and magnitude of…
Descriptors: Testing Programs, Educational Assessment, Measures (Individuals), Program Effectiveness
Brennan, Robert L. – Applied Psychological Measurement, 2008
The discussion here covers five articles that are linked in the sense that they all treat population invariance. This discussion of population invariance is a somewhat broader treatment of the subject than simply a discussion of these five articles. In particular, occasional reference is made to publications other than those in this issue. The…
Descriptors: Advanced Placement, Law Schools, Science Achievement, Achievement Tests
Petersen, Nancy S. – Applied Psychological Measurement, 2008
This article discusses the five studies included in this issue. Each article addressed the same topic, population invariance of equating. They all used data from major standardized testing programs, and they all used essentially the same statistics to evaluate their results, namely, the root mean square difference and root expected mean square…
Descriptors: Testing Programs, Standardized Tests, Equated Scores, Evaluation Methods

Gohmann, Stephen F. – Journal of Educational Measurement, 1988
One method to correct for selection bias in comparing Scholastic Aptitude Test (SAT) scores among states is presented, which is a modification of J. J. Heckman's Selection Bias Correction (1976, 1979). Empirical results suggest that sample selection bias is present in SAT score regressions. (SLD)
Descriptors: Regression (Statistics), Sampling, Scoring, Selection

Gao, Xiaohong; And Others – Applied Measurement in Education, 1994
This study provides empirical evidence about the sampling variability and generalizability (reliability) of a statewide performance assessment for grade six. Results for 600 students at individual and school levels indicate that task-sampling variability was the major source of measurement error. Rater-sampling variability was negligible. (SLD)
Descriptors: Achievement Tests, Educational Assessment, Elementary School Students, Error of Measurement

Shepard, Lorrie – Studies in Educational Evaluation, 1979
Assessment generally refers to large-scale, system-wide measurement programs for pupil diagnosis; pupil certification; program evaluation; research; accountability; resource allocations; or teacher evaluation. The purpose of assessment should determine the test content, construction, administration, and examinees sampled. Assessment methods for…
Descriptors: Accountability, Diagnostic Tests, Educational Assessment, Educational Research

Marascuilo, Leonard A. – Journal of Experimental Education, 1979
The utility of the biomedical model of adjusted statistics is demonstrated. The model is recommended for use by educational researchers to randomize subjects for a more accurate estimate of school programs' success or failure when compared across classrooms or other units. (Author/MH)
Descriptors: Academic Achievement, Analysis of Variance, Comparative Analysis, Criterion Referenced Tests