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50 Years of ERIC
50 Years of ERIC
The Education Resources Information Center (ERIC) is celebrating its 50th Birthday! First opened on May 15th, 1964 ERIC continues the long tradition of ongoing innovation and enhancement.

Learn more about the history of ERIC here. PDF icon

Showing all 11 results
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Rusticus, Shayna A.; Lovato, Chris Y. – Practical Assessment, Research & Evaluation, 2014
The question of equivalence between two or more groups is frequently of interest to many applied researchers. Equivalence testing is a statistical method designed to provide evidence that groups are comparable by demonstrating that the mean differences found between groups are small enough that they are considered practically unimportant. Few…
Descriptors: Sample Size, Equivalency Tests, Simulation, Error of Measurement
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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
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Williams, Matt N.; Gomez Grajales, Carlos Alberto; Kurkiewicz, Dason – Practical Assessment, Research & Evaluation, 2013
In 2002, an article entitled "Four assumptions of multiple regression that researchers should always test" by Osborne and Waters was published in "PARE." This article has gone on to be viewed more than 275,000 times (as of August 2013), and it is one of the first results displayed in a Google search for "regression…
Descriptors: Multiple Regression Analysis, Misconceptions, Reader Response, Predictor Variables
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Han, Kyung T. – Practical Assessment, Research & Evaluation, 2012
For several decades, the "three-parameter logistic model" (3PLM) has been the dominant choice for practitioners in the field of educational measurement for modeling examinees' response data from multiple-choice (MC) items. Past studies, however, have pointed out that the c-parameter of 3PLM should not be interpreted as a guessing parameter. This…
Descriptors: Statistical Analysis, Models, Multiple Choice Tests, Guessing (Tests)
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Nordstokke, David W.; Zumbo, Bruno D.; Cairns, Sharon L.; Saklofske, Donald H. – Practical Assessment, Research & Evaluation, 2011
Many assessment and evaluation studies use statistical hypothesis tests, such as the independent samples t test or analysis of variance, to test the equality of two or more means for gender, age groups, cultures or language group comparisons. In addition, some, but far fewer, studies compare variability across these same groups or research…
Descriptors: Nonparametric Statistics, Statistical Analysis, Error of Measurement, Statistical Data
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Osborne, Jason W. – Practical Assessment, Research & Evaluation, 2011
Large surveys often use probability sampling in order to obtain representative samples, and these data sets are valuable tools for researchers in all areas of science. Yet many researchers are not formally prepared to appropriately utilize these resources. Indeed, users of one popular dataset were generally found "not" to have modeled the analyses…
Descriptors: Best Practices, Sampling, Sample Size, Data Analysis
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Schafer, William D.; Coverdale, Bradley J.; Luxenberg, Harlan; Jin, Ying – Practical Assessment, Research & Evaluation, 2011
There are relatively few examples of quantitative approaches to quality control in educational assessment and accountability contexts. Among the several techniques that are used in other fields, Shewart charts have been found in a few instances to be applicable in educational settings. This paper describes Shewart charts and gives examples of how…
Descriptors: Charts, Quality Control, Educational Assessment, Statistical Analysis
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Osbourne, Jason W.; Waters, Elaine – Practical Assessment, Research & Evaluation, 2002
Discusses assumptions of multiple regression that are not robust to violation: linearity, reliability of measurement, homoscedasticity, and normality. Stresses the importance of checking assumptions. (SLD)
Descriptors: Error of Measurement, Regression (Statistics), Reliability
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Cassady, Jerrell C. – Practical Assessment, Research & Evaluation, 2001
Studied the accuracy and trends of deviation noted in undergraduates' self-reported Scholastic Assessment Test (SAT) and grade point average (GPA) values. Results for 89 undergraduates show that students had highly reliable ratings of cumulative GPA, but the overall accuracies of self-reported SAT scores were considerably lower than the accuracy…
Descriptors: Error of Measurement, Grade Point Average, Higher Education, Trend Analysis
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Rudner, Lawrence M. – Practical Assessment, Research & Evaluation, 2001
Provides and illustrates a method to compute the expected number of misclassifications of examinees using three-parameter item response theory and two state classifications (mastery or nonmastery). The method uses the standard error and the expected examinee ability distribution. (SLD)
Descriptors: Ability, Classification, Computation, Error of Measurement
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Kellow, J. Thomas; Willson, Victor L. – Practical Assessment, Research & Evaluation, 2001
Explores the consequence of failing to incorporate measurement error in the development of cut scores in criterion-referenced measures, using the example of Texas and the Texas Assessment of Academic Skills to illustrate the impact of measurement error on false negative decisions. Findings support those of W. Haney (2000). (SLD)
Descriptors: Criterion Referenced Tests, Cutting Scores, Decision Making, Error of Measurement