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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 1 to 15 of 32 results
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Osborne, Jason W. – Practical Assessment, Research & Evaluation, 2015
Exploratory factor analysis (EFA) is one of the most commonly-reported quantitative methodology in the social sciences, yet much of the detail regarding what happens during an EFA remains unclear. The goal of this brief technical note is to explore what "rotation" is, what exactly is rotating, and why we use rotation when performing…
Descriptors: Factor Analysis, Social Sciences, Engineering Education, Evaluation Methods
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Zumbach, Joerg; Funke, Joachim – Practical Assessment, Research & Evaluation, 2014
In two subsequent experiments, the influence of mood on academic course evaluation is examined. By means of facial feedback, either a positive or a negative mood was induced while students were completing a course evaluation questionnaire during lectures. Results from both studies reveal that a positive mood leads to better ratings of different…
Descriptors: Course Evaluation, Psychological Patterns, Student Attitudes, Feedback (Response)
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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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Beaujean, A. Alexander – Practical Assessment, Research & Evaluation, 2014
A common question asked by researchers using regression models is, What sample size is needed for my study? While there are formulae to estimate sample sizes, their assumptions are often not met in the collected data. A more realistic approach to sample size determination requires more information such as the model of interest, strength of the…
Descriptors: Regression (Statistics), Sample Size, Sampling, Monte Carlo Methods
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Stoffel, Heather; Raymond, Mark R.; Bucak, S. Deniz; Haist, Steven A. – Practical Assessment, Research & Evaluation, 2014
Previous research on the impact of text and formatting changes on test-item performance has produced mixed results. This matter is important because it is generally acknowledged that "any" change to an item requires that it be recalibrated. The present study investigated the effects of seven classes of stylistic changes on item…
Descriptors: Test Construction, Test Items, Standardized Tests, Physicians
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Wyse, Adam E.; Seo, Dong Gi – Practical Assessment, Research & Evaluation, 2014
This article provides a brief overview and comparison of three conditional growth percentile methods; student growth percentiles, percentile rank residuals, and a nonparametric matching method. These approaches seek to describe student growth in terms of the relative percentile ranking of a student in relationship to students that had the same…
Descriptors: Academic Achievement, Achievement Gains, Evaluation Methods, Statistical Analysis
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Warne, Russell T. – Practical Assessment, Research & Evaluation, 2014
Reviews of statistical procedures (e.g., Bangert & Baumberger, 2005; Kieffer, Reese, & Thompson, 2001; Warne, Lazo, Ramos, & Ritter, 2012) show that one of the most common multivariate statistical methods in psychological research is multivariate analysis of variance (MANOVA). However, MANOVA and its associated procedures are often not…
Descriptors: Multivariate Analysis, Behavioral Science Research, Discriminant Analysis, Psychological Studies
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Randolph, Justus J.; Falbe, Kristina; Manuel, Austin Kureethara; Balloun, Joseph L. – Practical Assessment, Research & Evaluation, 2014
Propensity score matching is a statistical technique in which a treatment case is matched with one or more control cases based on each case's propensity score. This matching can help strengthen causal arguments in quasi-experimental and observational studies by reducing selection bias. In this article we concentrate on how to conduct…
Descriptors: Statistical Analysis, Probability, Experimental Groups, Control Groups
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Adelson, Jill L. – Practical Assessment, Research & Evaluation, 2013
Often it is infeasible or unethical to use random assignment in educational settings to study important constructs and questions. Hence, educational research often uses observational data, such as large-scale secondary data sets and state and school district data, and quasi-experimental designs. One method of reducing selection bias in estimations…
Descriptors: Educational Research, Data, Statistical Bias, Probability
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de Winter, J. C .F. – Practical Assessment, Research & Evaluation, 2013
Researchers occasionally have to work with an extremely small sample size, defined herein as "N" less than or equal to 5. Some methodologists have cautioned against using the "t"-test when the sample size is extremely small, whereas others have suggested that using the "t"-test is feasible in such a case. The present…
Descriptors: Sample Size, Statistical Analysis, Hypothesis Testing, Simulation
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Osborne, Jason W. – Practical Assessment, Research & Evaluation, 2013
Osborne and Waters (2002) focused on checking some of the assumptions of multiple linear regression. In a critique of that paper, Williams, Grajales, and Kurkiewicz correctly clarify that regression models estimated using ordinary least squares require the assumption of normally distributed errors, but not the assumption of normally distributed…
Descriptors: Multiple Regression Analysis, Least Squares Statistics, Computation, Statistical Analysis
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Stone, Clement A.; Tang, Yun – Practical Assessment, Research & Evaluation, 2013
Propensity score applications are often used to evaluate educational program impact. However, various options are available to estimate both propensity scores and construct comparison groups. This study used a student achievement dataset with commonly available covariates to compare different propensity scoring estimation methods (logistic…
Descriptors: Comparative Analysis, Probability, Sample Size, Program Evaluation
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Derzon, James H.; Alford, Aaron A. – Practical Assessment, Research & Evaluation, 2013
Forest plots provide an effective means of presenting a wealth of information in a single graphic. Whether used to illustrate multiple results in a single study or the cumulative knowledge of an entire field, forest plots have become an accepted and generally understood way of presenting many estimates simultaneously. This article explores…
Descriptors: Spreadsheets, Graphs, Statistical Analysis, Meta Analysis
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Courtney, Matthew Gordon Ray – Practical Assessment, Research & Evaluation, 2013
Exploratory factor analysis (EFA) is a common technique utilized in the development of assessment instruments. The key question when performing this procedure is how to best estimate the number of factors to retain. This is especially important as under- or over-extraction may lead to erroneous conclusions. Although recent advancements have been…
Descriptors: Factor Analysis, Computer Software, Open Source Technology, Computation
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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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