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Simsek, Ahmet Salih – International Journal of Assessment Tools in Education, 2023
Likert-type item is the most popular response format for collecting data in social, educational, and psychological studies through scales or questionnaires. However, there is no consensus on whether parametric or non-parametric tests should be preferred when analyzing Likert-type data. This study examined the statistical power of parametric and…
Descriptors: Error of Measurement, Likert Scales, Nonparametric Statistics, Statistical Analysis
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Lane, David; Oswald, Frederick L. – Educational Measurement: Issues and Practice, 2016
The educational literature, the popular press, and educated laypeople have all echoed a conclusion from the book "Academically Adrift" by Richard Arum and Josipa Roksa (which has now become received wisdom), namely, that 45% of college students showed no significant gains in critical thinking skills. Similar results were reported by…
Descriptors: College Students, Critical Thinking, Thinking Skills, Statistical Analysis
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Vaughan, Timothy S. – Journal of Statistics Education, 2015
This paper introduces a dataset and associated analysis of the scores of National Football League (NFL) games over the 2012, 2013, and first five weeks of the 2014 season. In the face of current media attention to "lopsided" scores in Thursday night games in the early part of the 2014 season, t-test results indicate no statistically…
Descriptors: Team Sports, Success, Scores, Statistics
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Zimmerman, Donald W. – Psicologica: International Journal of Methodology and Experimental Psychology, 2012
In order to circumvent the influence of correlation in paired-samples and repeated measures experimental designs, researchers typically perform a one-sample Student "t" test on difference scores. That procedure entails some loss of power, because it employs N - 1 degrees of freedom instead of the 2N - 2 degrees of freedom of the…
Descriptors: Correlation, Statistical Analysis, Statistical Significance, Scores
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Vanhove, Jan – Studies in Second Language Learning and Teaching, 2015
I discuss three common practices that obfuscate or invalidate the statistical analysis of randomized controlled interventions in applied linguistics. These are (a) checking whether randomization produced groups that are balanced on a number of possibly relevant covariates, (b) using repeated measures ANOVA to analyze pretest-posttest designs, and…
Descriptors: Randomized Controlled Trials, Intervention, Applied Linguistics, Statistical Analysis
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Horakova, Tereza; Houska, Milan – International Education Studies, 2014
The paper shows how the methodology for a pedagogical experiment can be improved through including the pre-research stage. If the experiment has the form of a test procedure, an improvement of methodology can be achieved using for example the methods of statistical and didactic analysis of tests which are traditionally used in other areas, i.e.…
Descriptors: Educational Research, Educational Experiments, Research Methodology, Statistical Analysis
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What Works Clearinghouse, 2014
This "What Works Clearinghouse Procedures and Standards Handbook (Version 3.0)" provides a detailed description of the standards and procedures of the What Works Clearinghouse (WWC). The remaining chapters of this Handbook are organized to take the reader through the basic steps that the WWC uses to develop a review protocol, identify…
Descriptors: Educational Research, Guides, Intervention, Classification
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Carvajal, Jorge; Skorupski, William P. – Educational and Psychological Measurement, 2010
This study is an evaluation of the behavior of the Liu-Agresti estimator of the cumulative common odds ratio when identifying differential item functioning (DIF) with polytomously scored test items using small samples. The Liu-Agresti estimator has been proposed by Penfield and Algina as a promising approach for the study of polytomous DIF but no…
Descriptors: Test Bias, Sample Size, Test Items, Computation
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Solanas, Antonio; Manolov, Rumen; Sierra, Vicenta – Psicologica: International Journal of Methodology and Experimental Psychology, 2010
In the first part of the study, nine estimators of the first-order autoregressive parameter are reviewed and a new estimator is proposed. The relationships and discrepancies between the estimators are discussed in order to achieve a clear differentiation. In the second part of the study, the precision in the estimation of autocorrelation is…
Descriptors: Computation, Hypothesis Testing, Correlation, Monte Carlo Methods
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Fan, Xitao; Nowell, Dana L. – Gifted Child Quarterly, 2011
This methodological brief introduces the readers to the propensity score matching method, which can be used for enhancing the validity of causal inferences in research situations involving nonexperimental design or observational research, or in situations where the benefits of an experimental design are not fully realized because of reasons beyond…
Descriptors: Research Design, Educational Research, Statistical Analysis, Inferences
Murphy, Richard; Weinhardt, Felix – Centre for Economic Performance, 2013
We find an individual's rank within their reference group has effects on later objective outcomes. To evaluate the impact of local rank, we use a large administrative dataset tracking over two million students in England from primary through to secondary school. Academic rank within primary school has sizable, robust and significant effects on…
Descriptors: Foreign Countries, Class Rank, Progress Monitoring, Effect Size
Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research
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Schochet, Peter Z. – National Center for Education Evaluation and Regional Assistance, 2009
This paper examines the estimation of two-stage clustered RCT designs in education research using the Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the…
Descriptors: Control Groups, Causal Models, Statistical Significance, Computation
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Moses, Tim – ETS Research Report Series, 2006
Population invariance is an important requirement of test equating. An equating function is said to be population invariant when the choice of (sub)population used to compute the equating function does not matter. In recent studies, the extent to which equating functions are population invariant is typically addressed in terms of practical…
Descriptors: Equated Scores, Computation, Error of Measurement, Statistical Analysis
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Yuan, Ke-Hai; Bentler, Peter M. – Educational and Psychological Measurement, 2004
In mean and covariance structure analysis, the chi-square difference test is often applied to evaluate the number of factors, cross-group constraints, and other nested model comparisons. Let model M[a] be the base model within which model M[b] is nested. In practice, this test is commonly used to justify M[b] even when M[a] is misspecified. The…
Descriptors: Statistical Significance, Item Response Theory, Computation, Statistical Analysis
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