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Showing 1 to 15 of 17 results Save | Export
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Weller, Susan C. – Field Methods, 2015
This article presents a simple approach to making quick sample size estimates for basic hypothesis tests. Although there are many sources available for estimating sample sizes, methods are not often integrated across statistical tests, levels of measurement of variables, or effect sizes. A few parameters are required to estimate sample sizes and…
Descriptors: Sample Size, Statistical Analysis, Computation, Hypothesis Testing
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Ryan, Wendy L.; St. Iago-McRae, Ezry – Bioscene: Journal of College Biology Teaching, 2016
Experimentation is the foundation of science and an important process for students to understand and experience. However, it can be difficult to teach some aspects of experimentation within the time and resource constraints of an academic semester. Interactive models can be a useful tool in bridging this gap. This freely accessible simulation…
Descriptors: Research Design, Simulation, Animals, Animal Behavior
Spencer, Bryden – ProQuest LLC, 2016
Value-added models are a class of growth models used in education to assign responsibility for student growth to teachers or schools. For value-added models to be used fairly, sufficient statistical precision is necessary for accurate teacher classification. Previous research indicated precision below practical limits. An alternative approach has…
Descriptors: Monte Carlo Methods, Comparative Analysis, Accuracy, High Stakes Tests
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Long, Mark C. – Journal of Research on Educational Effectiveness, 2016
Using a "naïve" specification, this paper estimates the relationship between 36 high school characteristics and 24 student outcomes controlling for students' pre-high school characteristics. The goal of this exploration is not to generate casual estimates, but rather to: (a) compare the size of the relationships to determine which inputs…
Descriptors: Hypothesis Testing, Effect Size, High School Students, Student Characteristics
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Fan, Weihua; Hancock, Gregory R. – Journal of Educational and Behavioral Statistics, 2012
This study proposes robust means modeling (RMM) approaches for hypothesis testing of mean differences for between-subjects designs in order to control the biasing effects of nonnormality and variance inequality. Drawing from structural equation modeling (SEM), the RMM approaches make no assumption of variance homogeneity and employ robust…
Descriptors: Robustness (Statistics), Hypothesis Testing, Monte Carlo Methods, Simulation
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Price, Larry R. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…
Descriptors: Sample Size, Time, Bayesian Statistics, Structural Equation Models
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Paek, Insu – Applied Psychological Measurement, 2010
Conservative bias in rejection of a null hypothesis from using the continuity correction in the Mantel-Haenszel (MH) procedure was examined through simulation in a differential item functioning (DIF) investigation context in which statistical testing uses a prespecified level [alpha] for the decision on an item with respect to DIF. The standard MH…
Descriptors: Test Bias, Statistical Analysis, Sample Size, Error of Measurement
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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Olejnik, Stephen F. – Journal of Experimental Education, 1984
This paper discusses the sample size problem and four factors affecting its solution: significance level, statistical power, analysis procedure, and effect size. The interrelationship between these factors is discussed and demonstrated by calculating minimal sample size requirements for a variety of research conditions. (Author)
Descriptors: Effect Size, Error of Measurement, Hypothesis Testing, Research Design
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Kaplan, David – Multivariate Behavioral Research, 1990
A strategy for evaluating/modifying covariance structure models (CSMs) is presented. The approach uses recent developments in estimation under nonstandard conditions and unified asymptotic theory related to hypothesis testing, and it determines the extent of sample size sensitivity and specification error effects by relying on existing statistical…
Descriptors: Error of Measurement, Estimation (Mathematics), Evaluation Methods, Goodness of Fit
Clark, Sheldon B.; Huck, Schuyler W. – 1983
In true experiments in which sample material can be randomly assigned to treatment conditions, most researchers presume that the condition of equal sample sizes is statistically desirable. When one or more a priori contrasts can be identified which represent a few overriding experimental concerns, however, allocating sample material unequally will…
Descriptors: Analysis of Variance, Error of Measurement, Hypothesis Testing, Power (Statistics)
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Smith, Margaret H. – Journal of Statistics Education, 2004
Unless the sample encompasses a substantial portion of the population, the standard error of an estimator depends on the size of the sample, but not the size of the population. This is a crucial statistical insight that students find very counterintuitive. After trying several ways of convincing students of the validity of this principle, I have…
Descriptors: Sample Size, Error of Measurement, Mathematics Instruction, College Mathematics
Vasu, Ellen S.; Elmore, Patricia B. – 1975
The effects of the violation of the assumption of normality coupled with the condition of multicollinearity upon the outcome of testing the hypothesis Beta equals zero in the two-predictor regression equation is investigated. A monte carlo approach was utilized in which three differenct distributions were sampled for two sample sizes over…
Descriptors: Correlation, Error of Measurement, Factor Structure, Hypothesis Testing
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Long, Jeffrey D. – Psychological Methods, 2005
Often quantitative data in the social sciences have only ordinal justification. Problems of interpretation can arise when least squares multiple regression (LSMR) is used with ordinal data. Two ordinal alternatives are discussed, dominance-based ordinal multiple regression (DOMR) and proportional odds multiple regression. The Q[superscript 2]…
Descriptors: Simulation, Social Science Research, Error of Measurement, Least Squares Statistics
Thompson, Bruce – 1987
This paper evaluates the logic underlying various criticisms of statistical significance testing and makes specific recommendations for scientific and editorial practice that might better increase the knowledge base. Reliance on the traditional hypothesis testing model has led to a major bias against nonsignificant results and to misinterpretation…
Descriptors: Analysis of Variance, Data Interpretation, Editors, Effect Size
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