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Vaske, Jerry J. – Sagamore-Venture, 2019
Data collected from surveys can result in hundreds of variables and thousands of respondents. This implies that time and energy must be devoted to (a) carefully entering the data into a database, (b) running preliminary analyses to identify any problems (e.g., missing data, potential outliers), (c) checking the reliability and validity of the…
Descriptors: Surveys, Theories, Hypothesis Testing, Effect Size
Eudey, T. Lynn; Kerr, Joshua D.; Trumbo, Bruce E. – Journal of Statistics Education, 2010
Null distributions of permutation tests for two-sample, paired, and block designs are simulated using the R statistical programming language. For each design and type of data, permutation tests are compared with standard normal-theory and nonparametric tests. These examples (often using real data) provide for classroom discussion use of metrics…
Descriptors: Statistical Distributions, Hypothesis Testing, Relationship, Statistical Significance
Peer reviewedCoombs, William T.; And Others – Review of Educational Research, 1996
Methods to compare population means in the univariate case and population mean vectors in the multivariate case are presented in terms of hypotheses tested by various procedures. Tests relevant to each hypothesis are described and compared in terms of maximizing power while controlling Type I error rates over the widest variety of conditions. (SLD)
Descriptors: Comparative Analysis, Hypothesis Testing, Multivariate Analysis
Peer reviewedFerrando, Pere J.; Lorenzo-Seva, Urbano – Multivariate Behavioral Research, 1999
Describes the implementation of a standard Pearson chi-square statistic to test the null hypothesis of bivariate normality for latent variables in the Type I censored model. Assesses the behavior of the statistic through simulation and illustrates the statistic through an empirical example. Discusses limitations of the test. (Author/SLD)
Descriptors: Chi Square, Evaluation Methods, Hypothesis Testing, Multivariate Analysis
Huberty, Carl J.; Smith, Jerry D. – 1981
A particular strategy for investigating effects resulting from a multivariate analysis of variance (MANOVA) is proposed. The strategy involves multiple two-group multivariate analyses. The two groups result from considering multivariate pairwise group contrasts or multivariate complex group contrasts. Assuming a given two-group analysis yields…
Descriptors: Analysis of Variance, Comparative Analysis, Discriminant Analysis, Hypothesis Testing
Ferron, John; Jones, Peggy K. – Journal of Experimental Education, 2006
The authors present a method that ensures control over the Type I error rate for those who visually analyze the data from response-guided multiple-baseline designs. The method can be seen as a modification of visual analysis methods to incorporate a mechanism to control Type I errors or as a modification of randomization test methods to allow…
Descriptors: Multivariate Analysis, Data Analysis, Inferences, Monte Carlo Methods
Maxwell, Scott E. – Psychological Methods, 2004
Underpowered studies persist in the psychological literature. This article examines reasons for their persistence and the effects on efforts to create a cumulative science. The "curse of multiplicities" plays a central role in the presentation. Most psychologists realize that testing multiple hypotheses in a single study affects the Type I error…
Descriptors: Psychology, Psychological Studies, Effect Size, Research Methodology
Bartolucci, Francesco – Psychometrika, 2007
We illustrate a class of multidimensional item response theory models in which the items are allowed to have different discriminating power and the latent traits are represented through a vector having a discrete distribution. We also show how the hypothesis of unidimensionality may be tested against a specific bidimensional alternative by using a…
Descriptors: Simulation, National Competency Tests, Item Response Theory, Models
Peer reviewedHoeksma, Jan B.; Knol, Dirk L. – Multivariate Behavioral Research, 2001
Makes the case that hierarchical linear models or longitudinal multilevel models are a better alternative than standard regression models for empirical tests of predictive developmental hypotheses. Describes a multivariate longitudinal model linking developmental data to a criterion and presents an example from a study of the prediction of infant…
Descriptors: Behavior Patterns, Case Studies, Development, Hypothesis Testing

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