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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
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Willinger, Ulrike; Schmoeger, Michaela; Deckert, Matthias; Eisenwort, Brigitte; Loader, Benjamin; Hofmair, Annemarie; Auff, Eduard – Journal of Psycholinguistic Research, 2017
Specific language impairment (SLI) comprises impairments in receptive and/or expressive language. Aim of this study was to evaluate a screening for SLI. 61 children with SLI (SLI-children, age-range 4-6 years) and 61 matched typically developing controls were tested for receptive language ability (Token Test-TT) and for intelligence (Wechsler…
Descriptors: Screening Tests, Clinical Diagnosis, Language Impairments, Preschool Children
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
Heausler, Nancy L. – 1987
Each of the four classic multivariate analysis of variance (MANOVA) tests of statistical significance may lead a researcher to different decisions as to whether a null hypothesis should be rejected: (1) Wilks' lambda; (2) Lawley-Hotelling trace criterion; (3) Roy's greatest characteristic root criterion; and (4) Pillai's trace criterion. These…
Descriptors: Analysis of Variance, Discriminant Analysis, Factor Analysis, Hypothesis Testing
Tate, Richard L. – 1980
This paper reviews multivariate hypothesis testing, interval estimation, and discriminant analysis procedures for an aptitude-treatment interaction (ATI) analysis and illustrates the procedures with data from a large experimental study of science education at the secondary school level. The treatment variable of interest is time allowed for study…
Descriptors: Academic Achievement, Aptitude Treatment Interaction, Discriminant Analysis, Hypothesis Testing