Abstract
This chapter reviews the independent-samples t-test and the one-way analysis of variance, inferential statistics that are commonly used to test null and alternative hypotheses about mean differences among independent populations. Because both procedures assume equal population variances, Levene’s test for homogeneity of variances is discussed, as are methods for hypothesis testing when homogeneity of variances cannot be safely assumed. The chapter continues by using a measure of effect size, partial eta squared, to distinguish between statistical and clinical significance, and concludes with a discussion of post hoc multiple comparisons and contrast analysis.
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CDC BRFSS.sav obtained from: Centers for Disease Control and Prevention (CDC): Behavioral Risk Factor Surveillance System Survey Data. US Department of Health and Human Services, Centers for Disease Control and Prevention, Atlanta (2005). Public domain. For more information about the BRFSS, visit http://www.cdc.gov/brfss/. Accessed 16 Nov 2014
Framingham.sav obtained from: Dupont, W.D.: Statistical Modeling for Biomedical Researchers, 2nd edn. Cambridge University Press, New York (2009). (With the kind permission of Sean Coady, National Heart, Blood, and Lung Institute)
From: Barker, S., Jerome, J., Woods, D., Zaika, C., Brown, R.G., Holmes, W.H.: The Sit and Reach Test as a Measure of Flexibility for Predicting Lower Extremity Injury in Division III Athletes. Unpublished data. Le Moyne College, Syracuse (2010)
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Holmes, W., Rinaman, W. (2014). Comparing Means of Independent Samples. In: Statistical Literacy for Clinical Practitioners. Springer, Cham. https://doi.org/10.1007/978-3-319-12550-3_10
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DOI: https://doi.org/10.1007/978-3-319-12550-3_10
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Online ISBN: 978-3-319-12550-3
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