Parts of this section are adapted from M. L. Abbott, Understanding Educational Statistics Using Microsoft Excel® and SPSS® (Wiley, 2011), by permission of the publisher.
15. Statistical Methods of Difference: t Test†
Published Online: 11 JAN 2013
Copyright © 2013 John Wiley & Sons, Inc.
Understanding and Applying Research Design
How to Cite
Lee Abbott, M. and McKinney, J. (2012) Statistical Methods of Difference: t Test, in Understanding and Applying Research Design, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118647325.ch15
- Published Online: 11 JAN 2013
- Published Print: 29 NOV 2012
Print ISBN: 9781118096482
Online ISBN: 9781118647325
- analysis of variance (ANOVA);
- independent t tests;
- post facto designs;
- statistical methods
There are many types of statistical procedures that attempt to detect statistical differences between and among research conditions. Statistical procedures that use a higher level of data, namely interval data, also detect differences among variables. Independent t tests determine whether the two categories of a predictor (independent) variable result in statistically different mean values of an outcome (dependent) variable. Analysis of variance (ANOVA) tests do essentially the same thing, but instead of comparing two categories of a predictor variable, they compare three or more categories. The added complexity is that the procedure uses special drill-down methods to determine the differences among each of the pairs of categories if the overall test is significant. Post facto designs compare group performance on an outcome measure after group differences have already taken place. These designs can be correlational or comparative depending on how the researcher relates one set of scores to the other.
Controlled Vocabulary Terms
analysis of variance; T-test