# 15. Statistical Methods of Difference: t Test†

1. Martin Lee Abbott and
2. Jennifer McKinney

Published Online: 11 JAN 2013

DOI: 10.1002/9781118647325.ch15

## 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

#### Author Information

1. Seattle Pacific University, USA

1. 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.

#### Publication History

1. Published Online: 11 JAN 2013
2. Published Print: 29 NOV 2012

#### ISBN Information

Print ISBN: 9781118096482

Online ISBN: 9781118647325

## SEARCH

### Keywords:

• analysis of variance (ANOVA);
• independent t tests;
• post facto designs;
• statistical methods

### Summary

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