Using a truly accessible and reader-friendly approach, this comprehensive introduction to statistics redefines the way statistics can be taught and learned. Unlike other books that merely focus on procedures, Reid’s approach balances development of critical thinking skills with application of those skills to contemporary statistical analysis. He goes beyond simply presenting techniques by focusing on the key concepts readers need to master in order to ensure their long-term success. Indeed, this exciting new book offers the perfect foundation upon which readers can build as their studies and careers progress to more advanced forms of statistics. Keeping computational challenges to a minimum, Reid shows readers not only how to conduct a variety of commonly used statistical procedures, but also when each procedure should be utilized and how they are related. Following a review of descriptive statistics, he begins his discussion of inferential statistics with a two-chapter examination of the Chi Square test to introduce students to hypothesis testing, the importance of determining effect size, and the need for post hoc tests. When more complex procedures related to interval/ratio data are covered, students already have a solid understanding of the foundational concepts involved. Exploring challenging topics in an engaging and easy-to-follow manner, Reid builds concepts logically and supports learning through robust pedagogical tools, the use of SPSS, numerous examples, historical quotations, insightful questions, and helpful progress checks.

# Finding Differences With Interval and Ratio Data—IV : The One-Way Within-Subjects ANOVA

Finding Differences With Interval and Ratio Data—IV : The One-Way Within-Subjects ANOVA

The null hypothesis is never proved or established, but is possibly disproved, in the course of experimentation.

—R. A. Fisher

The logic of the one-way within-subjects analysis of variance (ANOVA) is very similar to what we already learned for the one-way between-subjects ANOVA. Both include one independent variable (IV) with two or more levels and culminate in the calculation of an F ratio. To review, the F is the ratio of two estimates of the population variance, ${\mathrm{\sigma }}_{X}^{2}$. With the one-way between-subjects ANOVA, the estimate in the numerator is based on the variability of the sample means. This estimate of ${\mathrm{\sigma }}_{X}^{2}$ is called the mean square between (MSBet). It includes the effect of our ...