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 Nominal Data—II : The Chi-Square Test of Independence

Finding Differences With Nominal Data—II : The Chi-Square Test of Independence

Science is not a collection of facts but a way of interrogating the world.

—Sharon Begley

The chi-square statistic is not limited to analyzing frequencies obtained with a single variable, as was the case in Chapter 7. Another form of the chi-square statistic, the chi-square test of independence, is used when we have a design that involves nominal data and two variables. This test is underlined in Table 8.1.

Analyzing a Difference Design With Two Variables, Each With at Least Two Outcomes

In Chapter 7, we noted that in the field of statistics, “independent” has a very specific meaning, for it signifies that two events, samples, or variables are not related in a predicable fashion. The term ...

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