Ordinal Data Nonparametric Statistical Analyses and SPSS Applications

“Ordinal Data Nonparametric Statistical Analyses and SPSS Applications” by Clifton W. Hamilton is a comprehensive resource published by Cognella, Incorporated on April 8, 2024. This 120-page book is presented in English and serves as an introduction to the statistical analysis of ordinal data, which is frequently used in health, wellness, and behavioral sciences. It provides foundational knowledge necessary for understanding various data types, including Likert-like scales and ordinal rankings, and covers essential concepts such as hypothesis testing and p-values.
Readers will find detailed explanations of nonparametric tests and distribution-free methods, along with specific procedures for conducting tests like the Rank Sum Test and the Signed Rank Test. The text emphasizes the computation of statistical measures and the interpretation of results, highlighting that analysis outcomes should support research hypotheses without definitively proving them. This book is particularly suitable for undergraduate or graduate courses in nonparametric statistics, quantitative analysis, and research methods within the health and behavioral sciences.
Official synopsis Publisher
Ordinal Data: Nonparametric Statistical Analyses and SPSS Applications familiarizes students with the statistical analysis of ordinal data, commonly utilized in the fields of health, wellness, and behavioral sciences. The book offers foundational knowledge required for understanding and handling data types such as Likert-like scales, ordinal rankings, and nonparametric datasets. It is designed for learners who have a basic understanding of statistics, covering concepts such as hypothesis testing, p-values, and sampling.
The text delineates the principles of nonparametric tests and distribution-free methods, as well as detailed procedures for conducting specific tests like the Rank Sum Test and the Signed Rank Test. It discusses the computation of various statistical measures and the interpretation of results, emphasizing that outcomes of analyses should support, but not definitively prove, research hypotheses. The content is structured to provide clarity on methods to analyze data from smaller and larger sample sizes, correct for biases such as ties in data, and report findings according to APA standards.
Ordinal Data is ideal for undergraduate or graduate courses focused on nonparametric statistics, quantitative analysis, as well as research methods in the health and behavioral sciences.
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