Data Analysis and Statistics for Nursing Research
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Introductory text covers the statistical methods and procedures needed to analyze research findings. Special focus is given to quantitative methods. Other features include management of data, how to "clean" data, and how to work around missing data. Graduate nursing students. Margaret Nield This unique focus of this statistics textbook is on the process of nursing research data analysis -- the how, when, and why of analysis -- rather than on statistical theory and calculation of statistics. The purpose is to present statistical techniques within a context of data analysis so that the student learns how to readily apply and interpret the results. The author succeeds in meeting the book's objective. The target audience is nursing students in an introductory statistics course. However, graduate students would find it a useful supplement to a course on research design. The clear writing style, the inclusion of healthcare research and computer printout examples, and a logical progression from statistics to causal modeling make the book a valuable teaching aid. A particularly practical feature for each chapter is the identification of specific research questions appropriate to the statistical procedure. Because of the conceptual nature of the book, the statistical calculations are kept at a minimum. Graphs and figures are used to clarify complex relationships and add to the textbook's readability. The references are pertinent. The book would be a useful addition to the personal library of the clinical nurse researchers and healthcare libraries.
PrefaceAcknowledgments1Introduction to Data Analysis12Univariate Statistics: Tabulating and Displaying Data193Univariate Statistics: Central Tendency and Variability434Bivariate Description695Statistical Inference916Testing the Difference Between Two Means: The Two-Sample t-Test1277Analysis of Variance1558Chi-Square and Other Nonparametric Tests1939Correlation and Simple Regression22510Multiple Regression25711Analysis of Covariance, Multivariate ANOVA, and Canonical Analysis30512Factor Analysis34513Discriminant Analysis and Logistic Regression38114Causal Modeling: Path Analysis and Linear Structural Relations Analysis413References439Glossary445Appendix A. Answers for Selected Exercises461Appendix B. Theoretical Probability Distribution Tables469Appendix C. Tables for Power Analyses481Name Index489Subject Index493
