Statistics and data analysis for nursing research
Author(s)
Bibliographic Information
Statistics and data analysis for nursing research
Pearson, c2010
2nd ed
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Note
Rev. ed. of: Data analysis & statistics for nursing research / Denise F. Polit. c1996
Includes bibliographical references and index
Contents of Works
- Introduction to data analysis in an evidence-based practice environment
- Frequency distributions : tabulating and displaying data
- Central tendency, variability, and relative standing
- Bivariate description : crosstabulation, risk indexes, and correlation
- Statistical inference
- T tests : testing two mean differences
- Analysis of variance
- Chi-square and other nonparametric tests
- Correlation and simple regression
- Multiple regression
- Analysis of covariance, multivariate ANOVA, and related multivariate analyses
- Logistic regression
- Factor analysis and internal consistency reliability analysis
- Missing values
Description and Table of Contents
Description
The second edition of Statistics and Data Analysis for Nursing, uses a conversational style to teach students how to use statistical methods and procedures to analyze research findings. Readers are guided through the complete analysis process from performing a statistical analysis to the rationale behind doing so. Special focus is given to quantitative methods. Other features include management of data, how to "clean" data, and how to work around missing data. New to this edition are
updated research examples utilizinging examples from an international mix of studies published by nurse researchers in 2006-2009.
Table of Contents
Chapter Chapter Title
1 Introduction to Data Analysis in an Evidence-Based Practice Environment
2 Frequency Distribution: Tabulating and Displaying Data
3 Central Tendency, Variability, and Location
4 Correlation, Crosstabulation, and Risk Indexes: Describing Relationships:
5 Statistical Inference
6 t Tests
7 Analysis of Variance
8 Chi Square and Other Nonparametric Tests
9 Correlation and Simple Regression
10 Multiple Regression
11 Analysis of Covariance, MANOVA, and Other Related Multivariate Techniques
12 Using Logistic Regression
13 Factor Analysis and Internal Consistency Reliability Analysis
14 Missing Values
Appendix A: Theoretical Probability Tables
Appendix B: Power Analysis/Effect Size Tables
Appendix C: Tips on Handling Missing Data
Appendix D: Answers for Selected Exercises
by "Nielsen BookData"
