TY  - GEN
AU  -  Alan C.Elliott
AU  - Wayne A. Woodward
TI  - IBM SPSS by Example: A Practical Guide to Statistical Data Analysis 
SN  - 9781483319032
U1  - 005.55 ELL 
PY  - 2016///
CY  - New Delhi
PB  - SAGE
N1  - Chapter 1: Introduction 
Getting the Most Out of IBM SPSS by Example
 
A Brief Overview of the Statistical Process
 
Understanding Hypothesis Testing, Power, and Sample Size
 
Understanding the p-Value
 
Planning a Successful Analysis
 
Guidelines for Creating Data Sets
 
Preparing Excel Data for Import
 
Guidelines for Reporting Results
 
Opening Data Files for Examples
 
  Chapter 2: Describing and Examining Data 
Example Data Files
 
Describing Quantitative Data
 
Describing Categorical Data
 
  Chapter 3: Creating and Using Graphs 
Introduction to SPSS Graphs
 
Chart Builder
 
Graphboard Template Chooser
 
Legacy Plots
 
Scatterplots
 
Histograms
 
Bar Charts
 
Pie Charts
 
Box Plots
 
  Chapter 4: Comparing One or Two Means Using the t-Test 
One-Sample t-Test
 
Two-Sample t-Test
 
Paired t-Test
 
  Chapter 5: Correlation and Regression 
Correlation Analysis
 
Simple Linear Regression
 
Multiple Linear Regression
 
  Chapter 6: Analysis of Categorical Data 
Contingency Table Analysis
 
McNemar’s Test
 
Mantel-Haenszel Comparison
 
Tests of Interrater Reliability
 
Goodness-of-Fit Test
 
Other Measures of Association for Categorical Data
 
  Chapter 7: Analysis of Variance and Covariance 
One-Way ANOVA
 
Two-Way Analysis of Variance
 
Repeated-Measures Analysis of Variance
 
Analysis of Covariance
N2  - The updated Second Edition of Alan C. Elliott and Wayne A. Woodward’s “cut to the chase” IBM SPSS guide quickly explains the when, where, and how of statistical data analysis as it is used for real-world decision making in a wide variety of disciplines. This one-stop reference provides succinct guidelines for performing an analysis using SPSS software, avoiding pitfalls, interpreting results, and reporting outcomes. Written from a practical perspective, IBM SPSS by Example, Second Edition provides a wealth of information—from assumptions and design to computation, interpretation, and presentation of results—to help users save time, money, and frustration
ER  -