IBM SPSS by Example: A Practical Guide to Statistical Data Analysis by Alan C. Elliott

By: Alan C. Elliott [Author]Contributor(s): Wayne A. WoodwardPublisher: New Delhi SAGE 2016Edition: Second EditionDescription: xxiii, 342 pages; Illustrations: 22 cmISBN: 9781483319032DDC classification: 005.55 ELL
Contents:
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
Abstract: 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.
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Non-fiction 005.55 ELL (Browse shelf) Available 001276

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

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.

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