Data Analysis with IBM SPSS Statistics

Master data management & analysis techniques with IBM SPSS Statistics 24
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Data Analysis with IBM SPSS Statistics

Kenneth Stehlik-Barry, Anthony J. Babinec

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Master data management & analysis techniques with IBM SPSS Statistics 24

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Book Details

ISBN 139781787283817
Paperback446 pages

Book Description

SPSS Statistics is a software package used for logical batched and non-batched statistical analysis. Analytical tools such as SPSS can readily provide even a novice user with an overwhelming amount of information and a broad range of options for analyzing patterns in the data.

The journey starts with installing and configuring SPSS Statistics for first use and exploring the data to understand its potential (as well as its limitations). Use the right statistical analysis technique such as regression, classification and more, and analyze your data in the best possible manner. Work with graphs and charts to visualize your findings. With this information in hand, the discovery of patterns within the data can be undertaken. Finally, the high level objective of developing predictive models that can be applied to other situations will be addressed.

By the end of this book, you will have a firm understanding of the various statistical analysis techniques offered by SPSS Statistics, and be able to master its use for data analysis with ease.

Table of Contents

Chapter 1: Installing and Configuring SPSS
The SPSS installation utility
Licensing SPSS
Launching and using SPSS
Setting parameters within the SPSS software
Executing a basic SPSS session
Summary
Chapter 2: Accessing and Organizing Data
Accessing and organizing data overview
Reading Excel files
Reading delimited text data files
Saving IBM SPSS Statistics files
Reading IBM SPSS Statistics files
Demo - first look at the data - frequencies
Variable properties
Summary
Chapter 3: Statistics for Individual Data Elements
Getting the sample data
Descriptive statistics for numeric fields
Discovering coding issues using frequencies
Explore procedure
Summary
Chapter 4: Dealing with Missing Data and Outliers
Outliers
Missing data
Summary
Chapter 5: Visually Exploring the Data
Graphs available in SPSS procedures
Summary
Chapter 6: Sampling, Subsetting, and Weighting
Select cases dialog box
Selecting a random sample of cases
Split File
Weighting
Summary
Chapter 7: Creating New Data Elements
Transforming fields in SPSS
The RECODE command
The COMPUTE command
The IF command
The DO IF/ELSE IF command
General points regarding SPSS transformation commands
Summary
Chapter 8: Adding and Matching Files
SPSS Statistics commands to merge files
Example of one-to-many merge - Northwind database
One-to-one merge - two data subsets from GSS2016
Example of combining cases using ADD FILES
Summary
Chapter 9: Aggregating and Restructuring Data
Using aggregation to add fields to a file
Aggregating up one level
Second level aggregation
Matching the aggregated file back to find specific records
Restructuring rows to columns
Summary
Chapter 10: Crosstabulation Patterns for Categorical Data
Percentages in crosstabs
Summary
Chapter 11: Comparing Means and ANOVA
SPSS procedures for comparing Means
Post hoc comparisons
Summary
Chapter 12: Correlations
Pearson correlations
Listwise versus pairwise missing values
Pivoting table editing to enhance correlation matrices
Visualizing correlations with scatterplots
Rank order correlations
Partial correlations
Summary
Chapter 13: Linear Regression
Assumptions of the classical linear regression model
Example - motor trend car data
Multiple regression - Model-building strategies
Summary
Chapter 14: Principal Components and Factor Analysis
Choosing between principal components analysis and factor analysis
PCA example - violent crimes
Factor analysis - abilities
Summary
Chapter 15: Clustering
Overview of cluster analysis
Overview of SPSS Statistics cluster analysis procedures
Hierarchical cluster analysis example
K-means cluster analysis example
Twostep cluster analysis example
Summary
Chapter 16: Discriminant Analysis
Descriptive discriminant analysis
Predictive discriminant analysis
Assumptions underlying discriminant analysis
Example data
Statistical and graphical summary of the data
Discriminant analysis setup - key decisions
Examining the results
Scoring new observations
Summary

What You Will Learn

  • Install and set up SPSS to create a working environment for analytics
  • Techniques for exploring data visually and statistically, assessing data quality and addressing issues related to missing data
  • How to import different kinds of data and work with it
  • Organize data for analytical purposes (create new data elements, sampling, weighting, subsetting, and restructure your data)
  • Discover basic relationships among data elements (bivariate data patterns, differences in means, correlations)
  • Explore multivariate relationships
  • Leverage the offerings to draw accurate insights from your research, and benefit your decision-making

Authors

Table of Contents

Chapter 1: Installing and Configuring SPSS
The SPSS installation utility
Licensing SPSS
Launching and using SPSS
Setting parameters within the SPSS software
Executing a basic SPSS session
Summary
Chapter 2: Accessing and Organizing Data
Accessing and organizing data overview
Reading Excel files
Reading delimited text data files
Saving IBM SPSS Statistics files
Reading IBM SPSS Statistics files
Demo - first look at the data - frequencies
Variable properties
Summary
Chapter 3: Statistics for Individual Data Elements
Getting the sample data
Descriptive statistics for numeric fields
Discovering coding issues using frequencies
Explore procedure
Summary
Chapter 4: Dealing with Missing Data and Outliers
Outliers
Missing data
Summary
Chapter 5: Visually Exploring the Data
Graphs available in SPSS procedures
Summary
Chapter 6: Sampling, Subsetting, and Weighting
Select cases dialog box
Selecting a random sample of cases
Split File
Weighting
Summary
Chapter 7: Creating New Data Elements
Transforming fields in SPSS
The RECODE command
The COMPUTE command
The IF command
The DO IF/ELSE IF command
General points regarding SPSS transformation commands
Summary
Chapter 8: Adding and Matching Files
SPSS Statistics commands to merge files
Example of one-to-many merge - Northwind database
One-to-one merge - two data subsets from GSS2016
Example of combining cases using ADD FILES
Summary
Chapter 9: Aggregating and Restructuring Data
Using aggregation to add fields to a file
Aggregating up one level
Second level aggregation
Matching the aggregated file back to find specific records
Restructuring rows to columns
Summary
Chapter 10: Crosstabulation Patterns for Categorical Data
Percentages in crosstabs
Summary
Chapter 11: Comparing Means and ANOVA
SPSS procedures for comparing Means
Post hoc comparisons
Summary
Chapter 12: Correlations
Pearson correlations
Listwise versus pairwise missing values
Pivoting table editing to enhance correlation matrices
Visualizing correlations with scatterplots
Rank order correlations
Partial correlations
Summary
Chapter 13: Linear Regression
Assumptions of the classical linear regression model
Example - motor trend car data
Multiple regression - Model-building strategies
Summary
Chapter 14: Principal Components and Factor Analysis
Choosing between principal components analysis and factor analysis
PCA example - violent crimes
Factor analysis - abilities
Summary
Chapter 15: Clustering
Overview of cluster analysis
Overview of SPSS Statistics cluster analysis procedures
Hierarchical cluster analysis example
K-means cluster analysis example
Twostep cluster analysis example
Summary
Chapter 16: Discriminant Analysis
Descriptive discriminant analysis
Predictive discriminant analysis
Assumptions underlying discriminant analysis
Example data
Statistical and graphical summary of the data
Discriminant analysis setup - key decisions
Examining the results
Scoring new observations
Summary

Book Details

ISBN 139781787283817
Paperback446 pages
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From 2 reviews

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