Name: Data Analysis with IBM SPSS Statistics
Author: Kenneth Stehlik Barry Anthony J. Babinec
ISBN: 9781787280700

微信读书书城Data Analysis with IBM SPSS Statistics

首页我的书架登录

翻译完成

切换

Data Analysis with IBM SPSS Statistics

Kenneth Stehlik Barry Anthony J. Babinec

扉页
cover
+书签
Title Page
Copyright
Data Analysis with IBM SPSS Statistics
Credits
About the Authors
Acknowledgement
About the Reviewers
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
Installing and Configuring SPSS
The SPSS installation utility
Installing Python for the scripting
Licensing SPSS
Confirming the options available
Launching and using SPSS
Setting parameters within the SPSS software
Executing a basic SPSS session
Summary
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
Variable properties - name
Variable properties - type
Variable properties - width
Variable properties - decimals
Variable properties - label
Variable properties - values
Variable properties - missing
Variable properties - columns
Variable properties - align
Variable properties - measure
Variable properties - role
Demo - adding variable properties to the Variable View
Demo - adding variable properties via syntax
Demo - defining variable properties
Summary
Statistics for Individual Data Elements
Getting the sample data
Descriptive statistics for numeric fields
Controlling the descriptives display order
Frequency distributions
Discovering coding issues using frequencies
Using frequencies to verify missing data patterns
Explore procedure
Stem and leaf plot
Boxplot
Using explore to check subgroup patterns
Summary
Dealing with Missing Data and Outliers
Outliers
Frequencies for histogram and percentile values
Descriptives for standardized scores
The Examine procedure for extreme values and boxplot
Detecting multivariate outliers
Missing data
Missing values in Frequencies
Missing values in Descriptives
Missing value patterns
Replacing missing values
Summary
Visually Exploring the Data
Graphs available in SPSS procedures
Obtaining bar charts with frequencies
Obtaining a histogram with frequencies
Creating graphs using chart builder
Building a scatterplot
Create a boxplot using chart builder
Summary
Sampling, Subsetting, and Weighting
Select cases dialog box
Select cases - If condition is satisfied
Example
If condition is satisfied combined with Filter
If condition is satisfied combined with Copy
If condition is satisfied combined with Delete unselected cases
The Temporary command
Select cases based on time or case range
Using the filter variable
Selecting a random sample of cases
Split File
Weighting
Summary
Creating New Data Elements
Transforming fields in SPSS
The RECODE command
Creating a dummy variable using RECODE
Using RECODE to rescale a field
Respondent s income using the midpoint of a selected category
The COMPUTE command
The IF command
The DO IF/ELSE IF command
General points regarding SPSS transformation commands
Summary
Adding and Matching Files
SPSS Statistics commands to merge files
Example of one-to-many merge - Northwind database
Customer table
Orders table
The Customer-Orders relationship
SPSS code for a one-to-many merge
Alternate SPSS code
One-to-one merge - two data subsets from GSS2016
Example of combining cases using ADD FILES
Summary
Aggregating and Restructuring Data
Using aggregation to add fields to a file
Using aggregated variables to create new fields
Aggregating up one level
Preparing the data for aggregation
Second level aggregation
Preparing aggregated data for further use
Matching the aggregated file back to find specific records
Restructuring rows to columns
Patient test data example
Performing calculations following data restructuring
Summary
Crosstabulation Patterns for Categorical Data
Percentages in crosstabs
Testing differences in column proportions
Crosstab pivot table editing
Adding a layer variable
Adding a second layer
Using a Chi-square test with crosstabs
Expected counts
Context sensitive help
Ordinal measures of association
Interval with nominal association measure
Nominal measures of association
Summary
Comparing Means and ANOVA
SPSS procedures for comparing Means
The Means procedure
Adding a second variable
Test of linearity example
Testing the strength of the nonlinear relationship
Single sample t-test
The independent samples t-test
Homogeneity of variance test
Comparing subsets
Paired t-test
Paired t-test split by gender
One-way analysis of variance
Brown-Forsythe and Welch statistics
Planned comparisons
Post hoc comparisons
The ANOVA procedure
Summary
Correlations
Pearson correlations
Testing for significance
Mean differences versus correlations
Listwise versus pairwise missing values
Comparing pairwise and listwise correlation matrices
Pivoting table editing to enhance correlation matrices
Creating a very trimmed matrix
Visualizing correlations with scatterplots
Rank order correlations
Partial correlations
Adding a second control variable
Summary
Linear Regression
Assumptions of the classical linear regression model
Example - motor trend car data
Exploring associations between the target and predictors
Fitting and interpreting a simple regression model
Residual analysis for the simple regression model
Saving and interpreting casewise diagnostics
Multiple regression - Model-building strategies
Summary
Principal Components and Factor Analysis
Choosing between principal components analysis and factor analysis
PCA example - violent crimes
Simple descriptive analysis
SPSS code - principal components analysis
Assessing factorability of the data
Principal components analysis of the crime variables
Principal component analysis – two-component solution
Factor analysis - abilities
The reduced correlation matrix and its eigenvalues
Factor analysis code
Factor analysis results
Summary
Clustering
Overview of cluster analysis
Overview of SPSS Statistics cluster analysis procedures
Hierarchical cluster analysis example
Descriptive analysis
Cluster analysis - first attempt
Cluster analysis with four clusters
K-means cluster analysis example
Descriptive analysis
K-means cluster analysis of the Old Faithful data
Further cluster profiling
Other analyses to try
Twostep cluster analysis example
Summary
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
Priors
Pooled or separate
Dimensionality
Syntax for the wine example
Examining the results
Scoring new observations
Summary

简介

版权

Data Analysis with IBM SPSS Statistics