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Data Analysis with IBM SPSS Statistics 1st Edition by Kenneth Stehlik Barry, Anthony Babinec ISBN 9781787280700 1787280705

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Product details:
ISBN 10: 1787280705
ISBN 13: 9781787280700
Author: Kenneth Stehlik Barry, Anthony Babinec

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.

Data Analysis with IBM SPSS Statistics 1st Edition Table of contents:

  1. What this book covers
  2. What you need for this book
  3. Who this book is for
  4. Conventions
  5. Reader feedback
  6. Customer support
  7. Downloading the example code
  8. Errata
  9. Piracy
  10. Questions
  11. Installing and Configuring SPSS
  12. The SPSS installation utility
  13. Installing Python for the scripting
  14. Licensing SPSS
  15. Confirming the options available
  16. Launching and using SPSS
  17. Setting parameters within the SPSS software
  18. Executing a basic SPSS session
  19. Summary
  20. Accessing and Organizing Data
  21. Accessing and organizing data overview
  22. Reading Excel files
  23. Reading delimited text data files
  24. Saving IBM SPSS Statistics files
  25. Reading IBM SPSS Statistics files
  26. Demo – first look at the data – frequencies
  27. Variable properties
  28. Variable properties – name
  29. Variable properties – type
  30. Variable properties – width
  31. Variable properties – decimals
  32. Variable properties – label
  33. Variable properties – values
  34. Variable properties – missing
  35. Variable properties – columns
  36. Variable properties – align
  37. Variable properties – measure
  38. Variable properties – role
  39. Demo – adding variable properties to the Variable View
  40. Demo – adding variable properties via syntax
  41. Demo – defining variable properties
  42. Summary
  43. Statistics for Individual Data Elements
  44. Getting the sample data
  45. Descriptive statistics for numeric fields
  46. Controlling the descriptives display order
  47. Frequency distributions
  48. Discovering coding issues using frequencies
  49. Using frequencies to verify missing data patterns
  50. Explore procedure
  51. Stem and leaf plot
  52. Boxplot
  53. Using explore to check subgroup patterns
  54. Summary
  55. Dealing with Missing Data and Outliers
  56. Outliers
  57. Frequencies for histogram and percentile values
  58. Descriptives for standardized scores
  59. The Examine procedure for extreme values and boxplot
  60. Detecting multivariate outliers
  61. Missing data
  62. Missing values in Frequencies
  63. Missing values in Descriptives
  64. Missing value patterns
  65. Replacing missing values
  66. Summary
  67. Visually Exploring the Data
  68. Graphs available in SPSS procedures
  69. Obtaining bar charts with frequencies
  70. Obtaining a histogram with frequencies
  71. Creating graphs using chart builder
  72. Building a scatterplot
  73. Create a boxplot using chart builder
  74. Summary
  75. Sampling, Subsetting, and Weighting
  76. Select cases dialog box
  77. Select cases – If condition is satisfied
  78. Example
  79. If condition is satisfied combined with Filter
  80. If condition is satisfied combined with Copy
  81. If condition is satisfied combined with Delete unselected cases
  82. The Temporary command
  83. Select cases based on time or case range
  84. Using the filter variable
  85. Selecting a random sample of cases
  86. Split File
  87. Weighting
  88. Summary
  89. Creating New Data Elements
  90. Transforming fields in SPSS
  91. The RECODE command
  92. Creating a dummy variable using RECODE
  93. Using RECODE to rescale a field
  94. Respondent’s income using the midpoint of a selected category
  95. The COMPUTE command
  96. The IF command
  97. The DO IF/ELSE IF command
  98. General points regarding SPSS transformation commands
  99. Summary
  100. Adding and Matching Files
  101. SPSS Statistics commands to merge files
  102. Example of one-to-many merge – Northwind database
  103. Customer table
  104. Orders table
  105. The Customer-Orders relationship
  106. SPSS code for a one-to-many merge
  107. Alternate SPSS code
  108. One-to-one merge – two data subsets from GSS2016
  109. Example of combining cases using ADD FILES
  110. Summary
  111. Aggregating and Restructuring Data
  112. Using aggregation to add fields to a file
  113. Using aggregated variables to create new fields
  114. Aggregating up one level
  115. Preparing the data for aggregation
  116. Second level aggregation
  117. Preparing aggregated data for further use
  118. Matching the aggregated file back to find specific records
  119. Restructuring rows to columns
  120. Patient test data example
  121. Performing calculations following data restructuring
  122. Summary
  123. Crosstabulation Patterns for Categorical Data
  124. Percentages in crosstabs
  125. Testing differences in column proportions
  126. Crosstab pivot table editing
  127. Adding a layer variable
  128. Adding a second layer
  129. Using a Chi-square test with crosstabs
  130. Expected counts
  131. Context sensitive help
  132. Ordinal measures of association
  133. Interval with nominal association measure
  134. Nominal measures of association
  135. Summary
  136. Comparing Means and ANOVA
  137. SPSS procedures for comparing Means
  138. The Means procedure
  139. Adding a second variable 
  140. Test of linearity example
  141. Testing the strength of the nonlinear relationship
  142. Single sample t-test
  143. The independent samples t-test
  144. Homogeneity of variance test
  145. Comparing subsets
  146. Paired t-test
  147. Paired t-test split by gender
  148. One-way analysis of variance
  149. Brown-Forsythe and Welch statistics
  150. Planned comparisons
  151. Post hoc comparisons
  152. The ANOVA procedure
  153. Summary
  154. Correlations
  155. Pearson correlations
  156. Testing for significance
  157. Mean differences versus correlations
  158. Listwise versus pairwise missing values
  159. Comparing pairwise and listwise correlation matrices
  160. Pivoting table editing to enhance correlation matrices
  161. Creating a very trimmed matrix
  162. Visualizing correlations with scatterplots
  163. Rank order correlations
  164. Partial correlations
  165. Adding a second control variable
  166. Summary
  167. Linear Regression
  168. Assumptions of the classical linear regression model
  169. Example – motor trend car data
  170. Exploring associations between the target and predictors
  171. Fitting and interpreting a simple regression model
  172. Residual analysis for the simple regression model
  173. Saving and interpreting casewise diagnostics
  174. Multiple regression – Model-building strategies
  175. Summary
  176. Principal Components and Factor Analysis
  177. Choosing between principal components analysis and factor analysis
  178. PCA example – violent crimes
  179. Simple descriptive analysis
  180. SPSS code – principal components analysis
  181. Assessing factorability of the data
  182. Principal components analysis of the crime variables
  183. Principal component analysis – two-component solution
  184. Factor analysis – abilities
  185. The reduced correlation matrix and its eigenvalues
  186. Factor analysis code
  187. Factor analysis results
  188. Summary
  189. Clustering
  190. Overview of cluster analysis
  191. Overview of SPSS Statistics cluster analysis procedures
  192. Hierarchical cluster analysis example
  193. Descriptive analysis
  194. Cluster analysis – first attempt
  195. Cluster analysis with four clusters
  196. K-means cluster analysis example
  197. Descriptive analysis
  198. K-means cluster analysis of the Old Faithful data
  199. Further cluster profiling
  200. Other analyses to try
  201. Twostep cluster analysis example
  202. Summary
  203. Discriminant Analysis
  204. Descriptive discriminant analysis
  205. Predictive discriminant analysis
  206. Assumptions underlying discriminant analysis
  207. Example data
  208. Statistical and graphical summary of the data
  209. Discriminant analysis setup – key decisions
  210. Priors
  211. Pooled or separate
  212. Dimensionality
  213. Syntax for the wine example
  214. Examining the results
  215. Scoring new observations
  216. Summary

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Tags: Kenneth Stehlik Barry, Anthony Babinec, Data Analysis, IBM SPSS Statistics