| Foreword |
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xxiii | |
| Introduction |
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xxvii | |
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Part I Advanced Statistics |
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1 | (128) |
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Chapter 1 Comparing and Contrasting IBM SPSS AMOS with Other Multivariate Techniques |
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3 | (40) |
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7 | (16) |
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8 | (5) |
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13 | (10) |
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Factor Analysis and Unobserved Variables in SPSS |
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23 | (3) |
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26 | (17) |
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Revisiting Factor Analysis and a General Orientation to AMOS |
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26 | (3) |
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29 | (14) |
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Chapter 2 Monte Carlo Simulation and IBM SPSS Bootstrapping |
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43 | (28) |
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44 | (1) |
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Monte Carlo Simulation in IBM SPSS Statistics |
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44 | (1) |
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Creating an SPSS Model File |
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45 | (14) |
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59 | (12) |
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63 | (3) |
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66 | (2) |
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Bootstrap and Linear Regression |
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68 | (3) |
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Chapter 3 Regression with Categorical Outcome Variables |
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71 | (30) |
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Regression Approaches in SPSS |
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72 | (1) |
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73 | (1) |
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Ordinal Regression Theory |
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74 | (3) |
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Assumptions of Ordinal Regression Models |
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77 | (1) |
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Ordinal Regression Dialogs |
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77 | (4) |
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Ordinal Regression Output |
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81 | (5) |
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Categorical Regression Theory |
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86 | (1) |
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Assumptions of Categorical Regression Models |
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87 | (1) |
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Categorical Regression Dialogs |
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87 | (6) |
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Categorical Regression Output |
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93 | (8) |
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Chapter 4 Building Hierarchical Linear Models |
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101 | (28) |
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Overview of Hierarchical Linear Mixed Models |
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102 | (2) |
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A Two-Level Hierarchical Linear Model Example |
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102 | (2) |
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104 | (9) |
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Mixed Models Linear (Output) |
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113 | (3) |
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Mixed Models Generalized Linear |
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116 | (4) |
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Mixed Models Generalized Linear (Output) |
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120 | (6) |
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Adjusting Model Structure |
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126 | (3) |
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Part II Data Visualization |
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129 | (142) |
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Chapter 5 Take Your Data Visualizations to the Next Level |
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131 | (16) |
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Graphics Options in SPSS Statistics |
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132 | (4) |
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Understanding the Revolutionary Approach in The Grammar of Graphics |
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136 | (2) |
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138 | (5) |
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143 | (4) |
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Chapter 6 The Code Behind SPSS Graphics: Graphics Production Language |
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147 | (26) |
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Introducing GPL: Bubble Chart Case Study |
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147 | (8) |
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155 | (1) |
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Bubble Chart Case Study Part Two |
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156 | (4) |
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Double Regression Line Case Study |
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160 | (3) |
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163 | (4) |
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MBTI Bubble Chart Case Study |
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167 | (6) |
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Chapter 7 Mapping in IBM SPSS Statistics |
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173 | (20) |
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Creating Maps with the Graphboard Template Chooser |
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174 | (19) |
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Creating a Choropleth of Counts Map |
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175 | (4) |
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179 | (6) |
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Creating Maps Using Geographical Coordinates |
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185 | (8) |
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Chapter 8 Geospatial Analytics |
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193 | (24) |
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Geospatial Association Rules |
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194 | (1) |
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Case Study: Crime and 311 Calls |
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194 | (13) |
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Spatio-Temporal Prediction |
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207 | (1) |
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Case Study: Predicting Weekly Shootings |
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207 | (10) |
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Chapter 9 Perceptual Mapping with Correspondence Analysis, GPL, and OMS |
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217 | (32) |
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220 | (4) |
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224 | (10) |
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Multiple Correspondence Analysis |
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234 | (8) |
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234 | (8) |
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Applying OMS and GPL to the MCA Perceptual Map |
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242 | (7) |
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Chapter 10 Display Complex Relationships with Multidimensional Scaling |
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249 | (22) |
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Metric and Nonmetric Multidimensional Scaling |
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251 | (1) |
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Nonmetric Scaling of Psychology Sub-Disciplines |
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251 | (2) |
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Multidimenional Scaling Dialog Options |
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253 | (6) |
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Multidimensional Scaling Output Interpretation |
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259 | (5) |
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Subjective Approach to Dimension Interpretation |
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264 | (2) |
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Statistical Approach to Dimension Interpretation |
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266 | (5) |
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Part III Predictive Analytics |
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271 | (122) |
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Chapter 11 SPSS Statistics versus SPSS Modeler: Can I Be a Data Miner Using SPSS Statistics? |
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275 | (28) |
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275 | (1) |
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What Is IBM SPSS Modeler? |
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276 | (2) |
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Can Data Mining Be Done in SPSS Statistics? |
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278 | (2) |
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Hypothesis Testing, Type I Error, and Hold-Out Validation |
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280 | (4) |
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Significance of the Model and Importance of Each Independent Variable |
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284 | (1) |
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The Importance of Finding and Modeling Interactions |
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284 | (3) |
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Classic and Important Data Mining Tasks |
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287 | (16) |
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Partitioning and Validating |
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288 | (3) |
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291 | (3) |
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294 | (1) |
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Comparing Results from Multiple Models |
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295 | (2) |
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297 | (3) |
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300 | (3) |
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Chapter 12 IBM SPSS Data Preparation |
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303 | (22) |
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304 | (11) |
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Identify Unusual Cases Dialogs |
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305 | (6) |
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Identify Unusual Cases Output |
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311 | (4) |
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315 | (10) |
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316 | (5) |
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321 | (4) |
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Chapter 13 Model Complex Interactions with IBM SPSS Neural Networks |
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325 | (30) |
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326 | (7) |
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The Famous Case of Exclusive OR and the Perceptron |
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328 | (4) |
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What Is a Hidden Layer and Why Is It Needed? |
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332 | (1) |
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Neural Net Results with the XOR Variables |
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333 | (8) |
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How the Weights Are Calculated: Error Backpropagation |
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337 | (3) |
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Creating a Consistent Partition in SPSS Statistics |
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340 | (1) |
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Comparing Regression to Neural Net with the Bank Salary Case Study |
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341 | (14) |
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Calculating Mean Absolute Percent Error for Both Models |
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344 | (5) |
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Classification with Neural Nets Demonstrated with the Titanic Dataset |
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349 | (6) |
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Chapter 14 Powerful and Intuitive: IBM SPSS Decision Trees |
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355 | (24) |
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Building a Tree with the CHAID Algorithm |
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355 | (5) |
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Review of the CHAID Algorithm |
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360 | (6) |
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Adjusting the CHAID Settings |
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363 | (3) |
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366 | (8) |
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Understanding Why the CRT Algorithm Produces a Different Tree |
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368 | (1) |
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369 | (1) |
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Changing the CRT Settings |
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369 | (2) |
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Comparing the Results of All Four Models |
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371 | (2) |
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Alternative Validation Options |
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373 | (1) |
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374 | (5) |
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Chapter 15 Find Patterns and Make Predictions with K Nearest Neighbors |
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379 | (14) |
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Using KNN to Find "Neighbors" |
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380 | (1) |
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The Titanic Dataset and KNN Used as a Classifier |
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381 | (5) |
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The Trade-Offs between Bias and Variance |
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386 | (2) |
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Comparing Our Models: Decision Trees, Neural Nets, and KNN |
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388 | (3) |
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391 | (2) |
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Part IV Syntax, Data Management, and Programmability |
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393 | (80) |
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Chapter 16 Write More Efficient and Elegant Code with SPSS Syntax Techniques |
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395 | (26) |
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A Syntax Primer for the Uninitiated |
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396 | (8) |
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Making the Connection: Menus and the Grammar of Syntax |
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401 | (2) |
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What Is "Inefficient" Code? |
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403 | (1) |
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404 | (17) |
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406 | (1) |
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407 | (2) |
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Addressing Case Sensitivity of City Names with UPPER() and LOWER() |
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409 | (1) |
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Parsing Strings and the Index Function |
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410 | (1) |
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Aggregate and Restructure |
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410 | (2) |
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Pasting Variable Names, TO, Recode, and Count |
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412 | (2) |
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414 | (1) |
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415 | (2) |
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417 | (4) |
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Chapter 17 Automate Your Analyses with SPSS Syntax and the Output Management System |
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421 | (20) |
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Overview of the Output Management System |
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422 | (1) |
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423 | (1) |
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Automatically Writing Selected Categories of Output to Different Formats |
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424 | (5) |
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429 | (7) |
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436 | (2) |
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438 | (3) |
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Chapter 18 Statistical Extension Commands |
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441 | (32) |
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What Is an Extension Command? |
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441 | (3) |
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TURF Analysis---Designing Product Bundles |
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444 | (6) |
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449 | (1) |
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Quantile Regression---Predicting Airline Delays |
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450 | (9) |
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Comparing Ordinary Least Squares with Quantile Regression Results |
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455 | (4) |
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Operational Considerations |
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459 | (9) |
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Support Vector Machines---Predicting Loan Default |
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461 | (1) |
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461 | (3) |
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464 | (3) |
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467 | (1) |
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Computing Cohen's d Measure of Effect Size for a T-Test |
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468 | (5) |
| Index |
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473 | |
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