SPSS Categories® 11.0

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Contents 1 Introduction to SPSS Optimal Scaling Procedures for Categorical Data 1 What Is Optimal Scaling? 1 Why Use Optimal Scaling? 1 Optimal Scaling Level and Measurement Level 2 Selecting the Optimal Scaling Level 3 Transformation Plots 4 Category Codes 5 Which Procedure Is Best for Your Application? 7 Categorical Regression 8 Categorical Principal Components Analysis 9 Nonlinear Canonical Correlation Analysis 9 Correspondence Analysis 10 Homogeneity Analysis 12 Multidimensional Scaling 13 Displays with More than Two Dimensions 14 Three-Dimensional Scatterplots 14 Scatterplot Matrices 15 Aspect Ratio in Optimal Scaling Charts 16 2 Categorical Regression (CATREG) 17 To Obtain a Categorical Regression 18 Define Scale in Categorical Regression 19 To Define the Scale in CATREG 20 Categorical Regression Discretization 20 Categorical Regression Missing Values 22 Categorical Regression Options 23 Categorical Regression Output 24 Categorical Regression Save 25 Categorical Regression Plots 26 CATREG Command Additional Features 26 vii
Page 1 and 2: SPSS Categories® 11.0 Jacqueline J
Page 3 and 4: Installation Compatibility Serial N
Page 5: Tell Us Your Thoughts Your comments
Page 9 and 10: 6 Homogeneity Analysis (HOMALS) 59
Page 11 and 12: Example 2: Perceptions of Coffee Br
Page 13 and 14: 1 Introduction to SPSS Optimal Scal
Page 15 and 16: Introduction to SPSS Optimal Scalin
Page 17 and 18: Category Codes Introduction to SPSS
Page 21 and 22: Categorical Principal Components An
Page 27 and 28: Scatterplot Matrices Introduction t
Page 29 and 30: 1 2 Categorical Regression (CATREG)
Page 31 and 32: Define Scale in Categorical Regress
Page 33 and 34: Figure 2.3 Categorical Regression D
Page 35 and 36: Categorical Regression Options Cate
Page 37 and 38: Categorical Regression (CATREG) 25
Page 39 and 40: 3 Categorical Principal Components
Page 41 and 42: Figure 3.2 Categorical Principal Co
Page 43 and 44: To Define the Scale and Weight in C
Page 45 and 46: Categorical Principal Components Mi
Page 51 and 52: Categorical Principal Components Op
Page 53 and 54: 4 Nonlinear Canonical Correlation A
Page 55 and 56: Figure 4.1 Optimal Scaling dialog b
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Nonlinear Canonical Correlation Ana
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Figure 4.5 OVERALS Options dialog b
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5 Correspondence Analysis One of th
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Select a row variable. Select a col
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Correspondence Analysis 53 straints
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Standardization Method. Choose one
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Correspondence Analysis Plots The P
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6 Homogeneity Analysis (HOMALS) Hom
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Figure 6.2 Homogeneity Analysis (HO
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Homogeneity Analysis Options Homoge
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7 Multidimensional Scaling (PROXSCA
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Proximities in Matrices across Colu
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Proximities in One Column Multidime
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Measures Dialog Box Figure 7.6 Mult
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Multidimensional Scaling (PROXSCAL)
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Multidimensional Scaling Options Mu
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Multidimensional Scaling (PROXSCAL)
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Figure 7.12 Multidimensional Scalin
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8 Categorical Regression Examples T
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Figure 8.2 Regression coefficients
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Categorical Regression Examples 85
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Model Fit and Coefficients Categori
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Figure 8.14 Residuals for categoric
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To compute the new variables as sug
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Figure 8.16 Regression coefficients
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Figure 8.20 Transformation plot for
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108 Chapter 9 Example 1: Interrelat
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110 Chapter 9 Number of Dimensions
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112 Chapter 9 Object Scores Figure
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114 Chapter 9 Component Loadings pe
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116 Chapter 9 Figure 9.9 Model summ
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118 Chapter 9 Figure 9.12 Three-dim
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120 Chapter 9 To produce categorica
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122 Chapter 9 Quantifications for S
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124 Chapter 9 Object Scores which m
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126 Chapter 9 Table 9.4 Object scor
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128 Chapter 9 Differential Developm
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130 Chapter 9 With respect to sexua
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132 Chapter 10 Examining the Data T
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134 Chapter 10 Figure 10.1 Object s
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136 Chapter 10 Figure 10.4 Componen
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138 Chapter 10 Component Loadings T
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140 Chapter 10 In contrast, the tra
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142 Chapter 10 constraint is applie
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144 Chapter 10 Figure 10.14 Centroi
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146 Chapter 10 The multiple-fit and
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148 Chapter 10 Figure 10.20 display
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11 Correspondence Analysis Examples
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Correspondence Analysis Examples 15
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Inertia Figure 11.6 Column profiles
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Dimensionality The Euclidean distan
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Model... Dimensions in solution: 2
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Plots... Scatterplots Biplot (dese
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Figure 11.15 Contributions of dimen
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Example 2: Perceptions of Coffee Br
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Contributions Figure 11.21 Inertia
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Plots Correspondence Analysis Examp
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Symmetrical Normalization Correspon
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Figure 11.28 Correspondence table f
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12 Homogeneity Analysis Examples Th
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Multiple Dimensions Now, to obtain
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Homogeneity Analysis Examples 187 T
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Figure 12.4 Plot of discrimination
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Figure 12.6 Selected category quant
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Figure 12.9 Object scores labeled w
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Figure 12.11 Eigenvalues Dimension
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13 Multidimensional Scaling Example
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Figure 13.1 Scree Plot Normalized R
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Plots... Stress (deselect) Common
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Final Coordinates of the Common Spa
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Figure 13.8 Transformed degree and
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Figure 13.9 Transformed proximities
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Syntax Reference
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212 Syntax Reference Syntax Rules
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ANACOR Overview Options ANACOR TABL
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Casewise Data Table Data ANACOR 217
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ANACOR 219 column and its correspon
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MATRIX Subcommand ANACOR 221 • Th
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ANACOR 223 • The table cell value
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226 Syntax Reference Overview Optio
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228 Syntax Reference Example CATPCA
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230 Syntax Reference Level Keyword
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232 Syntax Reference DISTR Keyword
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234 Syntax Reference DIMENSION Subc
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236 Syntax Reference VAF Variance a
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238 Syntax Reference CATEGORY(varli
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240 Syntax Reference SAVE Subcomman
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242 Syntax Reference OUTFILE Subcom
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244 Syntax Reference Options Basic
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246 Syntax Reference • VARIABLES
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248 Syntax Reference SPORD and SPNO
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250 Syntax Reference SUPPLEMENTARY
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252 Syntax Reference PLOT Subcomman
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256 Syntax Reference Options Basic
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258 Syntax Reference Table Data Exa
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260 Syntax Reference EQUAL Subcomma
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262 Syntax Reference PRINT Subcomma
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266 Syntax Reference • The table
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268 Syntax Reference Writing matric
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270 Syntax Reference • The maximu
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272 Syntax Reference The following
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274 Syntax Reference • A variable
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276 Syntax Reference Basic Specific
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278 Syntax Reference SETS Subcomman
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280 Syntax Reference CONVERGENCE Su
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282 Syntax Reference SAVE Subcomman
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PRINCALS Overview Options PRINCALS
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Example PRINCALS 287 can use COMPUT
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PRINCALS 289 NUME Numerical. This i
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DEFAULT QUANT and OBJECT. ALL All a
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MATRIX Subcommand PRINCALS 293 PRIN
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PROXSCAL PROXSCAL varlist [/TABLE =
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PROXSCAL 297 Output. You can produc
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PROXSCAL 299 sourceid Source identi
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PROXSCAL 301 • PROXSCAL reads two
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CONDITION Subcommand PROXSCAL 303 C
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MODEL Subcommand PROXSCAL 305 MODEL
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Example PROXSCAL 307 PROXSCAL aunt
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PROXSCAL 309 STRESS Stress measures
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PROXSCAL 311 CORRELATIONS Correlati
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Bibliography Barlow, R. E., D. J. B
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Bibliography 315 Lebart L., A. Mori
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Subject Index active row in Corresp
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importance in Categorical Regressio
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ow scores in Correspondence Analysi
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324 Syntax Index CENTROID (keyword)
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326 Syntax Index PROXSCAL command,
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328 Syntax Index CATREG command, 25
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330 Syntax Index TRDATA (keyword) C
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