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26 Chapter 2 Categorical Regression Plots The Plot dialog box allows you to specify the variables that will produce transformation and residual plots. Figure 2.8 Categorical Regression Plot dialog box Transformation Plots. For each of these variables, the category quantifications are plotted against the original category values. Empty categories appear on the horizontal axis but do not affect the computations. These categories are identified by breaks in the line connecting the quantifications. Residual Plots. For each of these variables, residuals (computed for the dependent variable predicted from all predictor variables except the predictor variable in question) are plotted against category indicators and the optimal category quantifications multiplied with beta against category indicators. CATREG Command Additional Features You can customize your categorical regression if you paste your selections into a syntax window and edit the resulting CATREG command syntax. <strong>SPSS</strong> command language also allows you to: Specify rootnames for the transformed variables when saving them to the working data file (with the SAVE subcommand).
3 Categorical Principal Components Analysis (CATPCA) This procedure simultaneously quantifies categorical variables while reducing the dimensionality of the data. Categorical principal components analysis is also known by the acronym CATPCA, for categorical principal components analysis. The goal of principal components analysis is to reduce an original set of variables into a smaller set of uncorrelated components that represent most of the information found in the original variables. The technique is most useful when a large number of variables prohibits effective interpretation of the relationships between objects (subjects and units). By reducing the dimensionality, you interpret a few components rather than a large number of variables. Standard principal components analysis assumes linear relationships between numeric variables. On the other hand, the optimal-scaling approach allows variables to be scaled at different levels. Categorical variables are optimally quantified in the specified dimensionality. As a result, nonlinear relationships between variables can be modeled. Example. Categorical principal components analysis could be used to graphically display the relationship between job category, job division, region, amount of travel (high, medium, and low), and job satisfaction. You might find that two dimensions account for a large amount of variance. The first dimension might separate job category from region, whereas the second dimension might separate job division from amount of travel. You also might find that high job satisfaction is related to a medium amount of travel. Statistics and plots. Frequencies, missing values, optimal scaling level, mode, variance accounted for by centroid coordinates, vector coordinates, total per variable and per dimension, component loadings for vector-quantified variables, category quantifications and coordinates, iteration history, correlations of the transformed variables and eigenvalues of the correlation matrix, correlations of the original variables and eigenvalues of the correlation matrix, object scores, category plots, joint category plots, transformation plots, residual plots, projected centroid plots, object plots, biplots, triplots, and component loadings plots. Data. String variable values are always converted into positive integers by ascending alphanumeric order. User-defined missing values, system-missing values, and values less than 1 are considered missing; you can recode or add a constant to variables with values less than 1 to make them nonmissing. 27
Page 1 and 2: SPSS Categories® 11.0 Jacqueline J
Page 3 and 4: Installation Compatibility Serial N
Page 5 and 6: Tell Us Your Thoughts Your comments
Page 7 and 8: Contents 1 Introduction to SPSS Opt
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: Categorical Regression (CATREG) 25
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
Page 57 and 58: Nonlinear Canonical Correlation Ana
Page 59 and 60: Figure 4.5 OVERALS Options dialog b
Page 61 and 62: 5 Correspondence Analysis One of th
Page 63 and 64: Select a row variable. Select a col
Page 65 and 66: Correspondence Analysis 53 straints
Page 67 and 68: Standardization Method. Choose one
Page 69 and 70: Correspondence Analysis Plots The P
Page 71 and 72: 6 Homogeneity Analysis (HOMALS) Hom
Page 73 and 74: Figure 6.2 Homogeneity Analysis (HO
Page 75 and 76: Homogeneity Analysis Options Homoge
Page 77 and 78: 7 Multidimensional Scaling (PROXSCA
Page 79 and 80: Proximities in Matrices across Colu
Page 81 and 82: Proximities in One Column Multidime
Page 83 and 84: Measures Dialog Box Figure 7.6 Mult
Page 85 and 86: Multidimensional Scaling (PROXSCAL)
Page 87 and 88: 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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