SPSS Categories® 11.0

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4 Chapter 1 Transformation Plots The different levels at which each variable can be scaled impose different restrictions on the quantifications. Transformation plots illustrate the relationship between the quantifications and the original categories resulting from the selected optimal scaling level. For example, a linear transformation plot results when a variable is treated as numerical. Variables treated as ordinal result in a nondecreasing transformation plot. Transformation plots for variables treated nominally that are U-shaped (or the reverse) display a quadratic relationship. Nominal variables could also yield transformation plots without apparent trends by changing the order of the categories completely. Figure 1.1 displays a sample transformation plot. Transformation plots are particularly suited to determining how well the selected optimal scaling level performs. If several categories receive similar quantifications, collapsing these categories into one category may be warranted. Alternatively, if a variable treated as nominal receives quantifications that display an increasing trend, an ordinal transformation may result in a similar fit. If that trend is linear, numerical treatment may be appropriate. However, if collapsing categories or changing scaling levels is warranted, the analysis will not change significantly. Figure 1.1 Transformation plot of price (numerical) Although HOMALS does not currently offer the transformation plot in Figure 1.1 as an option, creating this plot is a straightforward procedure. For each variable, create a new variable containing the values (and labels) of the categories. Create a new variable containing the quantifications for that variable from the Categories analysis. Use the line facility with the data in the chart representing the values of individual cases. You can use the quantifications for the line in the chart and use the categories to label the chart.
Category Codes Introduction to <strong>SPSS</strong> Optimal Scaling Procedures for Categorical Data 5 Some care should be taken when coding categorical variables, because some coding schemes may yield unwanted output or incomplete analyses. Possible coding schemes for job are displayed in Table 1.2. Table 1.2 Alternative coding schemes for job Scheme Category A B C D intern 1 1 5 1 sales rep 2 2 6 5 manager 3 7 7 3 Some Categories procedures require that the range of every variable used be defined. Any value outside this range is treated as a missing value. The minimum category value is always 1. The maximum category value is supplied by the user. This value is not the number of categories for a variable; it is the largest category value. For example, in Table 1.2, scheme A has a maximum category of 3 and scheme B has a maximum category value of 7, yet both schemes code the same three categories. The variable range determines which categories will be omitted from the analysis. Any categories with codes outside the defined range are omitted from the analysis. This is a simple method for omitting categories but can result in unwanted analyses. An incorrectly defined maximum category can omit valid categories from the analysis. For example, for scheme B, defining the maximum category value to be 3 indicates that job has categories coded from 1 to 3; the manager category is treated as missing. Because no category has actually been coded 3, the third category in the analysis contains no cases. If you wanted to omit all manager categories, this analysis would be appropriate. However, if managers are to be included, the maximum category must be defined as 7, and missing values must be coded with values above 7 or below 1. For variables treated as nominal or ordinal, the range of the categories does not affect the results. For nominal variables, only the label and not the value associated with that label is important. For ordinal variables, the order of the categories is preserved in the quantifications; the category values themselves are not important. All coding schemes resulting in the same category ordering will have identical results. For example, the first three schemes in Table 1.2 are functionally equivalent if job is analyzed at an ordinal level. The order of the categories is identical in these schemes. Scheme D, on the other hand, inverts the second and third categories and will yield different results than the other schemes.
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: Introduction to SPSS Optimal Scalin
Page 19 and 20: Introduction to SPSS Optimal Scalin
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
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
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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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SPSS Categories® 11.0

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