# Spss For Discriniment Analysis

Essay by 24 • November 24, 2010 • 1,693 Words (7 Pages) • 1,380 Views

**Page 1 of 7**

SPSS Syntax for creating a scoring model using all segmenting variables:

1. First, select Ð²Ð‚?analyzeÐ²Ð‚™, then Ð²Ð‚?discriminantÐ²Ð‚™ from the top menu.

2. Then select your grouping variable as the variable you use to name your clusters. In this example, ours is called Ð²Ð‚?clus4Ð²Ð‚™

a. You will have to manually define the range of this variable. The range is equal to the total # of clusters. In this example, the range is 1 to 4

3. Then select as your Ð²Ð‚?independentsÐ²Ð‚™ all of the segmenting / basis variables. In this example we have 68 in total

4. Then select Ð²Ð‚?enter independents togetherÐ²Ð‚™ underneath the field that shows which independent variables youÐ²Ð‚™ve selected

5. Then, under the Ð²Ð‚?statisticsÐ²Ð‚™ sub-menu, select Ð²Ð‚?FisherÐ²Ð‚™sÐ²Ð‚™ under the Ð²Ð‚?Function CoefficientsÐ²Ð‚™ sub-menu

a. Note: FisherÐ²Ð‚™s coefficients would provide you with the eventual coefficients that would be input into the Excel scoring model tool to help type respondents

i. The Ð²Ð‚?COEFFÐ²Ð‚™ after the Ð²Ð‚?/STATISTICSÐ²Ð‚™ command is the syntax for producing the Ð²Ð‚?FisherÐ²Ð‚™s CoefficientsÐ²Ð‚™

6. Under the Ð²Ð‚?classifyÐ²Ð‚™ sub-menu, you need to select the following

a. Ð²Ð‚?Compute from group sizesÐ²Ð‚™ under Ð²Ð‚?prior probabilitiesÐ²Ð‚™

b. Ð²Ð‚?Within groupsÐ²Ð‚™ under Ð²Ð‚?Use Covariance MatrixÐ²Ð‚™, or Ð²Ð‚?separate groupsÐ²Ð‚™ if your Box M results are significant. By selecting Ð²Ð‚?separate groupsÐ²Ð‚™ if the Box M results are significant, you may be able to get a better cross-validation score in the end

c. Ð²Ð‚?Summary TableÐ²Ð‚™ and Ð²Ð‚?Leave-one-out classificationÐ²Ð‚™ under Ð²Ð‚?DisplayÐ²Ð‚™

d. Ð²Ð‚?Territorial MapÐ²Ð‚™ under Ð²Ð‚?PlotÐ²Ð‚™

7. Then select Ð²Ð‚?pasteÐ²Ð‚™ and you should see a syntax window come up with the following code:

DISCRIMINANT

/GROUPS=clus4(1 4)

/VARIABLES=g4a g4b g4c g4e g4f g4g g4h g4i g4j g4k g4l g4m g4n g4o g4r g4s

g4t g4v g4w g4bb g4cc i1a i1b i1c i1d i1f i1h i1i i1j i1u i1w i1x i1y i1z

i1aa i1bb i1cc j2b j2c j2d j2e j2f j2g j2h j2i j2j j2l j2m j2n j2o j2p j2q

l1a l1b l1c l1e l1f l1h l1j m1b m1c m1d m1e m1f m1g m1j m1l m1q

/ANALYSIS ALL

/PRIORS SIZE

/STATISTICS=COEFF TABLE CROSSVALID

/PLOT=MAP

/CLASSIFY=NONMISSING POOLED .

8. Then select Ð²Ð‚?runÐ²Ð‚™ in the syntax menu bar and you will see the results plot out. HereÐ²Ð‚™s what you need to look for in your results

a. Look under Ð²Ð‚?summary of canonical discriminant functionsÐ²Ð‚™ at the table called: Eigenvalues. See how well the two discriminant functions explain the variance of the market. If itÐ²Ð‚™s over 80-85%, then weÐ²Ð‚™re probably OK to proceed

b. Then look at the chi-square column under the Ð²Ð‚?WilksÐ²Ð‚™ LambdaÐ²Ð‚™ table. If the proportion of the chi-square for 1 through 3 and 2 through 3 are by and large greater than 3 alone, then weÐ²Ð‚™re probably OK to proceed

c. Look at the Ð²Ð‚?Territorial MapÐ²Ð‚™ plot and see how the clusters map out relative to one another on the two discriminant functions. If it looks like there is good separation among all clusters, then weÐ²Ð‚™re probably OK to proceed

9. If a, b, and c from #8 seem indicative that a two discriminant function solution is valid to show a perceptual map, then weÐ²Ð‚™ll want to add one command in the syntax to force SPSS to collapse down the solution into 2 functions only. Copy and paste the Ð²Ð‚?/FUNCTION=2Ð²Ð‚™ command into the appropriate spot after the Ð²Ð‚?ANALYSISÐ²Ð‚™ command. See below. Now we will be able to run a new DA and be able to see how well the map coordinates will look (these will come from the structure matrix for attitudes and the group centroids for clusters). We will then export these to the Excel DA Pmap solver to finalize the segment positioning map

10. Finally, weÐ²Ð‚™ll look at the bottom of the output for two tables

a. The table Ð²Ð‚?classification function coefficientsÐ²Ð‚™ will be the Fisher coefficients that get dumped into the Excel scoring model template to be multiplied by future respondentsÐ²Ð‚™ answers to the segmenting attitudes

b. The table Ð²Ð‚?classification resultsÐ²Ð‚™ shows the cross-validated accuracy results which tell us how accurate our scoring model is. In general, weÐ²Ð‚™d like to see our Ð²Ð‚?cross-validated accuracyÐ²Ð‚™ to be Ð¿‚Ñ- 80%. This is equal to saying that we have an 80% chance of accurately assigning an MD to the correct segment based on their responses

DISCRIMINANT

/GROUPS=clus4(1 4)

/VARIABLES=g4a g4b g4c g4e g4f g4g g4h g4i g4j g4k g4l g4m g4n g4o g4r g4s

g4t g4v g4w g4bb g4cc i1a i1b i1c i1d i1f i1h i1i i1j i1u i1w i1x i1y i1z

i1aa i1bb i1cc j2b j2c j2d j2e j2f j2g j2h j2i j2j j2l j2m j2n j2o j2p j2q

l1a l1b l1c l1e l1f l1h l1j m1b m1c m1d m1e m1f m1g m1j m1l m1q

/ANALYSIS ALL

/FUNCTION=2

/PRIORS SIZE

/STATISTICS=COEFF TABLE CROSSVALID

/PLOT=MAP

/CLASSIFY=NONMISSING POOLED .

SPSS Syntax for creating a scoring model using five variables only:

1. Copy the syntax command below into a new syntax window

2. Change the Ð²Ð‚?clus4Ð²Ð‚™

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