1. Discriminant Analysis
Introduction
Types of DA
Assumptions
Model representation, data type/sample size
Measurements
Steps to solve DA problems
An numerical example
SPSS commands
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2. Discriminant Analysis
is a powerful statistical tool used to study the differences between groups of objects
Here, objects could be
an individual person or firms, and
classifying them can be based on prior or posterior factors or characteristics
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3. Types of DA Two groups Three or more group
refer to as two-group discriminant analysis
Its dependent variable is termed as dichotomous
Three or more group
Refer to as multiple discriminant analysis (MDA)
Its corresponding dependent variables are termed as multichotomous
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4. Assumptions 1) multivariate normality, 2) equal covariance matrices
uses the normal probability plot approach
uses the most common statistical tests are the calculation of skewness value
2) equal covariance matrices
Use covariance to check their corelations
3) multicollinearity, among independent variables
That is to check independent variables are not correlated to each other
4) Outliers
"the observations with a unique combination of characteristics identifiable as distinctly different from the other observations".
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5. Model representation Data type:
Dependent variables = non-metric format
Indep variables = metric format
Sample size : between 5-20 obs for each independent variables
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6. Measurements Group categorizations Hit ratio Discriminating power
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7. Group categorizations
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8. Hit ratio Used to measure the model fitness
Is a maximum chance criteria
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Note: We need to compute this value for our original sample size and then compare
to the value that produced by the SPSS; and computer value should not be less than
the formal value in order to claim the significant of fitness of model

9. Discriminating power References: refer to “hit ratio” for details
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References: refer to “hit ratio” for details

10. Steps to solve DA problems
Step 1: Assess the assumptions
Step 2: Estimate the discriminant function(s)
and its (their) significance
Step 3: Assess the overall fit
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11. Example
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You can obtain this paper by clicking Discriminant paper from my web site

12. Objective:
To discriminate the difference practices between the high and low performance of firms practicing TQM is ISF
Use score of overall satisfaction as a mean for discriminating factor
Steps:
Step 1, refer to p 762
Step 2, refer to p763
Step 3, refer to p763
Discussion, you can refer to the “discussion” section
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13. Step 1, refer to p 762
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14. Step 2, refer to p763
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15. Step 3, refer to p763
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16. SPSS commands
SPSS Windows
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17. SPSS windows Learn from iconic base – Pls refer to my website
Steps to compute Discriminant Analysis
Step 0
Prior the study of analysis, we need to firstly define a new variable as follows:
- Define “group” and assign a value of either 0, 1, 2 to them, as 0 as neural
Step 1
Select “Analyze”
Select “Classify”
Select “Discriminant”
click “group variable”
and select “group” variable as above
click “define range”
state its max and min ranges
(this range same as min=1, and max=2 for above case)
click “Independent”
select “variables”
that a group of factors that wish to be clustering
Click option “use stepwise method”
select “Statistics”
Learn from iconic base – Pls refer to my website