discriminant analysis
DESCRIPTION
Discriminant Analysis. Introduction Types of DA Assumptions Model representation , data type/sample size Measurements Steps to solve DA problems An numerical example SPSS commands. (to p2). (to p3). (to p4). (to p5). (to p6). (to p10). (to p11). (to p16). Discriminant Analysis. - PowerPoint PPT PresentationTRANSCRIPT
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Discriminant Analysis
1. Introduction
2. Types of DA
3. Assumptions
4. Model representation, data type/sample size
5. Measurements
6. Steps to solve DA problems
7. An numerical example
8. SPSS commands
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Discriminant Analysis• is a powerful statistical tool used to study
the differences between groups of objects
• Here, objects could be 1. an individual person or firms, and
2. classifying them can be based on prior or posterior factors or characteristics
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Types of DA
• Two groups
– 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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Assumptions
1) multivariate normality, – 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". (to p1)
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Model representation
Data type:Dependent variables = non-metric formatIndep variables = metric format
Sample size : between 5-20 obs for each independent variables
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Measurements
1. Group categorizations
2. Hit ratio
3. Discriminating power
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Group categorizations
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Hit ratio
• Used to measure the model fitness• Is a maximum chance criteria
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
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Discriminating power
References: refer to “hit ratio” for details
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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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Example
You can obtain this paper by clicking Discriminant paper from my web site
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• 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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Step 1, refer to p 762
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Step 2, refer to p763
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Step 3, refer to p763
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SPSS commands
SPSS Windows (to p17)
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SPSS windows• 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