Multivariate statistical analysis
Introductions and basic data
analysis
Multivariate
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Variate (變量) vs. variable (變數)
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The attributes that the researcher concerned and
observed performance
The attributes that the researcher could operate
for the expected performance
Uni-variate (單變量) vs. multi-variate (多變量)
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Single concerned performance
Multiple concerned performance vector
Measurement scale
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Nominal
Ordinal
Interval
Ratio
ref. p.10 表1.2-1 四種衡量尺度之比較
Four types of measuring scale
Measuring Variables
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Measuring variables: used to describe
the attitudes of specific concerned
attributes
Analytical variables: internal scale, ratio
scale
Categorical variables: nominal scale,
ordinal scale
ref. p.11, 表1.2-2,-3,-4
Example
Cost of measurement
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Error cost: the impact resulted from the
deviation to the true attitude
Measuring cost: the difficulty of
accurate measuring
Reliability
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Retest reliability
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Split half reliability
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Verify the stability of the responses
Designing the contrast questions
Cronbach’s α (>0.7)
Cronbach’s α
Validity
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Effectiveness to reflect the concerned
issues
Content validity
Criteria-related validity
Construct validity
Problems of validity
Likert scale
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Quasi-interval scale
5-scale, 7-scale, (in the form of 2/3
negative scale and 2/3 positive scale
around the original)
Data format
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Cases: the observant, the experimental
subjects/objects
Variables: the set of concerned
attributes
Observations: the collected data
Observation vector: the set of all
attributes retained from a specific case
Data format
Classification of multivariate
models
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Functional relation model
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Interdependence relation model
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Variables interdependence
Cases interdependence
Systemic relation model
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Responsive variates=f (independent variables)
Path analysis
LISREL model
ref. p.33, 表1.7-1 多變量統計模式之歸類;
p.40,表1.7-2; p.41,表1.7-3
Multivariate analysis models
Multivariate analysis models
Multivariate analysis models
SAS/SPSS introductions