View
227
Download
3
Category
Preview:
Citation preview
Multivariate statistical analysis
Introductions and basic data analysis
Multivariate Variate ( 變量 ) vs. variable ( 變數 )
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 ) Single concerned performance Multiple concerned performance vector
Measurement scale
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 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
Error cost: the impact resulted from the deviation to the true attitude
Measuring cost: the difficulty of accurate measuring
Reliability
Retest reliability Verify the stability of the responses
Split half reliability Designing the contrast questions
Cronbach’s α (>0.7)
Cronbach’s α
Validity
Effectiveness to reflect the concerned issues
Content validity Criteria-related validity Construct validity
Problems of validity
Likert scale 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
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 Functional relation model
Responsive variates=f (independent variables) Interdependence relation model
Variables interdependence Cases interdependence
Systemic relation model 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
Recommended