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Multivariate statistical analysis Introductions and basic data analysis

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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

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Measurement scale Nominal Ordinal Interval Ratio ref. p.10 表 1.2-1 四種衡量尺度之比較

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Four types of measuring scale

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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

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Example

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Cost of measurement Error cost: the impact resulted from the deviation to the true attitude Measuring cost: the difficulty of accurate measuring

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Reliability Retest reliability Verify the stability of the responses Split half reliability Designing the contrast questions Cronbach ’ s α (>0.7)

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Cronbach ’ s α

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Validity Effectiveness to reflect the concerned issues Content validity Criteria-related validity Construct validity

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Problems of validity

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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)

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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

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Data format

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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

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Multivariate analysis models

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SAS/SPSS introductions

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