Advanced Research Methods part B – Technique consultation Factor Analysis
Meaning construct/variable/measurement arrow
E = latent construct, zoals employee satisfaction
X1/X2/X3 = items, de vragen die employee satisfaction meten
E1/E2/E3 = measurement error
Lambda 1/lambda 2/lambda 3 = factor loading = de correlatie van de individuele variabelen
met de factor
Principal Components Analysis
- Looks at the total variance in the data
- Diagonal of the correlation matrix consists of unities
- Full variance is brought into the factor matrix
- Primary concern: minimum number of factors that will account for maximum variance
- The factors are called ‘’principal components’’
- Mathematically, each variable is expressed as a linear combination of the
components
- The covariation among the variables is described in terms of a small number of
principal components
- If the variables are standardized, the principal component model may be represented
as:
-
Common Factor Analysis
- Factors are estimated based only on the common variance
- Communalities are inserted in the diagonal of the correlation matrix
- Primary concern: identify the underlying dimensions and their common variance
- Also known as principal axis factoring
- Mathematically, each variable is expressed as a linear combination of underlying
factors
- The covariation among the variables is described in terms of a small number of
common factors + a unique factor for each variable
- Meer confirmatory, meer a priory expectations van de loadings
- If the variables are standardized, the factor model may be represented as:
'
1
Meaning construct/variable/measurement arrow
E = latent construct, zoals employee satisfaction
X1/X2/X3 = items, de vragen die employee satisfaction meten
E1/E2/E3 = measurement error
Lambda 1/lambda 2/lambda 3 = factor loading = de correlatie van de individuele variabelen
met de factor
Principal Components Analysis
- Looks at the total variance in the data
- Diagonal of the correlation matrix consists of unities
- Full variance is brought into the factor matrix
- Primary concern: minimum number of factors that will account for maximum variance
- The factors are called ‘’principal components’’
- Mathematically, each variable is expressed as a linear combination of the
components
- The covariation among the variables is described in terms of a small number of
principal components
- If the variables are standardized, the principal component model may be represented
as:
-
Common Factor Analysis
- Factors are estimated based only on the common variance
- Communalities are inserted in the diagonal of the correlation matrix
- Primary concern: identify the underlying dimensions and their common variance
- Also known as principal axis factoring
- Mathematically, each variable is expressed as a linear combination of underlying
factors
- The covariation among the variables is described in terms of a small number of
common factors + a unique factor for each variable
- Meer confirmatory, meer a priory expectations van de loadings
- If the variables are standardized, the factor model may be represented as:
'
1