Confirmatory factor analysis
8 important questions on Confirmatory factor analysis
What is the main difference between EFA en CFA regarding correlations between factors and variables?
The factors and correlations among factors and variables are determined a priori --> CFA.
What are the main similarities and differences of EFA and CFA?
- they both try to determine the number and nature of latend variables or factors that account for variation among a set of observed variables. (reflective)
- Both try to reproduce the relationship among a set of indicators with smaller set of latend variables.
Differences:
EFA
- Data driven
- Standardized observed variables
- Correlation matrix is analyzed
- Errors are assumed to be uncorrelated
CFA
- Theory driven
- Unstandardized observed variables
- Evaluation based on how the sample covariance is reproduced
- Error may be correlated.
What is evaluation of measurement invariance and with which of the two types of factor analysis can you check this?
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What do you do in the first step of CFA, model specification?
Within the step model specification, the number of factors and observed variables that load on a construct are specified in advance. What is the difference between setting values fixed, free or constrained?
Free - parameters will be estimated
Constrained - parameters wil lie estimated acknowledging the constraints
What do you determine in the second step of CFA: Identification? What is the difference between the deductive and inductive part?
Deductive: the model structure and parameter values (fixed, free or constraint) determine the variances and covariances of the observed variables.
Inductive:comparing the empirical values with the model. How close are these to the original values? You want the differences to be as small as possible. The empirical variances and covariances yield estimates of unknown (free) parameter values given the structure of the model.
What do you do within the third step of CFA: estimation?
Estimation techniques:
- Maximum likelihood (ML) = most common approach. Maximizes the likelihood of getting a model with minimum discrepancy between the sample covariance and implied covariance matrix.
- Unweighted least squares (ULS)
- Generalized least squares (GLS)
- Distribution-free (ADF)
In the fifth step, eventually specification, you can improve the model fit. How can this be done and what is the danger you should avoid?
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