CFA Flashcards
What is CFA?
CFA is extremely versatile method to test different psychometric properties of the scale
Psychometric properties- quality of the scale, determining how we can use it in further research, how trustworthy are the results we get utilizing the scale
CFA- confirms specific structure of the scale- dimensions
CFA- shows how reliable are indicators- which are significant and how much do they correlate with specific factors
CFA- confirms integrity of the scale- separated from other constructs
CFA- shows whether we can use scale for different groups of respondents
CFA analyses require the researcher to hypothesize, in advance, the number of factors, whether or not these factors are correlated, and which items/measures load and reflect which factors while in EFA, researcher is not required to have any specific hypotheses about how many factors will emerge, and what items or variables these factors will comprise.
General Purpose – Procedure
Defining individual construct: First, we have to define the individual constructs. The first step involves the procedure that defines constructs theoretically. This involves a pretest to evaluate the construct items, and a confirmatory test of the measurement model that is conducted using confirmatory factor analysis (CFA), etc.
Developing the overall measurement model theory: In confirmatory factor analysis (CFA), we should consider the concept of unidimensionality between construct error variance and within construct error variance. At least four constructs and three items per constructs should be present in the research.
Designing a study to produce the empirical results: The measurement model must be specified. Most commonly, the value of one loading estimate should be one per construct. Two methods are available for identification; the first is rank condition, and the second is order condition.
Assessing the measurement model validity: Assessing the measurement model validity occurs when the theoretical measurement model is compared with the reality model to see how well the data fits. To check the measurement model validity, the number of the indicator helps us. For example, the factor loading latent variable should be greater than 0.7. Chi-square test and other goodness of fit statistics like RMR, GFI, NFI, RMSEA, SIC, BIC, etc., are some key indicators that help in measuring the model validity.
Path analysis
Used to test structural equations.
Path diagram
Shows the graphical representation of cause and effect relationships of the theory
Endogenous variable
The resulting variables that are a causal relationship
Exogenous variable
The predictor variables
Confirmatory analysis
Used to test the pre-specified relationship/ model on a new dataset
Goodness of fit
The degree to which the observed input variance-covariance matrix is predicted by the estimated model.
Latent variables
Variables that are inferred, not directly observed, from other variables that are observed.
