Testing Scales and Confirmatory Factor Analysis Flashcards
What is structural equation modeling (SEM) ?
Structural equation modeling (SEM) is a collection of statistical techniques that allow a set of relationships between one or more IVs, either continuous or discrete, and one or more DVs, either continuous or discrete, to be examined.
SEM allows questions to be answered that involve multiple regression analyses of factors.
When factor analysis is combined with multiple regression analyses, you have SEM.
what is meant by Psychometric properties?
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
In CFA should a model be underidentified?
NO.
If there are more data points than parameters to be estimated, the model is said to be overidentified, a necessary condition for proceeding with the analysis.
- Overidentified model: knowns > unknowns
If there are the same number of data points as parameters to be estimated, the model is said to be just identified.
- Just identified model: knowns = uknowns
If there are fewer data points than parameters to be estimated, the model is said to be underidentified and parameters cannot be estimated.
- Underidentified model: known < unknowns
Advantages of CFA
Testing errors and cross-loadings
o Helps us to refine scales, possibly kick out items, notice some problems with phrasing.
o A very objective way of improving a scale
Allows for comparison of models
* Statistical evaluation of the fit with and without restrictions on the model
Restricting parts of the model between groups, methods
o Checking whether the scale works the same for different groups of individuals
Help assess reliability and validity
o i.e. test for common variance bias.
What does the p-value tell us in CFA?
P-value: null hypothesis significance testing, the p-value is the probability of obtaining test results at least as extreme as the results observed, under the assumption that the null hypothesis is correct.
Mention some other goodness of fit measures, and their cut off points.
OTHER GOODNESS OF FIT MEASURES
SRMR- standardized root mean square residual- absolute fit
- Cut off point- lower than or close to 0.08
RMSEA- root mean square error of approximation
- Cut off point- lower than or close to 0.06
CFI-comparative fit index: thinking of a series of models all nested within one another.
- High values (greater than .95) are indicative of a good‑fitting model.
TLI- Tucker-Lewis index
- Cut off point- higher than or close to 0.95
AIC- Akaike information criterion and BIC -Bayes information criterion
- No cut-off points, but smaller number better
- Can compare non-nested models
What measures are used to test reliability of CFA?
Cronbach alpha -> above 0,6
McDonald’s Omega
How is validity checked with CFA?
Content validity: are the items good representation of the targeted construct? Do they cover the content domain?
Criterion validity/Convergent validity: can the construct predict criterion variable?
* Concurrent and predictive: is the data collected on both at the same time or separately?
Construct validity: does the construct fit with other constructs already in existence?
Discriminant validity: distinguished from constructs it should be distinguished from (especially if they could be very similar- like cultural intelligence and global mindset)
What are the three priorities for scales?
Reliability -> tested with Cronbach Alpha should be higher than 0,6. However, Cronbach alpha assumes Tau equivalence. Alternative McDonald’s Omega can be used.
Validity: content validity, criterion validity, and construct validity (also entail discriminant validity)
Measurement invariance: if yes the scale can be compared across genders, nationality, etc. -> scales are stable for different groups of people, variance found is then due to true variance.
