efa for 3factors (construal,trust & respect) py4108 data

8/2/2019 EFA for 3factors (Construal,Trust & Respect) PY4108 Data

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EXTRA: Under SUMMARY OF DATA - number of missing data

patterns: This gives the number of different patterns of

missingness present in the variables included in the model.

Large numbers of missing data patterns can result in difficulty

estimating the model

Covariance Coverage: I f any of the variables in the model

have missing values, MPlus provides information on the

number and distribtion of missing values. The covariancecoverage matrix gives the proportion of values present for

each variable individually ( on the diagonal) and pairwise

combination of variables (below the diagonal)


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Eigenvalues for sample correlation matrix - An

eigenvalue is the variance of the factor. In the in

factor solution, the first factor will account for t

most variance, the second will account for the n

highest amount of variance and so on.


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Chi-square test of model fit: Compares the fit of the model to a model with

no restrictions (i.e. all variables correlated freely). Chi-square values can be

used to test the difference in fit between nested models.


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Fit Indices - The Comparative Fit Indeix (CFI) and the

Tucker Lewis Index (TLI) are measure of model fit. They

have a range from 0 to 1 with higher values indicating

better fit.

*Extra: Under Information Criteria;

- Number of Free Parameters

- Akaike (AIC)

- Bayesian (BIC)

- Sample-size Adjusted BIC

: The Akaike Information Criteria (AIC) and the Bayesian

Information Criteria (BIC, Schwarz criterion), can also be used to

compare models, including non-nested models


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RMSEA : The root mean square error of approximation

is another measure of model fit. Smaller values

indicate better model fit .

** GEOMIN ROTATED LOADINGS (in this case

OBLIMIN) : the rotated loadings are the linear

combination of variables that make up the factor. In

addition to the factor loadings, to completely interpret

an oblique rotation one which needs to take into

account both the factor pattern and the factor

structre matrices (shown below) and the correlations

among factors. Note that orthogonal rotations produce

only ONE (A) single matrix, which gives the

correlations between the variable and the factor


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** GEOMIN factor correlations (OBLIMIN in our case): the

factor correlations matrix gives the correlations between the

factors. For e.g., the correlation between factor 1 and factor

1 is 1.000.... etc.

Estimated Residual Variances : there are the variances of the

observed variables after accounting for all of the vairance in the

EFA model.


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Below are the standard errors for the OBLIMIN rotated loadings, factor correlations, and est imated residual

variances. These values can be used to perform hypothesis tests and estimate confidence intervals


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Below are the z-statistics (i.e. estimate/standard error) for the OBLIMIN rotated loadings, factor correlations, and

estimated residual variances. These values can be compared to a normal distribution to perform hypothesis test .


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Factor Structure: With an oblique rotation, the factor

structre matrix presents the correlations between the

variables and the factors. For e.g., the correlation

between U1 and factor 1 is 0.256. As noted above, the

factor structure matrix is used along with the factor

loadings and fac tor correlations to interpret the model

* EXTRA: FACTOR DETERMINACIES (come after

factor structre - before the end) :

are the proportion of variance in each factor that

is explained by the observed variables. Higher

proportions of variance explained indicate better

fit .


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efa for 3factors (construal,trust & respect) py4108 data

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