Factor analysis is a method of data reduction.
It does this by seeking underlying latent variables that are reflected in the observed variables.
Table 1 represents the Communalities. This explains the variance in the variables has been accounted for by the extracted factors.
For example, here, we see that, 88.4% of the variance in online learning runs smooth at all times is accounted for while 58.7% of the variance in the interpersonal interaction is not affected as a result of online classes is accounted for.
Table 2 explains the Total variance explained, mean that the percent of variance attributable to each factor, and the cumulative variance of the factor and the previous factors.
Here, we see that first factor accounts for 13.863% of the variance, the second 11.759%, the third 9.369%, fourth 8.001%, fifth accounts for 7.525%, the sixth 6.947%, the seventh 6.305%, the eight 5.166% and the third 4.758%.
All the remaining factors are not significant. The table 3 represents the component matrix which shows the loadings of the 23 variables on the nine factors extracted.
The higher the absolute value of the loading, the more the factor contributes to the variable. The gap on the table represent loadings that are less than 0.4, this makes reading the table easier.
We suppressed all loadings less than 0.4.
The table 3 represents the component matrix which shows the loadings of the 23 variables on the nine factors extracted. The higher the absolute value of the loading, the more the factor contributes to the variable.
The gap on the table represent loadings that are less than 0.4, this makes reading the table easier. We suppressed all loadings less than 0.4."