Interpretation. Examine the loading pattern to determine the factor that has the most influence on each variable. Loadings close to -1 or 1 indicate that the factor strongly influences the variable. Loadings close to 0 indicate that the factor has a weak influence on the variable.

1. Factor Analysis in SPSS To conduct a Factor Analysis, start from the “Analyze” menu.
2. This dialog allows you to choose a “rotation method” for your factor analysis.
3. This table shows you the actual factors that were extracted.
4. E.
5. Finally, the Rotated Component Matrix shows you the factor loadings for each variable.

In order to provide evidence that a measure does or does not measure what it intended to, a Factor Analysis must be conducted to produce what we call Factor Loadings. Factor Loadings are scaled from 0 to 1 and are essentially coefficients that tell us how strong the relationship is between the variable and the factor.

Factor loading: Factor loading shows the variance explained by the variable on that particular factor. In the SEM approach, as a rule of thumb, 0.7 or higher factor loading represents that the factor extracts sufficient variance from that variable.

Loadings are interpreted as the coefficients of the linear combination of the initial variables from which the principal components are constructed. From a numerical point of view, the loadings are equal to the coordinates of the variables divided by the square root of the eigenvalue associated with the component.

Actually, loadings are the covariances/correlations between the original variables and the unit-scaled components. This answer shows geometrically what loadings are and what are coefficients associating components with variables in PCA or factor analysis.

Factor loadings should be reported to two decimal places and use descriptive labels in addition to item numbers. Correlations between the factors 2 Page 3 should also be included, either at the bottom of this table, in a separate table, or in an appendix.

Factor loadings are correlation coefficients between observed variables and latent common factors. Factor loadings can also be viewed as standardized regression coefficients, or regression weights.

Positive loadings indicate a variable and a principal component are positively correlated: an increase in one results in an increase in the other. Negative loadings indicate a negative correlation. Large (either positive or negative) loadings indicate that a variable has a strong effect on that principal component.

In SEM Analysis, factor loading 0.55 or above are acceptable. You can refer to Hair et al (2020).

## What does it mean if Bartlett test is significant?

The critical value of chi square is 9.488. If the Bartlett test statistic is greater than this critical value, there is a significant difference in the variances. If the Bartlett test statistic is less than this critical value, there is not a significance difference. In this example, X02 < 9.488.