Interpret the key results for Factor Analysis (2024)

FAQs

How to interpret the results of factor analysis? ›

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. Some variables may have high loadings on multiple factors. Unrotated factor loadings are often difficult to interpret.

To interpret each principal components, examine the magnitude and direction of the coefficients for the original variables. The larger the absolute value of the coefficient, the more important the corresponding variable is in calculating the component.

Factor analysis is the practice of condensing many variables into just a few, so that your research data is easier to work with. For example, a retail business trying to understand customer buying behaviours might consider variables such as 'did the product meet your expectations?

A factor loading of 0.7 or higher typically indicates that the factor sufficiently captures the variance of that variable.

The multiplicity of interests in government was a key factor. Choosing to take part was a key factor. The problematic narrative of the book is a key factor. Costs are a key factor in the policy arena.

Uniqueness is the variance that is 'unique' to the variable and not shared with other variables. It is equal to 1 – communality (variance that is shared with other variables). For example, 61.57% of the variance in 'ideol' is not share with other variables in the overall factor model.

Interpretation of the principal components is based on finding which variables are most strongly correlated with each component, i.e., which of these numbers are large in magnitude, the farthest from zero in either direction. Which numbers we consider to be large or small is of course a subjective decision.

Interpretation. Use the plot to identify which variables have the largest effect on each component. Coefficients can range from -1 to 1. Coefficients close to -1 or 1 indicate that the variable strongly influences the component.

What are factor scores in principal component analysis? ›

Principal component scores are actual scores. Factor scores are estimates of underlying latent constructs. Eigenvectors are the weights in a linear transformation when computing principal component scores. Eigenvalues indicate the amount of variance explained by each principal component or each factor.

Factor analysis (FA) allows us to simplify a set of complex variables or items using statistical procedures to explore the underlying dimensions that explain the relationships between the multiple variables/items.

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.

Factor analysis is a statistical technique that reduces a set of variables by extracting all their commonalities into a smaller number of factors. It can also be called data reduction. When observing vast numbers of variables, some common patterns emerge, which are known as factors.

Usually, you summarize the results of the EFA into one table which contains all items used for the EFA, their factor loadings and the names of the factors. Then you indicate in the notes of the table the method of extraction, the method of rotation and the cutting value of extracting factors.

Eigenvalues represent the total amount of variance that can be explained by a given principal component. They can be positive or negative in theory, but in practice they explain variance which is always positive. If eigenvalues are greater than zero, then it's a good sign.