Exploratory factor analysis was conducted to determine the underlying constructs of the questionnaire. The results show the % variance explained by each factor. What does % variance mean in EFA? How to interpret the results? How can I explain the % variance to a non-technical person in simple non-statistical language?
Nidhi Joshi The % of Variance column tells you how much of the variance in the dataset can be explained by each factor. The first few factors account for relatively large proportions of the variance compared to the latter (in SPSS).
How much the data is far from the mean value, is explained (%) by a similar set of questions (factors).
Factors are the set of variables that loads according to a similar interaction (for ex: negative questions=factor 1 / positive questions=factor 2)
The % variance explained by a set of factors in a given variable is often called communality (h^2). It gives the proportion of individual differences in an observed (measured) variable that is accounted for by the common factors. The communality is similar to R-squared in regression, except that the independent variables are latent factors. Under certain conditions, the communality is equal to the reliability of a variable, namely when there is not specific variance (only random measurement error) left after accounting for the factors.
In summary, the communality gives you an estimate of a variable's ability to reliably measure common factors (latent variables). The closer the communality to 1, the more reliable the variable as a measure of the factors.
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