What are the factors that can distort correlation coefficients?
The authors describe and illustrate 6 factors that affect the size of a Pearson correlation: (a) the amount of variability in the data, (b) differences in the shapes of the 2 distributions, (c) lack of linearity, (d) the presence of 1 or more “outliers,” (e) characteristics of the sample, and (f) measurement error.
Is a high or low correlation better?
In other words, the values cannot exceed 1.0 or be less than -1.0. A correlation of -1.0 indicates a perfect negative correlation, and a correlation of 1.0 indicates a perfect positive correlation. If the correlation coefficient is greater than zero, it is a positive relationship.
What should the factor loading of a variable be?
They are usually the ones with low factor loadings, although additional criteria should be considered before taking out a variable. As a rule of thumb, your variable should have a rotated factor loading of at least |0.4| (meaning ≥ +.4 or ≤ –.4) onto one of the factors in order to be considered important.
How many uncorrelated factors are there in factor analysis?
Although you initially created 42 factors, a much smaller number of, say 4, uncorrelated factors might have been ‘retained’ under the criteria that the minimum eigenvalue be greater than 1 and the factor rotation will be orthogonal. As it turns out, the first factor has in eigenvalue of 8.5. Question: What does all that mean?
How are some items of my data highly correlated?
To specify, some items of my data are highly correlated r = 0.8, some items stem from a similar/partially same test [Example: Persons had to remember 20 words, they had to repeat them directly after (one item) and many minutes after (second item).]
Do you have to eliminate correlated variables in PCA?
Do I have to eliminate variables that are highly correlated before doing an exploratory factor analysis, like it has been discussed for PCA already here?