I have problem while analyzing items using Lisrel program. I'm trying to validate four items to measure extrinsic dimension in Learning Motivation. But the factor loading score of all items have negative score. then I drop one item, then analyze three items to measure it. The result is The model is over fit, with p-value 1.000 and RMSEA 0.000. So what should I do?
If you have one fewer explanatory variables than data points, the model fit will be perfect by definition. Imagine you're trying to model Y as a linear function of X, but have only two points. Any line you draw will fit perfectly.
More generally, model overfitting is when your function fits the training data a lot better than it fits new data. It tends to happen when you have too many explanatory variables and/or too small a sample.
I had this happen a long time ago when I was trying to be too clever with interaction terms. After writing out the equation, I then noticed I could cancel terms. I ended up proving that 1=1 with an R^2 of 1.0. I decided not to publish that particular result.
As Maia states, it can also be from having too many explanatory variables relative to data points. This source of such variables does not matter. They can be measured or interactions. Polynomial regression is fun because you can always get a perfect fit with a high enough degree polynomial.
Agree. Allow some degrees of freedom. Probably all errors are correlated.
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