Data was collected through observation and measurement scale was the second. If I use the percentage of agreements or errors I'll probably find very low values. Is there any method which overcomes these and realizes the proximity of values? The data is attached.
well, considering the overall percentage of agreement (ie. time_1 = time_2) I only have 68.9%. And in this case does not take into account differences in, for example, 1 second. I think that in these data may not be an error of reliability but a redundancy in function of the measuring instrument (stopwatch).
I made a Pearson Correlation and I find 0,967. I think that is not bad!
I calculate the root mean squared error (RMSE) via "Analyze > Forecasting > Create Models..." is this the right way?
I find RMSE= 3,721. what does this mean?
There is some theoretical support for this? Any reference that I can cite to justify this approach?
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