**GOOGLE SHEET:** https://docs.google.com/spreadsheets/d/1q48rqUMAzi1N1_4AtIbYdyz2sdH-nga4yKcsFqIVhwY/edit?ts=5c1bd816#gid=1339014359 - This google sheet provides Q-Correlation and Squared Euclidean Distance examples for ASEBA T-Scores and the BFI. - Explanations and interpretations of the scores and graphs are explained in tagged comments. - Also included is a "calculator for you to enter your own scores and receive a calculated Difference in Scores, Q-correlation, Euclidean Distance and a graph comparing the two informants' scores. **Patterns of Agreement** - Way to look at difference in scores between two informants - Makes it easier to interpret dyadic agreement. - Can be used as predictors - Provides understanding on the picture of the informant's mental health, emotional and behavioral profile. - Informative on what could influence agreement. - **Shape** of agreement for the multivariate model, which is the pattern of similarity in scores. - **Dispersion** is the spread of the scores or the variance of agreement. Similar to standard deviation. - **Level** is how high or low the average of the T-scores are in this case. The magnitude of the problem. **Q-Correlation** - Captures/Sensitive: Shape and Dispersion of Agreement - How similar is the youth and the caregiver's ranking of ASEBA syndromes from most to least concerning behavior/problem. - Larger, the stronger the overall agreement is as a pattern across the eight ASEBA youth behaviors. Interpret the same way as bivariate correlations for strength and direction. - Q-correlation can be high but the distance between the child and dyad scores could be vast and you would not know unless you look at the Squared Euclidean Distance **Squared Euclidean Distance** - Captures: Shape, Dispersion, and Level of Agreement - Describes the difference between the caregiver and youth’s scores. - Measure of similarity or proximity, and so the larger the distance value the more disagreement between the caregiver and the youth on the youth’s behaviors across the eight ASEBA syndromes. - This shows the variance between scores or distance more than Q-correlation.