Figure 2 shows a model that was tested without adjusting for people’s personal characteristics. This means the analysis did not take those characteristics into account when examining the relationships shown in the model.
How well did the model fit?
Researchers use fit statistics to check how closely a model matches the data. The results were:
- Comparative Fit Index (CFI) = 0.96: This suggests the model fits the data well.
- Root Mean Square Error of Approximation (RMSEA) = 0.06: This suggests the model’s errors are fairly small.
- Standardized Root Mean Square Residual (SRMR) = 0.05: This also suggests a good fit between the model and the data.
Together, these scores indicate that the model matches the observed data reasonably well.
What do the other notes mean?
- Standardized β (beta) coefficients: These numbers show the direction and strength of relationships in the model, using a common scale so the results are easier to compare. A positive value means the variables tend to increase together; a negative value means one tends to decrease as the other increases.
- ADHD: This stands for attention-deficit/hyperactivity disorder.
- a, b, and c: These letters mark results that meet different levels of statistical significance: a means p < .001, b means p < .05, and c means p < .01. A smaller p-value generally means the result would be less likely to appear by chance if there were no real relationship.