A Study On Cohens D Essay
Thus, it can be safely concluded that the effect size of the intervention is small and not clinically significant. Association and causality is not proven in this case. The data set given was imported to Stata and summary statistics were used and graphs and scatter plots on Stata to visualise and analyse the data.
3 A linear regression analysis was done with Stata to find the treatment effect to adjust for baseline co variates. (propensity score matching)
The results are as below- weight after treatment being the dependent variable (continuous variables) and the rest independent variables- fitted to a linear regression model with the intervention (being the treatment) compared to controls.
Treatment effect Pearson’s Coeff
Std. Err 95% Conf. Interval
Control vs intervention
N (lifestyle changes). .0009792 .0025689 -.0040558 .0060142
Treatment-effects estimation - linear transformation
Number of observations = 92. Estimator regression adjustment Outcome model: linear Combining other variables as age, sex, weight etc.
The treatment effect is still very small if at all. The adjusted change in BMI is .000972 with a confidence interval of -004 to .005.
However, the definitions of obesity and ‘treatment’ and ‘control’ need to be more…