I have the survey among three groups of health professionals to answer the survey.
The survey is a Likert scale for seven different treatment options across different disease conditions (6 conditions).
The goal is to assess if there's a statistically significant difference between the mean scores of these different groups of health professionals for various treatment options.
I have 6 different disease condition (or 6 different question) each question with 7 different treatment option.
I am thinking to run ANOVA works here, but if I want to run ANOVA, then will need to run that 42 times. Because (6*7=42)
Then Applying corrections like the Bonferroni correction to adjust for the increased risk of Type I errors (false positives) when conducting multiple statistical tests, such as multiple ANOVAs. But still will need to apply ANOVA 42 times, is that correct method to do for this? Is there Any better method?
if I want to calculate the overall mean score for one specific treatment option across all disease conditions, then will run the ANOVA just seven times.
If I want to go with the first approach, what method do you suggest?
Am I correct to apply anova?
Thanks
Hello,
Yes, ANOVA is correct for assessing differences, but consider MANOVA to efficiently analyze multiple factors at once. For overall mean scores by treatment, ANOVA is suitable. Adjust for multiple comparisons carefully.
Hope this helps.
Running ANOVA for each combination of disease condition and treatment option (42 times) and applying corrections like the Bonferroni correction is a valid method, but it may lead to an increased risk of Type I errors due to multiple testing. It's essential to control for this increased risk to avoid drawing incorrect conclusions.
An alternative suitable method would be multivariate analysis of variance (MANOVA). MANOVA allows you to simultaneously analyze the differences in mean scores across multiple dependent variables (in your case, the Likert scale responses for each treatment option) while controlling for the overall Type I error rate. This method provides a more efficient and powerful way to analyze the data, as it takes into account the interdependence of the dependent variables.
I agree with Hsin-Yuan Chen . MANOVA would be more suitable for your dataset and experimental design. It helps to control the Type I errors more effectively.
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