Hi there. I´ve read many articles, tutorials, posts etc. about this condition but I´m not clear on the following:
Outcome variable 1:
Outcome variable 2:
Background:
I have a test group with 19 subjects being tested on 4 outcome variables with 5 test phases for each subject: measure 1 (=baseline); treat 1; measure 2; treat 2; and measure 3. Data has been collected continuously (not sampled) for each of the phases, which range from 1 week (baseline) to 2 months (treat 2) in length. For each subject daily mean values of the outcome variables were calculated (from the raw data). For each subject the mean of these values for each test phase was calculated. The data samples comprise the respective phase means for each of the subjects.
Slight complication which leads to a supplementary question: Only 17 of the subjects also include "treat 2" data and only 15 of the subjects also include "measure 3" data. Whether via parametric or non-parametric tests can I compare the "measure 3" sample (n=15) with all 19 subjects in the baseline or should I compare the 15 "measure 3" subjects only with the same 15 subjects at baseline.
- Do ALL the samples (paired, before/after type) that are being compared need to be normally distributed; or is it sufficient if only the baseline values are normally distributed (see examples below)?
- I´ve obtained kurtosis and skewness values for each time point using Excel. Do both values have to be within critical values? What if one is and one isn´t?
- I´ve obtained the critical skewness value with the formula 2 x sqrt(6/n). Is this correct (I´ve often seen +/- 2 as the acceptable values)?
- Is the same formula used to calculate the critical kurtosis value?
- In a real-life research project is it normally sufficient and acceptable to base normality tests only on the skewness and kurtosis values (for the purpose of deciding whether parametric tests can be applied) or does one need to do a more formal test (such as Kolmogorov-Smirnov)?
- To what extent does a sample size larger than ten offset non-normality when doing the students t-test or an ANOVA? Can this offset some sample data (in the time series) not being normally distributed?
- I wish to compare each of 4 test phases with the baseline (I´m less interested in comparing say phase 3 with phase 2). Would 4 separate t-tests be robust enough (assuming sufficient normality) or would it be expected to do a repeated measures ANOVA? I ask because most studies in the literature use ANOVA).
Outcome variable 1:
Outcome variable 2:
Background:
I have a test group with 19 subjects being tested on 4 outcome variables with 5 test phases for each subject: measure 1 (=baseline); treat 1; measure 2; treat 2; and measure 3. Data has been collected continuously (not sampled) for each of the phases, which range from 1 week (baseline) to 2 months (treat 2) in length. For each subject daily mean values of the outcome variables were calculated (from the raw data). For each subject the mean of these values for each test phase was calculated. The data samples comprise the respective phase means for each of the subjects.
Slight complication which leads to a supplementary question: Only 17 of the subjects also include "treat 2" data and only 15 of the subjects also include "measure 3" data. Whether via parametric or non-parametric tests can I compare the "measure 3" sample (n=15) with all 19 subjects in the baseline or should I compare the 15 "measure 3" subjects only with the same 15 subjects at baseline.
Last edited:
