Hi all,
Myself and my supervisor are looking at carrying out a statistical analysis to see whether there is a difference in patient outcomes for our 10-week pain management programme according to whether the patient did the programme pre-COVID (in person programme) or post-COVID (online programme).
We have four sets of data from questionnaires:
1. Pre-programme questionnaire scores for patients who attended in person programme
2. Post-programme questionnaire scores for patients who attended in person programme
3. Pre-programme questionnaire scores for patients who attended online programme
4. Post-programme questionnaire scores for patients who attended online programme
Essentially we want to see whether there is any statistical differences between pre- and post- intervention questionnaire results and between each type of intervention (in person/online).
At first we thought it would be a 2x2 factorial ANOVA, however we are unsure if maybe a t-test is more suitable instead?
Any help would be greatly appreciated - we think maybe we are thinking too much into it and second guessing ourselves!
Thank you in advance.
Because your pre- and post-test scores come from the same participants, you should consider repeated measures ANOVA. Another alternative is to compute a difference score from the pre- and post- test scores and treat that as your dependent variable.
Dear Morgan: This kind of data is suitable to be analysed by t- test, or chi- square, both are giving you good reliable conclusions.. Cheers
to see the effectiveness of intervention, paired t-test is used, and to assess whether online or in-person is effective, for that chi-square.
Annie Caddle
Prior to decide which test you should use, you should observe the type of the data.
Here are 2 groups of paired data and 2 groups of independent data.
Paired data:
1. results from the pre- and post- in person programme
2. results from the pre- and post- online programme
Independent data:
1. outcome of the in person programme
2. outcome of the online programme
Since these data are obtained from the questionnaire that collects data about personal sense of pain in each phase (I assume on the 1-10 scale), we can conclude that these data are ordinal. They are not measured and even though expressed as numbers, they should not be considered for t-test or ANOVA.
To test the difference between 2 groups of paired data you should use Wilcoxon rank test.
Then you should continue to test 2 groups of independent data; probably your data for the outcome would be of the nominal attribute (pain-no pain; different frequencies for the 1-10 scale). You can use Fisher exact test for n100.
If I got it clearly, you are interested in two things.
1. If the intervention has an impact on pain management
2. If there is a difference between the implication of the intervention for "in person or pre covid" and for "online or post covid".
For these, it's two paired sample t-test that would be conducted and then a Z test could be applied to further test the outcome for the understanding of the variances in both.
The dependent t-test will reveal the difference between the pre test and posttest result of both groups and the mean variances could be used to understand which group experienced higher changes based on intervention.
The level of significance could also be compared, while a z test could be applied to further confirm the possible variance in the outcome.
ANOVA repeated measure is not suitable because the participants were not tested more than twice and its results of repeated test from two different participants.
In addition, 2 X 2 F ANOVA is not applicable because the independent factor is more of the intervention while you have 4 different scores on the DV. 2 X 2 ANOVA is only visible where the IVs are in 2 levels and one is interested in the interactions between the levels.
Here, you are not interested in the interaction between pretest pre covid and posttest post covid, but just the impact of the IV (intervention) and comparing the outcome of the DV at 2 instances, thus factorial analysis is not applicable.
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