I am measuring scores from 5 different questionnaires at 3 time points (baseline, 3 months and 6 months) in the same sample of people (120 nurses).
I wondered what statistical test would be appropriate? I was thinking repeated measures ANOVA but am not sure if that would mean having to run it 5 times for each questionnaire?
Do I have enough participants for this purpose?
Thanks in advance for any help.
Hi, Natalie,
1 > having to run it 5 times for each questionnaire?
If questionnaires are independent ( have different targets/aims ) - yes
2 > Do I have enough participants for this purpose?
Depend on population size and error size in Your researсh.
https://www.surveysystem.com/sscalc.htm
First I would combine the data from the 5 questionnaires into one data set making sure the participants are matched in each case. The number of analyses per questionnaire depends on how many questions you had per questionnaire, but you need to do one repeated measures analysis to answer each question you want answered. If you combine the 5 questionnaires into one data set, you could also do analyses across the questionnaires, e.g. comparing the answers to Question 1 over the 5 questionnaires if that is appropriate.
Thanks very much for your responses, very helpful. The sample size calculator is very useful too.
Hi Natalia
1. Repeated measures test has to be applied for each survey.
2. The assumptions of Repeated measures test have to be investigated carefully.
3. Repeated measures ANOVA is the equivalent of the one-way ANOVA, but for related, not independent groups, and is the extension of the dependent t-test. A repeated measures ANOVA is also referred to as a within-subjects ANOVA or ANOVA for correlated samples.
4. We can analyse data using a repeated measures ANOVA for two types of study design. Studies that investigate either (1) changes in mean scores over three or more time points, or (2) differences in mean scores under three or more different conditions.
5. One of the major assumptions of this type of repeated measure analysis is that of sphericity. If this assumption of sphericity is violated, then the value of F statistic will come out with severely biased results. In other words, if the assumption of sphericity is violated, then the researcher might end up committing Type I error.
6. There are options available for the researcher to override this violation of assumptions while performing this type of repeated measure analysis. The researcher can do an adjusted degree of freedom test or use the Green house-Geisser method to overcome the effects of violation.
7. Wilcoxon signed rank test is the appropriate non-parametric alternative.
8. For more details in theoretical side, please refer to;
i. William G. Cochran, Gertrude M. Cox (1977). Experimental Design, Wiley.
ii. Snedecor, W.G. and Cochran,W.G. (1989). Statistical Methods, Blackwell.
iii.Geoffrey Keppel Professor Emeritus, Thomas D. Wickens (2004). Design and
Analysis: A Researcher's Handbook (4th Edition). International Edition.
9. For practical side, please refer to:
iv. Landau, S. and Everitt, B. S.(2004). A Handbook of Statistical analyses using SPSS.
v.Howitt, D. and Cramer, D.(2008). Introduction to SPSS.
Regards,
Zuhair
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