Data analysis for aim 2

Our quantitative analysis will use descriptive statistics to summarize PPQ/FPQ items’ distribution characteristics. Generalized linear mixed models will be applied to determine differences between the IG’s and the CG’s knowledge of cancer pain management throughout the intervention. The influence of missing data on the model results will be examined using sensitivity analysis [46]. Change scores for each patient/FC as well as Cohen’s d will be calculated for each variable of interest.

For qualitative data analysis, transcript data will be stored and analysed in ATLAS.ti 7. Field notes and audiotapes of telephone calls, home visits and interviews will serve as qualitative data to explore both the IG’s and the CG’s the learning processes concerning knowledge of cancer pain and pain self-management. Data will be analysed via interpretive description–an approach using stepwise, systematic and iterative processing of data to arrive at a meaningful description and interpretation [57].

Information from quantitative and qualitative data collection will be combined within a mixed method matrix. Qualitative and quantitative results related to each German PPQ/FPQ item will be integrated in a final synthesis [39, 58].