In this paper, we show how automation on the side of the quantitative strand of research may help to alleviate this issue. For that purpose, we focus on explanatory sequential designs, where a quantitative strand of research is followed by a qualitative strand of research (Creswell, 2009). This is a common research design found in MMMR where quantitative results are further explained using qualitative methods (Schoonenboom, Johnson, & Froehlich, 2018). For instance, a survey may be followed by in-depth interviews with individuals from the survey population to help with contextualizing and interpreting the results. We report how R Markdown, a tool for report automation based on R (Froehlich, 2018b; Xie, 2013), may be used to increase research efficiency when applying such designs. We strongly believe that the quantitative strands of explanatory sequential designs lend themselves to such automation in order to free up resources for the (often labor intensive) qualitative strand. Next to increasing research efficiency, this measure is also helpful in aiding practitioners that do want to apply scientific methods, but do not possess the necessary in-depth knowledge about (quantitative) research methods.



Cite as

Froehlich, D. E. (2018). Explanatory sequential research designs on autopilot: Using R Markdown to increase research and evaluation efficiency. https://doi.org/10.17605/OSF.IO/JD8PF