Quantitative Ethnography

Epistemic Network Analysis is a method for identifying and quantifying connections among elements in coded data and representing them in dynamic network models. A key feature of the ENA tool is that it enables researchers to compare different networks, both visually and through summary statistics that reflect the weighted structure of connections. The interface also allows users to see the original data that contributed to each of the connections in the network representation. ENA can thus be used to address a wide range of qualitative and quantitative research questions.

I ran a t-test in webENA to see if there was a statistically significant difference between the codes and their connections at KSU vs UW. 

Along the X axis, a two sample t test assuming unequal variance showed Ksu (mean=0.00510, SD=1.15660, N=114 was not statistically significantly different at the alpha=0.05 level from Uw (mean=-0.01210, SD=0.73810, N=48; t(134.58270)= 0.11300, p=0.91020, Cohen's d=0.01630).

Along the Y axis, a two sample t test assuming unequal variance showed Ksu (mean=-0.16840, SD=0.79830, N=114 was statistically significantly different at the alpha=0.05 level from Uw (mean=0.39990, SD=0.63850, N=48; t(109.50410)= -4.78850, p=0.00001, Cohen's d=0.75280). 

The model I have above tells the story about the conversations about marketability in the virtual internship during the “Reflection team discussion of surfactants” section of the internship. My model shows that there is a connection between the use of learning support tools and discussions about marketability and that the UW group tended to talk about marketability in terms of DiscussingNumericalDetails while KSU tend to talk about marketability in terms of a loop between UseOfLearningSupportTools and DiscussingClientImpacts. There is a statistically significant difference in the frequency and interaction of utterances referencing marketability between the ksu and uw groups.