During the ICPSR summer stats camp, I had the honour and pleasure of taking two courses with William Jacoby, a political scientist well known for his contribution to the fields of measurement theory and data visualization. One of the courses I took with him was a short course on statistical graphics for visualizing data. In this post, I will briefly share some of the resources and takeaways I garnered from him.
This course focused a lot on analytical graphs (e.g. the graphs we use to help us gain insight into our data by making sense of patterns or relationships). Why might a researcher go to the trouble of coding a graph that they had no intention on including in a publication? Graphics prevent mistakes. Using graphics to analyze data was extended and popularized by statistician John Tukey (developer of the boxplot, among other innovations).
Presentational graphs, in contrast to analytical graphs, communicate the researcher’s main point to the intended audience. Thus, the purposes and uses of these two kinds of graphical displays of quantitative information are very different. However, often researchers treat them as the same thing, which can be a problem. According to Cleveland, the components of interpreting or decoding presentational graphics are: detection (can you see the data?), assembly (can you put things together into a structure?), and estimation (to what extent does the graphic facilitate accurate estimation?).
In all fields of social research, and particularly in social work research, presentation of results is highly important. Often we research phenomena for and with communities that may not be familiar with scientific or statistical methods. We can and should make the salient points of our analysis easier to understand through graphical representation. If we fail to do so, our research is likely not to have the kind of individual, community, and social impacts that we would like it to.
Resources for Information on Data Visualization (not at all exhaustive):
New Directions for Evaluation has special issues on Data Visualization. see Autumn (139), and Winter (2013) issues.
Anscombe, F. J. Graphs in Statistical Analysis [An amazing paper]
Cleveland, W. S. Statistics Research Homepage [An excellent resource for using and understanding the trellis package from S-PLUS (lattice in R)]
Glenn, R. W. Data Graphics [A basic overview of data visualization history and theory]
Jacoby, W. Statistical Graphics for Visualizing Data [Slides, code, and lecture notes from the ICPSR course]
Tufte, E. R. The Graphical Display of Quantitative Information [A bible of sorts]