ASQ Improving Evidence Presentation: Resources and Tools

This is a post that contains resources and tools to improve evidence presentation, echoing one of ASQ’s most important initiatives. The ASQ blog will keep updating the post and we welcome crowdsourcing and sharing of ideas from management scholars all around the world. Get in touch with to share your thoughts!

What is ASQ Improving Evidence Presentation initiative?(#ASQEvidencePresentation)

As you have seen in the “From the Editor” that Administrative Science Quarterly (ASQ) published in June 2017, the editors of ASQ strongly encourage that authors show the data in their manuscripts, by using graphical approaches to give an indication of the most important features of the data and their theoretical explanation before estimating models. Preferably this should be done as early as the introduction in order to spur the reader’s interest and give an indication of why the paper is valuable. Such use of graphical methods is rare in organizational theory and management research more generally, so we will gradually introduce methods of graphical analysis that can be used by researchers.

Graphical methods for showing the data are integrated into Stata, the most common software used by management researchers, and the Stata commands offer a good blend of simplicity and flexibility. Nevertheless, they need some training, especially because statistical training is model-focused in many schools, and highly variable in how well graphical methods are taught. New analytical tools like R and Python are also on the rise and increasingly used to visualize data, which requires even more training and knowledge sharing. Here we collaborate with the ASQ blog to home a resource center where tools and techniques to improve evidence presentation are crowdsourced and curated. The resource center will be updated, and editors of ASQ will contribute examples with data and do-files to demonstrate evidence presentation. We hope that the resource center will help improve the way you present evidence in your research.

—-Henrich Greve, the Editor of Administrative Science Quarterly

ASQ paper development workshop materials

  1. 2017 ASQ paper development workshop, AOM in Atlanta

Editor’s examples

  1. why indie books sell well, an evidence presentation example based on Greve & Song (2017), “Amazon Warrior: How a Platform Can Restructure Industry Power and Ecology.” Data and do-file are available.

Tools using Stata

  1. introduction to some important methods including scatterplots, lineplots, bar graphs, box plots, and kernel (full distribution) plots.
  2. example of more advanced programming, which is needed because stata does not (yet) have a simple way of showing a grouped bar graph with error bars, which is an important graph for taking a first look at group differences.
  3. introduction to spmap, an add-on procedure for producing mapped data displays. Displaying statistics on a map can be very helpful for any kind of research involving spatial relations, before the add-on such mapping required changing to different software and exporting data, which is both time consuming and a potential source of errors.
  4. introduction to the coefplot function, a graphical display of coefficient magnitudes. This is a very informative way of giving a comparative view of a full regression model, or parts of it, in a compact graph. This routine has a very flexible and intriguing set of plots as displayed on this link.

Tools for R, Python, JavaScript, and other programming languages

  1. ggplot for Python and ggplot2 for R: a system for declaratively creating graphics, based on The Grammar of Graphics.
  2. Seaborn: a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
  3. Matplotlib: a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Python’s pandas DataFrame plot is built-in functions using Matplotlib.
  4. Bokeh: an interactive visualization library that targets modern web browsers for presentation. Bokeh provides elegant, concise construction of versatile graphics with high-performance interactivity over very large or streaming datasets in a quick and easy way from Python. rbokeh is the R interface of Bokeh.
  5. D3.js: a JavaScript library for manipulating documents based on data. D3 bind arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document. Its emphasis on web standards allows for bringing data to life using HTML, SVG, and CSS and not being tied to a proprietary framework.
  6. plotly is a company that provide interactive visualization tools for multiple languages (R, python, JavaScript) and has a web-based interface Dash app. examples for R and examples for Python

Guides for evidence presentation & data visualization

  1. An Economist’s Guide of Visualising Data, written by Jonathan A. Schwabish (2014) in Journal of Economic Perspectives, 28(1): 209–234
  2. From the Editors—A Brief Primer on Data Visualization Opportunities in Management Research, written by Gokhan Ertug, Marc Gruber, Anthony Nyberg, H. Kevin Steensma (2018) in Academy of Management Journal, 61(5): 1613–1625
  3. 15 Most Common Type of Data Visualisation
  4. 8 More Common Types of Data Visaualization
  5. UW Interactive Data Lab and Stanford Visualization Group:  resources from computer scientists, Stanford Vsiualization Group is old archive

Fun facts about data visualization

  1. Data visualisation, from 1987 to today at The Economist, Aug 29, 2018
  2. Data journalism at The Economist gets a home of its own in print, Oct 18, 2018


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