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 asqblog@gmail.com with email subject “ASQ Evidence Presentation” to share your thoughts!

AOM ASQ workshop materials (August 2022 updated! )

2022 AOM PDW – Narrative & Evidence: Situating Research in Prior Literature to Establish a Coherent Contribution

A workshop featuring ASQ editors and authors that focuses on comparability, and specifically on developing theoretically-informed, empirical narratives that explicitly connect the focal study to prior research, facilitating knowledge accumulation and establishing a contribution. A collection of papers featured in the PDW can be found here 

1. ASQ Editor Christine Beckman introduction slides, AOM in Seattle
2. ASQ author and Dissertation Award winner Nate Wilmers slides discussing “numerical narrative”, AOM in Seattle
3. ASQ Associate Editor and author Greta Hsu slides discussing mixed methods (qual+quant), AOM in Seattle
4. ASQ author Amanda Ferguson slides detailing the abductive narrative, AOM in Seattle
5. ASQ Associate Editor Chris Rider’s blog post on “Numerical Narrative”

2019 AOM PDW – Evidence Presentation: How to Visualize Your Data and Why It is Important

A workshop featuring ASQ editors and authors that encourages using pre-estimation data visualizations for transparency, and authors of exemplar ASQ papers demonstrated good evidence presentation using different methods.

1. 2019 Evidence Presentation PDW Aruna Ranganathan slides, AOM in Boston
2. 2019 Evidence Presentation PDW Florenta Teodoridis, Michael Bikard, Keyvan Vakili slides, AOM in Boston
3. 2019 Evidence Presentation PDW Ryan Raffaelli slides, AOM in Boston
4. 2019 Evidence Presentation PDW Chris Yenkey slides, AOM in Boston

Background: 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, Editor of Administrative Science Quarterly (2016-2020)

ASQ Editor’s examples

1. 2017 ASQ paper development workshop: improving evidence presentation, AOM in Atlanta
2. 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.

Other guides for evidence presentation

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. Best Practices For Reliable Research, a website featuring videos of journal editors, academic researchers, and industry leaders on how to create robust and reliable research. Managed by Professor Gwendolyn K Lee from Warrington College of Business, University of Florida

Exemplary papers

1. The Artisan and His Audience: ASQ article by Aruna Ranganathan, featured in AOM 2019 Evidence Presentation PDW
2. Creativity at the Knowledge Frontier: ASQ article by Florenta Teodoridis, Michael Bikard, and Keyvan Vakili, featured in AOM 2019 Evidence Presentation PDW
3. Technology Reemergence: ASQ article by Ryan Raffaelli, featured in AOM 2019 Evidence Presentation PDW
4. Mobilizing a Market: ASQ article by Chris Yenkey, featured in AOM 2019 Evidence Presentation PDW
5. The Outsider’s Advantage: AJS article by Chris Yenkey, featured in AOM 2019 Evidence Presentation PDW
6. Fraud and Market Participation: ASQ article by Chris Yenkey, featured in AOM 2019 Evidence Presentation PDW
7. Facilitate knowledge sharing in online communities: MOR article with neat use of scatter plots

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
7. tableau is a company that provide a visual analytics platform to do data visualization. Professor Joost Rietveld from UCL has an example on Twitter
8. Charts in Excel provides guidelines to create nice visualisation in Excel! a nice Twitter thread from A. Michael have great tips

Guides for data visualization

1. 15 Most Common Type of Data Visualisation
2. 8 More Common Types of Data Visaualization
3. UW Interactive Data Lab and Stanford Visualization Group:  resources from computer scientists, Stanford Vsiualization Group is old archive
4. Depict Data Studio: visualization choosing tool and resources

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