Data and visualization can be powerful tools to explain complex topics or present nuanced points simply. However, the easiest and simplest chart or graph may not always be the best one to communicate the point you have in mind. Data communication is a complex field that many people study for years. True mastery will take a long time, but to get you on the right path, consider these guidelines.
However, we all have a responsibility to ensure that our arguments and presentations match what the data says, and not the other way around. With the many data sources and visualization tools available today, it is increasingly easy to pick the most favorable data or edit chart axes and visualization methods to support your ideas more - even if an objective interpretation might tell another story.
For examples of misleading and unethical visualizations, see this article by The Economist, or this blog post of common ways writers can use visualizations to mislead readers.
Selected Data Sources
These are only a few data sources that you may find particularly useful for this course. Additional sources can be found on the Duke Libraries website here.
Evaluating Data Sources
It is wonderful and often necessary to expand your search for data beyond the databases listed here or those recommended by a professor. However, you should always use the most reliable, high-quality data sources available. All of the guidance listed on the Evaluating Sources tab of this guide still apply.
Data can, of course, be more tricky than some other kinds of sources to evaluate for quality. How can you tell if a data source is reliable and of high quality? The most common way is to ensure that it comes from a respected and reputable source. Institutions like the World Bank and UN, major data portals like Statista, or reputed surveying bodies like the World Values Survey have longstanding reputations of providing the most reliable data available or conducting reliable research. Secondary sources like Datahub or Gapminder list the sources for all of their data, most of which are large, respected, and reputable sources. Evaluating a data source is similar to evaluating other sources of information: do some background research on the institution providing them to see if they are peer-reviewed or reputed in some way, but always remain skeptical and critical, even toward sources that are usually reliable.
Often, however, we cannot find the kind of data we need from a major source like the UN. What happens when our only source for some data may be un-reputed or un-reviewed? As above, we must always remain skeptical and critical and do some investigation of the methodologies, asking "how was this data gathered? What does it really show?" The most important thing is to be open about the quality of your data. If the only data you can find is from an obscure source, or gathered by hand, or lacks a reputation for quality in some way, let your audience know by using phrases such as "I am uncertain about the reliability of the dataset, but it was the only source for this information". And avoid using such uncertain information as the center of your argument.
Concealing from your readers or audience where your data comes from or how it was acquired can often be a big warning sign that the data is not reliable. This applies equally to evaluating a dataset yourself, as well as presenting your data and analysis to others.
Always remember that if you need help finding a data source, or evaluating the quality of an unknown source, you can consult your professor or a librarian.
Remember that data must be cited exactly like articles, books, or any other piece of information that is not pubic knowledge.
For help on how to cite a data source, please see here, or follow the following templates:
Minimum requirements based on instructions and example for dataset reference:
Milberger, S. (2002). Evaluation of violence against women with physical disabilities in Michigan, 2000-2001 (ICPSR version) [data file and codebook]. doi:10.3886/ICPSR03414
With optional elements:
Milberger, S. (2002). Evaluation of violence against women with physical disabilities in Michigan, 2000-2001 (ICPSR version) [data file and codebook]. Detroit: Wayne State University [producer]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. doi:10.3886/ICPSR03414
Minimum requirements based on instructions and examples for books and web publications:
Milberger, Sharon. Evaluation of Violence Against Women With Physical Disabilities in Michigan, 2000-2001. ICPSR version. Inter-university Consortium for Political and Social Research, 2002. Web. 19 May 2011.
With optional elements:
Milberger, Sharon. Evaluation of Violence Against Women With Physical Disabilities in Michigan, 2000-2001. ICPSR version. Detroit: Wayne State U [producer]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2002. Web. 19 May 2011. doi:10.3886/ICPSR03414
Bibliography style (based on documentation for books):
Milberger, Sharon. Evaluation of Violence Against Women With Physical Disabilities in Michigan, 2000-2001. ICPSR version. Detroit: Wayne State University, 2002. Distributed by Ann Arbor, MI: Inter-University Consortium for Political and Social Research, 2002. doi:10.3886/ICPSR03414.
Author-Date style:
Milberger, Sharon. 2002. Evaluation of Violence Against Women With Physical Disabilities in Michigan, 2000-2001. ICPSR version. Detroit: Wayne State University. Distributed by Ann Arbor, MI: Inter-University Consortium for Political and Social Research. doi:10.3886/ICPSR03414.
For any questions about additional data sources or tools, or for any help with finding, analyzing, or visualizing your data, please contact:
Scott Mauldin
Data and Visualization Services Librarian
scott.mauldin@dukekunshan.edu.cn
