Data Across the Curriculum: The Explanatory Power of Data in Global Development & Geography

The field of geography is split into two camps: critical scholars, who are skeptical of data because they believe it silences certain voices within society and fails to explain process and context, and empirical scholars, who incorporate data to create empirical models that explain geographic concepts and trends.  Leif  Brottem, Assistant Professor of Political Science with a PhD in Geography, is a firm believer in the importance of both critical and empirical approaches.  Data analysis can compensate for and expand upon the limits of text and qualitative evidence. His focus on data analysis as a tool that illustrates narrative is evident in the work of each of his three classes, Introduction to Global Development Studies (GDS), Introduction to Geographical Analysis and Cartography, and Climate Change, Development, and Environment.

In his Introduction to Global Development Studies, for instance, Brottem utilizes infographics & charts to explain basic concepts, and utilizes data tools such as GapMinder to illustrate change over time and regional differences pertaining to a variety of development indicators. His students also complete two data analysis exercises as a part of the class: one exercise asks students to study the relationship between economic development and social development indicators, and the second has students explore different aspects of population dynamics such as carrying capacity, limits to growth and the determinants of population growth.

In Brottem’s Introduction to Geographical Analysis and Cartography course, students learn both the basic critical perspectives on how to evaluate maps and understand their overt and covert messages and practical techniques for making maps using Geographical Information Systems software.  Students complete in-class exercises and take-home labs that require creating data and using data to solve problems.

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Finally, in Climate Change, Development, and Environment, Brottem utilizes data analysis in the form of topic-modeling: students investigate textual trends in various sources, from tweets to scholarly articles, using the MALLET topic model package. In addition, his students also work with Nvivo to conduct further qualitative analysis, and GIS to visualize spatial trends.

Working with data builds data literacy, a marketable and necessary skill in the real world that Brottem says isn’t typically developed in a liberal arts settings. Building data literacy is especially important in his introductory classes, because he has students who wouldn’t otherwise be exposed to data, and aims to get them comfortable with using data and reduce their fears of data, numbers, and data analysis. Brottem strongly believes that data is a powerful explanatory tool that helps students think of different ways to look at the world and their studies, beyond theory.

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10 Suggestions for Making an Effective Poster

saracasson

Written papers are the traditional way to share research results at professional meetings, but poster sessions have been gaining popularity in many fields. Posters are particularly effective for sharing quantitative data, as they provide a good format for presenting data visualizations and allow readers to peruse the information at leisure.  For students they are a great teaching tool, as preparing a good poster also requires clear and concise writing.

Making a poster is easy, but making a really good poster is hard.  I have found the guidelines below helpful to students.  The most important piece of advice, however, is the one true for all writing—write, read and revise; write, read and revise; write, read and revise!

  1. Make your poster using PowerPoint. This will allow you to put in text via text boxes as well as to paste in charts, graphs, tables, maps, and pictures.  It is easy! To get your pictures and text boxes to line up consistently, use snap to grid.  In the Format tab choose Arrange>>Align and then Grid Setting. Select to view the grid and to snap to the grid.  You can set the grid size here as well.
  1. Use a single slide. In the Design Tab pick Page Setup, select custom, and then set the width and height to maximize your slide, given the locally-available paper size. At Grinnell the paper width available is 36”, so we set the width to 45” and the height to 36”.  Use “landscape” for your orientation.
  1. As in a written paper, have a descriptive title. Put the title (in 68 point type or larger) at the top of the poster.  Place your name and college affiliation in slightly smaller type immediately below it.
  1. The exact sections of the poster will vary some depending on the project, but include an abstract placed either under the title or in the upper left column.
  1. As in a written paper, be sure you have a good thesis and present it early in the poster, support it with evidence, then remind your audience of it as you conclude. Finish with a minimum of citations and acknowledgements in the lower right hand corner.
  1. Posters should read sequentially from the upper left, down the left column, then down the central column (if you have one) and finally down the right column. Alternative layouts are possible, but the order in which the poster is read must be obvious.
  1. Use a large font–a minimum of 28 point.
  1. Limit the number of words. Be concise and think of much of your text as captions for illustrations.
  1. Use lots of charts, graphs, maps, and other pictures. Be sure to label your figures and refer to them in the text.
  1. Make your poster attractive. Use color.  Pay attention to layout.  Do not have large empty areas.

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The #Holidays, According to Twitter

The holiday season is among us, and people are flocking to social media to share their thoughts about the holidays with friends, family, and followers. Using Nvivo, a text-analysis software, DASIL tracked tweets with the hashtags #Kwanzaa, #Hanukkah, and #Christmas published on Dec. 10th and earlier to create word clouds that demonstrate the top 100 words most frequently associated with each holiday.
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For Christmas, tweets highlight how highly commercialized the nature of the holiday has become. The most common words found alongside “Christmas” include “win,” “giveaway,” “luck,” “enter,” “chance,” and others, attesting to how advertisements for giveaways and contests to win prizes are dominating talk of Christmas in the Twittersphere. A handful of tweets reference the holiday’s secular traditions, with words like “decked,” “festive,” “tree,” and “lighting.”

Like Christmas tweets, Hanukkah tweets mention words relating to its traditions (“menorah,” “candles,” “lighting,” and “latkes”). Hanukkah tweets differ from Christmas tweets by centering instead on sentimental values and inclusivity, with words like “happy,” “everyone,” “celebrating,” and “family.” Some Hanukkah tweeters are also using the holiday to highlight political issues, with mentions of “Netanyahu,” Israel’s prime minister, as well as “police,” “western,” “border,” and “#muslim.” Surprisingly, both Hanukkah and Christmas tweets completely ignore their religious roots, with no mentions of words relating to the respective origins of each holiday.

The words associated with Kwanzaa, the week-long celebration of African heritage by the West African diaspora, are a grab-bag: words like “celebration,” “promoted,” “happy,” “family,” and “submissions” reveal a mixture of sentimental and commercial connotations. Interestingly, the “#blacklivesmatter” hashtag crops up as one common word associated with Kwanzaa, showing how Kwanzaa, like Hanukkah, is being used as a vehicle to spotlight social and political issues revolving around racial injustice.

All in all, the big takeaways from this short text analysis are that, of the three current holidays, Christmas is the most commercial.  Hannukkah celebrates family and inclusivity, but also has a political edge, while Kwanzaa has a less focused identity.

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Data Across the Curriculum: Integrating Data Analysis with Narrative in Political Science

From a pedagogical standpoint, Danielle Lussier, Assistant Professor of Political Science, stresses data as a tool for helping students approach problems from multiple perspectives. Working interactively with data allows them to better compare narratives and better understand the research process in both lower-level and upper-level material.

Political science is both a quantitative and qualitative field, so students at all levels of Lussier’s political science classes delve into both data types extensively and build data analytic skills as students progress in the major. Every class taught by Lussier involves data labs that draw on both cross-national data with countries as the unit of measure and on data with individuals as the unit of measure. The labs directly relate to readings, concepts, and/or countries that students study.

At the 100-level, students gain both an introduction to fundamental data concepts such as the construction and measurement of variables and to analytical computer programs like STATA, a statistical package, and ArcGIS, which analyses spatial data. The image below is of a GIS map her introductory political science students make in a data lab.

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At the 200-level, Lussier’s students delve into applied data analysis and write in-depth data reports that compare data analyses from the course readings to data analyses that students reconstruct and update from the readings.

At the 300-level, students get the opportunity to pose questions about class readings and use lab time to test their inquiries with actual data from the readings. In addition, Lussier assigns students research modules that allow them to create their own qualitative variables from cross-national data that they then transform into quantitative data, giving students the opportunity to apply the data skills they’ve accumulated in each course level.

The positive impact of incorporating data into classroom work is not lost on students. Students in all levels of her courses are widely receptive to data in coursework and have viewed working with data in her classes as an integral stepping stone to both academic and professional pursuits. Adam Lauretig ’13, the first Post-Baccalaureate Fellow for DASIL, was inspired by Lussier’s data-driven coursework to pursue more advanced courses in spatial statistics, and subsequently created visualizations like the interactive timeline map of historical coups d’etat. Additionally, many of her students have cited the research and data skills developed in her class work as marketable to employers and graduate programs.

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Understanding Population Estimates Based Upon Stratified Random Samples

When a researcher is interested in examining distinct subgroups within a population, it is common to use a stratified random sample to better represent the entire population. This method involves dividing the population of interest into several small subgroups (called strata) based on specific variables of interest and then taking a simple random sample from each of these smaller groups. To account for stratified random samples, weights are used to better estimate population parameters.

Many people fail to recognize that data from a stratified random sample should not treated as a simple random sample (SRS), as Kathy Kamp, Professor of Anthropology, mentions in an earlier blog post. The following example explains why it is important to treat stratified random samples and SRS differently.

In 2010, CBS and the New York Times conducted a national phone survey (a stratified random sample) of 1,087 subjects as part of “a continuing series of monthly surveys that solicit[ed] public opinion on a range of political and social issues” (ICPSR 33183, 2012 March 15). In addition to political preference, they gathered information on race, sex, age, and region of residence.

The figure below demonstrates how population estimates vary depending on the use of weights. The unweighted graph incorrectly overestimates the number of females in the democratic party (52% Democrat and 40% Republican). This leads to an incorrect overestimate of the number of democrats in the nation. However, when weights are properly incorporated into the analysis we see that the ratios are actually much closer (46% Democrat and 45% Republican).

 

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As demonstrated above, there is a difference between the weighted and unweighted graphs and resulting proportions. Specifically, the number and percent of Republican supporters increases when we take into account the weights. The weighted graph and proportions give a more accurate estimation of Political Preference by Sex in the population than the unweighted graph.

Try it on your own!

Through a summer MAP with Pam Fellers and Shonda Kuiper, we have created a Political Data app using this dataset. Follow this link in  to view the influence of weights on the population estimates for all the subgroups within this dataset. For example, select the X Axis Variable to be “Region” and the Y Axis Variable to be “Political Preference”. What do you notice about the weighted graph in comparison to the unweighted graph? You can also find datasets and several student lab activities giving details for proper estimation and testing for survey (weighted) data at this website.

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