Data Across the Curriculum: Using Qualitative Data Analysis in Teaching Spanish

When Spanish Professor Pérez incorporates NVivo, a qualitative research tool, into her teaching of Spanish, she sees it as a way to prepare her students for their future careers. Based on the trajectory of the field, she believes that “the digital humanities are here to stay.” While she realizes that not every student that studies Spanish plans on a career in academia or as a Spanish teacher, she hopes that working with digital technology will prepare her students to adapt to a variety of digital research tools in a wide range of fields.

After learning about NVivo, Professor Pérez decided to try using the program in her own research on festival books. Her initial project included only a small number of texts; however, with NVivo’s capacity for large-scale comparison between digital texts, her project has expanded to include around 700 texts.

Once she was familiar with NVivo, Professor Pérez decided to include a short assignment using the program in her Spanish seminar focused on Miguel de Cervantes’ classic novel, Don Quijote.

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SaraSanders ‘14, the 2014-15 DASIL Post-Baccalaureate Fellow, gave an introductory workshop in the class, and Professor Pérez assigned three chapters of the Quijote to each small group of students to analyze digitally. Students then produced reports that included their analytical findings and reflections on NVivo’s usefulness.

So far, Professor Pérez has noted differences in how students respond to NVivo: the majority of her science-major students critiqued the program, wishing that it included detailed quantitative analysis, while humanities majors were usually complimentary. Eventually, she hopes to share further observations about the connection between digital technology and pedagogy at conferences and in a published article. As one of the first professors in Grinnell’s Spanish department to utilize digital analysis in her classes, she also hopes that her experiences with the developing field of digital humanities will facilitate other professors’ explorations of new technologies.

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This past summer, Professor Pérez received a Steven Elkes Grant to develop the use of technology in a new course.  With the help of her research assistant, Alex Claycomb ’18, she is in the process of designing a course entitled “Designing Empire: Plazas, Power and Urban Planning in Habsburg Spain and its Colonies,” which integrates two new NVivo assignments as well as work with GIS and mapping.

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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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Exploring Racial Disparities in New York City’s Stop-and-Frisk Policies

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A comparison of the two maps above yields a surprising conclusion: African-Americans are much less likely to be arrested in areas with higher African-American populations!

One of the best examples of the use of statistics in policy research is in the controversy about New York City’s Stop-Question-and-Frisk policies, which give police officers the right to stop, search, or arrest any suspicious person with reasonable grounds for action. These policies were an effort to reduce crime rates, under the philosophy that stopping suspicious persons will prevent smaller crimes from escalating into more violent ones. In recent years, the NYPD had been under fire for alleged racial discrimination in their stops. Research on approximately 175,000 stops from January 1998 through March 1999, for example, showed that Blacks and Hispanics represented 51% and 33% of the stops, while only representing 26% and 24% of the New York City population respectively. The NYPD defended their practices, saying that since crimes mostly happen in black neighborhoods, it is natural that more black people would be found suspicious of crimes.

Using the stop-and-frisk dataset provided by the NYPD and 2010 census data, numbers were compiled into an interactive heat map of arrests directly related to the stop-and-frisk policy in New York City, as an aid to visualizing this disparity in race.

For each precinct, the visualization allows you to compare the racial make-up of the population with the proportion of arrests by race. For instance, this shows that in Precinct 104 while less than 2 % of the population in this precinct is African-Americans, over 15% of the arrests were of African-Americans.

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Evidence of racial disparity is clear.  African-Americans are consistently overrepresented in arrests compared to the population in each precinct.

The exception to this trend which was alluded to at the beginning of the post:  in areas with high African-American populations, the disparity disappears, and even reverses in a few precincts! Thus African-Americans are much less likely to be arrested in neighborhoods with high African-American populations.

Use this visualization to explore the trends in arrests due to the stop-and-frisk policies in New York:

Another visualization on stops and arrests in New York City can be accessed here. You can also go here for more information on these data visualizations.  These visualizations were created as part of a Grinnell College Mentored Advanced Project with Ying Long and Zachary Segall under the direction of Shonda Kuiper.

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Historical Approach to Climate Change

We often think about climate change as what is going to happen—what will the future world be like as a result of the changing climate? But in some ways, looking back at the past can also inform our understanding of climate change.

The Old Weather project does just that. Volunteers or “citizen scientists” can assist in the reading and transcribing historical documents (ship log books, mostly).
Looking back at the past allows for scientists to better model climate change. These log books from ships kept records of weather patterns, including data on temperatures and air pressure.
But why does a climate change project need help of citizen scientists?

By crowdsourcing the process of transcribing these historical documents, there is a smaller margin for mistakes. If multiple people transcribe the same document, errors can be caught and corrected. Additionally, it saves time.

For more information, visit the Old Weather project or read this NPR article.

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