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Software Review: Tableau as a Teaching Tool

Tableau is unique and a valuable teaching tool because it provides an easy interface for the creation of charts, graphs and even maps.  Students can explore data in sophisticated ways with only a short training session.  Even better, as students they can get free licenses for the software, allowing faculty to use it for classes without ensuing large financial commitments.

A map showing fatality and even types of different violent events in Africa

What sets Tableau apart from other data visualization or business intelligence software is its intuitive, user-friendly drag-and-drop interface. For more sophisticated applications this is supplemented by a variety of easy to understand menus. By using contextual menus and panels instead of typing in code, Tableau lowers the learning curve needed to create visualizations. For example, creating a line graph or a map is as easy as selecting the variables in question and selecting the appropriate type of visualization.

Classic tables like the one below are easy to construct and can also be augmented with color-coded hotspot analyses.

A highlight table showing the number of violent events happening in Egypt, Libya, South Sudan, and Sudan broken down by Country and Event Type

Tableau provides the opportunity to construct data visualizations that are more complex than those generated by most traditional statistical packages.  For example, the graphic below compares the number of conflicts over time for four North African countries in a fairly normal plot, but add an additional variable, the number of fatalities by varying line thickness.

A line graph showing the trend of the number of violent events in 4 African countries (Egypt, Libya, South Sudan, and Sudan) between 1997 and 2015. The thickness of the lines represent number of fatalities.

For classes working with data, Tableau presents a significant opportunity for instructors to integrate more data into the classroom, especially with students who might not have experience with more advanced statistical software. Making it easier for students to explore and understand data, as well as to ask their own questions through investigative learning, encourages them to gain a deeper appreciation for data as it relates to their discipline. In fact, as of the time of writing, Tableau is currently being successfully used in several of our classes at Grinnell College.

However, Tableau does have its drawbacks. In particular, visualizations created with Tableau are not as customizable as more powerful languages such as R or Javascript. In addition, Tableau is not created for data analysis.  It is a data visualization tool, not a statistical package. Another small downside is that data entered into Tableau must be formatted in a specific way.  While Tableau is able to do some data manipulation, spreadsheet programs like Excel are much easier for this. So, Tableau’s role in classrooms or in research might only be restricted to surface-level explorations of the data in question. Despite this limitation, Tableau remains a tool with great potential, especially in the possibilities it presents to the user in creating quick and easy visualizations.

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Student Spotlight: Racial Bias in the NYPD Stop-and-frisk Policy

Donald Trump recently came out in favor of an old New York Police Department’s (NYPD) “stop-and-frisk” policy that allowed police officers to stop, question and frisk individuals for weapons or illegal items. This policy was under harsh criticism for racial profiling and was declared in violation of the constitution by a federal judge in 2013.

An earlier post by Krit Petrachainan showed a potential racial discrimination against African-Americans within different precincts. Expanding on this topic, we decided to look at data in 2014, one year after the policy had been reformed, but when major official policy changes had not yet taken place.

More specifically, this study examined whether race (Black or White) influenced the chance of being frisked after being stopped in NYC in 2014 after taking physical appearance, population distribution among different race suspect, and suspected crime types into account.

2014 Data From NYPD and Study Results

For this study, we used the 2014 Stop, Question and Frisked dataset retrieved from the city of New York Police Department. After data cleaning, the final dataset has 22054 observations. To address our research question, we built a logistic regression model and ran a drop-in-deviance test to determine the importance of Race variable in our model.

Our results suggest that after the suspect is stopped, race does not significantly influence the chance of being frisked in NYC in 2014. A drop-in-deviance tests after creating a logistic regression model predicting the likelihood of being frisked gave a G-statistic of 8.99, and corresponding p-value of 0.061. This marginally significant p-value shows we do not have enough evidence to conclude that adding terms associated with Race improves the predictive power of the model.

Logistic regression plot predicting probability of being frisked from precinct population Black, compared across race

Figure 1. Logistic regression plot predicting probability of being frisked from precinct population Black, compared across race

To better visualize the relationship in interactions between race and other variables, we created logistic regression plots predicting the probability of being frisked from either Black Pop or Age, and bar charts comparing proportion of suspects frisked across sex and race.

Interestingly, given that the suspects are stopped, as the precinct proportion of Blacks increases, both Black and White suspects are more likely to be frisked. Furthermore, this trend is more profound for Black than White suspects (Figure 1).

Additionally, young Black suspects are much more likely than their White counterparts to be frisked, given that they are stopped. This difference diminishes as suspect age increases (Figure 2).

Logistic regression plot predicting probability of being frisked from age, compared across age

Figure 2. Logistic regression plot predicting probability of being frisked from age, compared across age

Finally, male suspects are much more likely to be frisked than females, given they are stopped (Figure 3). However, the bar charts indicate that the effect of race on the probability of being frisked does not depend on gender.

Proportion frisked by race, compared across sex

Figure 3. Proportion frisked by race, compared across sex

Is stop-and-frisk prone to racial bias?

Our results suggest that given that the suspect is stopped, after taking other external factors into account, race does not significantly influence the chance of being frisked in NYC in 2014. However, after looking at relationships between race and precinct population Black, age, and sex, there is a possibility that the NYPD “stop-and-frisk” practices are prone to racism, posing threat to minority citizens in NYC. It is crucial that the NYPD continue to evaluate its “stop-and-frisk” policy and make appropriate changes to the policy and/or police officer training in order to prevent racial profiling at any level of investigation.

*** This study by Linh Pham, Takahiro Omura, and Han Trinh won 2nd place in the 2016 Undergraduate Class Project Competition (USCLAP).

Check out the 2016 USCLAP Winners here.

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Software Review: NVivo as a Teaching Tool

nvivo-logoFor the past few weeks, DASIL has been publishing a series of blog posts comparing the two presidential candidates this year – Hillary Clinton and Donald Trump – using NVivo, a text analysis software. Given the increasing demand for qualitative data analysis in academic research and teaching, this blog post will discuss the strengths and weaknesses of NVivo as a teaching tool in qualitative analysis.

Efficiency and reliability

Using software like NVivo in content analysis can add rigor to qualitative research. Doing word search or coding using NVivo will produce more reliable results than doing so manually since the software rules out human error. Furthermore, NVivo proves to be really useful with large data sets – it would be extremely time-consuming to code hundreds of documents by hand with a highlighter pen.

Ease of use

NVivo is relatively simple to use. Users can import documents directly from word processing packages in various forms, including Word documents and pdfs, and code these documents easily on screen via the point-and-click system. Teachers and students can quickly become proficient in use of this software.

NVivo and social media

NVivo allows users to import Tweets, Facebook posts, and Youtube comments and incorporate them as part of their data. Given the rise of social media and increased interest in studying its impact on our society, this capability of NVivo may become more heavily employed.

Segmenting and identifying patterns 

NVivo allows users to create clusters of nodes and organize their data into categories and themes, making it easy for researchers to identify patterns. At the same time, the use of word clouds and cluster analysis also provides insight into prevailing themes and topics across data sets.

Limitations

While NVivo seems to be a great software that serves to provide a reliable, general picture of the data, it is important to be aware of its limitations. It may be tempting to limit the data analysis process to automatic word searches that yield a list of nodes and themes. While it is alluring to do so, in-depth analyses and critical thinking skill are needed for meaningful data analysis.

Although it is possible to search for particular words and derivations of those words, various ways in which ideas are expressed make it difficult to find all instances of a particular usage of words or ideas. Manual searches and evaluation of automatic word searches help to ensure that the data are, in fact, thoroughly examined.

Once individual themes in a data set are found, NVivo doesn’t provides tools to map out how these themes relate to one another, making it difficult to visualize the inter-relationships of the nodes and topics across data sets. Users need to think critically about ways in which these themes emerge and relate to each other to gain a deeper understanding of the data.

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Meet Yujing Cao, DASIL’s new data scientist!

This year, DASIL welcomes a new member of our staff, Yujing Cao, who will be serving as the new data scientist. In her position at DASIL, Yujing will bring her expertise in data analysis and visualization to further expand DASIL’s capability to help students and faculty members integrate data analysis into research and classroom work.  In today’s big data era, enormous quantities of data are available, and Yujing will help Grinnell students and faculty explore them.

Yujing Cao is excited about joining DASIL and bringing a new level of data analysis to faculty research and teaching!

Yujing Cao is excited about joining DASIL and bringing a new level of data analysis to faculty research and teaching!

Originally from China, Yujing got her bachelor degree in Statistics from Anhui University. Her passion for data science led her to a PhD program in Statistics at the University of Texas at Dallas, where she obtained her degree in 2016. Her research was on graphical modeling of biological pathways in genomic studies. She is also interested in network analysis, machine learning, and trying different tools for data visualization. In her spare time, she enjoys reading, hiking, and exercising.

Yujing was excited about the position at Grinnell because of her strong interests in teaching and in data visualization. As she puts it:

“I wanted to look for a position which provides opportunities to create interesting data visualizations along with other data analysis work. I love using graphs to tell stories behind different data sets.

Working environment is another factor that led to my decision to come to Grinnell.  I strongly resonate with the core values of a liberal arts education. At Grinnell College, I can work in an academic environment helping faculty and students while promoting the use of data in research and learning.

Yujing also discusses a number of skills crucial to succeed in the field of data science. Data science is an interdisciplinary field requiring knowledge from mathematics, statistics, data mining and machine learning. Statistical knowledge and knowledge from other fields can help form good questions and seek direction, while programming skills (e.g. joining data sets and visualizing data) are needed for implementing our ideas. To be a good data scientist, you should possess strong programming and analytical skills.”

According to Yujing, “One of the most important qualities for any data scientist is curiosity. Curiosity encourages us to dig in and make interesting discoveries about data. Also, good communication skills can make a great data scientist. You should be able to clearly articulate your results and the implications of your findings to others, including other data scientists and people who don’t share a similar background.”

Her tip for students interested in a career in data science is to keep an open mind to learn from different disciplines and sharpen your programming skills.  In addition, a student who is interested in being a data scientist should take advantage of any opportunities to get hands-on projects that use real data.”

Faculty or students interested in meeting with Yujing should drop by DASIL(ARH 130) or her office (Goodnow 103) or contact her via email at caoyujin@grinnell.edu for an appointment.

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Data Across the Curriculum: Qualitative Literary Analysis in the Humanities

This semester, students in Professors James Lee and Erik Simpson’s special topic seminar, “Milton, Blake, and Frankenstein,” will use NVivo, a software for qualitative literary analysis, to create word trees visualizing the use of the word “sublime” in Milton’s work. This is an outgrowth of teaching that Lee began in 2013 when, as DASIL’s first Faculty Fellow, he designed a seminar on Shakespeare and Renaissance literature that used NVivo to investigate first the corpus of Shakespeare’s work and then over 20,000 documents from the Early English Books Online (You can see previous DASIL blog posts written by Prof. Lee about this research here and here).

Lee’s classes use NVivo to visualize data and generate descriptive statistics about datasets that can be exported to other programs, such as Tableau (another software for data visualization). These programs are particularly useful to students because they provide a user interface, allowing students to manipulate data easily, without having to learn a new programming language.

Lee’s personal research incorporates some of the same methodologies that he teaches to his students in class. His current project, the Global Renaissance Project, is partially funded by a “Digital Bridges for Humanistic Inquiry” grant from the Mellon Foundation. It uses network analysis and topic modeling to examine discourses surrounding race in Renaissance texts. The figure below is a still of the prototype for the topic modeling aspect of the project, which identifies clusters of words with a disproportionately high probability of occurring together in text.

Screen Shot 2016-03-24 at 6.41.14 PM

So far, the project has revealed that Renaissance representations of race were centered on cultural, geographic, and commercial factors; race as a biological or physical concept emerged as a justification for English imperialism after the Renaissance. Lee currently collaborates with professors at the University of Iowa on a “linked reading” project that combines two databases to discover how networks of printers and publishing houses contributed to the Renaissance discourse on race.

For Lee, the biggest challenge presented by the integration of digital analysis into classes is changing his students’ mindsets. He observes that humanities students are often used to classes in which students and professors develop ideas through discussion. In contrast to discussion-based classes, working with the digital humanities can mean that students exert effort in a particular line of inquiry that doesn’t yield any concrete results.

The iterative process of data analysis can be frustrating, especially since students often don’t anticipate undertaking it in their humanities courses. Professor Lee hopes continuing to integrate digital humanities into classes like the seminar he is co-teaching this semester, will help to convert students’ frustration into a “tinkering mentality” so that students come prepared to continually adjust their hypotheses based on their analysis and visualization of the data.

To explore the Global Renaissance Project prototype, click here.

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