Data Seeking Behavior of Economics Undergraduate Students: An Exploratory Study

By Garrison, Betty; Exner, Nina | Reference & User Services Quarterly, Winter 2018 | Go to article overview

Data Seeking Behavior of Economics Undergraduate Students: An Exploratory Study


Garrison, Betty, Exner, Nina, Reference & User Services Quarterly


This article investigates the information seeking behavior of undergraduate economics students to determine their effectiveness in locating data sets for a multiple regression analysis assignment and seeks to discover how students pursue the process of learning to find and use data. A study was conducted in fall and spring 2015 to find out (1) what influences affect students' ways of seeking data sets; and (2) what changes occur over the course of students' data search. The findings say that while only about 10% of students started with the library, either a library database or a librarian, nearly half eventually used the library in some form for this course project. The conclusion reached as a result of the survey was that undergraduates have widely varying data search concepts, that more of the students look for personal interest data than business discipline data, and that the searching part of economics students' first regression project can add a noticeable amount of time to the assignment before they can even get started working on the regression itself. Included are ideas for further research and ways to reach students before data searching gets frustrating, as well as thoughts on how to structure data search learning and how to use insights into student behaviors to overcome the reluctance of some faculty.

Data are everywhere. It is impossible to get through the day without hearing about so-called alternative facts with misused statistics, seeing an infographic or chart to explain a theory or trend, or studying data as proof of a concept, either on television news, online news, on Facebook, or on websites visited frequently. Quantification adds clarity to our often confusing, disorganized, and increasingly interdependent world.

In order for undergraduate students at university to be considered well-educated and highly employable for consideration of the best paying jobs in today's employment market, they must be data literate, "the ability to understand and use data effectively to inform decisions." (1)

Data skills are considered mandatory in many fields of employment. (2) Economists, for example, use data to offer solutions for problems in government, international trade, finance, the environment, agriculture, immigration, climate change, and more. Students pursuing an economics degree are required to show proficiency in working with data. As economists, following graduation, they will be dependent on those data skills to a greater degree than in many other professions.

A study was conducted in 2015 at Elon University, a midsized private university in the Piedmont region of North Carolina, with economics undergraduates in a required statistics course to find out how the students search for data sets on self-selected topics to complete a required regression analysis assignment. The authors hoped to find out what influences affect students' ways of seeking data sets, and what changes occur over the course of students' data research. Our intention was to better understand what affected how often students were successful finding the anticipated data set on their own and use that insight to understand ways to optimize data literacy interventions from a librarian.

BACKGROUND: THE IMPETUS FOR THE SURVEY

A few economics undergraduate students per semester find the business librarian's office, usually after stopping by the information service desk in the library to ask for help finding data sets for their class, ECO 203--Statistics for Decision-Making. A typical student usually begins the research interview by saying that he has already spent hours looking for relevant data sets, has been unsuccessful, and is hopeful the librarian can help him. Often the librarian can elucidate the information request and provide the resource needed in short order.

Data reference interviews require special skills. (3) Unlike most reference interactions, data reference interviews involve significant discussion back and forth between student and librarian regarding what data the student hopes to find, what he hopes to prove with it, and articulating the boundaries surrounding the types of data he requires, including time or date range, geographic location, whether data sets or statistics, the units of analysis, and the type of data, for example demographic or financial. …

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