How Post-Secondary Journalism Educators Teach Advanced CAR Data Analysis Skills in the Digital Age1
Yarnall, Louise, Johnson, J. T., Rinne, Luke, Ranney, Michael Andrew, Journalism & Mass Communication Educator
Survey responses from 232 journalism educators in 33 nations were analyzed for descriptions of how they have taught a subset of the most pedagogically challenging computer-assisted reporting (CAR) skills-advanced data analysis. Respondents' programs were sorted into three instructional groupings: (1) Comprehensive programs offering coherent curricula for learning three basic and six advanced analytic reporting competencies, (2) mixed adoption programs that make data analytic learning optional and student directed, and (3) lagging programs that provide weak learning opportunities. We also statistically address U.S. versus non-U.S. contrasts, and features of U.S. programs offering analytic training also are statistically addressed. Barriers to expanding such training are discussed.
Philip Meyer noted that computerassisted reporting (CAR) has "come to apply to such a wide variety of skills, from database searching to statistical analysis, that it needs its elements specified and standards set"2-underscoring the need for a clearer conceptualization of CAR skills taught in journalism education. Studies tracking CAR instructional trends have traditionally measured the frequency of various research activities involving computer technology. Research activities have included basic searches for background articles through "the Internet, CD-ROMS, commercial online databases, newspaper morgues or archives,"3 and data analysis skills such as constructing relational databases and conducting statistical analyses.4 Yet these two types of CAR skills-colloquially, "search" versus "analysis"-differ in complexity and instructional demands. Past research indicates key cognitive distinctions between CAR skills used for searching textual databases such as LexisNexis and those used for setting up and analyzing data. For example, searching archival databases, commonly used to check facts and develop story context, requires the systematic skills of database navigation and clear query formation.5 By contrast, setting up a useful relational database of campaign contributions or school test scores and analyzing such data involve other skills: hypothesis formation, understanding relevant quantitative variables, data cleaning, and tabulation.6
Reflecting this underlying conceptual distinction, studies showed marked differences in the availability of journalism courses in these two types of CAR skills. While 92% of journalism programs train students to conduct Internet searches, only half teach spreadsheet and database software skills.7 Even the search offerings are cursory at best: Only 12% of programs offer "multiple" research skills courses covering various forms of information search.8 Recent program shifts toward news media convergence represent a fresh challenge to improved instruction in data analysis. Such convergence imposes even more technical training requirements9 on faculty and students, and one study indicates such pressures might be perceived as diluting the depth of reportorial training.10 Such trends have led some CAR scholars to voice a familiar criticism that post-secondary journalism training has become overly oriented to craft, rather than profession.11 Although taught less frequently to journalists than search skills, data analytic CAR skills are widely recognized as important. Data analysis often distinguishes the most celebrated journalistic work,12 and such skills serve as an important intellectual foundation for journalistic skepticism and interviewing.13 These skills have long been considered underdeveloped among journalists,14 so professional institutes15 and accreditation agencies16 call for greater numeracy/analysis to be taught by journalism educators.17 To foster more of such instruction, CAR scholars suggest teaching data analysis skills in ways that are relevant to journalists' professional work and critical thinking dispositions.18
Teaching data analysis skills has been termed "daunting,"19 and data analysis course adoptions were largely hindered by a lack of qualified faculty. …