{"id":3234,"date":"2024-10-24T09:10:41","date_gmt":"2024-10-24T13:10:41","guid":{"rendered":"https:\/\/krieger.jhu.edu\/writing-program\/?page_id=3234"},"modified":"2024-10-24T09:10:41","modified_gmt":"2024-10-24T13:10:41","slug":"writing-with-data","status":"publish","type":"page","link":"https:\/\/krieger.jhu.edu\/writing-program\/writing-in-the-majors\/teaching-writing\/writing-with-data\/","title":{"rendered":"Writing With Data"},"content":{"rendered":"\n

When we ask our students to collect, evaluate, and analyze data, our instructional focus often first falls on ensuring they have the tools they need to interpret<\/em> data. We teach students how to identify patterns, explore relationships, and assess comparisons.<\/p>\n\n\n\n

Yet moving from the ability to understand data representations<\/em> to the ability to effectively incorporate data <\/em>into an argument can mark a threshold concept<\/a> in data literacy<\/a>. This means that once a student learns how to incorporate data in an argument, it can permanently and dramatically change their perception of what data is and does\u2014often leading to more precise understanding and deeper critical thinking.<\/p>\n\n\n\n

Ensuring students can deploy data to suit their ends in multiple contexts and genres\u2014from research reports and presentations to editorials and policy briefs\u2014is thus a key task for instructors across disciplines. But communicating effectively with data is no easy task, even for the most experienced writers. Here are three common problems found in novice writing with (and about) data, and some tips for how to help students overcome them.<\/p>\n\n\n\n

Problem: Saying too little. Students think that data \u201cspeaks for themselves.\u201d<\/h2>\n\n\n\n

Solution: Help students identify their purpose\u2014and make it explicit.<\/strong><\/p>\n\n\n\n

The prospect of writing about data can sometimes overwhelm students. The technicalities involved with statistical evidence, for example, or the variety and richness of data visualizations<\/a> available, may make inexperienced writers feel lost in the weeds when they attempt to discuss their data. Rather than putting in the effort to get back on the right course, they might reasonably decide that the path of least resistance is to \u2018let the data speak for themselves.\u2019 This is a common problem in undergraduate writing, in which students include data in the text but ultimately say little, if anything, about what they think it means. <\/p>\n\n\n\n

Faced with this situation, instructors may find it useful to demonstrate to students that everything\u2019s an argument<\/a>\u2014even discussions of data and findings. Much writing in the social and natural sciences, of course, aims for a tone of impartiality with its interpretation of data. But this doesn\u2019t mean that there is no argument\u2014writers are always taking positions<\/a> as they decide what they want readers to take away from their text.<\/p>\n\n\n\n

Experienced writers know that, far from speaking for themselves, nearly all data can be interpreted multiple ways. Impressing on students the importance of articulating their interpretation as an implicit argument\u2014i.e., that their interpretation is correct\u2014can go a long way in rectifying this problem.<\/p>\n\n\n\n

A good first step may be to help students identify what their purpose<\/em> is. Getting a student to recognize that data is (or should be) included for a reason\u2014to present a pattern, for example\u2014can help them understand that an effective discussion must explicitly tell the reader what<\/em> that pattern is, and why<\/em> it matters.<\/p>\n\n\n\n

Problem: Saying too much. Students try to explain every datum.<\/h2>\n\n\n\n

Solution: Help them subordinate their evidence to their purpose.<\/strong><\/p>\n\n\n\n

Some students will err in the other direction, choosing to say too much<\/em> about their evidence\u2014including excessive information or explaining every datum. This may lead to a discussion that is irrelevant (at best) or incoherent (at worst).<\/p>\n\n\n\n

To find the happy medium between \u201csaying too little\u201d and \u201csaying too much,\u201d instructors should help students lean into their purpose<\/em>. When selecting data or figures to include and discuss, students should do so in a way that serves the broader goals of the assignment<\/a>. Including a piece of evidence for its own sake will distract readers and muddy the analysis.<\/p>\n\n\n\n

Giving students examples of various uses of data and figures that are purpose-driven is a useful strategy. These purposes include, for example:<\/p>\n\n\n\n