7.1 What makes a good term paper

In your term paper, your task is to develop an interesting research question, find literature about a linguistic phenomenon, and extract data that you then analyze and interpret.

  1. Form: A good paper adheres to general conventions for writing papers (see below), and also linguistic conventions (cf. tip of the day #3).

  2. Language: A good paper is written in an academic style. The more academic language you have read, the easier this will be for you to emulate. Of course, you should also follow proper spelling and punctuation conventions. Use clear and concise language and build up your arguments logically and easy to follow.

  3. Terminology: Naturally, you should use linguistic terminology correctly, i.e. in accordance with convention. One of the most common mistakes, however, is not identifying the right places to use terminology, which is often a sign of bad literature research or a lack of linguistic knowledge. If a structure has a name in linguistics, use it. For example, an adverb referring to time is a temporal adverb; an adjective appearing in front of a noun is an attributive adjective, etc…

  4. Operationalization: You need be able to make the linguistic concepts you discuss measurable. In most cases, this comes down to the question of, “how can I count occurrences of x.” If you use counts, you need to make sure these counts represent your phenomenon. If you code data, you need to take decisions that are conceptually motivated.

  5. Methodology: Your paper should make use of the empirical methods we have learned over the course of this semester. A good paper not only gathers valid corpus data reproducibly, but also describes them with the right metrics. An excellent paper is also aware of statistical significance.

  6. Line of argument: A good paper builds up a compelling line of argument that is aware of limitations, without sacrificing the meaningfulness of the study. Common mistakes are on both extreme ends of a scale: either completely refuting the validity of the applied method or data; or over-generalizing results and accepting a hypothesis without sufficient evidence.