The following are general scenarios / thoughts / recommendations on choosing a topic for a dissertation. Note that each recommendation has exceptions -- without exception!
Examples:
A very narrow topic may be easier and quicker to execute, but you can get bored, experience La Nausée (i.e., become depressed), or experience a feeling of emptiness ("so what?"). Example: redo a small group experiment with the door to the lab open rather than closed.
There is no need to be anxious about the length of your dissertation. Once you start writing and you add the tables and the figures and the references and the footnotes and the appendices, before you know it you will have a huge document.
This may simply mean keeping an open mind and avoiding premature conclusions. If one sets out to prove that variable X interacts with variable Z to cause variable Y, what does one do if the interaction is non significant? Change topic, drop out of graduate school, beat up your dissertation adviser, start drinking heavily...? Better to define topic as a study of the interrelationships of X, Y, and Z, without committing to a specific conclusion in advance. E.g., in a paper on home cleaning services I once read where the author was determined to show that the power relations described by Hochschild were at work for the new cleaning services, even though her data showed exactly the opposite (relations between cleaning services and customers were essentially impersonal and bureaucratic).
People sometimes get too heavily invested in a complicated, difficult model, and eventually discouraged when their reciprocal effects cannot be estimated or their modifications indices tell them to correlate more errors. Keep it simple! (See next point.)
For example, define a topic as "determinants of interracial and interethnic dating relations among adolescents", not "a LISREL model of interracial and interethnic dating relations among adolescents". AFTER you are done with your research you may want to use the "LISREL model..." title, but it is better to keep your methodological options open beforehand.
In my research with Art Alderson we hit on the idea that statistical techniques for unbalanced pooled time series of cross sections (i.e., unequal numbers of observations per unit over time) were ideally suited to analyzing cross national data on income inequality. We were able to produce several successful papers out of that realization. Art used these techniques in his dissertation.
If, that is, you have a straightforward topic, you use existing data, and you don't do too many things besides you dissertation. E.g., Biz Pressler-Marshall's dissertation on intergenerational transmission of attitudes on gender roles was done in one year, a period during which she also had her first baby! In general, try to avoid collecting your own data!
Being engaged may be a powerful motivation for research. But on the other hand it is crucial to be able to look at a topic dispassionately. If one sets out to prove a predetermined conclusion preferred on external grounds one runs the risk of being empirically wrong and not being able to cope with this, or of distorting the importance of a topic out of reasonable proportion. Examples: a student active in a battered women shelter studying the decisions of judges to make the mother or father legal guardian of children, with the theory that judges systematically decide in favor of the father; deciding to study the gay-lesbian community in the wake of discovering one's own homosexuality; deciding to study AA as a recently sober alcoholic.
Last modified 16 Oct 2007