Dissertators often struggle with theory. Virtually all dissertators must grapple with theory when they propose their research projects to their graduate school reviewers. Many whose proposals I’ve edited seem to think they can just ignore theory altogether. They seem to have a hard time choosing a theory, applying a theory… or even understanding why they need a theory in the first place.
The two fundamental theoretical approaches—deductive and inductive reasoning—offer dissertators two viewpoints to help them organize their thinking when they plan their projects.
Deductive reasoning
Deductive reasoning starts with a general theory about how something works. We collect observations to confirm the theory. It’s a birds-eye view of your topic.
For example, we can start with a theory, like this:
PROPOSED THEORY: Dissertators who don’t get enough sleep are cranky and dissatisfied with their doctoral experience.
Using that theory, we write a statement (hypothesis) that we can test: “Getting at least 8 hours of sleep a night significantly reduces dissertator dissatisfaction.” Then we collect observations of dissertators who get 8 hours of sleep per night and compare them to the data for dissertators who don’t. I bet the theory is confirmed: Dissertators who don’t get enough sleep act a lot like cranky teenagers who don’t want to go to school.
What can go wrong? The main drawback with research approaches that use deductive reasoning is the fact that the entire research project depends on the validity of the original theory or premise. The premise is assumed to be true. If the premise is incorrect, your conclusions may be valid, based on your theory, but only for that sample. Generalizing or transferring your conclusions to some other population or setting may be impossible.
For example, you might assume that toy preferences among six-year-olds are determined by gender.
PROPOSED THEORY: Girls prefer dolls and boys prefer toy trucks.
If you think this, you wouldn’t be alone: the toy industry has assumed this theory for years. Children may not know much about theory, but they know what they like. With two brothers, I had many opportunities to enjoy toy trucks, racing cars, little green army men, and cap guns. However, I also had my share of Barbies and troll dolls. Preferences vary by child, but overall, apparently, there is evidence to confirm the theory that children’s toy preferences are gender-typed from an early age. See here and here. But not all researchers have agreed: see here.
Your study of 50 six-year-olds may confirm or disconfirm this theory, thus adding to the body of knowledge on the topic and helping parents feel a little less guilty about watching their little girls grab for Barbies and their little boys grab for Legos.
For more on deductive reasoning, visit one of my favorite websites, changing minds.
Inductive reasoning
Inductive reasoning works from the specific and generates a theory from the observations. We observe some phenomenon, analyze the observation data, draw some inferences about it (our theory), and then collect more data to (we hope) confirm our conclusions.
- Observe
- Analyze
- Infer
- Confirm
Instead of starting with a theory, we collect observations of a phenomenon and build a theory from the ground up. That’s why grounded theory approaches used in qualitative research are considered inductive.
For example, you notice dissertators in the School of Education seem to sit around the library and cry a lot. You wonder what’s up. You talk to 10 dissertators from different programs and find some similarities and differences in their responses. Based on these patterns, you formulate a tentative hypothesis:
PROPOSED THEORY: Education dissertators worry more about everything, compared to dissertators in the business or computer departments, who usually worry about nothing.
Your data led you to generate this proposed theory. Thus, out of your data comes a broad theory about the tenderhearted education dissertator. You didn’t start with a theory: you got down and dirty with the data and the theory emerged from the patterns and themes you extracted from your data.
What can go wrong? The main issue with inductive reasoning approaches is that data collection can be an open-ended process. This is primarily a concern among qualitative dissertators. You can’t know ahead of time how big your sample needs to be, because you plan to collect data until you reach data saturation—that point at which new data generate no new insights.
That might sound like fun to you, but it’s a red flag for your Chair, Committee, and grad school reviewers. They are nervous enough letting a novice researcher collect data from human subjects: They know what can happen when overly enthusiastic interviewers go off the rails. What do I mean? Think about it: Pumped up (desperate) dissertators may badger, cajole, and otherwise manipulate their subjects into talking, thus injecting bias in their data, and setting up potential IRB issues for the institution that gave these dissertators permission to conduct research. That is why the idea of giving you a blank check to talk to an unknown number of people makes IRB reviewers shiver in their boots.
For more on inductive reasoning, visit changing minds.
Which approach should you use?
Most dissertators’ projects fall into the category of deductive reasoning. They start with a theory, collect some data, and see how their findings confirm or disconfirm the theory. This time-tested approach is safe and familiar, has lots of support in the literature, and your reviewers know and understand it. You are more likely to get your dissertation proposal approved if you use a deductive reasoning approach.
However, for all you off-the-beaten-path dissertators, if you can convince your Chair and Committee members that you can implement it successfully, the inductive approach is for you.
Misusing theory (or not using theory at all) is one of the reasons you might fail to get your dissertation proposal approved. If you need some help with your proposal, check out my book, available now through Amazon in both print and Kindle formats.
