Getting Started: Identifying Candidate Propositions #to full tutorial

To begin constructing a theory module from existing work comb related publications in search of basic, general, causal claims. Occasionally authors will list propositions explicitly, but more often the analyst will need to extract them from other writing. We recommend focusing especially on the theoretical sections of the writing, and extracting those sentences or paragraphs that appear to boil down and communicate the essence of the theory. Avoid statements that merely provide illustrations, opinions, reviews of previous work, discussion of implications, speculation about future directions, summaries of the approach or framework, and other non-essential material. While these can be interesting and useful for the analysis, it is unlikely they will yield useful propositions.

Propositions vs. Hypotheses #to full tutorial

Propositions are abstract and general statements, applicable across many different contexts. Hypotheses are statements asserting relationships between concrete instances of a theory’s abstract terms. Hypotheses may help the analyst to interpret ambiguous propositions, but ultimately we need to express propositions abstractly so they can generate hypotheses that apply in a wide variety of situations.

Propositions vs. Derivations #to full tutorial

Derivations are statements logically derived from a set of propositions. They rely on propositions for their terms and logical connectives, and so they also are abstract and general in nature.

Avoiding Non-causal Language #to full tutorial

One common mistake in proposition statements is the use of non-causal language. This can result in imprecise and unclear propositions. It is important that propositions state, instead of imply, causal relationships. And when stating propositions, the greater the level of precision the better.

A major benefit of formalizing theory is that it maximizes the value of empirical tests. When the rigor of empirical methods outstrip the theories under consideration data become less valuable. This is due to fundamental differences between theory testing and other non-scientific epistemologies like common sense assessments, forecasting efforts, and rote empiricism. Without good theory suitable empirical tests are difficult to design and at risk of misinterpretation or reframing.

Theories and their empirical tests are continuously interacting with each other. Well-articulated theories generate precise and fruitful testing opportunities, and quality empirical tests build confidence in the theory and point to potential new opportunities for refinement. The value of theory relies on evidence and vice versa.

Science must be shared and collective in nature. All else equal, theories which are communicable, logically sound, and parsimonious are superior. Those that suffer by these standards are fundamentally less successful at fulfilling their role, regardless of the empirical evidence surrounding them.

Keep It Simple! Maximizing Logical Parsimony #to full tutorial

Remember to always simplify, simplify, simplify! It is best to minimize the number of propositions in a module, and terms used in those propositions. This will ensure that the module is as clear as possible to readers. Propositions can be simplified by reducing the verbiage included in them, finding clearer wording, and using simpler language.

Arranging Propositions#to full tutorial

At this point in the process you should have a set of tentative logically consistent and parsimonious statements. Now they must be arranged into a logical argument. This may require some reordering of the statements because propositions are sometimes presented out of order.