Hypotheses, in Research

The hypothetico-deductive method together with observing phenomena and collecting facts complete the dominant paradigm for scientific research. Hypotheses are tentative answers to problems that can be subjected to empirical testing. Hypotheses link abstract theories and sensory experience by operationalizing and connecting the ideas that underlie a research project with measurable variables and thus making sense of what is observed. The key characteristic of a hypothesis is that it is falsifiable; that is, it can be refuted if there is strong enough evidence contradicting what has been hypothesized. It is important to realize that the test of hypotheses is indirect, by determining whether the statements derived from the theory (that is, the hypotheses) are valid, rather than by addressing the status of the underlying theory directly.

Testing hypotheses thereby makes it possible to test scientific theories, which are sets of generalizations that are connected through deductive reasoning. All hypotheses should be deducible from or be used to deduce other statements contained in a theory. For a concept to be testable, two fundamental circumstances must be true.

First, the hypotheses should be clear conceptually and make sense to anyone who studies the field in which the hypotheses are generated. A fancy way of saying this is that the hypotheses should provide for the intersubjective transmissibility of knowledge. You can tell this has been achieved when other researchers agree on how to interpret the results of their studies using the same hypotheses.

Second, the concepts that are used in hypotheses must be defined operationally and be measurable, with minimal expenditure of resources. In short, if you can't measure it, you can't test it. For example, a study applying Astin's input-environment-output (I-E-O) model needs to be able to make clear distinctions between what variables count (a) as student input characteristics at the time they start college (e.g., high school grade point average, high school class rank, ACT or SAT scores, number of high school units of mathematics, or family income); (b) as environmental conditions they encounter in their higher-education experiences (such as whether they are in a learning community, receive supplemental instruction, or live on- or off-campus); and (c) as output variables (for instance, retention to the sophomore year, cumulative college grade point average, or graduation within 6 years).

In addition, each of these variables needs to be measurable and operationalized to be able to evaluate the hypothesized connections between student outcomes and what background the students bring with them into the campus environment, as well as how that environment mediates the connection between the input and output variables. For example, to determine whether participating in supplemental instruction makes a difference in whether students with lower ACT scores perform better in a freshman physics course, you need to have a pretty good idea of how much participation in supplemental instruction is likely to make a difference. Merely attending one session in a semester probably isn't going to do much, so a dichotomous measure of attending or not attending doesn't seem ideal. Instead, you might want to operationalize supplemental instruction participation as the proportion of all available sessions that each student attends.

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