The building blocks of hypotheses are variables.
A variable is anything that varies, changes, or has differences.
Something that never changes is called a constant. Variables that only have two extremes are called attributes. In social science research, we deal mostly with two types of variables:
independent and dependent. Independent variables are those things thought to be the cause or bring about change in other variables. Dependent variables are those things changed or affected by independent variables, sometimes through other variables. Independent variables always come before dependent variables in time and space.
Hypotheses are simply if-then sentences that can be categorized in certain logical forms, such as no difference (null hypothesis), associated difference, directionality of difference, and magnitude of difference. A good hypothesis implies all these in a single sentence, and the trick is to express them as briefly as possible and in simple English. Let's take a look at how this is done:
Differences in Variable A have no relationship to differences in Variable B (null hypothesis)
If Variable A changes, then Variable B changes, or
There is a relationship between Variable A and Variable B, orVariable A affects Variable B (all examples of associated difference, sometimes callednondirectional hypotheses)
If Variable A increases, then Variable B increases, orIf Variable A decreases, then Variable B decreases (both examples of directional hypotheses)
but you can also have inverse relationships, such asIf Variable A increases, then Variable B decreases, orIf Variable A decreases, then Variable B increases
If Variable A increases by 2 points (12% or whatever), then Variable B increases by 3 points (or whatever) (magnitude of difference)
and, you can also have directionality with magnitude in an inverse relationship, as in
If Variable A decreases by 5%, then Variable increases by 3%.
The point is that the more specific you make your hypotheses, the better. Not only may you be able to use more powerful statistics, but you will be engaging in what is called confirmatory research, instead of what is called exploratory research. The more your topic has been previously researched by others, the more it is expected that you will use confirmatory research.
Exploratory research is used only in previously uncharted areas, or by those who honestly don't know anything about their topic. You'll note that the last example above contains all the elements of previous examples, so you can always drop down to a less rigorous hypothesis, but you can't or shouldn't move up to a more specific one after you've already done your research.
Also, the null hypothesis remains unstated or implied as no differences, no matter how complex your other (called alternative) hypotheses get. Technically, all statistical tests are tests of the null hypothesis first, which is rejected in favor of degrees of confidence in the alternatives. An important part of the research process that goes along with hypothesis formulation is constructing your operational definitions. These are definitions of your variables for research purposes. They are important so others can understand and replicate your research.
Researchers need to define their variables very precisely, especially the dependent variables. If "crime" is your dependent variable, you need to be very precise about exactly what kind of crime you have in mind: violent crime; property crime; vice crime; etc.
