Five Big Words
Research involves an eclectic blending of an enormous range of skills and activities. To be a good social researcher, you have to be able to work well with a wide variety of people, understand the specific methods used to conduct research, understand the subject that you are studying, be able to convince someone to give you the funds to study it, stay on track and on schedule, speak and write persuasively, and on and on.
Here, I want to introduce you to five terms that I think help to describe some of the key aspects of contemporary social research. (This list is not exhaustive. It's really just the first five terms that came into my mind when I was thinking about this and thinking about how I might be able to impress someone with really big/complex words to describe fairly straightforward concepts).
I present the first two terms -- theoretical and empirical -- together because they are often contrasted with each other. Social research is theoretical, meaning that much of it is concerned with developing, exploring or testing the theories or ideas that social researchers have about how the world operates. But it is also empirical, meaning that it is based on observations and measurements of reality -- on what we perceive of the world around us. You can even think of most research as a blending of these two terms -- a comparison of our theories about how the world operates with our observations of its operation.
The next term -- nomothetic -- comes (I think) from the writings of the psychologist Gordon Allport. Nomothetic refers to laws or rules that pertain to the general case (nomos in Greek) and is contrasted with the term "idiographic" which refers to laws or rules that relate to individuals (idios means 'self' or 'characteristic of an individual ' in Greek). In any event, the point here is that most social research is concerned with the nomothetic -- the general case -- rather than the individual. We often study individuals, but usually we are interested in generalizing to more than just the individual.
In our post-positivist view of science, we no longer regard certainty as attainable. Thus, the fourth big word that describes much contemporary social research is probabilistic, or based on probabilities. The inferences that we make in social research have probabilities associated with them -- they are seldom meant to be considered covering laws that pertain to all cases. Part of the reason we have seen statistics become so dominant in social research is that it allows us to estimate probabilities for the situations we study.
The last term I want to introduce is causal. You've got to be very careful with this term. Note that it is spelledcausal not casual. You'll really be embarrassed if you write about the "casual hypothesis" in your study! The term causal means that most social research is interested (at some point) in looking at cause-effect relationships. This doesn't mean that most studies actually study cause-effect relationships. There are some studies that simply observe -- for instance, surveys that seek to describe the percent of people holding a particular opinion. And, there are many studies that explore relationships -- for example, studies that attempt to see whether there is a relationship between gender and salary. Probably the vast majority of applied social research consists of these descriptive and correlational studies. So why am I talking about causal studies? Because for most social sciences, it is important that we go beyond just looking at the world or looking at relationships. We would like to be able to change the world, to improve it and eliminate some of its major problems. If we want to change the world (especially if we want to do this in an organized, scientific way), we are automatically interested in causal relationships -- ones that tell us how our causes (e.g., programs, treatments) affect the outcomes of interest.
Types of Questions
There are three basic types of questions that research projects can address:
1. Descriptive.When a study is designed primarily to describe what is going on or what exists. Public opinion polls that seek only to describe the proportion of people who hold various opinions are primarily descriptive in nature. For instance, if we want to know what percent of the population would vote for a Democratic or a Republican in the next presidential election, we are simply interested in describing something.
2. Relational.When a study is designed to look at the relationships between two or more variables. A public opinion poll that compares what proportion of males and females say they would vote for a Democratic or a Republican candidate in the next presidential election is essentially studying the relationship between gender and voting preference.
3. Causal.When a study is designed to determine whether one or more variables (e.g., a program or treatment variable) causes or affects one or more outcome variables. If we did a public opinion poll to try to determine whether a recent political advertising campaign changed voter preferences, we would essentially be studying whether the campaign (cause) changed the proportion of voters who would vote Democratic or Republican (effect).
The three question types can be viewed as cumulative. That is, a relational study assumes that you can first describe (by measuring or observing) each of the variables you are trying to relate. And, a causal study assumes that you can describe both the cause and effect variables and that you can show that they are related to each other. Causal studies are probably the most demanding of the three.
Time in Research
Time is an important element of any research design, and here I want to introduce one of the most fundamental distinctions in research design nomenclature: cross-sectional versus longitudinal studies. Across-sectional study is one that takes place at a single point in time. In effect, we are taking a 'slice' or cross-section of whatever it is we're observing or measuring. A longitudinal study is one that takes place over time -- we have at least two (and often more) waves of measurement in a longitudinal design.
A further distinction is made between two types of longitudinal designs: repeated measures and time series. There is no universally agreed upon rule for distinguishing these two terms, but in general, if you have two or a few waves of measurement, you are using a repeated measures design. If you have many waves of measurement over time, you have a time series. How many is 'many'? Usually, we wouldn't use the term time series unless we had at least twenty waves of measurement, and often far more. Sometimes the way we distinguish these is with the analysis methods we would use. Time series analysis requires that you have at least twenty or so observations. Repeated measures analyses (like repeated measures ANOVA) aren't often used with as many as twenty waves of measurement.
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