Description
Statistics in Plain English, Third Edition
Introduction to Social Science Research Principles and Terminology
When I was in graduate school, one of my statistics professors often repeated what passes, in statistics, for a joke: “If this is all Greek to you, well that’s good.” Unfortunately, most of the class was so lost we didn’t even get the joke. The world of statistics and research in the social sciences, like any specialized field, has its own terminology, language, and conventions. In this chapter, I review some of the fundamental research principles and terminology including the distinction between samples and populations, methods of sampling, types of variables, and the distinction between inferential and descriptive statistics. Finally, I provide a brief word about different types of research designs.
Populations and Samples, Statistics and Parameters
A population is an individual or group that represents all the members of a certain group or category of interest. A sample is a subset drawn from the larger population (see Figure 1.1). For example, suppose that I wanted to know the average income of the current full-time, tenured faculty at Harvard. There are two ways that I could find this average. First, I could get a list of every full-time, tenured faculty member at Harvard and find out the annual income of each member on this list. Because this list contains every member of the group that I am interested in, it can be considered a population. If I were to collect these data and calculate the mean, I would have generated a parameter, because a parameter is a value generated from, or applied to, a population. Another way to generate the mean income of the tenured faculty at Harvard would be to randomly select a subset of faculty names from my list and calculate the average income of this subset. The subset is known as a sample (in this case it is a random sample), and the mean that I generate from this sample is a type of statistic. Statistics are values derived from sample data, whereas parameters are values that are either derived from or applied to population data.
It is important to keep a couple of things in mind about samples and populations. First, a population does not need to be large to count as a population. For example, if I wanted to know the average height of the students in my statistics class this term, then all of the members of the class (collectively) would comprise the population. If my class only has five students in it, then my population only has five cases. Second, populations (and samples) do not have to include people. For example, suppose I want to know the average age of the dogs that visited a veterinary clinic in the last year. The population in this study is made up of dogs, not people. Similarly, I may want to know the total amount of carbon monoxide produced by Ford vehicles that were assembled in the United States during 2005. In this example, my population is cars, but not all cars—it is limited to Ford cars, and only those actually assembled in a single country during a single calendar year.