Even More About Measurement 2

3. Who Is Predicting Whom?

 In general, we believe that most variables are continuous. People aren’t just smart and stupid, they vary on a continuous scale of intelligence. People are not just rich and poor, their earnings are better described by a continuous variable. Even drug abuse can be considered on a continuous scale (amount of drugs consumed).

Although the underlying variable is continuous, how the questions are worded can make the data appear discrete. Although years of school is a continuous variable, the question “have you ever gone to school?” would result in non-continuous (discrete) data. “Are you employed?” produces discrete information, but the number of days worked is continuous. It is possible to study a continuous variable in a way which makes it look discrete.

Continuous data, then, is a factor which can describe people on a large scale with small steps; discrete data is a continuous variable chopped up into parts (high, medium, low; fast, slow). A discrete variable with only two levels (e.g., yes, no) has its own name: dichotomous.

Traditionally, a differentiation is made between independent and dependent variables. It is a characterization based on locus of control. A dependent variable depends on the performance of the subjects. It is anything that we measure, observe or record. A dependent variable is an outcome. In contrast, an independent variable is independent of the subjects’ control. It is something the researcher selects, manipulates or induces.

The distinction is clearest in a traditional experiment: an independent variable is manipulated and a dependent variable is measured. Such a structure provides confidence in making inferences of causation. You stomp on a foot, the person says “ouch.” You don’t stomp on the foot and the person says nothing. The clear inference is that stomping on a foot causes a person to say “ouch.”

Notice that the independent variable is a discrete variable: stomp or not-stomp. It is not measured in continuous increments of pressure but is either there or absent. A variation of this theme is to select high, medium and low levels of an independent variable but, again, the independent variable is a discrete variable that is manipulated to see what impact it has on a continuous dependent variable.

In many areas of research, variables can not be directly manipulated, if at all. It would be ridiculous and unethical to assign children to abusive and non-abusive environments to see what impact the independent variable (abuse) has on the dependent variable (self-esteem, for instance). Consequently, the independence of many “independent variables” is in question.

Also, the more complicated models of human behavior include many variables, each impacting and being impacted upon by others. These experimental designs do not lend themselves to the independent-dependent variable distinction. Consequently, there is much to recommend the replacement of independent-dependent variables with the designation of predictor-criterion.

As an alternative to the independent-dependent variable characterization, the predictor-criterion designation provides more flexibility and more accurately depicts the relationships between model components.

Predictor-criterion is more flexible because it includes discrete and continuous variables. Although a discrete predictor (stomp or don’t stomp) is good, a continuous predictor would give more information about the amount of pressure needed before you said “ouch.” Also, when it is impossible to manipulate a situation (such as height, gender, or personality type), the term “independent” doesn’t aptly describe the variable. Predictors can be discrete (like a traditional independent variable) or continuous (like a correlation or regression). The predictor-criterion distinction also is a better description of the relationship between the variables. When subjects cannot be randomly assigned to treatments, the independence of variables is in question. It is clearer to note that a particular variable is being used as a predictor of another.

This approach accommodates both traditional experimental designs and complex correlational and causal modeling designs. In addition to simple discrete predictor and continuous criterion, the same nomenclature can be used for continuous predictors, moderator variables (ones that influence only part of a model), intervening variables (variables stuck between a predictor and a criterion) and suppressor variables (variables that filter out noise).

It is important to note that in an actual research study, any variable can be a predictor or a criterion. Annual income, level of education, self-esteem, intelligence-any could be used as a predictor of another. And each could be a criterion. Since the choice is arbitrary, the choice of model components and the hypothesized interrelationships should be determined by the theory being studied. The type of relationships between model components is determined by our theoretical questions

4. Who Are You Going To Study? Sometimes researchers want to study an entire population: the total number of subjects in a particular area of interest. As the focus of interest changes, the size of the population being studying changes. If you’re only interesting in what happens to you, the population of interest is 1.

Although we think of population as the number of people in a city or country, in research, a population is any group of interest. It can the number of people in a family, the number of dogs in a town, or the number of lights on a Christmas tree. Sometimes the population of interest is too large to measure directly. It is usually not convenient to talk to all of the people in a county or inspect all of the paper clips made daily. When the population is too large, a sample is chosen.

A selected part of a larger group is called a sample. Any group can be thought of as both a collection of smaller groups (a population) and a sample of a larger group. The students in Ms. Mendoza’s class are a population to her, a sample of all the 4th graders in the school, a sample of all of the students in the school district, etc.

Obviously, how a sample is chosen determines how well it represents the population. If the first 10 children who enter the class are selected, Ms. Mendoza might have excluded those who rode on the bus (if it ran late that day). A common practice is random selection. Each subject is selected at random from the convenient pool of subjects. Subjects are randomly selected from those students taking introductory psychology classes who want to participant in a study.

An alternative method is called stratification. Like rock walls, groups of people are composed of segments or layers. When there are certain subgroup comparisons you want to make (male-female, rich-poor or tall-medium-short, for example), subjects are randomly selected from within the categories. First, categories of interest are selected. Then, subjects are randomly selected within each category.

However, the best way to pick a sample is random sampling. If everyone in the population of interest has an equal opportunity to be selected, the sample is unlikely to be biased in favor of any particular subgroup. This is very seldom done…for very good reasons. Although researchers want to draw conclusions about the people in general, each person does not have an equal chance of being selected. People living in rural areas-and the disabled, elderly and very young-are generally not included in studies. When interpreting research results, it is important to remember the limitations of sampling.