The following module explains the different types of variables in quantitative research and discusses operational definitions of variables.
Define and explain the types of variables in a quantitative research project.
Identify the variables in research examples.
Define and explain operational definitions and provide an example.
The goal of quantitative research is to examine the relationships between variables. A variable is a characteristic or attribute of interest in the research study that can take on different values and is not constant. Variables may be straightforward and easy to measure including characteristics such as gender, weight, height, age, size, and time. Other variable may be more complex and more difficult to measure. Examples of these types of variables may include socioeconomic status, attitudes, achievement, education level, and performance.
This module will focus on five types of variables: independent, dependent, extraneous, moderator and mediator variables. The two primary types of variables are dependent and independent variables. An independent variable is the variable manipulated or changed by the researcher. The independent variable affects or determines the values of dependent variable. The dependent variable is sometimes referred to as the outcome variable because the resulting outcome of manipulating the independent variable is typically the focus of the research study. The dependent variable is the one that the researcher is attempting to predict or explain. The distinction between independent and dependent variables is especially important when studying cause-effect relationships. Following are two examples:
A researcher wants to study the effectiveness of different dosages of a particular antibiotic in clearing an infection.
The independent variable - varying dosages of antibiotic.
The dependent variable - the presence or absence of infection following a specific time period.
The researcher plans to study the relationship between the amount of time spent in a study group and test scores.
The independent variable – number of hours spent in a study group.
The dependent variable – test scores.
Extraneous variables, sometimes referred to as nuisance or confounding variables, are not the variables of primary interest. However, they are believed to be related to the independent or dependent variable and therefore, may impact the results. Researchers should attempt to control extraneous variables in order to attain meaningful results. If they cannot be controlled, extraneous variables should at least be considered when interpreting results.
A moderator variable is a variable that interacts with the independent variable and may influence the strength of the relationship between the independent and dependent variables. This variable is measured and taken into consideration, making it different than an extraneous variable. For example, if studying the relationship between exercise and weight loss, the number of calories consumed maybe a moderating variable.
Mediating variables, commonly referred to as intervening variables, are processes that may not be observable but link the independent and dependent variables. An instructor may have a new teaching approach for a mathematical concept and plans to study the use of this approach and its relationship to test scores. The differing levels at which students in the class are able to process abstract mathematical concepts is a mediating variable.
While it is important to identify, understand, and consider the variables within a study, the researcher must also consider the measurement of those variables and the types of values that may be collected. When measuring the values of variables, there are two main classifications: categorical and quantitative variables. Categorical variables are those that express a qualitative attribute and do not express a numerical ordering. These variables refer to different types or categories of phenomenon or characteristic. Some examples would include gender, eye color, race, religion, payment method, or social status. Quantitative variables vary in degree or amount and are expressed using numerical ordering. Height, weight, shoe size, income, and test scores are quantitative variables.
The specific way in which a variable is measured in a particular study is called the operational definition. It is critical to operationally define a variable in order to lend credibility to the methodology and to ensure the reproducibility of the results. Another study may measure the same variable differently. The operational definition also helps to control the variable by making the measurement constant. Therefore, when it comes to operational definitions of a variable, the more detailed the definition is, the better. For example, if the researcher was planning to weigh research subjects, there would several constructs that should be spelled out including what the subjects were to wear, whether or not they would wear shoes, what type of scale was being used, and time of day. It may also be important to define the measurement of the outcome. For example, if a study was examining the relationship of swimming on overall fitness, the researcher would need to define how the outcome of overall fitness would be measured. Similarly, if a researcher was studying the impact of a nutrition education program, the outcome to be used in measuring the program’s effectiveness would need to be defined.
Identifying and defining variables is a critical first step in a research study and will impact the validity and reliability of the study. For further discussion of variables, the following video, Independent, Dependent and Confounding Variables in Quantitative Research, offers a detailed look at identifying variables and includes examples of different types of variables.
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