One of the key factors in developing and testing scientific theories is the identification and operational definition of the variables that describe phenomena observed in the real world. Variables are measured in a research study, and they can have more than one value. There are several types of variables of interest to the researcher. Independent variables are stimuli that are manipulated in order to determine their effect on the value of dependent variables. Extraneous variables are variables that affect the value of the dependent variable but that are not related to the question under investigation in the study. Intervening variables are variables that occur between the manipulation of the independent variable and the measurement of the dependent variable and that contaminate the relationship between the two. In order to be of use in scientific research, variables need to be operationally defined so that they can be measured and their effects analyzed.
Sociologists attempt to make sense out the world by observing the behavior of people within society, developing theories to explain this behavior, translating their theories into working hypotheses that can be tested, and conducting empirical research to test whether or not their theories are supported. Based on the results of the research, they then either accept or revise their theories in a continuing attempt to explain the world around them. One of the key factors in this process is the identification and operational definition of variables -- traits, characteristics, or other measurable factors that can have different values -- that impact the phenomenon of interest.
One is primarily interested in two types of variables: independent variables and dependent variables. The independent variable is the variable that is being manipulated by the researcher. For example, Dr. Harvey has a theory that the way that people dress affects how they are treated by others in the workplace. He believes that if people dress as if they are successful professionals (e.g., well-groomed, business attire), they will be treated that way and receive a disproportionately high percentage of raises, promotions, high performance appraisals, and other recognition. The independent variable in this theory is whether or not people dress like successful professionals. This is the variable that Dr. Harvey will manipulate in his research study to determine how it affects the way that people are treated in the workplace. The second major variable of interest to researchers is the dependent variable. The dependent variable (so called because its value depends on which level of the independent variable the subject received) is the response to the independent variable. In Dr. Harvey's research study, the dependent variable is the way that people are treated in the workplace. Dr. Harvey's theory is that the value of this variable (i.e., whether or not people receive recognition in the workplace) is dependent on how they dress.
Concepts such as "dressing as if one is a successful professional" and "how one is treated in the workplace," however, are rather nebulous and open to different interpretations. To one person, "professional attire" may be a power suit with white shirt and tie while to another it may be a clean polo shirt with Bermuda shorts rather than cutoffs. Therefore, to be of use to researchers, variables need to be operationally defined in such a way that they can be tested and statistically analyzed. An operational definition is a definition that is stated in terms that can be observed and measured. To turn his question into a hypothesis, Dr. Harvey needs to operationally define both the independent and dependent variables. For example, he may decide that "dressing professionally" means that the person wears a dark suit with a white shirt and tie for men and a dark suit with white blouse and pearls for women. Of course, this is not the only definition of "professional dress" possible. Business casual, blazers and slacks, or any number of other possibilities is also possible. However, since it is typically impossible to consider the entire range of possibilities in one research study, Dr. Harvey will have to restrict his study to include only those values of the independent variable that are of most interest. Similarly, Dr. Harvey will have to operationally define what it means not to dress professionally in the workplace (e.g., jeans and a t-shirt). These definitions, of course, restrict Dr. Harvey's hypothesis. His results will not really answer the question about "professional" versus "not professional" attire, but only about the difference in treatment that people wearing power suits receive from those who wear casual attire.
Based on this discussion, it would seem that Dr. Harvey would be well off to pick multiple operational definitions for the independent variable. Although in some ways this is true, operationally defining a variable can be a tricky proposition. The goal of operationally defining variables is not just so that they can be tested in research, but to adequately and accurately define them so that they completely represent the underlying concept as much as possible. For example, as discussed above, the concept of "dressing professionally" means different things to different people. These differences affect not only the persons who need to decide how to dress for success, but also the persons who judge them based on the clothing choices. For example, if one's boss is "old-fashioned" and dresses in a suit and tie, dressing in a suit and tie would be more likely to impress this person even if the standard for "business attire" for that company was jeans and a polo shirt.
In addition, Dr. Harvey will have to operationally define what he means by "how one is treated in the workplace." Operational definitions of this dependent variable could include the supervisor's performance appraisal ratings of the individual, the average time it takes before the person receives a raise or bonus, or whatever other factors Dr. Harvey thinks are indicative of success. Some statistical techniques allow researchers to design experiments where to test multiple conditions of both the independent and dependent variables (e.g., power suit, blazer and slacks, business casual, and casual clothing). However, given the infinite variety of human nature and behavior, it is unlikely that he will be able to include every possible condition in his operational definitions.
Operationally defining dependent variables in human research can be a complicated process. For example, the construct underlying the variable "success in the workplace" is a nebulous and complex concept. Unless one is willing to wait for the end of the subject's career and look back to determine the value of the ultimate criterion of how successful that person was in the end, one can only estimate the ultimate criterion of success in the workplace using one or more predictor measures that one can operationally define. The underlying criterion is a dependent or predicted measure that is used to judge the effectiveness of persons, organizations, treatments, or predictors. However, one does not truly know whether or not a person is successful until s/he retires and can look back on the entire career. Practically, however, this is typically not possible in social science research. Rather than choosing an ultimate criterion of success such as success at the point of retirement, it is typically necessary instead to pick an intermediate criterion of success such as how many promotions one receives within a given period of time, how many (or how large) the raises are that the person receives during that same time period, or the performance appraisal...
In the course of writing your thesis, one of the first terms that you encounter is the word variable. Failure to understand the meaning and the usefulness of variables in your study will prevent you from doing good research. What then are variables and how do you use variables in your study? I explain the concept below with lots of examples on variables commonly used in research.
You may find it difficult to understand just what variables are in the context of research especially those that deal with quantitative data analysis. This initial difficulty about variables becomes much more confusing when you encounter the phrases “dependent variable” and “independent variable” as you go deeper in studying this important concept of research as well as statistics.
Understanding what variables mean is crucial in writing your thesis proposal because you will need these in constructing your conceptual framework and in analyzing the data that you have gathered. Therefore, it is a must that you should be able to grasp thoroughly the meaning of variables and ways on how to measure them. Yes, the variables should be measurable so that you will be able to use your data for statistical analysis.
I will strengthen your understanding by providing examples of phenomena and their corresponding variables below.
Definition of Variables and Examples
Variables are those simplified portions of the complex phenomena that you intend to study. The word variable is derived from the root word “vary”, meaning, changing in amount, volume, number, form, nature or type. These variables should be measurable, i.e., they can be counted or subjected to a scale.
The following examples of phenomena from a global to a local perspective. The corresponding list of variables is given to provide a clear illustration of how complex phenomena can be broken down into manageable pieces for better understanding and to subject the phenomena to research.
- Phenomenon: climate change
Examples of variables related to climate change:
- sea level
- the amount of carbon emission
- the amount of rainfall
- Phenomenon: Crime and violence in the streets
Examples of variables related to crime and violence:
- number of robberies
- number of attempted murders
- number of prisoners
- number of crime victims
- number of laws enforcers
- number of convictions
- number of car napping incidents
- Phenomenon: poor performance of students in college entrance exams
Examples of variables related to poor academic performance:
- entrance exam score
- number of hours devoted to studying
- student-teacher ratio
- number of students in the class
- educational attainment of teachers
- teaching style
- the distance of school from home
- number of hours devoted by parents in providing tutorial support
Examples of variables related to fish kill:
- dissolved oxygen
- water salinity
- age of fish
- presence or absence of parasites
- presence or absence of heavy metal
- stocking density
- Phenomenon: Poor crop growth
Examples of variables related to poor crop growth:
- the amount of nitrogen in the soil
- the amount of phosphorous in the soil
- the amount of potassium in the ground
- the amount of rainfall
- frequency of weeding
- type of soil
Notice in the above examples of variables that all of them can be counted or measured using a scale. The expected values derived from these variables will, therefore, be in terms of numbers, amount, category or type. Quantified variables allow statistical analysis. Variable correlations or differences are then determined.
Difference Between Independent and Dependent Variables
Which of the above examples of variables are the independent and the dependent variables? The independent variables are just those variables that may influence or affect the other variable, i.e., the dependent variable.
For example, in the first phenomenon of climate change, temperature (independent variable) may influence sea level (dependent variable). Increased temperature will cause expansion of water in the sea. Thus, sea level rise on a global scale may occur. In the second phenomenon, i.e., crime and violence in the streets, the independent variable may be the number of law enforcers and the dependent variable is the number of robberies.
I will leave to you the other variables so you can figure out how this works.
How will you know that one variable may cause the other to behave in a certain way? Finding the relationship between variables require a thoroughreview of the literature. Through a review of the relevant and reliable literature, you will be able to find out which variables influence the other variable. You do not just simply guess relationships between variables. The whole process is the essence of research.
At this point, I believe that the concept of the variable is now clear to you. Share this information to your peers who may have difficulty in understanding what the variables are in research.
©2012 October 22 P. A. Regoniel
Cite this article as: Regoniel, Patrick A. (October 22, 2012). What are Examples of Variables in Research?. In SimplyEducate.Me. Retrieved from http://simplyeducate.me/2012/10/22/what-are-examples-of-variables-in-research/
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