THE USE OF VARIABLES IN RESEARCH.
The central purpose of research is to solve problems and improve the welfare of the society. To research is to search or investigate exhaustively. It is a careful or diligent search, studious inquiry or examination especially investigation or experimentation aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts or practical application of such new or revised theories or laws, it can also be the collection of information about a particular subject, Webster(1985). Research cannot be possible without taking into consideration measurable factors that are subject to change due to circumstances. Anything that can vary in research due to circumstances is called a variable. However, based on what has been learnt in the current research course, the principle aim of this paper is to explain in parts, how the types of variables, the relationship between dependent and independent variables and their importance can be used in research.
2.0. Variables in Research
There are so many variables in research that it could be impossible or extremely difficult to account for all of them due to the fact that what can be considered a variable in one study may not necessarily be a variable in another study.
2.1 Definition of a variable
A variable is an object, event, idea, feeling, time period, or any other type of category you are trying to measure. There are two major types of variables-independent and dependent and these will be explained more later. A variable is something that can change, such as gender, which can be either male or female, age which can be 15 years old, 16 years old, 20, 38 or 30 years old and variables are typically the focus of a study. Associated to variables are attributes which are sub-values of a variable, such as ‘male’ and ‘female’ in the example variable given above. In other ways, under the variable gender, male and female are the attributes of that variable. It is important to note that variables may have the following characteristics: firstly, they have a period when they starts and stops. Secondly, they may have a pattern such as daily, weekly, ad-hoc and monthly. Thirdly, they are quiet detailed with an overview of in depth. They may be latency which is the time between measuring dependent and independent variable because some things take time to take effect.
2.2 Types of Variables
Variables are of different types and kinds. There are descriptive variables which refer to those variables in research which will be reported on, without relating them to anything in particular. There are also Categorical variables which result from a selection from categories, such as ‘agree’ and ‘disagree’. In quantitative studies, nominal and ordinal variables are categorical in nature because they result from some selected category. Variables like numeric give a number, such as age. Discrete variables are numeric variables that come from a limited set of numbers. They may result from answering questions such as ‘how many’, ‘how often’ and how far while continuous variables are numeric variables that can take any value, such as weight.
An independent variable is exactly what it sounds like. It is a variable that stands alone and isn’t changed by the other variables you are trying to measure. For example, someone’s age might be an independent variable. Other factors such as what they eat, how much they go to school, how much television they watch aren’t going to change a person’s age. In fact, when you are looking for some kind of relationship between variables you are trying to see if the independent variable causes some kind of change in the other variables, or dependent variables. An independent variable by definition is one which is manipulated by the researcher. It is like the knob on a dial that the researcher turns.
A dependent variable is one which changes as a result of the independent variable being changed. Just like an independent variable, a dependent variable is exactly what it sounds like. It is something that depends on other factors. For example, a test score could be a dependent variable because it could change depending on several factors such as how much you studied, how much sleep you got the night before you took the test, or even how hungry you were when you took it. Usually when you are looking for a relationship between two things you are trying to find out what makes the dependent variable change the way it does.
Every experiment has at least two types of variables: independent and dependent. The independent variable (IV) is often thought of as our input variable. It is independent of everything that occurs during the experiment because once it is chosen it does not change easily. For example in one of the experiments on college performance, researchers chose two groups at the onset, namely, those with work experience and those without. This variable makes up the two independent groups and is therefore called the independent variable.
On the other hand, the dependent variable (DV), or outcome variable, is dependent on our independent variable or what we start with. In the study exemplified above, college grades would be our dependent variable because it is dependent on work experience. If we chose to also look at men versus women, or older students versus younger students, then these variables would be other independent variables and the outcome, our dependent variable (college grades), would be dependent on them as well. Remember that whatever is the same between the two groups is considered a constant because they do not vary between groups but rather remain the same and therefore do not affect the outcome of each group differently.
Kalof, Dan, and Dietz (2008:37) suggest is to look at the time ordering of the variables: “If one variable is describing things that occur before the things described by another variable happen, then the first variable can be usually taken as the independent variable and the second as the dependent variable”. To illustrate, imagine higher unemployment rates (independent variable) are thought to contribute to an increase in suicide rates (dependent variable). A signal that the unemployment rate is an independent variable would be that the years or months marking the data for unemployment rates would be earlier than the years or months marking the data for suicide rates. Employment rate data would precede suicide rate data because the underlying thought would be: Insofar as unemployment might trigger suicide, first, people become unemployed. Then they become distraught. Then they commit suicide.
There are also Confounding Variables in research. Researchers must be aware that variables outside of the independent variable(s) may confound or alter the results of a study. As previously discussed, any variable that can potentially play a role in the outcome of a study but which is not part of the study is called a confounding variable. If, for instance, we had two groups in the above mentioned study but did not control for age then age itself may be a confound. Imagine comparing students with work experience with a mean age of 40 with students without work experience and a mean age of 18. Could we reasonably say that work experience caused the student to receive higher grades? This extraneous variable can play havoc on our results as can any intervening variable such as motivation or attention. Addressing confounds before they alter the results of your study is always a wise decision.
There are also extraneous variables which are additional variables which could provide alternative explanations or cast doubt on conclusions. It should be noted here that in an experiment there may be many additional variables beyond the manipulated independent variable and the measured dependent variables. It is up to the research to put up measures to control these factors in the research process.
2.3 The Relationship between dependent and independent Variables
The relationship between independent variable and dependent variables is those independent variables causes a change in dependent variable and that it not possible that a dependent variable could cause a change in an independent variable. For example, time spent studying causes a change in test score and it is not possible that test scores could cause a change in time spent studying. Therefore, “Time Spent Studying” must be the independent variable and “Test Score” must be the dependent variable because the sentence doesn’t make sense the other way around.
The other relationship can be traced from its terms independent and dependent refering to the relationship between these two types of variables. The terms have meaning only with respect to each other. In the case of the dependent variable, its value or behavior is considered reliant, to an extent, upon the value of the independent variable but not the other way around. That is why it is considered “dependent.” The independent variable, on the other hand, is truly independent from the dependent variable. Its value does not change according to the value of the dependent variable.
In many researches, the major task for researchers is to be able to determine the relationship between the independent and dependent variables, such that if the independent variable is changed, then the researcher will be able to accurately predict how the dependent variable will change. When this correlation is determined, a further question is whether varying the independent variable caused the independent variable to change. This adds complexity and debate to the situation.
2.4 The importance between dependent and independent variables.
The importance of dependent and independent variables is that they guide the researchers to per sue their studies with maximum curiosity. Dependent and independent variables are important because they drive the research process. As defined earlier, a variable as opposed to a constant is simply anything that can vary and that many researchers consistently look at the relationship between these two variables. While the variation of an independent variable will influence the variation of dependent variable, both variables give the study a focus. If we were to study the effects of work experience on college performance, we might look at the grades of students who have worked prior to starting college and the grades of students who did not work prior to starting college. In this study, you may notice that both groups are students so student status remains constant between the two groups. You may also notice that work experience is not the same between the two groups, therefore work experience varies and is considered a variable. If we choose students for each group who are of similar age or similar background, we are holding these aspects constant and therefore, they too will not vary within our study.
Dependent and independent variables are also important because they determine the cause and effects in research. Although not all indepedent and dependent variables are causal related variables, the notion of cause and effect can help clarify the idea of “independence” in the independent variable and “dependence” in the dependent variable. In the studying and scores example, cause-effect is fairly obvious, and therefore, it is relatively easy to understand what the independent dependent variables are. However, as Kalof, Dan, and Dietz (2008:36) state, “It can be hard to understand what variables are independent (causes) and what variables are dependent (effects) when we are reading research or thinking about the implications of theory” .
It can be concluded that the paper has shown the different types of variables, the relationship between dependent and independent variables and their importance in research. It was pointed out that there are several types of variables but the major ones are for every research are the independent and dependent variables. It was also pointed out that dependent variable depends on the independent variable when it comes to relationships and finally it was pointed out that the importance of these variables is that they help research get their studies focused with maximu curiosity in the course of their studies.
Kalof, L., Dan, A., & Dietz, T. (2008). Essentials of social research. Berkshire, England: Open University.
Vogt, W.P. (2005). Dictionary of statistics and methodology. Thousand Oaks, CA: Sage.