Variables Research28th January 2021
A variable is, as the name applies, something that varies. Age, sex, export, income and expenses, family size, country of birth, capital expenditure, class grades, blood pressure readings, preoperative anxiety levels, eye color, and vehicle type are all examples of variables because each of these properties varies or differs from one individual to another.
A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using.
Types of Variable
An important distinction between variables is between the qualitative variable and the quantitative variable.
Qualitative variables are those that express a qualitative attribute such as hair color, religion, race, gender, social status, method of payment, and so on. The values of a qualitative variable do not imply a meaningful numerical ordering.
The value of the variable ‘religion’ (Muslim, Hindu, ..,etc.) differs qualitatively; no ordering of religion is implied. Qualitative variables are sometimes referred to as categorical variables.
Categorical variables may again be described as nominal and ordinal.
Ordinal variables are those which can be logically ordered or ranked higher or lower than another but do not necessarily establish a numeric difference between each category, such as examination grades (A+, A, B+, etc., clothing size (Extra-large, large, medium, small).
Nominal variables are those who can neither be ranked nor logically ordered, such as religion, sex, etc.
A qualitative variable is a characteristic that is not capable of being measured but can be categorized to possess or not to possess some characteristics.
Quantitative variables, also called numeric variables, are those variables that are measured in terms of numbers. A simple example of a quantitative variable is a person’s age.
The age can take on different values because a person can be 20 years old, 35 years old, and so on. Likewise, family size is a quantitative variable, because a family might be comprised of one, two, three members, and so on.
That is, each of these properties or characteristics referred to above varies or differs from one individual to another. Note that these variables are expressed in numbers, for which we call them quantitative or sometimes numeric variables.
A quantitative variable is one for which the resulting observations are numeric and thus possesses a natural ordering or ranking.
Discrete and Continuous Variables
Quantitative variables are again of two types: discrete and continuous.
Variables such as some children in a household or number of defective items in a box are discrete variables since the possible scores are discrete on the scale.
A discrete variable, restricted to certain values, usually (but not necessarily) consists of whole numbers, such as the family size, number of defective items in a box. They are often the results of enumeration or counting.
The variable that is used to describe or measure the problem or outcome under study is called a dependent variable.
In a causal relationship, the cause is the independent variable, and the effect is the dependent variable. If we hypothesize that smoking causes lung cancer, ‘smoking’ is the independent variable and cancer the dependent variable.
A continuous variable is one that may take on an infinite number of intermediate values along a specified interval. Examples are:
- The sugar level in the human body
- Blood pressure reading
- Height or weight of the human body
- Rate of bank interest
- Internal rate of return (IRR)
The variable that is used to describe or measure the factor that is assumed to cause or at least to influence the problem or outcome is called an independent variable.
The definition implies that the experimenter uses the independent variable to describe or explain the influence or effect of it on the dependent variable.
Variability in the dependent variable is presumed to depend on variability in the independent variable.
Dependent and Independent Variables
In many research settings, there are two specific classes of variables that need to be distinguished from one another, independent variable and dependent variable.
Many research studies are aimed at unrevealing and understanding the causes of underlying phenomena or problems with the ultimate goal of establishing a causal relationship between them.
In almost every study, we collect information such as age, sex, educational attainment, socioeconomic status, marital status, religion, place of birth, and the like. These variables are referred to as background variables.
These variables are often related to many independent variables so that they influence the problem indirectly. Hence, they are called background variables.
Most studies concern the identification of a single independent variable and the measurement of its effect on the dependent variable.
But still, several variables might conceivably affect our hypothesized independent-dependent variable relationship, thereby distorting the study. These variables are referred to as extraneous variables.
In any statement of relationships of variables, it is normally hypothesized that in some way, the independent variable ’causes’ the dependent variable to occur. In simple relationships, all other variables are extraneous and are ignored. In actual study situations, such a simple one-to-one relationship needs to be revised to take other variables into account to better explain the relationship.
In many cases, we have good reasons to believe that the variables of interest have a relationship within themselves, but our data fail to establish any such relationship. Some hidden factors may be suppressing the true relationship between the two original variables.
Such a factor is referred to as a suppressor variable because it suppresses the actual relationship between the other two variables.
Often an apparent relationship between two variables is caused by a third variable.
For example, variables X and Y may be highly correlated, but only because X causes the third variable, Z, which in turn causes Y. In this case, Z is the intervening variable.
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