In research, a variable is any qualitative or quantitative characteristic that can change and have more than one value, such as age, height, weight, gender, or income.
Before starting a study, it’s crucial to identify which variables need to be measured or analyzed. This helps in selecting the right statistical test and ensures that your findings are both reliable and valid.
Example:
If you want to conduct a test on plants to see if hybridization of plants affects human health, the following are the key variables to consider such as agricultural techniques, types of plants shielded with hybridization.
To design a sound experiment, you need to know how each variable works within your research type.
Types of Variables
Experiments often investigate how one variable influences another. To do this, researchers use three main types of variables.
- Independent variables
- Dependent variable
- Control variable
| Type of Variable | Definition | Example |
|---|---|---|
| Independent Variable (IV) | The variable that is intentionally manipulated or changed by the researcher, or is observed as a factor that is presumed to cause or influence an outcome. | In a study testing a new drug, the dosage of the drug (e.g., 0mg, 10mg, 20mg) is the Independent Variable. It is manipulated by the experimenter. |
| Dependent Variable (DV) | The variable that is measured or observed to assess the effect of the independent variable. It is the outcome or response. | To measure the drug’s effect, the symptom severity score is the Dependent Variable. It is expected to change in response to the drug dosage. |
| Control Variable | A variable that is kept constant or regulated by the researcher to minimize its influence on the relationship between the IV and DV. | In the drug study, the age, gender, and diet of the participants are kept as similar as possible (controlled) across all dosage groups to ensure that any change in symptom severity is due only to the drug dosage. |
he research includes finding ways:
- To change the independent variables.
- To prevent the controlled variables from changing.
- To measure the dependent variables.
Note: The terms dependent and independent are not applicable in correlational research, as this is not a controlled experiment. A researcher doesn’t have control over the variables. The association between two or more variables is measured. If one variable affects another one, then it’s called the predictor variable and outcome variable.
Example:
Correlation between investment (predictor variable) and profit (outcome variable).
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Types of Variables Based on Data
Data is referred to as the information and statistics gathered for analysis of a research topic. It is broadly divided into two categories:
Quantitative data: Numerical and measurable.
Categorical data: Group-based and descriptive.
Quantitative Variables
The quantitative variable is associated with measurement, quantity, and extent, like how many. It follows the statistical, mathematical, and computational techniques in numerical data, such as percentages and statistics. The research is conducted on a large group of population.
Example:
Find out the weight of students in the fifth standard studying in government schools.
Quantitative Variable Types
The quantitative variable can be further categorised into
- Continuous
- Discrete
| Type of Quantitative Variable | Definition | Example |
|---|---|---|
| Continuous Variable | A quantitative variable that can take on any value within a given range. It has an infinite number of possible values, including fractions and decimals. | Height ($\text{1.75}$ meters), Weight ($\text{72.3}$ kg), Temperature ($\text{98.6}^{\circ}\text{F}$), Time (e.g., $\text{10.5}$ seconds). These are typically measured on an Interval or Ratio scale. |
| Discrete Variable | A quantitative variable that can only take on a countable number of distinct values, usually whole numbers (integers). | Number of cars owned ($\text{0, 1, 2, 3}$, etc.), Number of students in a class, Number of errors on a test, Number of votes cast. These are typically measured on a Ratio scale. |
Categorical Variables
The categorical variable includes measurements that vary in categories, such as names, but not in terms of rank or degree. It means one level of a categorical variable cannot be considered better or greater than another level.
Example:
Gender, colors, brands, or postal codes.
Types of Categorical Variables
The categorical variable is further categorised into three types:
- Dichotomous (Binary) Variable
- Nominal Variable
- Ordinal Variable
| Type of Categorical Variable | Definition | Key Property | Example |
|---|---|---|---|
| Dichotomous (Binary) Variable | A variable with only two mutually exclusive categories or outcomes. It is the simplest type of categorical variable. | Categories have no inherent order but represent a simple choice or presence/absence. | Gender (Male/Female), Treatment Status (Treated/Control), Outcome (Pass/Fail, Yes/No). |
| Nominal Variable | A variable where the categories serve only as labels and have no intrinsic order or ranking. | Categories are distinct but cannot be mathematically ordered (A is not greater than B). | Eye Color (Blue, Brown, Green), Religion (Christianity, Islam, Buddhism), Type of Car (Sedan, SUV, Truck). |
| Ordinal Variable | A variable where the categories have a logical, meaningful order or rank, but the distances between the categories are not equal or meaningful. | Categories are rankable, but the difference between ranks cannot be quantified (e.g., 2nd place is not necessarily twice as good as 4th place). | Likert Scales (Strongly Disagree to Strongly Agree), Socioeconomic Status (Low, Medium, High), Educational Level (High School, Bachelor’s, Master’s). |
Note: Sometimes, ordinal variables can act as quantitative variables. Ordinal data has an order, but the intervals between scale points may be uneven.
Example:
Numbers on a rating scale represent the reviews’ rank or range from below average to above average. However, it also represents a quantitative variable showing how many stars and how much rating is given.
Other Types of Variables
Beyond independent, dependent, quantitative, and categorical variables, researchers often use additional classifications.
| Type of Variable | Definition | Function/Role | Example |
|---|---|---|---|
| Confounding Variable | An unobserved or hidden variable that is related to both the independent variable (IV) and the dependent variable (DV), creating a spurious (false) association between them. | Creates an alternative explanation for the observed relationship between the IV and DV, threatening internal validity. | Observed Association: Ice cream sales (IV) are positively associated with the number of drownings (DV). The Confounding Variable is Hot Weather, which causes both increased ice cream sales and increased swimming/drowning risk. |
| Latent Variable | A theoretical construct that cannot be directly observed or measured but is inferred from a set of observable variables (indicators). | Used in complex models (like Structural Equation Modeling) to represent unobservable concepts. | Intelligence is a latent variable measured through observable indicators like scores on various subtests (e.g., verbal reasoning, spatial ability). Other examples: Anxiety, Motivation, Self-Esteem. |
| Composite Variable | A new variable created by combining or aggregating multiple related measured variables into a single score or index. | Simplifies data and creates a measure for a multidimensional concept based on observed data. | A researcher sums the scores from 10 different questions about diet, exercise, and sleep to create a single “Overall Health Score” (the composite variable). Another example is a GPA, which is a composite of multiple course grades. |
| Moderator Variable | A variable that influences the strength or direction of the relationship between the independent variable (IV) and the dependent variable (DV). | Answers the question: “When” or “for whom” does the IV-DV relationship hold true? | Relationship: Hours of Study (IV) $\rightarrow$ Exam Score (DV). The Moderator could be Prior Knowledge. The effect of studying on the score may be much stronger for students with low prior knowledge than for those with high prior knowledge. |
Frequently Asked Questions
Independent, dependent, control, confounding, continuous, discrete, categorical, nominal, ordinal, extraneous.
In programming, a variable is a symbolic name for a storage location that holds data or values. It allows data storage and retrieval for computational operations. Variables have types, like integer or string, determining the nature of data they can hold. They’re fundamental in manipulating and processing information in software.
In science, a controlled variable is a factor that remains constant throughout an experiment. It ensures that any observed changes in the dependent variable are solely due to the independent variable, not other factors.
Ideally, an investigation should have one independent variable to clearly establish cause-and-effect relationships. Manipulating multiple independent variables simultaneously can complicate data interpretation.
