Understanding Variables in Research Methodology
Defining Variables in Research
A variable is any attribute, phenomenon, or characteristic that can assume different values among members of a class, subjects, or events. However, it holds a single value for any specific member of that class at a given point. A variable describes a unit of analysis and varies (in quality, order, or mode) across different units of analysis or at different times.
Examples:
- Variables such as age (e.g., 15 years) and height (e.g., 170 cm) take numeric values.
- Variables like sex (male, female), marital status, and marital satisfaction (very satisfied, satisfied, dissatisfied, very dissatisfied) adopt categories.
- Other examples include education level, learning outcomes, academic performance, and level of participation.
Dimensions of a Variable
A dimension represents a partial aspect of a variable that has a degree of independence relative to other aspects. Together, these dimensions help in understanding the full meaning of the variable.
Classifying Variables
A. Qualitative or Non-Metric Variables
These variables represent categories or attributes.
1. Nominal Variables
Each value is assigned to a category that has a name. No inherent order or ranking can be established between these categories (e.g., marital status: single, married, divorced). The categories are mutually exclusive.
2. Ordinal Variables
The categories of these variables can be sorted or ranked according to some criterion. For example, the variable ‘level of schooling’ can be ordered as: initial level, primary level, secondary level, higher level.
B. Quantitative or Metric Variables
These variables are measured numerically.
1. Interval Variables
Interval variables possess the property of assigning a measure of distance between their values. They not only indicate which value is greater but also by how much. Distances between values can be quantified exactly, thanks to the establishment of a standard unit of measurement (e.g., years, dollars, hours, minutes). Temperature in Celsius or Fahrenheit is a common example (0°C does not mean no temperature).
2. Ratio Variables
In addition to the characteristics of an interval variable, ratio variables feature a true or absolute zero point. This allows for the calculation of ratios and the performance of all mathematical operations (e.g., height, weight, income). A value of 0 means the complete absence of the attribute.
Measurement Scales for Variables
Measurement scales refer to how variables are quantified or categorized. Some variables are directly susceptible to being expressed in numerical values according to a unit of measure.
1. Discrete Variables
These variables can only take specific, distinct integer values and cannot take values in between. There are no intermediate values. Examples:
- Number of students in a class (e.g., 25 students)
- Number of errors on a test (e.g., 3 errors)
- Number of cars owned (e.g., 1 car)
2. Continuous Variables
These variables can take any value (integer or fractional) within a given range. Examples:
- Height (e.g., 175.5 cm)
- Weight (e.g., 68.2 kg)
- Time (e.g., 2.5 hours)
- Age (e.g., 25 years, 3 months, 4 days)
Variables by Their Role in Research
A. Independent Variable (IV)
The independent variable is the variable that is manipulated or selected by the researcher to act as a potential cause. It is presumed to influence or condition the dependent variable(s).
B. Dependent Variable (DV)
The dependent variable is the variable that is observed and measured to determine the effect of the independent variable. It represents the outcome or effect that the researcher is interested in.
C. Intervening (Mediating) Variables
Intervening variables help to interpret the relationship between an independent variable and a dependent variable. They are conceptualized as being influenced by the independent variable and, in turn, influencing the dependent variable. Their presence can explain how or why the IV affects the DV (often introduced by phrases like “due to” or “mediated by”).
Variables by Level of Abstraction
A. General Variables (Constructs)
These are broad, generic, and abstract concepts that cannot be directly observed or measured. For example, social status is a general variable that requires specific indicators for its measurement.
B. Intermediate Variables (Dimensions)
These express some dimension or partial aspect of a general variable. They are more specific than general variables but may still not be directly measurable. For example, educational level can be an intermediate variable (or dimension) used as part of measuring the general variable social status.
C. Empirical Variables (Indicators)
These represent specific, observable, and measurable aspects of the dimensions (intermediate variables), which in turn constitute the general variable. They differ from more abstract concepts because they can be directly measured. Indicators are properties empirically related to a latent property (one that cannot be observed directly). Example: Number of academic courses completed can be an empirical indicator for the dimension ‘educational level,’ which in turn contributes to understanding ‘social status’.