<\/span><\/h2>\nIn statistics, a variable refers to an attribute or characteristic of an object of study. It can be measured, controlled, and manipulated. Variables take different values. There are <\/span>different<\/span> types of variables used in research.<\/span><\/p>\n<\/span>Major Categories of Variables<\/span><\/strong><\/span><\/h3>\nQuantitative variables<\/span><\/strong>\u00a0are those that contain quantitative data. These can <\/span>be further classified<\/span> into:<\/span><\/p>\n\n- Discrete variables<\/span><\/strong>: Counts of individual items or values (example: number of participants who have signed up for a camping trip)<\/span><\/li>\n
- Continuous variables<\/span><\/strong>: Measurement of <\/span>continuous<\/span> or non-finite values (examples: volume or age)<\/span><\/li>\n<\/ul>\n
Categorical variables<\/span><\/strong>\u00a0represent categories or groupings. These variables <\/span>are further categorized<\/span> as:<\/span><\/p>\n\n- Binary or dichotomous variables<\/span><\/strong>: Have only two categories <\/span>as<\/span> in yes or no outcomes<\/span><\/li>\n
- Nominal variables<\/span><\/strong>: Show groups or have two or more categories <\/span>which have<\/span> no rank or order between them (example: color)<\/span><\/li>\n
- Ordinal variables<\/span><\/strong>: Present groups that <\/span>are ranked<\/span> in a specific order (example: rating scale responses in a survey)<\/span><\/li>\n<\/ul>\n
<\/span>Other Types of Variables<\/span><\/strong><\/span><\/h3>\nLatent variables<\/span><\/strong>\u00a0cannot be directly observed or measured<\/span>, but are represented through a proxy.\u00a0Confounding variables,<\/strong> although not the main variables in the study,<\/span>\u00a0can<\/span> distort or influence the relationship between the dependent and independent variables under study.<\/span><\/p>\nThe most common types of variables are <\/span>the<\/span> explanatory and response variables <\/span>and<\/span> these<\/span> are detailed<\/span> in the following section.<\/span><\/p>\n<\/span>Differences Between Explanatory and Response Variables<\/span><\/strong><\/span><\/h2>\nExplanatory and response variables facilitate the investigation of relationships during<\/span>\u00a0experiments and observations.<\/span> In order to<\/span> ensure the validity of your study, <\/span>especially<\/span> in the scientific discipline, it is vital <\/span>to clearly articulate the explanatory and response variables<\/span>.<\/span><\/p>\nTerminology note:<\/strong> Explanatory variables are often interchangeably used in research with terms such as independent variable or predictor variable, while response variables are interchangeably used with terms such as dependent variable, outcome, or criterion variable.<\/span><\/p>\n<\/span>What is an Explanatory Variable?<\/span><\/strong><\/span><\/h3>\nAn explanatory variable has three key characteristics:<\/span><\/p>\n\n- Manipulation<\/span><\/strong>: It is the variable <\/span>that is<\/span> manipulated by the researcher in an experimental study to observe its effect on the response variable<\/span><\/li>\n
- Explanation<\/span><\/strong>: This variable explains the variations in the response variable<\/span><\/li>\n
- Causation<\/span><\/strong>: This variable is the expected cause<\/span><\/li>\n<\/ul>\n
<\/span>What is a Response Variable?<\/span><\/strong><\/span><\/h3>\nA response variable has three defining features:<\/span><\/p>\n\n- Outcome measurement<\/span><\/strong>: It is the outcome that <\/span>is measured<\/span> through manipulation of the explanatory variable<\/span><\/li>\n
- Expected result<\/span><\/strong>: This variable is the expected result<\/span><\/li>\n
- Effect relationship<\/span><\/strong>: Changes in the explanatory variables <\/span>are expected<\/span> to cause changes in the response variable<\/span><\/li>\n<\/ul>\n
<\/span>Examples of Explanatory and Response Variables<\/span><\/strong><\/span><\/h2>\n<\/span>Example 1: Piano Practice and Performance<\/span><\/strong><\/span><\/h3>\nExplanatory variable:<\/span><\/strong>\u00a0Number of hours that a student spends practicing piano in a week<\/span><\/p>\nResponse variable:<\/span><\/strong>\u00a0Score in the practical examination<\/span><\/p>\nIn this example, the explanatory variable is the amount of time (in hours) spent by the student going through their<\/span>\u00a0piano lessons and practice.<\/span> The response variable is the assessment score in the practical examination taken by the tutor every quarter. The parents, tutor, and student will be able to see how the time spent on daily practice has impacted the student’s quarterly score.<\/span><\/p>\n<\/span>Example 2: Badminton Training Programs<\/span><\/strong><\/span><\/h3>\nExplanatory variable:<\/span><\/strong>\u00a0Type of training program adopted by the coach<\/span><\/p>\nResponse variable:<\/span><\/strong>\u00a0Effect of the different types of training <\/span>programs<\/span> on the effectiveness of the players’ hitting the overhead smash<\/span><\/p>\nAnother example can be when a badminton coach wants to analyze the effect of different training programs on his students. <\/span>The coach aims to assess the <\/span>effect<\/span> of these programs on the players’ effectiveness<\/span>\u00a0in hitting the overhead smash.<\/span> Here, the coach categorizes students into three groups to follow three different training programs. This could be divided into training schedules A, B, and C for a fortnight.<\/span><\/p>\n<\/span>Example 3: Film Budget and Box Office Success<\/span><\/strong><\/span><\/h3>\nExplanatory variables:<\/span><\/strong> Costs of production, marketing,\u00a0<\/span>and<\/span> distribution, and their relationship <\/span>with<\/span> each other<\/span><\/p>\nResponse variable:<\/span><\/strong>\u00a0How ultimately the film fares or its box office performance<\/span><\/p>\nAnother simple example to illustrate explanatory and response variables is how <\/span>the <\/span>budget<\/span>
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