2.3 Research Variables

Research variables are fundamental elements in any statistical analysis and research. They are the characteristics or properties that researchers measure, manipulate, and control. In the context of sports studies, understanding these variables is crucial for designing experiments, analysing data, and interpreting results.

2.3.1 Types of Variables

Research variables can be classified into several types based on their roles in an experiment and their characteristics.

How Many Variables are Needed in an Experiment?

In an experiment, at least two types of variables are essential: the independent variable (what you are testing) and the dependent variable (the outcome you measure). Including control variables enhances the internal validity of the experiment by reducing the impact of extraneous variables (Hendl, 2012).

Independent Variables

Independent variables (IV) are the variables that researchers manipulate or change to observe their effect on dependent variables. They are not influenced by other variables in the experiment but can influence other variables. Examples:

  • Training Program: The type of training program (e.g., strength training, endurance training) is an independent variable that can affect athletes' performance.
  • Dietary Supplement: The intake of a specific dietary supplement can be manipulated to study its effects on muscle recovery.

Dependent Variables

Dependent variables (DV) are the outcomes that researchers measure in an experiment. They depend on the changes made to the independent variables. Examples:

  • Performance Metrics: The time taken to complete a sprint, which depends on the type of training received.
  • Muscle Mass: The change in muscle mass resulting from a specific diet or training regimen.

Control Variables

Control variables are constants throughout the experiment. They are not changed and do not affect the independent or dependent variables, ensuring the validity of the results. Examples:

  • Age of Participants: Keeping the age range of participants consistent in a study comparing different training methods.
  • Equipment Used: Using the same type of equipment for all participants to control for equipment variability.

Intervening (Mediator) Variables

Intervening variables are theoretical constructs that explain the relationship between independent and dependent variables. They provide a deeper understanding of how or why an effect occurs. Example:

  • Motivation: If the independent variable is a new coaching technique and the dependent variable is improved performance, the intervening variable could be increased motivation among athletes due to the new technique.

Moderating Variables

Moderating variables affect the strength or direction of the relationship between independent and dependent variables. They can either strengthen or weaken this relationship. Examples:

  • Age: The relationship between training intensity (IV) and injury rates (DV) may vary with the age of the athletes, with older athletes potentially experiencing higher injury rates at higher training intensities.

Extraneous Variables

Extraneous variables are any variables not intentionally studied that can affect the dependent variable. They introduce unwanted variability and can confound the results. Examples:

  • Weather Conditions: In a study on outdoor running performance, variations in weather can affect the results if not controlled.

In the study of variables, it is essential to understand the distinction between dependent and independent variables, which are often referred to by different terms across various disciplines. For instance, the independent variable may also be known as the predictor, explanatory, or manipulated variable, while the dependent variable is sometimes called the outcome, response, or measured variable. These alternative names reflect the roles these variables play in research design. For a visual summary of these synonyms, refer to Figure 25.

Common Synonyms for Dependent and Independent Variables
Figude 25: Common Synonyms for Dependent and Independent Variables

2.3.2 Classification by Value Types

Variables can also be classified based on the type of values they hold and their measurement scales.

Quantitative (Numerical) Variables

Quantitative variables represent numerical values and quantities.

  • Discrete Variables: Countable values, such as the number of goals scored.

  • Continuous Variables: Values that can be measured infinitely precisely, like running time in seconds.

Examples: Height (measured in centimetres, cm); Weight (measured in kilograms, kg).

Qualitative (Categorical) Variables

Qualitative variables represent categories or non-numerical values.

  • Nominal Variables: Unordered categories, such as eye colour.

  • Ordinal Variables: Ordered categories, like ranks or grades.

Examples: Position Played (categories like goalkeeper, defender, midfielder, and forward), Injury Severity (categorised as mild, moderate, or severe)..

Composite Variables

Composite variables are combinations of two or more variables to form a more complex measure.

Examples: Overall Health (a composite variable that includes weight, blood pressure, and absence of injury)

Review Questions

  • Explain the difference between independent and dependent variables. Provide an example of each from a sports-related experiment.

  • What are control variables, and why are they important for maintaining the internal validity of an experiment? Give an example of a control variable in a fitness study.

  • Describe the role of moderating variables in research. How can they influence the relationship between independent and dependent variables? Provide a sports-related example.

  • Define extraneous variables and explain how they can confound the results of an experiment. What strategies can researchers use to control for these variables?

  • What is the difference between discrete and continuous quantitative variables? Provide examples of each in the context of sports performance measurements.