Module 2A Non Experimental Research

Qualitative Variables: Categorize individuals.
- Examples: Sex, competition level, selection status (selected vs. non-selected).
Quantitative Variables: Continuous, numerical data.
- Examples: Heart rate, sprint speed, jump height.
- It's best to think of these two different types of variables as numerical and categorical.

The variable manipulated by the researcher.
Often called the treatment variable.
- Medical study example: Type of treatment received (drug vs. placebo).
- Strength and conditioning example: Types of training (percentage-based training, repetitions in reserve, velocity-based training, overspeed training, heavy sled training).
- Nutrition example: Diet (Mediterranean vs. low carb), supplement vs. no supplement.

The variable(s) impacted by the independent variable.
Observed or measured within the research protocol.
Changes as a result of changes in the independent variable.
- Example: If the independent variable is different forms of training, the dependent variable could be the change in $1RM$ strength.
- It is important that you pick out the most important or the key dependent variable to interpret that study. Because having multiple or many dependent variables can cause a large amount of issues with interpretation because it reduces the statistical power of the study.

Variables that could impact the outcome of the study but are not deliberately changed.
Often called confounding variables.
Researchers need to strictly control these.
Various methods exist within research design to remove confounding variables.