VARIABLES

While generating theories and hypotheses, we must know the importance of our collected data in testing hypotheses or deciding between competing theories. Therefore, we need to decide on two things:

1. What to measure?

2. How to measure it?

Variables are data items whose values can change/vary between individuals or testing conditions (e.g. weight, RBC count, severity of disease, and tumor markers’ concentrations).

On the other hand, the values of the other data type, Constants, are always the same.

Variables are usually classified according to two standards:

1. The causal relationship.

2. The level of measurement.

The variables are classified into two types with regard to causality: Dependent and Independent variables. In other words, you can think of them in terms of cause and effect: an Independent variable is the variable the researcher thinks is, or has a significant influence on, the cause, while a Dependent Variable is the effect, or a consequence thereof.

For example, if you plan to study the effects of smoking on lung cancer:

 The Independent Variable is a better fit to represent smoking.

 The Dependent Variable will be representing lung cancer, as it is dependent on smoking.

If there is a third variable in a study examining a potential cause-and-effect relationship, we call that variable a Confounding Factor. For example, a study investigating the association between obesity and heart disease might be “confounded” by age, diet, or smoking status.

It's difficult to separate the true effect of the independent variable from the effect of the confounding variable. Therefore, in your research design, it is important to identify potential confounding variables and plan how you will reduce their impact on your statistical methods.

Depending on whether you are measuring your variables qualitatively or quantitatively, you should choose a class of variables fitting for the type of data you have.

Categorical Variables are more fitted for qualitative data, while Numerical Variables are better to describe quantitative data.

Categorical Variables take Category/Label values and measure the frequencies of each category. They do NOT establish a numeric difference between categories.

Categorical variables are often further classified as either:

Characteristics

 Can NOT be logically ordered or ranked.

 Dichotomous (Binary) if there are only two categories like (yes/no or alive/dead).

 Polytomous if there are more than two categories.

 Can be logically ordered or ranked.

Numerical Variables, on the other hand, have a numeric difference between their values and are often further classified as either:

Characteristics

 Have infinite intermediate numbers along a specified interval.

 Measurable; therefore, they have “Units of Measurement.”

 Countable but NOT infinitely intervalized.

Example

 Blood Pressure

 Temperature

 Glucose Level.

 Number of Children

 Previous MI Attacks.