Types of Variables

Types of Variables

• Variable is a characteristic or property that can take on different values.

Discrete and Continuous Variables

Discrete variables:

• Quantitative variables can be further distinguished in terms of whether they are discrete or continuous.

• The word discrete means countable. For example, the number of students in a class is countable or discrete. The value could be 2, 24, 34 or 135 students, but it cannot be 23/32 or 12.23 students.

• Number of page in the book is a discrete variable. Discrete data can only take on certain individual values.

Continuous variables:

• Continuous variables are a variable which can take all values within a given interval or range. A continuous variable consists of numbers whose values, at least in theory, have no restrictions.

• Example of continuous variables is Blood pressure, weight, high and income.

• Continuous data can take on any value in a certain range. Length of a file is a continuous variable.

Difference between Discrete variables and Continuous variables

Approximate Numbers

• Approximate number is defined as a number approximated to the exact number and there is always a difference between the exact and approximate numbers.

• For example, 2, 4, 9 are exact numbers as they do not need any approximation.

• But √2, л, √3 are approximate numbers as they cannot be expressed exactly by a finite digits. They can be written as 1.414, 3.1416, 1.7320 etc which are only approximations to the true values.

• Whenever values are rounded off, as is always the case with actual values for continuous variables, the resulting numbers are approximate, never exact.

• An approximate number is one that does have uncertainty. A number can be approximate for one of two reasons:

a) The number can be the result of a measurement.

b) Certain numbers simply cannot be written exactly in decimal form. Many fractions and all irrational numbers fall into this category

Independent and Dependent Variables

• The two main variables in an experiment are the independent and dependent variable. An experiment is a study in which the investigator decides who receives the special treatment.

 1. Independent variables

• An independent variable is the variable that is changed or controlled in a scientific experiment to test the effects on the dependent variable.

• An independent variable is a variable that represents a quantity that is being manipulated in an experiment.

• The independent variable is the one that the researcher intentionally changes or controls.

 • In an experiment, an independent variable is the treatment manipulated by the investigator. Mostly in mathematical equations, independent variables are denoted by 'x'.

• Independent variables are also termed as "explanatory variables," "manipulated variables," or "controlled variables." In a graph, the independent variable is usually plotted on the X-axis.

2. Dependent variables

• A dependent variable is the variable being tested and measured in a scientific experiment.

• The dependent variable is 'dependent' on the independent variable. As the experimenter changes the independent variable, the effect on the dependent variable is observed and recorded.

• The dependent variable is the factor that the research measures. It changes in response to the independent variable or depends upon it.

• A dependent variable represents a quantity whose value depends on how the independent variable is manipulated.

• Mostly in mathematical equations, dependent variables are denoted by 'y'.

• Dependent variables are also termed as "measured variable," the "responding variable," or the "explained variable". In a graph, dependent variables are usually plotted on the Y-axis.

• When a variable is believed to have been influenced by the independent variable, it is called a dependent variable. In an experimental setting, the dependent variable is measured, counted or recorded by the investigator.

 • Example: Suppose we want to know whether or not eating breakfast affects student test scores. The factor under the experimenter's control is the presence or absence of breakfast, so we know it is the independent variable. The experiment measures test scores of students who ate breakfast versus those who did not. Theoretically, the test results depend on breakfast, so the test results are the dependent variable. Note that test scores are the dependent variable, even if it turns out there is no relationship between scores and breakfast.

Observational Study

• An observational study focuses on detecting relationships between variables not manipulated by the investigator. An observational study is used to answer a research question based purely on what the researcher observes. There is no interference or manipulation of the research subjects and no control and treatment groups.

• These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

• Observational studies are generally used in hard science, medical and social science fields. This is often due to ethical or practical concerns that prevent the researcher from conducting a traditional experiment. However, the lack of control and treatment groups means that forming inferences is difficult and there is a risk of confounding variables impacting user analysis.

Confounding Variable

• Confounding variables are those that affect other variables in a way that produces spurious or distorted associations between two variables. They confound the "true" relationship between two variables. Confounding refers to differences in outcomes that occur because of differences in the baseline risks of the comparison groups.

• For example, if we have an association between two variables (X and Y) and that association is due entirely to the fact that both X and Y are affected by a third variable (Z), then we would say that the association between X and Y is spurious and that it is a result of the effect of a confounding variable (Z).

• A difference between groups might be due not to the independent variable but to a confounding variable.

• For a variable to be confounding:

a) It must have connected with independent variables of interest and

b) It must be connected to the outcome or dependent variable directly.

• Consider the example, in order to conduct research that has the objective that alcohol drinkers can have more heart disease than non-alcohol drinkers such that they can be influenced by another factor. For instance, alcohol drinkers might consume cigarettes more than non drinkers that act as a confounding variable (consuming cigarettes in this case) to study an association amidst drinking alcohol and heart disease.

• For example, suppose a researcher collects data on ice cream sales and shark attacks and finds that the two variables are highly correlated. Does this mean that increased ice cream sales cause more shark attacks? That's unlikely. The more likely cause is the confounding variable temperature. When it is warmer outside, more people buy ice cream and more people go in the ocean.