There are several different types of variables. There are
qualitative variables, quantitative variables, and continuous and intervening
variables. These types of variables differ in the ways that they are defined.
Knowing these distinctions is crucial to the success of your research. The variables you use in your research will help you design a proper study
and improve the quality of your findings.
Qualitative
variables:
When it comes to data, there are many types of
variables in research to choose from. Some are discrete, and others are
continuous. A discrete variable is a number, such as the number of children in
a household. Continuous variables are not numerical values; they
simply allude to attributes that do not have a numerical value.
Qualitative variables, on the other hand, are a more
complicated matter. They can be classified as either ordinal or continuous. For
instance, several women may be grouped as having only one pregnancy or two.
In either case, the researcher can report the number of women who have a
pregnancy.
Quantitative
variables:
Quantitative variables are variables that are measured with
numbers. For example, we can measure height in inches or the weight of a
backpack in pounds. These variables have practical implications in data
analysis. For instance, if a child weighs three pounds and another child weighs
four pounds, the weight of their backpack will be different.
Categorical variables, on the other hand, are qualitative
variables. They do not have a numerical value and instead describe the quality
of something. These variables can't be added together but can be subtracted or
multiplied. Qualitative variables include eye colour, state of
residence, and dog breed.
Researchers often use different categories to categorise
risks and exposures. Studies analysing quantitative exposure variables most
often use four or five types. This practice is standard in epidemiology.
Continuous
variables:
A continuous variable can have many different
values within a range. Unlike a discrete variable, a continuous one can never
have the same value twice. It doesn't have to be essentially infinite, though.
A good example is a time. For example, the time a body dies is a continuous
variable.
Another example is the number of pregnancies among women.
This can be a continuous variable or an ordinal variable. It can be reported as
the number of women with one or two pregnancies or written as the
number of pregnancies among all women.
One way to measure the variability of a continuous variable
is to use the standard deviation. This measure is the most widely used way to
assess how often variable changes.
Intervening
variables:
In research, intervening variables are
not directly related to the dependent variable. They can also be called
mediators or intermediaries. They help to clarify the relationship between the
dependent and independent variables. There are several ways to identify
intervening variables. The following are some examples: a.
Intervening variables may be a factor in an experiment but
are not observable. In the study of a new drug, the intervention could be an
intangible one. It could be a physical phenomenon or a social situation that
affects a person's health. An example of an intervening variable is poverty.
Although poverty is associated with a shorter life span, it does not always
cause it. Similarly, poor nutrition and lack of health care may be intervening
variables.
The effect of an intervening variable can be estimated
through two tests, one of which uses the multivariate delta method. The method
involves multiplying the partial derivatives of a function by the covariance
matrix. Then, the standard error is calculated.
Author Bio:
Carmen Troy is a research-based
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