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Week 2 Discussion
A variable is a feature or characteristic that has quality or quantity that changes or varies. For instance, in a study conducted to investigate the existing differences between females and males, sex or gender would be the variable (Pandey, 2020). Variables can either be continuous, discrete or categorical. The commonly used are continuous and discrete since categorical variables may fall in either of the two main types.
Continuous variables are quantitative variables used to describe data that can be measured in some way. They are variables that can take any value between its lower limit and upper limit hence making it difficult to count. Continuous variables are non-countable since even forever is not enough to count them. Examples of continuous variables include age, height, date, time, eye color, among others. These variables cannot be counted, and if they can, one would never exhaust all values (Kaliyadan & Kulkarni, 2019). These variables are mostly measured in small infinite units. Many continuous variables, as highlighted above, are physical traits. Psychological traits, such as creativity and intelligence, are also continuous variables. A continuous variable can be numeric like in the case of height or time and date, like the time when something was done.
Discrete variables are types of variables that can only take finite number values. Qualitative variables fall under this category of variables. However, some quantitative variables may be discrete such as performance when rated as either 1, 2, 3, 4, or 5. Discrete variables are numeric and can be counted, unlike continuous variables (Kaliyadan & Kulkarni, 2019). These variables are always numeric. Examples of discrete variables include the number of cars, number of people in a hotel or class, questions answered correctly in a test, number of defects in producer goods, number of books published, number of coin flips, among others. When things like height and distance are rounded off to the nearest whole number, instead of fractions or decimals, then they become discrete variables.
Categorical variables are variables that have two or more categories with no intrinsic order of the classes. In other words, they contain a fixed and finite number of distinct groups called categories. They usually lack a logical order. Both continuous and discrete variables can become categorical if they are separated into finite categories. Categorical variables include gender, hair color, payment method, race, age group and level of education (Kaliyadan & Kulkarni, 2019). These variables are like multiple choice questions where you are given a fixed number of options to choose from, and none of the alternatives is powerful than the other hence cannot be arranged sequentially. For instance, gender: one can either be male or female, and there is no intrinsic way of ordering them. Similarly, age can be categorized into several age groups, such as; below 18 years, 19 to 40 years, 41 to 65 years, and 65+ years. A person can belong to only one of the age groups at a time.
A dichotomous variable can be confused with a categorical variable since both have distinct groups. However, dichotomous variables are those that can only take one of only two possible values; that is, they are categorical variables with only two categories. For instance, gender is a categorical variable with only two possible values: male or female. Therefore, gender is a dichotomous variable. Other examples include measurements that require yes or no answers, high or low, etc. age can be transformed to become dichotomous by dividing it into two distinct age groups like below 50 and over 50 years. Continuous variables are transformed into a dichotomous variable in a process called dichotomization (McShane & Gal, 2017). Variables are essential in research since they are the driving tools.
Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample size. Indian dermatology online journal, 10(1), 82.
McShane, B. B., & Gal, D. (2017). Statistical significance and the dichotomization of evidence. Journal of the American Statistical Association, 112(519), 885-895.
Pandey, S. (2020). Types of variables in medical research. Journal of the Practice of Cardiovascular Sciences, 6(1), 4.