A variable, also known as a data item, is any characteristics, number, or quantity that can be measured and it may vary between data units. It changes in value with time. There are two types of variables namely numeric variables and categorical variables. Examples of variable include age, sex, business incomes, and expenses, class grades (Hagger-Johnson, 2014). In contrast, a parameter is any numerical value that characterizes a given population. It generally gives information about a whole population. Common examples of a parameter are mean median, mode, and average.
Data provided is quantitative data and not qualitative since it is expressed in the form of numerical value. Quantitative data is one that deals with quantity, and is expressed numerically (Hagger-Johnson, 2014). In most cases, it is used in computation and statistical tests, while qualitative data is used to describe the qualities of a particular item and cannot be measured.
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A variable, as stated earlier, is any characteristics, number, or quantity that can be measured and it may vary between data units. It can also be described as an entity that varies with respect to another entity in a particular system depending on conditions (Hagger-Johnson, 2014). Data is systematically recorded information, which consists of facts or figures from which conclusions can be drawn. Data is represented either numerically or by use of text. The major difference between a variable and data is that a variable is used to define how data is spread out, while data is the entity itself.
There are four ways of representing data namely nominal, ordinal, interval and ratio. Nominal and ordinal data are under the categorical types of data, while interval and ratio are under the numerical types of data (Hagger-Johnson, 2014). Nominal data represents values of discrete units and is used to label variables that do not have a quantitative value. Nominal data does not have an order, and as such, changing order of values does not change anything. Ordinal data is similar to nominal data since it represents values of discrete units. However, there is a slight difference since besides to representing discrete units; ordinal data represents ordered units unlike nominal data. Ordinal data is used to measure non-numeric concepts such as happiness. Interval data represents ordered units with a similar difference (Hagger-Johnson, 2014). Therefore, the variables represented by interval data are those with a numerical value, have an order, and there is a similar difference between the values. The interval values of data do not have an absolute zero. An example is the temperature of a given place. Ratio data also represents ordered units that have an exact difference. However, the interval values of data have an absolute zero. An example is weight of a substance.
There are three measures of central tendency namely mean, mode, and median. Mean or average is the most popular measure of central tendency. It is equivalent to the sum of all values in a dataset divided by the number of values in that dataset. It is mainly used with continuous data although it can also be used for discrete data (Hagger-Johnson, 2014). Mode is defined as the most occurring value in a dataset. It is used for categorical data to determine the most common category. Median is the middle value at a set of data that has been arranged in either in ascending or descending order. It applies for ordinal data.
There are different measures of variation, which include range, interquartile range, variance, and standard deviation. Range is the difference between the largest data item in a set and the smallest (Hagger-Johnson, 2014). It is usually used together with a measure of central tendency to give an overall illustration of data. Inter-quartile range divides data into quarters and it is used when one wants measure how data is spread around the mean. Variance is used to determine how far data is spread out and is used to calculate standard deviation.
References
Hagger-Johnson, G. (2014). Introduction to research methods and data analysis in the health sciences. New York, NY: Routledge
