The Role, Collection and Presentation of Quantitative
Data in the Business Decision Making Process
1. What is data
Data is important for many business decisions. Data can be qualitative or quantitative. Data that is specifically collected first-hand for the project is called primary data and data that was collected for a different project and used in this project is called secondary data. It is always important when collecting data to ensure that the sample collected is random and representative of the whole population. 2. Data Types
Two main categories of data are categorical and quantitative.
2.1 Categorical data
Categorical data is measured in discrete units or groups, usually depicting qualitative categories. Examples of categorical variables are gender, race and age group. Note that while “age” is a quantitative variable, it is often convenient to categorise variables such as age into a small number of groups, thereby converting “age” to categorical data. The two types of categorical data are nominal data and ordinal data:
• Nominal - data sorted into mutually exclusive (an observation cannot belong to more than one category) categories. The numerical value of the category has no meaning, so statistical analysis or relationships cannot be examined.
• Ordinal - Ordinal data are categorical data where there is a logical ordering to the categories. A good example is the Likert scale that you see on many surveys: 1=Strongly disagree; 2=Disagree; 3=Neutral; 4=Agree; 5=Strongly agree. The categories can be compared with each other, however statistics are normally useless.
2.2 Quantitative data
Quantitative data is measured in continuous units. Examples are income, age and temperature. The two types of quantitative data are interval and ratio data:
• Interval - Interval data is continuous data where differences are interpretable, but where there is no "natural" zero. A good example is temperature in degrees
© Abigail Maandig 2014
• Ratio -