Statistical measures, consensus calculation is an important part of running a Delphi survey. Statistics are calculated the moment an event happens such as a panelist submits answer to a question, or a panelist revises existing answer to a question.

 

In mathematical and statistical analysis, data is defined as a collected group of information. Information, in this case, could be anything which may be used to prove or disprove a scientific guess during an experiment.

 

Data collected may be age, name, a person's opinion, type of pet, hair color etc. Although there is no restriction to the form this data may take, it is classified into two main categories depending on its nature - namely; categorical and numerical data.

 

Categorical data, as the name implies, are usually grouped into a category or multiple categories. Similarly, numerical data, as the name implies, deals with number variables.

 

Categorical data is a collection of information that is divided into groups. I.e, if an organisation or agency is trying to get a biodata of its employees, the resulting data is referred to as categorical. This data is called categorical because it may be grouped according to the variables present in the biodata such as sex, state of residence, etc.

 

Categorical data can take on numerical values (such as “1” indicating Yes and “2” indicating No), but those numbers don’t have mathematical meaning. One can neither add them together nor subtract them from each other.

 

There are two types of categorical data, namely; the nominal and ordinal data.

 

Nominal Data: This is a type of data used to name variables without providing any numerical value. Coined from the Latin nomenclature “Nomen” (meaning name), this data type is a subcategory of categorical data.  Nominal data is sometimes called “labelled” or “named” data. Examples of nominal data include name, hair colour, sex etc.  Mostly collected using surveys or questionnaires, this data type is descriptive, as it sometimes allows respondents the freedom to type in responses. Although this characteristic helps in arriving at better conclusions, it sometimes poses problems for researchers as they have to deal with so much irrelevant data.

 

Ordinal Data: This is a data type with a set order or scale to it. However, this order does not have a standard scale on which the difference in variables in each scale is measured.  Although mostly classified as categorical data, it is said to exhibit both categorical and numerical data characteristics making it in between. Its classification under categorical data has to do with the fact that it exhibits more categorical data character.  Some ordinal data examples include; Likert scale, interval scale, bug severity, customer satisfaction survey data etc. Each of these examples may have different collection and analysis techniques, but they are all ordinal data.

 

 

 

There consist of two categories of categorical data, namely; nominal data and ordinal data. Nominal data, also known as named data is the type of data used to name variable, while ordinal data is a type of data with a scale or order to it.

 

Categorical data is qualitative. That is, it describes an event using a string of words rather than numbers.

 

Categorical data is analysed using mode and median distributions, where nominal data is analysed with mode while ordinal data uses both. In some cases, ordinal data may also be analysed using univariate statistics, bivariate statistics, regression applications, linear trends and classification methods.

 

It can also be analysed graphically using a bar chart and pie chart. A bar chart is mostly used to analyse frequency while a pie chart analysis percentage. This is done after grouping into a table.

 

In the case of ordinal data, which has a given order or scale, the scale does not have a standardised interval. This is not applicable for nominal data.

 

Although categorical data is qualitative, it may sometimes take numerical values. However, these values do not exhibit quantitative characteristics. Arithmetic operations can not be performed on them.

 

Categorical data may also be classified into binary and non binary depending on its nature. A given question with options “Yes” or “No” is classified as binary because it has two options while adding “Maybe” to the given options will make it non binary.

 

 

Below $30,001

$30,001 - $40,000

$40,001 - $50,000

$50,001 and above

This is a closed ended nominal data example.

 

What is your highest level of education? 

School SAT

High School

BSc.

MSc.

PhD

 

Male

Female

This is a binary and closed-ended nominal data example.

 

Male

Female

Others _____

This is a nonbinary and open-closed ended nominal data example.

 

Very poor

Poor

Neutral

Good

Very good

 

Lux

Dove

Olay

 

Blonde

Brunette

Brown

Black

Red

 

1

2

3

4

5

 

Business

Leisure

Family

Study

Health

Others _____

 

Below 21 years

21 to 35 years

36 to 58 years

59 years and above

 

 

Likert scale: A Likert scale is a point scale used by researchers to take surveys and get people's opinion on a subject matter. 

 

How will you rate the desert served tonight? 

Very good

Good

Neutral

Bad

Very bad