LECTURE POD 2- DATA TYPES

Summary:

There are 4 different types of data that are discussed in this lecture pod. They include:

  1. Nominal
  2. Ordinal
  3. Interval
  4. Ratio

Nominal data derives from the Latin word, ‘nomen’ meaning ‘pertaining to names.’

An example of nominal data is if an individual went grocery shopping and observed that various items would fall into different categories. ‘Nominal data’ is inherently unordered. You cannot take the ‘average’ of nominal data. When there are 2 categories, the data is then referred to as ‘dichotomous.’

Ordinal data can be defined as when numbers are assigned to determine something that is quantitatively immeasurable. For example, a survey may ask a shopper to rate their experience from one to five- one meaning ‘very unsatisfied’ and five representing ‘very satisfied.’ As emotions are subjective to each individual, this would be the most appropriate method to gain an insight into a consumers thoughts and feelings.

MD-1348_MultipleChoice_graphic3b

https://www.google.com.au/search?q=survey+1+to+10&source=lnms&tbm=isch&sa=X&ved=0ahUKEwjUu_rzsafVAhUGPrwKHfeGCuUQ_AUICigB&biw=1920&bih=901#imgrc=Hd-9iRZZ7qpZpM:

Interval data can be defined when numbers are assigned to determine something that is quantitatively measurable, unlike ordinal data. For example, the time of a clock. When you say ‘0:00 am’ this does not mean that there is an absence of time. It just means that it is the beginning of a new day. Other examples of interval data in every day life include temperature.

Unlike ratio data, ‘zero’ does not mean the absence of a variable but simply a measurement.

clock_in_flash8.jpg

https://www.google.com.au/search?biw=1920&bih=901&tbm=isch&sa=1&q=digital+clock&oq=digital+clock&gs_l=psy-ab.3..0l4.43962.46168.0.46343.17.13.0.0.0.0.291.2015.0j2j7.9.0….0…1.1.64.psy-ab..8.8.1831.0..0i67k1.BiiryTcuQL4#imgrc=Z9vAr5sAgrS_wM:

Ratio data can be defined when numbers are assigned to determine something that is quantitatively measurable. However, unlike ‘interval data’, the value of 0 indicates an absence of whatever you are measurable.

For example, 0 minutes or 0 dairy products in the basket. Some other frequent examples of ratio data include:

  • Height
  • Weight
  • Age
  • Income

Furthermore, an example of qualitative would be an individual stating  “I drink coffee every day.”

Quantitative, on the other hand, is numerical information. There are two types:

Discrete (counted)-  “I drink 4 coffees everyday”
Continuous (measured)- “I drink 80 grams of coffee everyday”

Reflection:

I understand that there are several different types of data but I like how the lecture pod describes each of the four types of data thoroughly and with effective examples. I now acknowledge the difference between quality and quantifiable data. At first, I was confused between ‘interval data’ and ‘ratio data’ but the lecture pod explains the the differences well. I feel confident in my new gain knowledge of the four types of data and believe they will act as effective tools to help me enhance my data visualization skills in this unit.

Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s