Data Driven Decision Making Types of DataChapter Two

What you will learn:

• What a data handling cycle is
• A definition of qualitative data
• A definition of quantitative data
• Why it is important to know what types of data exist

Introduction

Using data to drive decision making means finding and/or collecting the relevant data and then making sense of it to inform your decisions. In this module we will introduce the data handling cycle and describe what types of data are available to be found or collected.

The Data Handling Cycle

The data handling cycle starts with a problem or a specific question that you want answered.

The Data Model

Diagram Sourced from Pintrest

For example, the university might want to know the proportion of students who work during the day in order to determine if putting on night classes would be beneficial to students.

Starting at the beginning of the cycle: [1.], The question might be ‘What percentage of students work during the day?’

Activity

Reflect upon whether this question is a good one to answer. Who might use the results of that research?

Moving on to the second part of the model: [2], The type of data we would need to determine that question would be the number of students who work during the day and then divide that by the total population to get the percentage.

Activity

How might this data be collected in the most practical and efficient way?

Moving on to the third part of the model: [3], We will discuss data collection techniques in another module but one way to get this data would be to ask the student via a survey or via a phone interview.

In this module we will define the types of data there are out there.

Types of Data

All data falls under two groups: Qualitative or Quantitative.

Qualitative data (often abbreviated to ‘qual’ when spoken about) is descriptive data, which is data that is not defined by numbers. Examples could be interviews, personal stories, or observations. Qualitative data may also be looked at as data that is categorized or placed into categories, for example whether you drive a Holden or a Toyota, are you male or female, your level of education. Qualitative data can also be divided into:

1. Nominal (by name): That is, categories such as species, brands or groups.
2. Ordinal (by its place in a series): that is, ordered by categories (nominal data but with an implied order), for example: small, medium, large.

Quantitative (often abbreviated to ‘quan’) data is numerical data – that is data that is based on numbers and upon which statistical calculations can be performed, such as finding the mean and other statistics. Quantitative data is measured (not observed like qualitative data). Under quantitative data there are two types:

1. Continuous data: That is data that is measured, for example the height of every student in the class.
2. Discrete data: Data that is counted, for example how many goals Geelong kicked in the 2011 Grand Final.

Here is a link to a video which goes into the difference between qualitative and quantitative data in a little more detail:

Quantitative vs Qualitative Data | 4:08 mins

Why it is important to know about (and use) both types of data

It is important to know the differences between the types of data as this will influence how you will collect the data and what type of analysis you will do. For example, you would not calculate the average value on qualitative data as this value would be meaningless. Neither type of data is ‘superior’ to the other; whichever type you use will depend on the question that you want answered, which will then help you in your decision making process.

The Australian Bureau of Statistics (ABS) regularly conducts a census of the population – they may collect both quantitative and qualitative data. For example:

Quantitative data:

1. How many children per family
2. Family income level
3. Square meterage of the family house.

Qualitative data:

1. In which industry the main income earner works
2. What his/her occupation is
3. In which country they were born.

By combining both types of data a bigger and more accurate picture can be built of the Australian population than just using one type of data in preference to the other.

Conclusion

The starting point for all data collection is what question do you want answered. Once this is established then it becomes much easier to determine your data requirements – that is what data you need to answer your question. The next module will discuss the collection phase of the data handling cycle. That is, the ways that data is collected.

Activity

Post your results from the activity in this module on the discussion board for your peers to discuss. In preparation for the next module, think about all the ways that data could be collected.

To move on to Chapter Three of this series on Data Driven Decision Making (which gives a case study example), please click on the button below.

References

Pace MySPH 2012, ‘Quantitative vs. qualitative data’, YouTube, viewed 5 December 2017, https://www.youtube.com/watch?time_continue=15&v=EcKrT_IegoU

https://www.pinterest.com.au/pin/404761085233437754/

Developed for the Practice and Portfolio Program for the Associate Degrees, University College, Robert Lewis, 26th January 2018.

Credits:

Created with images by Luke Chesser - "Desk Setup" • Crew - "Desks in an open office space" • rawpixel.com - "Table at the florist’s" • Bookblock - "Weskin Notebook"

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