Levels and Types of Data

Question 1

What are the three levels of data?

Answer 1

• Nominal
• Ordinal
• Interval

Question 2

What is nominal data? Provide examples

Answer 2

It is categorical data, named categories
E.g. hair colour:
Brown - 1
Blonde - 4
Black - 2
–> Order does not matter

Question 3

What is ordinal data? Provide examples

Answer 3

It is ordered data
E.g. places in a race:
1st, 2nd , 3rd place OR ranking experiences: excellent, good, satisfactory, bad
–> Cannot measure the differences between each one

Question 4

What is interval data? Provide examples

Answer 4

It is data that is in order, but the differences between each one CAN be measured
E.g. temperature: 30 degrees, 60 degrees, 90 degrees. There is 30 degree difference between each
–> No true “0” starting point, can go below e.g. -30 degrees

Question 5

What are the two types of data?

Answer 5

• Quantitative
• Qualitative

Question 6

What is quantitative data?

Answer 6

It is numerical data

Question 7

What is qualitative data?

Answer 7

It is data in the form of words, sentences and images

Question 8

What are the strengths of quantitative data?

Answer 8

1. Easy to analyse - numerical data can be summarized using descriptive statistics E.g. mean, median, mode
   2, Less researcher bias - numerical data cannot be biased, making results more valid
2. Easy to draw conclusions
3. Easy to compare
4. Easier to establish reliability

Question 9

What are the weaknesses of quantitative data?

Answer 9

1. Over simplifies reality - in real life we do not just deal with numerical data, making it reductionist
2. Can be less valid - no real range to express yourself, so lessens how meaningful the data is
3. Isn’t true to real life - people don’t describe feelings with numbers, so makes it less ecologically valid

Question 10

What are the strengths of qualitative data?

Answer 10

1. More holistic - a complex explanation is provided
2. Provides rich, detailed and meaningful explanations
3. Allows for people to express a range of opinions - more meaningful, so more valid
4. Higher ecological validity - more true to real life to respond with words

Question 11

What are the weaknesses of qualitative data?

Answer 11

1. Harder to draw conclusions - harder to detect patterns and trends
   –> Harder to analyse
2. Prone to researcher bias - researcher can interpret words, reducing validity
3. Harder to compare between participants
4. Less reliable - not as consistent as numerical data

Question 12

What are the two sources of data?

Answer 12

• Primary
• Secondary