levels of measurement

• -          Sometimes its difficult to distinguish between ordinal and interval data – and if unsure opt for ordinal as it is a form of interval data
• -          Interval data is similar to ordinal data as it involves data that can be ordered but the difference is that there are equal intervals on the points of the scale someone finishing a race in 22 seconds is twice as fast as somebody who did in 44 seconds  – have a scale 20- 40 and each person finishing time on a race is plotted exactly
• -          An example of ordinal data would be ordering your classmates in terms of height or in a race 1st , 2nd and 3rd
• -          Ordinal data can be categorised into nominal data – by counting how many people scored 1 on an attitude test
• -          Ordinal data is also nominal data but is more informative as it shows us how the data relates to each other because of the order – however although we know the order we don’t know the difference between each place – the difference between 1st and 2nd could have been seconds faster
• -          Ordinal data is when the data is ranked in place order sometimes rating scales are sued to achieve this
• -          Nominal data – reference to counting frequency data and this data fall into separate categories and each piece of data can only belong in one category and then you simply count the number of participants that belong in each category – this level of measurement does not tell us much about the data except its quantity – eg – counting whether participants are happy or sad
• -          There are different types of data you can collect which is only relevant to quantitative data – these levels of measurement are important as they help us decide what graph and statistical test is appropriate to use