This becomes relevant when gathering descriptive statistics about your data. Although you can say that two values in your data set are equal or unequal (= or ≠) or that one value is greater or less than another (), you cannot meaningfully add or subtract the values from each other. For example, 1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Often, and 5 = Always.īut it’s important to note that not all mathematical operations can be performed on these numbers. Since these values have a natural order, they are sometimes coded into numerical values. How important do you think it is to reduce your carbon footprint? Examples of Likert-type questions How frequently do you buy energy efficient products? Likert scales are made up of 4 or more Likert-type questions with continuums of response items for participants to choose from. In the social sciences, ordinal data is often collected using Likert scales. The more precise level is always preferable for collecting data because it allows you to perform more mathematical operations and statistical analyses.
You could collect ratio data by asking participants for their exact age.You could collect ordinal data by asking participants to select from four age brackets, as in the question above.Some types of data can be recorded at more than one level.
In the past three months, how many times did you buy groceries online? Examples of ordinal scale survey questions Question These are user-friendly and let you easily compare data between participants. Ordinal variables are usually assessed using closed-ended survey questions that give participants several possible answers to choose from.
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Variableĭiscover proofreading & editing How to collect ordinal data In social scientific research, ordinal variables often include ratings about opinions or perceptions, or demographic factors that are categorized into levels or brackets (such as social status or income).
Interval data differs from ordinal data because the differences between adjacent scores are equal. Nominal data differs from ordinal data because it cannot be ranked in an order. While nominal and ordinal variables are categorical, interval and ratio variables are quantitative. The levels of measurement indicate how precisely data is recorded. Ordinal is the second of 4 hierarchical levels of measurement: nominal, ordinal, interval, and ratio.
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