Slide 16 To summarize what has been presented in this lecture, I first talked about the different levels of measurement: nominal, ordinal, interval, and ratio. I provided several examples of each type of measure. I talked about the different properties for these different levels of measurement in terms of ranking items, the distance between intervals, and knowing whether or not a there’s meaningful zero point. In other words, I talked about order, distance, and origin. I closed by briefly discussing the statistics that are appropriate for these different levels of measurement. I hope that you understand the decisions you make regarding your questionnaires, in terms of the levels of measure, will have implications when the data into a computer and you’ve entered begin to analyze it. If you insist on using rank-order measures, then you be able to say won’t something like average rank for item #1 was such and such and the average rank for item ‘the #2 was so and because that requires you to compute a mean for ordinal data and so’, that’s statistically problematic.
Slide 16 To summarize what has been presented in this lecture, I first talked about the different levels of measurement: nominal, ordinal, interval, and ratio. I provided several examples of each type of measure. I talked about the different properties for these different levels of measurement in terms of ranking items, the distance between intervals, and knowing whether or not there’s a meaningful zero point. In other words, I talked about order, distance, and origin. I closed by briefly discussing the statistics that are appropriate for these different levels of measurement. I hope that you understand the decisions you make regarding your questionnaires, in terms of the levels of measure, will have implications when you’ve entered the data into a computer and begin to analyze it. If you insist on using rank-order measures, then you won’t be able to say something like ‘the average rank for item #1 was such and such and the average rank for item #2 was so and so’, because that requires you to compute a mean for ordinal data and that’s statistically problematic.