Likert scale survey results. Presenting your findings is always tricky. In my example the children I taught filled in forms with question that are statements about which they can strongly agree, agree, be netural, disagree or strongly disagree. So ordinal options, the 5-point Likert scale.
I need to provide an overview of the results. I’m thinking of a stacked bar like this:
(Click to embiggenatte.)
Trouble is there are many good reasons why analysis of ordinal data like this is a bad thing. Furthermore I’ve taken some lierties myself.
1) Interval data (e.g. an absolute measures of, say, temperature over time) can be plotted like this. Ordinal data is arbitrary. E.g. who is to say that Person A’s “strongly agree” is the same as Person B’s strongly agree? Therefore mapping their results together is flawed.
2) The same is true between questions answered by the same person. Statement 1 might be “strongly agree” as might Statement 2, but is it the same strength of feeling? What if Statement 1 was “Murder is bad” and Statement 2 is “Mars Bars taste nice”. I strongly agree with both, but the strength of feeling is obviously different.
3) Similar issues exist between all the options in a single question. E.g. is the gap between “agree” and “strongly agree” the same as between “neutral” and “agree”?
4) I’ve taken the liberty of opposing the “positive” and “negative” responses so as to give instant visual feedback on how the responses balance each other.
5) I’ve also taken the liberty of equally distributing “neutral” responses across the +ve and -ve axis. A bit naughty. I could ‘zero’ the neutral responses, but then they drop off the chart completely. But doing this would lead to a fairer comparison between the explicitly +ve and -ve responses.
Having said all this, the stacked bar gives a good overall representation of the survey results.
I could present the combined data as clustered bars. This lessens the cross-question and inter-question-answers comparison inference, but it’s basically the same data. Like this:
(Click for enlargification.)
Question is: is this misleading and/or can you think of any other way I could present the data?