I have couple of questions. Q1: How important is it to have a "Neutral" option in Likert scale. Does it depend on the queries asked? Or is it always preferable to have a "middle" option.
The context of this question is that a questionnaire study was conducted in which passengers of a new bus system in the city were asked several questions regarding their "level of satisfaction" of various attributes. The actual survey had 5 options: (i) highly satisfied, (ii) adequately satisfied (iii) satisfied (iv) dissatisfied & (v) highly dissatisfied. It does seem that it is skewed more towards "satisfied" option; however having a "neutral" option would also imply that those users are "okay" with the system, thus making them move towards "satisfied group". So, Q2: In this scenario, should "adequately satisfied and "satisfied" be combined as one category since they essentially tell the same thing. OR should the "satisfied" option be treated as "neutral" since they're essentially saying "we're okay with it"??
Thank you in advance.
Solved – When is it okay not to use “middle” or “neutral” option for Likert scale
likert
Best Answer
If you do not have the "neutral" option, you force your responders to state their preference. The "neutral" option enables them not to have opinion. If people are forced to make a choice you end up with some answer, on another hand, if people feel that they don't have an opinion they may not answer your questions, or provide random, rubbish answers. There is lots of examples of surveys and questionnaires where the "neutral" option was, or was not included. It is up to you.
The scale you quote is strange--are you sure that the options are correct? Nonetheless, the "neutral" option should be neutral. If it is not then you can get rubbish results. Creating asymmetric scales does not sound reasonable. I guess, many people would not read the options carefully and treat the middle option as "in the middle" rather then as "satisfied". By creating asymmetric scales you are playing with fire and you could come into issues with interpretability of your results.
How you aggregate and present your results is up to you and depends on your aims. Any way of aggregating may make sense in different scenarios. For example, you could as well aggregate everything other then "very dissatisfied" if you want to talk about people who are not "very dissatisfied" etc.