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Likert Scale Questions
Likert Scale Questions
When voters go to the polls, it is not easy to assess what drives their voting behaviour. Do voters consider all issues relating to which way they are going to cast their vote in equal measures? What do they feel strongly about, and what issues matter little or not at all?
Surveys carried out can indicate which way the vote will go but don’t always explain results on Election Day. Voters can be asked to rate issues in order of importance, however, this doesn’t always show if one issue is more important than another to the voter.
This is just one example where the use of Likert scale questions can illuminate the detail behind people’s opinions, beliefs and attitudes.
Likert scale advantages and disadvantages
There are advantages and disadvantages to every type of research, and they are quite clear with this question type.
The main advantage of Likert Scale questions is that they use a universal method of collecting data, which means it is easy to understand them. Working with quantitative data, it is easy to draw conclusions, reports, results and graphs from the responses. Furthermore, because Likert Scale questions use a scale, people are not forced to express an either-or opinion, rather allowing them to be neutral should they so choose. Once all responses have been received, it is very easy to analyse them. Last but not least, it is very quick and easy to run this type of survey and it can be sent out through all modes of communication, including text messages.
However, one disadvantage of using Likert Scales can be that respondents either lean towards choosing the most extreme option or express no opinion at all. This can lead to results being clustered around the middle or at each end of the scale, making it hard to distinguish between strong and weakly held opinions, implying the space between each possibility is equidistant, which is not true in real life. As a result, a true attitude is not actually measured. Furthermore, you must realise that your previous questions will have influenced responses to any further survey questions that have been asked. People also have a tendency to automatically avoid “extremes”, therefore answering the way they think they are expected to, rather than providing real honesty.
However, the use of Likert scale in online survey can be viewed as positive overall, particularly if you understand the limitations it presents. By knowing that it is by no means a perfect tool, you are able to read data less as set in stone but more as a generalised picture of a certain issue, which can be all you need for improvement.
Likert scale examples
Likert scales come in a number of different styles, either in terms of the number of options provided to respondents (3-scale, 6-scale, 9-scale etc), or the manner in which beliefs, attitudes or opinions are expressed (e.g. frequency, satisfaction levels, degree of agreement with a statement).
The two most commonly used Likert scale options are 5-point and 7-point:
Likert questions can also come in ranges of just 3-point and 4-point scales:
Among the longer Likert scale questions used in questionnaires are options using 6-point and even 9-point scales:
When developing a Likert scale for a survey, it is imperative to ensure that the statements or questions are clear and unequivocal. Respondents should have no difficulty comprehending the statement or question being posed and their response options.
Additionally, it is critical to ensure that the response options are balanced, such that there is an equivalent number of affirmative and negative options. This aids in reducing the likelihood of response bias, where respondents may be inclined to answer more positively or negatively simply based on the phrasing of the questions.
Likert scales are a beneficial instrument for measuring attitudes, beliefs, and perceptions in surveys. They allow researchers to collect data that can be utilized to gain greater insight into people’s thoughts and emotions about a particular subject. By crafting Likert scale questions and response options carefully, researchers can collect high-quality data that is both reliable and valid.
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