Likert Scale Examples For Surveys: Fill & Download for Free

It is used for quantitative research.In simple words we can not measure outcomes of qualitative research in number or we can not quantify it whereas in quantitative research, results are always measured in numbers.In Likert Scaling method, we scale responses of audience collected through survey on basis of ratings. for example:-Are you satisfied with the services of McDonalds?1 - Strongly agree2 - Agree3 - Neither agree nor disagree4 - Disagree5 - Strongly disgree

You can use EmailMeForm for your surveys.Our tool is a drag-and-drop online form and survey builder, letting you create a survey in minutes even if you don't have a coding experience.We have survey-specific fields like Likert scales, drop-down menus, and text box that you can readily use in your survey. Once your survey is done, you can send your target participants a link to the survey via email.Below is an example survey:You can check out more available ready-to-use survey templates here.

Though analysis of Likert scale data can be quite the scientific and mathematical undertaking (especially in determining validity, distortions, etc.), I’ll oonly discuss a simple way in which to interpret the data you’ve collected.I determine the percentage of your customers that respond a certain way to each individual question.Most commonly, Likert scales are evaluated by giving each option value and then adding these values together to create a score for every customer.Though relatively simple, this reporting method makes it easy to evaluate the opinions revealed by each Likert option.A chart of scores can offer visual insight into sentiment on a particular Likert scale.By doing this for each question, you’ll be able to determine areas in need of improvement, as well as areas in which your company is thriving.You also might notice areas of concern where you initially might have thought things were running smoothly.The most important factors in reporting on the Likert scale are consistency in values and cohesiveness in questions or items that are evaluated together.Questions that are out of place can skew the results, making it harder to take the right actions based on the answers your customers give you.Speaking of skewed results…Possible Survey DistortionsGoing along with the last notion, the answers your customers provide when completing satisfaction surveys might not always be entirely accurate.Simply put: human nature sometimes gets in the way of customers responding openly and honestly.The most efficient way to combat these distortions is to always give your respondents the option of providing more detail – or discussing confusion – regarding a specific question or set of questions.However, it’s still important to understand the possible biases your customers come to the table with, in order to identify possible distortions or discrepancies among your data.The biases you’re most likely to encounter are:Central Tendency BiasAs the name implies, central tendency bias refers to the notion that some respondents may avoid choosing the most extreme options provided.The most common explanation for this tendency is that respondents don’t have a clear definition of the extreme high or extreme low with regard to a specific question.For example, when responding to the question“How would you rate our company’s customer service?” (with responses ranging from “Unhelpful” to “Extremely helpful”), a customer who did receive exemplary customer service might get caught up in the semantics of what “extreme” actually means.It’s possible that, although they acknowledge the service was exquisite, they are hesitant to report that it was the be-all-end-all of customer service.Another explanation for central tendency bias is that customers might initially “save up” their “extreme” answers for later questions.If they answer the first question with an “extreme” answer, they might view the rest of their answers through the lens of this first answer. (i.e., to respond with an “extreme” answer to subsequent questions, their level of satisfaction will have to match the level of satisfaction regarding that first question).In addition to providing opportunities for respondents to expand on their answers, you can also avoid falling victim to central tendency bias by either providing context for what certain terms (such as “excellent”) mean, or allowing respondents to define the terms in their own words.Extreme Response BiasIn contrast to central tendency bias, extreme response bias is the tendency for some respondents to, only, answer in extremes.There are, again, a number of reasons this might occur, including:Cultural attitudesThe intelligence level of respondentsLevel of effort respondents put into completing a surveyThe way in which questions and choices are wordedOf these four reasons, the only one you truly have control over is the last one. Ensure the questions you ask don’t lead respondents toward a certain answer, and also that each option is clearly defined and understandable.You can address the issues of cultural and intellectual diversity by soliciting demographic and other personal information from each respondent.While not an absolute determinant by any stretch, it’s possibly the closest you can get to figure out why a respondent answered as they did (short of asking them, of course).Acquiescence BiasAcquiescence bias refers to a respondent’s tendency to go along with a statement in an effort to avoid ruffling feathers or insulting anyone.For example, say a customer received subpar service from an employee who truly did their best to meet the customer’s needs.Though the customer didn’t end up with the result they desired, they might report they received “excellent” customer service from the employee simply because they tried to be helpful.But, for the purposes of the survey, such a response isn’t helpful at all.To avoid such a discrepancy, ask clear and specific questions throughout your survey. Using the above example, if the customer was asked about the employee’s willingness to help (in addition to being asked about the service they received), they would have a chance to show that the employee tried to help, but ultimately wasn’t able to.Another way to possibly avoid acquiescence bias is to make clear to respondents that the purpose of the survey in the first place is to improve customer service across the board.Though there will certainly still be cases of such bias, this simple disclaimer might open the door for more honesty from many of your customers.