Likert 5 point scale examples

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Likert 5 point scale examples

Learn about different types of Likert Scale and how to create them in the right way. Learn why the 5-Point Likert Scale is the most popular. Get Likert Scales Examples and Tips. The definition of the Likert Scale The Likert Scale is a type of rating scale used in questionnaires to measure people's attitudes and opinions. It is named after its inventor, psychologist Renis Likert, who devised the approach in 1932. Respondents choose from a range of possible responses expressing their level of agreement or disagreement with the specified statement. Please rate the extent to which you agree/disagree with the following: "TV campaigns are a good way to persuade people to stop smoking in the presence of children" Please rate the extent to which you agree/disagree with the following: "TV campaigns are a good way to persuade people to stop smoking in the presence of children" 1 ? Strongly disagree, 2 ? Disagree, 3 ? Somewhat disagree, 4 ? Neither agree nor disagree, 5 ? Somewhat agree, 6 ? Agree, 7 ? Strongly agree You can add these templates to your AidaForm account, customize logos and texts and start collecting valuable business information in five minutes. The Likert Scale doesn't have to state "agree" or "disagree"; dozens of variations are possible on themes like agreement, frequency, quality, and importance. For example: Frequency: Never ? Rarely ? Sometimes ? Always ? Often Quality: Poor ? Fair ? Good ? Very good ? Excellent Likelihood: Extremely Unlikely ? Unlikely ? Neutral ? Likely ? Extremely Likely Importance: Not at all important ? Low importance ? Slightly important ? Neutral ? Moderately important ? Very important ? Extremely important These labels are called Likert Scale Response Anchors. Note that some of the scales also have their own names like Frequency Scales or Semantic Differential Scale. See the complete list of types of rating scales that you will be able to create with the help of the AidaForm service. How likely are you to recommend... 1 - Extremely likely, 4 - Neutral, 7 - Extremely unlikely When purchasing electronic products, how often do you do the following? The size of a Likert scale may vary, but scales with odd numbers of points prevail as they can have a midpoint (neutral answer). An even number of labels, as in a 4-point or 6-point Likert scale, make respondents choose strictly between "for" or "against". Most commonly, survey specialists use a 5-point scale (see examples A, B, D). A larger scale, for example, a 7-point scale, could offer more choices to respondents, but it has been noticed that the larger the scale, the more people tend to avoid labels for the extreme notions. Also, take into consideration that both rating scales with a big and small number of labels may cause difficulties because they provide too much or too little differentiation between points. How to Make Your Likert Scale Survey More Effective: Your survey questions and statement should be phrased in a way that only asks about one characteristic per question or statement. This will make clear what the person is responding to. For example, "I think politicians are honest and helpful" will not work because it asks about two separate issues (honesty and helpfulness). If you ask about satisfaction with the service at a hotel, do you mean the service from hotel desk administrators, cleaning ladies, or the kitchen personnel? All of the above? Are you asking whether your guests were satisfied with the speed of service, the courteousness of the staff, or the quality of breakfast? Ask more specific questions and there will be a higher chance that your Likert questions will provide you with more trustworthy responses. How satisfied or dissatisfied are you with the quality of ... 1 - Not satisfied at all, 3 - Neutral, 5 - Completely satisfied, N/A - Not applicable Likert-type scales can be scored in different ways. It's a common practice to score each item so that higher scores would indicate "more" of some characteristic and then take the average of all of the items. Note that the numbers will not have any inherent meaning. For example, if you are measuring attitudes towards politicians, scoring a 3.4 actually means that, on average, an individual was slightly favorable in his/her attitudes and can then be compared to the distribution of the remaining responses. Read our article which shows how using the wrong interpretation techniques helps manipulate polls results How Likert Scales Helped Vladimir Putin's Rating Likert-Type Scale Response Anchors Don't hesitate to download and consult the list of Likert-Type Scale Response Anchors here. Let's wrap it up with theory for now. It's time to create and conduct polls and surveys. AidaForm is a cloud-based service allowing you to create online polls and surveys. Give it a try right away. And best wishes with your research! AidaForm is a complete online service where you can create forms, share them, gather responses, and evaluate them in your member's area. Statistics Definitions > What is the Likert Scale? Variations of the Likert Scale.A Likert Scale is a type of rating scale used to measure attitudes or opinions. With this scale, respondents are asked to rate items on a level of agreement. For example: Strongly agree Agree Neutral Disagree Strongly disagree Five to seven items are usually used in the scale. The scale doesn't have to state "agree" or "disagree"; dozens of variations are possible on themes like agreement, frequency, quality and importance. For example: Agreement: Strongly agree to strongly disagree. Frequency: Often to never. Quality: Very good to very bad. Likelihood: Definitely to never. Importance: Very important to unimportant. These items are called Likert Scale Response Anchors. Once the respondents have answered, numbers are assigned to the responses. For example: Strongly agree=5 Agree=4 Neutral=3 Disagree=2 Strongly disagree=1 This enables you to assign meaning to the responses. For example, a survey on customer service can enable you to see which of your customer service representatives are providing good service (an average score of 4-5) and which are providing poor service (an average score of 1-2). Steps to Developing a Likert Scale Define the focus: what is it you are trying to measure? Your topic should be one-dimensional. For example "Customer Service" or "This Website." Generate the Likert Scale items. The items should be able to be rated on some kind of scale. The image at the top of this page has some suggestions. For example, polite/rude could be rated as "very polite", "polite", "not polite" or "very impolite." Politeness could also be rated on a scale of 1 to 10, where 1 is not polite at all and 10 is extremely polite. Rate the Likert Scale items. You want to be sure your focus is good, so pick a team of people to go through the items in step 2 above and rate them as favorable/neutral/unfavorable to your focus. Weed out the items that are mostly seen as unfavorable. Administer your Likert Scale test. Hypothesis Tests on Likert Scales If you known that you're going to be performing analysis on Likert scale data, it's easier to tailor your questions in the development stage, rather than to collect your data and then make a decision about analysis. What analysis you run depends on the format of your questionnaire. There is some disagreement in education and research about whether you should run parametric tests like the t-test or non-parametric hypothesis tests like the Mann-Whitney on Likert-scale data. Winter and Dodou(2010) researched this issue, with the following results: "In conclusion, the t test and [Mann-Whitney] generally have equivalent power, except for skewed, peaked, or multimodal distributions for which strong power differences between the two tests occurred. The Type I error rate of both methods was never more than 3% above the nominal rate of 5%, even not when sample sizes were highly unequal." In other words, there seems to be no real difference between the results for parametric and non-parametric tests, except for skewed, peaked, or multimodal distributions. Which avenue you take is up to you, your department, and perhaps the journal you are submitting to (if any). The most important step at the decision stage is deciding if you want to treat your data as ordinal or interval data. Then read the section below for your data type. A couple of general guidelines: For a series of individual questions with Likert responses, treat the data as ordinal variables. For a series of Likert questions that together describe a single construct (personality trait or attitude), treat the data as interval variables. Two Options Most Likert scales are classified as ordinal variables. If you are 100% sure that the distance between variables is constant, then they can be treated as interval variables for testing purposes. In most cases, your data will be ordinal, as it's impossible to tell the difference between, say, "strongly agree" and "agree" vs. "agree" and "neutral." Ordinal Scale Data With most variable types (interval, ratio, nominal), you can find the mean. This is not true for Likert scale data. The mean in a Likert scale can't be found because you don't know the "distance" between the data items. In other words, while you can find an average of 1,2, and 3, you can't find an average of "agree", "disagree", and "neutral." "The average of `fair' and `good' is not `fair-and-a-half'; which is true even when one assigns integers to represent `fair' and `good'!" ? Susan Jamieson paraphrasing Kuzon Jr et al. (Jamieson, 2004) Statistics Choices Statistics you can use are: Hypothesis Testing In hypothesis testing for Likert scales, the independent variable represents the groups and the dependent variable represents the construct you are measuring. For example, if you survey nursing students to measure their level of compassion, the independent variable is the groups of nursing students and the dependent variable is the level of compassion. Types of test you can run: Kruskal Wallis: determines if the median for two groups is different. Mann Whitney U Test: determines if the medians for two groups are different. Simple to evaluate single Likert scale questions, but suffers from several forms of bias, including central tendency bias, acquiescence bias and social desirability bias. In addition, validity is usually hard to demonstrate. More Options for Two Categories If you combine your responses into two categories, for example, agree and disagree, more test options open up to you. Chi-square: The test is designed for multinomial experiments, where the outcomes are counts placed into categories. McNemar test: Tests if responses to categories are the same for two groups/conditions. Cochran's Q test: An extension of McNemar that tests if responses to categories are the same for three or more groups/conditions. Friedman Test: for finding differences in treatments across multiple attempts. Measures of Association Sometimes you want to know if a one group of people has a different response (higher or lower) from another group of people to a certain Likert scale item. To answer this question, you would use a measure of association instead of a test for differences (like those listed above). If your groups are ordinal (i.e. ordered) in some way, like age-groups, you can use: If your groups aren't ordinal, then use one of these: Phi coefficient. Contingency coefficient. Cramer's V. Interval Scale Data Statistics that are suitable for interval scale Likert data: Mean. Standard deviation. Hypothesis Tests suitable for interval scale Likert data: References Beyer, W. H. CRC Standard Mathematical Tables, 31st ed. Boca Raton, FL: CRC Press, pp. 536 and 571, 2002. Levine, D. (2014). Even You Can Learn Statistics and Analytics: An Easy to Understand Guide to Statistics and Analytics 3rd Edition. Pearson FT Press Vogt, W.P. (2005). Dictionary of Statistics & Methodology: A Nontechnical Guide for the Social Sciences. SAGE. Salkind, N. (2016). Statistics for People Who (Think They) Hate Statistics: Using Microsoft Excel 4th Edition. -----------------------------------------------------------------------------Need help with a homework or test question? With Chegg Study, you can get step-by-step solutions to your questions from an expert in the field. Your first 30 minutes with a Chegg tutor is free! Comments? Need to post a correction? Please post a comment on our Facebook page. Shape Parameter: Definition and Examples Restricted Range

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