{"id":51510,"date":"2024-09-26T10:27:57","date_gmt":"2024-09-26T09:27:57","guid":{"rendered":"https:\/\/www.idsurvey.com\/?p=51510"},"modified":"2024-10-03T11:18:25","modified_gmt":"2024-10-03T10:18:25","slug":"how-to-create-and-analyze-likert-scale-data","status":"publish","type":"post","link":"https:\/\/www.idsurvey.com\/en\/how-to-create-and-analyze-likert-scale-data\/","title":{"rendered":"How to create and analyze likert scale data"},"content":{"rendered":"<style>h3{margin-block-end:0px;}<\/style>\n<p>The Likert scale is a key tool in psychometrics and social research, used in surveys to collect data on people&#8217;s opinions, perceptions and attitudes. Introduced by Rensis Likert in 1932, it allows participants to express their degree of agreement or disagreement through questions posed in the form of statements and answers that allow them to express their opinions.<br \/>\nIn this article we will explore how to create effective questions with the Likert scale and how to analyze the results in depth.<\/p>\n<h2>What is a likert scale?<\/h2>\n<p>The Likert scale is a psychometric scale that measures the intensity of an opinion through a series of ordered answers, called items. Answers range from extremely positive to extremely negative, for example, \u201cStrongly Agree,\u201d \u201cAgree,\u201d \u201cNeutral,\u201d \u201cDisagree,\u201d and \u201cStrongly Disagree.\u201d This method is useful to get a clear idea of respondents&#8217; feelings about a particular issue.<\/p>\n<div style=\"padding: 20px; border-radius: 5px; margin: 50px auto; width: 80%; height: 180px; background-color: #27446a; text-align: center;\">\n<div style=\"margin-bottom: 30px; color: #fff;\"><b>Explore the features ofIdSurvey: the powerful, versatile, and easy-to-use survey software.<\/b><\/div>\n<p><a style=\"background: #72A04E; color: #fff; padding: 10px 15px; border-radius: 5px;\" alt=\"discover IdSurvey\" href=\"https:\/\/www.idsurvey.com\/en\/\"><strong>Discover IdSurvey<\/strong><\/a><\/p>\n<\/div>\n<h2>How to create an effective likert scale?<\/h2>\n<ul>\n<li>\n<h3>Define the goal of the survey<\/h3>\n<p>Before you start creating questions, it is essential to have a clear goal for the survey. What do you want to find out? What aspects do you wish to explore? Having a well-defined objective helps to structure the questions in a clear and relevant way.<\/p>\n<h3>Make clear and specific statements<\/h3>\n<p>Likert Scale questions should be phrased as statements that respondents can answer indicating their degree of agreement or disagreement. Avoid vague wording, which could confuse participants and lead to inaccurate answers. Statements should be designed to be clear, specific and targeted to the construct you intend to measure. For example, to measure job satisfaction, it is preferable to use statements such as \u201cI am satisfied with my work environment\u201d rather than generic questions such as \u201cAre you happy at work?\u201d.<\/li>\n<li>\n<h3>Balance the statements<\/h3>\n<p>Include both positive and negative statements to reduce the risk of acquiescence, i.e., the tendency of respondents to always answer positively or negatively. This approach helps to mitigate answer bias and get a more accurate picture of opinions. If you want to interpret positive and negative statements in the same way, the coding of answers to negative statements should be reversed so that a high score always represents a positive assessment and a low score a negative assessment, or vice versa.<\/li>\n<li>\n<h3>Avoid bias<\/h3>\n<p>It is important to write questions while avoiding influencing the respondent, for example suggesting a particular answer or introducing an implicit bias. Instead of asking \u201cThis product is outstanding,\u201d use a neutral statement such as \u201cThis product meets my expectations.\u201d<\/p>\n<p>Some of the most common biases in this context are:<br \/>\n<strong> Central tendency bias: <\/strong> the tendency to choose central options, such as \u201cneutral\u201d or \u201cindifferent,\u201d to avoid taking a clear position. To reduce this bias, you can use scales without neutral options (e.g., a 4-point scale), and avoid extreme item definitions.<br \/>\n<strong> Acquiescence bias: <\/strong> the tendency of participants to agree with statements regardless of their content. This bias can be avoided by alternating positive and negative statements to encourage more reflective answers.<br \/>\n<strong>Social desirability bias:<\/strong> participants may respond in a way that makes them appear socially desirable or morally correct, rather than expressing their true thoughts. To reduce this bias, it is helpful to ensure anonymity and to phrase questions neutrally.<\/li>\n<li>\n<h3>Choose the number of options:<\/h3>\n<p>usually, the Likert scale provides 5 or 7 answer options. An odd number of options allows for a neutral option in the middle, while an even number forces participants to take a position. The choice depends on the type of analysis you want to perform. If the goal is to measure the indifference regarding a specific topic, it is important to include the neutral option.<\/li>\n<li>\n<h3>Maintain consistency:<\/h3>\n<p>if you use multiple Likert scale questions within the same survey, it is important to maintain the same format and order of answers to avoid confusion and to facilitate data analysis.<\/li>\n<\/ul>\n<div style=\"padding: 20px; border-radius: 5px; margin: 50px auto; width: 80%; height: 380px; background-color: #27446a; text-align: center;\">\n<div><img decoding=\"async\" src=\"https:\/\/www.idsurvey.com\/is-content\/uploads\/2022\/03\/survey-report-and-dashboard.png\" alt=\"Likert Scale data analysis\" width=\"40%\" height=\"\" \/><\/div>\n<div style=\"margin-bottom: 30px; color: #fff;\"><b>Explore the features of IdSurvey: the powerful, versatile, and easy-to-use survey software.<\/b><\/div>\n<p><a style=\"background: #72A04E; color: #fff; padding: 10px 15px; border-radius: 5px;\" alt=\"discover IdSurvey\" href=\"https:\/\/www.idsurvey.com\/en\/\"><strong>Discover IdSurvey<\/strong><\/a><\/p>\n<\/div>\n<h2>Likert Scale data analysis<\/h2>\n<p>From a statistical point of view, we should remember that Likert scales produce ordinal data.<\/p>\n<div style=\"margin-left: 50px; margin-bottom: 50px;\"><i>Note:<br \/>\nThis means that while we can determine that one response is ranked higher or lower than another (for example, \u201cstrongly disagree\u201d is lower than \u201cstrongly agree\u201d), we cannot assume that the intervals between responses are equal, as the exact value of each item isn&#8217;t clearly defined. For instance, the numerical gap between \u201csatisfied\u201d and \u201cvery satisfied\u201d may not be the same as that between \u201cneutral\u201d and \u201cdissatisfied.\u201d As a result, ordinal data can be arranged in order of magnitude or importance, but the precise differences between items cannot be accurately measured.<\/i><\/div>\n<p>After collecting the data, it&#8217;s crucial to analyze them systematically to draw meaningful conclusions. Below are some statistical metrics that can be computed from Likert Scale questions:<\/p>\n<ul>\n<li>\n<h3>Mode analysis<\/h3>\n<p>The mode represents the value that occurs most frequently in a dataset. If multiple values share the highest frequency, the dataset is considered multimodal.<\/li>\n<li>\n<h3>Median Analysis<\/h3>\n<p>The median is the value that lies in the middle of an ordered data set. It divides the data into two halves: 50% of the answers are lower than the median and 50% are higher. This value gives us a clue about the distribution of the data, for example, a high median suggests that the distribution of answers tends toward positive or high values.<\/li>\n<li>\n<h3>Correlation analysis<\/h3>\n<p>Checking whether there are correlations between answers to different questions can offer interesting insights. For example, if a strong correlation emerges between customer service satisfaction and brand loyalty, this could indicate an area on which to focus improvement efforts. The use of correlation coefficients (such as Spearman&#8217;s coefficient) can help identify relationships between answers to different Likert questions, providing an in-depth view of the interactions between the constructs being measured.<\/li>\n<li>\n<h3>Other non-parametric tests<\/h3>\n<p>To test for statistical significance between groups of data collected through Likert scale questions, specific tests for ordinal data are typically applied. When comparing two groups, the Mann-Whitney U Test is often used to obtain a p-value, which is then compared to a significance threshold to determine whether the differences between groups suggest a meaningful relationship or are due to chance. For comparisons involving three or more groups, the Kruskal-Wallis Test is commonly employed. Although the Chi-square Test can also be used, it is designed for categorical data, meaning it disregards the ordinal nature of the responses. However, the Chi-square Test can still be effective if the data are grouped into broader categories, such as combining responses into &#8220;positive&#8221; and &#8220;negative&#8221; opinions, rather than analyzing each Likert scale level individually.<\/li>\n<\/ul>\n<h2>Other types of data analysis<\/h2>\n<p>Although this practice is incorrect, some researchers treat Likert scales as interval data, assuming that the distances between response options are equal. This approach attempts to force the analysis of ordinal data as though they were numerical (or interval) scale data. Instead of using the item values (e.g., &#8220;satisfied,&#8221; &#8220;very satisfied,&#8221; etc.), numerical scores are assigned to each option, allowing arithmetic calculations, such as mean and standard deviation, and enabling the application of tests typically used for quantitative variable analysis.<\/p>\n<div style=\"margin-left: 50px; margin-bottom: 50px;\"><i>Note<br \/>\nThis practice is theoretically problematic because it disregards the true ordinal nature of the data. While calculating the average of responses may offer a general sense of the trend in opinions, it is crucial to remember that the arithmetic mean cannot accurately capture the actual distribution of opinions on an ordinal scale. Likert scale responses are inherently ordinal, meaning they represent a rank order (e.g., &#8220;strongly agree&#8221; &gt; &#8220;agree&#8221; &gt; &#8220;neutral&#8221;), but the intervals between responses are not necessarily equal. We cannot assume, for instance, that the gap between &#8220;agree&#8221; and &#8220;neutral&#8221; is the same as that between &#8220;neutral&#8221; and &#8220;disagree.&#8221;\u2028<\/i><\/div>\n<ul>\n<li>\n<h3>Mean Analysis<\/h3>\n<p>The arithmetic mean is a measure of central tendency that represents the average value of answers. Using the mean, other statistics typical of quantitative variables can be calculated.<\/li>\n<li>\n<h3>Variance and Standard Deviation<\/h3>\n<p>The standard deviation is the mean distance of values with the arithmetic mean. It is important for understanding the dispersion of answers. A high standard deviation indicates a large variability in opinions, while a low one suggests a consensus among participants.<\/li>\n<li>\n<h3>Parametric tests<\/h3>\n<p>Even if one chooses to force the interpretation of Likert scale data as interval-scale &#8211; allowing for the calculation of the mean and standard deviation &#8211; the use of parametric tests is strongly discouraged. Tests like the T-test, <span id=\"urn:enhancement-777cd7f4-12a4-4f22-b744-4d11b855aab5\" class=\"textannotation disambiguated wl-creative-work\" itemid=\"https:\/\/data.wordlift.io\/wl76787\/entity\/anova\"><a alt=\"Crosstab\" href=\"https:\/\/www.idsurvey.com\/en\/crosstab\/\">ANOVA<\/a><\/span>, or Pearson&#8217;s Correlation Test are designed for quantitative data that follow a normal distribution, a condition that is not met by data collected using a Likert scale. Thus, applying these tests can lead to inaccurate or misleading results.<\/li>\n<\/ul>\n<p>Interpreting data collected using the Likert scale requires caution, as it is essential to consider the context and characteristics of the sample. For instance, a high level of job satisfaction observed within one company may not necessarily be applicable or generalizable to other industries or cultural settings. Understanding these nuances is key to drawing accurate and relevant conclusions.<\/p>\n<p>Types for likert scale survey<\/p>\n<p><strong>Avoid cognitive overload:<\/strong> do not include too many likert scale questions in a row, as this may tire respondents and reduce the quality of answers.<br \/>\n<strong>Test the survey:<\/strong> before distributing it, test the survey with a small group to check the clarity of the questions and understandability of the answers.<\/p>\n<div style=\"padding: 20px; border-radius: 5px; margin: 50px auto; width: 80%; height: 180px; background-color: #27446a; text-align: center;\">\n<div style=\"margin-bottom: 30px; color: #fff;\"><b>Explore the features of IdSurvey: the powerful, versatile, and easy-to-use survey software.<\/b><\/div>\n<p><a style=\"background: #72A04E; color: #fff; padding: 10px 15px; border-radius: 5px;\" alt=\"discover IdSurvey\" href=\"https:\/\/www.idsurvey.com\/en\/\"><strong>Discover IdSurvey<\/strong><\/a><\/p>\n<\/div>\n<h2>Examples of application of the Likert Scale question<\/h2>\n<p>Below are some areas of application that show how the Likert scale can be a versatile and powerful tool for collecting detailed and meaningful data on a wide range of issues, facilitating strategic decisions based on structured feedback.<\/p>\n<h5>Customer satisfaction surveys<\/h5>\n<p>Likert scales are commonly used to assess customer satisfaction with products, services or brand experiences. Companies can ask questions to obtain detailed feedback that allows them to identify strengths and areas for improvement.<\/p>\n<p>Example question:<br \/>\n\u201cI feel satisfied with customer service.\u201d<\/p>\n<p>Answer options:<\/p>\n<p>\u2022 Strongly disagree<br \/>\n\u2022 Disagree<br \/>\n\u2022 Neutral<br \/>\n\u2022 Agree<br \/>\n\u2022 Strongly agree<\/p>\n<h5>Assessment of employee experience<\/h5>\n<p>Likert scales are ideal for collecting employees&#8217; opinions on aspects of their work environment, such as the level of support received from superiors, opportunities for professional growth, and the quality of internal communications. This type of survey helps organizations measure employee <a alt=\"employee engagement\" href=\"https:\/\/www.idsurvey.com\/en\/employee-experience\/\">engagement<\/a> and <a href=\"https:\/\/www.idsurvey.com\/en\/customer-experience-software\/\">satisfaction<\/a>.<\/p>\n<p>Sample question:<br \/>\n\u201cDoes my supervisor support me in my professional development?\u201d<\/p>\n<p>Answer options:<\/p>\n<p>\u2022 Not at all<br \/>\n\u2022 Slightly<br \/>\n\u2022 Medium<br \/>\n\u2022 Fairly<br \/>\n\u2022 Very<\/p>\n<h5>Measuring brand perceptions and corporate image<\/h5>\n<p>Companies can use Likert scales to understand how the public perceives their brand, products or advertising campaigns. Questions such as \u201cDoes our brand represent sustainability values\u201d allow them to assess the effectiveness of communication and marketing strategies.<\/p>\n<p>Example question:<br \/>\n\u201cHow much do you agree with the statement: Our brand stands for innovation and quality?\u201d<\/p>\n<p>Answer options:<\/p>\n<p>\u2022 Strongly disagree<br \/>\n\u2022 Disagree<br \/>\n\u2022 Neutral<br \/>\n\u2022 Agree<br \/>\n\u2022 Strongly agree<\/p>\n<h5>Purchasing behavior analysis<\/h5>\n<p>The Likert scale is useful for exploring consumer buying habits and preferences. For example, structured questions such as the example below can provide important data on consumer trends, helping companies adapt their offerings to market needs.<\/p>\n<p>Example question:<br \/>\n\u201cI often buy organic products.\u201d<\/p>\n<p>Answer options:<\/p>\n<p>\u2022 Strongly disagree<br \/>\n\u2022 Disagree<br \/>\n\u2022 Neutral<br \/>\n\u2022 Agree<br \/>\n\u2022 Strongly agree<\/p>\n<h5>Evaluation of the effectiveness of training and learning<\/h5>\n<p>In education and business, Likert scales can be used to measure the effectiveness of training courses and educational programs. Questions such as \u201cDo I feel prepared after completing this course?\u201d allow educators and trainers to assess the impact of their program and make any improvements.<\/p>\n<p>Example question:<br \/>\n\u201cI feel prepared to use the skills learned during this course\u201d<\/p>\n<p>Answer options:<\/p>\n<p>\u2022 Strongly disagree<br \/>\n\u2022 Disagree<br \/>\n\u2022 Neutral<br \/>\n\u2022 Agree<br \/>\n\u2022 Strongly agree<\/p>\n<h5>Likert phrased in the form of a question<\/h5>\n<p>Deviating from the original model, it is common to find questionnaires with Likert questions not phrased in the affirmative form. This form is not properly correct but nevertheless has become commonly used.<\/p>\n<p>Example of a non-affirmative question:<\/p>\n<p>\u201cHow likely are you to purchase our products again?\u201d<br \/>\nAnswer options:<\/p>\n<p>\u2022 Extremely unlikely<br \/>\n\u2022 Unlikely<br \/>\n\u2022 Neutral<br \/>\n\u2022 Probable<br \/>\n\u2022 Extremely likely<\/p>\n<h2>Conclusions of likert scale guide<\/h2>\n<p>Likert scales are a versatile and powerful tool for collecting data on opinions and attitudes.<\/p>\n<p>It is critical to consider ethical implications in the design and interpretation of Likert scale surveys. Questions should be phrased in a respectful and neutral manner, avoiding influencing answers. It is essential to ensure the anonymity and confidentiality of participants, especially when addressing sensitive issues.<\/p>\n<p>A rigorous and scientific approach to survey <a alt=\"design and analysis\" href=\"\/en\">design and analysis<\/a> can provide valuable insights and support strategic decisions in academic, business, and social settings.<\/p>\n<div style=\"padding: 20px; border-radius: 5px; margin: 50px auto; width: 80%; height: 180px; background-color: #27446a; text-align: center;\">\n<div style=\"margin-bottom: 30px; color: #fff;\"><b>Explore the features of  IdSurvey: the powerful, versatile, and easy-to-use survey software.<\/b><\/div>\n<p><a style=\"background: #72A04E; color: #fff; padding: 10px 15px; border-radius: 5px;\" alt=\"discover IdSurvey\" href=\"https:\/\/www.idsurvey.com\/en\/\"><strong>Discover IdSurvey<\/strong><\/a><\/p>\n<\/div>\n<h2 style=\"margin-top: 50px;\">Likert scale FAQ<\/h2>\n<p><strong>What is the likert scale?<\/strong><br \/>\nThe Likert scale is a rating scale used in surveys to measure respondents&#8217; opinions. Each Likert scale question consists of a statement and a set of answer options that allow respondents to express their degree of agreement or disagreement. The answers are structured with an ordinal scale, for example from \u201cStrongly disagree\u201d to \u201cStrongly agree.\u201d<\/p>\n<p><strong>What are the advantages of using the Likert scale?<\/strong><br \/>\nAdvantages include ease of use and understanding and the ability to measure the complexity of respondents&#8217; feelings and perceptions. The range of answers a likert scale allows can be easily adapted to different research needs. For example, you can create questions with options ranging from \u201cStrongly disagree\u201d to \u201cStrongly agree,\u201d or from \u201cExtremely dissatisfied\u201d to \u201cExtremely satisfied,\u201d etc..<\/p>\n<p><strong>How to analyze Likert scale data statistically?<\/strong><br \/>\nLikert scale data can be analyzed using descriptive statistics such as mode and median. By forcing the type of data, some researchers turn the ordinal scale into an interval scale, allowing the calculation of mean and standard deviation. Nonparametric tests can also be used to compare groups or check correlations. The use of correlation coefficients such as Spearman can help identify relationships between variables.<\/p>\n<p><strong>How many answer options should I use in a Likert scale question?<\/strong><br \/>\nGenerally, Likert scales use 5 or 7 answer options. A 5-point scale offers a good balance between simplicity and accuracy, while a 7-point scale can provide greater sensitivity in measurement. Odd-numbered scales allow a neutral answer to be included, while even-numbered scales force the respondent to take a positive or negative position.<\/p>\n<p><strong>When to use a Likert scale?<\/strong><br \/>\nLikert scales are ideal for assessing respondents&#8217; feelings on specific topics, such as customer satisfaction, service quality, employee experience, and effectiveness of products or programs. They are especially useful when you want to get a detailed view of opinions while avoiding the use of open-ended answers.<\/p>\n<p><strong>What is the difference between the Likert scale and other rating scales?<\/strong><br \/>\nThe Likert scale differs from other scales &#8211; such as the nominal scale or the ordinal scale &#8211; in that it allows measurement not only of the presence of an opinion, but also the degree of intensity with which it is expressed. Unlike a dichotomous variable (Yes\/No), the Likert scale allows a wider range of answers, providing more detail on the degree of agreement or disagreement.<\/p>\n<p><strong>What common mistakes can be made when designing Likert questions?<\/strong><br \/>\nCommon mistakes include: using ambiguous or complex statements, formulating questions that suggest an answer (wording bias), and using too many or too few answer options. It is important to maintain clear, neutral language and make sure that all answer options are easily understood.<\/p>\n<p><strong>Why is the Likert scale preferred over other question types?<\/strong><br \/>\nThe Likert scale is preferred because it allows the collection of detailed and easily quantifiable data on opinions, easy to implement, and allows a large volume of answers to be collected quickly. In addition, the use of closed questions facilitates data analysis compared to open-ended questions, reducing the time and resources required for interpretation.<\/p>\n<p><strong>How can I improve the quality of Likert scale surveys?<\/strong><br \/>\nIt is important to: formulate clear questions focused on a specific topic, balance positive and negative statements to reduce bias, test the survey with a small group before distribution, and make sure the answer options are consistent and understandable.<\/p>\n<p><strong>What are the disadvantages of the Likert scale?<\/strong><br \/>\nDisadvantages include a greater possibility of acquiescence bias (tendency to answer positively), the risk of respondents choosing the neutral answer to avoid taking a position (central tendency bias), and the difficulty in dealing with ordinal data in statistical analysis. In addition, the Likert scale may not be suitable for measuring complex opinions that require numerical or more detailed answers.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Likert scale is a key tool in psychometrics and social research, used in surveys to collect data on people&#8217;s opinions, perceptions and attitudes. Introduced by Rensis Likert in 1932, it allows participants to express their degree of agreement or disagreement through questions posed in the form of statements and answers that allow them to [&hellip;]<\/p>\n","protected":false},"author":9,"featured_media":51565,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"wl_entities_gutenberg":"","footnotes":""},"categories":[38],"tags":[],"wl_entity_type":[86],"class_list":["post-51510","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-world","wl_entity_type-article"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.0 (Yoast SEO v26.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to create and analyze likert scale data - IdSurvey<\/title>\n<meta name=\"description\" content=\"Likert Scale: a guide to designing and interpreting data to achieve reliable results and 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