Journal articles: 'Likert scale'

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Kim, Kwangmin. "Likert Scale." Korean Journal of Family Medicine 32, no. 1 (2011): 1. http://dx.doi.org/10.4082/kjfm.2011.32.1.1.

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Koo, Malcolm, and Shih-Wei Yang. "Likert-Type Scale." Encyclopedia 5, no. 1 (2025): 18. https://doi.org/10.3390/encyclopedia5010018.

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The Likert-type scale is a widely used psychometric instrument for measuring attitudes, opinions, or perceptions in research contexts. It presents respondents with a series of statements accompanied by symmetrical response options, typically structured on a five-point scale ranging from “Strongly Disagree” to “Strongly Agree”. Each point on the scale represents a gradation of agreement or sentiment, allowing researchers to transform subjective responses into quantifiable data for statistical analysis and interpretation.

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Albaum, Gerald. "The Likert Scale Revisited." Market Research Society. Journal. 39, no. 2 (1997): 1–21. http://dx.doi.org/10.1177/147078539703900202.

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This study examined the effect of alternative scale formats on reporting of intensity of attitudes on Likert scales of agreement. A standard one-stage format and an alternate two-stage format were tested in three separate studies on samples of university students in three countries. In general the two-stage format generated the greatest percentage of extreme-position (i.e. most intense) responses across scales. A test of predictive ability showed that the two-stage format was a better predictor of product preferences. Underlying data structures did not differ much between the two.

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Joshi, Ankur, Saket Kale, Satish Chandel, and D. Pal. "Likert Scale: Explored and Explained." British Journal of Applied Science & Technology 7, no. 4 (2015): 396–403. http://dx.doi.org/10.9734/bjast/2015/14975.

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Gunderman, Richard B., and Stephen Chan. "The 13-Point Likert Scale." Academic Radiology 20, no. 11 (2013): 1466–67. http://dx.doi.org/10.1016/j.acra.2013.04.010.

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Leonor, M. M. Rosa, G. S. Manaces Easud, and P. P. Luis Fernando. "Indeterminate Likert Scale in Social Sciences Research." International Journal of Neutrosophic Science 19, no. 1 (2022): 289–97. http://dx.doi.org/10.54216/ijns.190125.

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The Likert scale is by far the most popular psychometric tool for collecting data. The ordinal structure and confined style of the Likert scale make it prone to information misinterpretation and loss. Depending on the consumers' moods, replies in the real world are sometimes erratic, imprecise, and ill-defined. Neutrosophy (the study of the implementation of the provisions and indeterminacy) is utilized to accurately portray the answers. This work introduces a neutrosophic-informed, agnostic version of the Likert scale. Clustering users based on their comments is an efficient method of segmenting the population and marketing to them. In this research, we offer a clustering approach for responses received using arbitrary Likert scales. When dealing with real-world events, indeterminate Likert scales are superior in recording replies properly.

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Simamora, Bilson. "Skala Likert, Bias Penggunaan dan Jalan Keluarnya." Jurnal Manajemen 12, no. 1 (2022): 84–93. http://dx.doi.org/10.46806/jman.v12i1.978.

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Many researchers refer to any graded scale as a Likert scale. In addition to misnaming, this assumption can lead to errors in the data scale and analysis technique. This paper aims to explain the instrument scales in the survey and the data types they generate. Thus, the position of the Likert scale compared to other measurement scales becomes apparent so that researchers can give the right name to the scale they use and choose the proper data analysis technique according to its type.

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Albaum, Gerald, and Brian D. Murphy. "Extreme Response on a Likert Scale." Psychological Reports 63, no. 2 (1988): 501–2. http://dx.doi.org/10.2466/pr0.1988.63.2.501.

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This study examined the effect of alternative scale formats on reporting of extreme attitudes on Liken scales of agreement. The formats were tested on samples of university students who responded to a set of statements about economic systems. In general, a two-stage format generated a greater percentage of extreme-position responses than did the common one-stage format, with a modified one-stage version falling in between.

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Gregoire, Timothy G. "Analysis of Likert-scale data revisited." Psychological Bulletin 105, no. 1 (1989): 171. http://dx.doi.org/10.1037/h0092469.

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Pervez, A. K. M. Kanak, Md Maniruzzaman, Ashfaq Ahmad Shah, Nur Nabi, and Abdou Matsalabi Ado. "The Meagerness of Simple Likert Scale in Assessing Risk: How Appropriate the Fuzzy Likert is?" NUST Journal of Social Sciences and Humanities 6, no. 2 (2021): 138–50. http://dx.doi.org/10.51732/njssh.v6i2.55.

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Social scientists around the world commonly use the Likert scale. The scale has some limitations; in many cases, researchers are ignoring those limitations. Many social scientists have been trying to find out an alternative, but all initiatives do not correctly solve the problems. Among all limitations, the most critical issue is that Likert scale adopts a similar variance between two successive scale points. Fuzzy-Likert scale is a useful alternative for solving the existing limitation of the traditional Likert scale. Therefore, the current article describes the limitations of existing Likert scale and application of Fuzzy-Likert scale in perceived risk assessment. Naturally, risks are interrelated with different factors. Assessing risks with simple existing Likert scale is not entirely appropriate. A well-structured Fuzzy-Likert scale can be used to mitigate the existing problems. This article clarifies how efficiently researchers can use a Fuzzy-Likert scale for assessing the perceived risk in agriculture using a simple structured questionnaire with the help of an example. To reach the conclusions and recommendations, the researchers used different published articles, online repositories, report etc. through content analysis.

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Louangrath, P.I., and C. Sutanapong. "Validity and Reliability of Survey Scales." Inter. J. Res. Methodol. Soc. Sci. 4, no. 4 (2018): 99–114. https://doi.org/10.5281/zenodo.2545038.

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The objective of this paper is to evaluate Likert and non-Likert scales for quantitative survey. The data used in the evaluation of the scale is the scale components. The scales used for the evaluation include the following types: (0,1,2,3), (1,2,3,4,5, (1,2,3,4,5,6,7), and (1,2,3,4,5,6,7,8,9,10). These scales are categorized into two types, namely Likert and non-Likert. The scale (0,1,2,3) is classified as non-Likert; the remaining scales are Likert scales. The efficacy of various scales is evaluated on the basis of fitness. We defined fitness as the ratio between shape and scale of the scaled obtained through the QQ plot linear equation. We found that scale (0,1,2,3) is the most effective scale type for quantitative response choice. The efficacy of the scale was measured by the absolute error of the scale’s fitness CDF. The absolute error of the CDF of the fitness were 0.14, 0.22, 0.25 and 0.26 for the following types: (0,1,2,3), (1,2,3,4,5, (1,2,3,4,5,6,7), and (1,2,3,4,5,6,7,8,9,10), respectively. The results of GOF under the likelihood ratio test, Wald statistic and Langrangian multiplier shows that the non-Likert scale (0,1,2,3) has the best fit in the probability space of the unit circle: 0.71, 0.68, and 0.70, respectively. Response in a form of (0,1,2,3) is the best form of response scale for quantitative survey. This finding is a contribution to the field because the common use of the Likert scale has made findings and conclusion in many cases in social science research lacking validity due to low accuracy.

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Louangrath, P. I. "Reliability and Validity of Survey Scales." Inter. J. Res. Methodol. Soc. Sci. 4, no. 1 (2018): 50–62. https://doi.org/10.5281/zenodo.1322695.

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In this paper, we answered two questions: What is the reliability of a response scale in a question? What is the validity of a response scale in a question? The purpose of this paper is to present practical tools for measuring the reliability and validity of response scales used in written survey. Reliability measures consistency and validity measures precision. Our objective is to determine the reliability and validity of Likert and non-Likert scales used in research instrument. The data came from the numerical values of each type of scale. The Likert-type of scales include (1,2,3,4,5), (1,2,3,4,5,6,7) and (1,2,3,4,5,6,7,8,9,10). Non-Likert scale was (0,1,2,3). Reliability was measured by the estimated of  under system analysis. The response space was proxied as a system to create a range between maximum and minimum values in the scale. Validity was tested by using the Fisher transformation of the estimated Z score of  series. Empirical evidence shows that non-Likert scale (0,1,2,3) is 92% reliable while the Likert-type of scale had 90, 89, and 88% reliability. Validity test showed that non-Likert scale was 93% reliable, while the Likert-type scale had 89, 61, and 57% precision. Through Monte Carlo simulation and NK landscape method for optimization, the ability of information retention for non-Likert scale was 0.96 and 0.73, 0.75, and 0.77 for Likert scales. We standardize the scale efficacy in a 5.0 system, the non-Likert scale is 4.73 and 2.35, 2.45, and 2.41 for Likert scales.In this paper, we answered two questions: What is the reliability of a response scale in a question? What is the validity of a response scale in a question? The purpose of this paper is to present practical tools for measuring the reliability and validity of response scales used in written survey. Reliability measures consistency and validity measures precision. Our objective is to determine the reliability and validity of Likert and non-Likert scales used in research instrument. The data came from the numerical values of each type of scale. The Likert-type of scales include (1,2,3,4,5), (1,2,3,4,5,6,7) and (1,2,3,4,5,6,7,8,9,10). Non-Likert scale was (0,1,2,3). Reliability was measured by the estimated of  under system analysis. The response space was proxied as a system to create a range between maximum and minimum values in the scale. Validity was tested by using the Fisher transformation of the estimated Z score of  series. Empirical evidence shows that non-Likert scale (0,1,2,3) is 92% reliable while the Likert-type of scale had 90, 89, and 88% reliability. Validity test showed that non-Likert scale was 93% reliable, while the Likert-type scale had 89, 61, and 57% precision. Through Monte Carlo simulation and NK landscape method for optimization, the ability of information retention for non-Likert scale was 0.96 and 0.73, 0.75, and 0.77 for Likert scales. We standardize the scale efficacy in a 5.0 system, the non-Likert scale is 4.73 and 2.35, 2.45, and 2.41 for Likert scales.

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Lindner, James R., and Nicholas Lindner. "Interpreting Likert type, summated, unidimensional, and attitudinal scales: I neither agree nor disagree, Likert or not." Advancements in Agricultural Development 5, no. 2 (2024): 152–63. http://dx.doi.org/10.37433/aad.v5i2.351.

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This paper provides a rationale and convention for discussing the true limits and interpretation of data collected using unidimensional, summated, Likert-type, and attitudinal scales used in research investigating human behavior, sociology, education, psychology, and other related fields of study. All vague quantifiers must be described in methods and findings. The true limits of the scale and of each vague quantifier should be described. This information should be placed in the methods section. A five-point summated scale, for example, can be interpreted as follows: Strongly Agree = 5 - 4.51, Agree = 4.5 - 3.51, Neither Agree nor Disagree = 3.5 - 2.51, Disagree = 2.5 - 1.51, Strongly Disagree = 1.5 - 1. This paper also provides a rationale and convention for the use of nonstandardized effect size (ES) estimates to describe the magnitude and strength of the effect. This is accomplished by subtracting one summated M from another summated M and interpreted using the following convention: Small (ES = .19 and lower); Medium (ES = .20 - .49); and Large (ES = .50 and higher). The rationale for this is based on the intuitiveness of the measure, true limits of the scale, and scale intervals.

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Duşa, Adrian, and Valeriu Frunzaru. "The Visual Analogue Scale. An Alternative to the Likert Type Response Scales. An Alternative to the Likert Type Response Scales." International Review of Social Research 1, no. 1 (2011): 91–103. http://dx.doi.org/10.1515/irsr-2011-0005.

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Abstract For about eight decades, research instruments in the social sciences have been orbiting around Likert’s proposal for his famous response scale. Before him, and also after he managed to impose it, many researchers have tried to find a better solution. This, however, has proven difficult. While solving methodological problems for measuring concepts, by concentrating all the responses in only five categories brings major disadvantages as well: it has extremely low variation, it does not produce metric scores unless combined with similar items, and it cannot be used as such for advanced statistical analysis. In this article, we propose using a continuous response scale as a solution to each of these problems. In our opinion, the possible application of this solution has an extremely high potential to advance social science research methodology.

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Mirahmadizadeh, Alireza, Hamed Delam, Mozhgan Seif, and Reyhaneh Bahrami. "Designing, Constructing, and Analyzing Likert Scale Data." Journal of Education and Community Health 5, no. 3 (2018): 63–72. http://dx.doi.org/10.21859/jech.5.3.63.

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Uz Zaman, Nadeem, Zainab Bibi, Sana Ur Rehman Sheikh, and Abdul Raziq. "Manualizing Factor Analysis of Likert Scale Data." Journal of Management Sciences 7, no. 2 (2020): 56–67. http://dx.doi.org/10.20547/jms.2014.2007204.

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Yamashita, Taichi. "Analyzing Likert scale surveys with Rasch models." Research Methods in Applied Linguistics 1, no. 3 (2022): 100022. http://dx.doi.org/10.1016/j.rmal.2022.100022.

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Kang, Man-Ki, Chang-Eun Lee, and Gue-Tak Chio. "Fuzzy Hypotheses Testing of Likert Fuzzy Scale." Journal of Korean Institute of Intelligent Systems 15, no. 5 (2005): 533–37. http://dx.doi.org/10.5391/jkiis.2005.15.5.533.

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FLASKERUD, JACQUELYN H. "Is the Likert Scale Format Culturally Biased?" Nursing Research 37, no. 3 (1988): 185???186. http://dx.doi.org/10.1097/00006199-198805000-00013.

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Iyo, Iyo, Manuel Ricardo Vel� .., Javier D. Bosquez .., and Marina Abdurashidova. "Neutrosophic Likert Scale for Evaluating Environmental Sanctions." International Journal of Neutrosophic Science 24, no. 1 (2024): 24–34. http://dx.doi.org/10.54216/ijns.240103.

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One of the fundamental tools to guarantee compliance with environmental laws is the sanctions, which act as deterrent mechanisms against possible violations. The present study focused on evaluating public perceptions towards environmental policies and sanctions, applying a methodology that integrates neutrosophy and indeterminate Likert scales to capture the complexity of human opinions on environmental legislation. By distributing a structured questionnaire to a representative sample, data was collected and subsequently analyzed using neutrosophic logic and cluster analysis techniques. The results underlined the importance of considering indeterminacy and uncertainty in the study of public perceptions towards environmental sanctions, providing valuable data for the improvement of environmental policies and communication in this area. This study demonstrates the relevance of addressing human attitudes and behaviors toward environmental challenges inclusively and effectively.

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Cheng, Che, Keng-Ling Lay, Yung-Fong Hsu, and Yi-Miau Tsai. "Can Likert scales predict choices? Testing the congruence between using Likert scale and comparative judgment on measuring attribution." Methods in Psychology 5 (December 2021): 100081. http://dx.doi.org/10.1016/j.metip.2021.100081.

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Nikita Dewi and Joko Soebagyo. "Analisis Bibliometrik terhadap Likert Scale Mathematics Berbasis VOSViewer." Buana Matematika : Jurnal Ilmiah Matematika dan Pendidikan Matematika 12, no. 1 (2022): 71–82. http://dx.doi.org/10.36456/buanamatematika.v12i1.5837.

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This bibliometric analysis focuses on publications related to the Likert scale using VOSViewer based on Google Scholar data. This study was conducted to determine the number of international publications on the trend of research development using a Likert scale in the time interval between years 0-0. The data search was carried out using the PoP (Publish or Perish) software based on Google Scholar with the keywords Likert Scale Mathematics. The data is in the form of journals with a sample of 1000 journals. Then the data were analyzed using VOSViewer and Ms Excel. The results showed that the number of research publications related to Likert scale mathematics experienced an unstable situation where the most publications occurred in the 2012-2016 interval, namely as many as 246 article titles. The publishers who publish the most scientific journals are Elsevier, Springer, Taylor & Francis, and Wiley Online Library. Likert scale mathematics research with a fairly high density, namely the Likert scale, the rest has a low density such as validation, engineering, mathematics performance so that it is possible to be used as a theme for the next research.

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Lipovetsky, Stan, and Michael Conklin. "Decreasing Respondent Heterogeneity by Likert Scales Adjustment via Multipoles." Stats 1, no. 1 (2018): 169–75. http://dx.doi.org/10.3390/stats1010012.

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A description of Likert scales can be given using the multipoles technique known in quantum physics and applied to behavioral sciences data. This paper considers decomposition of Likert scales by the multipoles for the application of decreasing the respondents’ heterogeneity. Due to cultural and language differences, different respondents habitually use the lower end, the mid-scale, or the upper end of the Likert scales which can lead to distortion and inconsistency in data across respondents. A big impact of different kinds of respondent is well known, for instance, in international studies, and it is called the problem of high and low raters. Application of a multipoles technique to the row-data smoothing via prediction of individual rates by the histogram of the Likert scale tiers produces better results than standard row-centering in data. A numerical example by marketing research data shows that the results are encouraging: while a standard row-centering produces a poor outcome, the dipole-adjustment noticeably improves the obtained segmentation results.

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Marzuki, Hanis Nabilah, Izzah Zahirah, May Nak Lau, Elavarasi Kuppusamy, Nik Mukhriz Nik Mustapha, and Asma Ashari. "Likert scale versus the visual analogue scale in evaluating dentofacial aesthetics: a systematic review." Australasian Orthodontic Journal 40, no. 1 (2024): 158–68. http://dx.doi.org/10.2478/aoj-2024-0010.

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Abstract Background Numerous studies rating dentofacial aesthetics have been conducted by using the Likert Scale or the Visual Analogue Scale (VAS). The Likert Scale is an assessment tool consisting of items that require respondents to rank a query using graded declarative statements. Alternatively, the VAS is a tool that requires the respondents to place a mark along a horizontal line to rate a particular item. Objective This systematic review aimed to identify the validity and reliability of the Likert scale and VAS in evaluating dentofacial aesthetics. A second objective was to identify which scale is preferable for use by clinicians and patients in determining dental aesthetics. Search methods With no language limitations, a comprehensive electronic database search was conducted in the PubMed, Cochrane Central Register of Controlled Trials, Scopus, Web of Science, Ovid, and OpenGrey databases using keywords and Mesh terms combinations: (“Likert scale” OR “Likert”) AND (“visual analogue scale” OR “VAS”) AND (“aesthetic*” OR “facial*” OR “dental*” OR “dentist*”). The selection criteria were set based on the PICO format. Population (P): Laypeople and/ or dental clinicians; Intervention (I) and Control (C): Likert Scale and Visual Analog Scale for measuring dentofacial aesthetics; Outcome (O): Validity, reliability, and the patient’s or clinician’s preference for using the Likert Scale or the Visual Analog Scale for measuring dentofacial aesthetics. The risk of bias was assessed using the STROBE checklist for observational studies and Version 2 of the Cochrane Risk of Bias Tool for Randomized Trials (RoB 2) for randomized controlled trials. The results were summarised qualitatively; no meta-analysis was conducted due to the high level of heterogeneity of the included studies. Results of the synthesis Both the Likert Scale and VAS are valid and reliable for scoring dentofacial aesthetics but each have their own advantages in aesthetic evaluation. There were different preferences for the two scoring methods. Conclusions Overall, there is insufficient data to draw a conclusion that the VAS or the Likert scale is superior. Either of these scales may be used to rate dentofacial aesthetics.

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Baghaei, Purya, and Farshad Effatpanah. "Nonparametric Kernel Smoothing Item Response Theory Analysis of Likert Items." Psych 6, no. 1 (2024): 236–59. http://dx.doi.org/10.3390/psych6010015.

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Likert scales are the most common psychometric response scales in the social and behavioral sciences. Likert items are typically used to measure individuals’ attitudes, perceptions, knowledge, and behavioral changes. To analyze the psychometric properties of individual Likert-type items and overall Likert scales, mostly methods based on classical test theory (CTT) are used, including corrected item–total correlations and reliability indices. CTT methods heavily rely on the total scale scores, making it challenging to directly examine the performance of items and response options across varying levels of the trait. In this study, Kernel Smoothing Item Response Theory (KS-IRT) is introduced as a graphical nonparametric IRT approach for the evaluation of Likert items. Unlike parametric IRT models, nonparametric IRT models do not involve strong assumptions regarding the form of item response functions (IRFs). KS-IRT provides graphics for detecting peculiar patterns in items across different levels of a latent trait. Differential item functioning (DIF) can also be examined by applying KS-IRT. Using empirical data, we illustrate the application of KS-IRT to the examination of Likert items on a psychological scale.

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Anjaria, Kushal. "Knowledge derivation from Likert scale using Z-numbers." Information Sciences 590 (April 2022): 234–52. http://dx.doi.org/10.1016/j.ins.2022.01.024.

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Wakita, Takafumi, Natsumi Ueshima, and Hiroyuki Noguchi. "Psychological Distance Between Categories in the Likert Scale." Educational and Psychological Measurement 72, no. 4 (2012): 533–46. http://dx.doi.org/10.1177/0013164411431162.

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Seval; TOKER, DÖNMEZ. "Construction of a likert-type transformational Leadership Scale." Ankara Üniversitesi Dil ve Tarih-Coğrafya Fakültesi Dergisi - DTCF Dergisi 57, no. 2 (2017): 753–75. http://dx.doi.org/10.1501/dtcfder_0000001537.

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Wadgave, Umesh, and Mahesh R. Khairnar. "Parametric tests for Likert scale: For and against." Asian Journal of Psychiatry 24 (December 2016): 67–68. http://dx.doi.org/10.1016/j.ajp.2016.08.016.

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Lee, Jerry W., Patricia S. Jones, Yoshimitsu Mineyama, and Xinwei Esther Zhang. "Cultural differences in responses to a likert scale." Research in Nursing & Health 25, no. 4 (2002): 295–306. http://dx.doi.org/10.1002/nur.10041.

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Drasgow, Fritz, Oleksandr S. Chernyshenko, and Stephen Stark. "75 Years After Likert: Thurstone Was Right!" Industrial and Organizational Psychology 3, no. 4 (2010): 465–76. http://dx.doi.org/10.1111/j.1754-9434.2010.01273.x.

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For over three-quarters of a century researchers and practitioners have analyzed rating scale data using methods that assume a dominance response process wherein an individual high on the trait assessed is assumed to answer positively with high probability. This approach derives from Likert's famous 1932 approach to the development and analysis of rating scales. In this paper, we argue that Likert scaling and related methods are misguided. Instead, we propose that methods that have evolved from Thurstone (1927, 1928, 1929) scaling provide a better representation of the choice process underlying rating scale judgments. These methods hypothesize an ideal point response process where the probability of endorsement is assumed to be directly related to the proximity of the statement to the individual's standing on the assessed trait. We review some research showing the superiority of ideal point methods for personality assessment and then describe several settings in which ideal point methods should provide tangible improvements over traditional approaches to assessment.

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Muhenje, Chimwani Pamela. "A Theoretical Examination of the Composite Index or Measuring a Variable Using Likert Scale." Research Journal of Business and Finance 1, no. 1 (2023): XX. http://dx.doi.org/10.58721/rjbf.v1i1.125.

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The assumption whilst using Likert scales is that the intensity or strength of character and personality traits such as attitudes can be measured on a linear continuum from strongly agree to strongly disagree. This paper discusses Likert scale items vis-a-vis Likert type items and highlights the difference between the two. Likert scale used interrelated items that can be converted into a composite score during data analysis. As such appropriate inferential statistical techniques can be applied. Conversely Likert items are individual items based on an ordinal scale and data analysis is specific to the item and only non-parametric statistical methods are appropriate.

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Brown, Simon, and David C. Simcock. "Some fundamental issues arising from averaging Likert scale scores." Deviot Institute Working Papers 2023 (March 1, 2023): 01. https://doi.org/10.5281/zenodo.7954234.

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Likert scale questions are very widely employed to assess the attitudes of respondents. Each respondent is asked to select the descriptor (“disagree” or “strongly agree”, for example) that most closely reflects her or his attitude to a statement or question. In analysing the data the descriptors are often assigned numerical labels which are then treated as though they are numbers. In such cases, a mean is often reported, sometimes accompanied by the variance or standard deviation. This approach has obvious implications for the interpretation of the descriptors, but the mean of the numerical labels is also problematic in that there can be very many ways of achieving any one mean score. The statistical analysis of the labels has mathematical implications that are used here to analyse some of the weaknesses in that approach. We suggest that the mean and the variance are relatively ambiguous measures that would be improved by showing the distribution.

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Belén Catalán-Gregori. "The Use of Likert Scale in Children. Theoretical Review." Power System Technology 49, no. 1 (2025): 1816–32. https://doi.org/10.52783/pst.1919.

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The use of the Likert scale in children as a data collection tool has not been extensively researched or utilized due to the children cognitive development level, the studies conducted present different results, all agreeing on the necessity to adapt each questionnaire to the age level of the sample. This article presents a collection of twelve scientific studies aimed at categorizing the samples, methodologies, results and conclusions with the main objective of reviewing the existing literature on the use of Likert scales with children in order to summarize the most commonly implemented actions that have proven to be effective. As a result, it is presented the use of emoji-based scale, the effect of the number of answer choices, the use of neutral mid-point and the reason for the extreme positive responses. In the discussion part it is found that the use of Emoji-based scale needs further research and his effectivity depends on what it is wanted to measure, that the 4 or 5-point response scale it’s the optimal, the younger child do not understand de negative questions and tend to have more extreme positive responses due to satisficing or social desirability or to their low level of reading ability. As a conclusion, age-adapted modifications, such as using fewer response points or incorporating visual aids such as emojis, improve children's comprehension and engagement. However, more research is needed to determine how these adaptations affect the reliability and validity above all in early childhood. DOI : https://doi.org/10.52783/pst.1919

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Yaska, Mutah, and Bata Malgwi Nuhu. "Assessment of Measures of Central Tendency and Dispersion Using Likert-Type Scale." African Journal of Advances in Science and Technology Research 16, no. 1 (2024): 33–45. http://dx.doi.org/10.62154/ajastr.2024.016.010379.

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This research aims to analyzing measures of central tendency (mean, median, and mode), and measures of dispersion (variance and standard deviation) and to graphically present the simulated data using Likert scale measurement. Mean (μ), Median (Md), Mode (Mod), Variance ( ), and Standard deviation (σ) have been calculated on simulated data using 5 and 4 points Likert scales with four different statements. Data analysis is key in any development especially when there is need to understanding people's opinions on a particular issue that has attracted the attention of a specific population. Likert scale of measurement is one of the methods that use statement problems in collecting statistical data in which the respondent agrees with the opinion or disagrees. Considering the 5-point Likert scale, the measure of dispersion in which statement two had the highest standard deviation of 1.53 and the mean value of 2.35 being the least of the mean values implies the significant spread of the data across opinions. Statement one of the 4-points Likert scale has two modes (bimodal) where the opinions “Strongly Agree” and “Agree” have ties of 20 responses each. This implies that interpretation of such data may be difficult, because the data can be roughly split into two different groups, and can be useful for further research to find a mode to draw conclusions. The best form of Likert scale can be recommended for 5-point Likert scale so as to give room for undecided opinion. Other measures of central tendency and dispersion can be useful in interpretation but the recommended statistical tool is standard deviation as it shows the spread of the opinions about the mean.

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Vickers, Andrew J. "COMPARISON OF AN ORDINAL AND A CONTINUOUS OUTCOME MEASURE OF MUSCLE SORENESS." International Journal of Technology Assessment in Health Care 15, no. 4 (1999): 709–16. http://dx.doi.org/10.1017/s0266462399154102.

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The relationship between an ordinal (Likert) and a continuous (visual analog) measure of pain was investigated in 400 long-distance runners experiencing delayed-onset muscle soreness. Subjects completed a 100-mm visual analog scale (VAS) and a 7-point Likert scale twice a day for 5 days, starting at 9:00 PM on the evening of the run. The relationship between scales was modeled by calculating the median VAS for each Likert score and the modal Likert score for each point on the VAS. Standardized means were calculated by dividing the total of 5 day scores by the standard error of 5 day scores for all subjects. The relationship between scales was approximately linear, but VAS scores recorded concurrently with each Likert score varied enormously. VAS responses for a Likert score of zero were lower than expected, apparently because subjects chose zero only if they were completely free of soreness. The standardized mean of the Likert scale was higher (34.1 [SD 20] versus 30.4 [SD20] p <<< .0001), suggesting greater responsiveness. The Likert scale can be recommended as a method of measuring muscle soreness, but researchers should not anchor the lowest score to zero pain.

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Tóth, Zsuzsanna E., Gábor Árva, and Rita V. Dénes. "Are the ‘Illnesses’ of Traditional Likert Scales Treatable?" Quality Innovation Prosperity 24, no. 2 (2020): 120. http://dx.doi.org/10.12776/qip.v24i2.1439.

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Purpose: The main aim of this paper is to introduce the development and the application of a fuzzy rating scale in measuring customer satisfaction which are to be demonstrated through a healthcare example in order to illustrate how the proposed methodology is able to enhance the reliability of traditional Likert scale-based evaluations.Methodology/Approach: The proposed methodology is built on fuzzy sets the membership function of which is composed of two sigmoid functions by applying Dombi’s conjunction operator. The possible ‘values’ of the linguistic variable expressing customer satisfaction are to be expressed by these functions which can also be linked to the level of organizational performance allowing the illustration of the mainly nonlinear relationship between the provided and perceived service performance.Findings: The application of the proposed fuzzy rating scale confirms its ability to reflect the unambiguity of human ratings as well as the context-dependency of ratings resulting in a more precise representation of human judgements.Research Limitation/implication: The presented methodology may be viewed as a viable approach in any kind of service quality evaluations where Likert-type scales are traditionally applied to handle its weaknesses.Originality/Value of paper: The proposed methodology is not only able to reflect the satisfaction of customers and the organizational performance simultaneously, but the expectations of customers related to the desired level of performance can also be incorporated into the establishment of the scale yielding to more reliably supported managerial decisions.

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Armstrong, Robert L. "The Midpoint on a Five-Point Likert-Type Scale." Perceptual and Motor Skills 64, no. 2 (1987): 359–62. http://dx.doi.org/10.2466/pms.1987.64.2.359.

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Previous studies on the points of the Likert-scale format have not addressed the question of the effect on the score resulting from the use of “neutral” or “undecided” as the midpoint of a five-point scale. The present study addressed this topic with a scale on attitude toward the school board, using 389 undergraduate and 190 graduate students in education, the latter having at least one year of teaching experience, in seven geographically separated universities in the United States. The two formats of the scale were identical (strongly agree to strongly disagree) except that one scale midpoint was designated “undecided” and the midpoint of the other scale was designated as “neutral” with “undecided” as a no-answer alternative. Analysis showed differences were negligible and little if any erosion of score appears to result.

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Phelps, Andrew S., David M. Naeger, Jesse L. Courtier, et al. "Pairwise Comparison Versus Likert Scale for Biomedical Image Assessment." American Journal of Roentgenology 204, no. 1 (2015): 8–14. http://dx.doi.org/10.2214/ajr.14.13022.

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Arnulf, Jan Ketil, Kai R. Larsen, and Øyvind L. Martinsen. "Respondent Robotics: Simulating Responses to Likert-Scale Survey Items." SAGE Open 8, no. 1 (2018): 215824401876480. http://dx.doi.org/10.1177/2158244018764803.

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Harpe, Spencer E. "How to analyze Likert and other rating scale data." Currents in Pharmacy Teaching and Learning 7, no. 6 (2015): 836–50. http://dx.doi.org/10.1016/j.cptl.2015.08.001.

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Li, Qing. "A novel Likert scale based on fuzzy sets theory." Expert Systems with Applications 40, no. 5 (2013): 1609–18. http://dx.doi.org/10.1016/j.eswa.2012.09.015.

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Kodrič, Borut, Anita Trnavčevič, Tine Bertoncel, and Nada Trunk Širca. "Garbage in garbage out: Likert scale in management research." International Journal of Innovation and Learning 37, no. 6 (2025): 1–21. https://doi.org/10.1504/ijil.2025.145684.

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Cheung, K. C., and L. C. Mooi. "A Comparison Between the Rating Scale Model and Dual Scaling for Likert Scales." Applied Psychological Measurement 18, no. 1 (1994): 1–13. http://dx.doi.org/10.1177/014662169401800101.

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Harpale, Dr. Dattatraya V., and Dr. Smita S. Harane. "Assessing Tourism Potential in Nashik District: A Likert Scale Approach to Culture, History, and Heritage Promotion." International Journal of Advance and Applied Research 5, no. 27 (2024): 75–80. https://doi.org/10.5281/zenodo.13860297.

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This research paper, titled "Assessing Tourism Potential in Nashik District: A Likert Scale Approach to Culture, History, and Heritage Promotion," aims to explore and evaluate the tourism potential of Nashik District, Maharashtra. Nashik, known for its rich cultural heritage, historical significance, and religious landmarks, presents a unique opportunity for tourism development that can enhance both cultural preservation and economic growth. The study employs a Likert Scale to quantitatively measure the perceptions and attitudes of local residents, tourists, and stakeholders regarding various aspects of tourism in the district, including its cultural, historical, and heritage attractions. The research focuses on identifying key tourist destinations within Nashik that hold significant cultural and historical value. It examines how these sites contribute to the overall tourism experience and the district's economic development. By analyzing the data collected through the Likert Scale, the study provides insights into the strengths and weaknesses of the current tourism offerings and suggests strategies for enhancing the promotion of Nashik's cultural and historical assets. The findings of this research are expected to inform policymakers, tourism planners, and local communities on how to effectively leverage Nashik's rich heritage for sustainable tourism development, ultimately contributing to the district's socio-economic growth. For this study the factor wise Likert scale is calculated. Tourism satisfaction is a psychological outcome and it is concerned with the attribute of behaviors itself. Likert scale is a technique for the measurement of attitude a scale giving values for an individual’s reactive attitude. The test is required to choose from a possible answer per item and thus to show a degree of agreement or disagreement with the attitude represented. The response to each question was rated on seven points 'Likert Scale' ranging from 1 to 7.

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Westland, J. Christopher. "Information loss and bias in likert survey responses." PLOS ONE 17, no. 7 (2022): e0271949. http://dx.doi.org/10.1371/journal.pone.0271949.

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Likert response surveys are widely applied in marketing, public opinion polls, epidemiological and economic disciplines. Theoretically, Likert mapping from real-world beliefs could lose significant amounts of information, as they are discrete categorical metrics. Similarly, the subjective nature of Likert-scale data capture, through questionnaires, holds the potential to inject researcher biases into the statistical analysis. Arguments and counterexamples are provided to show how this loss and bias can potentially be substantial under extreme polarization or strong beliefs held by the surveyed population, and where the survey instruments are poorly controlled. These theoretical possibilities were tested using a large survey with 14 Likert-scaled questions presented to 125,387 respondents in 442 distinct behavioral-demographic groups. Despite the potential for bias and information loss, the empirical analysis found strong support for an assumption of minimal information loss under Normal beliefs in Likert scaled surveys. Evidence from this study found that the Normal assumption is a very good fit to the majority of actual responses, the only variance from Normal being slightly platykurtic (kurtosis ~ 2) which is likely due to censoring of beliefs after the lower and upper extremes of the Likert mapping. The discussion and conclusions argue that further revisions to survey protocols can assure that information loss and bias in Likert-scaled data are minimal.

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Watson Todd, Richard. "Analyzing and Interpreting Rating Scale Data from Questionnaires." rEFLections 14 (March 3, 2018): 69–77. http://dx.doi.org/10.61508/refl.v14i0.114230.

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Questionnaires comprising Likert rating scale items are probably the most commonly used instrument for data collection in education and educational research, yet there is much less guidance available concerning how to analyze and interpret such rating scales than there is on how to design them. Likert scale questionnaire data is most commonly analyzed using means, although this assumes that the Likert scale descriptors are equally spaced points on a continuum. Alternative ways to analyze rating scale data include percentage frequencies, medians and modes. If means are used, there are several different approaches to interpretation depending on how concrete, intuitive or unbiased the interpretations should be. This paper presents the options available in analyzing and interpreting Likert scale data and shows how the choice between options depends on the purposes of the research.

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Abdelhafez Qasem Al-Shayeb, Abdelhafez Qasem Al-Shayeb. "The Effectiveness of the Extended Forced-Choice Format as an Alternative to the Likert Scale Format in Improving the Factor Structure of the Psychological Scales: فاعلية استخدام صورة الاختيار القسري المطوّل كأسلوب بديل لصورة "ليكرت" في تحسين البناء العاملي للمقاييس النفسية". مجلة العلوم التربوية و النفسية 6, № 17 (2022): 129–49. http://dx.doi.org/10.26389/ajsrp.b041021.

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The study aimed to examine the relative effectiveness of using the extended forced-choice format as an alternative to the Likert scale format in improving the factor structure of the psychological scales. The neuroticism factor, which is one of the five factors of the Jordanian version of NEO personality Inventory was used as an example of the psychological scales. The Descriptive correlational approach was used to achieve the objectives of the study. The data were collected from (532) available undergraduate male and female students of different academic levels and different majors who are enrolled in different sections and different colleges in four Jordanian Universities, where the selected sections were randomly assigned into two groups (Likert format, and extended forced-choice format). The results of the exploratory as well as the confirmatory factor analyses of the scale in each of its two forms revealed that the extended forced-choice format produced better construct validity indices compared to the Likert scale format, where it was found that the factor structure of the Likert scale format is contaminated; that is, the scale does not measure a unidimensional trait as it is supposed. The results also indicated a better stability index (Cronbach-Alpha) for the extended forced-choice format which was found to be (0.847) compared to the stability index of the Likert scale format which was found to be (0.705). It was recommended to conduct more studies on using the extended forced-choice format as an alternative to the Likert scale format in other psychological scales, especially if the scale includes reversed worded items besides positively worded items.

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Abdul Malik, Mohd Azry, Muhammad Firdaus Mustapha, Norafefah Mohamad Sobri, et al. "Optimal Reliability and Validity of Measurement Model in Confirmatory Factor Analysis: Different Likert Point Scale Experiment." Journal of Contemporary Issues and Thought 11 (July 1, 2021): 105–12. http://dx.doi.org/10.37134/jcit.vol11.9.2021.

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Designing a questionnaire is one of the most difficult challenges in research design, particularly when deciding which level of Likert point scale is appropriate for the instrumentation. Suitable Likert point scale used in the instrumentation able to reduce the risk of facing potential problems of not achieving reliability (indicator and internal consistency reliability) and validity (convergent validity, discriminating validity, and construct validity) and simultaneously preventing the occurrence of multicollinearity. This study compares the performance of reliability and validity of measurement construct in Confirmatory Factor Analysis (CFA) by using Likert 5-point Likert scale, 6-point Likert scale, 7-point Likert scale, 9-point Likert scale, and 10-point Likert scale. The study uses primary data based on a questionnaire data collection method which involves 100 samples from similar population characteristics for each Likert. The data were analysed using Smart-PLS software. The results suggest that expanding the range of the Likert point scale optimizes the performance of reliability and validity of the measurement model. This study offers an insight to researchers in deciding the best choice of Likert point scale to adapt in instrumentation for a better result in the quantitative analysis process.

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Cowley, Jennifer A., and Heather Youngblood. "Subjective Response Differences between Visual Analogue, Ordinal and Hybrid Response Scales." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 53, no. 25 (2009): 1883–87. http://dx.doi.org/10.1177/154193120905302506.

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The Human Factors and Ergonomics (HF/E) discipline employs different subjective response scale formats to measure subjective phenomena (e.g., hazard perception). Per the psychometrics literature, different scale formats can yield different participant responses, which is a potential threat to validity in replication studies if response scales are not consistent across study iterations. If ordinal response scales (e.g., Likert scales) yield ordinal data and continuous response scales (e.g., Visual Analogue Scales) yield continuous data then it is inappropriate to substitute one response scale for the other if they potentially yield different responses for the same question. The current research compared mean participant ratings for the same question on VAS, Likert and Hybrid response scales and found that VAS scales had significantly lower mean ratings than Likert and Hybrid response scales. Two scale features, the number of anchors (5 or 9) and the scale length (10.0cm and 19.2cm), were varied and no significant main effects or interactions resulted. In conclusion, scale types, not scale features, produced significant mean participant rating differences. To support the validity of replication research, this paper also provided a response scale taxonomy based on the scale features studied herein, that can be used to classify and report different response scales. Implications for these results and future research directions are discussed.

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Journal articles on the topic 'Likert scale'

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