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Journal articles on the topic 'Likert'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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1

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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Stratton, Samuel J. "Likert Data." Prehospital and Disaster Medicine 33, no. 2 (2018): 117–18. http://dx.doi.org/10.1017/s1049023x18000237.

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Emerson, Robert Wall. "Likert Scales." Journal of Visual Impairment & Blindness 111, no. 5 (2017): 488. http://dx.doi.org/10.1177/0145482x1711100511.

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4

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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Pye, Hayley, Saurabh Singh, Joseph M. Norris, et al. "Evaluation of PSA and PSA Density in a Multiparametric Magnetic Resonance Imaging-Directed Diagnostic Pathway for Suspected Prostate Cancer: The INNOVATE Trial." Cancers 13, no. 8 (2021): 1985. http://dx.doi.org/10.3390/cancers13081985.

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Objectives: To assess the clinical outcomes of mpMRI before biopsy and evaluate the space remaining for novel biomarkers. Methods: The INNOVATE study was set up to evaluate the validity of novel fluidic biomarkers in men with suspected prostate cancer who undergo pre-biopsy mpMRI. We report the characteristics of this clinical cohort, the distribution of clinical serum biomarkers, PSA and PSA density (PSAD), and compare the mpMRI Likert scoring system to the Prostate Imaging–Reporting and Data System v2.1 (PI-RADS) in men undergoing biopsy. Results: 340 men underwent mpMRI to evaluate suspected prostate cancer. 193/340 (57%) men had subsequent MRI-targeted prostate biopsy. Clinically significant prostate cancer (csigPCa), i.e., overall Gleason ≥ 3 + 4 of any length OR maximum cancer core length (MCCL) ≥4 mm of any grade including any 3 + 3, was found in 96/195 (49%) of biopsied patients. Median PSA (and PSAD) was 4.7 (0.20), 8.0 (0.17), and 9.7 (0.31) ng/mL (ng/mL/mL) in mpMRI scored Likert 3,4,5 respectively for men with csigPCa on biopsy. The space for novel biomarkers was shown to be within the group of men with mpMRI scored Likert3 (178/340) and 4 (70/350), in whom an additional of 40% (70/178) men with mpMRI-scored Likert3, and 37% (26/70) Likert4 could have been spared biopsy. PSAD is already considered clinically in this cohort to risk stratify patients for biopsy, despite this 67% (55/82) of men with mpMRI-scored Likert3, and 55% (36/65) Likert4, who underwent prostate biopsy had a PSAD below a clinical threshold of 0.15 (or 0.12 for men aged <50 years). Different thresholds of PSA and PSAD were assessed in mpMRI-scored Likert4 to predict csigPCa on biopsy, to achieve false negative levels of ≤5% the proportion of patients whom who test as above the threshold were unsuitably high at 86 and 92% of patients for PSAD and PSA respectively. When PSA was re tested in a sub cohort of men repeated PSAD showed its poor reproducibility with 43% (41/95) of patients being reclassified. After PI-RADS rescoring of the biopsied lesions, 66% (54/82) of the Likert3 lesions received a different PI-RADS score. Conclusions: The addition of simple biochemical and radiological markers (Likert and PSAD) facilitate the streamlining of the mpMRI-diagnostic pathway for suspected prostate cancer but there remains scope for improvement, in the introduction of novel biomarkers for risk assessment in Likert3 and 4 patients, future application of novel biomarkers tested in a Likert cohort would also require re-optimization around Likert3/PI-RADS2, as well as reproducibility testing.

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Duncan, Otis Dudley, and Magnus Stenbeck. "Are Likert scales unidimensional?" Social Science Research 16, no. 3 (1987): 245–59. http://dx.doi.org/10.1016/0049-089x(87)90003-2.

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7

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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8

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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9

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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Shiina, Kenpei. "Cursor movement in Likert ratings." Proceedings of the Annual Convention of the Japanese Psychological Association 82 (September 25, 2018): 3EV—046–3EV—046. http://dx.doi.org/10.4992/pacjpa.82.0_3ev-046.

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11

Gritsch, Simone. "Die Likert-Skala – Meinungen abbilden." ergopraxis 5, no. 01 (2012): 16–17. http://dx.doi.org/10.1055/s-0031-1300814.

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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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K.S.N, Prasad, Vijaya Saradhi M.V, and Rudra Kumar M. "Likert-Rating Consensus for SRS Documentation." HELIX 11, no. 5 (2021): 1–10. http://dx.doi.org/10.29042/2021-11-5-1-10.

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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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15

Böckenholt, Ulf. "Measuring response styles in Likert items." Psychological Methods 22, no. 1 (2017): 69–83. http://dx.doi.org/10.1037/met0000106.

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Pell, Godfrey. "Use and misuse of Likert scales." Medical Education 39, no. 9 (2005): 970. http://dx.doi.org/10.1111/j.1365-2929.2005.02237.x.

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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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18

Hartley, James. "Some thoughts on Likert-type scales." International Journal of Clinical and Health Psychology 14, no. 1 (2014): 83–86. http://dx.doi.org/10.1016/s1697-2600(14)70040-7.

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Watson, Nicholas C. "Likert or Not, We Are Biased." Anesthesiology 116, no. 5 (2012): 1160. http://dx.doi.org/10.1097/aln.0b013e3182503a86.

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เทพวรชัย, สุชาดา. "บทวิจัย : ควรใช้ X กับข้อมูลจากแบบสอบถาม Likert หรือไม่ ?" Journal of Education Studies 28, № 3 (2000): 121–26. http://dx.doi.org/10.58837/chula.educu.28.3.15.

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21

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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22

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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23

Darnton, Geoffrey. "Likert scales and questions: uses and abuses." European Conference on Research Methodology for Business and Management Studies 22, no. 1 (2023): 44–49. http://dx.doi.org/10.34190/ecrm.22.1.1748.

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when conducting business and management research, the most common strategy employed for collecting primary data is either interviews or questionnaires (or both). When questionnaires are used to collect primary data a very common approach used is to construct questions which can be called Likert-type questions or Likert scales. Numbers are allocated to the question responses of each question.. Often, this is followed by performing arithmetic or statistical operations on the allocated numbers. In many cases encountered by the author, the analysis has even included techniques such as parametric statistics and factor analysis. This paper explains why such simplistic approaches are completely invalid and should never be used. It goes on to explain how analysis can be done whilst avoiding typical hazards. Often, the writers of such papers do not understand or explain levels of measurement. Of course, Likert scales are at an ordinal level of measurement which would normally preclude the use of arithmetic, statistical, factor analysis techniques there is an additional problem of reliability because different people will interpret terms such as strongly disagree and disagree, differently. Likert was aware of these problems when writing the original paper in 1932 although at that time the term “levels of measurement” was not in use. This paper provides approaches and suggestions for avoiding the problems of data analysis when using Likert-type questions. This paper should be of assistance to those who intend to use Likert-type questions in the questionnaire.

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24

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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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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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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Gobo, Giampietro. "Ritorno a Likert. Verso una survey discorsiva." SOCIOLOGIA E RICERCA SOCIALE, no. 88 (December 2009): 5–28. http://dx.doi.org/10.3280/sr2009-088001.

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- For decades, the dilemma between open-ended and closed-ended response alternatives occupied the methodological debate. Over the years, dominant approaches in survey have reacted to this dilemma by opting for fixed response alternatives and the standardization of interviewer's behaviour. If this methodological decision has been the survey's fortune, making it the methodology most widely used in the social sciences, however it produces a large amount of biases well known in the literature. In order to re- medy these biases an alternative proposal can be designed by re-discovering and adapting two «old» proposals: Likert's technique called «fixed question/free answers», and Galtung's procedure named «open question/closed answer». Both procedures are guided by the same principle: make the interview into a conversation, let the interviewee answer freely in his/her own words, and thus release him/her from the researcher's schemes.

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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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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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Carr, Stuart C., Vanessa Powell, Maria Knezovic, Don Munro, and Malcolm MacLachlan. "Measuring motivational gravity: Likert or scenario scaling?" Journal of Managerial Psychology 11, no. 5 (1996): 43–47. http://dx.doi.org/10.1108/02683949610124816.

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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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Mellor, D., and K. A. Moore. "The Use of Likert Scales With Children." Journal of Pediatric Psychology 39, no. 3 (2013): 369–79. http://dx.doi.org/10.1093/jpepsy/jst079.

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Flaskerud, Jacquelyn H. "Cultural Bias and Likert-Type Scales Revisited." Issues in Mental Health Nursing 33, no. 2 (2012): 130–32. http://dx.doi.org/10.3109/01612840.2011.600510.

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Chan, Jason C. "Response-Order Effects in Likert-Type Scales." Educational and Psychological Measurement 51, no. 3 (1991): 531–40. http://dx.doi.org/10.1177/0013164491513002.

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37

Jamieson, Susan. "Likert scales: how to (ab)use them." Medical Education 38, no. 12 (2004): 1217–18. http://dx.doi.org/10.1111/j.1365-2929.2004.02012.x.

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Woods, Carol M. "Ramsay Curve IRT for Likert-Type Data." Applied Psychological Measurement 31, no. 3 (2007): 195–212. http://dx.doi.org/10.1177/0146621606291567.

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Hodge, D. R., and D. Gillespie. "Phrase completions: An alternative to Likert scales." Social Work Research 27, no. 1 (2003): 45–55. http://dx.doi.org/10.1093/swr/27.1.45.

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Doe, Raymond, Bryan A. Landrum, Kaelyn M. Lewis, Matthew E. Glenn, and Jacob D. Smith. "Likert Items: Should(n’t) We Really Care?" International Journal of Social Science Studies 11, no. 1 (2022): 32. http://dx.doi.org/10.11114/ijsss.v11i1.5747.

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One of the controversial methodological topics in the social and behavioral sciences is the (ab)use of Likert Scale items, Likert-type items and ranked ordered response categories. The debate is whether parametric tests can be legitimately conducted on technically ordinal response categories that are represented with numbers. Participants answered survey questions on moral disengagement, where we changed the intervals of seven response categories and tested whether assigning numbers made any difference in two separate studies. The results showed that participants’ ratings were not significantly different with or without numbers. Participants tend to covertly superimpose numbers where none were provided. Also, there were no significant interactions between assignment of numbers and ‘intervalness’. However, ratings were significantly different between two key interval groups. Knowing the assumptions of respondents to these Likert items even without numbers could inform researchers especially if parametric tests are to be conducted.

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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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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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Carifio, James, and Rocco J. Perla. "Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes." Journal of Social Sciences 3, no. 3 (2007): 106–16. http://dx.doi.org/10.3844/jssp.2007.106.116.

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Suasapha, Anom Hery. "SKALA LIKERT UNTUK PENELITIAN PARIWISATA; BEBERAPA CATATAN UNTUK MENYUSUNNYA DENGAN BAIK." JURNAL KEPARIWISATAAN 19, no. 1 (2020): 26–37. http://dx.doi.org/10.52352/jpar.v19i1.407.

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Artikel ini merupakan sebuah artikel studi literatur (literature review article) yang disusun dengan metode literatur review. Hal yang mendasari penulisan artikel ini adalah keinginan untuk memahami lebih baik mengenai Skala Likert dengan membaca beberapa literatur dan menuangkan hasilnya ke dalam bentuk manuskrip. Topik yang diulas melalui penulisan artikel ini adalah mengenai Skala Likert, sehingga kata kunci yang dipergunakan untuk mengumpulkan data, yang dalam hal ini adalah pustaka berupa buku maupun artikel jurnal adalah “Skala Likert”. Sebanyak 27 pustaka yang membahas mengenai Skala Likert telah dibaca. Mengingat Sebagian besar literatur yang dibaca adalah berupa buku yang terdiri atas banyak topik dan halaman, maka teknik membaca berupa scan, skim dan understand telah diaplikasikan. Ide utama dari penulisan artikel ini adalah untuk memaparkan semaksimal mungkin mengenai Skala Likert berdasarkan karya asli penemunya, untuk kemudian diperkuat dengan berbagai pendapat dari pakar lain mengenai Skala Likert, termasuk penggunaanya di bidang ilmu pariwisata. Bagi penulis, Literatur review mengenai Skala Likert ini menghasilkan pemahaman yang lebih baik mengenai Skala Likert dan apa saja yang harus dipertimbangkan untuk menyusun kuesioner Skala Likert dengan baik. Diharapkan, manfaat yang sama juga dapat dirasakan oleh pembacanya kelak.

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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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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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García-Fernández, Jaime, Marcelino Cuesta, Álvaro Postigo, Álvaro Menéndez-Aller, Covadonga González-Nuevo, and Eduardo García-Cueto. "Ítems tipo Likert vs. Ítems continuos: ¿Qué opción es mejor?" Revista Internacional de Sociología 82, no. 2 (2024): e252. http://dx.doi.org/10.3989/ris.2024.82.2.m23-04.

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Los ítems tipo Likert son comúnmente utilizados en ciencias sociales. Este estudio comprueba si se pueden mejorar las propiedades psicométricas de los autoinformes variando los formatos de respuesta. Los formatos comparados han sido el tipo Likert de cinco alternativas y el VAS (Visual Analog Scale). Para ello, 584 personas (75.2 % mujeres) con una edad media de 32.9 años (DT = 13.4) respondieron a dos cuestionarios online de personalidad que fueron aplicados una vez con formato tipo Likert (0-4) y una segunda vez con formato VAS (0-100). La discriminación, fiabilidad y pesos factoriales de las escalas con formato Likert han sido mayores que las del formato VAS. El formato de respuesta no afectó a la estructura factorial. Las escalas con formato VAS dan puntuaciones totales significativamente más altas. Aunque los ítems con formato de respuesta Likert muestran un mejor funcionamiento, estos resultados podrían estar influenciados por la longitud de la línea en el formato VAS.

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Sullivan, Gail M., and Anthony R. Artino. "Analyzing and Interpreting Data From Likert-Type Scales." Journal of Graduate Medical Education 5, no. 4 (2013): 541–42. http://dx.doi.org/10.4300/jgme-5-4-18.

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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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