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Article

An Approach to Developing Likert Scale Survey Results Based on the Example of a Research Study Involving a Limited Number of Students

by
Marek Gaworski
1,* and
Aleksandra Daśko
2
1
Department of Production Engineering, Institute of Mechanical Engineering, Warsaw University of Life Sciences, 02-787 Warsaw, Poland
2
Faculty of Production Engineering, Warsaw University of Life Sciences, 02-787 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2026, 16(5), 2602; https://doi.org/10.3390/app16052602
Submission received: 21 January 2026 / Revised: 4 March 2026 / Accepted: 6 March 2026 / Published: 9 March 2026
(This article belongs to the Special Issue New Trends in Model-Based Systems Engineering)
Review Reports Versions Notes

Abstract

Surveys are important tools for collecting knowledge, including student knowledge, and assessing their opinions and behavior. Survey results inspire information processing and selection of a processing method for further knowledge management. In this study, an improved approach to presenting survey results was developed, utilizing a Likert scale. In the survey, 20 students answered 10 questions (issues) that examined their opinions on the impact of modern technical equipment on dairy production assessment. The feature significance index (FSI) was utilized to inform the development of the survey study results. The FSI is the ratio of the percentage share of the highest to the lowest ratings on a Likert scale. In the case of four issues, none of the students indicated the options had very little impact and little impact. Therefore, the FSI could not be calculated, so a modified version was proposed. After ranking the issues in the survey based on the FSI, the difference in FSI between the best-rated and worst-rated issues was more than 13 times. This difference was less than two times in the modified version of the FSI. A larger difference allows for a more comprehensive interpretation of the survey results. The study confirmed that the small number of survey participants is a key limitation in developing the survey results.

1. Introduction

Surveys are among the most popular methods for collecting information on knowledge, preferences, satisfaction, and the behavior of society across various areas of life. By gaining insight into the opinions of the surveyed societal group, various goals are achieved, including scientific, cognitive, educational, marketing, and assessing respondents’ awareness of a specific issue [1]. The aim may also be to formulate observations concerning the acquisition of knowledge [2], which is important in assessing the effects of studying. Considering the broad spectrum of contributions, the surveys thus align with the general concept of sustainable knowledge development. In sustainable development research, the design is of key importance [3].

1.1. Study Framework

Designing research using a survey inspires the development of many scientific issues: how to formulate questions (open-ended or multiple-choice), how to collect responses from respondents (in person or via a website), which group of respondents the survey should be addressed to, and how many people should be included in the survey. These issues are taken into account in practice in studies covering various research areas, including food science [4], food safety [5,6], consumer preferences [7] and satisfaction [8], packaging evaluation [9,10], educational issues [11], selected aspects of agricultural production [12,13], engineering [14], the environment [15], assessment of production technology [16] and many others. Like any other research, a common feature of survey research is presenting the results of scientific considerations [17].
The survey results can be presented in various ways, and the formulation of the survey questions plays a crucial role [18]. Open questions provide an opportunity to quote respondents’ answers [19,20] as a basis for developing a discussion presenting the knowledge and thoughts of a group of people on a given topic. For open-ended survey questions, the idea is to use keywords and assess their frequency using a keyword cloud [21,22]. Arranging questions with simple answers (choice: yes, no, and no opinion) is a straightforward approach to presenting survey results as a percentage share of three answer options [23]. A similar approach to presenting survey results as a percentage of responses can also be used when responses are based on a Likert scale. The question remains how many degrees this scale should have, which translates into a detailed distribution of answers [24,25]. In practice, the elaboration of the survey results is based on five-, seven-, nine- and eleven-point scales; these numbers of odd responses are used in 90% of studies with a Likert scale measurement tool, and the most popular scale used by researchers is a five-point Likert scale [26]. In this context, a research question is posed [27]: Do data characteristics change according to the number of scale points used? The Likert scale may be the subject of model analysis [28], which confirms the need to improve the approach to developing survey results by considering its use. The need to improve the approach to Likert scale use was confirmed by Li [29], who highlighted the potential for information distortion and loss arising from closed-form scaling. As a result, a fuzzy Likert scale was proposed based on fuzzy set theory. Fuzzy numbers are often used to recode Likert-type variables [30], and their application is demonstrated in numerous studies, including those from industry and academia.
An approach to analyzing the results of a Likert scale survey might involve calculating a score for each questionnaire for each participant, averaging participants’ responses across all questions, and using statistical tests to assess the questionnaire’s internal consistency [31]. When processing data from Likert scale surveys, the option to convert seven-point rating labels to a three-point ordinal scale for further analysis may also be considered [32]. One-way ANOVA is also used in the analysis of Likert scale studies, such as those comparing distance-learning options [33]. Different approaches to developing survey results translate into a wide range of possible options for presenting research findings [34].

1.2. Study Purpose

The original aim of the research study was to examine students’ opinions on the impact of modern and innovative technical equipment on dairy production on selected aspects of its assessment. Using the Likert scale in the research questionnaire served as the basis for developing and refining the method of analyzing the research results, which was an additional benefit of the study.
The cognitive (scientific) aim of the research study was to develop a procedure for handling survey results when respondents did not select all the answer options in a survey based on a Likert scale. The practical (utilitarian) aim of the study was to develop the idea of using a ranking of answers in the analysis of survey results with a Likert scale.
The inspiration to take up the topic related to presenting the results of survey research was the previously developed method of analyzing research data [35], which, to some extent (for some cases), turned out to be imperfect.

2. Materials and Methods

The survey, in which the answers to the questions are graded on a scale of 1 to 5, has become an opportunity to propose a method for developing the survey results [35]. The idea of developing the results (answers) based on a 5-point Likert scale was to divide the percentage share of good and very good ratings (4 and 5 on the rating scale) by the percentage share of very bad and bad ratings (1 and 2 on the rating scale). Such a relationship was defined as the feature significance index. The formal notation of the formula for calculating the feature significance index (FSI) is as follows:
F S I   =   p s 4 , 5 p s 1 , 2
where ps1,2 is the percentage share of very poor (1) and poor (2) ratings [%], and ps4,5 is the percentage share of good (4) and very good (5) ratings [%].
The proposed approach to calculating the feature significance index (FSI) utilizes the extreme (minimum and maximum) values of the data collected in the survey. The theoretical rationale of the FSI equation is based on the concept of minimum and maximum values in mathematical analyses and the presentation of research results, taking into account descriptive statistics.
The essence of the proposed formula for the feature significance index (FSI) was to demonstrate the multiplicity of differentiation between the best and worst ratings for the feature under consideration. In basic research [35], the features of dairy products were ranked using the FSI. The ranking criterion was the value of the feature significance index.
However, applying the feature significance index to present the survey results using a Likert scale has some limitations. One limitation relates to a potential lack of ratings 1 and 2 from respondents. If no ratings 1 or 2 are indicated in the survey, i.e., their percentage share is 0, then the FSI cannot be calculated.
This situation, in which no ratings 1 or 2 (on a Likert scale from 1 to 5) were indicated by respondents, occurred in a survey conducted among students of the Faculty of Production Engineering at Warsaw University of Life Sciences in May 2023. The group consisted of 23 individuals who were studying in one of the master’s degree programs. The survey questions referred to the knowledge that students had acquired in a given field of study. Therefore, the survey scope was limited to a specific group of people. Twenty students participated directly in the survey, and the results from this group were further analyzed. The survey was anonymous and was conducted online. The online survey was open for three days. Students were informed about access to the survey via social networking sites. Participation in the survey was voluntary.
Students answered questions about the impact of modern technical equipment on dairy production, including product quality, consumption safety, price, taste, shelf life, availability, competitiveness, and efficiency of production processes. In addition, students were asked about the impact of innovative technologies on sustainable milk production, the development of the dairy industry, and the effect of modernity and innovation on technical equipment for dairy production, as well as the impact on investments in research. By participating in a survey with detailed questions, students could demonstrate their ability to manage acquired knowledge (information) at an academic level, confirming the essence of the functioning of academia [36].
For each question, students could choose one of five answers on a scale from 1 to 5, where the individual numbers had the following meanings: 1—very low impact; 2—low impact; 3—medium impact; 4—strong impact; and 5—very strong impact.
The dataset used in the research study was based on the results of a survey conducted among students. In an Excel spreadsheet, the sum of the answers given for each value on a scale of 1 to 5 was compiled for each of the 10 survey questions. This dataset was used to calculate the FSI and its modified versions. A confidence interval was also calculated in an Excel spreadsheet for the ps4,5 and ps1,2 proportions. The confidence interval calculation accounted for the significance level (α = 0.05), the standard deviation (calculated for the ps4,5-to-ps1,2 ratio), and the sample size (N = 20).
The answers given by a group of students to individual questions were used to calculate, based on Formula (1), the feature significance index (FSI). The calculated FSI for the issues raised in the questions was used to rank these issues and inform the discussion of the survey results.
A brief description of the study and its aim, along with a declaration of anonymity and confidentiality, was provided to the participants before they began the questionnaire. Respondents did not provide their names or contact information (including IP addresses) and could complete the survey at any stage. The answers were saved only by clicking the “submit” button after completing the questionnaire.

3. Results

Of the 10 questions in the survey, only the results of six could be used to calculate the feature significance index (FSI). The ranking of the issues raised in the study, based on the FSI value (from highest to lowest), is summarized in Table 1. The confidence interval for the results of this part of the study was 3.41.
For four of the survey questions, the feature significance index (FSI) could not be calculated because none of the students chose options 1 (very little impact) or 2 (little impact). These issues covered the impact of modern technical equipment on dairy production, including product quality and safety of consumption, the shelf life of dairy products, the competitiveness of companies in the dairy industry, and the development of the dairy industry.
Based on the FSI, such study results do not fully reflect the essence of the comparison between the individual issues in the survey. In the ranking (Table 1), no issues received the lowest scores (1 and 2) on the Likert scale. Such a case became an inspiration to propose a corrected, alternative version of the feature significance index.
The proposed revised version of the feature significance index (FSIbis) would be as follows:
F S I b i s   =   p s 4 , 5 p s 1 , 2 + p s 4 , 5
where ps1,2 is the percentage share of responses indicating very low impact (1) or low impact (2) [%], and ps4,5 is the percentage share of responses indicating strong impact (4) or very strong impact (5) [%].
The results of calculating the FSIbis for the ten issues considered in the research survey are presented in Table 2. A column with the calculated FSI is also included in the table to facilitate comparison.
An FSIbis of 1.0 does not provide a clear answer regarding the order of the four issues considered in the survey (Table 2). For this case, an additional calculation of FSIbis1.0 can be proposed, which would be as follows:
F S I b i s 1.0 =   p s 4 , 5 100
where FSIbis is the revised feature significance index, and ps4,5 is the percentage share of responses indicating strong impact (4) or very strong impact (5) [%].
Table 3 summarizes the results of the FSIbis1.0 calculation for four issues with an FSIbis value of 1.0 (Table 2).

4. Discussion

The primary objective of the presented research study was to enhance the approach to analyzing survey results. The need for such improvement resulted from the imperfection of the application (to some extent) of the feature significance index (FSI) to analyze research results. The paper [35] proposed that the FSI is based on extreme Likert scale ratings, assuming respondents use the full rating scale. However, if the respondents do not indicate the weakest ratings, calculating the FSI is impossible. The index is, therefore, of limited use in this case.
The designed survey included 10 questions addressed to students. These questions addressed the impact of modernity and innovation on the technical equipment used in dairy production, examining various aspects of dairy production and its products. Formulating questions with answer options based on a five-point Likert scale enabled the collection of research material for further analysis. A preliminary analysis of the answers revealed that, in the case of four questions, the respondents (students) did not assign very low or low ratings to the factor in question. For these questions (issues considered), it was therefore not possible to calculate the feature significance index (FSI) using Formula (1). Therefore, an attempt was made to correct the formula for calculating the feature significance index, considering the principle of using groups of extreme ratings (on the Likert scale) provided by respondents participating in the survey.
The corrected version of the formula for the feature significance index (FSIbis) enabled the calculation of its value for all survey questions. The calculation results of the corrected FSIbis are in a completely different range of values than the FSI (Table 2). This range includes values of 1.0 and lower. A calculated value of 1.0 indicates the proportion of only the highest ratings (4 and 5) in the survey regarding the issue under consideration. The value of the FSIbis for the remaining issues (questions) taken up in the study ranked them in the same order as the order resulting from the calculation of the FSI. The summary of calculation results in Table 2 prompts us to compare the ranges of FSIbis and FSI values for the six questions. Comparing the extreme range values for FSIbis, the highest value exceeds the lowest by nearly 66%. However, the FSI’s highest value is approximately 13.5 times higher than its lowest value. Such a comparison may indicate the usefulness of the considered indices for detailed analyses of the survey material. The construction of the FSIbis enables the calculation of the value of this index for all issues in the survey. This is certainly a valuable argument for assessing the FSIbis compared to the FSI. On the other hand, the FSI shows greater differences between the calculated values, which may be an argument for formulating more detailed conclusions.
When the FSIbis value is 1.0 for several issues in the survey, the analysis regarding the calculation of this indicator can be further deepened. The calculation value equal to 1.0—as follows from Formula (2)—can be obtained with a different number of ratings 4 and 5, when the number of ratings 1 and 2 is zero. Therefore, Formula (3) was proposed to more precisely identify the share of ratings 4 and 5 in the total ratings given by respondents. The result of the calculation, based on Formula (3), determined the order of four issues included in the survey, taking into account the value of the FSIbis1.0, from highest to lowest (Table 3), according to the criterion of the highest ratings indicated by respondents.
Determining the order of issues included in the survey based on the values of the FSI, FSIbis, and FSIbis1.0 may contribute to the analysis of the research problem under consideration, especially when the questions answered by respondents were concerned with the same main issue. In this research study, the primary objective was to examine the impact of modernity and innovation on the technical equipment used in dairy production, focusing on various factors that influence the assessment of this production. One example of ranking issues the survey considers is barriers to implementing sustainable agricultural practices [37]. These barriers were ranked based on the barrier significance index (BSI), which is derived from the feature significance index (FSI) proposed by Gaworski et al. [35]. The ability to rank responses based on the FSI values, similar to the BSI, is a key feature of the proposed approach to assessing survey results using a Likert scale. The use of Likert scales is subject to development [38], and the results of this research study attempt to confirm this development. One of the premises for the development is the use of Likert scales in research undertaken in various fields of science [39] and the solution to diverse research problems, including those involving both parametric and non-parametric research methods [40]. An essential issue in the research development using Likert scales is the proper interpretation of survey results, considering the use of statistical tools [41] to perform partial or complete assessments of the study [42]. In these assessments, which are the subject of in-depth scientific research, attention is paid to the accuracy and reliability of the results, as well as the scale analysis [39]. Regardless of the details of the Likert scale assessment and the results of studies, it seems crucial to disseminate basic knowledge among interested people about what the Likert scale is and how to use it [43]. An important element of this knowledge is the skillful formulation of a questionnaire with questions designed to account for the gradation of responses that can be assessed using a Likert scale. The responses provide feedback that can be used to improve the accuracy and quality of the questions generated [44]. This is an added value of the research undertaken with respondents.
The results of our own study were based on a relatively small group of respondents. This may limit the comparison of our results with other studies. Surveys are usually conducted with a relatively large group of people in mind. In our study, the small group of survey participants resulted from the limited number of students who met the criteria for studying Management and Production Engineering. The small number of people completing the survey can be related to the observation that some options were not indicated in the Likert scale survey. This was a reason to develop an approach to analyzing research data collected from a small population of respondents. Considering the methodology for developing the survey results, a point of reference may be the research conducted by Lamm et al. [37]. In this study, 17 factors classified as barriers to sustainable agricultural practices were ranked in order based on the calculated barrier significance index (BSI). In our study, we included 10 assessed factors; however, the FSI could only be calculated for six of these issues (Table 1). Another comparison is also valuable. In our own study, a 13.5-fold difference was noted, based on the FSI, between the highest-rated and lowest-rated issues in the ranking. In contrast, the survey by Lamm et al. [37] found, based on the BSI (analogous to the FSI), a 32-fold difference between the highest-rated and lowest-rated issues in the ranking. The greater variation in the ranking of the BSI (compared to the FSI in our study) could be due to the greater number of issues considered. However, such a thesis would need to be confirmed by other studies. Another important observation can be made from the comparison of the results of the two studies; the value of the FSI and BSI for the highest-rated issues in the rankings is at a similar level and amounts to 18.0 (FSI in our study) and 17.72 (BSI in the study by Lamm et al. [37]), respectively. The highest values of the FSI and BSI were found at a similar level, despite significant differences in the number of respondents between the compared studies. Our study, which included the calculation of the FSI, involved 20 students, while the study using the BSI involved 992 people.
The importance of the FSI in developing survey results lies in its ability to rank the issues considered in the survey by FSI value. This allows us to demonstrate, to some extent, the “strength” of the importance of individual issues addressed in the survey.
The essence of our research was to develop the feature significance index (FSI). In the discussion, it seemed logical to compare the results of our own research with studies where a similar or analogous index was generated. However, this does not close the search for other planes of comparison. It is justified to compare the results of our own research with studies covering the same or a similar thematic scope to that included in the survey. The specificity of our research was a narrow group of respondents (students in a given field of study) in connection with a specific thematic area of questions (issues). The specificity of respondents and the scope of questions may constitute a limitation in making an unambiguous comparison of the results of our study with those of other research teams. In practice, there are other examples of the survey’s specificity in addressing issues, including questions about income, which can lead to respondents being reluctant to answer, and problems with the quality of data for substantive analyses [45].
In discussions of research involving Likert scale questionnaires, it is emphasized that such research should, whenever possible, be combined with other forms of data collection [46], including, for example, visual analogue scales [47]; such an approach may facilitate a more thorough understanding of the issues under consideration, especially in the case of educational phenomena studied. It is even suggested that, in some cases, open-ended questions and other options for collecting information should also be included in survey questionnaires to interpret the research results more precisely [48]. One example is adding two additional options to five-point Likert scale questions, such as “don’t know” and “don’t understand” [49]. As a result of the development of research methods, including questionnaires [50], they are becoming increasingly sophisticated. Still, this sophistication should not be mistaken for accuracy [38]. On the other hand, despite the development of research using the Likert scale, problems still need to be addressed, including those related to the interpretation of research results. Such interpretation is, for example, difficult in cross-cultural comparisons using subjective Likert scales due to different reference groups [51]. Such observations show the specificity of research tasks undertaken using the Likert scale and the need to seek individual solutions to maintain the appropriate quality of research. Individual solutions may result from the thematic area of the survey research, which translates into the formulation of response options on a Likert scale. An example is the Likert scale, where 1 represents “Not at all plausible/adequate” and 5 indicates “Completely plausible/adequate” [52]. These individual solutions may be a result not only of the thematic area of the subject of the survey but also of the nation (nationality) of the respondents; in the study by [53], differences between nations were found in terms of difficulties with interpreting and completing the survey, skipping some questions, the frequency of choosing the middle option on the Likert scale, and the optimal range of the scale (number of answers).
By presenting various aspects of survey research using the Likert scale, a contribution is made to the creation of a forum for exchanging views and experiences regarding survey research and the development of its results. This is an interdisciplinary forum for exchanging scientific opinions, as research using the Likert scale falls not only in the social sciences but also in other areas of knowledge, including agricultural and technical fields. Each of these research areas has premises for linking survey questions with selected aspects of sustainable development. In assessing sustainable development, various social groups are considered, including students, who were also the leading respondents in the presented study. Studies with the participation of students who complete surveys (designed based on the Likert scale) can be used to identify the approach to the implementation of sustainable practices at the university [54], to assess the quality of environmental education [55,56], to assess environmental awareness [57], or to assess the quality of education on sustainability [58]. In our study, students were used in a specialized scope, i.e., to express their opinions regarding the importance of modernity and innovation of technical dairy production equipment for its assessment.
Questions about the importance of modernity and innovation of technical equipment in dairy production are part of technological progress in economically and environmentally sustainable agricultural production systems [59]. Dairy farms, equipped with various levels of modern technical equipment, play a crucial role in these production systems. According to the survey study, this modernity was identified as one of the key factors determining sustainable milk production (FSI = 17, Table 1). Such opinions are consistent with the experiences of implementing sustainable agriculture practices among a group of Polish farmers, who own farms with varying scales of production [60]. In the survey, students rated the importance of the efficiency of the production process even higher, depending on the modernity and innovativeness of the technical equipment (FSI = 18, Table 1). This assessment of students is consistent with detailed statements in the literature on the subject: Britt et al. [61] indicated that dairy farms’ profitability (expressing efficiency) would be the key to their sustainable production and development in the future. This development may result from the implementation of modernity and the approach to assessing this modernity in dairy production and farming, including animal welfare [62]. The modernity theme encompasses two aspects: negative (exploitation of nature and loss of tradition) and positive (progress, comfort, and efficiency) [63], which could be key issues for further development in assessing technical equipment for dairy production. This technical equipment, included in dairy production technology, should not be considered separately but rather as an integral part of sustainable dairy production [64]. Human interaction with technical equipment that supports work is crucial in sustainable dairy production. The elements that support farmers on dairy farms include sensor systems [65,66], milking automation devices [67], feeding automation [68,69], and precision livestock farming systems [70]. The key link in the development of dairy production in these areas is a person who makes decisions [71] and also expresses satisfaction with the activities carried out in agriculture [72]. Farmers can share their experiences with using farm equipment in surveys. Survey studies are, therefore, an important element in assessing the dairy production system and other farm activities.
The proposed revised version of the feature importance index (FSI) was developed because respondents did not provide negative answers (on the Likert scale) to some questions. This situation could have resulted from the small number of respondents participating in the survey. Therefore, including a larger social group in the study is justified. In this research study, the limited number of respondents resulted from the student population in the field of study under consideration. Small groups of respondents require an appropriate survey approach, considering the use of the Likert scale [73].
The conducted research study is an example of searching for an answer to a problem that arose during the development of survey results. The theoretical implication of the research study was to propose a revised approach to developing survey results, which builds upon the previous form of the feature significance index (FSI). The practical implication was to demonstrate the feasibility of creating a ranking of responses provided in the survey using a Likert scale, taking into account cases where a group of respondents did not select some answer options (on the Likert scale). In many previous survey studies, the results were typically presented in the form of a comparison of the share (or percentage share) of respondents’ responses on the issues under consideration, which served as a basis for discussing the survey results. In the case of the methodological approach proposed by us, it is easier to compare the results of different studies if a uniform, calculated (based on a formula) index is used, based on the survey results and the Likert scale. The method of calculating the feature significance index (FSI) and its modified versions is an example of searching for universal solutions to develop survey research results that incorporate theoretical knowledge and practical effects of processing research material (information). In practice, surveys provide a forum for addressing various issues that may arise during data collection and analysis. For example, France et al. [74] proposed a method for assessing nonresponse based on meta-analytic file drawer techniques (worst-case resistance testing—WCRT) that is suitable for a wide range of data collection scenarios.
The presented research problem and its solution method may be applicable to the areas of computer science and information sciences. Processing information from surveys may inspire the search for alternative methods of developing research results using IT tools. The area of IT tasks may include creating rankings of respondents’ responses in surveys. Developing rules for creating a ranking of responses in surveys is a field that requires implementing new ideas with IT support. The essence of the idea to create a ranking of responses in the presented study was to formulate the question (leading issue) in such a way as to generate a set of responses, each of which could be assessed on a scale of significance from 1 to 5. In practice, other methods of creating rankings of responses are also known, including, for example, those based on mutual assessment of descriptive responses provided by respondents [22]. Rankings of responses are an excellent material for visualizing research results. Visualization has been developed in detail in numerous survey studies using Likert scales, facilitating the interpretation of research results [75].
Regardless of the size of the group participating in the survey, the key issue is always the processing of information. The answers provided by respondents contribute to this information. If students are included in the survey, it is possible to collect information that identifies the effects of knowledge transfer during studies. In our study, the transfer of knowledge regarding dairy production technology was assessed, aligning with the search for interactions between science and technology, which is crucial for the fusion of knowledge communities [76]. Innovations are embedded in science and technology, which were the leading themes in the student survey questions. Innovations fill knowledge gaps [77] and require social acceptance, which may be a premise for developing appropriate survey research.
Survey research in the area of dairy production can inspire the development of scientific knowledge. However, the rationale and purpose of such research also, and perhaps primarily, lies in its practical application. The practical value of research, including survey research in dairy production, includes assessing factors influencing production, distribution, and the quality of dairy products [78,79], as well as consumer preferences in the dairy market [80].
The issues related to the processing of survey results discussed in this material, with a view to further development, may provide a basis for developing an extended mathematical model to adapt the presented methodology to surveys on a larger scale (a group of respondents). At the same time, further developing the practical nature of the research, it is possible to integrate the developed formulas within statistical software tools or a hypothetical algorithm.
Survey research using the Likert scale can be further developed to incorporate modern IT tools, including AI. Survey research studies using the proposed analytical approach can, in the long run, serve as a tool to support the mathematical and technical aspects of model-based engineering system development.

5. Conclusions

This study confirmed that a small number of participants could affect the distribution of survey responses and limit the processing of survey results. This, in turn, inspired the development of a method for processing survey results.
The proposed, modified formula for calculating the feature significance index enabled the assessment of all issues raised in the survey. However, the scale of differentiation of the index in the modified version was much smaller than in the original version of the feature significance index. The interpretation of the scale of variation in the feature significance index may provide an argument for continuing to refine the approach to assessing survey results using a Likert scale. This approach may incorporate modern IT tools supported by statistical analysis.
The survey results showed that, in students’ opinion, the modernity and innovation of technical equipment for dairy production can have a significant impact on the development of the dairy industry.

Author Contributions

Conceptualization, M.G. and A.D.; methodology, M.G.; software, M.G.; validation, M.G. and A.D.; formal analysis, M.G. and A.D.; investigation, A.D.; resources, A.D.; data curation, A.D.; writing—original draft preparation, M.G.; writing—review and editing, M.G.; visualization, M.G. and A.D.; supervision, M.G.; project administration, M.G. and A.D.; funding acquisition, M.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The online survey was conducted in full compliance with national and international regulations, as well as the Declaration of Helsinki (2000). The personal information and data of the participants were anonymized in accordance with the General Data Protection Regulation of the European Parliament (GDPR 679/2016). The survey did not require approval by the ethics committee due to its anonymous nature and the impossibility of tracking sensitive personal data.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. The participation was entirely voluntary. The questionnaires were anonymous, and no personal data were collected. The answers were saved only by clicking the “submit” button after filling out the questionnaire. Respondents did not provide their names or contact information (including their IP address) and could finish the survey at any stage.

Data Availability Statement

The data and materials are available at the following link: https://doi.org/10.18150/LKPDOD.

Acknowledgments

We thank Aleksander Lisowski for inspiring us to explore a modified approach to evaluating research results based on the Likert scale. We would also like to thank the students who chose to participate in the survey.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Ranking of issues raised in the survey with students based on the value of the feature significance index (FSI).
Table 1. Ranking of issues raised in the survey with students based on the value of the feature significance index (FSI).
The Issue Under Consideration in the Question:
Modernity and Innovations in Technical Equipment for Dairy Production Have an Impact on…		Percentage Share [%]FSI Value
ps4,5ps1,2
Efficiency of production processes90518.0
Sustainable milk production85517.0
Investments in research on dairy production technologies80516.0
Price of dairy products55105.5
Availability of dairy products70302.33
Taste of dairy products40301.33
Table 2. Ranking of issues raised in the survey with students based on the corrected value of the feature significance index (FSIbis).
Table 2. Ranking of issues raised in the survey with students based on the corrected value of the feature significance index (FSIbis).
The Issue Under Consideration in the Question:
Modernity and Innovations in Technical Equipment for Dairy Production Have an Impact on…		Percentage Share [%]FSIbisFSI Value
ps4,5ps1,2
Product quality and safety of their consumption8501.000–
Shelf life of dairy products9001.000–
Competitiveness of a dairy plant in the dairy product market7501.000–
Development of the dairy industry10001.000–
Efficiency of production processes9050.94718.0
Sustainable milk production8550.94417.0
Investments in research on dairy production technologies8050.94116.0
Price of dairy products55100.8465.5
Availability of dairy products70300.7002.33
Taste of dairy products40300.5711.33
Table 3. Ranking of issues raised in the survey with students based on the value of the feature significance index (FSIbis1.0).
Table 3. Ranking of issues raised in the survey with students based on the value of the feature significance index (FSIbis1.0).
The Issue Under Consideration in the Question:
Modernity and Innovations in Technical Equipment for Dairy Production Have an Impact on…		FSIbisFSIbis1.0
Development of the dairy industry1.001.00
Shelf life of dairy products1.000.90
Product quality and safety of their consumption1.000.85
Competitiveness of a dairy plant in the dairy product market1.000.75
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Gaworski, M.; Daśko, A. An Approach to Developing Likert Scale Survey Results Based on the Example of a Research Study Involving a Limited Number of Students. Appl. Sci. 2026, 16, 2602. https://doi.org/10.3390/app16052602

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Gaworski M, Daśko A. An Approach to Developing Likert Scale Survey Results Based on the Example of a Research Study Involving a Limited Number of Students. Applied Sciences. 2026; 16(5):2602. https://doi.org/10.3390/app16052602

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Gaworski, Marek, and Aleksandra Daśko. 2026. "An Approach to Developing Likert Scale Survey Results Based on the Example of a Research Study Involving a Limited Number of Students" Applied Sciences 16, no. 5: 2602. https://doi.org/10.3390/app16052602

APA Style

Gaworski, M., & Daśko, A. (2026). An Approach to Developing Likert Scale Survey Results Based on the Example of a Research Study Involving a Limited Number of Students. Applied Sciences, 16(5), 2602. https://doi.org/10.3390/app16052602

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