# Ten questions concerning the usage of subjective assessment scales in research on indoor environmental quality

Marcel Schweiker, Rune Korsholm Andersen, Salvatore Carlucci, Giorgia

Chinazzo, Simon Hodder, Ardeshir Mahdavi, Boris Igor Palella, Anna Laura

Pisello, Francesca Romana D'ambrosio Alfano, Marika Vellei

► To cite this version:

Marcel Schweiker, Rune Korsholm Andersen, Salvatore Carlucci, Giorgia Chinazzo, Simon Hodder, et al.. Ten questions concerning the usage of subjective assessment scales in research on indoor environmental quality. Building and Environment, 2025, 283, pp.113393. 10.1016/j.buildenv.2025.113393. hal-05411086

HAL Id: hal-05411086

https://hal.science/hal-05411086v1

Submitted on 11 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

# Ten questions concerning the usage of subjective assessment scales in research on indoor environmental quality

Marcel Schweiker<sup>a,b,*</sup>, Rune Korsholm Andersen<sup>c</sup>, Salvatore Carlucci<sup>d</sup>, Georgia Chinazzo<sup>e</sup>, Simon Hodder<sup>f</sup>, Ardeshir Mahdavi<sup>g</sup>, Boris Igor Palella<sup>h</sup>, Anna Laura Pisello<sup>i,j</sup>, Francesca Romana d'Ambrosio Alfano<sup>k</sup>, Marika Vellei<sup>l,m</sup>

## ARTICLE INFO

Keywords:

Assessment scales

Indoor environmental quality

Subjective ratings

Perception

Comfort

Wellbeing

## ABSTRACT

The usage of scales to assess subjective ratings related to indoor environmental quality (IEQ) is ubiquitous in built-environmental research. Despite their importance, research and discussions on the scales themselves are scarce and the number of variations in the field is huge, which is reducing the potential for cumulative research. Motivated by the increasing interdisciplinary exposures of the authors, this 10 questions-paper addresses ten key issues related to the current and potential future usage of subjective rating scales in built environment research. These issues are particularly relevant in the context of multi-domain research, where an increasing number of papers have revealed differences between IEQ-domains like thermal and visual aspects. The first question sets the basis for the following discussion by giving a brief summary of the history of scales' development. Questions 2 to 6 start with addressing specific challenges related to language and multi-domain applications and then broaden the scope to more general issues like the type and variation of scales and potential biases through their application. The third block of questions (Questions 7 and 8) discusses alternatives to scales like physiological signals and observing behavioural reactions. With the last two questions we ask ourselves and the community to what extend a fit-for-purpose approach to the use of scales would be beneficial, and what steps could lead to improving the use of scales in the future. We conclude by acknowledging the central role of questionnaires in advancing knowledge about IEQ and support their continued use with increased reliability and interpretability.

## Introduction

Being aware that we humans spend most of our lives indoors [1], understanding the impact of indoor environmental conditions on people's health, comfort, well-being, satisfaction, and productivity among others is of benefit for all of us. While it is possible to characterize many

parameters of the indoor environmental conditions by means of sensors objectively, the assessment of their influence on humans, especially on comfort and satisfaction, requires subjective methods to a large extent relying on questionnaires. The assessment scales used often ask respondents to rate their sensation of warmth or coldness, brightness, loudness etc., their level of comfort, and the degree of acceptability of

<footnote><sup>a</sup> Chair of Healthy Living Spaces, Faculty of Architecture, RWTH Aachen University, Germany</footnote>

<footnote><sup>b</sup> Healthy Living Spaces Lab, Institute for Occupational, Social and Environmental Medicine, Medical Faculty, RWTH Aachen University, Aachen, Germany</footnote>

<footnote><sup>c</sup> International Centre for Indoor Environment and Energy, Department of Environmental and Resource Engineering, Technical University of Denmark, Denmark</footnote>

<footnote><sup>d</sup> Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy</footnote>

<footnote><sup>e</sup> CARE Laboratory, Department of Civil and Environmental Engineering, Northwestern University, Evanston, USA</footnote>

<footnote><sup>f</sup> Environmental Ergonomics Research Centre, School of Design and Creative Arts, Loughborough University, UK</footnote>

<footnote><sup>g</sup> Institute for Building Physics, Services and Construction, Graz University of Technology, Graz, Austria</footnote>

<footnote><sup>h</sup> Department of Industrial Engineering, Università degli Studi di Napoli Federico II, Naples, Italy</footnote>

<footnote><sup>i</sup> EAPLAB at CIRIAF Interuniversity Research Centre on Pollution and Environment Mauro Felli, University of Perugia, Italy</footnote>

<footnote><sup>j</sup> Department of Engineering, University of Perugia, Italy</footnote>

<footnote><sup>k</sup> Department of Industrial Engineering, Università degli Studi di Salerno, Fisciano, Salerno, Italy</footnote>

<footnote><sup>l</sup> University of Bordeaux, CNRS, Bordeaux INP, I2M, UMR 5295, Talence F-33400, France</footnote>

<footnote><sup>m</sup> Arts et Metiers Institute of Technology, CNRS, Bordeaux INP, I2M, UMR 5295, Talence F-33400, France</footnote>

<footnote>* Corresponding author at: Chair of Healthy Living Spaces, Faculty of Architecture, RWTH Aachen University, Germany. E-mail address: mschweiker@ukaachen.de (M. Schweiker).</footnote>
the indoor environmental condition (either experimental or observational) to which they are exposed. Therefore, it is not surprising that scales are used ubiquitously within the research field of indoor environmental quality (IEQ). The use of scales offers several benefits, one of the most notable being the ability to quantify subjective responses as they are a fundamental tool in IEQ research for gathering quantitative subjective data (e.g., structured survey responses) and qualitative information on how people experience, perceive and evaluate the physical environmental conditions around them. Nevertheless, research and discussions on the scales themselves are scarce and not reflecting their importance and their influence on the results obtained that are used to define standards for indoor environmental conditions [2]. In addition, a large variety of scales is used to assess the same aspect (e.g., thermal sensation), which is reducing the potential to compare results across individual studies and for cumulative research [3].

To raise the awareness of potential pitfalls and biases and to promote more research on scales themselves, this 10 questions-paper addresses ten key issues related to the current and potential future usage of subjective rating scales in built environment research. These issues are particularly relevant in the context of multi-domain research, where an increasing number of reviews have revealed differences between IEQ-domains like thermal and visual aspects [3,4].<fnref n="1" /> The 10 questions are grouped into four blocks. The first question, a block itself, sets the basis for the following discussion by giving a brief summary of the history of scales in research and standards. The second block consists of questions 2 to 6, which reflect on several potential biases related to scales and their usage. Starting with two concrete aspects related to consistency across domains (Q2) and languages (Q3), the scope broadens towards general biases intrinsic to scales and their application (Q4) and due to the environmental context and personal situation of their application (Q5). The last question of this block examines to what extend scales are related at all to the construct of IEQ comfort as per its definitions (Q6). In the third block, we slightly leave the topic of scales and discuss alternative approach like physiological signals (Q7) and behavioural outcomes (Q8). In the fourth and last block, we suggest the introduction of a fit-for-purpose approach (Q9) and discuss general ways forward to improve the usage of scales (Q10).

Within the branches of IEQ research, not only the variety of scales is large, but also the terminology used when referring to individual elements of scales. Based on an intense discussion among the authors, we define the following terms (Fig. 1). A **questionnaire** is a collection of one or more scales. Within the IEQ research field, a **scale** is in most cases a single question (e.g., related to the loudness of the current environment). More complex constructs, as commonly used in psychology, may require several questions, which responses will be combined later by the researcher into a single scale. A **question** consists of the question phrase or a statement and either two or more response options or permits an open-ended (textual) response. **Response options**, if present, may have specific **labels** (e.g., “too dark” or “disagree”). Thereby, the nature of data derived from a questionnaire is most often subjective (e.g., compared to objective measurements of skin temperature), while format and analytical treatment of that data can be quantitative or qualitative depending on the type of response option (e.g., Likert scale vs. open-ended response).

As overall term for a set of scales related to a sensation, preference or acceptability, we will use the umbrella term **assessment**, (e.g., visual assessment). This term contrasts with earlier studies, which often use “perception” as the general term. The authors agreed that perception is well related to a sensation vote (i.e., the most direct and objective dimension, mainly judgement-free assessment of an environmental stimulus). In contrast, affective evaluations like comfort or preference (i.

e., the hedonic dimension of the assessment including a judgement to what extent that stimulus is positive, negative or neither of these) was considered to be less related to the word perception, but fitting into the umbrella of assessment. Each aspect of the assessment, including sensation, preference, acceptability among others, is referred to as a single **construct** and therefore needs to be assessed through different scales.

The authors are conscious that the questions posed in this paper are highly complex and may have neither unique nor simple answers. In addition, we acknowledge the central role that questionnaires have played (and continue to play) in advancing knowledge about IEQ, and design, control and operation of buildings. Rather than questioning their overall value, this paper highlights key methodological challenges and suggests strategies to enhance their reliability and interpretability. Our aim is to support their continued use in a way that strengthens the evidence base for building design and operation. Hence, the main objective of this study is to encourage the relevant scientific community to engage in critical discussion of the subject toward more robust future research in the IEQ domain. Beyond the application of scales, the utilization of suitable statistical methods is crucial for achieving reliable results. For instance, neglecting the type of data—such as ordinal, interval, or continuous—in the choice of a statistical technique—like a test or a regression model—can lead to distorted outcomes [5], as demonstrated for thermal responses by Favero et al. [6]. Another example is the decision, whether parametric or non-parametric statistics should be used [7]. An additional challenge in multi-domain research arises when the applied domain-specific scales vary in their format (e.g., number of response options or data type obtained) as this limits the ability to interpret cross-domain comparisons. However, such topic would be beyond the scope and length of this contribution.

# 1. How did scales evolve in research and standards?

To understand current usages and pitfalls of scales used in the context of IEQ research, a brief historical overview of their development and validation will be helpful. Modern (i.e., post-Ancient Greek) foundations date back to the period of Fechner and colleagues, who established the field of psychophysics [8]. In their work, the focus was on absolute levels to detect an environmental stimuli and relative levels to detect their differences. As such, they considered human assessment of intensities, typically referred to as sensation in IEQ research.

The advent of scales used in IEQ research varies between domains. The history of scales used in relation to the thermal domain is summarized in Schweiker et al. [9]. They refer to the study by Houghten and Yagloglou [10] from 1923 as one of the first using subjective feedback to assess subjects' perception of thermal comfort – in their case a three-item scale. According to Macfarlane [11] it took the field until Gagge et al. [12] in 1967 to consider the distinction between the assessment of a stimuli's intensity and its evaluation. Based on the work of Gagge et al. the 7-point ASHRAE scale and a four-point scale from comfortable to very uncomfortable evolved. Up to today, a nearly uncountable number of variations were used [3].

Within the indoor air quality (IAQ) domain, one needs to distinguish between the subjective assessment of odour intensity (OI) and perceived indoor air quality (pIAQ). According to Wargocki and Kostyrko [13] one of the first scales for odour intensity was used by Yaglou et al. [14] in 1936 and was a categorical intensity scale with 6-points ranging from “no odour” to “overpowering odour”. Later scales for OI include the category-ration scale from “0 nothing at all” to “10 extremely strong” by Borg and Borg from 2001 [15], which has unequal spacing in the lower part based on Fechner’ laws, and the equal-intensity matching scale by Müller et al. from 2005 [16]. The pIAQ was first assessed with a dichotomous acceptability statement (“acceptable” / “not acceptable”) in 1983 [17], while these days, continuous scales, first introduced by Gunnarsen and Fanger in 1992 [18] – some with, some without a middle response option – are most frequently used.

<footnote n="1">¹ Due to limitations in article length and because specific points related to scales are more researched or better explained in some domains compared to others, not all questions will cover each of the domains.</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. Graphical representation of terminology for scales used throughout this article.</figcaption></figure>

The major distinction to be made within the visual domain is between scales related to glare and visual comfort. Summarized by Quek et al. [19], a first scale for glare assessment was published in 1940 by Hopkinson [20] and was a 4-point scale from “just not perceptible” to “just intolerable”.<fnref n="2" /> As for other domains, also for glare, numerous variations of scales are used today without a common consensus on a single version. The same applies also to scales of visual comfort, which is assessed in relation to daylight satisfaction, view satisfaction and others [21].

No work summarizing the history of scales to assess the acoustic environment could be identified by the authors. Early scales to assess the loudness of an indoor environment used comparisons, see for instance Ising et al. [22] who asked their participants to compare the loudness of their office to one out of five noise sources, from “1 a refrigerator” to “5 a pneumatic drill”. Later, adjectives, for example from “1 quiet” to “5 very loud” got more common, while also in this domain, no single scale is used in all studies [23]. Ma et al. [24] highlight that there are also numerous constructs within this domain, including acoustic comfort, preference, or annoyance.

Much later than in research, scales were included in international ISO standards. While the sensation scale was mentioned earlier (e.g., ISO 7730 from 1984), ISO 10551 – published in 1995 – is the first international standard describing the background and also gathering other scales associated with the assessment of the thermal environment. ISO 10551 provides details on thermal sensation, thermal comfort, preference, acceptability, and pleasantness. While the version from 1995 addressed solely the thermal domain, the 2019 version of ISO 10551 extended its scope to all four main domains of IEQ [25]. In between, ISO 28802:2012 was published, which also presents examples for all four domains. It should be noted that even between ISO 10551 and ISO 28802, questions and response options vary.

Parallel to these two standards, which include multiple domains, domain-specific standards mention additional – again distinct scales. ISO 14505–3:2006 presents scales for the subjective assessment of the thermal environment in vehicles. ISO/TS 15666 (first version from 2003, latest version from 2021) presents methods for the assessment of noise annoyance, while ISO/TS 12913–2:2018 focuses on scales related to the assessment of the soundscape. ISO 22955:2021 which by title should focus on the acoustic quality of open office spaces, presents a questionnaire including several questions related to the acoustic domain, but also touches the visual and thermal domains.

## 2. How consistently is scale usage across individual IEQ domains?

The subjective judgement scales used for assessing physical environments differ across IEQ domains, reflecting the unique characteristics and measurement challenges of each domain. In the thermal

comfort, indoor air quality and visual domains, it is advised to ask participants to evaluate the perception of the stimulus and then to make an affective assessment of the stimulus [25]. This two-step approach ensures that both the immediate sensory experience and the subsequent affective assessments are captured.

In the thermal comfort domain, it is customary to ask for perception in the form of thermal sensation and the affective judgements are usually one or more constructs like acceptability/satisfaction, comfort, or preference. Sensation typically refers to the immediate perception of the thermal environment, often measured using the ASHRAE 7-point scale [26]. Acceptability assesses whether the thermal conditions are deemed acceptable by the occupants. Comfort is a broader measure that encompasses overall thermal satisfaction, while preference indicates the desired thermal feeling. This approach attempts to capture the various aspects of thermal assessment.

A similar procedure is often observed in air quality studies. Participants are asked to assess the perception of air quality in terms of odour intensity or the freshness/stuffiness of the air. Afterwards, they assess the acceptability of the air. This method ensures that both the sensory perception and the subjective evaluation of air quality are considered.

In the visual environment, ISO 10551:2019 [25] suggests using a visual analogue scale with endpoints “extremely dark” and “extremely light” to assess the perception of the visual environment. This scale captures the immediate sensory experience of brightness. Afterwards, participants evaluate comfort, preference, and acceptability. Comfort in the visual domain often relates to the absence of glare and the adequacy of lighting for tasks. However contextual factors such as view or colours also play a role [27] and these are not included in standards like ISO 10551:2019 [25].

The acoustic domain employs a different approach than the “perception before affective evaluation” approach used in the thermal, air quality and visual domains. ISO 10551:2019 [25] suggests using loudness as a perceptual term, equivalent to thermal sensation, odour intensity and brightness in the thermal, air quality and visual domains. However, this approach is not commonly practised by researchers in the acoustic field [28]. Instead, it is suggested to use personal annoyance before preference and acceptability [25]. ISO/TS 12913–2:2018 suggests identifying the sound source first by asking to what extend respondents hear sounds from various sources. The follow-up questions make affective assessments of each source of sound [29]. The focus on annoyance in [25] rather than a direct sensory measure like loudness reflects the unique challenges of assessing acoustic environments, where subjective experiences can vary widely based on individual aspects such as musical preferences and sensitivity [30].

Comparing subjective assessments across the four domains can be challenging due to the differing scales and measurement approaches. In the thermal domain, perception is usually assessed on the ASHRAE 7-point scale. However, the other three domains present higher degrees of complexity. The perception of air quality can be assessed on a fresh/stuffy scale or an odour intensity scale. The perception of the visual environment can be assessed using scales to assess brightness, glare, and other factors, while the perception of acoustic environments can be assessed using loudness or a quiet/noisy scale.

<footnote n="2">² As discussed later in Q4, this scale mixes perception and tolerance, which are two distinct aspects of an individuals’ environmental assessment and therefore to be avoided these days.</footnote>
The connection between perception and subjective judgement has more layers in the air quality and acoustic domain than in the thermal and visual domains. In the thermal domain, it is well established that the likelihood of thermal dissatisfaction increases with increasing distance to thermal neutrality [31]. Similarly, the visual dissatisfaction increases as the affective evaluations approach “extremely dark” or “extremely bright”. In contrast, a high odour intensity or a high loudness may be assessed as acceptable or even enjoyable if the odour or the sounds are pleasant while the opposite is true if the odour or the sounds are unpleasant. As such, a two-dimensional association between sensation and dissatisfaction like the one described in ISO 7730 in the thermal domain (see also Q4) may be difficult to establish in the air quality and acoustic domains.

### 3. How consistently can scales be used across languages?

In addition to the differences in scale usage across various domains (see Q2), translating the scale into the local language is essential for gathering information from occupants regarding IEQ levels. Focusing on the thermal domain, as an example, in the following paragraphs, this section addresses the question to what extent scale usage is consistent across languages and to what extent different languages affect results.

The basic functionality of the thermoregulatory system (as well as other sensory systems of the human body) is in healthy individuals irrespective of nationality or ethnicity [32], while participants in IEQ studies are asked to assess the IEQ, like perceived thermal sensation, in different languages in different countries [33–35]. Over the last fifteen years, the amount of thermal comfort research output from non-English-speaking countries has exceeded that from the English-speaking world [33,34].

Therefore, it is important to reflect on the influence of language and this challenge starts with international standards. For example, the ISO 10551:2019 standard was originally written in English, so that researchers applying its content in other languages need to discuss how to translate the subjective scales for evaluating IEQ from English into their languages [34]. Consequently, when researchers formulate surveys in their languages, it is not certain that translated thermal scales are consistent with the assumptions of the original English versions [35,36]. Differences can start already at the level of labels. It is commonly known in the thermal domain that depending on the language, one or more adjectives for warm and cold are used to characterise the thermal sensation scale [2,37]. These and other discrepancies between language versions imply potential unforeseen biases also because the meaning of a word is affected by the context in which it is used (e.g., textual, social, emotional, and cultural) [2,37,38].

The effect of such differences on the interpretation of subjective assessments is still under-explored. In the literature of the thermal domain, two aspects have been considered: (1) the influence of language on different categories (e.g., the operative temperature span of each category on the 7-point scale), and (2) the effect of language on the relationship between individual constructs, for instance between sensation and acceptability.

Data [39] including translations of the ASHRAE scale into several languages (e.g., Chinese, Greek, and Arabic)—where respondents were asked to mark on a line positions (e.g., as in Likert scales) corresponding to the scales as translated into their native language—revealed differences from the English version in the locations and that “neutrality” does not necessarily imply “acceptability”.

Differences in category widths of translated scales were also found by Humphreys [38], who considered subjective data collected within the SCATs database in English, French, Greek, Portuguese, and Swedish. Scales exhibit different category widths when translated into other languages, and such widths vary from category to category (e.g., neutral, cold, and so on). As such, the choice of response options can be affected by a semantic bias, that is, the original meaning of thermal comfort scale is altered when translated into different languages, which introduces a

systematic biases into the results [38].

The behaviour of the ASHRAE, Bedford, and Nicol thermal scales in Arabic [36] revealed that categories had irregular widths, and the middle sensation categories were the widest, indicating wide ranges of neutral and comfortable temperatures. In addition, the neutral category covered a range that differs from slightly cool [−1] to slightly warm [+1] with a shift to the cool side. This effect was most likely caused by the inability to distinguish between cool and cold in Arabic language [36], a point that also occurs in Greek [40]. These examples show that responses obtained through a simple translation can be inappropriate for use in a non-English speaking local context.

The Scales project [2,41] contributed to the above mentioned discussion creating a large-scale international survey (8225 respondents, 21 languages also including languages written from right to left as Arabic and Farsi) based on a novel questionnaire, which asked survey participants to state their perceived distance between the verbal anchors of the translated scales. Results show meaningful effects of the language, the presence of non-native speakers within respondents, and even the direction of writing.

A recent study [33] dealing with semantic discrepancies between the Korean and English versions of the ASHRAE scale showed a meaningful difference in the operative temperature span of each category. Moreover, the central three categories of the 7-point thermal sensation scale are not necessarily expressions of acceptable levels of thermal comfort in either language, as a shift towards the cool (warm) side of the scale in summer (winter) occurs. This also arises in Japanese, where the translated adjective “warm” expresses a thermally positive feeling [32].

Issues as above require targeted efforts to reduce possible linguistic/semantic biases, often due to a word-to-word literal translation [32], as the native equivalent wordings may not convey the associated physical feeling or impression. In a nutshell, translations must not necessarily be identical, but measured constructs should have comparable behaviour in statistical analysis [2], as required by the functional equivalence theory [42]. If we consider that languages developed under certain cultural, behavioural, social, and climatic contexts, future research requires close cooperation with linguists, aiming at validating existing and future verbal anchors in scales [2]. Additionally, employing a bipolar scale could minimise linguistic bias [42]. Alternative solutions could be removing descriptors altogether, and using a pictorial or visual analogue scale [43].

Further investigations are necessary to quantify the effect of translation on the objective metrics for evaluating the thermal sensation [44, 45]. These efforts will help develop specific guidelines and specific protocols [3,46] focused on how using the scale in different languages for questionnaire surveys and how interpreting results not only for the thermal domain, but for all domains of IEQ.

### 4. How do variations of scales and their elements influence results?

As described in Q1 to Q3, the scales used vary largely within research, among others between IEQ-domains and language. Hence, the effect of such variations on results needs to be considered. While this question applies to all domains of IEQ, this response focuses on the thermal and visual domains. The emphasis is on the formulation and application of questions and scales, as well as their broader implications.

As already touched in previous questions, questions and their associated scales can introduce bias, potentially leading to discrepancies between studies and contributing to inconsistent findings, which in turn may slow progress in understanding human assessments of the built environment [3]. Two types of bias are identified: those inherent to the *nature of scales* and those arising from *improper development and deployment of scales*.

Biases related to the *nature of scales* include: (1) responses constrained to a limited set of categories, which are often assumed to be uniformly spaced, though research suggests these intervals are
perceived as uneven and interpreted differently [9,47]; (2) responses skewed towards certain areas of the scale, such as higher glare ratings or avoidance of extreme ends, often due to highlighting specific environmental issues like glare. For example, if participants are asked about glare, few will report “no glare” [47–49]. Similarly, Schweiker et al. [9] observed a central tendency bias, that is, the avoidance of extreme category responses, which can skew the data collected from perception scales and affect model development (e.g., the fitted polynomial regression lines in that study).

While researchers should strive to acknowledge and mitigate these inherent biases, they must also ensure careful development and deployment of scales to prevent additional biases that could further skew responses and affect study outcomes. Specifically, researchers must carefully consider the type of questions and scales used, the precise wording and structure of each sentence, and the sequence of questions—particularly when addressing multi-domain aspects. The literature shows a wide variety of both being ignored, potentially contributing to inconclusive or contradictory results in the evaluation of indoor environmental assessments. Question formulation is particularly sensitive to wording, which can significantly alter how participants interpret them. For example, variations in question phrasing, such as “Express your vote from... to...,” “Right now, how do you...,” or “Give an overall evaluation of...,” can lead to different responses. An often-discussed example at conferences within the thermal domain is the difference between “How do you feel thermally?” in comparison to “How do you perceive the temperature of the room?”: while I can have a warmth sensation due to appropriate clothing level or activity, I could assess the room as cool. Research in this area has indicated that participants’ answers, and the variations therein, may be influenced by how the question directs—or fails to direct—their focus toward a specific stimulus (e. g., lighting or thermal conditions) and a specific time (e.g., right now). Without clearly targeting the intended stimulus, responses may be influenced by extraneous factors [50]. This finding underscores the critical importance of careful question wording and accurately reporting the instructions provided to participants, ensuring that they evaluate the precise stimulus the researchers intend. Moreover, it is essential to report verbatim the instructions given to participants to provide context and help understand how the questions and scales were introduced during the study.

Various types of scales—such as visual analogue, semantic differential, and Likert-type scales, or combinations thereof—have been developed and employed in the literature, potentially influencing how participants interpret questions and provide responses. In lighting research, semantic differential scales (i.e., seven-point rating scales anchored by pairs of opposite adjectives), were first adopted in the field by Flynn and colleagues [51,52]. However, their use has been criticized due to discrepancies between how participants interpret the questions and adjectives versus the researchers’ intentions, often resulting in ambiguous responses due to unclear stimuli [50]. Likert-type scales—also known as “category rating” [53] or “categorical scale” (CS) [54]—have undergone numerous studies in the IEQ field and beyond. Outside of the IEQ field, extensive research has explored how different aspects of survey design influence subjective responses. Studies have examined factors such as the number of response options, the effect of a midpoint, and the use of graphical CS or combinations with visual analogue scales on survey results [54–58]. Similar investigations are largely lacking within the IEQ field. As a result, the impact of scale formats and response structures on subjective evaluations in IEQ studies remains unclear and under-explored.

One aspect that warrants further attention in IEQ research is the number of response options used in subjective scales. IEQ studies have employed a wide range of scales, including the 7-point scale by Ngai and Boyce [59], the 9-point De Boer rating scale [60], and the Hopkinson scale [61] for assessing discomfort glare. Similarly, the widely adopted 7-point ASHRAE scale is used to evaluate thermal sensation. Studies have shown that the number of points used in IEQ scales can range from

two (dichotomous) to three, four, five, six, seven, eight, nine, ten, or even more [3,48]. The number of points is crucial, as participants must distinguish between categories to report their impressions accurately [47]. While some research suggests that scales with more than six or seven points might confuse participants and offer no psychometric advantage [57,62], there is no clear consensus on the optimal number of points or labels in IEQ research [53]. Even when the same question and number of categories are used, different wordings and word order can be found in the literature [3,63], along with varying label formats (e.g., 7-point scales labelled only at the endpoints versus fully labelled 5-point scales). Recommendations for choosing scale formats can be found in other fields [64]. Best practices in IEQ investigations suggest the use of a “pre-trial demonstration” or “pre-experimental standards” to clarify the upper and lower limits of the rating scale for participants [47,50,65]. Even if not done visually, scale clarifications can be accomplished through written definitions [66]. This “calibration” of subjective responses, proposed in the 1980s [50], was not widely adopted and has contributed to today’s limited understanding of human perceptual responses [67]. If this calibration approach is not feasible, it is advisable to provide clear definitions for each category to avoid misinterpretation [67].

Noteworthy, sometimes multiple psychological constructs (or continua) are merged into a single question, potentially leading to biased responses. A classic example is the Bedford scale, mixing thermal sensation and comfort [68]. Even though McIntyre did not observe differences between Bedford and ASHRAE scales [69], the use of Bedford scale is not recommended these days as it can lead to different results [9,36]. Another example from the visual domain is the combination of the constructs of acceptability and comfort, though an environment may be thermally or visually acceptable but not comfortable [63,67]. Questions should avoid merging these constructs, such as in the 9-point De Boer discomfort glare rating scale [60] and the glare assessment scale by Hopkinson [20] mentioned in Q1. Instead, separate questions and one-item scales should be used to measure individual constructs like sensation or comfort, ensuring that the intended metric is accurately captured [9].

## 5. What are we really measuring with assessment scales?

Given the potential biases introduced by applying scales and their effect on results as described in the previous question, the question arises what is and what can be measured with assessment scales and where are their limitations. While these tools are indispensable for assessing how IEQ conditions are perceived and judged by people, there is a concern regarding the precision and reliability of what these scales actually measure. Indeed, the complex relationship between survey design, respondent cognition, and measurement accuracy is a critical subject that started to gain more attention in the scientific community [70,71]. While researchers habitually dedicate considerable effort to crafting precise survey questions and response options, there remains a notable gap in understanding how respondents perceive and interpret these queries. Moreover, the extent to which individuals are consciously capable of discerning perception questions versus those who attempt to guess the “correct” response is a matter that is triggering deeper investigation.

Usually, researchers in the IEQ domain assume fixed distances between response options, such as “hot”, “warm” and “slightly warm” together with consistent and predetermined relationships across constructs (e.g., a quantitative relationship between thermal sensation and acceptability). However, the study by Schweiker et al. [2,41] already mentioned in Q2 showed that, in addition to language (Q2), contextual factors like climate and season influence respondents’ perceptions, challenging scales’ reliability. People’s interpretations of survey questions were influenced by various factors, including their understanding of the terminology used to write the questions, personal experience with the thermal environment, and cognitive, response and acquiescence
biases. These findings suggest that respondents may not always interpret questions as intended by researchers. Their interpretation is affected by contextual and individual factors as shown also in other studies [67,72, 73], potentially introducing response errors.

Regarding, instead, personal experience with the thermal environment, it is well-accepted that the concept of thermal comfort itself is non-static, dynamic, and subjective, meaning that it (a) varies across time, space and personal context, (b) adjusts with occupant behaviour and environmental interaction, and (c) is influenced by people's expectation, control and cultural adaptation [74]. Nicol and Humphreys [75], Humphreys [76], and de Dear and Brager [77] introduced what is nowadays called as adaptive hypothesis according to which comfort assessment is dynamically influenced by people's recent thermal history. In addition to a physiological component of thermal acclimatization, this model implies that the constructs that are measured with assessment scales might also be influenced by psychological and behavioural adaptive experiences of individuals rather being a sole static response due to instantaneous environmental stimuli. Additionally, Humphreys and Hancock [78] discussed how individuals' responses on perception scales may also be affected by their ability to control their environment rather than a sole measure of the direct thermal influences on thermal sensation, acceptability, or comfort. This point introduces a layer of complexity in interpreting assessment scale responses, as what they measure may not exclusively be influenced by IEQ stimuli but also respondents' perception of control opportunities or their recent exposure history. However, while these aspects can affect short-term studies, Li et al. [79] stated in a pre-print that "factors like age, gender, thermal history, and controllability, which influence short-term thermal comfort, did not significantly impact long-term thermal comfort" evaluations.

In addition, people's responses to questionnaires can be affected by additional factors, like mood, health status, contextual elements, or prior experiences. Specifically, according to Knez and Thorsson [80], the subjective nature of thermal comfort is affected by personal and psychological factors and can be influenced by the individual's mood and culture, indicating that responses might not solely reflect immediate thermal conditions but be filtered through a psychological layer. This hypothesis may suggest that affective assessment scales should be able to capture broader aspects of personal well-being and culture, not just sensation, to adopt techniques able to quantify the influence of psychological and cultural triggers. For example, Nikolopoulou and Lykoudis [81] further elaborate on the impact of context and environment, arguing that the same thermal conditions can be perceived differently depending on contextual factors such as the individual's location (outdoors versus indoors), climate context and cultural background. Such factors, hence, can introduce response biases that complicate the interpretation of data gathered from perceptual scales, suggesting again that what is measured is influenced by a complex interplay of environmental and personal factors.

On a different note, Sadick et al. [82] examined the extent to which humans can serve as reliable sensors, scrutinizing their ability to accurately perceive and report environmental conditions. Their findings emphasized the potential presence of an *acquiescence bias*. This cognitive tendency leads individuals to respond affirmatively or positively to survey questions, regardless of their actual perceptions or the objective conditions of the environment. This bias can influence self-reported data, potentially compromising the accuracy of subjective environmental assessments and calls for the need to account for it when interpreting subjective evaluations of environmental factors. That is a potential source of measurement error in comfort research that can skew survey results by inflating the prevalence of positive responses, leading to inaccurate assessments of comfort levels. Moreover, *cognitive biases* in the evaluation of comfort can arise, influenced by the tendency of respondents to provide answers that align with social desirability and their decision-making processes [83]. Similarly, respondents may rely on cognitive shortcuts or conform to prevailing social norms [84].

Additionally, Raccuglia et al. [85] introduced the notion of *anchoring bias*, suggesting that participants might unintentionally use prior sensory scores as reference points during repeated assessments. Over subsequent evaluations, responses might then be affected by these established benchmarks which are shaped by individual preconceptions and strengthened by memorization of previous scores. To mitigate this bias, Raccuglia et al. suggest not showing participants the whole set of scales during the fill out process and displaying a given scale only when it is required to avoid memorization and habituation to the scales. Also, reducing the evaluation frequency can attenuate memorization.

All these biases need careful consideration in survey design and interpretation, as they can hinder the true relationships between environmental parameters and their subjective assessment and the reported assessment. Based on the matters mentioned above, there remains a notable gap in understanding whether respondents genuinely perceive the distinctions between different questions related to sensation and comfort or whether they attempt to discern the “correct” answer based on social desirability or perceived researcher expectations.

## 6. Do subjective scales explicitly measure the qualities of comfort definitions?

Given all influences circumstancing the use of scales, one may ask whether it is possible to assess comfort with subjective scales at all. The quantification of a person's comfort with their environment is challenging due to its multi factorial nature. Often it is discomfort and negative aspects that is measured. Again, this response will start with the thermal domain, because thermal comfort has been the most heavily researched and standardized aspect, while the discussion can be applied to other domains as well.

Thermal comfort has been defined since 1966 as *"the condition of mind that expresses satisfaction with the thermal environment"* (ASHRAE) [86]. Gagge et al. [12] highlighted the complexity of determining the "comfort" of a person rather than their thermal state. They moved to the now accepted thermal sensation scale used by ASHRAE [87] and ISO [31] with neutral as the central point. These days, subjective scales used in thermal comfort research provide information about different aspects of a person's thermal state including thermal sensation, thermal comfort, preference, acceptability, and pleasantness (see also Q1). Each of the subjective scales provides a piece of information but none of these scales individually can resolve their output with the definition of thermal comfort which at its core aims to determine the persons satisfaction with the environment. Thermal sensation is the perception of warmth or cold, preference determines if the person would like to change their environment, acceptability helps identify whether a space is thermally acceptable and thermal comfort is used to estimate the persons comfort/discomfort with their thermal environment. The assessment of "thermal comfort" is the piece of information which is most challenging, and where there are the greatest variations in the design of correlated scales (questions, and responses) [9].

None of these principally used subjective scales, which are mentioned in ISO 10551 and ASHRAE standards, actively address the main focus of the above introduced definition which is the *satisfaction* with the environment. Thereby, above cited ASHRAE definition treats comfort and satisfaction synonymous, while such implicit relationship may break in cases people get dissatisfied with comfortable conditions (e.g., when actively seeking discomfort in a sauna for health reasons). It could be argued that the outputs from the widely used subjective scales triangulate the persons assessment of their environment to a concept of thermal comfort without explicitly confirming it. Whilst we can seek a binary expression of thermal satisfaction (are you satisfied with the thermal environment? Yes/No) and post-occupancy surveys exist that include multi-point satisfaction scales (e.g., including "slightly", "moderately"), there is no widely specified scale included in international standards that directly evaluates the satisfaction, or its magnitude, with the thermal environment. Satisfaction would be a positive
condition that integrates the thermal state, personal preference and individual tolerance. The development of such a scale and its validation (Q10) would require carefully designed studies, so the binary choice of satisfied / not satisfied maybe the best and most effective way of quantifying satisfaction with the thermal environment until then.

Similar issues can be seen when considering definitions for visual comfort, “the subjective condition of visual well-being induced by the lumi- nous environment” [88] and for acoustic comfort as a “state of contentment with acoustic conditions” [89]. Both again use positive descriptors to help define what the comfort condition is, but the scales used to determine a person’s visual assessment (discomfort, preference, acceptability) or acoustic assessment (tolerance, preference, acceptability) [25] do not align with these definitions.

So, would simply asking a person if they were satisfied with their environment be sufficient? From a user perspective this would probably have some benefits, but from research perspective it would not give the rich detail as to the background to what was driving the satisfaction or dissatisfaction. It does seem that there is a disconnect between what researchers and practitioners subjectively ask the occupants of buildings and the focus of the above-mentioned definitions of comfort highlighted here; thermal (satisfaction), visual (well-being), and acoustic (contentment).

Overall, it appears often easier to quantify negative aspects that lead to discomfort, too hot, too bright, too loud, and the drivers of that affect than to hone in what exactly the positive state of comfort is. Is it time for new definitions of environmental comfort parameters that align to the subjective qualities, researchers measure? Consensus on new definitions would be challenging but this review does highlight that the inclusion of a clear measure that aligns to the definition of comfort (thermal(satisfaction)/visual(well-being)/acoustic(contentment) should be included in the suite of subjective scales/tools used.

With respect to the lack of clarity on clear wordings in the fields of IEQ assessment, the authors appreciate recent approaches and initiated discussion related to reviewing existing definitions of comfort by Christoforou et al. [90] who discussed layperson and expert definitions of health, well-being and comfort applied to the built environment, and proposing new conceptual thoughts on the relationship between comfort, satisfaction, well-being, and health by Altomonte et al. [91].

## 7. To what extent can we replace scales with physiological signals?

Given the pitfalls and challenges in the application of scales described in previous questions, it is still unclear whether we can omit scales and rely findings solely on physiological signals (addressed in this question) or behavioural measures (Q8). The relationship between subjective comfort scales and unobtrusively collected physiological reactions has been debated in the context of environmental comfort assessments across all the domains such as thermal, acoustic, and visual comfort, in indoor and outdoor investigations. While traditional comfort assessment methods based on self-reported scales are designed to capture the conscious and evaluative perception, they may be discussed in the human capability to isolate the conscious perception while considering one domain at a time. This potential weakness indeed appeared to be evident since multi-domain comfort theories and experiments were going to be consolidated [3]. In fact, these scales often fail to reflect the nuanced and multidimensional nature of human comfort responses, which are mediated by both conscious and subconscious individual processes and a complex multisensory variety of contextual factors [4]. Wearable sensing technologies, capable of capturing physiological data such as heart rate variability, skin temperature, electrodermal activity, and brainwave patterns (i.e., electroencephalography), may therefore offer a co-occurring method, if not even an alternative and more objective mean, for investigating conscious and subconscious perception or evaluation of environmental stimuli and overall wellbeing conditions, potentially overcoming some of the limitations inherent in subjective

scales.

A growing body of literature has highlighted the discrepancies between subjective comfort ratings and physiological responses. For instance, studies have shown that while participants may report thermal comfort or discomfort at a given moment, their physiological data, particularly heart rate variability and skin conductance, may suggest ongoing autonomic regulation or stress responses not immediately reflected in their self-reported comfort levels [92]. This divergence points to the complex, non-linear relationship between subjective scales and physiological reactions. In particular, the individual assessment of environmental stimuli is a dynamic process that engages multiple neural pathways and brain regions [93], including the somatosensory cortex and ventral posterior insula for the intensity of the thermal stimuli, the limbic system for emotional processing and the prefrontal cortex for cognitive evaluation [93,94].

The perceptual process underlying comfort assessments involves different temporal phases of neural activation. For instance, the initial exposure to an environmental stimulus might elicit an autonomic nervous system response that is subconscious and reflected in physiological markers such as heart rate and skin conductance [95]. However, the conscious perception and evaluation of this experience—measured by traditional scales—may occur later. This information is indeed processed by the prefrontal cortex, which integrates it with prior knowledge, and generates a conscious, mediated, evaluative judgement. This final judgement is therefore mediated-to-biased by a variety of cross-modal and contextual factors, according to the multi-domain comfort theory [4]. The length of the time lag between physiological response and conscious evaluation could be between milliseconds and minutes. Therefore, each one of these options (i.e., subjective scales and physiological information) alone may not fully capture the dynamic nature of environmental assessment, and they may be limited in the possibility for driving HVAC predictive control actions, especially during transient environmental changes or during prolonged exposure to a variety of stimuli [96].

While traditional scales tend to simplify environmental assessments into discrete categories (e.g., comfortable vs. uncomfortable, seven-point ASHRAE thermal sensation scale with 0 representing neutral) [97], physiological data suggest that the exposure to environmental stimuli and its perception and evaluation may be a more continuous and multi-domain experience, influenced by individual factors such as adaptation, cognitive load, and emotional state, and socio-cultural factors (responsible for contextual effects) [4]. In this view, recent research calls into question whether conventional subjective comfort scales are adequately aligned with the diverse neural pathways and regions activated at different stages of perceptual processing [92].

The challenges of traditional scales in capturing this complexity has led to increased interest in integrating physiological data with subjective reporting to develop a more holistic understanding of how humans assess environmental stimuli [95]. By monitoring physiological markers in tandem with self-reported comfort scales, researchers can identify patterns of convergence and divergence between subjective and objective data. At the same time, the vast multitude of physiological data already collected by commercially available equipment such as smart watches, headphones etc. may disclose an unprecedented potential for data collection related to environmental parameters in close proximity of the individual position (air temperature in primis). For instance, periods of discomfort reported by a subject may coincide with heightened skin conductance, indicative of increased sympathetic nervous system activation [98]. Changes in LF/HF ratio demonstrate that the sympathetic nervous system plays a significant role in thermal discomfort detection. In detail, when the sympathetic nerve excites, the individual thermoregulation adaptation behaviour leads to thermal discomfort, and its relative perception [99]. This autonomic modification detectable via physiologic signals may also occur regardless of conscious discomfort reporting, demonstrating heart rate variabilities sensitivity to latent physiological strain and the possibility for tracking adaptive responses
that the subject may be even not aware of, or totally different (i.e., environmentally independent) triggers for generating such a reaction. Therefore, LF/HF ratio may represent an objective indicator for human thermal comfort levels [100].

This integration of subjective and objective data has the potential to enhance the individually tailored granularity of environmental assessments. This advancement is particularly valuable in understanding environmental assessments in complex and dynamic environments, such as urban settings or smart buildings, where perceptions and evaluations can be influenced by multiple, interacting environmental factors [71].

Therefore, subjective assessment scales provide valuable insights into the conscious evaluation of environmental stimuli and their effect on the human body (i.e., its personal perception and evaluation). However, they may not fully capture the underlying physiological processes that influence these experiences. The integration of wearable sensing technology and physiological data represents a significant advancement in comfort research. This approach potentially allows for a more comprehensive understanding of how environmental stimuli are perceived and processed by the human body within a multi-domain framework of indoor and outdoor multisensory environments.

While this innovative method holds great promise for the design of future buildings and urban environments, for the time being, they cannot replace the use of scales to capture human subjective assessment of the indoor environmental quality.

## 8. To what extent can we replace scales with behavioural signals?

In the previous question, we noted that increasingly more studies are attempting to replace subjective scales with objectively measurable physiological signals. However, this remains problematic, as physiological responses – such as heart rate, heart rate variability, skin temperature or galvanic skin response – do not show distinct patterns for single IEQ stressors. For example, these signals are influenced by individual characteristics and contextual non-environmental factors, which adds complexity to their interpretation. Therefore, we examine whether measuring behavioural outcomes can serve as another valid alternative to using scales.

Numerous observational studies have focused on directly observing and quantifying behaviours, rather than solely relying on subjective surveys. These studies have examined a variety of adaptive behaviours, such as shading use [101], window opening [102], and thermostat adjustments [103], often attempting to establish correlations between environmental factors and occupants' actions. The emergence of this practice in the field has been supported by the adaptive thermal comfort theory, which has emphasized the importance of behavioural adaptation [77]. As such, a behavioural action could be considered a sign of discomfort or the point at which conditions switch from being acceptable to unacceptable, and hence considered a suitable proxy for subjective assessments of the IEQ and a suitable candidate to replace scales. However, the sole use of behavioural outcomes, especially in field observation settings has its limitations as well. For instance, individuals may refrain from taking corrective actions because they lack control opportunities or a behaviour may be carried out for reasons unrelated to environmental causes (e.g., opening a window to call children inside rather than for ventilation). The same mistaken interpretation can happen between domains, as exemplarily seen for window opening behaviour, where some studies regarded thermal conditions as the major driver for window opening (i.e., if a window is opened, the occupants find the conditions too warm) [104,105] while others have found that the carbon dioxide concentration was a better predictor of window opening than temperature (i.e., if a window is opened, the occupants find the air quality too poor) [106,107]. These potential confounders make it difficult to isolate the causes of the observed adaptive behaviours.

In experimental IEQ research, the direct observation of behaviours,

has only been adopted in a minority of laboratory studies. In an early attempt, McIntyre tried to replace thermal sensation votes with a behavioural measure of the strength of grip but concluded that the variance of the strength is too high for being a useful measure of sensation [108]. Gagge allowed participants to adjust the air temperature in a test room until they reached their preferred temperature [109]. Similar active approaches were later employed by researchers such as Fanger et al. [110–115]. More recently, fan use preferences have been recorded in several controlled experiments [116,117]. Many other experimental variants for assessing behaviour exist; for example, in Valenza's experiment [118], participants adjusted the temperature of a cooling probe applied to their wrists to alleviate their thermal discomfort, while participants' shuttling behaviour between a cold and hot room has been observed in Schlader [119]. However, observing behaviour in experimental settings is also not without its challenges. For example, in the visual domain it has been noted that during illuminance adjustment tasks, the range of available illuminances significantly influences the preferred levels set by participants, which often fall in the middle of the stimuli range [120]. In the thermal domain, using a fan as a form of behavioural adjustment also presents a challenge, as it can be influenced by non-thermal factors such as noise, the discomfort due to increased airflow pressure, eye dryness, irritation of the eyes, nose, mucosa, and throat. Adaptation, habituation, and dynamic effects can also complicate the analysis and interpretation of behavioural outcomes during laboratory studies [121].

Beyond the inherent challenges of conducting behavioural observations and subjective assessments, the primary reason for employing behavioural indicators is the opportunity they offer to directly observe and quantify behaviour. This is shown exemplarily for the thermal domain as thermal behaviour constitutes the most powerful form of homeoeothermic thermoregulation available to humans and the first line of defence of body temperature [122]. It is therefore particularly important to understand what drives conscious thermal decisions. Thermoregulatory behaviour has been described as “an attempt to avoid what humans call thermal discomfort or displeasure and to obtain thermal pleasure” [123]. Although it is well-established that thermo-regulatory behaviour involves a conscious aspect, the connection between perception and behaviour remains elusive. It may be therefore time to reconcile subjective surveys with objective behavioural tests, particularly in experimental studies. Their integration will help us to improve our understanding of the different dimensions involved in human thermal assessment (e.g., sensation, comfort, preference, acceptance) and enable their comparison with behavioural actions, as a way to better identify the sensory pathways to behaviour. Combining the passive and active approach can also have important practical implications, for example, it can contribute to (1) inform occupant behaviour modelling in the context of building energy simulation; (2) transfer knowledge to be incorporated into novel building control strategies; and (3) improve advice on individual behaviours, for example as part of heat health action plans.

In summary, though they may appear closely related, behaviour still differs from subjective assessment. Therefore, depending on the research and data analysis objective (e.g., inferential, predictive), the use of scales is still necessary, especially when the goal is to capture and analyse the nuanced and subjective aspects of human comfort, well-being, or satisfaction.

## 9. Would a fit for the purpose approach for the application of scales improve their use?

The last point in the previous question regarding the fit between research objective and methods applied leads to the question on fit-for-purpose. It is generally recognized that the tools and models used in scientific and technical queries should fit the purposes of such queries. For instance, in the building sector, queries can be relevant to different stages of building design and operation processes, thus involving
varying degrees of detail and resolution. Consequently, the nature and complexity of the deployed tools and models should take the purpose of the queries and the respective levels of information and details into consideration [124]. This fit-for-purpose-paradigm can be argued to also apply to the design and application of scales in the IEQ field. However, there is little indication that a systematic and comprehensive juxtaposition of i) the variety of scale types and designs on the one hand, and the ii) the range of scale applications in the IEQ domain on the other hand, has been attempted.

As mentioned earlier in the responses to Q1 through Q4, there is large variety of scale designs and there is a rich variety of scales in terms of the kinds of subjective states they may target. Moreover, scales can be applied to not only target inherently dynamic aspects of the environment but also attitudes toward its more static aspects. This variety of scale formats and targets can be placed vis-à-vis a similarly diverse set of possible applications. For instance, scales may be applied in the course of fundamental scientific studies in neuroscience to explore correlations between individuals reports of their experiences and the simultaneously observed activities in various areas of their brains (see also Q7). Such studies may have no immediate intention to address practical questions such as those relevant to specification of adequate IEQ parameters. A typical instance of such questions is: What conditions are preferred by occupants in terms of thermal, visual, and acoustic environment and air quality? However, scales can also effectively address these kinds of practical issues in the course of applied IEQ research. A characteristic case in point is the insights gained via scale-based explorations of the relationship between objective – physically measurable – features of the indoor environments (e.g., air temperature and humidity, illuminance, sound pressure, carbon dioxide concentration) and people' subjective responses to such features (e.g., thermal comfort and pleasantness, visual discomfort, noise-induced annoyance, acceptability of air quality). Scales, thus, can not only capture occupants' primary sensory perceptions pertaining to thermal, visual, auditory, and olfactory stimuli in indoor environments, but also query positive or negative affective associations with such experiences (e.g., pleasantness, dissatisfaction, acceptability).

Past applications of scales in IEQ research and standardization do not appear to explicitly address the need for methods and strategies to match the significant variety of scale formats and formulations with the likewise considerable variety of scale applications toward building design and operation support. The preceding observations suggest that such a systematic “fit-for-purpose” matching could not only contribute to more informed IEQ research designs involving scales but also encourage a more fruitful application of scale-based insights and recommendations in practice toward high-quality indoor environments. In order to reach such systematic matching, researchers are encouraged to convey the reasons for selecting certain scales or for certain decisions surrounding the scales' application transparently (e.g., in the methods sections of their manuscripts). With more of such arguments being laid out, the growing body of related thoughts will support community wide standardization processes setting general guidance and rules for the selection of scales fit for the intended purpose.

## 10. How to move forward to improve the usage of scales?

Acknowledging the limitations, which was one of the purposes of this article, and being more conscious when selecting, designing, applying and interpreting the data derived from scales is an important first step forward towards improving the current situation of the field.

A second step is more research on scales themselves. As mentioned in Q4, using “pre-trial demonstrations” or “pre-experimental standards” is an important step to pilot any intended application of scales. Beyond that, formal validations of scales based on scientific criteria is meaningful. There is a large body of literature in other disciplines regarding the validation of scales including statistical measures (e.g., [125–127]). These measures include single item analyses to assess for instance item

difficulty, item variance or item selectivity relevant for their selection as well as scale- or test-related aspects such as objectivity, reliability and validity of scales. However, similar attempts are scarce with respect to those scales used in IEQ research. Schakib-Ekbatan et al. [128] summarized existing work in relation to IEQ- and post-occupancy evaluation questionnaires and found only three such attempts at that time [129–131]. In recent years, reliability measures of scales applied are mentioned more frequently in the IEQ literature of individual studies (e. g., [132,133–135]), while “we simply miss a conclusive treatment (e.g., a rigorous meta-study) of the expressive power and consistency of typical scales and formats used in IEQ research” [136].

Third, to address many of the challenges related to questions and scales and achieve more comparable data and results, the use of standardized tools is recommended. In the thermal domain, for instance, standardized questions and scales are available in standards like ASHRAE 55, EN ISO 10551, and EN ISO 28802. However, other domains, such as the visual domain, still lack a consensus on which questionnaires and scales to use [19]. As a result, significant progress is required from the scientific community, which should work toward proposing standardized questions and scales across all domains. Morandi et al. [137] emphasized that even when standards consider multiple environmental aspects, they do not always recommend the same wording or scales. This inconsistency poses a challenge for multi-domain research, as it hinders the ability to assess how different domains contribute to the overall subjective assessment and how these domains interact. Furthermore, in the absence of consensus and clear guidelines, researchers often develop their own sets of questions, as seen for example in Buratti et al. [138], where specific questions were created to evaluate perceptions of visual and acoustic environments. To overcome these issues, a collaborative effort from the IEQ research community is essential. Researchers need to develop and consistently use the same tools and methods within individual domains and, potentially, across domains to ensure that results are scalable and generalizable. Extensive collaborative activities leading for example to the ASHRAE Global Thermal Comfort database [34] may then serve for specific calibrations of scales in individual translations by assigning a new number appropriate to its meaning to each scale category as suggested already by Humphreys in 2008 [38].

Mentioned fourth here, but a prerequisite for standardization of scales, is a consensus on the right or at least best measure to assess individual constructs in individual domains for the intended purpose. Reflecting on the fit-for-purpose approach (Q9), a single type of scale will likely not be suitable for every research, and there remains a need for careful selection. As long as such consensus or even a clear definition of the construct to be assessed is lacking, aiming at multiple measures, both subjective, for example including at least scales related to sensation and evaluation, but also in combination with physiological and behavioural measures is a promising approach to increase our understanding on the interaction between environmental stimuli and human reactions.

## Conclusions

Questionnaires have been, and continue to be, essential tools in IEQ research and in assessing its impact on building occupants. The intention of this 10Q-paper is not to discourage their use, but rather to emphasise the importance of improving their design and implementation by developing valid and reliable questions, thereby ensuring the production of more robust, consistent, and interpretable data. Throughout this article, we have highlighted that the consistency of scale usage across and within individual IEQ domains varies significantly. For example, while the thermal, visual, and indoor air quality domains employ a similar two-step approach (perception followed by affective assessment), the acoustic domain uses different methods tailored to their characteristics. Nevertheless, scales are invaluable instruments for gathering people's feedback and assessing individual experiences. At the same time, the complexity of what these scales measure extends beyond the direct physical experience felt by people. They likely capture a
composite of psychological states, adaptive behaviours, contextual factors, and personal expectations, all modulated by potential biases which can be (involuntarily) introduced through survey design or (unconsciously) generated in the minds of the respondents. Alternatives such as the assessment by means of physiological signals only or behavioural measures are promising, but they are not yet mature enough to replace subjective self-reporting, which remains the most direct way to capture occupants' lived experiences. Therefore, to advance IEQ research and successfully adopt questionnaires for advanced building design, control and operation, it is essential to continue refining surveys and rating scales with an awareness of their limitations and an increased understanding of the multifaceted nature of human subjective assessment. In addition to the open issues discussed in this work, a key aspect of assessing IEQ through subjective scales is the preparation and administration of questionnaires. To obtain a reliable sketch of subjects' psychophysical conditions, minimize biases, and facilitate comparisons across different studies, it is essential to use standardized questionnaires and follow specific administration procedures. These should include specific questions about health (e.g., illnesses, medication use, psychological issues) and personal behaviours (e.g., clothing worn).

To overcome existing pitfalls and to advance the field, this 10Q- paper highlights the importance of rigorously applying a fit-for- purpose approach to match the intended purpose with the characteris- tics and possibilities of a scale (Q9), the need for more research on scales themselves (e.g., through validation activities more common in other fields), and the application of mixed methods combining subjective as- sessments with physiological and behavioural measurements (Q10). These efforts are not meant to replace existing tools, but to strengthen and expand them—so that future IEQ research continues to rely on high- quality, human-centred evidence to inform better buildings.

### CRediT authorship contribution statement

**Marcel Schweiker:** Writing – review & editing, Writing – original draft, Project administration, Methodology, Formal analysis, Conceptualization. **Rune Korsholm Andersen:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. **Salvatore Carlucci:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. **Giorgia Chinnazzo:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. **Simon Hodder:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. **Ardeshir Mahdavi:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. **Boris Igor Palella:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. **Anna Laura Pisello:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. **Francesca Romana d'Ambroasio Alfano:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. **Marika Vellei:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

M.S. was supported by a research grant (21055) by VILLUM FONDEN.

### Data availability

No data was used for the research described in the article.

### References

[1] N.E. Klepeis, et al., The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants, J. Expo. Sci. Environ. Epidemiol. 11 (3) (2001) 231–252.

[2] M. Schweiker, et al., Evaluating assumptions of scales for subjective assessment of thermal environments – do laypersons perceive them the way, we researchers believe? Energy Build. (2020).

[3] G. Chinazzo, et al., Quality criteria for multi-domain studies in the indoor environment: critical review towards research guidelines and recommendations, Build. Environ. 226 (2022) 109719.

[4] M. Schweiker, et al., Review of multi-domain approaches to indoor environmental perception and behaviour, Build. Environ. (2020), 106804–106804.

[5] L. Cohen, L. Manion, K. Morrison, Research Methods in Education, 5th ed., Routledge, New York, 2000.

[6] M. Favero, A. Luparelli, S. Carlucci, Analysis of subjective thermal comfort data: a statistical point of view, Energy Build. 281 (2023) 112755.

[7] S.E. Harpe, How to analyze Likert and other rating scale data, Curr. Pharm. Teach. Learn. 7 (6) (2015) 836–850.

[8] S.L. Hawkins, William James, gustav fechner, and early psychophysics, Front. Physiol. 2 (2011) 68.

[9] M. Schweiker, et al., Challenging the assumptions for thermal sensation scales, Build. Res. Inf. 45 (5) (2017) 572–589.

[10] F.C. Houghten, C.P. Yagloglou, Determining lines of equal comfort, J. ASHVE 29 (1923) 163–176.

[11] W.V. Macfarlane, Thermal comfort studies since 1958, Archit. Sci. Rev. 21 (4) (1978) 86–92.

[12] A.P. Gagge, J.A.J. Stolwijk, J.D. Hardy, Comfort and thermal sensations and associated physiological responses at various ambient temperatures, Environ. Res. 1 (1) (1967) 1–20.

[13] P. Wargocki, K. Kostyryko, C. Mandin, Measurements of perceived indoor air quality, in: Y. Zhang, P.K. Hopke (Eds.), Handbook of Indoor Air Quality, Editors, Springer Nature Singapore: Singapore, 2022, pp. 609–643.

[14] C. Yaglou, E. Riley, D. Coggins, Ventilation requirements, ASHVE Trans. 42 (1936) 133–162.

[15] G. Borg, E. Borg, A new generation of scaling methods: level-anchored ratio scaling, Psychologia 28 (2001) 15–45.

[16] B. Müller, et al., The use of gas sampling bags for evaluating the odors of building materials, in: Proceedings of the 10th International Conference on Indoor Air Quality and Climate -Indoor Air, Beijing, China, 2005.

[17] P.O. Fanger, B. Berg-Munch, Ventilation and body odor, in: Proceedings of the Engineering Foundation Conference on Management of Atmospheres in Tightly Enclosed Spaces, Santa Barbara, USA, ASHRAE, 1983.

[18] L. Gunnarsen, P.O. Fanger, Adaptation to indoor air pollution, Environ. Int. 18 (1) (1992) 43–54.

[19] G. Quek, et al., Comparison of questionnaire items for discomfort glare studies in daylight spaces, Light. Res. Technol. 55 (7–8) (2023) 730–758.

[20] R.G. Hopkinson, Discomfort glare in lighted streets, Trans. Illum. Eng. Soc. 5 (1–9_IESTrans) (1940) 1–32.

[21] N.S. Shafavi, et al., Occupants visual comfort assessments: a review of field studies and lab experiments, Sol. Energy 208 (2020) 249–274.

[22] H. Ising, W. Babisch, T. Günther, Work noise as a risk factor in myocardial infarction, J. Clin. Basic Cardiol. 2 (1) (1999) 64–68.

[23] W. Williams, E.F. Beach, M. Gilliver, Development of a subjective loudness rating scale, Int. J. Audiol. 52 (9) (2013) 650–653.

[24] K.W. Ma, C.M. Mak, H.M. Wong, Development of a subjective scale for sound quality assessments in building acoustics, J. Build. Eng. 29 (2020) 101177.

[25] ISO 10551, Ergonomics of the Thermal Environment – Assessment of the Influence of the Thermal Environment Using Subjective Judgment Scales (ISO 10551:2019), International Organization for Standardization, Geneva, 2019.

[26] ASHRAE, Standard 55-2017, Thermal Environmental Conditions for Human Occupancy, American Society of Heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2017.

[27] N.G. Vasquez, et al., Occupants' responses to window views, daylighting and lighting in buildings: a critical review, Build. Environ. 219 (2022) 109172.

[28] ISO/TS 15666, Acoustics Assessment of Noise Annoyance by Means of Social and Socio-Acoustic Surveys, International Organization for Standardization, 2021.

[29] ISO/TS 12913-2, Acoustics-Soundscape-Part 2: Data Collection and Reporting Requirements, International Organization for Standardization Geneva, 2018.

[30] J.M. Fields, et al., Standardized general-purpose noise reaction questions for community noise surveys: research and a recommendation, J. Sound Vib. 242 (4) (2001) 641–679.

[31] ISO 7730, Ergonomics of the Thermal Environment - Analytical determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria (ISO 7730:2005), International Organization for Standardization, Geneva, 2005.

[32] Y. Tochihara, et al., How humans adapt to hot climates learned from the recent research on tropical indigenes, J. Physiol. Anthropol. 41 (1) (2022) 27.
[33] J. Kim, et al., Semantic discrepancies between Korean and English versions of the ASHRAE sensation scale, Build. Environ. 221 (2022) 109343.

[34] V. Földváry Lícina, et al., Development of the ASHRAE global thermal comfort database II, Build. Environ. 142 (2018) 502–512.

[35] J.Y. Lee, et al., Warm or slightly hot? Differences in linguistic dimensions describing perceived thermal sensation, J. Physiol. Anthropol. 28 (1) (2009) 37–41.

[36] H. Al-Khatri, M.B. Gadi, Investigating the behaviour of ASHRAE, Bedford, and
Nicol thermal scales when translated into the Arabic language, Build. Environ.
151 (2019) 348–355.

[37] Z. Lian, Revisiting thermal comfort and thermal sensation, Build. Simul. 17 (2) (2024) 185–188.

[38] M.A. Humphreys, "Why did the piggy bark": some effects of language and context on the interpretation of words used in scales of warmth and thermal preference, in: Proceedings of the Windsor Conference on Air Conditioning and the Low Carbon Cooling Challenge, Cumberland Lodge, 2008.

[39] A. Pitts, The language and semantics of thermal comfort, in: Proceedings of the Windsor Conference, Windsor, UK, NCEUB, 2006.

[40] K. Pantavou, et al., Native influences on the construction of thermal sensation scales, Int. J. Biometeorol. 64 (9) (2020) 1497–1508.

[41] M. Schweiker, et al., The Scales Project, a cross-national dataset on the interpretation of thermal perception scales, Sci. Data 6 (1) (2019), 289–289.

[42] Zavala-Rojas, D., A procedure to prevent differences in translated survey items using SQP. 2014.

[43] J.Y. Lee, Y. Tochihara, Linguistic dimensions in descriptors expressing thermal sensation in Korean: 'warm' projects thermal comfort, Int. J. Biometeorol. 54 (4) (2010) 357–364.

[44] H. Zhang, et al., Thermal sensation and comfort models for non-uniform and transient environments, part III: whole-body sensation and comfort, Build. Environ. 45 (2) (2010) 399–410.

[45] F.R. d'Ambrosio Alfano, B.I. Palella, G. Riccio, THERMODE 2023: formulation and validation of a new thermo-physiological model for moderate environments, Build. Environ. 252 (2024) 111272.

[46] F.R. d'Ambrosio Alfano, E. Ianniello, B.I. Palella, PMV-PPD and acceptability in naturally ventilated schools, Build. Environ. 67 (2013) 129–137.

[47] S.A. Fotios, K.W. Houser, Research methods to avoid bias in categorical ratings of brightness, LEUKOS 5 (3) (2009) 167–181.

[48] C. Pierson, J. Wienold, M. Bodart, Review of factors influencing discomfort glare perception from daylight, LEUKOS 14 (3) (2018) 111–148.

[49] K.G. Van Den Wymelenberg, Visual comfort, discomfort glare, and occupant fenestration control: developing a research agenda, LEUKOS J. Illum. Eng. Soc. N. Am. 10 (4) (2014) 207–221.

[50] D.K. Tiller, M.S. Rea, Semantic differential scaling: prospects in lighting research, Light. Res. Technol. 24 (1) (1992) 43–51.

[51] J.E. Flynn, et al., Interim study of procedures for investigating the effect of light on impression and behavior, J. Illum. Eng. Soc. 3 (1) (1973) 87–94.

[52] J.E. Flynn, et al., A guide to methodology procedures for measuring subjective impressions in lighting, J. Illum. Eng. Soc. 8 (2) (1979) 95–110.

[53] S. Fotios, M. Kent, Measuring discomfort from glare: recommendations for good practice, LEUKOS 17 (4) (2021) 338–358.

[54] J.Y. Lee, et al., Issues in combining the categorical and visual analog scale for the assessment of perceived thermal sensation: methodological and conceptual considerations, Appl. Ergon. 41 (2) (2010) 282–290.

[55] S.Y. Chyung, et al., Evidence-based survey design: the use of a midpoint on the Likert scale, Perform. Improv. 56 (10) (2017) 15–23.

[56] R. Garland, The mid-point on a rating scale: is it desirable, Mark. Bull. 2 (1) (1991) 66–70.

[57] L.J. Simms, et al., Does the number of response options matter? Psychometric perspectives using personality questionnaire data, Psychol. Assess. 31 (4) (2019) 557.

[58] J. Wang, et al., The uncertainty of subjective thermal comfort measurement, Energy Build. 181 (2018) 38–49.

[59] P. Ngai, P. Boyce, The effect of overhead glare on visual discomfort, J. Illum. Eng. Soc. 29 (2) (2000) 29–38.

[60] J.B. de Boer, D.A. Schreuder, Glare as a criterion for quality in street lighting, Trans. Illum. Eng. Soc. 32 (2)IEstrans) (1967) 117–135.

[61] R.G. Hopkinson, Glare from daylighting in buildings, Appl. Ergon. 3 (4) (1972) 206–215.

[62] G.A. Miller, The magical number seven, plus or minus two: some limits on our capacity for processing information, Psychol. Rev. 63 (2) (1956) 81.

[63] L.M. Geerdinck, J.R. Van Gheluwe, M.C.J.M. Vissenberg, Discomfort glare perception of non-uniform light sources in an office setting, J. Environ. Psychol. 39 (2014) 5–13.

[64] B. Weijters, E. Cabooter, N. Schillewaert, The effect of rating scale format on response styles: the number of response categories and response category labels, Int. J. Res. Mark. 27 (3) (2010) 236–247.

[65] S. Bruce, et al., Response bias in category and magnitude estimation of difference and similarity for loudness and pitch, J. Exp. Psychol. Hum.Percept. Perform. 4 (3) (1978) 483.

[66] P.L. Vrabel, C.A. Bernecker, R.G. Mistrick, Visual performance and visual clarity under electric light sources: part II–visual clarity, J. Illum. Eng. Soc. 27 (1) (1998) 29–41.

[67] S. Fotios, Research note: uncertainty in subjective evaluation of discomfort glare, Light. Res. Technol. 47 (3) (2015) 379–383.

[68] T. Bedford, The warmth factor in comfort at work: a physiological study of heating and ventilation. Industrial Health Research Board Report, Medical Research Council, 1936.

[69] D.A. McIntyre, Seven point scales of warmth, Build. Serv. Eng. Res. Technol. 45 (1978) 215–226.

[70] V. De Giuli, O. Da Pos, M. De Carli, Indoor environmental quality and pupil perception in Italian primary schools, Build. Environ. 56 (2012) 335–345.

[71] V.L. Castaldo, et al., How subjective and non-physical parameters affect occupants' environmental comfort perception, Energy Build. 178 (2018) 107–129.

[72] X. Fuchs, et al., Subgroups holding different conceptions of scales rate room temperatures differently, Build. Environ. 128 (2018) 236–247.

[73] M.S. Rea, Calibration of subjective scaling responses, Light. Res. Technol. 14 (3) (1982) 121–129.

[74] J.L.M. Hensen, Literature review on thermal comfort in transient conditions, Build. Environ. 25 (4) (1990) 309–316.

[75] F. Nicol, M.A. Humphreys, Thermal comfort as part of a self-regulating system, Build. Res. Pract. 1 (3) (1973) 174–179.

[76] M.A. Humphreys, Outdoor temperatures and comfort indoors, Batim. Int. Build. Res. Pract. 6 (2) (1978) 92.

[77] R.J. de Dear, G.S. Brager, Developing an adaptive model of thermal comfort and preference, ASHRAE Trans. 104 (1) (1998) 145–167.

[78] M.A. Humphreys, M. Hancock, Do people like to feel 'neutral'?: exploring the variation of the desired thermal sensation on the ASHRAE scale, Energy Build. 39 (7) (2007) 867–874.

[79] Li, P., et al., Cognitive psychology effects in long-term thermal comfort evaluation. 2025. Available at SSRN 4551427.

[80] I. Knez, S. Thorsson, Influences of culture and environmental attitude on thermal, emotional and perceptual evaluations of a public square, Int. J. Biometeorol. 50 (5) (2006) 258–268.

[81] M. Nikolopoulou, S. Lykoudis, Thermal comfort in outdoor urban spaces: analysis across different European countries, Build. Environ. 41 (11) (2006) 1455–1470.

[82] A.M. Sadick, et al., Reliability of human environmental "sensors": evidence from first- and third-person methods, Build. Environ. 186 (2020) 107303.

[83] T.F. Van de Mortel, Faking it: social desirability response bias in self-report research, Aust. J. Adv. Nurs. 25 (4) (2008). The40-40.

[84] H. Allcott, Social norms and energy conservation, J. Public Econ. 95 (9) (2011) 1082–1095.

[85] M. Raccuglia, et al., Anchoring biases affect repeated scores of thermal, moisture, tactile and comfort sensations in transient conditions, Int. J. Biometeorol. 62 (11) (2018) 1945–1954.

[86] ASHRAE, Standard 55-1966, Thermal Environmental Conditions for Human Occupancy, American Society of Heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 1966.

[87] ASHRAE, Standard 55-2020, Thermal Environmental Conditions for Human Occupancy, American Society of Heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2020.

[88] EN 12665, Light and Lighting e Basic Terms and Criteria For Specifying Lighting Requirements (EN 12665:2002), European Committee for Standardization, Brussel, 2002.

[89] Navai, M. and J.A. Veitch, Acoustic satisfaction in open-plan offices: review and recommendations. Research Report RR-151. 2003. Institute for Research in Construction, National Research Council Canada: Ottawa, Canada.

[90] R. Christoforou, S. Lange, M. Schweiker, Individual differences in the definitions of health and well-being and the underlying promotional effect of the built environment, J. Build. Eng. 84 (2024) 108560.

[91] S. Altomonte, et al., What is NExT? A new conceptual model for comfort, satisfaction, health, and well-being in buildings, Build. Environ. 252 (2024) 111234.

[92] S.A. Mansi, et al., Measuring human physiological indices for thermal comfort assessment through wearable devices: a review, Measurement 183 (2021) 109872.

[93] S.A. Mansi, et al., A novel methodology for human thermal comfort decoding via physiological signals measurement and analysis, Build. Environ. 222 (2022) 109385.

[94] M. Abdallah, et al., Sensing occupant comfort using wearable technologies, in: Proceedings of the Construction Research Congress 2016, 2016.

[95] M. Jia, et al., Development of facial-skin temperature driven thermal comfort and sensation modeling for a futuristic application, Build. Environ. 207 (2022) 108479.

[96] K. Tamura, et al., Physiological and subjective comfort evaluation under different airflow directions in a cooling environment, PLoS ONE 16 (4) (2021) e0249235.

[97] R.J. de Dear, G.S. Brager, Thermal comfort in naturally ventilated buildings: revisions to ASHRAE Standard 55, Energy Build. 34 (6) (2002) 549–561.

[98] D.S. Bari, et al., The correlations among the skin conductance features responding to physiological stress stimuli, Skin Res. Technol. 27 (4) (2021) 582–588.

[99] R. McCraty, F. Shaffer, Heart rate variability: new perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk, Glob. Adv. Health Med. 4 (1) (2015) 46–61.

[100] W. Liu, Z. Lian, Y. Liu, Heart rate variability at different thermal comfort levels, Eur. J. Appl. Psychol. 103 (3) (2008) 361–366.

[101] J. Yuan, J. Yao, R. Zheng, Driving factors of shading adjustment behavior based on social cognitive theory and structural equation modeling, Sustainability 16 (16) (2024) 7103.

[102] B. Jeong, J.W. Jeong, J.S. Park, Occupant behavior regarding the manual control of windows in residential buildings, Energy Build. 127 (2016) 206–216.
[103] B. Huchuk, W. O'Brien, S. Sanner, A longitudinal study of thermostat behaviors based on climate, seasonal, and energy price considerations using connected thermostat data, Build. Environ. 139 (2018) 199-210.

[104] F. Haldi, D. Robinson, On the behaviour and adaptation of office occupants, Build. Environ. 43 (12) (2008) 2163-2177.

[105] H.B. Rijal, et al., Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings, Energy Build. 39 (7) (2007) 823-836.

[106] V. Fabi, et al., Occupants' window opening behaviour: a literature review of factors influencing occupant behaviour and models, Build. Environ. 58 (2012) 188-198.

[107] R.K. Andersen, et al., Window opening behaviour modelled from measurements in Danish dwellings, Build. Environ. 69 (2013) 101-113.

[108] D.A. McIntyre, Thermal sensation. A comparison of rating scales and cross modality matching, Int. J. Biometeorol. 20 (4) (1976) 295-303.

[109] A. Gagge, G. Rapp, J. Hardy, Mean radiant and operative temperature for high-temperature sources of radiant heat, ASHRAE Trans. 70 (1964) 419-424.

[110] P.O. Fanger, et al., Thermal comfort conditions during day and night, Eur. J. Appl. Physiol. Occup. Physiol. 33 (4) (1974) 255-263.

[111] P.O. Fanger, J. Højbjerre, J.O.B. Thomsen, Thermal comfort conditions in the morning and in the evening, Int. J. Biometeorol. 18 (1) (1974) 16-22.

[112] Y. Terai, et al., Circadian variation of preferred environmental temperature and body temperature, J. Therm. Biol. 10 (3) (1985) 151-156.

[113] F. Grivel, V. Candas, Ambient temperatures preferred by young European males and females at rest, Ergonomics 34 (3) (1991) 365-378.

[114] H.E. Kim, H. Tokura, Preferred room temperature self-selected by women under the influence of the menstrual cycle and time of day, Biol. Rhythm Res. 28 (4) (1997) 417-421.

[115] O. Shido, et al., Ambient temperatures preferred by humans acclimated to heat given at a fixed daily time, Physiol. Behav. 72 (3) (2001) 387-392.

[116] A.C. Boerstra, et al., Comfort and performance impact of personal control over thermal environment in summer: results from a laboratory study, Build. Environ. 87 (2015) 315-326.

[117] M. Chen, et al., Thermal comfort chamber study of Nordic elderly people with local cooling devices in warm conditions, Build. Environ. 235 (2023) 110213.

[118] A. Valenza, et al., Biophysical, thermo-physiological and perceptual determinants of cool-seeking behaviour during exercise in younger and older women, Exp. Physiol. 109 (2) (2024) 255-270.

[119] Z.J. Schlader, et al., Characteristics of the control of human thermoregulatory behavior, Physiol. Behav. 98 (5) (2009) 557-562.

[120] S. Fotios, C. Cheal, Stimulus range bias explains the outcome of preferred-illuminance adjustments, Light. Res. Technol. 42 (4) (2010) 433-447.

[121] M. Vellei, et al., Dynamic thermal perception: a review and agenda for future experimental research, Build. Environ. 205 (2021) 108269.

[122] A.D. Flouris, Functional architecture of behavioural thermoregulation, Eur. J. Appl. Physiol. 111 (1) (2011) 1-8.

[123] M. Cabanac, P. Serres, Peripheral heat as a reward for heart rate response in the curarized rat, J. Comp. Physiol. Psychol. 90 (5) (1976) 435-441.

[124] I. Gaetani, et al., Fit-for-purpose occupant modeling - choosing the right approach, in: W. O'Brien, F. Tahmasebi (Eds.), Occupant-Centric Simulation-Aided Building Design, Routledge: New York, 2023, pp. 1-23. Editors.

[125] J.M. Bland, D.G. Altman, Validating scales and indexes, BMJ 324 (7337) (2002) 606-607.

[126] G.O. Boateng, et al., Best practices for developing and validating scales for health, social, and behavioral research: a primer, Front. Public Health (2018) 6.

[127] J. Fenn, C.S. Tan, S. George, Development, validation and translation of psychological tests, BJPsych Adv. 26 (5) (2020) 306-315.

[128] K. Schakib-Ekbatan, S. Lechner, M. Schweiker, Reliability of an item set assessing indoor climate in offices—Results from field studies and laboratory research, Front. Built Environ. 5 (2019), 117-117.

[129] Z.D. Belafi, T. Hong, A. Reith, A critical review on questionnaire surveys in the field of energy-related occupant behaviour, Energy Effic. 11 (8) (2018) 2157-2177.

[130] D.A. Guerin, et al., Validity and reliability testing of a post-occupancy evaluation questionnaire on indoor environmental quality and occupants' satisfaction, Int. J. Sustain. Des. 2 (2) (2013) 128-148.

[131] K. Schakib-Ekbatan, et al., Does the occupant behavior match the energy concept of the building? - Analysis of a German naturally ventilated office building, Build. Environ. 84 (2015) 142-150.

[132] F. Sarhadi, V.B. Rad, The structural model for thermal comfort based on perceptions individuals in open urban spaces, Build. Environ. 185 (2020) 107260.

[133] M. Fakhari, V. Vahabi, R. Fayaz, A study on the factors simultaneously affecting visual comfort in classrooms: a structural equation modeling approach, Energy Build. 249 (2021) 111232.

[134] K. Herranz-Pascual, et al., Development of the Acoustic Comfort Assessment Scale (ACAS-12): psychometric properties, validity evidence and back-translation between Spanish and English, PLoS ONE 18 (2) (2023) e0281534.

[135] W.S.E. Ismaeel, A.G. Mohamed, Indoor air quality for sustainable building renovation: a decision-support assessment system using structural equation modelling, Build. Environ. 214 (2022) 108933.

[136] A. Mahdavi, C. Berger, Critical reflections on proxies of indoor-environmental quality, in: Proceedings of the 43rd AIVC-11th TightVent & 9th Venticool Conference, 2023.

[137] F. Morandi, et al., Assessing overall indoor environmental comfort and satisfaction: evaluation of a questionnaire proposal by means of statistical analysis of responses, in: Proceedings of the 41st AIVC/ASHRAE IAQ Conference, Athens, Greece, 2022.

[138] C. Buratti, et al., A new index combining thermal, acoustic, and visual comfort of moderate environments in temperate climates, Build. Environ. 139 (2018) 27-37.