Abstract
In the study of residential satisfaction in architectural design different physical comfort domains have received the most attention. But with this comfort-driven approach, residential satisfaction is reduced to a psychophysical relationship. Adding psychological substance to the design process, the paper argues that a distinction should be made between residential satisfaction and home attachment and that we need to consider home attachment as a mediator variable for comfort. The aim of the paper is to empirically assess whether the mediation, if it exists at all, is partial or complete. Distinguishing different forms of comfort, a set of alternative structural equation models are tested with data from a 14-nation population survey in Europe. The result of the model tests is that our wellbeing at home comes in two forms—satisfaction and attachment—and that there is partial as well as complete mediation of home attachment on satisfaction depending on the kind of comfort studied.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
1 Introduction
In view of its explanation and measurement, satisfaction in the home has been studied time and again. As explanatory factors, different physical comfort domains have received the most attention (temperature, light, indoor climate, noise), with studies often considering as controls also attributes and values of the residents as well as characteristics of the neighbourhood (see Aragonés et al., 2017, for review). Comfort measures are particularly useful predictors because they can easily be transformed into rules and standards for the building industry, guided by the belief that certain physical comfort levels elicit wellbeing at home. Thus, if examined empirically, the comfort-satisfaction relationship is modelled as a psychophysical relationship, S = f (C), with S indicating a measure of residential satisfaction and C a comfort measure of a particular physical modality.
However, following this comfort-driven approach, we should note that satisfaction is only one of several possible reactions to the stimulation of subjective delight. In particular, as psychological happiness research has convincingly established (Kahneman et al., 1999), we need to distinguish experienced pleasure from remembered pleasure, or, in more everyday terms: felt wellbeing from well-thought-out judgements of satisfaction (Kahneman, 1999). Whereas felt wellbeing is the immediate impression of pleasure, potentially captured in Benthamite instant utility functions, satisfaction is based on the more cognitive component of our wellbeing experience. Transferring this distinction to the domestic scene, feeling attached to one’s home is among the most striking features of felt wellbeing (Cooper Marcus, 2006; Dovey, 1985; Easthope, 2004). Therefore, extending the simple model of comfort-driven residential satisfaction that dominates this field of research, we propose the idea of attachment-driven satisfaction. The experience of comfort in the home prompts residential satisfaction; it does so, however, through the mediation process of attachment to the home. In its core then, wellbeing at home is the trilateral relation of physical comfort, home attachment, and satisfaction, with the size of the mediating effect to be determined (Fig. 1).
Why is this addition to the comfort-satisfaction scheme important? There are two arguments, giving reasons for the here presented research—one theoretical, one practical.
First, integrating home attachment into the equation is adding psychological substance to the architectural design process that is restricted to the consideration of physiological reactions otherwise. The concept of human comfort is used to study physical and physiological sensations in response to the environmental stimuli in our surroundings, but it does not involve motivation and the prospect of purposeful actions. Hence, the comfort-dominated approach is highly reductionistic when it comes to explaining residential satisfaction and far from being able to establishing a behavioural theory of satisfaction.
From a practical point of view, second, comfort is used by engineers to create building standards for optimizing the indoor environment for occupants. But when engineering comfort, we must consider that comfort, however defined, is of little value if we do not feel at home in the places, we experience the comfort in. Merging the standard-setting intensions of engineers with the sense of place, therefore, is what is needed for applying comfort rules in actual architectural settings.
In view of these theoretical and practical concerns, this paper addresses the causal relationship of comfort sensations, home attachment and residential satisfaction in an empirical mediation analysis (Pearl, 2014). Based on a large cross-country population survey in Europe, we examine which mediation model explains the data of this comparative survey best. The aim is to establish an empirical baseline for the analysis of the tripartite relationship of comfort, attachment, and satisfaction in residential satisfaction research.
2 Residential satisfaction as a concept
Residential satisfaction as a concept is of amorphous quality allowing for a multitude of definitions. Reviews of the research into this field include Easthope (2004) and Aragonés et al. (2017). The most comprehensive bibliometric analysis of residential satisfaction research is Biswas et al. (2021), citing 877 publications since 1961. They identify the most influential residential satisfaction contributions in terms of their citation frequencies. Emami and Sadeghlou (2019) structure their review according to the core determinants of residential satisfaction and dissatisfaction from an interdisciplinary perspective, and Lewicka (2011) covers the residential satisfaction literature within her extensive report on place attachment research on from the 70s.
Conceptually, the common feature in reviewing residential satisfaction research is the requirement to distinguish at least three dimensions that need specification in the formation of the concept: domains, relations, and processes. (1) According to Canter and Rees’s (1982) early distinction, the domain of residential satisfaction can address either the house, the neighbourhood, or the neighbours. The house is understood as the home. This is where the individual resides and has her or his primary environment. The home is more than having a roof and shelter, it is also a reflection of the individual’s socio-cultural place in society closely tight to the prevailing concept of the family. While there have been a number of studies that address satisfaction with home characteristics empirically (Shin, 2014, 2016; Shove, 2003), most of the research is focused on the neighbourhood domain (Aragonés et al., 2017, p. 313). The problem this research is confronted with, however, is the difficulty of defining what a neighbourhood is, how to determine its spatial boundary and meaningful demarcation (Marans & Rodgers, 1975). There is also the question of whether the emphasis should be on the physical aspects when drawing up neighbourhood boundaries, or on social and economic characteristics. To the extent that the latter is given preference over “neighbourhood as a place,” the focus is shifted to the third residential satisfaction domain, the neighbours. Here the residential environment is viewed in its social character. The interaction between residents is seen as either determined by neighbours’ physical proximity to each other or by their shared feelings of community. Both aspects can be studied from either a social-psychological and group-theoretical perspective (beginning with Festinger et al., 1950) or from that of urban sociology (Hunter, 1974; Kasarda & Janowitz, 1974).
(2) The second dimension in the concept formation of residential satisfaction is the relational aspect. We are satisfied or dissatisfied in relation to something. Following Amérigo and Aragonés’ (1997) classification, the objective of residential satisfaction can be physical or social, depending on whether the stimulus belongs to the physical or the social environment, and it is, second, either subjective or objective in the sense of being of factual reality versus the result of an evaluation. Amérigo and Aragonés’ (1997) and Aragonés et al. (2017) organize the possible predictors of residential satisfaction according to the resulting four-fold table of these types of relations.
(3) Finally, there is the process dimension. Its relevance received attention after the notion of place attachment was included in residential satisfaction reasoning. Focused primarily on the neighbourhood domain (Hidalgo & Hernández, 2001), attachment became an important variable in residential research, standing side by side with the concept of satisfaction (Bonaiuto et al., 1999). Because of the obvious proximity of both ideas, distinguishing them calls for considering different mental processes in generating satisfaction and attachment, respectively. It is generally held now that satisfaction is first of all a cognitive reaction to the residential environment, while attachment qualifies as a feeling and is as such linked to the emotional element of residential wellbeing (Aragonés et al., 2017, pp. 323–325). Because of these process differences, attachment to a place is of a different nature then residential satisfaction.
Based on the above distinctions of residential satisfaction of domains, relations, and processes, we proceed by delineating the two explanatory concepts of this study, comfort and home attachment, vis-à-vis satisfaction. While the residential satisfaction domain we study, clearly is the home, comfort indicates a particular relational aspect towards satisfaction, whereas home attachment is distinguished from residential satisfaction by a difference in process.
3 Home attachment and comfort measurements
3.1 Home attachment
The distinction between attachment and satisfaction, we focus on, refers to Daniel Kahneman’s theory of the mind and his idea of the self as being double layered: There is the self, existing in system 1, and the self, existing in system 2. The main difference is that in system 1 thinking is fast, in system 2 it is slow. As Kahneman writes: “System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control. System 2 allocates attention to the effortful mental activities that demand it, including complex computations” (Kahneman, 2013, pp. 20–21). Satisfaction is a system 2 notion then because it results from reflections and anticipations. Attachment, in contrast, is immediate indulgence, that is, a system 1 entity.
In terms of causality then, if we maintain this distinction, moving it to the domestic scene, we propose that home attachment should be thought of as contributing to residential satisfaction. We base our sense of satisfaction on our instantaneous enjoyment of feeling at home (Coolen & Meesters, 2012; Cooper Marcus, 2006; Easthope, 2004; Porteous, 1976; Soleimani & Gharehbaglou, 2021), relating it next to some comparison standard of previous experiences and benchmark frames as well as future prospects and concerns. But our attachment to the home, first of all, is what we ponder on when we derive residential satisfaction judgements. In our analysis therefore, we predict a causal relationship between home attachment and residential satisfaction or, in Kahneman’s phrasing, a system 1 phenomenon being causally relevant for a system 2 phenomenon (Kahneman, 1999, pp. 4–6).
3.2 Sense of place—directly perceived and socially constructed
The above argument is based on recent developments in environmental psychology and sense of place research. Environmental psychology, in as much as it was established during the second half of the twentieth century originating from James Gibson’s pioneering work in the field of perception (Gibson, 1979/2015) rejected the representational logic of cognitivism as well as the physicalist idea of the stimulus–response chain of behaviourism. Instead, perception is conceived as an active process in which the surroundings of an organism are related to possible actions, giving objects of perception ecological meaning. The substance of this organism-environment combination is, in Gibson’s term, affordance, reminiscent of the Aufforderungscharakter in the Gestaltist tradition (Lewin, 1936). As objects of perception, affordances are non-representational, instantaneous and without any reflective extras (McConnell & Fiore, 2017; Michaels, 2003).
The idea of perceptual directness and immediateness is picked up by place attachment researchers who are critical of the attention long-term processes of place bonding have received. In this research, it is generally accepted that place attachment develops over time, changes slowly and increases with the length of residence in a given place (Giuliani, 2003; Lewicka, 2010, 2011). Thus, place attachment research “privileges the slow” (Raymond et al., 2017, p. 2), the steady and temporal progression through cognitive and social construction of the meaning of place. In contrast, the theory of affordances “engages the fast” (Raymond et al., 2017, p. 4), the immediate and direct perception of place resulting from a perception–action process which couples the individual instantaneously with the actions she or he can undertake in a particular setting (Chemero, 2003; Raja, 2019).
Sense of place therefore comes in two forms, as direct perception that is immediate (the “fast”) and as a higher order socially constructed reasoning process consuming time (the “slow”). While dual-process theories in general have not been exempted from criticism (Evans & Stanovich, 2013), in place research there is widespread agreement that there is both: immediate place bonding as well as a reflective evaluation process which develops out of the direct sensation of being locally attached (Marsh et al., 2009; Lewicka, 2011, pp. 225f). In terms of causality then, the “fast” will normally function as a precondition for the “slow.”
In as much this distinction is mirrored into the concepts of (directly perceived) place attachment and (socially constructed) residential satisfaction, the relationship between both has mostly been studied without referencing affordance and dual-process theory (exceptions are Coolen & Meesters, 2012; Heft, 2003, 2010; Marsh et al., 2009; Raymond & Gottwald, 2020; Raymond et al., 2017). Rather, attachment and satisfaction are seen as attitudinal constructs (Jorgensen & Stedman, 2001; Scannell & Gifford, 2010) distinguished by appropriate measurement schemes and scales. As attitudes however, little can be said about causal priorities. Does place attachment promote residential satisfaction or is the latter a prerequisite for feeling locally attached?
Both views are exemplified in the sense of place literature. For instance, in the context of predicting pro-environmental behaviour (Daryanto & Song, 2021), Ramkissoon et al. (2013) consider place attachment as antecedent of place satisfaction in structural equation modelling (see Ramkissoon et al., 2012, for a review of similar studies), whereas others, e. g. Ramkissoon and Mavondo (2015) or Casakin and Reizer (2017), choose to reverse the relationship placing satisfaction before attachment. Vada et al. (2019) use satisfaction with tourism experience as predictor for place attachment, whilst Rollero and De Piccoli (2010) as well as Scannell and Gifford (2017) view place attachment as a cause for satisfaction experiences.
The obvious arbitrariness of such causal sequencing is inevitable as long as there is no theoretical frame for connecting both concepts, or, in Guiliani’s words, as long as “the distinction between satisfaction and attachment rests more on empirical results than on a theoretical basis” (Giuliani, 2003, p. 149; also Fried, 2000; Lewicka, 2011, p. 218). Without a theory of perception, the order of the relationship is bound to be ad hoc. In contrast, the dual-process scheme of fast and slow perceptions in place research, superseding empirical generalisations, allows for causal inference.
3.3 Comfort measurements
Also, sensations of comfort can contribute to residential satisfaction. The research in this area, however, does usually not address satisfaction with the home environment directly but bodily wellbeing in response to particular physical stimuli, i. e. the indoor environmental quality (IEQ) mostly assessed in terms of air temperature, air quality and speed, illuminance, and sound level.
Comfort measures are generated either objectively by defining comfort zones of specific energy levels of the respective physical modalities or subjectively through surveying occupants of buildings and record how pleasing they find certain thermal, lighting, or noise conditions. The prototypical project of assessing comfort by objective means was Fanger’s climate chamber research (Fanger, 1970) resulting in his famous PMV-PPD model of thermal comfort. The model is based on the difference of predicted mean vote (PMV) of experimental subjects and the predicted percentage of the dissatisfied (PPD). With some modification, the model has been cast into standard norms such as EN ISO 7730 (ISO, 1994), and is now widely used by engineers.
Architects are more inclined to follow the subjective measurement approach relying on field-based research that focuses on the occupants and their adaptive expectations, as it is implemented, for instance, in the Adaptive Comfort Standard (ACS) originating from the works of de Dear and Brager (1998). The ANSI/ASHRAE standard in its latest edition (ASHRAE, 2017) attempts to merge both types of measurement in its comfort standard. This is also true for the European standard EN15251, which provides guidance on IEQ measurement but is mainly used in energy simulations in accordance with Directive 2002/91 on the energy performance of buildings of the European Parliament and Council (EPBD, 2003; Olesen, 2012). Great effort has also been put into developing weighting schemes for combining effects of multiple environmental factors—e. g. indoor air quality, thermal environment, acoustics, lighting—to derive a holistic index of comfort in buildings (see Heinzerling et al., 2013; Altomonte et al., 2020; Larsen et al., 2020; Leccesse et al., 2021, for reviews).
3.4 Physical and functional comfort
Empirical survey-based studies of comfort in buildings have come up with an additional distinction. There are comfort dimensions proper and comfort dimensions related to the functionality of the house. The latter refer to operational parameters like energy consumption, renovation status, technical facilities, home size and, for instance, a dwelling’s resistance against mould. Functional considerations do not involve immediate feelings of physical comfort vis-à-vis temperature, proper lighting, or indoor climate. In fact, exploratory studies leading to the present research have established two salient factors of comfort measurement: physical comfort and the assessment of functionality (Wegener et al., 2014; Wegener & Fedkenheuer, 2016), a distinction already familiar in the measurement of the environmental quality of work life (Vischer, 1989, 2007) as well as in the literature on housing preferences and choice (Jansen, 2014; Jansen et al., 2011; Sirgi et al., 2005). Thus, we need to examine how these different elements of comfort relate to the attachment to home and, eventually, residential satisfaction.
4 Mediation analysis models
4.1 Causal analysis
Studying satisfaction predictors is different from studying causation. If we have established a close correlative relationship between satisfaction predictors and satisfaction, this does not tell us how both are connected or what mechanism is causally relevant for the outcome (Francescato et al., 2018). An advanced field of research is the causal analysis of estimating and testing mediation processes with latent and observed variables that, in contrast to conventional mediation analysis, takes random and non-random measurement errors into account (Muthén & Asparouhov, 2015; Pearl & Mackenzie, 2018; Vanderweele, 2015). As such, it has transformed the Hempel-Oppenheim covering-law model of explanation in defining the concept of causality (Hempel & Oppenheim, 1948) and has been shown to be appropriate for experimental as well as nonexperimental data (Pearl, 2010; Pearl & Mackenzie, 2018).
The core of causal analysis, based on directed cyclical graphs, is that it is assumed that a variable C is causing another variable S, the former being the causal variable, and variable S that it causes is the outcome. If we define C = comfort and S = satisfaction path c in Fig. 1 is the total effect of C on S. However, the effect of C on S may be mediated by a variable A (for attachment), while the variable C may still affect S. In this situation path c becomes the direct effect, and variable A is the mediator or process variable.
This mediational model is a causal model as it is assumed that the mediator causes the outcome. Complete (full) mediation is given when variable C no longer affects S after A has been introduced, cutting path c to zero. Partial mediation is the case in which the path from C to S is reduced in absolute size but is still different from zero. The amount of mediation in the model is called the indirect effect, and the total effect is defined as the direct effect plus the indirect effect. Often in research, there is inconclusive evidence whether there is full or partial mediation. It is important therefore to test the two alternative nested models, in particular if it turns out that direct and indirect effects have opposing signs causing the total effect to be diminished or disappear altogether (Muthén & Asparouhov, 2015).
4.2 Mediation models
For the different classes of predictors of residential satisfaction, we use mediation analysis for testing the appropriateness of competing models. First, we have the conventional relationship where physical comfort is assumed to determine satisfaction, S = f (C), with S indicating a direct measure of residential satisfaction and C indicating comfort measurements with regard to different physical modalities (Model Ia). According to our causal diagram of Fig. 1, the total effect of C on S in this case is c. Alternatively, this model can also take the form of S = f (F), with F symbolizing measurements of functional comfort instead of physical comfort (Model Ib).
The next model (Model IIa) takes physical comfort as the dominant driver of satisfaction but considers home attachment (A) as the comfort mediator, formally: S = f (C, (A ← C)). This is the classical partial mediation scheme with the direct effect of C on S. Again, reflecting on the role of different comfort realms, we can replace physical comfort with functional comfort such that S = f (F, (A ← F)). This is Model IIb.
Finally, we can leave out the direct effect of either physical or functional comfort on satisfaction in order to test for complete mediation (c = 0). We then have Model IIIa: S = f (A ← C), and Model IIIb: S = f (A ← F).
Thus, three different models are defined for the mediation analysis of physical and functional comfort, respectively: no mediation (Models I a–b), partial mediation (Models II a–b), and complete mediation (Models III a–b). These are summarised in Table 1.
It should be noted that the dependent variables C, F, and A can be conceived as being latent constructs; as such the intended mediation analyses of our study deviate from the classical meditation scheme applied to observables only (Hayes, 2018).Footnote 1 Accordingly, we will apply the alternative model strategy (Jöreskog, 1993; Kline, 2016, p. 11) within structural equations methodology (SEM) to the models, seeking appropriate measurements of the involved observables (Pearl, 2012). And we will ask whether the trilateral relationship of satisfaction, comfort and attachment captured in the models holds under all circumstances, in particular whether it is valid for different groups of respondents.
As will be described in the next section, we utilised recent survey data of a comparative housing quality study in 14 European countries to test the models. Because different climatic regions are covered, we differentiate between three climate zones in which we study residential satisfaction in our sample: the European Continental zone, the Atlantic zone, and the Mediterranean zone. Respondents living in these zones form three different groups. In addition, we define groups of respondents by country groups and test for model-specific group differences with regard to these different population groups.
5 Data
The database for the study is the Healthy Homes Barometer (HHB), an annual survey of attitudes and behaviour in Europe regarding housing quality, residential satisfaction, comfort, health perception, energy consumption, and environmental impact. It was first fielded in 2014 (HHB, 2015) and reported to the European Commission in 2015. In the present analysis, the Healthy Homes Barometer 2016 is used (Beranova et al., 2017; HHB, 2016). Participating countries in this cross-sectional wave were: Austria, Belgium, Czech Republic, Denmark, France, Germany, Hungary, Italy, Norway, Poland, Spain, Switzerland, the Netherlands, and the UK. The survey was administered as an online-panel survey in the involved countries in October 2015. The mean completion time of the questionnaire across all national samples was 17 min.
Based on online panel sampling procedures (Callegaro et al., 2014), national representative samples of 1,000 respondents, aged 18 and higher, were drawn in each country. The number of respondents from each country was set to ensure statistical representation. The samples were drawn from national online panels that secure representative distributions of key demographic variables (age, gender, education, regional representation).Footnote 2 The validity of results was checked with distributions from national resemblance census data and yielded no significant aberrations (Wilke, 2014). The 14 countries surveyed represent more than 430 million Europeans, accounting for more than 70% of the total European population. Furthermore, the selected countries embody a variety of sizes, geographic locations, and climate conditions.
When concluding on the European level, responses can be weighted according to a specific country’s share of the population of the 14 European countries surveyed as a whole. For analyses based on association measures (regression and factor analysis), however, unweighted estimates are usually preferred (Winship & Radbill, 1994). This approach is also followed in this study.
6 Measurement
The variables relevant for the satisfaction models were operationalised as questionnaire items in the Healthy Homes Barometer with category rating scaling methods. Constructing these items, we relied on exploratory case studies of comfort and satisfaction in residential housing. The studies were part of the Velux Model Home 2020 project (Velux, 2015). Covering a period of three years, the studies were carried out in model homes especially designed for this purpose. During the experiment, the houses were closely monitored both in terms of physical performance and of the psycho-social functioning of the residents. Several methods were used for exploring residential life: group discussions, structured face-to-face interviews with family members, self-reports using diary methods, and online questionnaires. These different measurements led to a very detailed recording of the family life in the houses. Eventually, using factor analytic methods and external validation studies, this exploratory material was transformed into questionnaire items suitable for population surveys. The methodological procedures that were employed are described in Wegener et al. (2014), Fedkenheuer and Wegener (2015), and Wegener and Fedkenheuer (2016, 2017). The outcomes were cross-checked with monitoring results of the complete Model Home 2020 series in Denmark, Austria, France, and the UK. These cross-country comparisons are reported in Fedkenheuer and Wegener (2014).
In the following, we describe the variables for assessing residential satisfaction, comfort, and home attachment that were included as part of the Healthy Homes Barometer, forming the database of the present study.
6.1 Satisfaction
As the dependent variable, residential satisfaction, a variant of the universally applied survey question on which satisfaction research is based, was modified to suit the domestic environment (Cheung & Lucas, 2014; Diener et al., 2012; Jovanović & Lazić, 2020).
All in all, how satisfied are you with your current home? (S),
respondents were provided with “Not at all satisfied/Slightly satisfied/Moderately satisfied/Very satisfied/Extremely satisfied” as response options.
6.2 Home attachment
Based on the exploratory case studies in residential housing mentioned above (Velux, 2015), the measurement of home attachment comprises two items that capture residents’ affection to their home best. The stimulus question was:
How do you perceive your current home? Please indicate to what extent the following statements are true?
Respondents could choose from “Very untrue/Somewhat untrue/Neutral/Somewhat true/ Very true.” Specifically, the two attachment items were:
I feel at home where I live (A1)
I don’t really like to spend much time in my dwelling (A2).
6.3 Comfort
For evaluating the physical comfort in the home, a battery of statements was used that were to be scaled by respondents according to their assumed truthfulness, using the same format as above. The physical comfort items, presented in randomised order, were:
In my home, I can make full use of the daylight (C1)
The temperature in my home can easily be adjusted according to my needs (C2)
The sleeping conditions in my bedroom allow a restful sleep (C3)
My dwelling can easily be aired out (C4).
The functionality aspects were measured with the following four (negative) statements, also in randomised order:
My dwelling is in need of renovation (F1)
I sometime wonder if my home uses too much energy (F2)
I have a problem with mould in my home (F3)
My dwelling is too small (F4).
6.4 Climate zones and country groups
Attempting to generalising results over Europe, it is necessary to control for geographic differences. Can we distinguish particular regions in Europe that are characterised by typical residential evaluation patterns? The climate zones classification of regions is the obvious candidate for running this test. Climate zones do not respect country borders, so classifying respondents according to the climatic zones of their residency calls for matching sub-national topological units to the climate zones in Europe.
Numerous studies have shown that climate provides a statistically significant explanation of cross-country variations in measures of subjective wellbeing (Maddison & Rehdanz, 2011; see Parker, 1995, for an early review listing more than 2000 studies from sociology, psychology and physiology). In most of these studies, either revealed preference techniques for expenditures or the household production function approach to measure the compensating surplus (CS) of climate are used (Maddison, 2003). In the so-called hedonic approach, the marginal changes in climate variables are measured directly via individual satisfaction estimates (Maddison, 2014; Maddison & Bigano, 2003; Maddison & Rehdanz, 2011; Mendelsohn, 2014; Rehdanz & Maddison, 2005, 2009; Van der Vliert et al., 2004; Zapata, 2022). Confronted with climate change, this area of research has gained imperative importance.
Based on the most widely used bio-geographical classification of regions (European Environmental Agency, 2020), we make use of the regional information in the Healthy Homes Barometer and derive a climate zone classification of respondents. The classification involves the international codes III2, III3, and IV1 and makes use of the bio-geographical labelling of regions in Europe: Continental climate, Atlantic climate and Mediterranean climate.
We can also look at country differences instead of climate zones in order to test whether political instead of topological divisions make a difference (Phillips et al., 2022). Grouping the individual 14 countries of the study, a natural classification of countries of the European map would be “North” (Austria, Belgium, Denmark, Germany, Norway, Switzerland, the Netherlands, the UK), “South” (France, Italy, Spain), and “East” (Czech Republic, Hungary, Poland) (Beranova et al., 2017).
6.5 Model specification
The specified models are tested with structural equation modelling (SEM) techniques allowing to test alternative models against each other and to control for measurement error. We also investigate the parameter invariances of the studied models with SEM’s group comparison options (Brown, 2015; Jöreskog, 1971; Kline, 2016).
As exogenous latent constructs, the models highlight physical comfort in Model Ia and functional comfort in Model Ib. In Model IIa and Model IIb, attachment is a mediation process variable for physical as well as for functional comfort on satisfaction (see Fig. 1). In Models IIIa and IIIb, complete mediation via attachment is assumed, setting the direct effect of both comfort measures on satisfaction to zero. For physical comfort, there are four indicators (C1–C4), functional comfort has also four observables (F1–F4), and the measurement model of attachment has two indicators (A1, A2). The final dependent (endogenous) variable is the observed satisfaction measure S.
6.6 Goodness of fit statistics
According to Hu and Bentler (1999), a two-index combination of incremental and absolute fit indices is advisable for reporting SEM results.Footnote 3 In this paper, we go beyond Hu and Bentler’s suggestion and report the two most commonly used incremental indices, CFI and NNFI (also known as TLI for Tucker-Lewis index), along with two absolute indices, RMSEA and SRMR.Footnote 4 Model chi-square values and their degrees of freedom are presented as well, even though there are well known limitations in using them in large samples (Bentler & Bonnett, 1980; Jöreskog, 1993). For additional information, we also report the overall coefficients of determination (R2) for the whole model (Bentler & Raykov, 2000).
All models are estimated in Mplus 8 (Muthén & Muthén, 2017) applying the robust maximum likelihood estimation method procedure (MLR) in order to compensate for skewed distributions of observables. We report standardised regression coefficients, the specified goodness of fit measures, and indirect and total effects and their significance based on bootstrap estimation and the Sobel test.
6.7 Exact measurement invariance test and alignment optimisation
When comparing results for groups, differences in means and regressions can only be interpreted meaningfully if the underlying measurements are invariant. If measurement invariance is established, we can safely assume that the same constructs are measured in the same way and that question wording and other conditions do not affect the outcomes across different groups (Davidov et al., 2018; Meredith, 1993; Millsap, 2011).
There are several techniques for testing for measurement invariance. The most widely used procedure is multigroup confirmatory factor analysis (MGCFA), which is applied across different groups of participants. Depending on which types of coefficients are constrained to be equal, three invariance levels are usually differentiated: configural, metric and scalar measurement invariance (Davidov et al., 2014). Configural invariance is established when a particular latent construct is measured by the same items in all groups, regardless of loading sizes. For metric measurement invariance, the loadings are assumed to be equal across groups, and for scalar invariance, in addition to the constrained factor loadings, also the intercepts are constrained to equality. Metric invariance is necessary for comparing covariances and regression coefficients over groups or countries; scalar invariance is necessary for comparing latent means (Davidov et al., 2014).
The problem with MGCFA is that it leads almost always to rejection with regard to scalar measurement invariance, even if only partial invariance is tested (Asparouhov & Muthén, 2014), because the test strictly demands that all relevant group parameters are exactly equal.Footnote 5
In this paper, we will make use of the exact measurement invariance test with MGCFA across population groups. However, in as much as it is likely that exact measurement invariance is not supported by the data, we also make use of an alternative. If exact measurement invariance does not hold, forms of criteria relaxation have been suggested for performing meaningful comparisons (Byrne et al., 1989; Muthén & Asparouhov, 2013). The approach receiving the most attention presently is the alignment optimisation method (Asparouhov & Muthén, 2014; Cieciuch et al., 2018). Tests of cross-cultural scale invariance of place attachment measures using alignment are reported by Nartova-Bochaver et al. (2022), whereas Jones et al. (2017) and Boley et al. (2021) use MGCFA only.
As integrated into the Mplus 8 program, the alignment optimisation procedure estimates group means and variances without constraining loadings and intercepts of indicators to be equal across groups. The method provides a list of model parameters that are noninvariant. According to the simulation results of Muthén and Asparouhov (2014), a maximum of about 25% noninvariant parameters, averaged over loadings and intercepts, are tolerable to conclude that the means based on the alignment optimisation are trustworthy.
6.8 Sequencing of model tests
Based on the forgoing methodological considerations, we proceed with the following sequence of structural equation models and measurement invariance tests (Fig. 2):
Step 1: Tests of full versus partial SEM mediation models (I[a, b], II[a, b], III[a, b]) for the cross-country sample (total, N = 14,000)
Step 2: Multigroup SEM of the best fitting partial meditation models for the three climate zones and three country groups
Step 3: Exact measurement invariance tests (MGCFA) of the best fitting partial mediation models for the three climate zones and three country groups
Step 4: Approximate measurement invariance tests by alignment optimisation for the three climate zones and three country groups
Step 5: In conclusion, we test the multiple mediation model for the cross-country sample (total, N = 14,000) determining the influence of physical and functional comfort and attachment on residential satisfaction simultaneously.
7 Results
7.1 Model tests, cross-country sample
In Table 2, goodness of fit values of the structural equation models are reported. The subsequent section takes up the results of the group comparisons of climate zones and country groups by detailing the goodness of fit measures of the group models, testing also for measurement invariance with both the exact invariance measurement and the alignment optimisation method.
In Table 2 we first see that the CFI index indicates a satisfactory fit for the no-mediation physical comfort model (Model Ia) for the full sample but displays a relatively low R2 value.Footnote 6 This is not particularly surprising, however, because the predictive strength is limited to only one independent variable. For functional comfort in Model Ib, results are very similar.
The path estimates of the mediation models are presented in Table 3. Noteworthy are the strong effects of physical comfort on satisfaction (0.452) and of (negatively phrased) functional comfort (− 0.503), expressed in the form of standardised coefficients, in Model Ia and Ib, respectively.
Of the models involving physical comfort as predictor, Model IIa is the most credible model, yielding the best overall goodness of fit indices CFI und TLI, well surpassing the conventional criterion values (Barrett, 2007; Bentler & Bonett, 1980; Hu & Bentler, 1999). The mediation process variable of attachment reduces the direct effect substantively to a coefficient of only 0.142 here. The next step leads to eliminating the direct effect in the hierarchical confirmatory analysis altogether, as is demonstrated in Model IIIa. While the CFI index is only minimally affected by this and TLI not at all, the overall R2 value increases notably, indicating a higher proportion of explained variance. From this we argue that home attachment tends to provide the complete mediation of physical comfort on satisfaction.
Functional comfort instead of physical comfort is inserted in Model IIb resulting also in a satisfactory, above threshold CFI index for partial mediation. The TLI index is notably lower, and the nonmediated direct effect changes from − 0.503 to − 0.305. If, however, the model is reduced to complete mediation (Model IIIb), both goodness of fit values fall below acceptable levels and large RMSEA values emerge. The conclusion here is that complete mediation does not fit the data for functional comfort. In contrast to physical comfort, functional comfort is only partially mediated by home attachment, leaving a substantial direct influence of functional comfort on satisfaction. Obviously, both forms of comfort sensations tend to stimulate different cognitions in the evaluation of our contentment in the home, thereby involving attachment to a different degree.
7.2 Climate zones and country groups differences
7.2.1 Exact measurement invariance tests using MGCFA
Next, we report the results of the multiple group structural equation models for the different mediation types of comfort, attachment, and satisfaction. The question that needs to be answered is whether the relationships that have been corroborated for the full sample hold also true for different population groups (Davidov et al., 2018). To this effect, we report climate zones and country group differences.
We look at the model fit of partial mediation, the most complete model, for the individual groups first (Models IIa and IIb). As documented in Table 4, the individual group analyses by and large indicate a satisfactory fit of the model with the group data. A fit slightly below standard is visible only in the functional comfort models in the Continental and Atlantic climate zones and in the Northern and Eastern country groups. Overall, after ruling out these exceptions, configural invariance of the geographic population groups is established.
Following the group comparison options for MGCFA, we test possible hierarchical model restrictions employing the free-baseline strategy of letting all parameters be unconstrained in the beginning and then constraining groups of parameters step-by-step. We report results of the three most relevant levels of invariance: configural invariance, metric invariance and scalar invariance. The goodness of fit indices for the invariance models are then compared, and the observed changes in fit statistics guide us in selecting the valid form of invariance (Cheung & Rensvold, 2002).
From the MGCFA results presented in Table 5, we can see that with regard to the two topological groupings, climate zones and country groups, configural and metric invariance is given for the latent constructs in the physical comfort models. Scalar invariance, however, is only established for the country grouping. In contrast, for the functional comfort models, measurement invariance at all three invariance levels must be rejected. Trying to fit models with either configural, metric, or scalar measurement invariance fails as evidenced by the insufficient goodness of fit measures obtained for these models.
7.2.2 Alignment approximation of climate zones and country groupings
For estimating means of the latent constructs, we next apply the approximate measurement invariance test with the alignment method, as described in the method section above. For the three latent constructs, physical and functional comfort, and attachment, we find that there are only very few invariance violations for the climate zones and country groups on the level of metric measurement invariance (equal factor loadings), whereas violation of scalar invariance (equal intercepts) is more prominent (see Table 7 in the Appendix). In all, there are 22% noninvariant parameters; over the two types of groupings, this includes 12% noninvariant loadings and 31% noninvariant intercepts. These results stay well below the cut-off criterion of 25% noninvariant parameters, averaged over loadings and intercepts, that are tolerable in order for means based on the alignment optimisation to be trustworthy.
Thus equipped, we turn our attention to the factor means in the climate zones and country groupings. In Table 6, for highlighting statistical differences, the means are arranged in descending order. We find that home attachment is highest in the Continental climate zone and in the South; physical comfort is lowest in the Atlantic zone as well as in the East and South; and functional comfort scores highest in the Atlantic zone and in the Eastern countries. For satisfaction, the dependent variable, the values are the highest for the Continental climate zone and the Northern countries. If nothing else, these results demonstrate that the three latent constructs capture very different concepts and realities, giving credence to the differences in the mediation of the two comfort types on satisfaction.Footnote 7
7.2.3 Multiple mediation model
After analysing the influence of physical and functional comfort on residential satisfaction independently, we are now in a position to conclude our analysis with a summative model of the full sample across all groupings: the multiple mediation model. The model includes both comfort types, with physical comfort (C) fully mediated by attachment (A) in determining satisfaction (S), and with functional comfort (F) partially mediated through attachment but also affecting satisfaction directly. This can be formalised as S = f (F, (A ← F), (A ← C)) and is presented graphically in Fig. 3.
This figure also includes standardised parameter estimates. Using the same robust estimation procedure as in the previous models, the model fit yields a chi-square value of 1026.876 (df = 37), CFI = 0.957, TLI = 0.937, RMSEA = 0.044, and SRMR = 0.027; R2 = 0.868. To mirror possible applications, residual error correlations between the functional comfort items F1–F3, that involve possible home renovation efforts, were allowed. Table 8 in the Appendix summarises the total direct and indirect effects of the comfort types on satisfaction.Footnote 8
8 Discussion
User-oriented architectural planning and post-occupancy evaluations usually rely on comfort measurements for determining residential satisfaction. An alternative predictor for the level of satisfaction, however, is the assessment of home attachment because the sense of home serves as a prerequisite for physical or functional comfort to shape our level of residential satisfaction. In terms of our model specification then, home attachment is a mediator variable for the comfort varieties that prompt satisfaction. Using recent European cross-country survey data, this proposition is brought to a straightforward test by comparing models of complete, partial, or no mediation of comfort in explaining residential satisfaction.
We find that physical comfort contributes to satisfaction via home attachment, whereas functional comfort affects satisfaction also directly, in other words: physical comfort is fully mediated by attachment, functional comfort only partially. Thus, referencing Kahneman’s (2013) distinction of fast versus slow thinking and Gibsonian perception theory, physical comfort turns out to be an element of system 1 experience, functional comfort is an element of system 2. This is so because attachment, mediating physical comfort fully, is an impulsive reaction whereas functional comfort stimulates a reflective response and as such affects satisfaction directly. This finding is well in line with recent attempts to conceptualise residential satisfaction within the means-end chain approach employing hierarchical value maps (Coolen, 2011; Coolen & Hoekstra, 2001; Sadeghlou & Emami, 2023). According to this approach, residential satisfaction is mediated by housing preferences that are motivated by the expected consequences of choosing a house as well as the realisation of abstract values—clearly a reflective process of Kahneman’s system 2 variety.
Explaining residential satisfaction, we therefore have a two-process situation characteristic of many cognitive and behavioural phenomena (Chaiken & Trope, 1999; Evans, 2010; Evans & Frankish, 2009; Strack & Deutsch, 2004). Though the causal analysis put forth in this article is clearly limited in scope as it utilises only a selective set of both types of comfort variables, it can well serve as a baseline model for delving more deeply into the duality of the causal dynamics of residential satisfaction.
Besides this theoretical outlook, residential satisfaction is also an important yardstick for the definition of comfort norms in architectural design. The engineering of comfort is guided by numerous standards and recommendations in national and international legislation the building industry is obliged to follow. But if comfort is mediated by home attachment in determining residential satisfaction, as the here reported research suggests, the simple comfort rules of these statutes are no longer appropriate. In order to generate satisfaction, the rigid physical and functional comfort norms need to be contextualised and transformed into adaptive comfort standards following recent developments in the measurement and implementation of thermal comfort (Halawa & van Hoof, 2012; Nicol et al., 2012; Kazanci et al., 2019; Nicol et al., 2020). Adaptive comfort rules take into account that perceptions, acceptability, and preferences vary depending on building characteristics and environmental conditions and therefore call for different comfort norms for different situations.
Situational differences need also be considered in view of home attachment. However, in spite of general agreement in place research that home is the prototypical place and that we are “domicentric” individuals (Porteous, 1976), the overwhelming quantity of research that deals with place attachment concerns attachment to the neighbourhood, not home (Giuliani, 2003; Lewicka, 2011). Moreover, as Hidalgo and Hernandez (2001) have shown, replicated by Lewicka (2010), there is an U-shaped relationship between place levels (home, neighbourhood, city) and strength of place attachment, home and city evoking the strongest attachment, neighbourhood the least. The search for predictors of these different attachment forces however overemphasises individual and social properties, whereas differences between places and the physical features of places receive less attention (Droseltis & Vignoles, 2010). Therefore, place attachment research is still in need of a theory of place that identifies causal factors for the development of emotional bonds in particular types of places (Lewicka, 2011).
Regarding the home environment in particular, researchers may want to return to the beginnings of place research in human geography and its roots in the phenomenological tradition (Merleau-Ponty, 1962; Norberg-Schultz, 1979; Graumann, 2002; Cresswell, 2006; Seamon, 2021). The claim of phenomenologists is that the descriptions of places, although subjective, are not idiosyncratic impressions of individual experiences, but reveal universal properties, giving places meaning and emotional value. In this tradition, the work of architectural theorist Christopher Alexander is pertinent (Alexander, 2003; Alexander et al., 1977). In his seminal book The timeless way of building, Alexander (1979) describes a number of architectural factors that should transform houses into homes, and hence generate attachment. Alexander’s order principles of liveable homes include balancing the public and the private, ownership status, size of buildings and number of floors, windows that give pleasing views, openness of access, and an abundance of other design principles. A theory of place attachment could benefit from taking these building and environmental features into account, testing to what extent they predict home attachment (Lewicka, 2011, p. 223; Iwańczak & Lewicka, 2020).
9 Concluding note
A research programme that is directed towards differences in home attachment and the adaptive norm setting of comfort rules needs to be based on causal reasoning and empirical tests of the kind illustrated in this paper. But there are at least five issues future research might improve on:
-
(1)
The number and complexity of contributing factors to the emergence of residential satisfaction should be extended including levels of place attachment based on a taxonomy of types of buildings and residential settings.
-
(2)
Also, differences in lifestyles should be considered. A certain level of satisfaction may well be contingent on particular objective features of the environment and still result in dissatisfaction. Therefore, lifestyle predictors may undercut the predictive power of physical attributes on satisfaction (Jansen, 2011). Future studies should provide to control for these variables.
-
(3)
As is quite obvious, more comfort measures of different physical modalities and functions should be added.
-
(4)
Causal effect differences between population groups and regions need to be accounted for.
-
(5)
Finally, it is also an option to design intervention studies to systematically vary comfort dimensions and attachment factors in quasi-experimental field studies.
But even without these extensions, the here reported research demonstrates already that comfort (physical and functional), home attachment, and residential satisfaction are the key elements of a core theory to explain our wellbeing at home.
Notes
Note that, in these models, we do not consider alternative causal directions or endogeneity effects that would require either randomized control trials (RCT) or longitudinal data or a set of appropriate instrumental variables not available in the data used for this study (Kline, 2016, p. 132; Antonakis, et al., 2010, pp. 1103–1105).
Fieldwork was carried out by Wilke A/S, Copenhagen, in association with three international panel providers: M3 Research (Norway), Wilke Wisdom (Denmark), and SSI Survey Sampling International (all other countries). The sampling was done differently among the three panel providers. M3 and Wilke Wisdom used stratified random samples from their panels in Norway and Denmark that were based on known proportions of various demographic segments. When applying their representative sampling, they quote samples into a matrix with 60 cells including gender, age, and region. The use of quotas in both the invitation process and in the closing process makes them reach nearly a perfect representative sample with almost no need for post weighting. In contrast, SSI employed survey routers. Participants were selected from SSI’s online sample stream, a consistently managed, diverse, and large frame. To minimise the risk of bias, SSI uses a three-stage randomisation process in matching a participant with a survey they are likely to qualify for (details are given in Wilke, 2014).
Incremental indices form a group of fit indices that compare the chi-square value of a baseline model to the chi-square of the tested model. LISREL’s normed fit index (NFI; Bentler & Bonnett, 1980) and its extensions, the comparative fit index (CFI) as well as the Tucker-Lewis non-normed fit index (NNFI; Bentler, 1990) fall into this category. Absolute indices, in contrast, determine how well a given model fits the sample data and demonstrate which model has the best fit (Jöreskog, 1993). Included in this category are the chi-square test, the root mean square error of approximation (RMSEA; Steiger, 1990), the root mean square residual (RMR), and the standardised root mean square residual (SRMR; West et al., 2012).
As cut-off points it is usually recommended that CFI (ranging from 0 to 1) should be equal or greater than .950. Somewhat lower values, .930 to .950, are common for the Tucker-Lewis non-normed fit index TLI (Hu & Bentler, 1995). RMSEA and SRMR can both vary from 1 to 0, with a lower value indicating better model fit. RMSEA should be equal or smaller than .060, and SRMR equal or smaller than .090 (Hooper et al., 2008; Hu & Bentler, 1999).
In MGCFA, model fit indices of increasingly constrained models are compared. If the loss in fit when moving from configural to metric and from metric to scalar is not substantial, the higher level of measurement invariance is considered to be valid. Cut-off criteria for the differences in the fit indices are given by Chen (2007) and Cieciuch et al. (2018). According to these sources, the CFI difference should be < .01, the RMSEA difference < .015, and the SRMR difference < .03 for the metric and < .01 for the scalar invariance model.
Since overall coefficients of determination are presently not implemented in Mplus 8, we take these values from the estimations with the Stata 17 structural equation modelling package with identical model specifications (StataCorp, 2021).
Measurement invariance, using MGCFA and the alignment approximation method, was also tested with regard to other groups of respondents based on regional and demographic attributes and on housing parameters (city size, age of building, type of dwelling, ownership, gender, respondent’s age and education). Due to space limitations, we do not report the results of these tests here. The authors will gladly provide the complete results upon request.
The multiple mediation model was also applied to all groupings of housing characteristics and sociodemographic attributes with satisfactory results in terms of goodness of fit measures. We conclude that the multiple mediation model is valid for the aggregate sample as well as for all of the studied individual groupings. The authors will gladly provide the complete results of these tests upon request.
References
Alexander, C. (1979). The timeless way of building. Oxford University Press.
Alexander, C. (2003). The luminous ground: An essay on the art of building and the nature of the universe (The nature of order, Book 4). The Center for Environmental Structure.
Alexander, C., Silverstein, M., Angel, S., Ishikawa, S., & Abrams, D. (1977). A pattern language. Oxford University Press.
Altomonte, S., Allen, J., Bluyssen, P. M., Brager, G., Heschong, L., Loder, A., Schiavon, S., Veitch, J. A., Wang, L., & Wargocki, P. (2020). Ten questions concerning well-being in the built environment. Building and Environment, 180, 106949. https://doi.org/10.1016/j.buildenv.2020.106949
Amérigo, M., & Aragonés, J. I. (1997). A theoretical and methodological approach to the study of residential satisfaction. Journal of Environmental Psychology, 17, 47–57. https://doi.org/10.1006/jevp.1996.0038
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21, 1086–1120. https://doi.org/10.1016/j.leaqua.2010.10.010
Aragonés, J. I., Amérigo, M., & Pérez-López, R. (2017). Residential satisfaction and quality of life. In G. Fleury-Bahi, E. Pol, & O. Navarro (Eds.), Handbook of environmental psychology and quality of life research (pp. 311–328). Springer.
ASHRAE. (2017). ANSI/ASHRAE Standard 55-2017—Thermal environmental conditions for human occupancy. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. https://doi.org/10.1615/atoz.a.american_society_of_heating_refrigeration_and_air-conditioning_engineers_ashrae_inc
Asparouhov, T. A., & Muthén, B. O. (2014). Multi-group factor analysis alignment. Structural Equation Modeling, 21, 495–508. https://doi.org/10.1080/10705511.2014.919210
Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42, 815–824. https://doi.org/10.1016/j.paid.2006.09.018
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246. https://doi.org/10.1037/0033-2909.107.2.238
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606. https://doi.org/10.1037/0033-2909.88.3.588
Bentler, P. M., & Raykov, T. (2000). On measures of explained variance in nonrecursive structural equation models. Journal of Applied Psychology, 85, 125–131. https://doi.org/10.1037/0021-9010.85.1.125
Beranova, S., Feifer, L., Wickmann Elkjær, M., Bang, U., Rasmussen, M. K., & Christoffersen, J. (2017). Healthy Home Barometer. A survey among European citizens. Proceeding ECEEE, 2017 Summer study on energy efficiency: Consumption, efficiency, and limits. https://www.eceee.org/library/conference_proceedings/eceee_Summer_Studies/2017/6-buildings-policies-directives-and-programmes/healthy-home-barometer-a-survey-among-european-citizens
Biswas, B., Sultana, Z., Priovashini, C., Ahsan, M. N., & Mallick, B. (2021). The emergence of residential satisfaction studies in social research: A bibliometric analysis. Habitat International, 109, 102336. https://doi.org/10.1016/j.habitatint.2021.102336
Boley, B. B., Strzelecka, M., Yeager, E. P., Ribeiro, M. A., Aleshinloye, K. D., Woosnam, K. M., & Mimbs, B. P. (2021). Measuring place attachment with the Abbreviated Place Attachment Scale (APAS). Journal of Environmental Psychology, 74, 101577. https://doi.org/10.1016/j.jenvp.2021.101577
Bonaiuto, M., Aiello, A., Perugini, M., Bonnes, M., & Ercolani, A. (1999). Multidimensional perception of residential environment quality and neighborhood attachment in the urban environment. Journal of Environmental Psychology, 19, 331–352. https://doi.org/10.1006/jevp.1999.0138
Brown, T. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford.
Byrne, B. M., Shavelson, R. J., & Muthén, B. (1989). Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105, 456–466. https://doi.org/10.1037/0033-2909.105.3.456
Callegaro, M., Baker, R., Bethlehem, J., Göritz, A. S., Kronick, J. A., & Lavrakas, P. J. (Eds.). (2014). Online panel research: A data quality perspective. Wiley.
Campbell, A., Converse, P. E., & Rodgers, W. L. (1976). The quality of life of America. Perceptions, evaluations, and satisfactions. Russell Sage.
Canter, D., & Rees, K. (1982). A multivariate model of housing satisfaction. International Review of Applied Psychology, 31, 185–208. https://doi.org/10.1111/j.1464-0597.1982.tb00087.x
Casakin, H., & Reizer, A. (2017). Place attachment, residential satisfaction, and life satisfaction: Traditional and renewed kibbutz. Journal of Human Behavior in the Social Environment, 27, 639–655. https://doi.org/10.1080/10911359.2017.1317313
Chaiken, S., & Trope, Y. (Eds.). (1999). Dual-process theories in social psychology. Guilford Press.
Chemero, A. (2003). An outline of a theory of affordances. Ecological Psychology, 15, 181–195. https://doi.org/10.1207/s15326969eco1502_5
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14, 464–504. https://doi.org/10.1080/10705510701301834
Cheung, F., & Lucas, R. E. (2014). Assessing the validity of single-item life satisfaction measures: Results from three large samples. Quality of Life Research, 23, 2809–2818. https://doi.org/10.1007/s11136-014-0726-4
Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255. https://doi.org/10.1207/s15328007sem0902_5
Cieciuch, J., Davidov, E., Algesheimer, R., & Schmidt, P. (2018). Testing for approximate measurement invariance of human values in the European Social Survey. Sociological Methods and Research, 47, 665–686. https://doi.org/10.1177/0049124117701478
Coolen, H. C. (2011). The meaning structure method. In Jansen, S. J. T., Coolen, H. C., & Goetgeluk, R. W. (Eds.), The measurement and analysis of housing preferences and choice (pp. 75–99). Springer. https://doi.org/10.1007/978-90-481-8894-9_8
Coolen, H. C., & Hoekstra, J. (2001). Values as determinants of preferences for housing attributes. Journal of Housing and the Built Environment, 16, 285–306. https://doi.org/10.1023/A:1012587323814
Coolen, H. C., & Meesters, J. (2012). Editorial special issue: House, home and dwelling. Journal of Housing and the Built Environment, 27, 1–10. https://doi.org/10.1007/s10901-011-9247-4
Cooper Marcus, C. (2006). House as a mirror of self. Exploring the deeper meaning of home. Nicolas Hays.
Cresswell, T. (2006). On the move: Mobility in the modern western world. Routledge.
Daryanto, A., & Song, Z. (2021). A meta-analysis of the relationship between place attachment and pro-environmental behaviour. Journal of Business Research, 123, 208–219. https://doi.org/10.1016/j.jbusres.2020.09.045
Davidov, E., Meuleman, B., Cieciuch, J., Schmidt, P., & Billiet, J. (2014). Measurement equivalence in cross-national research. Annual Review of Sociology, 40, 55–75. https://doi.org/10.1146/annurev-soc-071913-043137
Davidov, E., Schmidt, P., Billiet, J., & Meuleman, B. (Eds.). (2018). Cross-cultural analysis. Methods and applications (2nd ed.). Routledge.
de Dear, R., & Brager, G. (1998). Developing an adaptive model of thermal comfort and preference. ASHRAE Transactions, 104, 145–167.
Diener, E., Inglehart, R., & Tay, L. (2012). Theory and validity of life satisfaction scales. Social Indicators Research, 112, 497–527. https://doi.org/10.1007/s11205-012-0076-y
Dovey, K. (1985), Home and homelessness. In Altman, I., & Werner, C. M. (Eds.), Home environments (pp. 33–64). Plenum Press. https://doi.org/10.1007/978-1-4899-2266-3_2
Droseltis, O., & Vignoles, V. L. (2010). Towards an integrative model of place identification: Dimensionality and predictors of intrapersonal-level place preferences. Journal of Environmental Psychology, 30, 23–34. https://doi.org/10.1016/j.jenvp.2009.05.006
Easthope, H. (2004). A place called home. Housing, Theory and Society, 21, 128–138. https://doi.org/10.1080/14036090410021360
Emami, A., & Sadeghlou, S. (2019). Residential satisfaction: A narrative literature review towards identification of core determinants and indicators. Housing, Theory and Society, 38, 512–540. https://doi.org/10.1080/14036096.2020.1844795
EPBD (2003). European Parliament and Council. Energy performance of buildings directive (EPBD) 2003, 2002/91/EC (December 16, 2002). https://doi.org/10.1017/cbo9780511610851.032
European Environment Agency (EEA). (2020). Biogeographic Regions in Europe, 2015 data set. Retrieved October 2, 2020. http://www.eea.europa.eu/data-and-maps/figures/biogeographical-regions-in-europe-2.
Evans, J. S. (2010). Intuition and reasoning: A dual-process perspective. Psychological Inquiry, 21, 313–326. https://doi.org/10.1080/1047840x.2010.521057
Evans, J. S., & Frankish, K. (Eds.). (2009). In two minds: Dual processes and beyond. Oxford University Press.
Evans, J. S., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8, 223–241. https://doi.org/10.1177/1745691612460685
Fanger, P. O. (1970). Thermal comfort. Danish Technical Press Fountain. https://doi.org/10.1177/146642407209200337
Fedkenheuer, M., & Wegener, B. (2014). The Velux model homes 2020. An international experiment on housing and sustainability. Monitoring results and comparative housing well-being research. Velux group.
Fedkenheuer, M., & Wegener, B. (2015). The housing wellbeing inventory. Understanding how people interact with their homes. Daylight & Architecture, 23, 3–15.
Festinger, L., Schachter, S., & Back, K. (1950). Social pressure in social groups: A study of human factors in housing. Harper & Brothers.
Francescato G., Weidemann S., & Anderson J. R. (2018). Evaluating the built environment from the users’ perspective: Implications of attitudinal models of satisfaction. In Preiser, W., Hardy, A., & Schramm, U. (Eds.), Building performance evaluation (pp. 87–97). Springer. https://doi.org/10.1007/978-3-319-56862-1_7
Fried, M. (2000). Continuities and discontinuities of place. Journal of Environmental Psychology, 20, 193–205. https://doi.org/10.1006/jevp.1999.0154
Gibson, J. J. (1979/2015). The ecological approach to visual perception. Psychology Press.
Giuliani, M. V. (2003). Theory of attachment and place attachment. In Bonnes, M., Lee, T., & Bonaiuto, M. (Eds.), Psychological theories for environmental issues (pp. 137–170). Ashgate. https://doi.org/10.4324/9781315245720-11
Graumann, C. F. (2002). The phenomenological approach to people-environment studies. In R. B. Bechtel & A. Churchman (Eds.), Handbook of environmental psychology (pp. 95–113). Wiley.
Halawa, E., & van Hoof, J. (2012). The adaptive approach to thermal comfort: A critical overview. Energy and Buildings, 51, 101–110. https://doi.org/10.1016/j.enbuild.2012.04.011
Hayes, A. F. (2018). Mediation, moderation, and conditional process analysis. A regression-based approach (2nd ed.). Guilford Press.
Heft, H. (2003). Affordances, dynamic experience and the challenge of reification. Ecological Psychology, 15, 149–180. https://doi.org/10.1207/s15326969eco1502_4
Heft, H. (2010). Affordances and the perception of landscape: An inquiry into environmental perception and aesthetics. In C. Ward Thompson, P. Aspinall, & S. Bell (Eds.), Innovative approaches to researching landscape and health (pp. 9–32). Taylor & Francis Publishing. https://doi.org/10.4324/9780203853252
Heinzerling, D., Schiavon, S., Webster, T., & Arens, E. (2013). Indoor environmental quality assessment models: A literature review and a proposed weighting and classification scheme. Center for the Built Environment. Retrieved from https://escholarship.org/uc/item/5ts7j0f8
Hempel, C. G., & Oppenheim, P. (1948). Studies in the logic of explanation. Philosophy of Science, 15, 135–175. https://doi.org/10.1086/286983
HHB. (2015). Healthy homes barometer 2015. M. K. Rassmussen Publisher.
HHB. (2016). Healthy homes barometer 2016. M. K. Rassmussen Publisher.
Hidalgo, M. C., & Hernández, B. (2001). Place attachment: Conceptual and empirical questions. Journal of Environmental Psychology, 21, 273–281. https://doi.org/10.1006/jevp.2001.0221
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6, 53–60.
Hu, L. T., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76–99). Sage.
Hu, L., & Bentler, M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. https://doi.org/10.1080/10705519909540118
Hunter, A. (1974). Symbolic communities. The persistence and change of Chicago’s local communities. University of Chicago Press.
ISO (1994). International Standard 7730. Moderate thermal environments. Determination of the PMV and PPD indices and specification of the conditions of thermal comfort. International Standards Organization.
Iwańczak, B., & Lewicka, M. (2020). Affective map of Warsaw: Testing Alexander’s pattern language theory in an urban landscape. Landscape and Urban Planning, 204, 103910. https://doi.org/10.1016/j.landurbplan.2020.103910
Jansen, S. J. T. (2011). Lifestyle methods. In: Jansen, S. J. T., Coolen, H. C., & Goetgeluk, R. W. (Eds.), The measurement and analysis of housing preferences and choice (pp. 177–202). Springer. https://doi.org/10.1007/978-90-481-8894-9_8
Jansen, S. J. T. (2014). The impact of the have–want discrepancy on residential satisfaction. Journal of Environmental Psychology, 40, 26–38. https://doi.org/10.1016/j.jenvp.2014.04.006
Jansen, S. J. T., Coolen, H. C., & Goetgeluk, R. W. (Eds.). (2011). The measurement and analysis of housing preferences and choice. Springer. https://doi.org/10.1007/978-90-481-8894-9
Jones, P. C., Pendergast, L. L., Schaefer, B. A., Rasheed, M., Svensen, E., Scharf, R., et al. (2017). Measuring home environments across cultures: Invariance of the HOME scale across eight international sites from the MAL-ED study. Journal of School Psychology, 64, 109–127. https://doi.org/10.1016/j.jsp.2017.06.001
Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36, 409–426. https://doi.org/10.1007/bf02291366
Jöreskog, K. G. (1993). Testing structural equation models. In Bollen, K. A., & Long, J. S. (Eds.), Testing structural equation models (pp. 294–326). Sage. https://doi.org/10.1093/sf/73.3.1161
Jorgensen, B. S., & Stedman, R. C. (2001). Sense of place as an attitude: Lakeshore owners attitudes toward their properties. Journal of Environmental Psychology, 21, 233–248. https://doi.org/10.1006/jevp.2001.0226
Jovanović, V., & Lazić, M. (2020). Is longer always better? A comparison of the validity of single-item versus multiple-item measures of life satisfaction. Applied Research in Quality of Life, 15, 675–692. https://doi.org/10.1007/s11482-018-9680-6
Kahneman, D. (1999). Objective happiness. In Kahneman, D., Diener, E., & Schwarz, N. (Eds.), Well-being: The foundations of hedonic psychology (pp. 3–25). Russell Sage Foundation. https://doi.org/10.1017/s0953820806231972
Kahneman, D. (2013). Thinking, fast and slow. Farrar, Straus, and Giroux.
Kahneman, D., Diener, E., & Schwarz, N. (Eds.). (1999). Well-being: The foundations of hedonic psychology. Russell Sage Foundation. https://doi.org/10.1017/s0953820806231972
Kasarda, J. D., & Janowitz, M. (1974). Community attachment in mass society. American Sociological Review, 39, 328–339. https://doi.org/10.2307/2094293
Kazanci, O., Coakley, D., & Olesen, B. W. (2019). A review of adaptive thermal comfort implementation in international thermal comfort standards. ASHRAE Annual Conference, Kansas City, MO. https://www.researchgate.net/publication/334083781_A_Review_of_Adaptive_Thermal_Comfort_Implementation_in_International_Thermal_Comfort_Standards
Kline, R. B. (2016). Principles and practice of structural equation modeling, Fourth Edition (Methodology in the Social Sciences). Guilford Press.
Larsen, T. S., Rohde, L., Jønsson, K. T., Rasmussen, B., Jensen, R. L., Knudsen, H. N., Witterseh, T., & Bekö, G. (2020). IEQ-Compass—A tool for holistic evaluation of potential indoor environmental quality. Building and Environment, 172, 106707. https://doi.org/10.1016/j.buildenv.2020.106707
Leccese, F., Rocca, M., Salvadori, G., Belloni, E., & Buratti, C. (2021). Towards a holistic approach to indoor environmental quality assessment: Weighting schemes to combine effects of multiple environmental factors. Energy and Buildings, 245, 111056. https://doi.org/10.1016/j.enbuild.2021.111056
Lewicka, M. (2010). What makes neighborhood different from home and city? Effects of place scale on place attachment. Journal of Environmental Psychology, 30, 35–51. https://doi.org/10.1016/j.jenvp.2009.05.004
Lewicka, M. (2011). Place attachment: How far have we come in the last 40 years? Journal of Environmental Psychology, 31, 207–230. https://doi.org/10.1016/j.jenvp.2010.10.001
Lewin, K. (1936). Principles of topological psychology. McGraw-Hill.
Maddison, D. (2003). The amenity value of climate: The household production function approach. Resource and Energy Economics, 25, 155–175. https://doi.org/10.1016/s0928-7655(02)00024-6
Maddison, D. (Ed.). (2014). The amenity value of the global climate. Routledge.
Maddison, D., & Bigano, A. (2003). The amenity value of the Italian climate. Journal of Environmental Economics and Management, 45, 319–332. https://doi.org/10.1016/s0095-0696(02)00052-9
Maddison, D., & Rehdanz, K. (2011). The impact of climate on life satisfaction. Ecological Economics, 70, 2437–2445. https://doi.org/10.1016/j.ecolecon.2011.07.027
Marans, R. W., & Rodgers, W. (1975). Toward an understanding of community satisfaction. In A. H. Hawley & V. P. Rock (Eds.), Metropolitan America in contemporary perspective (pp. 299–352). Wiley.
Marsh, K. L., Johnson, L., Richardson, M. J., & Schmidt, R. C. (2009). Toward a radically embodied, embedded social psychology. European Journal of Social Psychology, 39, 1217–1225. https://doi.org/10.1002/ejsp.666
McConnell, D. S., & Fiore, S. M. (2017). A place for James J. Gibson. In Janz, B. (Ed.), Place, space and hermeneutics (pp. 261–273). Contributions to hermeneutics, 5. Springer International. https://doi.org/10.1007/978-3-319-52214-2_19
Mendelsohn, R. (2014). A hedonic study of the non-market impacts of global warming in the US. In Maddison, D. (Ed.), The amenity value of the global climate. Routledge. https://doi.org/10.4324/9781315071626
Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525–543. https://doi.org/10.1007/bf02294825
Merleau-Ponty, M. (1962). Phenomenology of perception. Routledge.
Michaels, C. F. (2003). Affordances: Four points of debate. Ecological Psychology, 15, 135–148. https://doi.org/10.1207/s15326969eco1502_3
Millsap, R. E. (2011). Statistical approaches to measurement invariance. Taylor & Francis.
Muthén, B. O., & Asparouhov, T. A. (2013). BSEM measurement invariance analysis. Mplus Web Notes, 17. https://www.statmodel.com/examples/webnotes/webnotel7.pdf
Muthén, B. O., & Asparouhov, T. A. (2014). IRT studies of many groups: The alignment method. Frontiers in Psychology, 5, 1–7. https://doi.org/10.3389/fpsyg.2014.00978
Muthén, B. O., & Asparouhov, T. A. (2015). Causal effects in mediation modeling: An introduction with applications to latent variables. Structural Equation Modelling, 22, 12–23. https://doi.org/10.1080/10705511.2014.935843
Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Muthén & Muthén.
Nartova-Bochaver, S., Reznichenko, S., Bardadymov, V., Khachaturova, M., Yerofeyeva, V., Khachatryan, N., Kryazh, I., Kamble, S., & Zulkarnain, Z. (2022). Measurement invariance of the Short Home Attachment Scale: A cross-cultural study. Frontiers in Psychology, 13, 834421. https://doi.org/10.3389/fpsyg.2022.834421
Nicol, J. F., Humphreys, M., & Roaf, S. (2012). Adaptive thermal comfort: Principles and practice. Routledge.
Nicol, J. F., Rijal, H., Imagawa, H., & Thapa, R. (2020). The range and shape of thermal comfort and resilience. Energy and Buildings, 224, 110277. https://doi.org/10.1016/j.enbuild.2020.110277
Norberg-Schultz, C. (1979). Genius loci. Towards a phenomenology of architecture. Rizzoli.
Olesen, B. W. (2012). Revision of EN 15251: Indoor environmental criteria. REHVA European HVAC Journal, 2012, 6–12.
Parker, P. (1995). Climatic effects on individual, social and economic behaviour: A physioeconomic review of research across disciplines. Greenwood Press.
Pearl, J. (2010). The foundations of causal inference. Sociological Methodology, 40, 75–149. https://doi.org/10.1111/j.1467-9531.2010.01228.x
Pearl, J. (2012). The causal foundations of structural equation modeling. In Hoyle, R. H. (Ed.), Handbook of structural equation modeling (pp. 68–91). Guilford Press. https://doi.org/10.21236/ada557445
Pearl, J. (2014). Interpretation and identification of causal mediation. Psychological Methods, 19, 459–481. https://doi.org/10.1037/a0036434
Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic Books.
Phillips, W., Janta, B., Gehrt, D., Flemons, L., Gkousis, E., Cole, S., Smith, P., & Hafner, M. (2022). Poor indoor climate: Its impact on health and life satisfaction, as well as its wider socio-economic costs. RAND Europe. https://doi.org/10.7249/rra1323-1
Porteous, J. D. (1976). Home: The territorial core. Geographical Review, 66, 383–390. https://doi.org/10.2307/213649
Raja, V. (2019). J. J. Gibson’s most radical idea: The development of a new law-based psychology. Theory & Psychology, 29, 789–806. https://doi.org/10.1177/0959354319855929
Ramkissoon, H., & Mavondo, F. T. (2015). The satisfaction–place attachment relationship: Potential mediators and moderators. Journal of Business Research, 68, 2593–2602. https://doi.org/10.1016/j.jbusres.2015.05.002
Ramkissoon, H., Smith, L. D. G., & Weiler, B. (2013). Testing the dimensionality of place attachment and its relationships with place satisfaction and pro-environmental behaviours: A structural equation modelling approach. Tourism Management, 36, 552–566. https://doi.org/10.1016/j.tourman.2012.09.003
Ramkissoon, H., Weiler, B., & Smith, L. D. G. (2012). Place attachment and pro-environmental behaviour in national parks: The development of a conceptual framework. Journal of Sustainable Tourism, 20, 257–276. https://doi.org/10.1080/09669582.2011.602194
Raymond, C., & Gottwald, S. (2020). Beyond the “local”: Methods for examining place attachment across geographic scales. In Manzo, L., & Devine-Wright, P. (Eds.). Place attachment: Advances in theory, methods, and applications (pp. 143–158). Routledge. https://doi.org/10.4324/9780429274442-9
Raymond, C. M., Kyttä, M., & Stedman, R. (2017). Sense of place, fast and slow: The potential contributions of affordance theory to sense of place. Frontiers in Psychology, 8, 1674. https://doi.org/10.3389/fpsyg.2017.01674
Rehdanz, K., & Maddison, D. (2005). Climate and happiness. Ecological Economics, 52, 111–125. https://doi.org/10.1016/j.ecolecon.2004.06.015
Rehdanz, K., & Maddison, D. (2009). The amenity value of climate to German households. Oxford Economic Papers, 61, 150–167. https://doi.org/10.1093/oep/gpn028
Rollero, C., & De Piccoli, N. (2010). Place attachment, identification and environment perception: An empirical study. Journal of Environmental Psychology, 30, 198–205. https://doi.org/10.1016/j.jenvp.2009.12.003
Sadeghlou, S., & Emami, A. (2023). Residential preferences and satisfaction: A qualitative study using means-end chain theory. Journal of Housing and the Built Environment. https://doi.org/10.1007/s10901-023-10017-1
Scannell, L., & Gifford, R. (2010). Defining place attachment: A tripartite organizing framework. Journal of Environmental Psychology, 30, 1–10. https://doi.org/10.1016/j.jenvp.2009.09.006
Scannell, L., & Gifford, R. (2017). Place attachment enhances psychological need satisfaction. Environmental Behavior, 49, 359–389. https://doi.org/10.1177/0013916516637648
Seamon, D. (2021). Place attachment and phenomenology: The dynamic complexity of place. In Manzo, L. C., & Devine-Wright, P. (Eds.), Place attachment: Advances in theory, methods, and applications (pp. 29–44). Routledge. https://doi.org/10.4324/9780429274442-2
Shin, J. (2014). Making home in the age of globalization: A comparative analysis of elderly homes in the U.S. and Korea. Journal of Environmental Psychology, 37, 80–93. https://doi.org/10.1016/j.jenvp.2013.12.001
Shin, J. (2016). Toward a theory of environmental satisfaction and human comfort: A process-oriented and contextually sensitive theoretical framework. Journal of Environmental Psychology, 45, 11–21. https://doi.org/10.1016/j.jenvp.2015.11.004
Shove, E. (2003). Comfort, cleanliness and convenience. The social organization of normality. Berg.
Sirgi, M. J., Grzeskowiak, S., & Su, C. (2005). Explaining housing preference and choice: The role of self-congruity and functional congruity. Journal of Housing and the Built Environment, 20, 329–347. https://doi.org/10.1007/s10901-005-9020-7
Soleimani, M., & Gharehbaglou, M. (2021). The role of self-determination needs and sense of home. Journal of Housing and the Built Environment. https://doi.org/10.1007/s10901-020-09804-x
StataCorp (2021). Stata: Release 17. Statistical Software. StataCorp LLC.
Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173–180. https://doi.org/10.1207/s15327906mbr2502_4
Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. https://doi.org/10.1207/s15327957pspr0803_1
Vada, S., Prentice, C., & Hsiao, A. (2019). The influence of tourism experience and well-being on place attachment. Journal of Retailing and Consumer Services, 47, 322–330. https://doi.org/10.1016/j.jretconser.2018.12.007
Van der Vliert, E., Huang, X., & Parker, P. (2004). Do hotter and colder climates make societies more, but poorer societies less, happy and altruistic? Journal of Environmental Psychology, 24, 17–30. https://doi.org/10.1016/s0272-4944(03)00021-5
Vanderweele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press.
Velux (2015). Model Home 2020. The buildings of tomorrow. Today. Final results and outlook of Model Home 2020. Velux group.
Vischer, J. C. (1989). Environmental quality in offices. Van Nostrand Reinhold.
Vischer, J. C. (2007). The effects of the physical environment on job performance: Towards a theoretical model of workspace stress. Stress and Health, 23, 175–184. https://doi.org/10.1002/smi.1134
Wegener, B., & Fedkenheuer, M. (2016). Assessing housing wellbeing in sustainable buildings and a large scale test. In Heiselberg, P. K. (Ed.), CLIMA 2016. Proceedings of the 12th REHVA World Congress. Volume 7. Department of Civil Engineering, Aalborg University. https://vbn.aau.dk/ws/portalfiles/portal/233762335/paper_375.pdf.
Wegener, B., & Fedkenheuer, M. (2017). The healthy homes barometer 2016: Theory and explanations. Velux group.
Wegener, B., Fedkenheuer, M., & Scheller, P. (2014). The psychophysics of wellbeing. Socio-psychological monitoring and benchmark measurement in energy-efficient housing. Proceedings World Sustainable Building Conference 2014. Volume 3 (pp. 691–700). Green Building Council España. https://gbce.es/archivos/ckfinderfiles/WSB14/CreatingNewResources_volume3.pdf
West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modelling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). Guilford Press.
Wilke (2014). Velux Index 2014—Methodology and quality ensurance procedures.: Velux group.
Winship, C., & Radbill, L. (1994). Sampling weights and regression analysis. Sociological Methods & Research, 23, 230–257. https://doi.org/10.1177/0049124194023002004
Zapata, O. (2022). Happiness in the tropics: Climate variables and subjective wellbeing. Environment and Development Economics, 27, 250–271. https://doi.org/10.1017/s1355770x21000267
Funding
Open Access funding enabled and organized by Projekt DEAL.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
No funding was received for this manuscript. The data for this study, collected under the first author’s supervision, was provided by the Velux group, Hørsholm, Denmark.
Research involving human participants and/or animals
No research involving human participants or animals was done as part of this research.
Informed consent
Given research did not involve human participants, no informed consent was needed as part of the study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Wegener, B.A., Schmidt, P. Wellbeing at home: a mediation analysis of residential satisfaction, comfort, and home attachment. J Hous and the Built Environ 39, 103–131 (2024). https://doi.org/10.1007/s10901-023-10068-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10901-023-10068-4