Chang, Choong Yew (2021) Window view quality: investigation of measurement method and proposed view attributes. PhD thesis, University of Sheffield.
Abstract
Previous studies have demonstrated that a room with good view to the outside can provide its occupants with certain psychological benefits. However, the characteristics that constitute a good (or bad) window view have remained unclear. From literature review, it was hypothesised in this study that the quality of a window view is attributed to seven factors: proportion of greenery, number of visual layers, view elements, balance of view, diversity of view, openness of view and depth of view.
To test these hypotheses, 12 urban and sub-urban scenes were selected; 62 subjects were recruited to perform on-site viewing and evaluation of the selected scenes. The method of the view quality evaluation was based on real scenes viewed through “virtual windows” as defined by a portable viewing box, which was set up on site by the researcher. The viewing box enabled the observer to view the actual scenes as if viewing the same scenes through a physical window of 1.2 metres by 1.2 metres in size. Instead of the conventional “view satisfaction” level used in the previous studies, the rating scale for this experiment employed two different dimensions of affective quality – i.e., “pleasantness” of view (POV) and “excitingness” of view (EOV) as the basis for the verbal descriptors, which were anchored to a 4-point and a 10-point numeric scales.
The results of the first experiment were used to test the view quality predictions made using the seven view attributes. In addition, the experiment results were used to test whether there was a significant difference in the subjects’ evaluations of view quality between the 4-point and 10-point scale formats after both primary scale data were rescaled into a common 101-point scale.
A second experiment was carried out to test the hypothesis that there is a significant difference in the perceived window view quality between actual-view and image-view modes. The second experiment was a systematic replication of the first: photographic images of the selected 12 window views were displayed on computer screen for a different group of 62 subjects to evaluate the view quality of the scenes using the same questionnaires for the first experiment.
Stepwise multiple regression and ordinal logistic regression analyses were conducted on the 10-point and 4-point scale data respectively to formulate prediction models of view quality. Results show that among the seven proposed view attributes, “view elements”, “balance of view” and “openness of view” were significant predictors of view quality in the linear model of POV. “Depth of view” appeared to be the poorest predictor of view quality – neither linear nor monotonic relationship could be established between this attribute and the view quality. “View elements” and “openness of view” were also significant predictors in the ordinal logistic model of POV. Validation of the proposed linear prediction model for POV was conducted using correlation analyses and one sample t-tests that compared the predicted view quality with a set of out-of-sample view evaluation data from a third experiment, which involved an independent group of 40 subjects.
The outcomes of analysis show that there is no significant difference in the mean POV (EOV) scores between the rescaled 4-point and rescaled 10-point ratings – whether the evaluation is carried out in actual or image viewing mode. In terms of scale reliability, the 4-point and 10-point scales in most cases showed moderate to excellent internal consistencies. Whether it is for actual or image view, 10-point scale appeared to have higher internal consistency and interrater reliability in most cases compared to the 4-point scale. Overall, the results confirm the construct validity of the rating scales (either 4-point or 10-point scales) that were used in the assessment of actual or image view quality. The results suggest that 10-point scale is probably too fine for the purpose of evaluating window view quality, whilst 4-point scale is perhaps too coarse to achieve a sufficient discriminating power between the scale points. The optimum number of response categories on a rating scale for evaluating window view quality may be either 6 or 8. The study shows that there is no significant difference in the perceived view qualities between actual and image views. However, POV (EOV) ratings of the actual views generally have larger variances compared to that of the image views, probably because the subjects were affected by other visual cues when looking at the window views in real space, which contrasted with window views in pictorial space.
Metadata
Supervisors: | Fotios, Steve |
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Keywords: | View attribute, assessment method, scale format, mode of view, stepwise regression, prediction model |
Awarding institution: | University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > School of Architecture (Sheffield) |
Identification Number/EthosID: | uk.bl.ethos.842822 |
Depositing User: | Mr Choong Yew Chang |
Date Deposited: | 26 Nov 2021 16:46 |
Last Modified: | 01 Jan 2022 10:54 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:29784 |
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