Multidimensional Scaling Of The Molar Physical Environment

Abstract

Two experiments were carried out in order to make tests of several hypotheses concerning perceptions of the molar physical environment. In the first, subjects did multiple category sorts of pictures of environments from three different functional orientations (what is done in or to the environment). In the second, subjects made similarity judgments of all pairs of the same pictures. Data from both experiments were analyzed by INDSCAL and MDSCAL and the resulting multidimensional stimulus spaces compared across conditions, experiments, and analysis methods. It was found that as functional orientations change (Expt. I), stimulus structures and dimensional interpretations of those structures remain invariant, while individual saliences of those dimensions change. Dimensions of naturalness and scale predominated in both experiments, and dimensional and stimulus structures were highly similar across judgment methods. INDSCAL and MDSCAL solutions were highly similar, but INDSCAL solutions were easier to interpret without rotation, while MDSCAL tended to extract fewer dimensions and to fit better with the original data. In the second experiment dimensional saliences were related to subjects' background characteristics and clusters of subjects were characterized according to differences in dimensional saliences.

Comparison of Methods for Collecting and Modeling Dissimilarity Data: Applications to Complex Sound Stimuli

“…However, the price of the increased efficiency is seldom considered: for example, free sorts are known to be less accurate than dissimilarity ratings (Subkoviak & Roecks, 1976). Further, other differences between free sorting and dissimilarity ratings are simply unknown: no study has compared their reliability, and redundancy studies (Bertino & Lawless, 1993;Bonebright, 1996;Cartier et al, 2006;Ward, 1977) are often carried out by focusing on MDS models, rather than on raw data (for an exception, see Harbke, 2003), despite the known inaccuracies of the MDS analysis of the binary freesorting dissimilarities (Goodhill, Simmen, & Willshaw, 1995;Kendall, 1975;Simmen, 1996) and the vulnerability of the fit of these models to variations in the distributional properties of the input data (Pruzansky, Tversky, & Carroll, 1982).…”

Perception of Physical Aspects of Homes, Schools, Neighborhoods, and University Residence Rooms

“…A dimension involving size or scale was also found by Ward (15) by means of multidimensional scaling on the responses of S s' ratings of 20 photographs covering a range of environments from an aerial view of San Francisco to a rubber raft on a river. He also found dimensions of manmade us.…”

“…2 The authors wish to thank Suzi Snook for computational assistance in various aspects of this study. 103 Downloaded by [Deakin University Library] at 03:58 15 March 2015 tial or other bipolar adjective sets in an attempt to examine aspects of environments or environmental displays (e.g., 1,2,3,4,5,7,9,10,13,14).…”

Mapas cognitivos: Revisión crítica

“…Otro acercamiento intenta captar las dimensiones subyacentes al significado ambiental mediante el empleo de MDS para analizar datos de similitudes entre lugares (Desbarats, 1976;Garling, 1976aGarling, , 1976bWard, 1977). Se presenta como una alternativa al uso de descriptores verbales, basándose en que al proporcionar a los sujetos una serie de descriptores o calificativos, se corre el riesgo de inducir a respuestas predeterminadas o reducir el espectro del significado ambiental.…”