Properties of individual differences scaling and its interpretation

2022 ◽  
Author(s):  
John C. Gower ◽  
Niël J. Le Roux ◽  
Sugnet Gardner-Lubbe
Keyword(s):  
Individual Differences ◽  
Individual Differences Scaling

PERCEIVED QUALITY OF NEIGHBOURHOODS IN A CITY IN NORTHWEST SPAIN: AN INDIVIDUAL DIFFERENCES SCALING APPROACH

1997 ◽  
Vol 17 (3) ◽  
pp. 243-252 ◽  
Author(s):  
Ricardo Garcı́a-Mira ◽  
Constantino Arce ◽  
José M. Sabucedo
Keyword(s):  
Individual Differences ◽  
Perceived Quality ◽  
Individual Differences Scaling

Finding a common ordination for several data sets by individual differences scaling

Vegetatio ◽  
1989 ◽  
Vol 83 (1-2) ◽  
pp. 137-145 ◽  
Author(s):  
Jari Oksanen ◽  
Pertti Huttunen
Keyword(s):  
Individual Differences ◽  
Data Sets ◽  
Individual Differences Scaling

Factorial Ecology in Space and Time: An Alternative Method

1976 ◽  
Vol 8 (7) ◽  
pp. 823-828 ◽  
Author(s):  
R Q Hanham
Keyword(s):  
Factor Analysis ◽  
Individual Differences ◽  
Common Factor ◽  
Published Data ◽  
Original Experiment ◽  
Factorial Ecology ◽  
Alternative Method ◽  
Time Space ◽  
Individual Differences Scaling ◽  
Time Periods

An experiment conducted by Taylor and Parkes (1975) to investigate the factorial ecology of a city within a time—space framework is reexamined by means of an alternative method. The original experiment used common-factor analysis (a two-mode procedure) in a three-mode problem. Using the published data of this experiment, the new method, Individual Differences Scaling (INDSCAL), identifies two major dimensions and defines their importance for each of the time periods being considered. INDSCAL has a greater potential as such a method, since it is capable of analyzing problems of up to seven-mode in nature.

Perceptual Scaling of Simultaneous Wind Instrument Timbres

1991 ◽  
Vol 8 (4) ◽  
pp. 369-404 ◽  
Author(s):  
Roger A. Kendall ◽  
Edward C. Carterette
Keyword(s):  
Individual Differences ◽  
Theoretical Model ◽  
Two Dimensions ◽  
Source Material ◽  
Alto Saxophone ◽  
Wind Instrument ◽  
Individual Differences Scaling ◽  
Third Dimension ◽  
Composite Data ◽  
Verbal Attribute

Timbral similarities among wind instrument duos were studied. Flute, oboe, E♭ alto saxophone, B♭ clarinet, and B♭ trumpet instrumentalists performed in all possible duo pairings (dyads). Source material included B♭4 unisons, unison melody, major thirds, and harmonized melody. Nonunison combinations had each instrument of the pair as the soprano, creating a total of six contexts. Music major and nonmusic major subjects rated the similarity of all possible pairs of dyads in each of the six contexts. Classical multidimensional scaling (MDS) was performed; contexts were treated as " subjects" in an individual differences scaling (INDSCAL) analysis of composite data. The resulting spaces had two stable, interpretable dimensions. From verbal attribute rating experiments ( Kendall & Carterette, in preparation, a), these were identified as " nasal" vs. " not nasal," and " rich" vs. " brilliant." A third dimension was interpreted as "simple" vs. "complex."Extrema in the space were associated with three of the five instruments: Trumpet (brilliant), saxophone (rich), and oboe ( nasal). Data that were amalgamated over contexts and plotted in two dimensions yielded a circumplicial configuration. Implications for orchestration are discussed and a theoretical model of timbre combinations and groupings is presented.

Sign in / Sign up

Export Citation Format

Share Document