1994
DOI: 10.4135/9781412984645
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Introduction to Facet Theory

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Cited by 227 publications
(86 citation statements)
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“…Here, we used Faceted Smallest Space Analysis (FSSA), a program developed by Shye (1992;Shye, Elizur, & Hoffman, 1994) that first maps the items, then mathematically partitions each conceptual map according to the predefined facets. The program also provides a separation index ranging from 0-1 to assess how well each facet scheme fits the data.…”
Section: Resultsmentioning
confidence: 99%
“…Here, we used Faceted Smallest Space Analysis (FSSA), a program developed by Shye (1992;Shye, Elizur, & Hoffman, 1994) that first maps the items, then mathematically partitions each conceptual map according to the predefined facets. The program also provides a separation index ranging from 0-1 to assess how well each facet scheme fits the data.…”
Section: Resultsmentioning
confidence: 99%
“…The higher the correlation or the more similar between two variables, the distance between them are shorter in this space [13]. SSA can provide two informal indicators to judge the quality of the configuration [14]: The first one is the coefficient of alienation, which reflects how the proposed dimension solution fits for the data (values of this index range from 0 (best) to 1 (worst). Another is the separation index, which is aim to examine the fit between the spatial solution constructed in the confirmatory SSA fits for the hypothesis.…”
Section: B the Small Space Analysismentioning
confidence: 99%
“…Another is the separation index, which is aim to examine the fit between the spatial solution constructed in the confirmatory SSA fits for the hypothesis. It has a range from 0 to 1 also, while value one means model fits the hypothesis perfectly [14].…”
Section: B the Small Space Analysismentioning
confidence: 99%
“…The location of each item is determined by assessing the similarity or dissimilarity to all of the other items. SSA can produce two indicators to evaluate the configuration quality [12]: the coefficient of alienation and the separation index, which reflects how it fit between the spatial configuration and the data (values of this index range from 0 (best) to 1 (worst), while the separation index examines the fit between the spatial solution found in the confirmatory SSA and the a priori spatial hypothesis derived from the mapping sentence, which ranges from 0 (worst) to 1 (best) [12].…”
Section: A the Smallest Space Analysismentioning
confidence: 99%