1970
DOI: 10.1177/002200277001400311
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Doing smallest space analysis

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Cited by 62 publications
(27 citation statements)
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“…This means that instead of metric dissimilarities with linear distance transformation, correlation coefficients between country scores will comprise similarities(!) with a monotonic transformation so that the inverse rank order between the coefficients are maintained (Bloombaum, 1970). Essentially, this means that if country i is more related to country j than say country p, the plotted distance between i and j ought to be smaller than i and p. Again, the algorithm will attempt to minimise stress.…”
Section: Equationmentioning
confidence: 99%
“…This means that instead of metric dissimilarities with linear distance transformation, correlation coefficients between country scores will comprise similarities(!) with a monotonic transformation so that the inverse rank order between the coefficients are maintained (Bloombaum, 1970). Essentially, this means that if country i is more related to country j than say country p, the plotted distance between i and j ought to be smaller than i and p. Again, the algorithm will attempt to minimise stress.…”
Section: Equationmentioning
confidence: 99%
“…Buscando agrupar as questões em fatores, utilizou-se a técnica Smallest Space Analysis -SSA, que permite uma análise com diversos itens, independentemente do tamanho da amostra, a fim de reduzir o número de variáveis e aglomerá-las em fatores a partir de sua forte correlação, uma vez que a SSA é baseada na ordem dos coeficientes de correlação (Bloombaum, 1970). Para aplicação dessa técnica, foi utilizada a ferramenta de Escala Multidimensional (Alternating Least Squares SCALing -ALSCAL) disponibilizada pelo programa SPSS (Statistical Package for the Social Sciences).…”
Section: Procedimentos De Análise De Dadosunclassified
“…Smallest Space Analysis is a graphic display of the interrelationships among the variables, reflecting the relation of each variable vis-a-vis all the others. The computer locates the variables as points on a map according to the strength of the correlation of each variable with each of the others: the stronger the positive correlation between two variables, the smaller the distance between the points representing them; the weaker the positive correlation (or the stronger the negative correlation) between two variables, the greater the distance between the points representing them (Bloombaum 1970;Canter 1985:268-271). 11 In this study a 'weak monotonicity correlation coefficient' {ji-j) was used.…”
Section: Masterymentioning
confidence: 99%