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| | Multiple Classification Analysis |
 | | In statistical terms, the MCA model specifies that a coefficient be assigned to each category of each predictor, and that each individual’s score on the dependent variable be treated as the sum of the coefficients assigned to categories characterizing that individual, plus the average for all cases, plus an error term. |
 | | This statistic is an approximate measure of the relationship between a predictor and the dependent variable, while holding constant all other predictors, i.e., assuming that in each category of a given predictor all other predictors are distributed as they are in the population at large. |
 | | In the case of Analysis of Variance, the problem of correlated predictors must be considered, whereas in the case of Multiple Regression or Discriminant Analysis, one is faced with the problem of predictors, which are not interval scale variables, but categories, often with scales as weak as the nominal level. |
| www.unesco.org /webworld/idams/advguide/Chapt5_3.htm (1098 words) |
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