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, 60, 61, 62, 64, 65, 66, 67, 70, 73, 76, 78, 86, 88, 102, 174, 233, 234, 237, 239, 242, 247 McCullough, M E, 449 McDaniel, P, 572 McDougall, W, 5, 463, 476 McFarland, C, 314, 355, 472 McFarland, S, 618 McGhee, D E, 305, 355, 356, 357, 519 Mcgill, K L, 333 McGlynn, E, 499 McGraw, K M, 602 McGue, M, 74, 76, 106 McGuf n, P, 77 McGuire, C V, 370 McGuire, W J, 275, 299, 308, 364, 365, 369, 370, 390, 601 McIntosh, W D, 195 McKenna, F P, 432 McKeough, K L, 391 McKinnon, C, 540 McLaughlin, D H, 40 McLaughlin-Volpe, T, 493 McLemore, C W, 212, 218 McMaster, M R, 475 McPhail, C J, 577 McRae, R R.

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value of 050 requires that at least 25% of the variable s variance be explained by a particular component PCA does not extract all the variance from the variables, only that proportion of the variance that is shared by several variables Communality represents the proportion of variance of a particular variable that is shared with other variables The communalities represent the overall importance of each of the variables in the PCA as a whole Communality values are calculated as the sum of squared component weights for a given variable Communalities less than 05 can be considered to be too low, since this would mean that the variable shares less than half of its variability in common with the other variables Factor analysis is related to principal components, but the two methods have different goals.

Principal components seeks to identify orthogonal linear combinations of the variables, to be used either for descriptive purposes or to substitute a smaller number of uncorrelated components for the original variables In contrast, factor analysis represents a model for the data, and as such is more elaborate Unfortunately, the factor solutions provided by factor analysis are not invariant to transformations Hence, the factors uncovered by the model are in essence nonunique, without further constraints The Kaiser Meyer Olkin measure of sampling adequacy and Bartlett s test of sphericity are used to determine whether a suf cient level of correlation exists among the predictor variables to apply factor analysis Factor loadings are analogous to the component weights in principal components analysis and represent the correlation between the ith variable and the jth factor To assist in the interpretation of the factors, factor rotation may be performed.

There is also a new family of algorithms called.

Factor rotation corresponds to a transformation (usually, orthogonal) of the coordinate axes, leading to a different set of factor loadings Often, the rst factor extracted represents a general factor and accounts for much of the total variability The effect of factor rotation is to redistribute the variability explained among the second, third, and subsequent factors Three methods for orthogonal rotation are quartimax rotation, varimax rotation, and equimax rotation Quartimax rotation tends to rotate the axes so that the variables have high loadings for the rst factor and low loadings thereafter Varimax rotation maximizes the variability in the loadings for the factors, with a goal of working toward the ideal column of zeros and ones for each variable Equimax seeks to compromise between the previous two methods Oblique rotation methods are also available in which the factors may be correlated with each other.

, 59, 60, 61, 62, 64, 72, 73, 77 McVey, S R, 578 Mead, G H, 213, 335, 467 Mead, M, 36 Meeks, B S, 451 Meertens, R M, 428, 438, 493, 514, 515, 516, 522 Mees, H, 138, 156 Meeus, W H J, 386 Megargee, E I, 549 Mehrabian, A, 291 Meichenbaum, D, 126, 129 Meindl, J R, 489, 490 Meleshko, K G A, 394 Mellers, B A, 292.

A user-de ned composite is simply a linear combination of the variables, which combines several variables together into a single composite measure User-de ned composites provide a way to diminish the effect of measurement error by combining multiple variables into a single measure User-de ned composites enable the analyst to embrace the range of model characteristics while retaining the bene ts of a parsimonious model Analysts should ensure that the conceptual de nition for their user-de ned composites lies grounded in prior research or established practice The variables comprising the user-de ned composite should be highly correlated with each other and uncorrelated with other variables used in the analysis..

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