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, 170, 172, 173, 174, 181 Dent, K M, 78 DePaola, S J, 391 DePascalis, V F, 90 DePaulo, B M, 335, 337, 338, 343, 393 D pret, E, 273 Depue, R A, 61, 62, 73, 91, 92, 93, 94, 188 De Raad, B, 72, 235, 236, 237, 239, 245, 246, 248, 251 De Rivera, J, 540 de Rivera, J H, 573, 581, 582, 585, 588, 589 Derlega, V J, 450, 494 Deschamps, J C, 489, 496 Desmarais, S, 549, 550 de St Aubin, E, 308 Desmond, J E, 95 Deutsch, F, 129 Deutsch, M, 336, 395, 398, 452, 456, 491, 492, 495, 540, 543, 544, 574 DeVault, S, 421 Devine, P G, 258, 261, 267, 268, 354, 367, 371, 510, 516, 517, 518 DeVinney, L C, 545 Devlin, K.



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D Kaplan, Understanding GPS: Principles and Applications, Boston: Artech House, 1996 9 E Korach, S Moran, and S Zaks, Optimal lower bounds for some distributed algorithms for a complete network of processors, Theoretical Computer Science, 64, 125 132, 1989 10 M C Loui, T A Matsushita, and D B West, Election in complete networks with a sense of direction, Information Processing Letters, 22, 185 187, 1986 11 N Lynch, Distributed Algorithms, Morgan Kaufmann Publishers, 1996 12 R M Metcalfe and D R Boggs, Ethernet: distributed packet switching for local computer networks, Communications of the ACM, 19, 395 404, 1976 13 R Motwani and P Raghavan, Randomized Algorithms, Cambridge: Cambridge University Press, 1995 14 K Nakano and S Olariu, Randomized O(log log n)-round leader election protocols in radio networks, Proceedings of International Symposium on Algorithms and Computation (LNCS 1533), 209 218, 1998 15 K Nakano and S.





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, 426 de Vries, N, 302, 311 de Waal, F, 476 Dhawan, N, 136 Diamond, D, 221 Diaz-Loving, R, 491 Dickerson, C A, 368 Dickinson, K A, 209, 221 Diedrich, O, 90 Diehl, M, 304, 316 Dien, D S, 38, 48 Diener, C, 37 Diener, E, 37, 45, 77, 196, 402, 403 Diener, M, 37, 45 Digman, J M, 59, 61, 86, 235, 242, 250 DiLalla, D L, 63 Dille, B, 602, 613 Dillehay, R C, 370 Dinnerstein, J L, 477.

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analysis with several predictors (with no missing data) and a dependent variable that is sometimes missing, for example, due to attrition If the dependent variable is missing due to an MCAR mechanism, then complete cases analysis is known to yield unbiased estimates If the missing data mechanism is MAR, that is, if the missingness on the dependent variable is due to some combination of the predictor variables, then the biases are completely controlled by the complete cases analysis (eg, see Heckman, 1979; also see Graham & Donaldson, 1993) If the cause of missingness on the dependent variable is an MNAR process, then the degree of bias due to complete cases will be the same as the bias that will occur with the acceptable analysis procedures described below (eg, Graham & Donaldson, 1993) Of course, this is true only under somewhat limited conditions.

Olariu, Randomized leader election protocols for ad-hoc networks, Proceedings of Sirocco 7, June 2000, 253 267..

In more complex analysis situations, complete cases analysis could introduce bias to the parameter estimates In short, we do not side with those who argue that complete cases analysis should not be used because of the potential for bias Rather, we argue that complete cases analysis should not be used because of loss of statistical power In virtually all research situations, using complete cases analysis means that the researcher must discard at least some information In fact, it is becoming more and more common that a large proportion of cases is lost if listwise deletion is used In order to illustrate this issue, consider two proportions: the proportion of complete cases, and the proportion of nonmissing data points.

The latter gure is calculated easily by considering the total number of data points in a data set, N k, where N is the number of cases and k is the number of variables One can simply divide the number of nonmissing data points by N k to determine the proportion of nonmissing data points Of course, if most of the data points are missing, the results may be suspect regardless of the analysis used However, there are many situations in which the proportion of nonmissing data points is actually quite high but the proportion of complete cases is disturbingly low.

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