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Fig. 6.5 Group size in uences hunting success in black-headed gulls. (From G ttmark, F., Winkler, D. and Andersson, M. (1986) Flock-feeding on sh schools increases success in gulls. Nature, 319, 589 91. Reprinted with permission from Nature. Copyright (1986) Macmillan Magazines Limited.)

Notice that because treatment C(B) is nested in treatment B, the model equation does not contain B C and A B C interaction terms. This design enables a researcher to test ve null hypotheses: H0: H0: H0:

gluten, soy protein isolates, carrageenan, and tenderizing enzymes in meat restructuring has been reported to affect the nished product avor (Miller et al. 1988; Chen and Trout 1991; Demos et al. 1994). Fraser et al. (1993) used a variety of hot-set binders in restructuring lamb roast and found that undesirable avors increased with increased storage time. Esguerra (1994) reported the presence of slight liver-like foreign avors in beefsteaks restructured using FibrimexTM. Similarly, Flores et al. (2007) reported that the avor of pork restructured with ActivaTM and FibrimexTM were described by some consumers as having a bad after-taste or livertaste off- avor, and one consumer in particular described the avor as having an iron avor or strong pig avor. The presence of high numbers of spoilage microorganisms will lead to the development of off-odors in chilled restructured meat products before any avor changes are detected by the consumer (Kotula et al. 1987).

And Edelson elaborates (p. 151):

WAP 2.x stack architecture (taken from WAP Forum Architecture, 2001).

Beck & Freeman (1990)

This syntax is exactly the same as the MsgBox syntax, except that it doesn t use the last two optional arguments (Helpfile and Context). MyMsgBox also uses the same pre-defined constants as MsgBox: vbOKOnly, vbQuestion, vbDefaultButton1, and so on.

population correlation or effect size is accepted as constant across studies and there is no search for moderators. Use of chi-square tests of homogeneity to estimate whether ndings in a set of studies differ more than would be expected from sampling error variance was originally proposed by Snedecor (1946). The chi-square test of homogeneity typically has low power to detect variation beyond sampling error (National Research Council, 1992). (Hedges and Olkin (1985) recommend that if theory suggests the existence of moderators, a moderator analysis should be conducted even if the homogeneity test is not signi cant. However, those using their methods typically ignore this recommendation.) Hence the meta-analyst will often conclude that the studies being examined are homogenous when they are not; that is, the metaanalyst will conclude that the value of x y or x y is the same in all the studies included in the meta-analysis when, in fact, these parameters actually vary across studies. A major problem here is that under these circumstances, the xed effects model of meta-analysis is then used in almost all cases. Unlike random effects meta-analysis models, xed effects models assume zero between-study variability in x y or x y in computing the standard error of the r or d, resulting in underestimates of the relevant standard errors of the mean. This in turn results in con dence intervals around the r or d that are erroneously narrow sometimes by large amounts. This creates an erroneous impression that the meta-analysis ndings are much more precise than in fact they really are. This problem also results in Type I biases in all signi cance tests conducted r or d, and these biases are often quite large (Hunter & Schmidt, 2000). As a result of this problem, the National Research Council (1992) report on data integration recommended that xed effects models be replaced by random effects models, which do not suffer from this problem. We have also made that recommendation (Hunter & Schmidt, 2000). However, the majority of published meta-analyses using the Rosenthal-Rubin methods and the Hedges-Olkin methods have used their xed effects models. For example, most of the meta-analyses that have appeared in Psychological Bulletin are xed effects meta-analysis. Most of these analyses employ the Hedges and Olkin (1985) xed effect meta-analysis model. Both Rosenthal and Rubin and Hedges and Olkin have presented random effects meta-analysis models as well as xed effects methods, but meta-analysts have rarely employed their random effects methods. The Hunter-Schmidt methods, described earlier in this chapter, are all random effects methods. Hedges (1982b) and Hedges and Olkin (1985) extended the concept of homogeneity tests to develop a more general

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