Pers Soc Psychol Bull OnlineFirst, published on July 26, 2007 as doi:10.1177/0146167207303028

Birth Order Has No Effect on Intelligence: A Reply and Extension of Previous Findings Aaron L. Wichman The Ohio State University Joseph Lee Rodgers University of Oklahoma Robert C. MacCallum University of North Carolina–Chapel Hill

We address points raised by Zajonc and Sulloway, who reject findings showing that birth order has no effect on intelligence. Many objections to findings of null birthorder results seem to stem from a misunderstanding of the difference between study designs where birth order is confounded with true causal influences on intelligence across families and designs that control for some of these influences. We discuss some of the consequences of not appreciating the nature of this difference. When between-family confounds are controlled using appropriate study designs and techniques such as multilevel modeling, birth order is shown not to influence intelligence. We conclude with an empirical investigation of the replicability and generalizability of this approach. Keywords:

ten paragraphs. We subsequently present two empirical extensions of our earlier work.

TEN RESPONSES First, Zajonc and Sulloway (2007) suggested that we ignored relevant evidence, and they spent much of their response reviewing cross-sectional data to argue for the existence of birth-order effects. Unfortunately, these crosssectional data, showing significant declines in means on relevant outcome variables for later birth orders, tell us nothing about the true existence of such effects within the family. Zajonc and Sulloway’s Figure 1 shows the patterns that hold in these (overwhelmingly) cross-sectional data. In this type of data, although birth order could potentially be the cause of the patterns, birth order is also a proxy variable, standing in and measuring potentially dozens or hundreds of other real causal factors, the majority of which occur at the between-family level (see Rodgers, Cleveland, van den Oord, & Rowe, 2000, for a more complete explanation). True birth-order effects can be studied only by using data that maintain information about family membership along with statistical models that can differentiate within-family from between-family effects. Our study did this. Virtually all of the evidence cited by Zajonc and Sulloway is drawn from studies that did not.

birth order; multilevel modeling; nested data; hierarchical linear modeling; intelligence; IQ

W

e recently published findings (Wichman, Rodgers, & MacCallum, 2006) showing that when the hierarchical nature of birth order is taken into account (i.e., children are nested within families) with modern statistical techniques, birth order is not related to intelligence. This finding is at odds with some studies done using less sophisticated approaches, and it is consistent with others. Given its significance, one would expect that this finding would invite criticism, which in turn provides an opportunity for further clarification of its conceptual underpinnings. We thank Zajonc and Sulloway (2007 [this issue]) for providing this opportunity. Some of their questions and criticisms may be shared by other researchers, and should be addressed. We believe that their criticisms are not warranted or defensible. In this response, we elaborate this position in

Authors’ Note: Please send correspondence to Aaron L. Wichman at [email protected]. PSPB, Vol. XX No. X, Month XXXX XX-XX DOI: 10.1177/0146167207303028 © by the Society for Personality and Social Psychology, Inc.

1 Copyright 2007 by Society for Personality and Social Psychology, Inc..

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PERSONALITY AND SOCIAL PSYCHOLOGY BULLETIN

Second, Zajonc and Sulloway (2007) suggested that we treated birth order “as a linear phenomenon” and thus ignored differential age patterns. This is incorrect. The method we used to represent birth order using dummy variables allowed for both nonlinear and linear patterns. A significant additional misunderstanding concerns the way cohort age was treated in our models. Zajonc and Sulloway suggested that we collapsed across cohorts to analyze our data. This also is incorrect. As explained in Wichman et al. (2006), we coded birth order as a series of dummy variables in a no-intercept model (Model 1) and then included dummy-coded cohorts by introducing its interactions with each dummy-coded birth order (Model 2). Rather than collapsing, our models simultaneously analyze IQ–birth order patterns separately for both cohorts. Furthermore, differential nonlinear patterns in the two cohorts can be identified by this modeling approach. Results showed a difference in level between cohorts but not a major difference in the pattern of the apparent birth-order effect. Both cohorts also can be separately plotted using this coding approach. Third, Zajonc and Sulloway (2007) suggested that we used a “problematic study design,” resulting in data “seriously skewed by family size.” They noted later that “there are only 158 fourthborns and 507 thirdborns in Wichman et al.’s sample, compared with 1,162 secondborns and 1,844 firstborns.” The data we used—the National Longitudinal Survey of Youth—came from a probability sample. Up to attrition and nonresponse, they are representative of the 1979 U.S. population of households that contained adolescents. Any skewness reflects the structure of the U.S. population. We fail to see how our use of national data from a probability sample constitutes a problematic study design. Fourth, they suggested that some of our results almost were significant and would have been so with a larger sample size. This comment seems to discount the large sample size that we did have; few within-family studies with thousands of respondents exist. However, this criticism also fails on a more critical point: We reduced the often-found cross-sectional pattern of significant birth-order effects (which we found in our Models 1 and 2 before controls for between-family variance were added) to nonsignificance using only one between-family covariate. We did not come close to using the full power of the multilevel approach in this demonstration. There are dozens of other betweenfamily control variables that should reduce the withinfamily variance in children’s intelligence even further. Contenders include family income, neighborhood characteristics (e.g., library support, school quality), mother’s IQ, parental education, and so forth. Any or

all of these between-family variables, if included within the analysis, likely would absorb more of the overall variance into the between-family part of the design. After further reducing the birth-order effect in this manner, enormous increases in sample size would then be required to again make it significant. Regardless of the ultimate sample size, though, the fact that betweenfamily variables reduce a supposedly within-family effect is damning evidence against cross-sectional approaches to the study of birth-order effects. Fifth, regardless of whether the sample size is a dozen or 400,000, using cross-sectional data to infer within-family processes is an example of the ecological fallacy (see Rodgers, 1988). This error in reasoning involves making inferences about individual-level phenomena based on group-level data. So long as cross-sectional, or betweenfamily, data are used to infer within-family birth-order effects, this fallacy persists. We note that many others have pointed out this issue in relation to birth-order research and the confluence model (e.g., Berbaum & Moreland, 1980; McCall, 1985). As Sulloway himself (1996) noted, “More research needs to be done on siblings who have grown up together in the same family.” Wichman et al. (2006) is an example of such research. Sixth, the ecological fallacy can lead to inappropriate analyses. As one example, consider the use of alerting correlations (Rosnow & Rosenthal, 2002) to analyze birth-order data (see Zajonc & Sulloway, 2007). Alerting correlations do not take into consideration within-family variance. In this context, they deliver results based only on between-family observations. Consider that “the special characteristic of ralerting is that it regards as noise or error only the disagreement between the predicted and obtained values of the means. That is, the level of noise or error found within conditions is simply set aside” (Rosnow & Rosenthal, 2002, p. 60). Of course, our own results also show these between-family patterns before we control for between-family variance (e.g., Model 1). Our point is that it is a critical mistake to infer that alerting correlations measure within-family birth-order effects. As noted by Rosnow and Rosenthal (2002), such within-family effects are considered “error” by the alerting correlation. We consider overlooking these (nonsignificant) within-family effects to be the primary error in studying birth order. Seventh, another example of inappropriate analyses involves Zajonc and Sulloway’s (2007) use of nonhierarchical methods to reanalyze our patterns of birthorder means; they tested planned contrasts between different pairs of birth-order levels. Typically, this type of contrast analysis follows a significant overall result across all of the birth orders (but our overall test was nonsignificant). Even granting this approach, however,

Wichman et al. / BIRTH ORDER HAS NO EFFECT ON IQ we are surprised that such a method would be chosen over multilevel modeling (MLM) as a preferred analysis technique. MLM is a modern improvement over such methods, one that explicitly accounts for the different sources of variance that underlie hierarchically structured data like birth order nested within families. The contrasts used by Zajonc and Sulloway in no way separate these sources of variance, nor do they model their interrelationship. It is hard to understand how these contrasts are valid in this context. Eighth, Zajonc and Sulloway (2007) suggested that the confluence model can make null predictions like those found in our analyses. Their response suggests slippage in the confluence age crossover previously identified in cross-sectional analyses as being somewhere around age 12 (Zajonc, Markus, & Markus, 1979). Now, Zajonc and Sulloway suggest that our age 13 to 14 results may not have picked up this crossover. Regardless of how much the crossover age is shifted to try to explain our results, this shift does nothing to explain our significant birth-order results before we controlled between-family variance. We know of no other highly parameterized nonlinear mathematical models whose developers would argue they were supported by null results. Complex mathematical models are unnecessary to explain effects that do not exist (see also Rodgers, 1984). Ninth, contrary to Zajonc and Sulloway’s (2007) claims, we note that Wichman et al. (2006) provided substantial information on properly done within-family studies of birth order as well as information on improperly done studies. We see only two studies that Zajonc and Sulloway mentioned that contain within-family patterns; the Tabah and Sutter (1955) study and the data from Zajonc et al. (1979). Each has weaknesses, but rather than debate those, we will simply grant their existence. In contrast, there are more than a dozen within-family studies that unequivocally fail to support birth-order effects on intelligence. Zajonc and Sulloway failed to acknowledge these within-family studies, studies that raise deep and fundamental issues regarding the legitimacy of the confluence model. This unfortunate pattern of overlooking results inconsistent with the confluence model has been noted before: “Zajonc’s citation of (at most) one half of the opinions regarding the model’s efficacy leave the naïve reader (at most) half informed” (Rodgers, 1988, p. 476). Tenth, we urge readers to recognize that the literature contains numerous previous criticisms of the confluence model. Shortly after the publication of the model in the mid-1970s, the criticism began in earnest (Page & Grandon, 1979; Valendia, Grandon, & Page, 1978), peaked in the early 1980s (Brackbill & Nichols, 1982; Galbraith, 1982; Pfouts, 1980; Price, Walsh, & Vilberg,

3

1984; Steelman & Mercy, 1980), and has continued more recently (Guo & VanWey, 1999; Retherford & Sewell, 1991). Virtually all of these criticisms still stand as originally stated. The standard and often-repeated defense of the model is the same as that offered now by Zajonc and Sulloway: The model seems able to capture patterns in cross-sectional data. This explanation has been refuted many times. Rodgers et al. (2000), Rodgers (2001), and Wichman et al. (2006) are the most recent articles to do so. In the face of such evidence against birth-order effects, confluence model advocates have not been able to explain why patterns in cross-sectional and within-family data are incompatible. Nor have they been able to explain why simple models perform better than the more complex confluence model, or why a large body of empirical data from exactly the domain the model describes does not match the predictions of the model. When the hierarchical nature of birth-order data is taken into account, birth order has no effect on intelligence. The existence of the confluence model hinges on avoiding recognition of this fact. TWO EMPIRICAL EXTENSIONS To be sure, the general ideas in the ten preceding paragraphs are not new. The fragility of birth order as an explanatory variable is known. For example, we agree with the following quote written about birth order as an explanation of radical thinking: Birth order is not the real cause . . . , even though it is strongly correlated with it. But birth order can be seen as a proxy for differences in age, size, power, and status within the family. Common sense tells us that causation probably lies in those other variables, not in birth order per se. (Sulloway, 1996, p. 373)

From a multilevel perspective, proxies logically can lie within the family, as this quote suggests. But they exist outside the family as well. Our findings show that birth order is confounded with between-family influences on intelligence and that, when these are controlled, birth order ceases to be associated with IQ. It follows that no matter what their size, cross-sectional samples have no internal validity for assessing birth order. There is nothing unique about the particular between-family control variable we chose, nor the outcome measures we used, nor the within-family dataset we used. Our analyses reflect the general principle of taking the hierarchical nature of birth-order data into account. To better demonstrate just how general this principle is and to respond to the earlier-raised concerns about the skewed distribution of birth order in our sample, we conducted two new analyses.

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PERSONALITY AND SOCIAL PSYCHOLOGY BULLETIN

TABLE 1:

Apparent Birth-Order Effects on IQ Before Between-Family Variance Is Controlled

Coefficient for birth order = 1 dummy variable Coefficient for birth order = 2 dummy variable Coefficient for Birth Order 1 × Cohort Variable Coefficient for Birth Order 2 × Cohort Variable Within-family residual variance for birth order 1 Within-family residual variance for birth order 2 Between-family residual variance Deviance statistic Test of birth-order effect

Math

Reading Recognition

Reading Comprehension

Nfamilies = 2,365 Nchildren = 2,978

Nfamilies = 2,359 Nchildren = 2,978

Nfamilies = 2,262 Nchildren = 2,827

101.41 (0.43) 99.70 (0.47) –3.36 (0.57) –2.81 (0.69) 96.49 (6.24) 89.23 (6.46) 58.82 (5.60) 23321.55 χ2(1) = 7.87 p = .005

105.62 (0.50) 103.70 (0.53) –3.44 (0.67) –3.93 (0.79) 135.32 (8.43) 118.54 (8.59) 72.96 (7.42) 24164.88 χ2(1) = 7.43 p = .006

106.23 (0.45) 104.64 (0.48) –9.46 (0.59) –10.22 (0.70) 101.44 (6.47) 90.76 (6.67) 48.28 (5.66) 22035.23 χ2(1) = 6.27 p = .012

NOTE: SEs are in parentheses. Estimates larger than twice their standard errors are significant.

TABLE 2:

Effect of a Between-Family (L2) Control Variable on the Birth-Order Effect

Coefficient for birth order = 1 dummy variable Coefficient for birth order = 2 dummy variable Coefficient for Birth Order 1 × Cohort Variable Coefficient for Birth Order 2 × Cohort Variable Coefficient for Birth Order 1 × Control Variable Coefficient for Birth Order 2 × Control Variable Within-family residual variance for birth order 1 Within-family residual variance for birth order 2 Between-family residual variance Deviance statistic Test of birth-order effect

Math

Reading Recognition

Reading Comprehension

Nfamilies = 2,108 Nchildren = 2,692

Nfamilies = 2,103 Nchildren = 2,693

Nfamilies = 2,023 Nchildren = 2,564

99.52 (0.55) 99.27 (0.50) –0.46 (0.72) –0.36 (0.80) 0.72 (0.09) 0.85 (0.11) 101.54 (6.52) 89.80 (6.65) 51.33 (5.67) 21017.97 χ2(1) = .121 p = .728

103.54 (0.63) 103.10 (0.57) –0.27 (0.83) –1.27 (0.92) 0.79 (0.10) 0.89 (0.13) 139.73 (8.78) 119.58 (8.86) 65.70 (7.54) 21801.83 χ2(1) = .281 p = .596

104.58 (0.56) 104.18 (0.52) –6.82 (0.73) –8.11 (0.82) 0.62 (0.09) 0.71 (0.12) 102.09 (6.63) 91.96 (6.84) 42.98 (5.69) 19911.51 χ2(1) = .292 p = .589

NOTE: SEs are in parentheses. Estimates larger than twice their standard errors are significant.

First, we reanalyzed birth orders 1 and 2 in both cohorts (i.e., deleting all birth orders 3 and 4, which were used in our earlier analysis), using data and models from our original publication. The first model separately estimated the birth-order effect in both cohorts (7- to 8-yearolds and 13- to 14-year-olds) without controlling for between-family variance in the relationship between birth order and intelligence (Model 2 in our original publication). The second model added to the first model a partial control for between-family variance in this relationship. The partial control was mother’s age at birth of first child (Model 3 in our original publication). If the skewed distribution of birth order present in our original sample were responsible for our effects, as Zajonc and Sulloway (2007) suggested, this reanalysis should show very different effects than our original analysis did. Our results are

displayed in Tables 1 and 2. Table 1 shows consistent effects of birth order across all outcome measures on the order of a 2-point difference between first- and secondborn children. However, once centered mother’s age at birth of first child is added as a between-family control variable, and the significant birth-order effect disappears, as before (Table 2). This reanalysis provides additional evidence for the value of the MLM approach and for the internal validity of the previous findings. However, the generalizability of our approach logically should not be limited merely to reanalyses of subsets of our original data. To test this principle, we conducted new analyses that extend the findings from Wichman et al. (2006). As before, data from the National Longitudinal Survey of Youth–Children (NLSY-Children) survey were used. This sample contains

Wichman et al. / BIRTH ORDER HAS NO EFFECT ON IQ TABLE 3:

Birth order N

TABLE 4:

Frequency Counts for Different Birth Orders in 7- to 8Year-Old Sample for Age-Adjusted Combined Digit Span Outcome Measure 1 781

2 627

3 344

4 105

5 43

Total: 1,900

5

designs are still considered by some to be legitimate in evaluating birth-order effects. This is a complex issue, but we have some ideas. One is that researchers are people, too. People like simple explanations, and birth order is simple. Everyone has a birth order, it does not change, and lay theories abound to suggest its systematic influence on outcomes from intelligence to personality. This is

Controlling Between-Family Variance Eliminates the Birth-Order Effect on Digit Span Memory

Number of families Total number of children Intercept Coefficient for birth order Coefficient for control variable Within-family residual variance Between-family residual variance Deviance statistic

Model 1: Digit Span Memory as Linear Function of Birth Order

Model 2: Controlling for Mother’s Age at Birth of First Child

Model 3: Controlling for Mother’s AFQT Score

1,709 1,900 9.99 (0.09) –0.19 (0.06) n/a 7.52 (0.64) 1.29 (0.61) 9522.16

1,476 1,646 9.80 (0.11) –0.02 (0.08) 0.09 (0.02) 7.88 (0.69) 0.83 (0.65) 8232.40

1,636 1,821 9.87 (0.09) –0.06 (0.07) 0.03 (0.00) 7.42 (0.63) 0.79 (0.59) 9001.61

NOTE: AFQT = Armed Forces Qualifying Test. SEs are in parentheses. Ns vary slightly across analyses because of missing data. Estimates larger than twice their standard errors are significant.

all biological children ever born to the women in the original NLSY 1979 survey. In comparison to our previous approach, the new analyses used a different outcome variable, a different covariate, and a series of simple linear multilevel models. We used data from the beginning of the child data collection program from 1986 to 1998. Additional information on this sample is available from the Center for Human Resource Research (CHRR, 1999) and in our original article (Wichman et al. 2006). We used a new children’s cognitive ability measure: digit span memory. The NLSYChildren dataset contains digit span data for a large number of children falling in the 7-to-8 age range (but not for those in the older age range). These data comprise the digit span subtest from the Wechsler Intelligence Test for Children–Revised (Wechsler, 1974). We used NLSY’s age-standardized measure of digit span (M = 10; SD = 3), which is composed of the aggregate of forward and backward digit span memory. The number of children of each birth order for the digit span outcome measure is given in Table 3. We specified our models as follows. In Model 1, we represented the outcome variable as a linear function of birth order, with birth order coded 0 to 4. We did not yet control for between-family influences on intelligence. Results are provided in Table 4. As expected, in this model, birth order was related to digit span memory. On average, earlier born children showed higher scores for digit span than did later born children. We then introduced two different between-family covariates in separate models. In Model 2, we introduced

centered mother’s age at birth of her first child. In Model 3, we introduced mother’s IQ as centered scores on the Armed Forces Qualifying Test (AFQT), a composite of the IQ measures contained in the Armed Services Vocational Aptitude Battery (U.S. Department of Defense, 1982). In both models, the coefficient representing the effect of birth order became nonsignificant (Table 4). The within-family MLM approach yields generalizable, replicable results showing that birth order has no effect on intelligence. In sum, we do not believe that any of the criticisms levied against our original article are valid. Our original sample had more than 2,000 families and was approximately representative of the household structure of the United States. Furthermore, we first replicated apparent birth-order effects on IQ before adding between-family control variables. This pattern suggests it is unlikely that our results were because of lack of power. When we reanalyzed our original data to test for potential problems with the skewed birth-order distribution, our results were even clearer than before. Finally, when we conducted another test of the possibility that uncontrolled betweenfamily variance was responsible for our results, using another cognitive measure (digit span) as an outcome variable and a new control variable (mother’s IQ), we again found that what appeared to be a birth-order effect disappeared into the between-family variance. Reflecting on the simplicity of the within–between explanation, one might ask why it is that some researchers still believe that birth order influences intelligence—or, even more puzzling, why cross-sectional

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PERSONALITY AND SOCIAL PSYCHOLOGY BULLETIN

designs are still considered by some to be legitimate in evaluating birth-order effects. This is a complex issue, but we have some ideas. One is that researchers are people, too. People like simple explanations, and birth order is simple. Everyone has a birth order, it does not change, and lay theories abound to suggest its systematic influence on outcomes from intelligence to personality. This is a simple statement of the “birth order trap” (see Rodgers, 2000). These qualities make birth-order effects alluring, although deceptive. For too long, a simple oversight (in retrospect) has deceived both researchers and the public about birth order’s real “effects.” There are a number of reasons that help explain why the truth has been so long in coming. One is that birth order is hard to study, and that the data’s complexity encourages such mistakes as the ecological fallacy. Another is that within-family and longitudinal data are considerably harder to collect than are between-family or cross-sectional data. These two factors have led to a great number of studies that unfortunately have little to say about the true influences on children’s intelligence. A multilevel approach to the research questions and the data structure helps explain both the apparent findings of previous between-family studies and the null effects of birth order within the family. We prefer this parsimonious explanation.

REFERENCES Berbaum, M. L. & Moreland, R. I. (1980). Intellectual development within the family: A new application of the confluence model. Developmental Psychology, 16, 500-515. Brackbill, Y., & Nichols, P. L. (1982). A test of the confluence model of intellectual development. Developmental Psychology, 18, 192-198. Center for Human Resource Research. (1999). The 1998 NLSY79 child assessments: Selected tables. Columbus: The Ohio State University, CHRR User Services. Galbraith, R. C. (1982). Sibling spacing and intellectual development: A closer look at the confluence models. Developmental Psychology, 18, 151-173. Guo, G., & VanWey, L. K. (1999). Sibship size and intellectual developmental: Is the relationship causal? American Sociological Review, 64, 169-187.

McCall, R. B. (1985). The confluence model and theory. Child Development, 56, 217-218. Page, E. G., & Grandon, G. (1979). Family configuration and mental ability: Two theories contrasted with U.S. data. American Educational Research Journal, 16, 257-272. Pfouts, J. H. (1980). Birth order, age-spacing, IQ differences, and family relations. Journal of Marriage and the Family, 42, 517-531. Price, G. G., Walsh, D. J., & Vilberg, W. R. (1984). The confluence models’ good predictions of mental age beg the question. Psychological Bulletin, 96, 195-200. Retherford, R. D., & Sewell, W. H. (1991). Birth order and intelligence: Further tests of the confluence model. American Sociological Review, 56, 141-158. Rodgers, J. L. (1984). Confluence effects: Not here, not now! Developmental Psychology, 20, 321-331. Rodgers, J. L. (1988). Birth order, SAT, and confluence: Spurious correlations and no causality. American Psychologist, 43, 476-477. Rodgers, J. L. (2000). The birth order trap. Politics and the Life Sciences, 19, 167-170. Rodgers, J. L. (2001). What causes birth order–intelligence patterns? The admixture hypothesis, revived. American Psychologist, 56, 505-510. Rodgers, J. L., Cleveland, H. H., van den Oord, E., & Rowe, D. C. (2000). Resolving the debate over birth order, family size, and intelligence. American Psychologist, 55, 599-612. Rosnow, R. L., & Rosenthal, R. (2002). Contrasts and correlations in theory assessment. Journal of Pediatric Psychology, 27, 59-66. Steelman, L. C., & Mercy, J. A. (1980). Unconfounding the confluence model: A test of sibship size and birth-order effects on intelligence. American Sociological Review, 45, 571-782. Sulloway, F. J. (1996). Born to rebel. New York: Vintage. Tabah, L., & Sutter, J. (1955). Le niveau intellectuel des enfants d’une meme famille. Annals of Human Genetics, 19, 120-150. U.S. Department of Defense. (1982). Profile of American youth: 1980 nationwide administration of the Armed Services Vocational Aptitude Battery. Washington, DC: Author. Valendia, W., Grandon, G., & Page, E. G. (1978). Family size, birth order, and intelligence in a large South American sample. American Educational Research Journal, 15, 399-416. Wechsler, D. (1974). Wechsler Intelligence Scales for Children–Revised. New York: Psychological Corporation. Wichman, A. L., Rodgers, J. L., & MacCallum, R. C. (2006). A multilevel approach to the relationship between birth order and intelligence. Personality and Social Psychology Bulletin, 32, 117-127. Zajonc, R. B., Markus, H., & Markus, G. B. (1979). The birth order puzzle. Journal of Personality and Social Psychology, 37, 1325-1341. Zajonc, R. B., & Sulloway, F. J. (2007). The confluence model: Birth order as a within-family or between-family dynamic? Personality and Social Psychology Bulletin, [PE TO INSERT VOL], [PE TO INSERT PAGES]. Received February 9, 2007 Revision accepted March 11, 2007

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