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RESEARCH ARTICLE |
Department of Psychology, University of Virginia, Charlottesville.
Address correspondence to Ryan P. Bowles, Department of Psychology, University of Virginia, PO Box 400400, Charlottesville, VA 22904-4400. E-mail: rpbowles{at}virginia.edu
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20,500). Results indicated that the vocabulary test is not unidimensional but bidimensional, with Basic Vocabulary and Advanced Vocabulary factors. An analysis of age differences indicates that basic vocabulary is highest around the age of 30, with a negative relation to age in late adulthood; in contrast, advanced vocabulary is unrelated to age between ages 35 and 70. Cohort effects may explain some of the differential age trend.
Vocabulary knowledge is one of the few cognitive skills that remain relatively intact over adulthood. Unlike most cognitive abilities, which peak when a person is around the age of 20 and then decline with age, vocabulary knowledge seems to peak around age 50 or possibly later, and decline only slowly, if at all, into old age. This distinction was recognized early in research on cognitive aging (Jones & Conrad, 1933
; Sorenson, 1938), and the finding has been confirmed in numerous types of studies, including cohort sequential (Schaie, 1996), repeated cross-sectional (Wilson & Gove, 1999b), and longitudinal (McArdle et al., 2003) studies. The shape of the age trend also seems to be consistent across types of tests of vocabulary knowledge, although the magnitude of age-related changes may vary (Bowles & Salthouse, 2003; Verhaeghen, 2003).
Several theoretical explanations for the age-related stability of vocabulary knowledge have been proposed. Among the most influential is the Cattell-Horn theory of fluid and crystallized intelligence (Cattell, 1941, 1963; Horn, 1998; Horn & Cattell, 1966), which posits that vocabulary knowledge, as a prototypical example of crystallized intelligence, is maintained across the life span, whereas cognition in general declines with age. Vocabulary knowledge is maintained because of new learning, and because of reinforcement through usage of previously learned material (Cattell, 1987). After the school years, most learning is in specific areas, suggesting that the increase or maintenance of vocabulary knowledge in later adulthood may be a result of increasing esoteric knowledge (Cattell, 1998).
A second theory of the aging of vocabulary knowledge is the dual-representation theory of knowledge (e.g., Brainerd & Reyna, 1992), or related theories of changing representations (McGinnis & Zelinski, 2003). Vocabulary knowledge may be characterized by two types of representations, a general "gist" definition and a detailed specific definition, or by a single representation varying along a specificity continuum (McGinnis & Zelinski, 2000). Age is associated with a reduction in the ability to generate and access the specific definition (McGinnis & Zelinski, 2003). Older adults may compensate for the age-related decline in cognition by relying more heavily on the general definition (Botwinick & Storandt, 1974; Tun, Wingfield, Rosen, & Blanchard, 1998), thus maintaining vocabulary knowledge despite cognitive decline.
A third theory of the aging of vocabulary knowledge is the transmission deficit hypothesis (MacKay & Abrams, 1998; MacKay & Burke, 1990). The transmission deficit hypothesis is based on node structure theory (NST), a version of a spreading activation model of the organization of lexical information (MacKay, 1987), with each individual representation of knowledge, whether semantic, phonological, or orthographic, contained in a node. Under NST, activation is passed (called priming in the terminology of NST) between nodes according to the strength of the connection between the nodes. The connections become universally weaker or less efficient with age (MacKay & Abrams) but strengthen with cumulative usage, so that, overall, vocabulary knowledge remains constant (Burke, MacKay, & James, 2000).
Some researchers have suggested that the age trend observed in tests of vocabulary knowledge is at least in part attributable to cohort effects (Alwin & McCammon, 1999, 2001; Alwin, McCammon, Rodgers, & Wray, 2003; Glenn, 1999; see also Wilson & Gove, 1999a, 1999b). What appears to be an effect of age may actually be explained by an intercohort decline in vocabulary knowledge, a result of changes in family structure (Alwin, 1991) or a decline in time spent reading (Glenn, 1994, 1999). Much of the decline is suppressed by an intercohort increase in schooling (Glenn, 1999), yielding what appears to be overall stability in vocabulary knowledge in adulthood.
One problem with these theories is that the structure of vocabulary knowledge is not generally studied in depth. Most researchers have assumed or concluded that vocabulary is a unitary construct. In particular, factor analyses of cognitive tasks including vocabulary tests generally yield the same results regardless of the particular vocabulary test employed (Carroll, 1993, p. 158). However, these results are based on the assumption that a single dimension of vocabulary knowledge is tested on any given vocabulary test. Vocabulary knowledge may be multidimensional, and there may be different age trends for different aspects of vocabulary knowledge.
Factor analyses of the item responses to vocabulary tests often yield two factors, although the identification of the factors differs across studies. Bailey and Federman (1979) identified Breadth and Depth factors; Gustafsson and Holmberg (1992) identified factors associated with word origin for a Swedish vocabulary test. In an analysis of the items from the Wechsler Adult Intelligence Scale–Revised, Beck and colleagues (1989) found that the vocabulary items split into two factors, with the easy items loading on a Verbal factor, along with most of the items from the standard verbal scales (Information, Similarities, Comprehension), and the difficult items loading on an Advanced Vocabulary factor, along with the difficult items from the Information scale. None of these studies, however, examined the relations between the vocabulary factors and age.
In this study, we assess the factor structure of a test of vocabulary for potential multidimensionality in vocabulary knowledge. We then compare dimensions of vocabulary knowledge with age to see if different types of vocabulary knowledge have different relations to age. These analyses are based on data from a nationally representative repeated cross-sectional sample, allowing improved generalizations of the results to the U.S. population.
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METHODS |
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), this includes approximately 97.3% of all U.S. adults. Only adults are included, and persons not living in households are excluded. The excluded individuals include 9.4% of adults aged 18 to 24, mostly military personnel and college students living in dormitories (but also incarcerated persons); 11.4% of adults aged 75 and older, mostly nursing home and other long-term-care residents; and between 0.8% and 1.4% of adults aged 25 to 74. Also excluded were non-English speaking households, which were 2% or less of the total adult population. Response rates were approximately constant across all survey years, generally between 75% and 80%.
In 15 years between 1974 and 2000, inclusive, a vocabulary test was administered as part of the GSS (generally every other year). The vocabulary test has 10 multiple-choice items, with each item consisting of a target word with five options (usually a single word, but in some cases a short phrase). Respondents were asked to choose the option that is most similar in meaning to the target word. The items are a subset of the items on the Thorndike–Gallup test of verbal intelligence (Thorndike, 1942; Thorndike & Gallup, 1944). The administrators of the GSS ask that the specific content of the items not be publicized. Therefore, in order to give readers a sense of the items on the test, we present five items selected at random from the full Thorndike–Gallup test in Table 1. We refer to the items on the GSS vocabulary test as Word A, Word B, ..., Word J. Responses to the items were coded as correct, incorrect, or no answer. We considered nonresponses to be incorrect. Other options for dealing with nonresponses yielded similar results. The GSS subsample we used is aggregated across the 15 survey years and consists of N = 20,560 adults who were given the vocabulary test. Because our unit of analysis is the adult person whereas the unit of sampling for the GSS was the household (from which one adult was selected at random), we reweighted the data for all analyses by the number of adults living in the household. In two of the survey years (1982 and 1987), Blacks were oversampled. In order to maintain consistency with other survey years, we removed this oversample from the data, although leaving them in the data set yielded nearly identical results. Consistent with previous analyses of the GSS vocabulary data (e.g., Alwin, 1991), we did not adjust for differential sample size across survey year, as the sample size was approximately equal in each year. (In three years, 1988, 1989, and 1990, the vocabulary test was given every year instead of every other year, but only to two thirds of the sample, so that, across the three years, the sample size was approximately equal to the sample size of any other two years). Age ranged from 18 to 89 years (M = 45.2), and birth year ranged from 1885 to 1982 (M = 1943). The sample was 57% female. The mean number of years of formal schooling was 12.6. Table 2 describes the demographic characteristics of the sample in more detail.
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). Previous work has shown that factor analyses of dichotomous items can yield biased factor loadings (Parry & McArdle, 1991) or generate spurious factors (McDonald & Ahlawat, 1974). Researchers have proposed several versions of nonlinear factor analysis to deal with these problems (McDonald & Ahlawat, 1974; Tate, 2003).
In this study, we report results from the version of nonlinear factor analysis implemented in the Mplus program (Muthén & Muthén, 2004). Mplus allows for both exploratory and confirmatory factor analysis of dichotomous items through the use of tetrachoric correlations of categorical data. The calculation of tetrachoric correlations involves the assumption that underlying each categorical variable is a continuous, normally distributed latent response propensity. Associated with each boundary between categories is a threshold, so that if a person has response propensity above the threshold, the person will respond in the higher category. Factor analysis of the tetrachoric correlations associated with the categorical variables can be conceptualized as standard linear factor analysis of the latent response propensities. (Alternatively, one can approximately achieve the version of nonlinear factor analysis implemented in Mplus by estimating the tetrachoric correlations externally and then using these correlations in any factor analysis or structural equation modeling program.) Thus, the nonlinearity between a factor and its indicators comes from the combination of a linear effect of the factor on the response propensities and a nonlinear dichotomization of the response propensities. For a more detailed introduction to nonlinear factor analysis, see Bartholomew, Steele, Moustaki, and Galbraith (2002). We made comparisons between factor solutions on the basis of the chi-square discrepancy function compared with the degrees of freedom (with 
=.01), the root mean square error of approximation (RMSEA; Browne & Cudeck, 1993), the number of eigenvalues of the tetrachoric correlation matrix greater than 1, the number of eigenvalues greater than simulated unidimensional data, and the overall interpretability of the factors.
An important disadvantage of Mplus is that it does not offer correction for guessing, so it may overestimate the number of factors (Hulin, Drasgow, & Parsons, 1983, chapter 8; Tate, 2003). We also employed versions of nonlinear factor analysis with correction for guessing as implemented in the NOHARM program (Fraser & McDonald, 1988; McDonald, 1981) and in the TESTFACT program (Bock et al., 2003). Corrections for guessing were small, and results from these analyses (available by request) were not different from results with Mplus.
Structural Relations to Chronological Age
After establishing the factor structure of the GSS vocabulary test, we assessed the relations between chronological age and the factor(s). To account for a wide range of nonlinear shapes while maintaining large group sizes, we created 12 dummy-coded variables to reflect 11 5-year age intervals and two extreme age groups, age younger than 25 and age greater than or equal to 80. Group sizes ranged from 731 for the oldest age group to 2,394 for the 30–34 group. We then regressed the factors on the age group variables, and adjusted the intercept so that the overall factor mean was set to 0. Comparability of the factor levels across factors is not clear, so that age trends across factors can be meaningfully compared, but not levels. We calculated the regressions in two steps. First, we ran a confirmatory factor analysis with item factor loadings less than.1 in absolute value from the exploratory factor analysis set to 0 (all other loadings were above.3). We then anchored the factor loadings so that the factor variances were equal to 1, and we regressed the factors on the age group variables directly in Mplus.
We aimed further analyses at modeling the age curves identified by the age group variable regressions. We employed the dual exponential growth curve model (McArdle, Ferrer-Caja, Hamagami, & Woodcock, 2002), which has been shown to fit age curves well and has easily interpretable parameters. The dual exponential model is given in Equation 1:
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, an unweighted sum of the item scores of items loading on a single factor. Results were similar for both estimates, so we report only results for the simpler item sum score. We estimated the dual exponential model by using the SAS NLIN procedure. Scripts are available by request.
An important concern in the interpretation of the age curves is that the age relations in the GSS vocabulary test may reflect at least in part a cohort effect (Alwin & McCammon, 1999, 2001; Wilson & Gove, 1999a, 1999b). In order to test this possibility, we repeated the dual exponential analysis, allowing for a linear effect of cohort. We then compared the age curves within cohorts (i.e., allowing for a cohort effect) with the age curves across cohorts.
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RESULTS |
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), but the two-factor solution had a substantially lower RMSEA. The two-factor solution fit significantly better than the one-factor solution (
21 factor = 1698, 22 factor = 282, 
2 = 1416, df = 9, p <.01). The three-factor solution did not converge as a result of a Heywood case. On the basis of these findings, we concluded that the two-factor solution best describes the GSS vocabulary test.
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, we also looked at word origin as a possible source of multidimensionality. One of the words was of Germanic origin, whereas the remaining nine can be traced to Latin through one of the Romance languages, in most cases French. Therefore, word origin did not provide a way to differentiate among the items. Next, we repeated the exploratory factor analyses, dropping the easiest item and separately by dropping the hardest item. Results were consistent with the two-factor solution with Basic and Advanced Vocabulary. We also considered a confirmatory factor analysis with an essentially random selection of items. We split the items into two factors on the basis of the order of presentation, with odd items on one factor and even items on the other. We also selected at random one item from each factor to cross load with the other factor, in order to match the two items cross loading on the Basic and Advanced Vocabulary factors. The odd–even factors fit substantially worse than the Basic and Advanced factors, with a RMSEA of.048 compared with.020. Finally, we considered slight perturbations to the Basic and Advanced factor structure by exchanging a randomly selected item from each factor (i.e., an easy item moved to the Advanced factor and a difficult item moved to the Basic factor). We repeated this procedure three times. In all cases the fit was substantially worse, with RMSEAs ranging from.049 to.050. Thus, we conclude that the Basic and Advanced factor structure is a robust result.
Because we were interested in the relations between the factors and age, we examined factorial invariance across age. Exploratory factor analyses of the GSS data from each group separately yielded similar factor patterns and loadings at all age levels. A confirmatory factor analysis with multiple groups yielded a significant gain in fit from allowing factor loadings to vary across groups (2 = 467, df = 120, p <.01). However, the RMSEA changed by only.002, indicating that there was no meaningful change in fit. Furthermore, there were no substantive or systematic differences in factor variances or correlations across the age groups. Therefore, we concluded that factorial invariance (both configural and metric) holds across age groups (see 1).
Age Relations
Results of the age regressions are displayed in Figure 2 for the two-factor solution. Numerical results are available by request. Constraining the age relations to be identical yielded a substantial loss in fit (2 = 607, df = 12, p <.01). It is clear that the two factors have different relations to age. Basic Vocabulary, on one hand, displays an age-related increase in early adulthood, has a peak at around age 35, and then shows an age-related decline in the later years. Advanced Vocabulary, on the other hand, displays an age-related increase throughout adulthood, reaching an asymptote around age 45, and showing an age-related decline only in very old age.
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Cohort Effects
The dual exponential models with linear cohort effect are displayed in Figure 3 for Basic Vocabulary and Figure 4 for Advanced Vocabulary. A small cohort effect is identifiable for Advanced Vocabulary, such that more recently born cohorts had lower Advanced Vocabulary knowledge (coefficient on cohort: –.0024, 95% CI = [–.0043, –.0006]). After allowing for the linear cohort effects, we found that there were larger growth (.074 vs.052) and decline (.020 vs.016) rates. Basic Vocabulary displays a stronger cohort effect. There was a positive effect of cohort (.0060, 95% CI = [.0048,.0073]), such that more recently born cohorts had higher Basic Vocabulary knowledge. Furthermore, after allowing for the linear cohort effect, we found that there were substantially smaller growth (.101 vs.162) and decline (.040 vs.067) rates, although these rates were both still larger than the rates for Advanced Vocabulary.
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, 1999), we also repeated the cohort analysis, using years of formal education instead of birth year as the covariate in the dual exponential model. More years of schooling was associated with higher vocabulary knowledge for both Basic Vocabulary (.024, 95% CI = [.021,.027]) and Advanced Vocabulary (.050, 95% CI = [.047,.053]), although the effect on Advanced Vocabulary was stronger. After allowing for the linear education effect, we found that there were small reductions in the growth (.149 vs.162) and decline (.055 vs.067) rates for Basic Vocabulary, whereas for Advanced Vocabulary there was essentially no change in either the growth (.051 vs.052) or decline (.012 vs.016) rates. |
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Difficulty Factors
Factors related to difficulty when dichotomous items are used can be problematic (McDonald & Ahlawat, 1974). Some researchers seem to indicate that difficulty factors are always artifactual (Hattie, Krakowski, Rogers, & Swaminathan, 1996), reflecting a misspecified relation between the true unidimensional factor and the observations. The misspecification can result from the use of a linear factor analysis instead of a nonlinear factor analysis (McDonald & Ahlawat, 1974), but it may still arise if the nonlinear function is not approximately correct. The misspecification may also arise from a lack of correction for guessing (Hulin et al., 1983, chapter 8). Our use of a nonlinear factor analysis may have minimized these problems, and correcting for guessing had no effect on the results, but it is not possible to assess to what degree we were successful in eliminating the potential for artifactual multidimensionality.
In contrast, difficulty factors may indicate true multidimensionality, reflecting differences in the cognitive processes required for easy and difficult items (McDonald & Ahlawat, 1974). Research into the sources of difficulty factors has not yielded definitive means of addressing potential artifactuality. One possibility is to use external validation. If the factors have different relations to other variables, then a conclusion of actual multidimensionality may be justified. We turn to the relations with age as external evidence of the validity of the bidimensionality of the GSS vocabulary test.
Generalizability
An important feature of these results is that, unlike many psychological studies of aging, which rely on convenience samples, the data come from a nationally representative sample. The sampling procedure is not completely representative, as institutionalized persons are not included. This includes nursing home residents, who are presumably less able at cognitive tasks than their uninstitutionalized peers, and college students, who are presumably more able at cognitive tasks than their uninstitionalized peers. Therefore, less able older adults and more able younger adults are likely underrepresented. The average ability of very old adults on both factors of vocabulary knowledge is likely overestimated, and the average ability of the young adults is likely underestimated. However, it is unlikely that this misestimation affects the general results of this study, as it probably affects both factors approximately equally.
In this study we looked at only one test, the multiple-choice GSS vocabulary test, and it is not known to what extent the results generalize to other vocabulary tests. Analyses of other tests have yielded less clear results. For example, using the same methodologies used for the GSS vocabulary test, we also analyzed Salthouse's (1993) Synonyms Vocabulary Test (see Bowles & Salthouse, 2003, for a description of the data). The Synonyms test replicated the bidimensionality with factors reflecting difficulty, although the factors were less well identified because of a lack of difficult items; the lowest proportion correct was.56, compared with.21 for the GSS vocabulary test. The differential age relations were also found, although again the difference was less clear because of the difficulty in identifying the Advanced Vocabulary factor.
Related Findings
Some other studies have found differences in age trends between basic and advanced vocabulary knowledge, although in no case was the difference a focus of the findings. Hambrick, Salthouse, and Meinz (1996) found stronger positive correlations between age and ability to complete crossword puzzles for the more difficult New York Times crossword puzzles than for relatively easier puzzles (.41 vs.03 and.07 in Study 2,.46 vs –.01 and.12 in Study 3, and.36 and.31 vs.16 in Study 4). In the Seattle Longitudinal Study, scores on an advanced vocabulary test peaked later and declined less in a person's late adulthood than scores on an easier vocabulary test when longitudinal data were examined (Schaie, 1996, Fig. 5.5, p. 124), although the difference did not appear in cross-sectional data (Fig. 4.6, p. 92).
Explanations for Differential Age Trends
It appears that no theory of the aging of vocabulary knowledge offers a good explanation for the seemingly counterintuitive finding that advanced vocabulary remains fairly stable in late adulthood, whereas basic vocabulary displays an age-related decline. Results from the dual exponential model suggest that a growth in esoteric knowledge does not account for the findings, as the decline rates of basic and advanced vocabulary differ, and advanced vocabulary, which presumably requires more esoteric knowledge, has a weaker growth rate. Dual-representation theories do not predict differences in the age trends between basic and advanced vocabulary, although it may be possible to develop an ad hoc and perhaps convoluted explanation. The transmission deficit hypothesis also does not explain the findings, as it suggests that advanced vocabulary should be more negatively related to age (Burke et al., 2000).
The analysis of cohort effects indicates that a theory involving cohort effects may provide the best explanation for the findings, although the currently conceptualized theories are insufficient to explain all the results. One possible explanation for the differential age trends is that there are two types of cohort effects: word obsolescence (Hauser & Huang, 1997), which would favor older adults and affect advanced vocabulary more strongly, and the intercohort increase in schooling (Alwin & McCammon, 2001), which affects both types of vocabulary knowledge and counteracts the obsolescence effect for advanced vocabulary, thus yielding only a small observed cohort effect for advanced vocabulary. These cohort effects would yield an observed intercohort increase in basic vocabulary, which appears as an age-related decline. However, the findings on the effect of years of education suggest that education has little explanatory power for the differential age trends. A more thorough theory of the mechanisms behind cohort effects on vocabulary knowledge is needed.
Measurement Issues
Issues of basic structural measurement are often ignored in psychological research (Bowles & Salthouse, 2003; Michell, 1990). The total score is often assumed to represent an assumed latent trait without testing the success of the representation, and all measurement properties are assumed without being tested. One commonly assumed measurement property is unidimensionality, which means that a single latent trait can account for the observations generated with the instrument. Although in some cases unidimensionality may hold, it need not, and, as illustrated in this study, the differences between the dimensions may be interesting and important. An assumption of unidimensionality can obscure the differences between the dimensions, so that findings based on the instrument may not be appropriate. Just as aggregating over persons can obscure important individual differences such that conclusions about the aggregate may not apply to any particular individual (Allport, 1937; Estes, 1956; Nesselroade & Molenaar, 1997), aggregating over dimensions of measurement with an untenable assumption of unidimensionality may yield conclusions that do not apply to any of the true underlying dimensions. Thus, it is important to test an assumption of multidimensionality, as well as other assumed measurement properties.
Previous analyses of the GSS vocabulary test have assumed that the test is unidimensional (e.g., Alwin & MacCammon, 1999; Wilson & Gove, 1999a, 1999b). Our results indicate that the test is bidimensional, suggesting that previous conclusions may have ignored some of the interesting and informative properties of the GSS vocabulary data. Future analyses should incorporate the two-factor model in a structural equation modeling framework, or at least approximate it with an appropriate selection of a factor score estimate. Although no optimal means of estimating factor scores with nonlinear factor analysis has been identified, Wackwitz and Horn (1971) suggest that the sum of scores on items loading on a single factor may closely approximate the factor score. Therefore, researchers could calculate two vocabulary scores, a basic vocabulary score equal to the sum of the scores on Word A, Word B, Word D, and Word I, and an advanced vocabulary score equal to the sum of the scores on Word C, Word G, Word H, and Word J. This system of equations cannot perfectly match the Basic and Advanced factors, but it approximates them while maintaining a simple raw score representation of vocabulary knowledge.
Conclusion
Researchers sometimes point to vocabulary knowledge as a fortunate exception to the general decline found in almost all cognitive tasks. However, looking at age-related changes in a unitary construct of vocabulary knowledge appears to be misleading, as vocabulary knowledge, at least as measured by the GSS vocabulary test, is better described as bidimensional. The two dimensions have dramatically different relations to age. Knowledge of basic vocabulary seems to peak at a relatively early age and then have an age-related decline, similar to most cognitive tasks, whereas knowledge of advanced vocabulary seems to be stable after reaching a peak around the age of 45. Some of the age differences may be accounted for by a cohort effect, but current theories of the aging of vocabulary knowledge are insufficient to explain the differential age trends.
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Acknowledgments |
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The lack of systematic differences in factor correlations across age goes against typical findings of dedifferentiation, such as those of Baltes, Cornelius, Spiro, Nesselroade, & Willis (1980). Studies of dedifferentiation focus on broad test batteries and do not tend to examine cognitive abilities at the level of detail used in this study. Therefore, we make no conclusions about the relation between our findings and the concept of dedifferentiation. 
Received for publication July 8, 2004. Accepted for publication March 18, 2005.
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