Career Landmarks in Science: Individual Differences …...career landmarks can be described in terms...

Developmental Psychology1991. Vol. 27, No. 1,119-130

Copyrighl 1991 by the American Psychological Association, Inc.0012-1649/91/53.00

Career Landmarks in Science: Individual Differencesand Interdisciplinary Contrasts

Dean Keith SimontonUniversity of California, Davis

A conceptual framework is introduced for interpreting individual differences in the developmentallocation of the first, best, and last contributions of a creative career. Eight hypotheses are offeredthat specify how the placement of the 3 landmarks over the life span should vary according to bothindividual differences (in age at career onset, lifetime productivity, and eminence) and interdisci-plinary contrasts (resulting from the inherent cognitive requirements of each field). The hypothesesare then confirmed on a sample of 2,026 scientists and inventors (even after introducing controls forpotential artifacts). The results (a) place further constraints on theoretical explanations of therelation between age and creative productivity, (b) lead to new predictions regarding how creativeachievement may vary across and within careers, and (c) suggest how to examine changes in creativepotential from childhood through old age.

Perhaps the oldest research topic in empirical life span devel-opmental psychology is the relation between age and creativeproductivity. More than 150 years ago, Quetelet (1835/1968)applied statistical methods to the problem of how the output ofsuccessful plays varies over the career course (see also Beard,1874). About a century later, Lehman launched the systematicresearch program that culminated in his well-known Age andAchievement (1953), thereby establishing an empirical tradi-tion that continues to this day (e.g., Horner, Rushton, & Vernon,1986; for a review, see Simonton, 1988a). Largely detached fromthis life span developmental literature is a second tradition thatconcentrates on individual differences in total lifetime output.This work began with Lotka's (1926) examination of the highlyskewed nature of the cross-sectional distribution of scientificproductivity, a topic granted a broader and more comprehen-sive study by Dennis (1954a, 1954c). Even though the dependentvariable in both developmental and individual-difference tra-ditions involved the same initial operation—the counting ofcreative products—few investigators have pursued both per-spectives simultaneously (for exceptions, see Dennis, 1954c;Over, 1982a, 1982b; Raskin, 1936; Simonton, 1977b, 1989b;Zusne, 1976).

Yet given that creative productivity is the same phenomenonfor both developmental and differential psychologists, it wouldseem more parsimonious to adopt a unified approach. Such anintegrated perspective may become especially instructive whenwe seek to comprehend the developmental placement of a cre-ative individual's career landmarks. That is, if the goal is tolearn what determines the ages at which creators produce theirfirst, best, and last influential contributions, we may have to

The computer analyses were made possible by a Faculty Grant fromthe University of California. I am grateful to the three anonymousreviewers for suggesting substantial improvements in the article.

Correspondence concerning this article should be addressed toDean Keith Simonton, Department of Psychology, University of Cali-fornia, Davis, California 95616.

consider simultaneously both longitudinal changes and cross-sectional variation. To argue this case, the present study offers aconceptual framework by which the age location of the threecareer landmarks can be described in terms of both individualdifferences and interdisciplinary contrasts. From this proposedscheme, we obtain eight hypotheses that will be examined us-ing a large sample of eminent creators. It must be emphasizedfrom the start that the aim is not to establish a particular inter-pretation of the phenomenon; the current state of our knowl-edge may render any such attempt premature. Nonetheless, byunifying two separate research traditions, the proposed modelleads to empirical results that narrow the range of theoreticaland empirical options.

The conceptual scheme builds upon two models developedin previous theoretical and empirical work (Simonton, 1988a).The first model entails an information-processing theory abouthow creative productivity shifts over the life span (Simonton,1984a, 1988b, 1989a). A mathematical formulation ofa postu-lated two-step cognitive process yielded the following equationfor predicting the rate of output at time t:

p(t) = (1)

where c = abmf(b - a) and e is the exponential constant (butwhen a- by p{t) = a1mte~a\ giving the same curves). This for-mula incorporates three features that are central to later deriva-tions. First, / is defined in terms of career age, not chronologi-cal age. Because the determination of the career course is endog-enous rather than exogenous, t = 0 at the moment that anindividual starts to devote full time to generating creative ideasin a discipline of choice. Consequently, a complete account ofhow separate careers differ must take into consideration sub-stantial individual differences in the age of career onset. Sec-ond, because the coefficient c scales the height of the age curve,it determines both the maximum output rate and the total life-time productivity, the latter being the total area under the curvefrom career onset until death (i.e., the definite integral). If a andb are held constant, then c is directly proportional to m, which

119

120 DEAN KEITH SIMONTON

o1—

CO=3

0t 0

Age 20

p(t) = 30(e-005t- e-006t)

— - p(t) = 20(e-004t-e-°05t)

1030

2040

3050

4060

5070

6080

Figure 1. Two hypothetical curves depicting annual productivity as aFunction of career age, assuming that creative potential equals 100 butwith different rates of ideation and elaboration, using Equation 1.

designates an individual's initial creative potential at / = 0. Byrepresenting the total number of contributions a person is capa-ble of making in an unrestricted life span, m makes allowancefor the considerable cross-sectional variation in lifetime out-put. Third, the two exponents represent the rates at which twomental processes take place: a is the ideation rate at whichcreative potential is transformed into "works-in-progress," andb is the elaboration rate at which these ideations are convertedinto finished products. According to this theory, these two in-formation-processing parameters are contingent on the num-ber and complexity of the concepts that must be manipulatedto originate new ideas in a given field. These contrasts are re-quired to interpret discrepancies in the typical career courseacross separate disciplines. Unlike the two individual differ-ence variables, these cognitive parameters govern the longitu-dinal shape of the curve, including such critical aspects as howrapidly productivity increases in youth, when it peaks in mid-career, and how gradually it declines in later life (e.g., Simonton,1989a). This implication is illustrated in Figure I, which plotsseparate curves for two individuals with identical career onsets(t = 0 at age 20) and initial creative potential (m = 100) but withslightly different rates of ideation and elaboration.

The curves shown in Figure I depict total output per age unit,whereas we are here interested in the career placement of genu-ine landmarks, namely, that subset of contributions that actu-ally had an impact on a creative enterprise. The necessary con-nection can be made by exploiting the constant-probability-of-success model (Simonton, 1988a), which states that quality is apositive though probabilistic function of quantity. Empirically,the correlation between socially valued creativity and merelybehavioral productivity holds both across and within individ-ual careers (Simonton, 1984b, 1988b). In the longitudinal case,for example, those periods in a career that generate the mosttotal output also tend, on the average, to produce the bestworks, a correspondence that occurs even after controlling forthe overall age trends (e.g., Over, 1989; Simonton, 1977a, 1985).Although it can be shown that this model is consistent with the

theory underlying Equation 1 in that both can be derived fromCampbell's (1960) blind-variation and selective-retentiontheory of creativity (Simonton, 1988b), these theoretical con-nections are not needed for us to arrive at the following eighthypotheses (which are enumerated approximately in order ofincreased complexity and predictive distinctiveness).1

Hypothesis 1. The mean ages of producing the first, best, andlast contributions are statistically and substantively differentacross disciplines.

As is evident from inspection of Figure 1—which shows peakproductivity rates at t = 18.2 versus / = 22.3 for only slightlydifferent information-processing parameters—disciplinesshould feature distinguishable career ages for the maximumoutput per annum. Given that the most outstanding work mustbe found among the elite selection of successful works and thatthe latter are most likely to appear in those career periods ofhighest productivity, interdisciplinary contrasts necessarily ex-ist in the most likely location of the single best contributionwithin a career. Likewise, because Equation 1 can accommo-date a diversity of shapes, including early or late productivepeaks and gradual or more precipitous postpeak declines, theages at first and last works should also vary appreciably acrossseparate creative endeavors. For example, in those disciplineswhere the curve grows sharply at the beginning of the career,the individual will be accumulating contributions morequickly, making it more probable that a notable work will ap-pear sooner.

A severe problem can potentially confound the above argu-ment, however. If different fields vary consistently in the age atwhich a career normally begins—owing to disparities in howlong it takes to master the requisite knowledge and to occupythe necessary professional positions—then this fact alonecould account for Hypothesis 1 even if the productivity curve interms of career age were perfectly equivalent for all creativedisciplines. Nevertheless, this rival explanation demands thatthe rank order of the ages for the three career landmarks shouldbe absolutely identical, and thus if control is introduced forcareer onset, interdisciplinary differences must disappear.This expectation provides the logic behind

Hypothesis 2. Interdisciplinary differences in the mean ages forcareer landmarks are statistically and suhstantively significantafter controlling for interdisciplinary variation in the mean age ofcareer onset.

For the next six hypotheses, let us simplify matters by restrict-ing the logic to creative individuals who all belong to the samediscipline, so that the predicted age curves all have a uniformshape regardless of what the outcome may be for the first twohypotheses. Assuming that the two key individual differencevariables are at least approximately orthogonal, we can derive afourfold classification of typical career courses within any

1 Actually, most hypotheses can be derived from the first modelalone if we assume that Equation X describes the total output of ideas,and then postulate that the least publishable unit for a certain disci-pline requires k ideas, where k > 1. But these alternative derivationscannot be empirically distinguished from those based on theconstant-probability-of-success model using the data available here.

CAREER LANDMARKS IN SCIENCE 121

CREATIVE POTENTIAL

CAREERONSET

Early |

•ao

Low

CL(e-at-e-bt)

Contribution

RrstBest Last

Late |3

OQ.

Contribution

C L < C H

(M L <M H )

First

High

= CH(e-al-e-bt)

Last

t O 10 20 30 40 50 60Age 20 30 40 50 60 70 80

0 10 20 30 40 50 6020 30 40 50 60 70 80

LastFirst

\

t - 0 10 20 30 40 50Age 20 30 40 50 60 70 80

0 10 20 30 40 5020 30 40 50 60 70 80

Figure 2. Four hypothetical curves expressing annual productivity as a function of career age, with ide-ation and elaboration rates held constant, but with individual variation in initial creative potential (high orlow) and in career onset (early or late).

given field. This typology is presented in Figure 2. The twoleft-hand curves represent the predicted career course for indi-viduals with low creative potential {tnL), whereas the two right-hand curves depict the same changes in output for those withhigh creative potential (mH). In contrast, the two curves in theupper portion of the figure hold for those whose career beganearly in chronological age (viz., t = 0 at age 20), whereas the twocurves in the lower portion describe the case for those with latercareer onsets (viz., t = 0 at age 30). Clearly, if two persons com-mence their careers at the same chronological age but differ increative potential, then the individual with the higher potentialwill begin making notable contributions earlier and will con-tinue making contributions later. Under the constant-probabil-ity-of-success model, the chance of a success is a positive func-tion of the number of trials, and consequently the higher thecreative potential, the faster attempted contributions will ap-pear and the sooner the individual can boast a hit. By the sametoken, the higher the initial creative potential, the greater theproductivity will be in the final career decades and thus themore successes there will be late in life. Because creative poten-tial underlies total productivity, we therefore obtain

Hypothesis 3. Lifetime creative productivity correlates nega-tively with the age of the first contribution and positively with theage of the last contribution.

Here, "lifetime creative productivity" is taken to signify thetotal output of truly important contributions—quality ratherthan quantity. Nonetheless, because the constant-probability-

of-success model is valid cross-sectionally as well as longitudi-nally, this hypothesis should earn support whether total outputis gauged as total output (major and minor works) or as justcreative output (major works alone), although in the presentstudy the latter definition alone is used. It is important to recog-nize that this deduction entails no hidden tautology (Simonton,1988a). If O is lifetime output, then it is evident that O = R(E -S), where R is the mean annual rate of output, E is the age thatcontributions ended (longevity), and S is the age that contribu-tions started (precocity). The three independent determinantsof lifetime output may adopt a wide range of correlations with-out violating this identity. In the absence of a theory affirmingthe contrary, any one of the three independent variables in theidentity can be made the exclusive correlate of the dependentvariable simply by constraining the other two. For example, thedifference between S and E, which yields the career length,could be a constant (i.e., those who start earlier end earlier), inwhich case total output would be a function solely of the meanoutput rate.

Unlike the first and last landmarks, the age at which the bestcontribution appears is not contingent upon creative potential.Because the ideation and elaboration parameters are constantfor individuals in the same discipline, m decides the height ofthe age curve but not the form of the curve. Accordingly, it isthese two rates alone that determine the location of the peakproductive age in the career course. If, as in Hypothesis 1, theage at which productivity maximizes is also the optimal age foroffering the creator's best work, we arrive at


Hypothesis 4. Lifetime creative productivity correlates zero withthe age at the best contribution.

Like Hypothesis 3, this prediction persists no matter how weoperationally define the productivity measure in terms of thequantity-quality distinction. Also as in the preceding predic-tion, the confirmation of Hypothesis 4 is by no means aforegone conclusion. Indeed, it has often been speculated thatthe most eminent creators tend to make their superior contri-butions earlier than is the norm (e.g., Lehman, 1953; Simonton,1988b, chap. 4; Zuckerman, 1977). Although the present propo-sition pertains only to lifetime productivity, this last variable,whether considered as total output or actual contributions, cor-relates highly with eventual eminence (Albert, 1975; Dennis,1954a, 1954b; Simonton, 1984b, chap, 4). If the only direct ante-cedent of eminence among the variables of the current model iscreative productivity, the following two expectations ensue:

Hypothesis 5. Eminence correlates zero with age at the best con-tribution.

Hypothesis 6. Eminence correlates negatively with age at thefirst contribution and positively with age at the last contribution,but once lifetime creative productivity is partialed out, the corre-lations of eminence with the ages at first, best, and last contribu-tions uniformly equal zero.

The point of the latter conclusion is simply that any correla-tion that eminence might have with the longitudinal location ofthe first and last landmarks is entirely mediated by the inter-vening variable of total output. To some extent, Hypotheses 5and 6 could be disconfirmed without rejecting the entire con-ceptual scheme. We would only be compelled to reject the sub-sidiary assumption that productivity provides the sole directcause of eminence. However, confirmation of Hypotheses 4and 5 provides unique evidence on behalf of the crucial suppo-sition that creative potential and age of career onset are orthogo-nal; if these latter two individual difference constructs arehighly correlated, the age at best work should correlate in thesame direction as both productivity and eminence, both ofwhich must be the upshot of the lifetime realization of creativepotential.

Aside from their respective correlations with productivityand eminence, the ages at first, best, and last contributionsmust exhibit a distinctive pattern of intercorrelations. Return-ing to Figure 2, we observe that the best work always falls in thesame career location relative to the first and last contributions,the magnitude of the initial creative potential and the age ofcareer onset be what they may. For the typical curves depicted,this career peak is about midway, but a bit closer to the firstcontribution. Thus, the ages at first and last works inform uswhere the best work is most likely to be placed, yielding

Hypothesis 7. Age at the best contribution correlates positivelywith ages at first and last contributions.

One can imagine realistic circumstances in which the twopredicted correlations would not emerge. For example, supposethat the career location of the best work is always placed—ow-ing to such exogenous causes as physiological, intellectual, orenvironmental changes—at approximately the same chronolog-ical year, say age 40. If the first work may appear anytime

before 40 and the last work anytime after 40, then Hypothesis 7would be disconfirmed (i.e., the covariance of a variable with aconstant is necessarily zero). Hence, if this hypothesis is veri-fied, it cannot be simply a matter of those who produce a greatdeal having started earlier and ended later, the career optima inquantity and quality occurring at roughly the same age. Themiddle landmark must be dictated endogenously by career agerather than exogenously by chronological age.

To predict the correlation between the ages at first and lastcontribution is a far more complicated matter because the twodimensions by which the four graphs in Figure 2 are differen-tiated function at cross-purposes. In one direction, holding cre-ative potential constant (i.e., comparing figures only vertically),it is obvious that an earlier career onset is positively associatedwith both a younger age for first work and a younger age for lastwork, mandating a positive correlation between the last twovariables. In the opposite direction, keeping career onset con-stant (i.e., comparing figures only horizontally), we see onceagain that the higher the creative potential, the younger the agefor the first contribution and the older the age for the last con-tribution, thus requiring a negative correlation between thesame two variables. Hence, the zero-order correlation betweenthe ages for first and last works is the composite of two antithe-tical relationships. Even so, if we had a sound estimate of thecareer age independent of either first or last major contribu-tion, we could rescale the chronological ages to the same careerage. Just such a recentering is provided by the age of the bestwork, which, if the framework holds, is governed solely by thecareer age, not by creative potential. Accordingly, we obtain aprediction that cannot be derived from any theory that does notsimultaneously acknowledge both individual differences in cre-ative potential and the endogenous determination of the careerpeak, namely,

Hypothesis 8. The first-order partial correlation between theages of first and last contributions is negative whenever the age atthe best contribution is statistically controlled.

The predictive uniqueness of this inference may not be imme-diately obvious. But by applying covariance algebra, we obtainthe partial correlation

(2)2\l)

where the subscripts index creative potential (m) and the ages atcareer onset (.4), first work (S), best work (B), and last work(E). All zero-order correlations are positive except /^, which isnegative according to Hypothesis 3 (and rmA = 0 by Hypothesis4). Given the inequalities rAB > rAS ̂ rAE (because the first corre-lation alone is determined solely by career onset and the lasttwo are differentially constrained by life span), the term ^ / ^can dominate the numerator, yielding a negative partial. Incontrast, if the age at best work were determined more by chro-nological age than by career age, fa >. rAE ̂ rAB, a positive corre-lation would become far more likely. Indeed, when rAB = 0, thepartial correlation between the ages at first and last work, whencontrolling for age of best work, must have the same sign as thezero-order correlation between the first and last career land-marks, the only change being an increase in absolute magni-


tude. By a similar line of reasoning, we can prove mathemati-cally that if the age of best work were influenced exclusively bythe initial creative potential, then a negative correlation be-tween the ages of first and last work would be impossible unlessrj^ were almost zero; the numerator for the partial would bermSrmE^ ~ rmB ) + rASrAE-

Before subjecting these eight derivations to an empirical test,two general observations are in order. First, it should bestressed that derivations of the above hypotheses are robust inthe face of various potential complications. For example, theideation and elaboration rates can vary across individuals in agiven discipline so long as the average rates differ systemati-cally across disciplines. Similarly, the hypotheses are un-changed if we allow both rates to slow down with age (Birren,Woods, & Williams, 1980), given that such longitudinal shiftswould serve only to elongate the latter section of the age curve.We may also permit creative potential to be resuscitated duringthe course of a career, so long as such influx is either (a) smallrelative to the total potential at career age t or (b) randomlydistributed throughout the career (Simonton, 1988b). Finally,the age curves need to hold only on the average, thereby allow-ing for "random shocks" (illnesses, life changes, war, etc.) todisrupt the course of any single career and therefore to displacethe specific developmental location of the career landmarks.Such deflections have been documented in previous analyses oflongitudinal data (Simonton, 1988a).

Second, past research has lended empirical plausibility tomost of the conjectures, although this support is at times onlyindirect. Regarding the first two hypotheses, in a large litera-ture from Lehman (1953) and Dennis (1966) onward, re-searchers have found noticeable interdisciplinary contrasts inthe shape of the age curve, although usually without conductingthe necessary significance tests (Simonton, 1988a). Further-more, when Equation 1 is fit to actual longitudinal data (withcorrelations between predicted and observed scores in the up-per .90s), the ideation and elaboration rates do indeed varyconsistently across fields (Simonton, 1984a, 1989a). For in-stance, the two rates are more rapid in poetry (0.04 and 0.07,respectively) than in history (0.02 and 0.03), a fact explainingthe independent finding that in the world's major literary tradi-tions, poets produce their best work at younger ages than dohistorians (Simonton, 1975). Concerning Hypothesis 3, whenwe look at total output either disregarding quality or includingjust distinguished work, lifetime productivity is negativelycorrelated with the age at which an individual first begins tocontribute and is positively correlated with the age at whichcontribution ends (Albert, 1975; Simonton, 1977b). Moreover,the total productivity levels seen in early, middle, and late de-cades of a career are all positively correlated (Dennis, 1954b;Zuckerman, 1977). In line with Hypotheses 5 and 6, the corre-lation between a psychologist^ eminence and the age at bestcontribution is almost exactly zero (Zusne, 1976), and the dif-ferential eminence of both scientists and literary figures is asso-ciated with the age at first work but not with the age at bestwork (Raskin, 1936). And last, consistent with Hypothesis 7 isthe correlation of .52 between the age at a psychologist's bestwork and the harmonic mean of the ages of first and last publi-cation, a mean that must estimate career age (Zusne, 1976).Despite these positive findings, no study has systematically ex-

amined the relationships among all three career landmarks,leaving many gaps with respect to the eight hypotheses.

Method

Samples: Inclusive and Exclusive

Even if the eight hypotheses may be evaluated on any creative en-deavor, in the present case attention is restricted to career landmarksin science and technology. This choice was inspired by (a) the wealth ofhigh-quality data regarding contributions to this endeavor and (b) theexcellent consensus that exists on what counts as an important discov-ery or invention. The primary sample consisted of 2,026 scientists andinventors who were sufficiently notable to be accorded entries in atleast one of three selective biographical dictionaries (Asimov, 1972;Howard, 1951; Williams, 1974). Over three dozen nationalities wererepresented, the individuals spanning the period from antiquity to the20th century, with an average year of birth of AXX 1717—but with overhalf born in the 19th century (Simonton, 1984c). Because women madeup less than 1% of the sample, we cannot examine here whether thefindings differ according to gender. Even so, nothing in the theorypredicts inherent contrasts between the sexes (cf. Simonton, 1988b).

Given that certain data for the earlier and the more obscure scien-tists were either missing or unreliable, a truncated sample was alsoexamined consisting solely of those figures who (a) were born after1450 and (b) had entries in every one of six biographical dictionaries(Asimov, 1982; Concise Dictionary of Scientific Biography, 1981; Dain-tith, Mitchell, & Tootill, 1981; Debus, 1968; Howard, 1951; Williams,1974). Although the 495 members of this sample still represented awide range of nationalities and disciplines, the earliest scientist in thesample was Copernicus, and no still-living scientist was included(mean birth year was 1790).

Over four dozen reference works provided the information for devel-oping a massive database of significant contributions by the sampledscientists. Besides the six biographical dictionaries mentioned earlierand encyclopedia entries, numerous historical works were used (e.g.,Daumas. 1957), especially several quite comprehensive chronologicallistings (e.g., Darmstaedter, 1908; Parkinson, 1985). In sum, 17,744sign i ficant contributions were attributed to the members of the inclu-sive sample, the exclusive sample alone accounting for 8,635 discover-ies and inventions.

Variables: Biographical, Historical and Integrated

The crucial variables were scientific discipline, eminence, productiv-ity, and the ages at first, best, and last works and at death. Except fordiscipline, eminence, and age at death (or life span), all variables weretabulated in three separate ways: the biographical sources alone, thehistorical sources alone, and the two sources of information inte-grated. Whenever it seemed advisable, we could then check the degreeof agreement between biographical and historical sources, thereby en-suring that conclusions did not hinge on a particular source type, giventhat the two types have distinct assets and deficiencies. The variableswere denned as follows (for all correlation coefficients in this section,p<.00\):

I. Each person was assigned to one and only one of the followingscientific disciplines: mathematics, astronomy, physics, chemistry, bi-ology, medicine, technology, earth sciences, and other (primarily be-havioral sciences and philosophy). These groupings were inclusiveenough to permit reasonably large sample sizes per field; these catego-ries possess the added virtue that scholars have found them to besufficiently generic to prove useful in classifying contributors of diver-gent historical and national origins (see, e.g., Concise Dictionary of Sci-entific Biography, 1981, pp. 749-773). The specific assignment was

124 DEAN KEITH S1MONTON

based on the primary designation provided by the majority of refer-ence works, which was almost invariably the discipline to which anindividual made the bulk of his or her significant contributions. Inhybrid disciplines, membership was decided by the noun rather thanthe adjective (e.g., a "physical chemist" was considered a "chemist"). Totest Hypotheses 1 and 2, this categorization was then translated into aseries of zero-one dummy variables, one for each discipline. However,when we regressed each of the three career landmark ages on thesedummies, one categorical variable had to be deleted to avoid multicol-linearity owing to the unit-intercept vector automatically placed in thedesign matrix. Accordingly, the dummy for chemistry was left out,marking this discipline as the comparison group against which otherfields could be contrasted (an arbitrary decision that did not affect theresults). (See Cohen and Cohen, 1983, chap. 5, for a treatment of howdummy variable regression is used to test for group mean differencesin the nonorthogonal analysis of variance.)

2. The eminence measure was adapted from an earlier investigationinto the differential distinction attained by all 2,026 scientists (Simon-ton, 1984c). This indicator resulted from a factor analysis of 23 separateindices compiled from sources separated by over a century of interna-tional scholarship, and operationalized in several distinct ways (e.g.,subjective ratings, page counts, line counts). The composite factorscore (obtained by summing scores transformed to unit variances) hasbeen shown to be reliable across disciplines (from .73 for earth sciencesto .90 for physics), nationalities (from .76 for Americans to .91 for theDutch), and historical periods (from .68 for the 20th century to .85 forthe 16th century; Simonton, 1984c). In addition, the eminence assess-ment was shown to correlate positively with citation indicators drawnfrom the Science Citation Index Five-Year Cumulation 1970-1974(1976). However, analysis of the item-composite correlations revealedthat the reliability of the summary measure could be enhanced bydeleting eight indicators that were either too generous (e.g., ConciseDictionary of Scientific Biography, 1981) or too stingy (e.g., the NobelPrize list) to exhibit much variance across the scientists (see Simonton,1984c). The new 15-item eminence measure still featured the judg-ments of a diversity of nationalities and a variety of operational defini-tions but now boasted a coefficient alpha of .94, with scores thatranged from 0 (e.g., John Waterston) to 95 (Isaac Newton). Because thisindicator was highly skewed to the right, the possibility of a logarith-mic transformation was considered. Yet the correlations between rawand log-transformed measures were sufficiently high (.82 and .90 forinclusive and exclusive samples, respectively) that all substantive con-clusions reported below hold for both, and we hence confine attentionto the raw measure, which is more directly interpretable as scientificprominence (Simonton, 1988b, chap. 4).

3. The creative productivity measure is simply a count of the totalnumber of discoveries and inventions credited to each scientist by thearchival sources. Biographical and historical indicatorsof productivitycorrelated .89 and .93, respectively, with the integrated indicator and.69 with each other (or.88, .95, and .71, respectively, for the exclusivesample). Obviously, this is not an index of total lifetime output butrather a gauge of actual contributions and hence constitutes an assess-ment of creative output as called for by the hypotheses. For the samereason, the restriction of the term contribution to works that actuallyleft a mark on scientific history also applies to the next three defini-tions.

4. Age at first work is the scientist's age when the first importantcontribution was made. The integrated measure was taken as theyounger age from the two possible sources. Biographical and historicalindicators correlated .93 and .76 with the integrated index and .63 witheach other (or .91, .79, and .66 for the exclusive sample).

5. Age at best work is the scientist's age when the most frequentlymentioned contribution was made (cf. Lehman, 1953; Simonton,1975). The term best is used in the generic sense of the most influential

work, which may or may not be the highest quality contribution bysome abstract standard (for further discussion, see Simonton, 1988b,chap. 4). Biographical and historical indicators correlated .89 and .84with the integrated measure and .73 with each other (or.88, .86, and .75for the exclusive sample). The best work was not always identical forbiographical, historical, and integrated sources because, occasionally,the most-cited contribution in both biographies and histories was notthe most-mentioned contribution when each source was taken sepa-rately.

6. Age at last work is the scientist's age when the last importantcontribution was made. The integrated measure was taken as the old-est age from the two available sources. The biographical and historicalindicators correlated .92 and .77 with the integrated index and .61 witheach other (or .90, .78, and .62 for the exclusive sample).

7. Age at death is the scientist's life span, denned as the differencebetween the year of death and the year of birth. The primary utility ofthis measure is as a potential control variable in certain analyses whereit may confound the inferences (Simonton, 1988a). Life span sets anobvious limit on the age of the last work and, to a lesser extent, restrictsthe appearance of the best work and even the first work.

Besides this last variable, a number of other controls were defined.The most significant of these were the scientist's birth year to correctfor possible historical trends and a dummy variable for whether thescientist died (or lived) in the 20th century to check for potential spuri-ous relationships resulting from the less comprehensive informationavailable for modern figures. Because these control variables did notin any way alter the results reported in the next section, we will discussonly the main findings, which are quite robust across alternativeanalyses.

Some concluding observations are in order about measure validity(see also Simonton, 1990b). One could argue that compilers and au-thors of data sources may entertain implicit theories about how emi-nence and productivity are related across and within careers, notionsthat are reflected in the information gathered for biographical dictio-naries and historical chronologies. As a consequence, testing the hy-potheses would amount merely to a confirmation of these unfoundedstereotypes, telling us more about the scholars than about the scien-tists. Against this skepticism we can impose three considerations.First, many of these notions in fact take the explicit form of "myths,"such as the recurrent statement that physicists are "over the hill" at age30 (Simonton, 1988b, p. 67). If such preconceptions truly permeatedthe raw data, then certain outcomes shown later could not occur (e.g.,the actual mean ages for best work). Second, for those propositionsthat are possibly vulnerable to this criticism (Hypotheses 1 and 3), it iseasy to cite parallel findings for productive quantity independent ofcreative quality (e.g., Albert, 1975; Dennis, 1954b, 1966; see Simonton,1988b, chap. 4). Consistent with the constant-probability-of-successmodel, the results for creativity are nothing more than rescaled ver-sions of those for productivity. Third, we have no real reason to doubtthat historians, in constructing inventories of notable contributions,merely acknowledge the fails accomplis of science (Lehman, 1962; Si-monton, 1984c). Hence, not one of the archival sources boosted New-ton's productivity and longevity scores by listing his massive (andwasted) efforts in the domain of alchemy among the landmarks of hiscareer—an act that would mark a gross dissent from the consensus ofthe scientific community no matter how illustrious the man.

Results

Table 1 offers the mean, standard deviation, range, and num-ber of cases without missing values for each of the six variables,with the statistics broken down by sample and by source. Ingeneral, the creative career begins on or a bit before age 30,attains a peak right before the 40th year, and closes around age


Table 1Basic Statistics for Substantive Variables by Sample and Source

Variable

EminenceAge at deathBiographical sources

ProductivityAge at first workAge at best workAge at last work

Historical sourcesProductivityAge at first workAge at best workAge at last work

Integrated sourcesProductivityAge at first workAge at best workAge at last work

M

8.469.7

5.831.439.651.7

5.033.839.749.4

8.830.639.753.8

Inclusive sample

SD

9.513.5

5.88.7

10.214.1

7.89.4

10.413.2

10.28.5

10.314.0

Range

0-9521-103

0-5111-7318-8518-102

0-8814-7717-8020-90

0-12111-7317-8120-102

N

2,0261,825

2,0261,8661,8451,865

2,0261,6151,5981,615

2,0261,8851,8841,884

M

16.369.4

10.428.439.055.8

11.530.639.454.0

17.427.339.158.5

Exclusive sample

SD

12.813.6

8.17.8

10.214.0

12.68.4

10.212.6

15.57.39.9

13.4

Range

0-9527-103

0-5115-6018-7923-102

0-8814-6018-7923-87

1-12114-6018-7923-102

AT

495494

495494490494

495486484486

495495495495

Note. To handle missing values, cases are deleted on a variable-by-variable basis (but virtually identicalresults obtain under listwise deletion).

50 or a little after.2 Although biographical and historicalsources yield virtually identical estimates of the age at bestwork, across both samples the biographical sources giveyounger ages at first work and older ages at last work, a differ-ence that reflects the superior attention that biographical d ictio-naries devote to the individual scientist's career. Because theintegrated measures always took the lower-bound estimate forage at first work and the upper-bound estimate for age at lastwork, the length of a creative career is longer for the integrateddata than for the biographical and historical sources taken sepa-rately. Additionally, judging from the productivity scores for thethree types of sources, the biographical and historical lists ofcontributions exhibited about 80% overlap. Finally, when wecompare the two samples, we see that the exclusive group fea-tures levels of eminence and productivity around double thosewitnessed for the inclusive sample (even though the ranges forboth variables are about the same). For the more elite sample,too, the first contribution comes at an earlier age and the lastcontribution comes at a later age, but the age for the best workremains virtually unaltered. The almost half-decade added tothe career of the exclusive group cannot be attributed to lifespan differences because the age at death is actually a bitsmaller. Despite these consistent contrasts between samples,the ranges for the age variables are about the same.3

Because testing Hypotheses 1 and 2 requires that we estimatestatistics for each field separately, for the moment let us focuson the inclusive sample (and integrated sources) to obtain thelargest number of cases per field. A large iv" per discipline al-lows between-group differences to emerge, notwithstandinglarge within-group variation resulting from such extraneousfactors as the already-hypothesized individual differences inthe age of career onset. Even so, the same substantive conclu-sions emerge with alternative samples and sources. Table 2shows the means, standard deviations, ranges, and sample sizes

for the scientists, split into the nine disciplines. Mathematic-ians apparently begin their careers at the youngest ages,whereas those in medical research tend to begin the latest,although most scientists still tend to make their first contribu-tion around age 30. Mathematicians are also young when theyoffer their best work to the world, although for this career land-mark, the physicists and chemists are younger still; likewise,those in medicine seem to have later career peaks, but earthscientists attain optima yet a bit older. The chemists close theircareers at the youngest ages, followed soon by the physicists,whereas those in astronomy, biology, and especially the earthsciences last the longest in terms of chronological age.

To determine if the means are statistically different, the agesat first, best, and last works and at death were each regressed onthe eight dummy variables, and in all four instances a signifi-cant proportion of the total variance was explained (Fs =6.14,5.86, 6.34, and 4.95, respectively, df^ 8,1875 for the first threestatistics, and df= 8,1816 for the last statistic, all ps < .001). Incomparison with chemists, the following can be observed: Forage at first work, mathematicians are indeed younger (b = -3.2,/ = -3,68, p < .001), and scientists in medicine (b = 1.8, t = 2.79,p < .01) and in other sciences (b = 2.9, / = 2.69, p < .01) aresignificantly older; for age at best work, researchers in earthsciences (b = 4.6, t = 3.75, p < .001), medicine (b = 4.1, / = 5.26,p< .001), other sciences (b = 3.6, /== 2.78, p<. 01), astronomy

2 Consistent with an earlier prediction (Simonton, 1984a), the vari-ance of age at last work is invariably larger than the variance of age atfirst work—even after removing the variation due to differential lifespan.

3 The life span of 103 belongs to Chevreul, a French chemist whobecame a pioneer gerontologist when in his 90s he investigated thepsychological consequences of old age—his last work being contrib-uted at age 102!


Table 2Ages at First, Best, and Last Work and at Death by Scientific Discipline

Discipline

MathematicsAstronomyPhysicsChemistryBiologyMedicineTechnologyEarth sciencesOther

M

27.330.529.730.529.432.331.630.933.4

First work

SD

8.58.77.88.17.28.29.58.2

11.0

Range

14-6017-7315-6614-7217-6417-6211-6017-5920-67

M

38.840.638.238.040.542.139.742.541.6

Best work

SD

10.711.29.19.0

11.010.410.812.111.1

Range

19-6818-7719-7018-7418-7722-8120-8017-7823-69

M

53.456.052.351.157.854.553.158.255.2

Last work

SD

15.014.013.313.213.714.014.913.814.4

Range

21-8120-8425-9022-10229-8727-9221-9327-8523-86

N

117162327425187280229

8572

M

63.470.569.570.872.069.569.971.368.7

SD

15.314.014.012.213.213.312.913.213.8

Death

Range

21-9522-9827-10230-10331-9531-9827-10030-9428-101

N

13315027739417627123389

102

Note. Descriptive statistics are based on inclusive sample and integrated sources. For significance tests for mean differences, see text.

{b = 2.7, t = 2.82, p < .01), biology (b = 2.6, t = 2.85, p < .01),and technology (b = 1.8, t = 2.10, p < .05) all peak later; and forage at last work, those in the earth sciences {b = 7.1, / = 4.33, p <.001), biology {b = 6.7,; = 5.53, p < .001), astronomy (b = 5.0, t=3.89, p < .001), other sciences (b = 4.2, t = 2.35, p < .05), andmedicine {b = 3.4, / = 3.21, p < .01) all end their careers later (forall, df= 1,1875, the 6s representing mean differences in years).Moreover, the expected age at death of mathematicians is infact significantly lower than the life expectancies found in theother disciplines 0 = -7.4, t = -5.50, p < .001, df= 1,1816).Still, because this constitutes the only difference, differentiallife span cannot account for the contrasts in the career land-marks, and. indeed, precisely the same results emerge aftercontrolling for life expectancy. Hypothesis 1 is confirmed.

Because the rankings of the ages across the nine disciplineschange as we advance across the three career landmarks, thevarious disciplines should feature distinct age curves; it is notsimply a matter of distinct starting times. For instance, mathe-maticians may begin earlier, and they are among the earliest toput forward their best work, but they are quite average when itcomes to the age at which they make their last contribution (thelatter despite the fact that mathematicians are plagued with thelowest life expectancy). Nonetheless, to verify fully the claim ofHypothesis 2 requires that we introduce some control for inter-disciplinary variation in career onset. The best available esti-mate for this confounding variable is the age at the first work,which must provide the upper bound. When we estimate theimpact of the dummy variables with age at first work in theequation, we find that interdisciplinary differences are still sig-nificant for both age at best work and age at last work: The peakages for earth scientists (b = 4.3, t = 4.06, p < .0001), biologists(b = 3.2, t= 4.04, p<.0001), medical researchers(b = 3.1, t =4.47, p < .0001), astronomers (b = 2.7, / = 3.22, p < .01), andmathematicians (b = 2.7, t = 2,82, p < .01) all surpass that forchemists; the ages at last contribution for earth scientists (b =7.1, t = 4.32, p < .00001), biologists (b = 6.8, / = 5.60, p <.00001), astronomers (b = 5.0, / = 3.89, p < .0001), other scien-tists {b= 3.9,/= 2.22, p<.05), and medical researchers(&=3.3,/ = 3.08, p < .01) all exceed the same comparison group. In-deed, by lowering within-discipline variability, the significancelevels most often greatly improve without any substantialchanges in most mean differences, which still hover close to a

half-decade. Alternatively, we can subtract the age at first workfrom age at best work and age at last work, yielding estimates ofcareer age at best work and career longevity, and still discoverthe same significant interdisciplinary differences across the sci-entific fields. These contrasts in the location of the career land-marks persist even after we control for lifetime creative produc-tivity, which, according to Hypothesis 3, affects career begin-ning and termination and thereby may confound the results ifsuch productivity systematically varies across fields. Hence, nomatter how we decide to control for career onset, Hypothesis 2is substantiated: If the landmarks fail to exhibit the same age-wise separation, the underlying age curves probably differacross disciplines, precisely as has been witnessed in longitu-dinal data. By the way, this is the first time that statistically andsubstantively significant differences across scientific disci-plines have been demonstrated, notwithstanding the introduc-tion of appropriate controls.

Turning to the remaining six hypotheses, it would seem advis-able to present the results separately by source and sample,owing to the rather special biases that might intrude on particu-lar tests. Yet judging from Table 3, which shows the zero-orderPearson product-moment coefficients for the variables, the pat-tern is quite similar across all six sets of correlations. Eminencealways correlates positively with productivity but features twoadditional correlations (of less than half the magnitude) withage at first work (negative) and age at last work (positive). Simi-larly, productivity correlates about equally with both age at firstwork and age at last work, although with the same switch in signthat was observed for eminence.4 Furthermore, no matter whatthe sample or the source, age at best work displays negligiblecorrelations with both eminence and productivity; only in theexclusive sample using historical sources does the correlation

4 One might suggest that an older age for the first work mandates alower lifetime productivity for the trivial reason that there would beless time until death necessarily ends the career. However, this con-straint only operates for those rare individuals who both begin theircareers extremely late and enjoy exceptionally high initial creative po-tential. Furthermore, as evident from Table 3, those who begin theircareers later also tend to end their careers later and to die later was well(see also Simonton. 1977b).


Table 3Correlation Coefficients for Substantive Wiriables by Sample and Source

Variables

Biographical sourcesProductivityAge at first workAge at best workAge at last work

Historical sourcesProductivityAge at first workAge at best workAge at last work

Integrated sourcesProductivityAge at first workAge at best workAge at last work

Eminence

.55***-.22***

.00

.26***

.64***-.23***

.01

.30***

.63***-.24***-.01

.26***

Inclusive sample

Productivity

—-.40***-.01

.47***

—-.36***-.04

.41***

—-.38***-.03

.42***

Age atDeath

.11***

.09***

.24***

.49***

.03

.16***

.26***

.40***

.08***

.10***

.25***

.53***

Age atfirst work

—52***

.10***

—.65***.22***

—.48***.05*

Age atbest work Eminence

.62***-.20***

— .04.47*** .25***

.64***-.26***

— .0054*** 31***

.64***-.23***

— .0144*#* 24***

Exclusive sample

Productivity

—-.40***-.03

4 5 *. .

—-.43***-.09*

43***

—-.40***-.07

.38***

Age atDeath

.16***

.0625***

.60***

.06

.14***32***

.52***

.12**

.08

.28***

.68***

Age atfirst work

—.42***.06

—.52***.04

—.41***.01

Age atbest work

—.36***

—,39***

—.31***

Note. Missing values were handled by pairwise deletion of cases (but virtually identical results obtain under likewise deletion). The correlationbetween eminence and age at death is nonsignificantly zero (to two decimal places) in both inclusive and exclusive samples.*p< .05 . **p<.01 . ***/?<.001.

between this landmark and productivity satisfy a conventionallevel of statistical significance, and, even with this exception,less than 1 % of the variance is shared, rendering the correlationsubstantively trivial. These results are in accord with Hypothe-ses 3 to 5. Moreover, the age at the best work is strongly andalmost equally correlated with the ages at first and last work,just as anticipated in Hypothesis 7.s Finally, the age at deathdisplays a small and inconsistently significant correlation withboth age at best work and age at last work, the latter correlationboasting about twice the value of the former. Hence, life spanweakly determines when a creative career terminates and evenaffects, although with more diminutive consequence, when thecareer peak emerges (see also Simonton, 1975),

The correlation between the age at first work and the age atlast work is also positive. Yet according to Hypothesis 8, thecorrelation should become negative once we control for the ageat best work, which centers the longitudinal changes aroundthe career age. When we calculate the correlation between ageat first work and age at last work, partialing out age at bestwork, the coefficient becomes significantly negative, and invari-ably so across all samples and sources (the range i s - . 11 to—.21,with a median correlation of —.20). Taking the integratedsources and inclusive sample, for instance, the first-order par-tial correlation becomes—.21 (p < .001). Given two individualswhose best works come at the same chronological age, the per-son who started contributing at the younger age will be the oneexpected to contribute at the older age as well. Even though wemight think that this negative correlation would be enhancedeven more if we introduced additional control for life span, itturns out that the second-order partial between the ages of firstand last works is virtually unchanged (e.g., r= -.22, p < .01, forthe inclusive sample with integrated sources). Furthermore, con-trolling for age at death alone without adding age at best workonly lowers the positive correlations displayed in Table 3, The

critical factor remains the chronological location of the careerpeak.

Using partial correlations, we can also demonstrate that thecorrelations between eminence and the ages at first and lastworks vanish once we control for productivity (e.g., for the inclu-sive sample with integrated sources, the two partials equal zeroto the second decimal place). In other words, the first and lastlandmarks exert no direct influences on eminence but ratheraffect eminence indirectry through their immediate relevancefor productivity—results in line with Hypothesis 6. On theother hand, we can also invert this partial correlation analysisto check for a potential artifact in testing Hypothesis 3. Sup-pose that a scientist's eminence determines space assignmentsin reference works and that these allotments translate directlyinto more extensive coverage of his or her contributions, includ-ing more discussion of earlier and later works. The correlationsbetween productivity and the ages at first and last works couldthen be spurious. This confounding would be most problem-atic for the biographical measures, with their separate entriesper scientist, whereas the historical measures emphasize thechief events in the history of science without respect to personal-ities (i.e., all histories used adopted either chronological or topi-cal formats). In any case, to rule out this serious concern weneed only to calculate the first-order partials, controlling foreminence. Across all samples and sources, the correlation be-

s This confirmation cannot be dismissed as resulting from thosescientists with short careers, because correlations confirming Hypoth-esis 7 remain significant even when the analysis is restricted to thosewhose careers were at least 20 years in length. Age at best work corre-lates between .29 and .55 (Mdn = .38) with age at first work and be-tween .20 and .42 {Mdn = .30) with age at last work, despite consider-able latitude granted to the placement of the best work when first andlast works are so distantly separated.


tween productivity and age at first work ranges from -.29 to-.35, with a median of-.34, and the correlation between pro-ductivity and age at last work ranges from .28 to .40, with amedian of .32 (all ps < .001). Despite the modest reduction inabsolute magnitude, these coefficients prove that Hypothesis 3holds even when all scientists are mathematically equalized ondistinction. Incidentally, even though a contaminating role foreminence is quite difficult to argue for the remaining hypothe-ses, it is worth reporting that these predictions also receive con-firmation after statistically matching the scientists on this vari-able (except, naturally, Hypotheses 5 and 6).

Of course, tests of Hypotheses 3 to 8 could be confounded bythe interdisciplinary contrasts, especially if there were largedifferences across disciplines in eminence and productivity.However, when all analyses were repeated with interdisciplin-ary variation partialed out, the results were practically identi-cal: 93% of the correlations reported in Table 3 departed fromthe eighth-order partials by no more than two integers in thesecond decimal place, and the average absolute difference was.01. In fact, the only notable alteration in the substantive con-clusions drawn earlier is that the case on behalf of Hypothesis 4is reinforced. The already marginal correlation between age atbest work and productivity in the exclusive sample with histori-cal sources is the spurious consequence of interdisciplinary dif-ferences in productivity, because when this variation is con-trolled using the eight dummy variables, the correlation van-ishes.

Discussion

Even if the confirmatory results do not provide conclusiveproof that the present theoretical model is correct, at least fu-ture attempts at explanation must take into consideration amore detailed matrix of empirical findings. In conjunctionwith the earlier studies of longitudinal changes in creative pro-ductivity (Simonton, 1988a), three sets of empirical constraintshave been imposed on all life span developmental accounts:

1. Any interpretation should predict the changes in creativeproductivity over the career course. Successful prediction in-cludes the correct specification of the characteristic single-peaked age curve, such as the concave downward onset andconcave upward termination, with an asymptotic approach tothe zero-productivity level (see Simonton, 1984a). Predictivesuccess also requires some interpretation for interdisciplinarycontrasts in the age curves, differences that lead to the contrast-ing locations of the three career landmarks. The predictionschemes must be sufficiently flexible that the resulting agecurves can vary not only according to the location of the peakbut also according to the slope of the postpeak decline, whichin some fields can prove negligible (Dennis, 1966; Simonton,1989a). Furthermore, given that many interdisciplinary differ-ences are cross-culturally and transhistorically invariant (Leh-man, 1962; Simonton, 1988a), any model probably must expli-cate these contrasts in terms of the intrinsic information-pro-cessing demands of a particular type of creative process ratherthan according to such extrinsic influences as the role expecta-tions of a society. Lastly, explanations should be compatiblewith the constant-probability-of-success model in both itscross-sectional and longitudinal forms; this requirement would

rule out any interpretation that implies that the proportion ofmajor works to total output may systematically change over thelife span.

2. Any theoretical account must make due provision for indi-vidual differences in creative potential (or some analogous theo-retical construct). At the very least, an interpretation shouldpropose some substantive basis for specifying exactly how life-time productivity relates to creative precocity, creative longev-ity, and the output rate per unit of career age, three variablesthat would otherwise take on almost any form whatsoever. Atthe same time, an explanatory effort must certainly provide arationale for why the age at best work alone among the threecareer landmarks is not affected by individual differences intotal productivity or eminence (two ultimate consequences ofdifferential creative potential). In any model, the peak age forproductivity, as well as the age for producing one's best work,must be endogenously fixed by career age rather than exoge-nously placed by chronological age. Hence, it seems implausi-ble to attribute the career peak to such exogenous circum-stances as changes in organic functioning (Simonton, 1988a).On the contrary, creative output over the life span is apparentlythe upshot ofsome processofself-actualization or self-organiza-tion that has some characteristic course depending solely on thenature of the contributions (Simonton, 1988b).

3. Allowance must be made for individual differences incareer onset. The addition of this factor adds an inherent ambi-guity to the assessment of creative precocity. Individuals maymake important contributions early in life either because theystarted their careers unusually young or because they startedwith exceptional amounts of creative potential. These two cir-cumstances can be easily discriminated if the theory predictstwo distinguishable trajectories for creative output from theonset of the career. In the case of a young start, the peak age ofthe career (defined by the best work or by maximum productiv-ity) will appear earlier than is normal for the discipline,whereas in the case of exceptional creativity, the peak age willemerge at the normal career location. Because these two deter-minants of the age of first work are apparently orthogonal, assuggested by the confirmation of Hypotheses 4, 5, and 8, anyexplanation must assume a more complex form than has beentypical for previous theories. This interpretative complexity ap-plies in equal force to the determination of creative longevity:High output late in life may result from a late career onset(indicated by a late career peak), an exceptional creative poten-tial (indicated by a high maximum output rate), or some combi-nation of the two factors.

More research is certainly necessary before we can settle on asingle interpretative theory. Obviously, it would be useful to testthe eight hypotheses on other disciplines, especially insofar asthe career landmarks could then be granted different opera-tional definitions. For instance, in classical music, the first,best, and last compositions would be defined by performancefrequencies in the repertoire (see, e.g., Simonton, 1977a, 1977b).More important, the present framework supports additionalpredictions that can be verified using different types of datathan those available here. In the first place, because creativepotential dictates the maximum output rate as well as the over-all lifetime output, the peak annual rate must correlate nega-tively with the age at first work and positively with the age at last


work, where the two career boundaries may be operationallydefined either by major contributions alone or by any contribu-tion whether major or minor (cf. Dennis, 1954b). Second, be-cause the constant-probability-of-success model specifies thatthe best work is most likely to appear in a period of maximumoutput, the age at best work should correlate more highly withthe age at maximum output than with the ages at first or lastwork. Finally close inspection of Figure 2 indicates that life-time output (as well as maximum output) should correlate nega-tively with the time elapsed between age at first work and age atcareer onset, where the latter is gauged by the receipt of a de-gree, diploma, or comparable certification. To test these hy-potheses, it is essential that total rather than successful outputbe examined to avoid serious floor effects on the indicator. Ofthe three predictions, the first and third are the most crucialbecause they cannot be generated by any psychological theorypublished between the works of Beard (1874) and Mumfordand Gustafson (1988).

Beyond these three specific predictions, the current concep-tual framework inspires several recommendations regardinghow future life span developmental research might best pro-ceed. To begin with, inquiries into the childhood and adoles-cent antecedents of adulthood creativity must take special careto differentiate three distinct sets of factors, namely those thatdetermine (a) the age of career onset, (c) the level of creativepotential, and (c) the choice of creative discipline. For example,intellectual precocity might determine only the age of careeronset, orphanhood might confine its influence to the acquisi-tion of creative potential, and birth order might affect solely thechoice of activity (cf. Simonton, 1987). Variables in each of thethree categories would affect the expected curve for creativeoutput, including the developmental location of the three ca-reer landmarks, but they would do so in quite contrary ways.

Furthermore, those investigations that assess adulthoodchanges in performance on psychometric measures of creativ-ity, divergent thinking, and problem-solving abilities wouldseem obliged by the present results to specify exactly how devel-opmental changes in these assessments vary according to theage of career onset and the discipline of creative expression (cf.Alpaugh & Birren, 1977; McCrae, Arenberg, & Costa, 1987).Given the dependency of the career course on these two factors,and assuming that these tests gauge creative potential (howeverapproximately), researchers should establish that changes inpsychometric creativity parallel the career trajectories dictatedby accelerated or retarded career onsets and by the selection ofendeavors with early or late career peaks (cf. Simonton, 1990a).

Finally, in the last portion of a life and career, more researchshould be conducted on how late-life potential is enhanced bydelayed career onsets, high initial levels of creative potential(whether psychometrically or behaviorally assessed), and theselection of slowly maturing creative activities or the decision tomake mid-life career changes. If the present theory has anyvalidity whatsoever, such gerontological inquiries should docu-ment tremendous cross-sectional and interdisciplinary varia-tion in late-life creativity, thereby providing more optimisticforecasts than has sometimes been the norm (see also Albert,1975; Over, 1982a, 1982b, 1989; Simonton, 1989b). As the fig-ures in Table 1 demonstrate, nothing necessarily prohibits anoctogenarian, or even a centenarian, from making noteworthycontributions to scientific knowledge.

Whatever the eventual status of the present conceptual frame-work, the present results should inspire further attempts tostudy creative achievement from an integrated longitudinal andcross-sectional perspective. Certainly, the particular location ofcareer landmarks in a scientist's career cannot be understoodwithout a unified analysis that accommodates both develop-mental change and individual variation.

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Received March 13,1989Revision received January 2,1990

Accepted January 4,1990 •

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