Interdisciplinary and military determinants of scientific productivity: A cross-lagged correlation analysis

Journal of Vocational Behavior 9, 53-62 (1976)

Interdisciplinary Productivity:

and Military Determinants A Cross-Lagged Correlation

DEAN

KEITH

of Scientific Analysis

SIMONTON

University of Arkansas

This paper explores the contemporaneous and intergenerational relationships among various scientific endeavors and military activity. Using European historical data from 1500 to 1900 A.D., generational (or 25yr) fluctuations were examined for nine categories of scientific discovery and invention and for two aspects of military activity. A cross-lagged correlational analysis indicated that (a) casualties (but not war duration) has a significant negative contemporaneous association with medical discoveries, (b) several scientific disciplines display positive intergenerational influences (e.g., medicine, geology, and chemistry on biology), and (c) astronomy exhibits a negative intergenerational impact on technology, medicine, biology, and geology. The findings were discussed in terms of both stimulating interdisciplinary information exchanges and inhibitory competitive recruitment.

Researchers have frequently maintained that scientific invention and discovery are largely a function of social, economic, and cultural forces rather than personal genius or creativity (cf. Kroeber, 1944, pp. 7-21; Kuhn, 1970 Schmookler, 1966; chap. 10). Two potential factors deserve special empirical attention: interdisciplinary and military. The first question is whether productivity in one scientific discipline can have an influence on another. Students of creativity have often suggested thai innovation depends on the recognition of overlap between two or more field: (e.g., Barnett, 1953, pp. 181-289; Bartlett, 1958, pp. 131-137; Koestler 1964, pp. 230-231; Kuhn, 1970, p. 90; Zuckerman & Merton, 1972, pp 311-314). Yet there exists relatively little research on whether discoveries ir some scientific disciplines have a consistent causal impact on discoveries ir other disciplines of science (cf. Simonton, 1975b; Sorokin, 1962, p. 663). One particular possibility which needs special consideration is whether a causa relationship exists between “pure” and “applied” research. Do discoveries ir The author is deeply grateful to Deredr de Solla Price for providing the Yuasa dat used for calculating some of the reliability coefficients. Requests for reprints directed to the author, Department of Psychology, University of California, 95616.

53 Copyright 0 1976 by Academic Press,Inc. m rights of reproduction

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DEANKEITHSIMONTON

physics possess a consistent stimulatory influence on technological inventions’? Does biology have an advantageous impact on medicine? Or might it be the case that “applied” areas, such as technology or medicine, have a causal influence on “pure” areas, such as physics or biology? The second issue concerns whether scientific productivity in some disciplines is positively or negatively affected by military events. Numerous social scientists have discussed the relationship between politico-military conditions and individual creativity, especially in science and technology, but usually without any consistent conclusions (e.g., Norling, 1970, chap. 6; Sorokin, 1937, Vol. 3; Toynbee, 1946). Further, what little quantitative research has been done on the subject fails to fin.d any connection between military and scientific activity (cf. Naroll, Benjamin, Fohl, Fried, Hildreth, & Schaefer, 1971; Simonton, 1975~). One difficulty with previous research, however, is that it fails to distinguish both among different types of scientific enterprises (cf. Roe, 1952; Gough & Woodworth, 1960) and among various aspects of warfare (cf. Wright, 1965, pp. 573-575). Yet surely different scientific disciplines should have varied determinants, and the diverse facets of war may or may not be engaged in such influences. For example, previous studies used the number of war years per unit time as the measure of war when the number of war casualties may be of paramount importance (e.g., Naroll et al., 1971; Simonton, 1975a). Even more significantly, warfare may have a positive effect on the “applied” sciences but a negative effect on the “pure” sciences. Certainly during war, technology seems to receive more intense emphasis, probably at the expense of such disciplines as mathematics whose relevance to national survival is much less manifest. Therefore, the present paper will try to analyze how scientific productivity may be the function of interdisciplinary and military factors. This goal is facilitated by recent developments in quasi-experimental designs (Campbell & Stanley, 1966), especially cross-lagged correlation analysis (cf. Crano, Kenny, & Campbell, 1972; Pelz & Andrews, 1964; Rozelle & Campbell, 1969). Essentially the logic of this method is as follows. Say that we have a series of observations over time on two variables, such as war and scientific productivity. For our current purposes the time units are 25yr periods called “generations” (cf. Bengston, Furlong, & Laufer, 1974, pp. 15-17; Buss, 1974, pp. 65-66; Mannheim, 1952; Marias, 1968). Then if war has a positive effect on scientific productivity, the “cross-lagged” correlation from war at generation g to scientific productivity at generation g + 1 should be significantly more positive than the reverse cross-lagged correlation from scientific productivity at generation g to war at generation g t 1. The assumption behind applying cross-lagged technique to generations is that some factors may operate as developmental period influences for the individual creators comprising the next generation (Simonton, 1975c, 1976). In other words, those scientists who are mature producers at generation g + 1

DETERMINANTS

OF SCIENTIFIC

PRODUCTIVITY

55

will be, on the average, youthful individuals more susceptible to political and interdisciplinary processes at generation g. To be sure, the causal relationships may in fact operate over intervals much shorter than 25 yr; but insofar as we are interested in the sociocultural context most favorable to creative development in the sciences, then cross-lagged analysis of generations can be of appreciable service.

METHOD Unit and variable definitions. Table 5 from the second volume of Sorokin’s (1937) Social and Cultural Dynamics presents frequency tabulations of 12,761 discoveries and inventions for each of the following nine areas: mathematics, astronomy, biology, medicine, chemistry, physics, geology, technology, and geography. These measures were derived from Darmstaedter’s (1908) Handbuch zur Geschichte der Naturwissenschaften und der Technik, a cooperative effort of 26 specialists (Sorokin, 1937, Vol. 2, p. 132). Although the measures extended from 3500 B.C. to 1908 A.D., only the period from 1500 to 1900 A.D. utilized 25-yr periods for the time unit. Since a quarter century alone, not a half or full century, seems a reasonable approximation to a “generation,” only 16 analytical units were adopted for the present study. In Tables 6 through 14 in the third volume of Sorokin’s (1937) work are presented two types of military measures: war duration (in years) and casualties (absolute number). These two military measures were then summed across all nations represented (France, Russia, England, Austria-Hungary, Germany, Italy, Spain, Holland, and Poland-Lithuania) to yield total indices of duration and casualties for most of Europe (minus Scandinavia). Because in different generations some nations either appeared (e.g., Germany before 1650) or vanished (e.g., Poland-Lithuania before 1800), the totals were divided by the number of nations active each quarter century. Thus the military measures consist of the average generational war duration and casualties per nation. To permit cross-lagged correlation analysis, lagged values were created for all scientific and military measures, thus losing one degree of freedom (i.e., df = 15). Data reliability. Every quantitative comparison of Sorokin’s measures with those independently compiled by others has produced significant reliability coefficients (e.g., Naroll et al., 1971; Simonton, 1974). For example, the measures of creativity in the biological and physical sciences have rank-order correlations of .87 and .93, respectively, with other measures derived from such sources as Kroeber (1944, pp. 100-122, 134-174) (Simonton, 1975b). Moreover, Sorokin (1937, Vol. 2, Table 5) also presents a second set of tabulations derived from Garrison’s (1929) An Introduction to

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DEANKEITHSIMONTON

the History of Medicine; the rank-order correlation between Darmstaedter’s and Garrison’s listings of medical discoveries is .95 (N = 10, p < .Ol). Finally, Yuasa (1974) has tabulated (using 50-yr periods) the number of scientists having entries in Webster’s Biographical Dictionary. The productmoment correlations between these measures and those tabulated by Sorokin are: mathematics - .22, astronomy .92, physics .999, chemistry .93, biology .98, and medicine .99. Except for mathematics, all correlations are significant at the .OOl level (iV = 7). The slightly negative reliability of the mathematics measure is probably due to the peculiar operational definition used by Yuasa (1974, p. 87). Sorokin’s political data have also been shown to be reliable (Simonton, 1974). For instance, his measure of war duration has been shown to have a rank-order correlation of .78 with an independent measure compiled from I-anger’s (1972) Encyclopedia of World History and other chronologies of political events (Simonton, 197%). Sorokin’s duration measure also has a rank-order correlation of .82 (N = 7, p < .05) with a war frequency measure devised by Naroll et al. (1971) and has a product-moment correlation of .68 (N = 77, p < .OOl) with a roughly comparable measure used in Wright’s A Study of War (1965, Table 56, p. 653). Needless to say, any measure of war casualties must necessarily be less reliable than a measure of war duration. Indeed, Wright (1965, Table 52, p. 657) has attempted to directly indicate the reliability of casualty data from France and England by contrasting Sorokin’s figures with those estimated by Wright’s research assistant. Even though the aim was to show how much they differ, the product-moment correlation between the two estimates is .46 (N = 24, p = .013). In sum, Sorokin’s measures of both war and science appear sufficiently reliable for our research purposes, especially for exploring a relatively neglected topic. Time trend control. Most of the measures exhibited conspicuous secular trends which tend to obscure the generational fluctuations. In the case of the war measures these trends were linear and consequently could be handled by introducing time as a control variable, where “time” is a number assigned to each consecutive generation. In the case of the science measures, however, an exponential trend was evident, necessitating a logarithmic transformation to render the time-series linear (Johnston, 1972, pp. 47-50; Simonton, 1975~). The cross-lagged correlation matrix was then computed for the raw military measures and the log transformed science measures after partialling out linear time trends. Accordingly, the analysis concerns solely the immediate generation to generation fluctuations. RESULTS AND DISCUSSION Table 1 displays the synchronous correlations among the de-trended scientific measures for contemporaneous and lagged values. Since the

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TABLE 1 SynchronousCorrelationsfor Scienceand Military Measures Measure

1. Mathematics 2. Astronomy 3. Biology 4. Medicine 5. Chemistry 6. Physics 7. Geology 8. Technology 9. Geography 10. Warduration 11. Casualties

1 36 -36 -15

-11 -22 -29 -56 -50 -21 06

2

3

4

5

6

7

8

9

10

04

-44 16

-36 -20 68

00 -18 05 28

-12

-34 -51 47 73 52 44

-51 02 76 70 56 68 69

-40 12 55 37 73 69 43 86

-02 00 14 -15 34 42 21 21 35

16 -08 -14 -44 -39 -26 -15 -06 -16

67 07 23 46 64 47 17 -48

33 04 73 53 25 -13 -56

51 55 58 71 32 -06

-03 41 28 57 28 73 73 48 -15

60 37 22 -20

88 31 -46

40 -18

11 29 -30 -51 -62 02 -24 -23 -52 -20 42

36

Note. Contemporaneouscorrelations are below the diagonal,laggedcorrelationsabove the diagonal (decimal points omitted). A product momentcorrelation must be .51 for the .05 level and .64 for the .Ol level for two-tailed tests. contemporaneous correlations are between the measures at generation g (g = 1, 2,3, . . . 15) and the lagged correlations are between measures at generation g t 1 (g = 1, 2, 3,. . . 15), the coefficients are based on virtually the same observations and therefore they should be equal except for sampling error (Simonton, 1975a, 1976). Table 2 presents the cross-lagged correlations and autocorrelations for the same measures. An autocorrelation is the productmoment coefficient between a measure’s value at generation g and its value at generation g + 1 (where g = 1, 2, 3, . . . . 15). A cross-lagged correlation is the product-moment coefficient between one measure’s value at generation g and another measure’s value at generation g + 1 (where g= 1, 2, 3, . . . . 15). Finally, Table 3 shows all cawl relationships having statistically significant differences between the two cross-lagged correlations for any pair of measures (using Person-Filon tests with z > 1.960, p < .05) (Kenny, Note 1). The Fortran program called PANEL was utilized for the analysis (Kenny, Note 2): Since there are several alternative approaches to cross-lagged causal inference (e.g., Howard & Krause, 1970; Rozelle & Campbell, 1969; Sandell, 1971; Yee & Gage, 1968), it is probably advisable to state the rules of thumb employed in this paper (adapted from Kenny, Note 1; Crano, Kenny & Campbell, 1972; Pelz & Andrews, 1964). These guidelines are: (a) if the two synchronous correlations are not significantly different from zero, then the direction of causality is determined by that cross-lagged correlation with the largest absolute value; (b) if the two synchronous correlations are significantly different from zero, then the direction of causality is determined by that cross-lagged correlation having the same sign as the synchronous correlations. An instance of the first criterion is where astronomy is inferred to have a negative impact on geography one generation later: The synchronous

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TABLE 2 Cross-laggedand Autocorrelations for Science and Military Measures Generation g+ 1 Generation ,g 1. Mathematics 2. Astronomy 3. Biology 4. Medicine 5. Chemistry 6. Physics 7. Geology 8. Technology 9. Geography 10. War duration 11. Casualties

1

2

3

4

5

6

7

8

9

10

11

31 46 -62 -62 -39 -55 -60 -74 -63 -04 32

01 46 42 20 02 -20 18 04 02 39 11

-53 -49 33 54 52 38 86 62 43 52 -07

-44 -81 -07 05 30 64 51 46 32 42 16

-17 -15 -11 -22 32 47 -01 42 48 11 01

-64 02 25 02 34 34 26 62 63 28 -10

-43 -66 -23 -01 59 59 34 51 56 20 14

-69 -49 37 38 55 60 68 85 72 33 -15

-46 -19 42 38 52 52 50 82 71 24 -35

-18 05 -10 -12 46 -07 16 14 37 04 12

44 34 -34 -23 08 -08 -45 -34 -22 -43 -30

Note. All decimal points have been omitted.

correlations are not significantly different from zero, and the cross-lagged correlation with the largest absolute value is the negative correlation between

astronomy at g and geography at g + 1. An example of the second criterion is where medicine is surmised to have a positive impact on biology one generation later: The two synchronous correlations are highly positive, and so the most positive cross-lagged correlation ‘is chosen, namely that between medicine at g and biology at g+ 1. Fortunately, there were no ambiguous cases in the present study (cf. Simonton, 1976). The number of statistically significant correlations and cross-lagged differences is appreciably greater than would be expected on chance alone. The general results should be discussed under two headings, military and interdisciplinary. Military Determinants As is seen in Table 3, no intergenerational relationships occur between the two war measures and any of the nine science measures. Thus warfare does not have any apparent causal association with any kind of discovery and invention studied here. So persons growing up in times of war do not differ from those developing during times of peace-at least in terms of scientific potential. The lack of a developmental impact notwithstanding, war does bear some connection with scientific creativity: Table 1 indicates that the number of casualties in any given generation has a significant negative correlation with medical discoveries. To be sure, since this correlation is contemporaneous we

59

DETERMINANTS OF SCIENTIFIC PRODUCTIVITY TABLE 3 Causal Relationship among Science and Military Measures Effect @l) Cause fg)

1

2

1. Mathematics 2. Astronomy 3. Biology 4. Medicine 5. Chemistry 6. Physics 7. Geology 8. Technology 9. Geography 10. War duration 11. Casualties

3

4

-

-

+ + +

5

6

7

8

-

-

9

10

11

+ + +

No@. Significant Pearson-Filon tests indicated by “+” for positive influences and by I‘-‘) for negative influences. Precise significance levels given in text.

do not know which way the causal association operates; still it appears far more likely that war casualties have an inhibitory impact on medical research than the reverse. It is possible that the negative relation is due to “competitive recruitment,” that is, when war injuries run high a large number of physicians are probably attracted or conscripted into the medical corps-and away from or out of medical laboratories. Hence the only significant effect war has on any scientific endeavor is to discourage the discovery and invention of drugs, surgical techniques, diagnoses, and the like. In terdisciplinaty Determinants. The associations among the nine scientific activities of two typescontemporaneous and lagged-which should be treated separately. Contemporary clustering. In the main, most of the scientific disciplines tend to fluctuate together. Thus medicine correlates positively with biology, geology with physics (see Table 1). Though science hence looks like a harmonious creative enterprise (cf. Simonton, 1975b), one conspicuous exception stands out: A significant negative correlation exists between technological and mathematical discoveries. Again, we do not know the causal direction, but it seems very improbable that either exerts a negative effect on the other. Rather it may be suggested that the sociocultural conditions conducive to each field are antagonistic in nature. Mathematics is the most “pure” of all the sciences, whereas technology is the most “applied.” Perhaps the environment which puts a great stress on pure research does so at the

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expense of applied research, and vice versa. In any case, this explanation deserves further inquiry. Intergenerational effects. Judging from Table 3, a large number of scientific disciplines have a stimulating impact on each other: (a) Chemistry has a positive impact on geology (z = 2.167, p < .05; (b) Physics (z = -2.015, p < .05) and geology (z = -2.239, p < .05) have a positive influence on medicine; and (c) Biology is beneficially affected by medicine (z = - 2.506, p < .05), chemistry (z = - 2.036, p < .05), and geology (z = -4.450, p < .OOl). Such intergenerational encouragement may be interpreted as evidence for positive “information transfer,” that is, where the discoveries of one field have repercussions upon those in another field. To give an illustration, it is significant that Charles Darwin’s primary reading while voyaging on the Beagle was Lyell’s Principles of Geology which was even written about a quarter century before the Origin of Species (Eiseley, 1958, chap. 6). Of course, an isolated anecdote proves nothing, but existence of significant cross-lag differences demonstrates that such influences may be quite common indeed. Moreover, the intriguing aspect about these intergenerational influences is the suggestion that the younger scientists may actually be more sensitive to the implications of discoveries in neighboring disciplines (cf. Kuhn, 1970, p. 90; Zuckerman & Merton, 1972, pp. 308-310): in most cases the cross-lagged correlation is greater than the synchronous correlations {compare Tables 1 and 2). This fact implies the need for additional research into the interdisciplinary interests of scientists in different fields and at various ages. Are younger scientists more likely to read literature outside their own specialty? Are medical researchers more prone than scientists in other fields to be influenced by the discoveries of a previous generation of physicists? Such questions as these may be approached most fruitfully either by using the Science Citation Index (cf. Garfield, Sher, & Torpie, 1964; Price, 1965) or by using some kind of vocational interest questionnaire (e.g., Johnson & Campbell, 1974). Nonetheless, not all intergenerational effects can involve stimulatory information transfer, for astronomy has a significant negative impact on technology (z = -2.086, p < .05), medicine (z = -3.706, p < .OOl), biology (z = 3.152, p < .OOl), and geology (z = 3.177, p < .Ol). How can discoveries in one discipline inhibit those in other diciplines? For the sake of promoting research we might suggest “competitive recruitment.” That is, most endeavors face the problem of recruiting members from the next generation in order to maintain continuity of research. Because the supply of intelligent individuals is limited, some disciplines may recruit members at the expense of others (cf. Simonton, 1975a). In particular, astronomy may be viewed with such awe as the “queen of the sciences” that major astronomical discoveries could tend to

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draw young future scientists away from more “mundane” work in technology, medicine, geology, and biology. Or alternatively, astronomical discoveries may tend to be so dramatic that they have a tendency to channel research money or investment away from more “earthly” matters. Whatever the case, more empirical inquiry is demanded before we can hope to comprehend why astronomy can detract from scientific activity in other fields. In conclusion, the above results suggest that future research should pursue two contrasting influences on scientific creativity: positive information transfer and negative competitive recruitment.

REFERENCES Bamett, H. G. Innovation: 7’he basis of cultural change. New York: McGraw-Hill, 1953. Bartlett, F. Thinking: An experimental and social study. London: Allen and Unwin, 1958. Bengston, V. L., Furlong, M. J., & Laufer, R. S. Time, aging, and the continuity of social structure: Themes and issues in generational analysis. Journal of Social Issues, 1974, 30, l-30. Buss, A: R. Generational analysis: Description, explanation, and theory. Journal of Social Issues, 1974, 30, 55-71. Campbell, D. T., & Stanley, J. C. Experimental and quasi-experimental designs for research. Chicago: Rand McNally, 1966. Crano, W. D., Kenny, D. A., & Campbell, D. T. Does intelligence cause achievement?: A cross-lagged panel analysis. Journal of Educational Psychology, 1972, 63, 258-275. Darmstaedter, L. Handbuch zur geschichte der naturwissenschaften und der technik. Berlin: Springer, 1908. Eisley, L. Darwin’s century: Evolution and the men who discovered it. Garden City, New York: Doubleday, 1958. Garfield, E., Sher, L. H., & Torpie, R. J. The use of citation data in writing the history of science. Philadelphia: Institute for Scientific Information, 1964. Garrison, F. H. An introduction to the history of medicine Philadelphia: Saunders, 1929, 4th ed. Cough, H. G., & Woodworth, D. G. Stylistic variations among professional research scientists. Journal of Psychology, 1960, 49, 87-98. Howard, K. J., & Krause, M. S. Some comments on techniques for estimating the source and direction of influence in panel data. Psychological Bulletin, 1970, 74, 219-224. Johnson, R. W. & Campbell, D. P. Basic Interests of men in 62 countries. Journal of Vocational Behavior, 1974, 5, 373-380. Johnston, J. Econometric methods New York: McGraw-Hill, 1972, 2nd ed. Koestler, A. 7’he act of creation. New York: Macmillan, 1964. Kroeber, A. Configurations of culture growth. Berkeley: University of California Press, 1944. Kuhn, T. S. The structure of scientific revolutions. Chicago: University of Chicago, 1970, 2nd ed. Ianger, W. L. (Ed.) An encyclopedia of world history. Boston: Houghton Mifflin, 1972, 5th ed.

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Mannheim, K. The problem of generations. In P. Kecskemeti (Ed.), L’ssays on the sociology of knowledge. London: Routledge & Kegan Paul, 1952. pp. 216322. Marias, J. Generations. I. The concept. In D. L. Sills (Ed.), International Encyclopedia of the Social Sciences. New York: Free Press, 1968. Vol. 6, pp. 88-92. Naroll, R., Benjamin, E. C., Fohl, F. K., Fried, M. J., Hildreth, R. E., & Schaefer, J. M. Creativity: A cross-historical pilot survey. Journal of Cross-Cultural Psychology, 1971, 2, 181-188. Norling, B. Timeless problems in history. Notre Dame, Indiana: University of Notre Dame Press, 1970. Pelz, D., & Andrews, F. Detecting causal priorities in panel study data. American Sociological Review, 1964, 29, 836-848. Price, D. Networks of scientific papers. Science, 1965, 149, 510-515. Roe, A. The making of a scientist. New York: Dodd, Mead, 1952. Rozelle, R. M., & Campbell, D. T. More plausible rival hypotheses in the cross-lagged panel correlation technique. Psychological Bulletin, 1969, 71, 74-80. Sandeli, R. G. Note on choosing between competing interpretations of cross-lagged panel correlations. Psychological Bulletin, 1971, 75, 367-368. Schmookler, J. Invention and economic growth Cambridge, Massachusetts: Harvard University Press, 1966. Simonton, D. K. The social psychology of creativity: An archival data analysis. Unpublished doctoral dissertation, Harvard University, 1974. Simonton, D. K. Interdisciplinary creativity over historical time: A correlational analysis of generational fluctuations Social Behavior and Personality, 1975, 3, 181-188. (a) Simonton, D. K. Invention and discovery among the sciences: A p-technique factor analysis. Journal of Vocational Behavior. 1975, 7, 275-281. (b) Simonton, D. K. Sociocultural context of individual creativity: A transhistorical time-series analysis. Journal of Personality and Social Psychology, 1975, 32, 1119-1133. (c) Simonton, D. K. The sociopolitical context of philosophical beliefs: A transhistorical causal analysis. Social Forces, 1976, 54, 513-553. Sorokin, P. A. Social and cultural dynamics (4 ~01s.) New York: American Book, 1937. Sorokin, P. A. Society, culture, and personality. New York: Cooper Square, 1962. Toynbee, A. J. A study of history (2 vol. abridgment by D. C. Somervell). New York: Oxford University Press, 1946. Wright, Q. A study of war. Chicago: University of Chicago, 1965, 2nd ed. Yee, A. H., & Gage, N. L. Techniques for estimating the source and direction of causal influence in panel data. Psychological Bulletin, 1968, 70, 115-126. Yuasa, M. The shifting center of scientific activity in the West. In S. Nakayama, D. L. Swain, & Y. Eri (Eds.), Science and society in modern Japan. Tokyo: University of Tokyo Press, 1974, pp. 81-103. Zuckerman, H., & Merton, R. K. Age, aging, and age structure in science. In M. W. Riley, M. Johnson, & A. Foner (Eds.), Aging and society. New York: Russell Sage Foundation, 1972, Vol. 3, pp. 292-356. REFERENCE NOTES 1. Kenny, D. A. research report, 2. Kenny, D. A. report, Harvard

Cross-lagged panel

correlation:

A test for

spuriousness.

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Harvard University, 1974. PANEL:

Panel data analysis

University, 1974.

Received: September 8, 1975.

computer

program.

Duplicated research