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Month of birth and academic achievement
0131.3869 Sh S:. 00 - 0.00 Pergmon Journals Lrd
MONTH
OF BIRTH
AND
ACADEMIC
ACHIEVEiMENT
R. J. H. RUSSELL Psychology
Department.
University
of London
Goldsmiths’
College,
London
SE 14. England
M. J. STARTUP Psychology
Department,
University (Received
of Surrey,
Guildford.
Surrey,
England
I5 IVovember 1983
Summary-The degree results of nearly 300,000 British graduates were tabulated by the month of their birth. The number of graduates varied as a function of month of birth. So too, but m a different say. did the quality of their degree results. A number of possible predictors of the results are examined. These analyses suggest that. among those who stay at school until the age of 18, the oldest in their year group are at an advantage but, by the time they graduate from university, the youngest perform best. It is concluded that some intellectually relevant quality peaks between 18 and 21 yr of age and then declines.
INTRODUCTION
Many empirical investigations, most of which were conducted over 20 yr ago, have shown that academic achievement at school varies with the child’s month or season of birth. Children born between September and December tend to succeed more than those born between January and April who, in turn, perform better than the remainder, born between May and August. This kind of effect has been found in the junior-school age range, among children with reading difficulties (Williams, 1964; Freyman, 1965) as well as among normal children (Freyman, 1965). The effect also appears to persist in secondary school. At this level there is general agreement that month of birth is associated with the stream in which the child is placed (Jinks, 1964), in both secondary modem (Freyman, 1965; Sutton, 1967) and comprehensive schools (Thompson, 1971). It has also been reported that success at gaining entry to selective secondary schools (Jinks, 1964; Freyman, 1965) and success in passing 0 level examinations (Thompson, 1971) are also associated with month of birth. For example, in a study of 1136 children who had stayed at comprehensive school for at least 5 yr, Thompson (1971) found that only 26% of those born in the autumn failed to gain any passes at 0 level compared with 40% of those born in the summer. Similarly, 34% of the autumn-born gained five or more passes compared with only 23% of the summer-born. However, this kind of result has not always been found; both Armstrong (1966) and Sutton (1967) failed to obtain a month-of-birth effect for entrance to selective secondary school and Armstrong failed to find such an effect for 0 level passes. Two major explanations for these month-of-birth effects have been advanced (Pidgeon and Dodds, 1961). One rests on the fact that many Local Education Authorities in Britain have long had a policy of admitting children to infant school at the beginning of the term in which they will become 5 yr old. Thus there are three intakes a year to these schools, and those that start school at the beginning of the summer term have about 7 months less schooling than those who start in September. Since every subsequent step up the academic ladder generally takes place at the beginning of the academic year, and examination for any one year group take place all at one time in the summer, children who start late in infants school never have a chance to make up for the one or two terms they have lost. Effects due to this factor are called ‘term-of-entry’ effects. The other major explanation for month-of-birth effects rests on the fact that summer-born children generally begin school as the youngest in their year-group, with the autumn-born being the oldest, and maintain their relative positions throughout their academic careers. Thus, when children born in June sit their examinations in June some years later, they are usually 9 months younger than the children born in September. Any effects due to this factor are called ‘age-group position’ effects. 839
WI
R. J H
RLSSELL end \I. J STARTLP
In practice it is a little difficult to t?lI which of the two theories is better supported by the data since. in Bri:ain. length of schooling is highly related to age; Pidgeon and Dodds (1961) found a correlation of r = 0.98 between the two. However. the evidence, such as it is. tends to be better for the age-group position effect. For example, Pidgeon (1965) has cited evidence showing that. if different countries with diffsrent ages of entry to formal schooling are compared, no difference in academic achievement is found at the age of 13. That is. differences betiveen countries as great as 2 >‘r in amount of schooling have no effect on their children’s subsequent performance 6. 7 or 8 yr later. On the other hand. a study carried out in Sweden. Lvhere all children born during any one )‘ear start school together and thus all receive the same amount of schooling. found that the youngest children in the group had significantly more educational problems (Berglund. lY67; Williams, Davies, Evans and Ferguson. 1970). Incidentally, since the age group in Sweden is based on the calendar year. the youngest in the group are those born in the autumn. Thus this finding also counts as evidence against the effects being due directly to the seasons since it is unlikely that SLbedish children react in radically different ways to climatic conditions compared with British children. Assuming that the month-of-birth effect at 0 level is genuine, it is possible that the effect could also be found at A level and possibly even beyond. They only study to address this question directly appears to be that by Craddick (1966) who examined the final degree grades of American college students. However, the births in this study were grouped into just two half-year ‘seasons’, with the summer being amalgamated with the autumn months, thus precluding the discovery of effects like those that ha\,e been discussed so far. It is the purpose of the present study, then, to investigate whether academic achievement in the later stages of education varies with the individual’s month of birth. If a month-of-birth effect is present at university graduation, it will be revealed by degree class. So far, the factors discussed have concerned timing of education. However, two other quite different season-of-birth effects have been reported which might be reflected in higher academic success in addition to, or instead of, the effects already discussed. One of these involves IQ scores, though the e\.idence bearing upon this is rather contradictory. Between 1931 and 1941 there were five major studies of intelligence and season of birth, each with a sample of at least 3000 Ss [Pintner (193 I), Pintner and Forlano (1933), who employed a sample of 17,500, Pintner and Mailer (1937), Fialkin and Beckman (1938) and Forlano and Ehrlich (1941)], and each of these studies found the mean IQ for people born in the winter quarter of the year to be the lowest. Agreement as to which season gave the highest IQ was not perfect but four out of the five studies indicated spring. By contrast, studies conducted more recently have generally failed to find a season-of-birth effect on intelligence (Crookes, 1963; Davies, 1964; Craddick, 1966; Farley, 1968; Mascie-Taylor, 1980) except among educationally handicapped people (Orme, 1962; Martindale and Black, 1970; Black, 1973; Whorton and Karnes, 1931). However, all of these more recent studies with normal people have grouped Ss born in the winter and spring and compared them with Ss born in the summer and autumn. Thus. if winter is really associated with the lowest IQ and spring with the highest, as the earlier studies suggest, the recent work has made the wrong comparison. Quite different from the association between season of birth and IQ is an association between season of birth and eminence in later life. Huntington (1938) comprehensively reviewed a number of studies of the month of birth of people who have achieved some form of fame or distinction. Investigations of these people typically show a tendency for them to have been born in disproportionally large numbers in the early months of the year, peaking in February. Several of these investigations have been well-conducted, using quite large numbers of notable people selected on the basis of a variety of criteria. Take, for example, an investigation by Ziegler, reported graphically (p. 305). Ziegler tabulated all American-born entries (n = 10,884) in the Dictionar_v of .-lr~~erican Biography by month of birth and the amount of coverage received in the book. Compared to a control sample, all entries show a peak in births in the early months of the year. The degree to which this occurs increases with coverage in the book. In other words, the most eminent people showed the most exaggerated peak, in February. In the context of the present study, it seems quite possible that factors responsible for great success later in life may show up by the time people graduate from university and be reflected in their degree results. In summary. there ssem to be several reasons for expecting academic performance at university
Month
of birth
and academic
achievement
841
to be related in some way to month of birth. The purpose of this paper is to examine the pattern of university performance as a function of month of birth to see whether there are any differences and, if there are, to see how this pattern can be explained with reference to the other variables. DATA
The data was provided by the Unicersities’ Statistical Record. The Ss were full-time students who completed a 3-yr first degree between 1972 and 1982 inclusive and who had been born in England and Wales. Only those using A levels as their entry qualification and whose degree class fell into one of the four categories-first, upper second, lower second or third-were retained. Those born on the 29th February were ignored. After these exclusions 295,700 Ss remained. The data is in the form of frequencies falling into categories defined by degree class, month of birth and year of graduation. PRELIMINARY
ANALYSES
The first analysis of the data involved fitting a saturated log linear model. The results of this analysis are displayed in Table 1. This analysis shows that all main effects and the interactions between year and grade and between month of birth and grade are highly significant. The significant main effects simply reflect the facts that the total number of graduates alters over the years, that the number of graduates born each month is not the same and that the proportions of students achieving particular grades varies. Of these facts the only one worth noting for present purposes is that the number of people graduating varies as a function of their month of birth. The non-significance of the year x month of birth interaction indicates that this pattern is constant over different years. In analyses to be reported below, the number of graduates born each month is treated as a dependent variable in need of explanation. These numbers are graphed in Fig. 1. The interaction between year and grade is an indication that boundaries between degree classes have altered position slightly with the passage of time. This is neither remarkable nor particularly interesting. The other significant interaction, that reflecting differential examination performance as a function of month of birth, is interesting. Furthermore, the total lack of a significant three-way interaction shows that the change in the proportions of students achieving particular grades as their birth date changes remains invariant over the years. It simplifies the analysis and obscures nothing relevant if results are collapsed over the years. The resulting 4 x 12 contingency table is displayed in Table 2. Table
I. Results
of a
IOR
linear analysis
Year Month Grade Year x Year x Month Year x
of birth month grade x grade month x grade
Table 2. Frequencies
Month
of birth
January February March April May June July August September October November
of the raw data df
Effect
achieving
Sxnificance
IO II 3 110 30 33 330
69’0.932 1265.529 128.014.638 133.864 246.397 76.390 326. I30
each degree class as a function
0.001 0.001 0.001 NS 0.001 0.001 NS
of their month of birth
First
UPPer second
Lower second
Third
Total
I866 I708 2124 1973 2067 1912 1771 1718 1774 1789 1632 1789
8609 8356 9663 9255 9441 8625 8562 7947 8693 8646 7734 7878
II.250 10,879 12,660 I 1,900 12,405 I I.212 10.682 10.130 I I.526 I I.680 IO.494 10,779
2839 2715 3227 3079 3186 2989 2703 2629 3033 2828 2698 2645
24.564
23,658 27.674 26.207 27,099 24,738 23.718 22,424 25.026 24.943
z2.558 23.09 I
R. J
H. RLSSELL and 31. J STARTLP
27.OC3 -
Month Fig.
I. Number
of
btrth
ofgraduatesby
month
of birth.
Some of the possible changes in grade proportions across the 12 months would be hard to interpret and of secondary interest, for example, if those born in June had a slight excess of firsts and lower seconds. To simplify the analysis further and focus on the main point of interest, degree grades were collapsed into two categories: number awarded an upper second or first class degree vs the number awarded a lower second or third. When the collapsed 2 x 12 frequency table is analysed as a saturated log linear model, the xX-value for the two-way interaction is 37.637 with 11 dir, a result significant at the 0.001 level. Thus it is clear that the quality of degree result obtained by U.K. graduates changes as a function of the month of their birth. The percentage obtaining an upper second or first class degree is graphed in Fig. 2. The results so far may be recapitulated as follows. The number of people graduating in the U.K. in recent years varies as a function of the month in which they were born, with a peak between March and May and a much smaller peak in September and October. The quality of degree they obtain also varies as a function of month of birth, peaking in July and August and reaching the lowest point in the months from September to December. REGRESSION
ANALYSES
In order to attempt to explain these findings, or at least to exclude incorrect explanations, a number of regression analyses were performed. In these analyses, the measure of quality of degree
434 z 0 : *
432-
: c g
422-
; z;
41a-
-
430-
420-
416-
a
Monrh Fig. 2. Degree
grade
of
birrh
by month
of birth
Slonth
of birth
ar.d academic
achievement
8-l]
result was the natural logarithm of the odds of gaining a first or upper second rather than a lower second or third. In the case of each dependent variable, frequency graduating and log odds of a good degree, some understanding of possible causes may be reached by seeing which potential predictors account for most of the variance. The potential predictors used were as follows. (I) A variable which ought to be included, if only in analyses of frequency of graduation by month of birth, is the frequency of birth each month in the general population, preferably broken down by socioeconomic class. The figures used here come from a 10% sample of legitimate births in England and Wales from July 1963 to June 1964. They have been taken from Table 1 of James (1971), having been provided by the Registrar General. Surprisingly, analysis of this table showed that there is no alteration in the proportion of people born each month as social class changes (x2 = 37.021, df= 44, NS). Accordingly, frequencies born each month in the general population, collapsed over socioeconomic class, gave the values of this predictor. (2) As explained in the Introduction, there are two possible educational variables which may influence academic performance. One is age on entering school, or university, or on graduating. (All of these are the same, plus or minus a constant.) Any class contains children varying in age from the youngest, born in August, to the oldest, born in September. If this has an effect at university, it should correlate with a function which is valued 12 for September, 11 for October and so on down to 1 for August. (3) There may be an effect on performance at university due to the policy pursued by many Local Education Authorities of choosing to admit children to infants school at the start of the term in which they will turn 5. Thus children entering junior school at or just before turning 7 have been exposed to differential amounts of infants’ schooling. The third variable employed here as a possible predictor is simply the number of months that the typical child can expect to spend in infants school, varying from 28 months for those born between May and August, through 32 months for those born between January and April, to 36 months for those born between September and December. (4) A potential predictor of academic performance is IQ, which has been reported as changing as a function of month of birth. Could changes in IQ with month of birth mediate changes in university exam success? Here mean figures on the Thurstone Psychological Examination for each month of birth were used. These were taken from Forlano and Ehrlich (1941), whose sample comprised 7897 male New York students. (5) It is plausible to suppose that whatever is responsible for eminence may show up earlier in life in grade of degree. The figures used here for estimating eminence by month of birth are based on the number of entries in Encyclopedia Britannica born each month, taken from Fig. 5 of Kaulins (1979). As the starting point for further analyses, the matrix of intercorrelations between all variables of interest was calculated. The results are displayed in Table 3. One salient feature of this matrix is the complete lack of any relationship between the two dependent variables. In other words, the profiles of the graphs in Figs 1 and 2 have nothing in common. It may have been expected that any pervasive effect of season of birth on intellectual performance would affect university intake Table
3. 41a1nx
I
Good
2 3
Number
4
Eminence
of
correlations x 100 between all
grade of graduates
IQ inm
-
5
Age
6
Months
at infmi
7
General
month
vanabler
of
potentialinterest
100
uni\rrsiry
9 30
100 46
19
61
-87 school
of birth
- 84 56 I
-4 -
IS 85 2
100 2
100
-
42
I9
-
36
21
49 3
47 4
100 95 -50
100 -58
5
6
100 7
FLU
R. J H. RLSSELL dnd \I. J STMTLP
(which must closely reflect the numbers graduating) and degree grade in a similar way. Clearly this is not the case. Let us first consider the total number of graduates born each month. These figures most closely reflect the general population’s birth frequencies each month. even though this general estimate is taken at a time a couple of years after the youngest of the graduates had been born. The percentage of variance shared between these two measures is 73% and the relationship is significant beyond the 0.001 level (two-tailed). Had the general estimate been taken 7 yr earlier. the match may have been closer. Another variable correlates with frequency graduating at the 0.05 level: the eminence effect. This correlation is largely due to the positive relationship bet\veen the eminence function and the general birth frequencies. When eminence and general month of birth are both regressed against the number of graduates each month, the p-coefficient for eminence is 0.27 (NS), whereas that for general month of birth is 0.72 (P < 0.005). It is safe to reach the unsurprising conclusion that the frequency of graduates born each month is largely a reflection of the frequency of births each month in the population at large. This is a good example of a general methodological problem which arises whenever month of birth effects are examined using frequency in a category as a dependent variable. Such research can only be of interest if there is an apt control available in the form of appropriate figures for month of birth of a large normal sample. Whenever it can be argued that the control is not completely appropriate, any reported month of birth findin, 0 of this sort must be viewed with suspicion. Fortunately, the other dependent variable used here is not open to this criticism, because it looks not at a frequency but a measure of quality of performance. The highest correlations between potential predictors of good degree results by month of birth are quite clearly the high negative correlations with age and months in infants school. which correlate very highly between themselves. When both are regressed against degree results, neither /I-coeficient is individually significant. although the coefficient for age ( - 0.65) shows it to be more exactly related to degree results than the term of entry effect whose coefficient is - 0.20. The correlational analyses have shown that the graphs in Figs 1 and 2 can be parsimoniously explained. The numbers of graduates born each month largely reflects the numbers born each month in the population at large. The month-of-birth figures for quality-of-degree results has 75% of common variance accounted for by a negative relationship with age. No term-of-entry effect is visible. In the two final analyses, the two independent variables which seem most relevant to the explanation of the patterns in the dependent variables are simultaneously entered as predictors. When both are regressed against the number of graduates born each month, the p-coefficients are I.10 (P < 0.001) for population month-of-birth figures and 0.51 (P < 0.005) for age within year. This shows that most of the variance in number of graduates by month which cannot be explained as reflecting population figures can be explained by a positive relationship with age. Between them, these two variables account for 92% of the variance exhibited in Fig. 2. When the same two independent variables are used to predict the odds of obtaining a good degree by month of birth, the addition of the population month-of-birth figures hardly improves the prediction, and the p-coefficient is small and non-significant. DISCUSSION Much of the literature on the effect of month of birth on educational achievement has reported that performance at school is a function of age. The youngest in each year are likely to end up in lower streams. They are more likely to end up classified as educationally handicapped. They fare less well at 0 level than their older classmates. Indeed, the results reported here show that, after population figures for monthly births have been used to adjust numbers of graduates, people who are relatively old on entry to university still seem to be at an educational advantage: a disproportionate number of them graduate. Thus, the advantages possessed by those who are old for their year must persist beyond 0 level qualifications until A level at least.
Month
of birth
and academic
achievement
Y-l5
However, the analyses of degree grade show that, by the time people graduate. at about the age of 32. the relationship has altered. For the first time, the youngest within each year are at an advantage, and tend to be the ones to leave university with better degrees. How can this unexpected reversal be explained? One possible argument goes as follows. Suppose that at A levels the summer-born are at a slight educational disadvantage compared to their autumn-born colleagues. Suppose also that univeristy selection effectively creams off people with the best A level grades. The summer-born will be underrepresented at university, as the autumn-born will be overrepresented. But those summer-born will be the ones who have succeeded despite an age problem. They may be fundamentally more able than their autumn-born contemporaries. By the time of their final exams, the drawback due to age will have diminished in importance and their greater ability should produce better exam results. The reason we reject this line of argument is that it can be applied just as easily to earlier stages of the process of educational filtering. Thus, the youngest in their class at 0 levels suffer a disadvantage, which could lead one to predict, using the argument above, that the few who took A levels should do well. What appears to happen is that those who are young on entry to junior school experience some educational difficulty compared to the eldest. This handicap persists at least until A levels, when people are about 1%yr old. By the time they are 21, the handicap has turned into an advantage. The only possible sort of explanation for this phenomenon may be that some quality needed for success in examinations increases as a function of age up to around 19 yr of age, and then declines. Whether this quality is fluid IQ, lack of proactive interference from previous learning, a receptive attitude to new information or something else is difficult to say. On the basis of a careful cross-sectional and longitudinal study of the effect of age on various measures of personality and cognitive and skilled performance, Schaie and Strother (1965) found that many instances of an apparent decline of ability with age are solely cross-sectional. Individuals studied over time do not reveal the generally expected decline in performance in many respects. The exceptions seem to be word fluency, which they describe as “the ability to emit in writing previously learned verbal material” (p. 676) and psychomotor speed, which they describe as “the rate of emission of familiar cognitive responses” (p. 678). Both of these attributes decline rapidly from early adulthood onwards. Both of these attributes also seem to be potentially important contributors to success at a task which primarily involves a rapid memory dump, namely university examinations. Thus, the most plausible explanation for the pattern of results reported here may be that success at university examinations partly depends on speed of memory retrieval, an ability which may have started to decline before the time many people graduate. If the pattern of results found here and the interpretation of this pattern were independently supported, the implication seems to be that, other things being equal, young people should be advised not to delay entry into university. 4cX-no~ledgemenrs-We
should
like to express
our thanks
to Cynthia
Holme
of the Unlcersirles’
Sfaristical
Record.
REFERENCES Armstrong H. G. (1966) A comparison of the performance of summer and autumn-born children at eleven and sixteen. Br. J. educ. Ps_vchol. 36, 72-76. Berglund G. W. (1967) A note on intelligence and season of birth. Br. J. Psychol. 58, 147-151. Black F. W. (1973) Season of birth and intelligence in a sample of learning-disabled children. J. gener. Psychol. 123, 31-34. Craddick R. .4. (1966) Effect of season of birth on achievement of college students. Psychol. Rep. 18, 329-330. Crookes T. G. (1963) A note on intelligence and date of birth. Br. J. med. Psyhol. 36, 355-356. Davies A. D. M. (1964) Season of birth, intelligence and personality measures. Br. J. Psychol. 55, 475376. Farley F. H. (1968) Season of birth, intelligence and personality. Br. J. Psychol. 59, 281-283. Fialkin H. N. and Beckman R. 0. (1938) The influence of month of birth on the intelligence test scores of adults. J. gene!. Psychol. 52, 203-209. Forlano G. and Ehrlich V. 2. (1941) Month and season of birth in relation to intelligence, introversionvextraversion and inferiority feelings. J. educ. Psychof. 32, l-12. Freyman R. (1965) Further evidence on the effect of date of birth on subsequent school oerformance. Educ. Res. 8. 58-64. Huntington E. (1938) Season of Birlh: irs Relation to Human Abilities. Wiley. New Ydrk. James W. H. (1971) Social class and season of birth. J. biosoc. Sci. 3. 309-320. Jinks P. C. (196-I) An investigation into the effect of date of birth on subsequent school performance. Educ. Res. 6,22&225. Kaulins A. (1979) Cycles in the birth of eminent humans. Cycles 30, 9-15. Martindale C. and Black F. W. (1970) Season of birth and intelligence. J. genet. Psychol. 117, 137-138.
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R. J H. RUSELL and \l
J STARXP
IMascie-Taylor C. G. N. (1980) Season of brrth, IQ components, and personality traits. J genrr. Ps,vchol. 137, 151-152 Orme J. E. (1962) Intelligence and season of birth. Br. J. med. PsJchai. 35, 233-23-i Pidgeon D. .A (1965) Date of berth and scholastic performance. &frtc Rrs. 8, 3-Y. Pidgeon D. .A. and Dodds E. M. (1961) Length of schooling and its effect on performance tn the jumor school. E&. Rev. 3, 211221. Pintner R. (1931) Intelligence and month of birth. J. app[. PsychoI. 15, 119-154. Pintner R. and Forlano G. (I9?3) The influence of month of birth on intelhgence quotients. J. rduc. Ps.whol. 21, 561-j8-t. Pintner R. and Mailer J. B. (1937) Month of birth and average intelligence among dtfferent ethnic groups. J genrr. PS~;C~IOI 50, 91-107. Schaie K. W. and Strorher C. R. (1968) A cross-sequential study of age changes in cognitive brhavror. Ps.~hol. Bull. 70, 67 I-680. Sutton P. (I 967) Correlatron between streaming and season of birth in secondary schoois. Br. J. educ. Pqchol. 37, 300-304. Thompson D. (1971) Season of birth and success m the secondary school. Educ. Res. 14, X-60. Whorton J. E. and Karnes F. A. (1981) Season of birth and intelligence in samples of exceptional children. PsychoI. Rep 49, 649-650. Williams P. (1963) Date of birth, backwardness and educational organization. Br. J. educ. PsychoI. 34, 147-255. William P., Davies P., Evans P. and Ferguson N. (1970) Season of birth and cognitive development. :Vnrure 228, 1033-1036.
MONTH
OF BIRTH
AND
ACADEMIC
ACHIEVEiMENT
R. J. H. RUSSELL Psychology
Department.
University
of London
Goldsmiths’
College,
London
SE 14. England
M. J. STARTUP Psychology
Department,
University (Received
of Surrey,
Guildford.
Surrey,
England
I5 IVovember 1983
Summary-The degree results of nearly 300,000 British graduates were tabulated by the month of their birth. The number of graduates varied as a function of month of birth. So too, but m a different say. did the quality of their degree results. A number of possible predictors of the results are examined. These analyses suggest that. among those who stay at school until the age of 18, the oldest in their year group are at an advantage but, by the time they graduate from university, the youngest perform best. It is concluded that some intellectually relevant quality peaks between 18 and 21 yr of age and then declines.
INTRODUCTION
Many empirical investigations, most of which were conducted over 20 yr ago, have shown that academic achievement at school varies with the child’s month or season of birth. Children born between September and December tend to succeed more than those born between January and April who, in turn, perform better than the remainder, born between May and August. This kind of effect has been found in the junior-school age range, among children with reading difficulties (Williams, 1964; Freyman, 1965) as well as among normal children (Freyman, 1965). The effect also appears to persist in secondary school. At this level there is general agreement that month of birth is associated with the stream in which the child is placed (Jinks, 1964), in both secondary modem (Freyman, 1965; Sutton, 1967) and comprehensive schools (Thompson, 1971). It has also been reported that success at gaining entry to selective secondary schools (Jinks, 1964; Freyman, 1965) and success in passing 0 level examinations (Thompson, 1971) are also associated with month of birth. For example, in a study of 1136 children who had stayed at comprehensive school for at least 5 yr, Thompson (1971) found that only 26% of those born in the autumn failed to gain any passes at 0 level compared with 40% of those born in the summer. Similarly, 34% of the autumn-born gained five or more passes compared with only 23% of the summer-born. However, this kind of result has not always been found; both Armstrong (1966) and Sutton (1967) failed to obtain a month-of-birth effect for entrance to selective secondary school and Armstrong failed to find such an effect for 0 level passes. Two major explanations for these month-of-birth effects have been advanced (Pidgeon and Dodds, 1961). One rests on the fact that many Local Education Authorities in Britain have long had a policy of admitting children to infant school at the beginning of the term in which they will become 5 yr old. Thus there are three intakes a year to these schools, and those that start school at the beginning of the summer term have about 7 months less schooling than those who start in September. Since every subsequent step up the academic ladder generally takes place at the beginning of the academic year, and examination for any one year group take place all at one time in the summer, children who start late in infants school never have a chance to make up for the one or two terms they have lost. Effects due to this factor are called ‘term-of-entry’ effects. The other major explanation for month-of-birth effects rests on the fact that summer-born children generally begin school as the youngest in their year-group, with the autumn-born being the oldest, and maintain their relative positions throughout their academic careers. Thus, when children born in June sit their examinations in June some years later, they are usually 9 months younger than the children born in September. Any effects due to this factor are called ‘age-group position’ effects. 839
WI
R. J H
RLSSELL end \I. J STARTLP
In practice it is a little difficult to t?lI which of the two theories is better supported by the data since. in Bri:ain. length of schooling is highly related to age; Pidgeon and Dodds (1961) found a correlation of r = 0.98 between the two. However. the evidence, such as it is. tends to be better for the age-group position effect. For example, Pidgeon (1965) has cited evidence showing that. if different countries with diffsrent ages of entry to formal schooling are compared, no difference in academic achievement is found at the age of 13. That is. differences betiveen countries as great as 2 >‘r in amount of schooling have no effect on their children’s subsequent performance 6. 7 or 8 yr later. On the other hand. a study carried out in Sweden. Lvhere all children born during any one )‘ear start school together and thus all receive the same amount of schooling. found that the youngest children in the group had significantly more educational problems (Berglund. lY67; Williams, Davies, Evans and Ferguson. 1970). Incidentally, since the age group in Sweden is based on the calendar year. the youngest in the group are those born in the autumn. Thus this finding also counts as evidence against the effects being due directly to the seasons since it is unlikely that SLbedish children react in radically different ways to climatic conditions compared with British children. Assuming that the month-of-birth effect at 0 level is genuine, it is possible that the effect could also be found at A level and possibly even beyond. They only study to address this question directly appears to be that by Craddick (1966) who examined the final degree grades of American college students. However, the births in this study were grouped into just two half-year ‘seasons’, with the summer being amalgamated with the autumn months, thus precluding the discovery of effects like those that ha\,e been discussed so far. It is the purpose of the present study, then, to investigate whether academic achievement in the later stages of education varies with the individual’s month of birth. If a month-of-birth effect is present at university graduation, it will be revealed by degree class. So far, the factors discussed have concerned timing of education. However, two other quite different season-of-birth effects have been reported which might be reflected in higher academic success in addition to, or instead of, the effects already discussed. One of these involves IQ scores, though the e\.idence bearing upon this is rather contradictory. Between 1931 and 1941 there were five major studies of intelligence and season of birth, each with a sample of at least 3000 Ss [Pintner (193 I), Pintner and Forlano (1933), who employed a sample of 17,500, Pintner and Mailer (1937), Fialkin and Beckman (1938) and Forlano and Ehrlich (1941)], and each of these studies found the mean IQ for people born in the winter quarter of the year to be the lowest. Agreement as to which season gave the highest IQ was not perfect but four out of the five studies indicated spring. By contrast, studies conducted more recently have generally failed to find a season-of-birth effect on intelligence (Crookes, 1963; Davies, 1964; Craddick, 1966; Farley, 1968; Mascie-Taylor, 1980) except among educationally handicapped people (Orme, 1962; Martindale and Black, 1970; Black, 1973; Whorton and Karnes, 1931). However, all of these more recent studies with normal people have grouped Ss born in the winter and spring and compared them with Ss born in the summer and autumn. Thus. if winter is really associated with the lowest IQ and spring with the highest, as the earlier studies suggest, the recent work has made the wrong comparison. Quite different from the association between season of birth and IQ is an association between season of birth and eminence in later life. Huntington (1938) comprehensively reviewed a number of studies of the month of birth of people who have achieved some form of fame or distinction. Investigations of these people typically show a tendency for them to have been born in disproportionally large numbers in the early months of the year, peaking in February. Several of these investigations have been well-conducted, using quite large numbers of notable people selected on the basis of a variety of criteria. Take, for example, an investigation by Ziegler, reported graphically (p. 305). Ziegler tabulated all American-born entries (n = 10,884) in the Dictionar_v of .-lr~~erican Biography by month of birth and the amount of coverage received in the book. Compared to a control sample, all entries show a peak in births in the early months of the year. The degree to which this occurs increases with coverage in the book. In other words, the most eminent people showed the most exaggerated peak, in February. In the context of the present study, it seems quite possible that factors responsible for great success later in life may show up by the time people graduate from university and be reflected in their degree results. In summary. there ssem to be several reasons for expecting academic performance at university
Month
of birth
and academic
achievement
841
to be related in some way to month of birth. The purpose of this paper is to examine the pattern of university performance as a function of month of birth to see whether there are any differences and, if there are, to see how this pattern can be explained with reference to the other variables. DATA
The data was provided by the Unicersities’ Statistical Record. The Ss were full-time students who completed a 3-yr first degree between 1972 and 1982 inclusive and who had been born in England and Wales. Only those using A levels as their entry qualification and whose degree class fell into one of the four categories-first, upper second, lower second or third-were retained. Those born on the 29th February were ignored. After these exclusions 295,700 Ss remained. The data is in the form of frequencies falling into categories defined by degree class, month of birth and year of graduation. PRELIMINARY
ANALYSES
The first analysis of the data involved fitting a saturated log linear model. The results of this analysis are displayed in Table 1. This analysis shows that all main effects and the interactions between year and grade and between month of birth and grade are highly significant. The significant main effects simply reflect the facts that the total number of graduates alters over the years, that the number of graduates born each month is not the same and that the proportions of students achieving particular grades varies. Of these facts the only one worth noting for present purposes is that the number of people graduating varies as a function of their month of birth. The non-significance of the year x month of birth interaction indicates that this pattern is constant over different years. In analyses to be reported below, the number of graduates born each month is treated as a dependent variable in need of explanation. These numbers are graphed in Fig. 1. The interaction between year and grade is an indication that boundaries between degree classes have altered position slightly with the passage of time. This is neither remarkable nor particularly interesting. The other significant interaction, that reflecting differential examination performance as a function of month of birth, is interesting. Furthermore, the total lack of a significant three-way interaction shows that the change in the proportions of students achieving particular grades as their birth date changes remains invariant over the years. It simplifies the analysis and obscures nothing relevant if results are collapsed over the years. The resulting 4 x 12 contingency table is displayed in Table 2. Table
I. Results
of a
IOR
linear analysis
Year Month Grade Year x Year x Month Year x
of birth month grade x grade month x grade
Table 2. Frequencies
Month
of birth
January February March April May June July August September October November
of the raw data df
Effect
achieving
Sxnificance
IO II 3 110 30 33 330
69’0.932 1265.529 128.014.638 133.864 246.397 76.390 326. I30
each degree class as a function
0.001 0.001 0.001 NS 0.001 0.001 NS
of their month of birth
First
UPPer second
Lower second
Third
Total
I866 I708 2124 1973 2067 1912 1771 1718 1774 1789 1632 1789
8609 8356 9663 9255 9441 8625 8562 7947 8693 8646 7734 7878
II.250 10,879 12,660 I 1,900 12,405 I I.212 10.682 10.130 I I.526 I I.680 IO.494 10,779
2839 2715 3227 3079 3186 2989 2703 2629 3033 2828 2698 2645
24.564
23,658 27.674 26.207 27,099 24,738 23.718 22,424 25.026 24.943
z2.558 23.09 I
R. J
H. RLSSELL and 31. J STARTLP
27.OC3 -
Month Fig.
I. Number
of
btrth
ofgraduatesby
month
of birth.
Some of the possible changes in grade proportions across the 12 months would be hard to interpret and of secondary interest, for example, if those born in June had a slight excess of firsts and lower seconds. To simplify the analysis further and focus on the main point of interest, degree grades were collapsed into two categories: number awarded an upper second or first class degree vs the number awarded a lower second or third. When the collapsed 2 x 12 frequency table is analysed as a saturated log linear model, the xX-value for the two-way interaction is 37.637 with 11 dir, a result significant at the 0.001 level. Thus it is clear that the quality of degree result obtained by U.K. graduates changes as a function of the month of their birth. The percentage obtaining an upper second or first class degree is graphed in Fig. 2. The results so far may be recapitulated as follows. The number of people graduating in the U.K. in recent years varies as a function of the month in which they were born, with a peak between March and May and a much smaller peak in September and October. The quality of degree they obtain also varies as a function of month of birth, peaking in July and August and reaching the lowest point in the months from September to December. REGRESSION
ANALYSES
In order to attempt to explain these findings, or at least to exclude incorrect explanations, a number of regression analyses were performed. In these analyses, the measure of quality of degree
434 z 0 : *
432-
: c g
422-
; z;
41a-
-
430-
420-
416-
a
Monrh Fig. 2. Degree
grade
of
birrh
by month
of birth
Slonth
of birth
ar.d academic
achievement
8-l]
result was the natural logarithm of the odds of gaining a first or upper second rather than a lower second or third. In the case of each dependent variable, frequency graduating and log odds of a good degree, some understanding of possible causes may be reached by seeing which potential predictors account for most of the variance. The potential predictors used were as follows. (I) A variable which ought to be included, if only in analyses of frequency of graduation by month of birth, is the frequency of birth each month in the general population, preferably broken down by socioeconomic class. The figures used here come from a 10% sample of legitimate births in England and Wales from July 1963 to June 1964. They have been taken from Table 1 of James (1971), having been provided by the Registrar General. Surprisingly, analysis of this table showed that there is no alteration in the proportion of people born each month as social class changes (x2 = 37.021, df= 44, NS). Accordingly, frequencies born each month in the general population, collapsed over socioeconomic class, gave the values of this predictor. (2) As explained in the Introduction, there are two possible educational variables which may influence academic performance. One is age on entering school, or university, or on graduating. (All of these are the same, plus or minus a constant.) Any class contains children varying in age from the youngest, born in August, to the oldest, born in September. If this has an effect at university, it should correlate with a function which is valued 12 for September, 11 for October and so on down to 1 for August. (3) There may be an effect on performance at university due to the policy pursued by many Local Education Authorities of choosing to admit children to infants school at the start of the term in which they will turn 5. Thus children entering junior school at or just before turning 7 have been exposed to differential amounts of infants’ schooling. The third variable employed here as a possible predictor is simply the number of months that the typical child can expect to spend in infants school, varying from 28 months for those born between May and August, through 32 months for those born between January and April, to 36 months for those born between September and December. (4) A potential predictor of academic performance is IQ, which has been reported as changing as a function of month of birth. Could changes in IQ with month of birth mediate changes in university exam success? Here mean figures on the Thurstone Psychological Examination for each month of birth were used. These were taken from Forlano and Ehrlich (1941), whose sample comprised 7897 male New York students. (5) It is plausible to suppose that whatever is responsible for eminence may show up earlier in life in grade of degree. The figures used here for estimating eminence by month of birth are based on the number of entries in Encyclopedia Britannica born each month, taken from Fig. 5 of Kaulins (1979). As the starting point for further analyses, the matrix of intercorrelations between all variables of interest was calculated. The results are displayed in Table 3. One salient feature of this matrix is the complete lack of any relationship between the two dependent variables. In other words, the profiles of the graphs in Figs 1 and 2 have nothing in common. It may have been expected that any pervasive effect of season of birth on intellectual performance would affect university intake Table
3. 41a1nx
I
Good
2 3
Number
4
Eminence
of
correlations x 100 between all
grade of graduates
IQ inm
-
5
Age
6
Months
at infmi
7
General
month
vanabler
of
potentialinterest
100
uni\rrsiry
9 30
100 46
19
61
-87 school
of birth
- 84 56 I
-4 -
IS 85 2
100 2
100
-
42
I9
-
36
21
49 3
47 4
100 95 -50
100 -58
5
6
100 7
FLU
R. J H. RLSSELL dnd \I. J STMTLP
(which must closely reflect the numbers graduating) and degree grade in a similar way. Clearly this is not the case. Let us first consider the total number of graduates born each month. These figures most closely reflect the general population’s birth frequencies each month. even though this general estimate is taken at a time a couple of years after the youngest of the graduates had been born. The percentage of variance shared between these two measures is 73% and the relationship is significant beyond the 0.001 level (two-tailed). Had the general estimate been taken 7 yr earlier. the match may have been closer. Another variable correlates with frequency graduating at the 0.05 level: the eminence effect. This correlation is largely due to the positive relationship bet\veen the eminence function and the general birth frequencies. When eminence and general month of birth are both regressed against the number of graduates each month, the p-coefficient for eminence is 0.27 (NS), whereas that for general month of birth is 0.72 (P < 0.005). It is safe to reach the unsurprising conclusion that the frequency of graduates born each month is largely a reflection of the frequency of births each month in the population at large. This is a good example of a general methodological problem which arises whenever month of birth effects are examined using frequency in a category as a dependent variable. Such research can only be of interest if there is an apt control available in the form of appropriate figures for month of birth of a large normal sample. Whenever it can be argued that the control is not completely appropriate, any reported month of birth findin, 0 of this sort must be viewed with suspicion. Fortunately, the other dependent variable used here is not open to this criticism, because it looks not at a frequency but a measure of quality of performance. The highest correlations between potential predictors of good degree results by month of birth are quite clearly the high negative correlations with age and months in infants school. which correlate very highly between themselves. When both are regressed against degree results, neither /I-coeficient is individually significant. although the coefficient for age ( - 0.65) shows it to be more exactly related to degree results than the term of entry effect whose coefficient is - 0.20. The correlational analyses have shown that the graphs in Figs 1 and 2 can be parsimoniously explained. The numbers of graduates born each month largely reflects the numbers born each month in the population at large. The month-of-birth figures for quality-of-degree results has 75% of common variance accounted for by a negative relationship with age. No term-of-entry effect is visible. In the two final analyses, the two independent variables which seem most relevant to the explanation of the patterns in the dependent variables are simultaneously entered as predictors. When both are regressed against the number of graduates born each month, the p-coefficients are I.10 (P < 0.001) for population month-of-birth figures and 0.51 (P < 0.005) for age within year. This shows that most of the variance in number of graduates by month which cannot be explained as reflecting population figures can be explained by a positive relationship with age. Between them, these two variables account for 92% of the variance exhibited in Fig. 2. When the same two independent variables are used to predict the odds of obtaining a good degree by month of birth, the addition of the population month-of-birth figures hardly improves the prediction, and the p-coefficient is small and non-significant. DISCUSSION Much of the literature on the effect of month of birth on educational achievement has reported that performance at school is a function of age. The youngest in each year are likely to end up in lower streams. They are more likely to end up classified as educationally handicapped. They fare less well at 0 level than their older classmates. Indeed, the results reported here show that, after population figures for monthly births have been used to adjust numbers of graduates, people who are relatively old on entry to university still seem to be at an educational advantage: a disproportionate number of them graduate. Thus, the advantages possessed by those who are old for their year must persist beyond 0 level qualifications until A level at least.
Month
of birth
and academic
achievement
Y-l5
However, the analyses of degree grade show that, by the time people graduate. at about the age of 32. the relationship has altered. For the first time, the youngest within each year are at an advantage, and tend to be the ones to leave university with better degrees. How can this unexpected reversal be explained? One possible argument goes as follows. Suppose that at A levels the summer-born are at a slight educational disadvantage compared to their autumn-born colleagues. Suppose also that univeristy selection effectively creams off people with the best A level grades. The summer-born will be underrepresented at university, as the autumn-born will be overrepresented. But those summer-born will be the ones who have succeeded despite an age problem. They may be fundamentally more able than their autumn-born contemporaries. By the time of their final exams, the drawback due to age will have diminished in importance and their greater ability should produce better exam results. The reason we reject this line of argument is that it can be applied just as easily to earlier stages of the process of educational filtering. Thus, the youngest in their class at 0 levels suffer a disadvantage, which could lead one to predict, using the argument above, that the few who took A levels should do well. What appears to happen is that those who are young on entry to junior school experience some educational difficulty compared to the eldest. This handicap persists at least until A levels, when people are about 1%yr old. By the time they are 21, the handicap has turned into an advantage. The only possible sort of explanation for this phenomenon may be that some quality needed for success in examinations increases as a function of age up to around 19 yr of age, and then declines. Whether this quality is fluid IQ, lack of proactive interference from previous learning, a receptive attitude to new information or something else is difficult to say. On the basis of a careful cross-sectional and longitudinal study of the effect of age on various measures of personality and cognitive and skilled performance, Schaie and Strother (1965) found that many instances of an apparent decline of ability with age are solely cross-sectional. Individuals studied over time do not reveal the generally expected decline in performance in many respects. The exceptions seem to be word fluency, which they describe as “the ability to emit in writing previously learned verbal material” (p. 676) and psychomotor speed, which they describe as “the rate of emission of familiar cognitive responses” (p. 678). Both of these attributes decline rapidly from early adulthood onwards. Both of these attributes also seem to be potentially important contributors to success at a task which primarily involves a rapid memory dump, namely university examinations. Thus, the most plausible explanation for the pattern of results reported here may be that success at university examinations partly depends on speed of memory retrieval, an ability which may have started to decline before the time many people graduate. If the pattern of results found here and the interpretation of this pattern were independently supported, the implication seems to be that, other things being equal, young people should be advised not to delay entry into university. 4cX-no~ledgemenrs-We
should
like to express
our thanks
to Cynthia
Holme
of the Unlcersirles’
Sfaristical
Record.
REFERENCES Armstrong H. G. (1966) A comparison of the performance of summer and autumn-born children at eleven and sixteen. Br. J. educ. Ps_vchol. 36, 72-76. Berglund G. W. (1967) A note on intelligence and season of birth. Br. J. Psychol. 58, 147-151. Black F. W. (1973) Season of birth and intelligence in a sample of learning-disabled children. J. gener. Psychol. 123, 31-34. Craddick R. .4. (1966) Effect of season of birth on achievement of college students. Psychol. Rep. 18, 329-330. Crookes T. G. (1963) A note on intelligence and date of birth. Br. J. med. Psyhol. 36, 355-356. Davies A. D. M. (1964) Season of birth, intelligence and personality measures. Br. J. Psychol. 55, 475376. Farley F. H. (1968) Season of birth, intelligence and personality. Br. J. Psychol. 59, 281-283. Fialkin H. N. and Beckman R. 0. (1938) The influence of month of birth on the intelligence test scores of adults. J. gene!. Psychol. 52, 203-209. Forlano G. and Ehrlich V. 2. (1941) Month and season of birth in relation to intelligence, introversionvextraversion and inferiority feelings. J. educ. Psychof. 32, l-12. Freyman R. (1965) Further evidence on the effect of date of birth on subsequent school oerformance. Educ. Res. 8. 58-64. Huntington E. (1938) Season of Birlh: irs Relation to Human Abilities. Wiley. New Ydrk. James W. H. (1971) Social class and season of birth. J. biosoc. Sci. 3. 309-320. Jinks P. C. (196-I) An investigation into the effect of date of birth on subsequent school performance. Educ. Res. 6,22&225. Kaulins A. (1979) Cycles in the birth of eminent humans. Cycles 30, 9-15. Martindale C. and Black F. W. (1970) Season of birth and intelligence. J. genet. Psychol. 117, 137-138.
816
R. J H. RUSELL and \l
J STARXP
IMascie-Taylor C. G. N. (1980) Season of brrth, IQ components, and personality traits. J genrr. Ps,vchol. 137, 151-152 Orme J. E. (1962) Intelligence and season of birth. Br. J. med. PsJchai. 35, 233-23-i Pidgeon D. .A (1965) Date of berth and scholastic performance. &frtc Rrs. 8, 3-Y. Pidgeon D. .A. and Dodds E. M. (1961) Length of schooling and its effect on performance tn the jumor school. E&. Rev. 3, 211221. Pintner R. (1931) Intelligence and month of birth. J. app[. PsychoI. 15, 119-154. Pintner R. and Forlano G. (I9?3) The influence of month of birth on intelhgence quotients. J. rduc. Ps.whol. 21, 561-j8-t. Pintner R. and Mailer J. B. (1937) Month of birth and average intelligence among dtfferent ethnic groups. J genrr. PS~;C~IOI 50, 91-107. Schaie K. W. and Strorher C. R. (1968) A cross-sequential study of age changes in cognitive brhavror. Ps.~hol. Bull. 70, 67 I-680. Sutton P. (I 967) Correlatron between streaming and season of birth in secondary schoois. Br. J. educ. Pqchol. 37, 300-304. Thompson D. (1971) Season of birth and success m the secondary school. Educ. Res. 14, X-60. Whorton J. E. and Karnes F. A. (1981) Season of birth and intelligence in samples of exceptional children. PsychoI. Rep 49, 649-650. Williams P. (1963) Date of birth, backwardness and educational organization. Br. J. educ. PsychoI. 34, 147-255. William P., Davies P., Evans P. and Ferguson N. (1970) Season of birth and cognitive development. :Vnrure 228, 1033-1036.