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A critique of the Flynn Effect: massive IQ Gains, methodological artifacts, or both?


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The Flynn Effect proposed by Flynn (1984; 1987) is reviewed and evaluated. Even in the presence of a skeptical and critical scrutiny of the effect, it appears that there is more than just methodological artifact to be explained. But the acceptance of the effect has been too quick. The proper explanations for the effect will not be meaningful until the nature of the effect is much better understood than it is now. Six questions are raised that have not been adequately answered. Two criticisms of the logic underlying the Flynn Effect are presented — one showing that even if IQ and SAT are highly correlated, their secular means will not necessarily track one another; the second showing that results by Flynn, (1984) are as consistent with a changing IQ variance as with a changing mean. The second of these is empirically evaluated with a re-analysis of a subset of the sources of Flynn's original 1984 data. Finally, 10 research strategies and designs are suggested that would help us better understand the effect. The critique is developed with the goal of clarifying the nature, meaning and causes of the Flynn Effect. The author hopes that this critique will stimulate both healthy skepticism about the Flynn Effect and careful research into its actual causes.
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A Critique of the Flynn Effect:
Massive IQ Gains,
Methodological Artifacts, or
Joseph L. Rodgers
University of Oklahoma, Norman, OK, USA
The Flynn Effect proposed by Flynn (1984;1987) is reviewed and evaluated. Even in the
presence of a skeptical and critical scrutiny of the effect, it appears that there is more than just
methodological artifact to be explained. But the acceptance of the effect has been too quick.
The proper explanations for the effect will not be meaningful until the nature of the effect is
much better understood than it is now. Six questions are raised that have not been adequately
answered. Two criticisms of the logic underlying the Flynn Effect are presented Ð one
showing that even if IQ and SAT are highly correlated, their secular means will not necessarily
track one another; the second showing that results by Flynn (1984) are as consistent with a
changing IQ variance as with a changing mean. The second of these is empirically evaluated
with a re-analysis of a subset of the sources of Flynn's original 1984 data. Finally, 10 research
strategies and designs are suggested that would help us better understand the effect. The
critique is developed with the goal of clarifying the nature, meaning and causes of the Flynn
Effect. The author hopes that this critique will stimulate both healthy skepticism about the
Flynn Effect and careful research into its actual causes.
The purpose of this article is to present a critique of the Flynn Effect, with particular
attention to the original two articles that established the existence of the effect (Flynn,
1984, 1987). The critique will be organized around six questions, two criticisms and 10
research proposals. The six questions are ones that need to be answered to clarify the
nature and meaning of the Flynn Effect. The two criticisms will point out methodological
and logical weaknesses in the original source articles and in the response of the community
of intelligence researchers to the proposed effect. The 10 proposals will suggest research
strategies and designs that could be (and hopefully will be) used to clarify the nature,
meaning and cause of the effect.
Direct all correspondence to: Joe L. Rodgers, Department of Psychology, University of Oklahoma, Norman, OK
73019, USA. E-mail: JRODGERS@OU.EDU
INTELLIGENCE 26(4): 337±356 Copyright D1999 by Elsevier Science Inc.
ISSN: 0160-2896 All rights of reproduction in any form reserved.
The Flynn Effect is one of the most surprising, most intriguing Ð and potentially most
important Ð findings in the recent psychology research literature. Flynn (1984) used
patterns in 73 studies to suggest the existence of ``massive gains'' in IQ in the US. He
calibrated the level of gain at around 0.33 IQ points per year in the US from 1932 to 1978,
an overall increase of around 15 IQ points over this period. Flynn (1987) followed with an
analysis of IQ scores from 14 economically developed countries around the world, and
found similar patterns that also supported IQ gains. These gains appear to reflect abstract
problem solving ability more than other intellectual abilities that involve learning material;
patterns from the Ravens Progressive Matrices, a relatively culture-free IQ test that
involves a great deal of problem solving, provide the strongest support for the Flynn Effect
across countries. In fact, Flynn (1987) suggested that there may have been declines in
abilities related to learned content, and that these have been suppressing rather than
contributing to IQ gains. Thus, he suggests that gains in specific problem solving abilities
may be, if anything, even greater than those he documented for general IQ.
The Flynn Effect has been tested and replicated (Flynn, 1987; Teasdale & Owen,
1987, 1989; Lynn, 1990; Lynn & Hampson, 1986), and treated as input to related
theoretical argument (Herrnstein & Murray, 1994; Stelzl, Merz, Ehlers, & Remer,
1995). The effect has been given attention in the popular press, which suggested that
the cause ``baffles intelligence experts'' (Horgan, 1995). In April of 1996, a group of
social scientist met at Emory University to ``discuss possible explanations'' for the
Flynn Effect, a meeting that also drew the attention of the press (Azar, 1996). Articles
by social scientists are beginning to emerge in journals and edited books (see, in
particular, the articles by various scholars in Detterman, 1996 and the article by
Neisser, 1997). Several causes of the Flynn Effect have been suggested. Lynn (1990)
proposed that nutritional changes underlie the Flynn Effect, Brand (1996) pointed to
the increased use of speeded tests, Mahlberg (1997) suggested a collective memory
interpretation, and other suggested causes include educational innovation and television.
Jensen (1996) proposed that the cause of the Flynn Effect may lie in ``the summation
of a great many possible causes, each of which alone has but a very small but real
effect on mental development'' (p. 149).
But before the effect is taken seriously by the community of social science
researchers, its very existence should not be questionable. In other words, research
addressing the legitimacy and meaning of the effect should precede research testing for
and evaluating causes of the effect. Surely, Flynn's analysis was complete and painstaking,
and his writing is clear and appropriately self-critical. In fact, he was very careful to
self-evaluate the strength of his arguments; for example, in Flynn (1987) he classified how
much support data from each country offered to the ``Massive Gains'' hypothesis, and also
classified the legitimacy of his interpretations. However, I will argue that Flynn's arguments
contain methodological weaknesses of which he was unaware, of which the community of
researchers has not been sufficiently critical. Because his self-evaluation was also blind to
these weaknesses, it tends to overstate the confidence we should have in the status of the
Flynn Effect. Certainly, scrutiny by independent investigators of the logic leading to the
claim is necessary before we will be able to understand what the Flynn Effect is, and
ultimately to identify what cause or causes lie behind it.
Thus, the purpose of this critique is not to resolve the issue of the meaningfulness
of the Flynn Effect or to specify the causes of the effect. Neither is the purpose to
present extensive empirical analysis to provide further data or evidence concerning its
legitimacy (although one suggestive empirical study will be presented). Rather, the
purpose is to frame an approach to studying the Flynn Effect by defining a set of
questions, criticisms and a research agenda. This critique opens discussion over what
the nature of the Flynn Effect is, and of whether the Flynn Effect is real or a
methodological artifact (or some combination). Other interpretations besides Flynn's
and the ones presented here certainly exist as well, and should also be subjected to
logical and empirical scrutiny.
Six Questions
Having read all of the literature I can find pertaining to the Flynn Effect, I am still not
sure what the Flynn Effect really is. I will explore this uncertainty in the following
questions, and answer them as best I can with reference to Flynn's articles and other
intelligence literature.
1. Does the IQ gain to which the Flynn Effect refers operate within the individual?
In other words, does Flynn propose that an individual's IQ increases system-
atically over time? The answer to this question is almost certainly ``No.'' In fact, the
literature on human abilities discusses whether cognitive abilities really decline with
age (Horn & Donaldson, 1976), and the large longitudinal study of Schaie (1994)
documents slight increases in some mental abilities during ages in the 20s and 30s,
with substantial declines following. There is no evidence Ð or suggestions from
Flynn Ð that within-individual IQ increases are causing the effect Flynn observed in
the studies he collected to establish the existence of the effect. In fact, most of the
studies he used were based on cross-sectional data, and did not even contain the
information necessary to evaluate within-individual change. One of the best data
sources reviewed in Flynn (1987) came from The Netherlands (deLeeuw & Meester,
1984; cited in Flynn, 1987), which contained Raven scores allowing comparison of
sons to fathers. No longitudinal studies containing within-individual information were
even evaluated.
2. Is the gain to which the Flynn Effect refers supposed to operate within the family?
In other words, should later-born children have higher IQs than earlier-born
children? Are the effects to which Flynn refers occurring within the family? The
Dutch data mentioned above showed substantial gains by the sons when compared to
their fathers' scores on the same test at the same age. This provides a powerful
design, and eliminates some (but not all) of the confounds caused by using cross-sec-
tional data. There is a large birth order literature studying IQ. Most empirical birth
order studies have used cross-sectional data. Large national studies have showed
declining IQ/achievement with increasing birth order (e.g., Breland, 1974 and
Steelman & Mercy, 1980, using US data; Velandia, Grandon, & Page, 1978, using
data from Columbia; Belmont & Marolla, 1983, using Dutch data), or in a few other
cases, a flat relationship (e.g., Zajonc, 1976, reviewing previous research on French
and Scottish data). Several theories have been proposed to explain the relationship of
IQ scores to birth order and family size (e.g., Zajonc & Markus, 1975; Page &
Grandon, 1979; Blake, 1981). Further, studies that have used within-family data have
typically found approximately flat patterns, suggesting little or no within-family
correlation between IQ and birth order (see Outhit, 1933; Berbaum & Moreland,
1980; Galbraith, 1982; Rodgers, 1984; Retherford & Sewell, 1991). An excellent
review of this literature was presented by Ernst and Angst (1983), who were
skeptical of the existence of any systematic birth order effects. These patterns
suggest the opposite of the Flynn Effect, with IQ score decreasing or staying flat
across birth order, which necessarily increases with time. If the Flynn Effect operated
within families (but not within individuals), then there should be systematic increases
in IQ across birth order in within-family data, and these would be translated into
positive IQ±birth order relationships in both cross-sectional and longitudinal data.
The absence of these effects can be used to infer that the Flynn Effect is not
operating within the family. Further, some of the other sources of the Flynn Effect
(discussed below) should be translated into within-family patterns, so that the
absence of birth order patterns within families is suggestive in helping to rule out
some interpretations.
3. Is the Flynn Effect an age, cohort, or period effect?
Psychologists are quite aware that changes across time Ð most effectively observed
in longitudinal data Ð may be caused by either age effects, cohort effects, or period
effects, and that only two of these types of effects operate independently (e.g., Adam,
1978; Costa & McCrae, 1982). I have already discussed and dismissed the possibility of
an age effect in treating question #1 above, which leaves the period and cohort effects.
Period effects would be ones caused by one or more social innovations that act equally
at a point in time on all individuals, regardless of age. A particular educational
innovation leading to improved educational quality experienced by all school-age
individuals would be an example of a period effect. A cohort effect would be one
acting on children or adults of a particular age, persisting across time. A particular
educational innovation experienced only by high school students during a restricted
period would be an example of a cohort effect; those students would potentially carry
the value of the innovation with them throughout their lives, but students outside of the
high school cohort would not. Theoretically, either a period or cohort effect or both
could be what the Flynn Effect is resting on.
In Flynn's treatment, he seems to lean in the direction of interpreting the effect as a
period effect. His calibration is applied to changes per unit of time, rather than to
changes per cohort. But in cross-sectional data, period and cohort effects are perfectly
confounded, and cannot be distinguished. Flynn does not, however, use this language,
but rather refers consistently to ``between-generation IQ differences.'' Because he does
not make any claims about the cause of the effect, we could very properly excuse his
treatment for not resolving this issue. However, a deeper consideration of the nature of
the Flynn Effect suggests that a cohort vs. period interpretation needs to be resolved,
since each has different implications for what the Flynn Effect means and what causes it.
Research designs that can distinguish between these two causal sources are needed in
this investigation.
4. Does the Flynn Effect operate equally within race categories?
Flynn (1987, p. 189) suggested that ``IQ differences cannot, at present, be used as
evidence'' for group differences because ``differences on IQ tests may not be equivalent to
intelligence differences.'' This conclusion further derives from his suggestion that ``Until
the causal problem of what factors engender between-generation IQ differences is solved,
no one knows what cultural variables are relevant'' ( p. 189). Thus, he reserves treatment
of this question until some more preliminary questions can be answered. However, the
empirical question of whether the IQ gains he observed would be the same within each
race is a meaningful and important question. Statistical paradoxes exist (e.g., Simpsons
Paradox; Simpson, 1951; Nunnally & Bernstein, 1994, p. 181) in which aggregate be-
havior is different than that of the individual groups contributing to the aggregate.
Consistent Flynn Effects within each race would strengthen the legitimacy of the claim
that the effect is real and meaningful. Inconsistent patterns would help point to the root
causes. If the effect disappeared within races, the paradoxical finding would still require
explanation, but more from a methodological perspective than a substantive one.
5. Does the Flynn Effect operate across the whole ability distribution?
In other words, is the effect operating equally on those of low, medium and high
ability, so that the whole ability distribution is being improved at a constant rate? Or is the
mean change observed repeatedly in all of the studies compiled by Flynn caused by
increases in the IQ of a restricted part of the distribution? This is an extremely important
methodological question that will be treated in more detail in the section on Criticisms.
Teasdale and Owen (1989) found that the Flynn Effect gains in a Danish dataset were
driven by gains in the bottom part of the intelligence distribution. If only a part of the
distribution is driving the overall effect, the increases must be even more remarkable for
that subgroup than if it applies to the overall distribution.
6. Does the Flynn Effect operate on all cognitive abilities, or only on certain abilities? Is
the effect one that applies to latent intelligence itself, or to artifacts of the measurement of
These two related questions have received a great deal of careful attention from
Flynn (and others). Particularly, Flynn's (1987) own self-critique of his results is
careful and thoughtful in addressing this issue. The major IQ improvements, if they are
real, clearly derive from the domain of abstract problem solving ability on relatively
culture-free tests. This finding all by itself rules out many causes that have strong
cultural and educational content bases. Others have also raised and treated this
problem. In particular, Jensen (1991) suggested separating superficial measurement
processes from the underlying distribution of ability by using chronometric methods to
anchor the test scores. Others (e.g., Loehlin, 1996) have also treated this problem.
Flynn's early work questioned whether the substantial increases in IQ actually reflected
gains in intelligence (Flynn, 1987). His recent article (Flynn, 1996) takes an even
stronger position on this question than his earlier work, suggesting that ``the portion of
IQ gains over time which represents an intelligence gain is very small indeed; ...
paradoxically, the exciting thing to explain is the huge non-intelligence gain'' ( p. 27).
Flynn (1996) repeatedly refers to ``ersatz intelligence gains'' as those caused by some
artifactual process not related to actual intelligence. Our goal, he suggests, is to find
measures of intelligence that will not register the artifactual gain but more validly
measure actual intelligence levels and changes. One of the biggest puzzles growing out
of the identification and research on the Flynn Effect is the elusive status of causal
explanations for the phenomenon. One of the main points of this article is to
emphasize the importance of understanding the nature of the Flynn Effect as a starting
point for explaining the causes Ð artifactual or real Ð of the Flynn Effect.
In summary, Flynn's treatment strongly addresses question #6, can be used along with
other literature and through implication to provide probable answers to #1, #2 and #3, and
is relatively silent with respect to #4 and #5. Before meaning and causes can be addressed,
however, better answers to these questions are prerequisite.
Two Criticisms
Flynn has presented the research community with a fascinating intellectual puzzle that has
important theoretical and policy implications. Many others have shared his interests, either
independently or in response to his work. Anyone working in this area must recognize that
Flynn (1984) and Flynn (1987) are superb examples of synthetic research, in which a large
corpus of existing empirical research was integrated into a coherent framework. Of course,
given the complexity of the task he undertook, it is not surprising that some important
points were treated quite well, while others were treated poorly or not at all. Two
methodological mistakes will be discussed in this section.
1. Flynn (1984) was perplexed that IQ was increasing at the same time that Scholastic
Aptitude Test (SAT) scores were declining. There is nothing perplexing at all about this
finding. Flynn's ``partitioning'' method to justify his confusion was neither necessary
nor correct.
This first criticism involves Flynn's concern over the relationship between rising IQ
scores and falling SAT scores in the US during the 1960s and 1970s. His error does no
damage to the Flynn Effect per se, but rather to the implications of the effect as
discussed in Flynn (1984). He noted that ``Between 1963 and 1981 ... , American high
school students who took the SAT showed a sharp decline in their average performance,
particularly on the SAT-Verbal, the test most significant as a predictor of college grades''
( p. 36). Following, he noted that past data showed correlations of around 0.5 to 0.8
between IQ tests and SAT scores, suggesting that ``IQ and aptitude tests measure general
intelligence to about the same degree and are functionally more or less equivalent'' (p.
37). He concluded: ``It seemed impossible that tests correlated at the 0.80 level and
measuring much in common would permit the following: that over a period of 18 years,
performance on the two kinds of tests had diverged by something between 0.288 SDU
[Standard Deviation Units] (safe estimate) and 0.648 SDU (speculative estimate)'' ( p.
46). Throughout his article, Flynn refers to this combination of patterns as ``unpalatable''
( p. 38), ``baffling'' ( p. 38) and ``inexplicable'' ( p. 48). I will show that a pattern of
increasing IQ, decreasing SAT and high correlation between them is, in fact, palatable,
understandable and explicable.
It is a well-known result that means and correlations are statistically independent of
one another. This fact suggests that knowledge of mean structure in the population gives us
no information about what correlation structure exists, or vice versa. (It is easy to observe
in the correlation formula how means are ``taken out'' of each score in the computation of
Pearson's r.)
Flynn found it surprising that two variables correlated around r= 0.8 could exist, with
one of them showing a mean increase across time and the other a mean decrease. In fact,
this pattern could occur even if the correlation were r= 1.0. Figure 1 shows a speculative
finding in which mean SAT is decreasing over time (shown by projections on the Yaxis),
mean IQ is increasing over time (shown by projections on the Xaxis), and the within-year
correlation between IQ and SAT is perfect.
How would we interpret such a finding substantively? A high correlation implies that
IQ scores above the mean occur in individuals who also have SAT scores above the mean,
Figure 1. Presentation of increasing IQ, decreasing SAT, with r
= 1.0.
and low IQ scores in individuals with low SAT scores, within a given year. Clearly, this
link could occur year after year after year, while the population means across years are
changing in any possible pattern. The IQ and SAT means and their relationships to r
is not at all baffling.
Flynn spent considerable effort ( pp. 36 ± 39) trying to interpret the part of IQ not
measured by SAT (i.e., the ``pure'' intellectual ability, uncontaminated by motivation and
effort) as creating an even bigger quandary than was apparent: If the IQ gains are ones
required to overcome the substantial parts of IQ that are measured by SAT that have been
declining, then the gains in IQ means must be even greater in their domain than they
appeared. Flynn (1984) developed a calculus of gains and losses in those means based on
the incorrect belief that high correlations imply trends in means. But the calculus is neither
necessary nor theoretically correct. Rather than conceptualizing the means as containing
overlapping and non-overlapping parts, a technically more correct approach would be to
use partial correlations or regression models to partition the explained variance. SAT
scores can be declining and IQ scores can be increasing simultaneously. At the completion
of his development, he noted ``It is precisely at this point that one's head begins to spin''
( p. 38) and ``Going beyond simple models to speculate about ultimate causes makes no
sense whatsoever of the trends in question'' ( p. 38).
He continued to use this ``mean partitioning calculus'' in Flynn (1987, p. 189), but
concluded that ``those entering American high schools were getting more and more
intelligent, and yet they were leaving high school with worse and worse academic
skills.'' Exactly! At this point he shifted to interpreting the patterns as substantively
meaningful (rather than baffling), and simply reported what the observation implies.
Both of those processes can be happening exactly as described, and yet the high positive
correlation can be maintained. As stated above, this problem does no damage whatso-
ever to the existence of the Flynn Effect, but resolves the suggested paradox as only
apparent. Explanations for simultaneous increasing IQ and decreasing SAT occur at the
aggregate mean level across years. Explanations for the correlation between IQ and SAT
occur at the individual level within a year. The two types of explanations may or may
not overlap.
2. The methodology used in Flynn (1984) does not necessarily lead to the conclusion that
IQ is increasing. Other interpretations are plausible and meaningful.
It is important that Flynn (1984) did not directly observe the gains he reported. Rather,
he inferred their existence from a logical argument, applied to a certain type of data. The
conclusion of IQ increase proposed in Flynn (1984) is presented as though this inference is
not only plausible (which it is), but also automatic (which it is not). This second criticism
could potentially undermine Flynn's whole argument. (Although, as it turns out, it
apparently does not; Flynn's original article by itself leaves the proper conclusion
unresolved, but his later work helps strengthen the legitimacy of his particular interpreta-
tion as probably correct.) He assumed that only rising IQ scores could have produced
results like those in the datasets he analyzed. In fact, there are other population changes
that could cause these effects as well.
The original argument of Flynn (1984) was based on evaluating a certain type of data
structure. Many studies have been run in which a single sample was administered two IQ
tests, one from a recently normed instrument and one from an older instrument. If norming
is done properly, then an individual's score can be interpreted in relationship to the
population mean and standard deviation (usually set to be m= 100 and s=15or16forIQ
instruments). Because the population values may potentially change over time, test
instruments must be re-normed periodically.
Flynn (1984, Table 2) found 73 studies based on a total of 7431 subjects that had
compared scores from different versions (i.e., versions scaled using different norming
samples) of the Stanford Binet and the Wechsler IQ tests. These comparisons accounted
for 18 different pairs of IQ scales, with replications ranging from 1 for seven of the pairs,
to as many as 17 for two of the pairs (for the 1932 SB and the 1947 WISC, and the 1947
WISC and the 1972 WISC-R). Generally, the mean across replications of the mean IQ
scores for the two forms showed higher scores for the earlier IQ instrument. There was
only one category inconsistent with this pattern, based on two studies; all other 17 pairs of
IQ scales showed a higher mean for the earlier instrument. (Note that several other
individual studies were inconsistent with the pattern, although averaging across studies in
each category showed the consistent result.) For example, the 17 studies that compared the
1932 SB to the 1947 WISC showed a mean gain (indicated by a lower score on the later
IQ test) of 5.49 IQ points. Collecting these 73 studies and identifying this remarkably
consistent pattern is what led Flynn to conclude that IQ had been increasing at a fairly
constant rate of around 1/3 IQ point per year for at least the 46 years between the first and
last norming samples.
Between the identification of the patterns and the conclusion, however, exists a
logical argument that is plausible and reasonable. Others exist as well, however. This
argument presumes that subjects performing better on earlier IQ forms is prima facie
evidence that an increase in IQ has occurred. Suppose a sample in 1947 took the
WISC 1947 IQ test and scored an average of 100. They would be exactly average
compared to the 1947 population. If they also scored 105 on the 1932 WB IQ test
(consistent with Flynn's empirical patterns), however, they would be around 1/3
standard deviations above the population mean in 1932, suggesting that this average
1947 sample would be have been above average in 1932. Another way of stating the
same logic suggests that if a test must become increasingly more difficult to reflect a
normatively average score, then the latent ability that is being measured must be
increasing as well.
Flynn Ð as well as both his critics and supporters in the Psychological
community Ð have treated this argument as though it were unassailable. While it is
plausible, it is certainly not the only possible interpretation. I re-emphasize that Flynn
(1984) results did not demonstrate the existence of IQ gains. His data showed
consistent differences within a single sample (i.e., on tests administered to a single
group at one point in time) across pairs of tests in which one had been normed earlier
in time than the other. The existence of IQ gains over time was his logical inference
based on these data patterns.
Without loss of generality (and simply to provide a reasonable graphical tool), assume
a normal distribution of intelligence in the population. In Figure 2 (top), I present the
pattern that Flynn assumed to be underlying his findings. If (latent) intelligence increases,
mean (observed) IQ increases as well. This pattern does indeed explain his results. If mean
intelligence is increasing systematically across time, then the gain scores found in his
samples would indeed obtain.
A change in population standard deviation of intelligence across time Ð combined
with either fixed or changing population mean Ð could also produce the results that Flynn
obtained. Before changing s's could masquerade as Flynn's changing m's, however, the
sample taking the two tests must be a select sample (i.e., its sample mean must be different
from the population mean). It is important to note that only slight selection Ð even at a
level caused by accidents of random sampling Ð could lead to misinterpreting changing
variability for changing means.
Suppose that population mean intelligence remains fixed, but the population standard
deviation systematically decreases. Now draw a sample of subjects (Sample #1) that is
Figure 2. IQ distributions normed at two different times, with samples drawn to take both
IQ forms.
positively select (i.e., their average intelligence is above the fixed population mean). This
situation is shown in Figure 2 (bottom). Obviously, the sample will be expected to score
further out in the tail of the newer intelligence distribution; the newer IQ test Ð normed on
this more recent and less variable population Ð will respond by assigning a higher score
to the later test than to the earlier test. The reverse effect will occur if the sample is
negatively selected, if a sample (Sample #2) has a lower mean intelligence than that of the
population. Then IQ scores from later tests will be systematically lower than those from
earlier tests. This is exactly the pattern that Flynn observed, which is as consistent with an
interpretation of negatively selected samples and reduced variance as with generally
increasing means. Alternatively, if the population standard deviation in intelligence
systematically increases over time, the opposite patterns will be observed. Samples that
are positively selected will score lower on the newer IQ test, and negatively selected
samples will score higher on the newer IQ test.
Thus, Flynn's findings are consistent with increased variability in the upper half of
the intelligence distribution, and/or decreased variability in the lower half of the
distribution. While the simple models implied above suggest changing variability at
the overall distributional level, different processes can in theory occur in the different
halves of the intelligence distribution. For example, increased variability in the upper
half of the intelligence distribution with no change in the lower half would result in a
Flynn Effect, detectable in positively selected samples taking two different IQ tests.
Decreased variability in the lower half of the intelligence distribution would result in a
Flynn Effect, detectable in negatively selected samples. Finally, if variability in
intelligence increased over time in the upper half and simultaneously decreased over
time in the lower half (possibly caused by two entirely different processes), this would
also produce a Flynn Effect that would be detectable in either positively or negatively
selected samples.
These types of changes in variability Ð decreasing variance in the lower half of the
intelligence, or increasing variance in the upper half Ð will cause the mean to increase
automatically. But the dynamics underlying these changes would be interpreted differently
under these different causal models. For example, a nutritional improvement that affected
all children's intelligence positively and equally would be reflected in an overall mean IQ
change (reflected in the need to renorm the IQ tests to adjust for this mean change),
without changing the shape of the IQ distribution. A nutritional program for low SES
children that reduced variability in the lower half of the distribution by improving
intelligence for underprivileged children would also show an overall mean IQ change,
but the intelligence in the upper half of the distribution would not change at all. The
process driving the change could be interpreted as either a variance-reduction or
mean-increasing process in the lower half of the distribution (and the overall shape of
the distribution would change substantially). While these two nutritional effects could each
produce the patterns that Flynn observed, distinguishing them is an important step in
understanding the factors causing the Flynn Effect. These arguments suggest that
researchers should attend to the overall structure of the IQ distribution, rather than just
to means or standard deviations.
Previous empirical evidence exists supporting the contraction of variability in the
bottom half of the distribution (e.g., Herrnstein & Murray, 1994, p. 308). Teasdale and
Owen (1989) studied a representative sample of Danish draftees and concluded that ``gains
appeared to be concentrated among lower intelligence levels, and we find no evidence of
gains at the higher levels'' (p. 255). Their conclusion was to support educational system
changes as driving the change. Lynn and Hampson (1986) noted that ``in our studies of the
rise of mean IQ in Britain over the period 1932±1982, we have found that the rise in the
lower half of the intelligence distribution has been about double that in the upper half.'' On
the other hand, Flynn (1996) suggested that ``IQ gains extend to every IQ level'' because
``score variance remains unchanged over time'' ( p. 25).
This issue is obviously of considerable concern, yet Flynn's (1984) original article
provided no discussion of the select nature of his samples. He did spend considerable
effort accounting for the representativeness of the norming samples used in the different
IQ forms (which is indeed prerequisite for the types of conclusions he wanted to draw, and
in fact for the validity of the test score procedures in the first place). But virtually no
reference is made to the nature of the samples. Two different empirical results will be
presented here that begin to address this critical question. First, the means presented in
Table 2 of Flynn (1984) will be evaluated in relationship to the arguments presented above
(i.e., to account for positively and negatively selected samples in relation to possible
changes in the variabilities). Second, the variability of several of the studies summarized in
Flynn (1984) will be presented to help in this evaluation.
Flynn (1984, p. 33, Table 2) presented 18 sets of means for ``Test 1'' (the earlier
IQ test) and ``Test 2'' (the later IQ test). These means were weighted means derived
from multiple studies that he identified that evaluated comparisons between combina-
tions of IQ test forms. To evaluate the select nature of each sample, it would be ideal
to have an IQ score from a test normed to the population in the same year that the test
was taken. Although this did not occur, in each case, ``Test 2'' scores were closer to
defining the ``contemporary IQ'' than the ``Test 1'' scores. In the column labeled ``Test
2,'' six of the sample combinations had IQ means above 105, five had IQ means
below 95, and the remaining seven had IQ means between 95 and 105 (three above
100, and four below 100). The correlation between these IQ means and the gain from
Test 1 to Test 2 was r=ÿ0.14, a non-significant correlation. The conclusion from this
evaluation is that there is no particular pattern to the selected nature of the samples. If
the overall variability were the source of the Flynn Effect, it should be reflected in a
relationship between the gain scores and the select nature of the sample, as described
above, and such a pattern was not observed. However, it should be noted that these
means were weighted means based on from 1 to 17 studies, and the selection issue
described above applies to the individual study and not to combinations of studies. It is
certain that many of the categories included both positively and negatively selected
samples. Only by re-evaluating the original studies from Flynn (1984) can this issue be
address directly.
A complete evaluation of all of the individual studies in Flynn's (1984) Table 2 should
be undertaken in future research. A highly suggestive smaller evaluation of the original
studies will be presented here. I obtained the original studies from six of the 18 IQ form
combinations. These six were chosen on the following grounds. The first choice was the
two studies (Knopf, Murfett, & Milstein, 1954; Price & Thorne, 1955) from the single
category that contributed to the only anomalous finding in Flynn's (1984) Table 2, the only
finding inconsistent with an IQ increase (a comparison of the Wechsler± Bellevue Form I
Ð normed in 1936, with the WISC Ð normed in 1947). The additional five categories
were chosen as all of those that Flynn rated as indicating an IQ rate gain of 0.44 per year or
higher (i.e., they were the five highest IQ gain categories, including rates of 1.13 per year,
0.63 per year, 0.57 per year, 0.50 per year and 0.44 per year.) The logic in this choice was
that the basic structure (as well as the causes) of the Flynn Effect should be best revealed
in settings in which the effect is magnified. The nine original studies within each of these
categories were Triggs and Cartee (1953), Quereshi (1968), Querishi and Miller (1970),
Hannon and Kicklighter (1970), Simpson (1970), Brooks (1977), Rasbury, McCoy, &
Perry (1977), Sewell (1977) and Wechsler (1974). The last of these (Wechsler, 1974) was
contained in a testing manual that was not available. The means from Wechsler (1974)
were estimated, however, by using the other study in this category to infer the means
(although the standard deviations were not available). The reason for obtaining these
original studies was to observe the mean and standard deviation patterns in each; only
category means averaged across studies, and no standard deviations, were reported in
Flynn (1984).
In the two studies showing a higher mean on the later test, Knopf et al.'s sample
had WISC IQ means ranging from 103 for Verbal IQ to 105 for Performance IQ (with
full scale IQ of 104.030), and Wechsler±Bellevue scores ranging from 99 on Verbal IQ
to 103 on Performance IQ (with full scale IQ of 100.63); Price and Thorne's sample had
mean WISC IQs ranging from 103 to 109 (with a composite Full-Scale IQ of 106.1) and
Wechsler±Bellevue IQs ranging from 95 to 110 (with a composite Full-Scale IQ of
104.5). In both cases, subjects were slightly positively selected compared to the most
recent norming population. The combination of positive selection and higher IQ scores
on the later form is consistent with either an IQ loss (based on Flynn's mean argument)
or on an upper-tail reduced variance interpretation (based on the argument from the
right-hand side of Figure 2b). Thus, for example, if the other studies reviewed by Flynn
were based on negatively selected samples (or, in fact, if the ``mean study'' in each
category of Table 2 were negatively selected), this finding would be as consistent with a
generally decreasing variance in the IQ distribution as with an increase in the mean, and
would furthermore resolve the confusion resulting from the two anomalous studies.
However, inspection of the other studies showed that this was not the case.
The means and standard deviations from the eleven studies are presented in Table 1.
In the nine studies from categories Flynn classified as indicating the most extreme rates
of IQ gain, four were positively selected (IQ2 mean greater than 105), three were
negatively selected (IQ2 mean less than 95) and two were unselected (IQ2 mean
between 95 and 105). By category, the Stanford± Binet (Form M) vs. WISC comparison
was positively selected; the WISC vs. WAIS on average had no selection; the WISC vs.
Stanford±Binet comparison was negatively selected; the WPSSI vs. Stanford±Binet
comparison was negatively selected; and the WPSSI vs. WISC comparison was
positively selected. Further, inspection of the standard deviations showed that in half
of the positively selected samples, the sample standard deviation was lower for the
earlier test than for the later test, while in the other half it was higher for the earlier than
for the later test. Among the three negatively selected samples, the later standard
deviation was lower than the earlier two times, and the earlier was lower than the later
one time. Thus, there appears to be no pattern relating the select nature of the samples
and the overall standard deviations. These results suggest strongly that the cause of the
Flynn Effect is not an overall change in the variance (like that shown in Figure 2).
In his second article, Flynn (1987) used a different methodology than that in Flynn
(1984). This second approach involved direct comparisons of IQ patterns across different
ages of cross-sectional respondents. For example, among the ``strongest'' data used by
Flynn (1987) came from Belgium, where all 18-year old men took a battery of tests
upon military induction. Between 1958 and 1967, cross-sectional means increased
between 2.65 and 4.50 IQ units on the several tests. Because these data are based on
(essentially) a whole population, the selection problem from the 73 studies in Flynn
(1984) did not exist.
Because many of these studies rested on direct observation of means, the problem of
potentially misplaced inference that occurred in Flynn (1984) is not of any concern.
Further, the finding of Flynn (1987) of direct mean changes over time in cross-sectional
samples suggests that Flynn's (1984) interpretation of his results as deriving from mean
changes is likely to be correct (assuming the causal processes underlying these patterns are
the same in the US as in the other countries studied). However, I emphasize that mean
changes and variance changes can be occurring simultaneously, and the distributional
nature of these changes lies exactly at the heart of our understanding of what the Flynn
Effect is and what it means.
The re-analysis of Flynn's (1984) data and Flynn's (1987) research account for
changes in the overall distributions. However, whether the mean changes are systematic
ones across the whole distribution, or whether they are caused by decreasing variance in
the lower half of the distribution and/or increasing variance in the upper half is not
resolved by this investigation. Since some empirical evidence (reviewed above) suggests
contraction in the lower half of the distribution, the issue of whether the Flynn Effect is
a fundamental shift in the mean of the overall distribution, or in the variance from a
subset of the distribution is still open to future research. In fact, other moments of the IQ
Table 1. Means and Standard Deviations from Studies Used in Flynn (1984) that Came from
Categories at the Positive and Negative Extremes of the IQ-Change Continuum
Test 1 Test 2 Study
SB-M, 1932 WISC, 1947.5 Triggs (1953) 124.1 107.6 9.7 13.2
WBI, 1936.5 WISC, 1947.5 Knopf (1954) 100.6 104.0 10.1 10.0
Price (1955) 104.5 106.1 13.8 12.3
WISC, 1947.5 WAIS, 1953.5 Hannon (1970) 104.1 103.2 24.1 18.7
Quereshi (1968) 110.9 107.0 10.9 8.3
Simpson (1970) 82.5 88.8 11.2 8.6
Quereshi (1970) 114.2 111.0 13.3 9.0
WISC, 1947.5 SB72, 1971.5 Brooks (1977) 96.4 87.2 19.1 16.0
WPPSI, 1964.5 SB72, 1971.5 Sewell (1977) 95.8 91.5 12.3 13.8
WPSSI, 1964.5 WISC-R, 1972 Rasbury (1977) 119.3 114.5 9.3 10.8
Wechsler (1974) 99.8
Studies are identified by only the name of the first author, to economize space in the table; see References for
full citations.
The subscripts on the means and standard deviations refer to the ``age'' of the IQ test in the comparison. X
and S
refer to the mean and standard deviation of the earlier test, X
and S
refer to the later test. Following the logic
assumed in Flynn (1984), a mean decline in sample means from the earlier to the later test implies increasing
population mean IQ over time. The logic developed in this paper suggests that changes in the sample means can
also result from changes in the population standard deviations as well.
Means were inferred and standard deviations were not available.
distribution besides the mean and variance Ð those related to skewness and kurtosis Ð
are of theoretical interest, and are substantively interpretable. We will only begin to
understand the Flynn Effect when we interpret it at the distribution level, with particular
focus on the raw scores themselves. Only at this level can the issues of select samples,
differential contraction or expansion of parts of the distribution, and the substantive
interpretations attached to these patterns begin to clear up. Micceri (1989) studied many
empirical distributions found in nature, and few of those followed the traditional normal
form. He found, for example, many more ``lumpy'' distributions than standard statistical
procedures (or researchers who account for them) typically expect. Following Micceri,
we need to understand in much more detail the nature of the whole distributions of IQ
scores to unravel the puzzles underlying the Flynn Effect.
Ten Proposals for Future Research
Excellent research often results in more questions than answers. Flynn's work has
identified an important and fascinating empirical pattern that begs for additional
research in response to the questions it has raised. In this final section, I will present
10 research projects that would inform our understanding of the nature, meaning and
causes of the Flynn Effect. Some of these lines of research will be defined by
specifying the research questions that should be addressed (and data, designs and
analyses will be suggested that might help answer the research question). In other cases,
direct research designs or data collection efforts will be suggested. These research
efforts will be organized around efforts to understand the nature, the meaning and the
causes of the Flynn Effect.
The actual nature of the empirical pattern underlying the Flynn Effect is still far from
being well defined. Empirical analysis directed toward answering several additional
questions about its nature would be helpful.
1. Does the Flynn Effect show up in major demographic subgroups, or only in aggre-
gate patterns?
Flynn has documented its cross-cultural occurrence, but there are many gender, race
and regional differences in intellectual functions. A great deal of research has been
devoted to understanding such subgroup differences, and some of the knowledge gained
from that research could be applied to understanding the Flynn Effect if we knew how it
behaves within those subgroups.
2. Is the Flynn Effect a period or cohort phenomenon, and how does it apply to
within-individual patterns of intellectual development (i.e., are there any age effects)?
A whole series of articles from the 1960s to the early 1980s developed long-
itudinal, cross-sequential and cross-sectional designs that can be used to resolve this
important question (e.g., Schaie, 1965, 1976; Baltes, 1968; Adam, 1978; Costa &
McCrae, 1982). If the Flynn Effect has some cohort component, then we need to know
to what cohorts the effect applies, and why. If the Flynn Effect is purely a period effect,
we need to know what is happening in the US and other economically developed
countries to create such systematic IQ increases (with a particular interest in continuing
to promote its elevation and extending it to other domains besides abstract problem
solving skills). If there is any aging implication to the Flynn Effect, we need to know
at what part of the lifespan it applies, whether there are other compensatory processes
that additively or interactively compete, and what are the specific mechanisms of its
contribution to aging.
3. If the Flynn Effect does have a strong period interpretation, how far back does it
go? Is it continuing? Why has the pace been so consistent during the period that Flynn
has studied?
Presumably, IQ Ð or abstract problem solving skills, as Flynn (1987) suggested
underlie the ``massive IQ gains'' Ð has not been systematically increasing forever, and
cannot keep doing so in perpetuity. Is there evidence that can be found to define the
beginning of the Flynn Effect, to project the end, or to suggest changes in its pace?
Different paradigms may be necessary to answer these questions than those used in Flynn
(1987) and, especially, Flynn (1984). But the statement of the effect can be used to make
specific predictions about IQs (and other outcomes) that have been measured outside the
intervals that Flynn has studied. The constancy of its pace is one of the intriguing (and
most surprising) pieces of the ``Flynn puzzle.'' Further efforts to understand this constant
pace would help our understanding of the effect.
4. Does the Flynn Effect occur only in economically developed countries, or also in LDCs?
It will be much harder to obtain data to identify a Flynn Effect in Lesser-Developed
Countries, but there certainly exists IQ information in some LDCs.
Besides many additional questions about the nature of the Flynn Effect (some of
which can be answered with the help of available Ð or still-to-be-collected Ð empirical
data and standard designs), there are also a number of questions about the meaning of the
Flynn Effect.
5. What does it mean to say ``massive gains in IQ?''
This question harkens back to a number of issues raised earlier in this critique, and
these issues define the requisite ``next steps'' before any additional meaningful progress
can be made in understanding the Flynn Effect. Question #5 implies several sub-questions:
Do the ``massive gains'' refer to the overall distribution of IQ, to sub-parts of the
distribution, to family units themselves, and /or to each individual within that distribution?
A similar and critical question is whether the gains only show up in IQ distributions, or
whether they can also be detected in raw score distributions themselves.
Another way to address the ``meaning'' question is through a series of
thought-experiments: If a US individual from 1950 ages normally to 1970, would
we expect that exact individual to arrive with an IQ around seven points higher? If all
individuals below (say) the median IQ in 1950 age normally to 1970, would they
arrive with a mean around seven points higher? What about all 10-year-olds? All high
school students? All US Hispanics? All US males? Those above the median IQ? What
if different years were used than 1950 and 1970? What if the aging occurred over a
20-year period, but for all individuals from age 20 to 40? What if the aging occurred
over a 20-year period to all high school students born between 1930 and 1950? What
if all of these questions were posed at the level of the raw score, rather than in
relation to IQ scores normed against the population? Do the increases in IQ actually
represent a higher probability of answering IQ questions correctly? If so, to what kinds
of questions does the increase probably apply? The repeated reference to ``massive
gains'' falls short of answering these types of specific questions, and until they are
addressed with appropriate designs, we will not really understand what the term
``Flynn Effect'' means.
Early in this critique, I suggested that we cannot properly search for causes until
we better understand the nature and meaning of the Flynn Effect. The search has
already begun, however, even if prematurely. In fact, extensive research to help us
understand what social and biological factors influence intellectual performance has
been conducted for many years, and work on the Flynn Effect falls into this much
larger domain. For example, many studies of the effect of nutrition on IQ have already
been performed, and these may have a great deal to say about the role of nutrition in
understanding the Flynn Effect. Research on the effect of schooling, or Head Start, or
nutrition, or dropping out, or having a certain number of siblings can all be brought to
bear on understanding the Flynn Effect. In return, research on the Flynn Effect can
ultimately help us understand the general underlying causes of individual differences in
intelligence and the various measures of it.
It is not premature to speculate about what kind of data and designs will be necessary
to help identify causes of the Flynn Effect.
6. Research on existing data like that in Flynn (1984) could be done in which common
items are used to link across time (or, alternatively, in which whole batteries are compared
across time).
Flynn (1984) used studies based on a common sample and different IQ forms as his
basic design. Flynn (1987) used studies based on common IQ forms (for the most part).
Even different IQ forms contain common questions, which could be used for equating
purposes. This might, for example, allow research on IQ scores from earlier in the 20th
century than Flynn (1984) was able to define. Extensive recent work published in the
psychometric literature on test equating and linking procedures could be applied
fruitfully to studying the Flynn Effect.
Arthur Jensen (personal communication, 1998) suggested a broader version of
this idea. If a whole battery of tests (or subtests from a common IQ form) have been
administered to two samples at disparate points in time, the raw score difference on
these batteries could be investigated to elucidate the patterns behind and the causes
of the Flynn Effect. Further, factor structures of the batteries could be compared
across time, and subtest patterns could be investigated across time to identify specific
domains that contribute (or that do not) to the Flynn Effect. Identifying data that
would permit either item-level comparisons across time, or comparisons of intellec-
tual subscales across time, would provide the basis for valuable and fascinating
research projects.
7. Research explicitly using the information contained in select samples could help us
understand the nature, meaning and causes of the Flynn Effect.
As discussed in the second criticism, Flynn (1984) erred in failing to account for the
selection processes involved in the studies he reviewed. Going back through these
studies and accounting for the select nature of those samples would be very helpful
research effort. Alternatively, identifying highly select samples (from either the upper or
lower tail or both) and studying their IQ patterns would help us identify where our
causal models should be applied. It seems axiomatic that many of the processes driving
individuals into the upper tail of the intelligence distribution are very different from
those driving them into the lower tail.
8. A re-analysis of the Flynn (1984) data as a formal meta-analysis would be a helpful
Flynn's (1984) Table 2 is the basis for a formal meta-analysis. When he conducted
that research, however, the methodology of meta-analysis was early in its development. A
less biased estimate of the effect size estimated by Flynn would be one product of such an
effort. An even more useful contribution would be to code additional information from the
studies reviewed in Flynn (1984), and correlate that information with the size of the
effects. Through such an effort, the role of the period, the role of selection, the role of
differential effects in different parts, and other problems raised above concerning the IQ
distributions could all be formally evaluated.
9. Presumably IQ forms change systematically over time. Research into the specific nature
of the questions that causes differences between two IQ forms would help us understand
the causes of the Flynn Effect.
One reason samples who take two different forms of an IQ test (as in Flynn, 1984)
do better on the earlier form may be that some of the questions from the earlier form
become more a part of the general cultural knowledge than they were for the norming
sample for the older test. A content analysis of these question changes with the Flynn
Effect specifically in mind would be a useful study. The idea of a cultural collective
memory developed by Mahlberg (1997) provides a useful theoretical basis for this type
of analysis.
10. At some point, when the nature and meaning of the Flynn Effect is better specified and
understood, some of the social innovations that might have influenced intelligence should
be evaluated using appropriate quasi-experimental design procedures.
Many evaluation studies have been performed of educational and social innovations,
and the methods for those are well-developed and sophisticated (e.g., Cook & Campbell,
1979). Creative designs and analyses to identify potential causes of the Flynn Effect can be
derived from such methods.
I have provided motivation and suggested questions and methods to address the nature,
meaning and causes of the Flynn Effect. I have assumed throughout that careful scrutiny of
the logic and constructive criticism of the methods on which it is built is prerequisite to
acceptance of the Flynn Effect as a well-defined phenomenon in search of an explanation.
Even with a healthy dose of skepticism, the effect rises above purely methodological
interpretation, and appears to have substantive import. But its nature, meaning and causes
are still far from being well understood.
Acknowledgements: The author would like to acknowledge discussions with Amalia
Bjornsdottir and Robert Terry that improved this article. Presentations to and comments
from the Bi-Weekly Quantsem group from the Oklahoma University Psychology Depart-
ment also contributed to the ideas developed in this article. Finally, the reviewers Ð
Arthur Jensen, Lee A. Thompson and Keith Widaman Ð and the editor Ð Douglas
Detterman Ð made many suggestions that substantially improved both the ideas and
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... A number of broad summaries of these theories have been published (e.g., Ang et al. 2010;Pietschnig and Voracek 2015). Rodgers (1998) suggested that research on the Flynn effect had moved forward more aggressively than the empirical understanding of the effect could justify. Almost two decades later, Rodgers (2015) was more sanguine about the knowledge base, because of a closer match between the empirical evidence and the theories that had been proposed. ...
... Because James Flynn argued in his early papers (e.g., Flynn 1984Flynn , 1987 for a cohortbased interpretation of the observed changes in intelligence, most theories have (implicitly or explicitly) searched for causes of the effect that are cohort-based. Whether the causes of the Flynn effect are predominantly cohort, period, or aging phenomena (or some combination of these) has seldom been addressed however (see critique relevant to this issue in Clouston et al. 2021;Rodgers 1998). The methodology for separating these three (confounded) processes with regard to the Flynn effect, or other processes in general, is limited in key ways (Bell and Jones 2013;Fienberg 2013;Fosse and Winship 2019a;, although models are estimable with constraints (Fosse and Winship 2019b;Keyes et al. 2010). ...
Full-text available
The Flynn effect refers to increases over time in measured (particularly fluid) intelligence of approximately 3 IQ points per decade. We define the Flynn effect at the family level, using longitudinal data and two new family-level cohort definitions. Multilevel growth curve analyses of the National Longitudinal Survey of Youth 1979 data showed that children in families with later-born mothers had higher average PIAT math scores, and lower average reading comprehension scores and growth, in young and middle childhood. Children in families where the first child was born later had higher average PIAT math, reading recognition, and reading comprehension scores, as well as larger developmental growth. The latter family-level Flynn effects were of higher magnitudes than the usual individual-level Flynn effect found in previous studies. Our results, showing family level-intercept and slope Flynn effects for both maternal birthyear and first child birthyear, have implications for research aiming to explain the Flynn effect.
... Also, the practice effect identified when solving cognitive tests and tasks improves the obtained scores but leave unchanged the tapped common latent factor (Estrada et al., 2015). Rodgers (1998) published a cogent commentary about the secular changes in intelligence test scores making a set of relevant questions. Question number 6 was this: "does the Flynn effect operate on all cognitive abilities, or only on certain abilities? ...
... The well-known Flynn effect (FE) refers to an observed rise in average IQ scores that occurs over time, in the order of decades, with each emerging generation showing increased performance on IQ tests (Pietschnig & Voracek, 2015;Rodgers, 1998;te Nijenhuis & van der Flier, 2013;Trahan, Stuebing, Fletcher, & Hiscock, 2014). This effect was originally found in North American populations, where James Flynn conducted large-scale studies that showed substantial gains in mean IQ scores, with an increase of three IQ points per decade in fullscale IQ scores (Flynn, 1984;Flynn, 1987a). ...
Several studies have investigated and found the gradual rise in IQ over time or the Flynn effect (FE) but inconsistent results on the FEs across types of countries and age groups were reported. The current cross-temporal meta-analysis aimed to examine the temporal correlations between mean IQ scores from Standard Progressive Matrices and year of publication, moderated by types of countries and age groups covering seven decades (1948-2020). The given relationships were weighted by sample sizes. The dataset included 1038 independent samples (N = 299,155) from 72 countries. The results generally supported the FE with the IQ gain of 0.22 points per year, but the magnitudes of the IQ gains depended on types of countries and age groups. Stronger FEs were evident in middle-income countries and younger generations. A multicausal explanatory framework should be utilized to explain the underlying mechanism of the secular IQ gains across factors.
... Third, the Flynn effect was investigated with different intelligence tests and developmental tests, but no research has yet been done on the ASIS intelligence scale. Finally, the literature on Flynn Effect studies is very limited in economically developing countries (Rodgers, 1999), such as Turkey. Kagitcibasi and Biricik (2011) presented IQ gains for Turkey in the Goodenough-a-Man Draw test with 258 fifth graders. ...
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Flynn effect which advocated that there was a rise in the IQ score was widely accepted by the relevant scientific community. However, this effect has been reversed in recent years. In this study both Flynn and anti-Flynn effects were investigated. The purpose of this study is to determine the trend of students' general, verbal, and visual intelligence scores in the last 6 years (2016-2021). The participant of study included 2192 students who were first grade. To measure general, verbal, and visual intelligence of the participant, Anadolu-Sak Intelligence Scale (ASIS) was used. ASIS is the first intelligence test developed and standardized in Turkey. Participants’ mean of general, verbal, and visual intelligence scores in different years were compared by trend analysis. The research findings showed that there was a downward tendency in general, verbal, and visual scores between the years of 2016 to 2021. Moreover, our findings reveal that there is a sharp decrease between 2020 and 2021. Girls' GIQ, VIQ, and NIQ scores of 2021 showed a sharp decline compared to boys. As a result of this study, it can be said that the Flynn effect has gone into reversed.
... Finally, the idea that cognitive abilities, measured by IQ tests, are merely superficial cultural constructs and therefore that the Flynn effect is a mirage or a methodological artefact (Rodgers, 1998;Beaujean and Osterlind, 2008), contradicts the observation, 20 in a very large number of studies, that IQ scores predict mortality (Calvin et al, 2011). In fact, even IQ change in adolescence, from age 10 to 20, is found to predict later life mortality (Lager et al, 2009). ...
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Research about the Flynn effect, the secular rise in IQ, is heavily based on conscript data from successive male birth cohorts. This inevitably means that two distinct phenomena are mixed: fertility differences by IQ group (‘compositional Flynn effect’), and any difference between parents and children (‘within-family Flynn effect’). Both will influence trends in cognitive ability. We focused on the latter phenomenon, exploring changes in cognitive abilities during adolescence within one generation, and between two successive generations within the same family. We identified determinants and outcomes in three linked generations in the Stockholm Multigenerational Study. School and conscript data covered logical/numerical and verbal scores for mothers at age 13, fathers at 13 and 18, and their sons at 18. Raw scores, and change in raw scores, were used as outcomes in linear regressions. Both parents’ abilities at 13 were equally important for sons’ abilities at 18. Boys from disadvantaged backgrounds caught up with other boys during adolescence. Comparing fathers with sons, there appeared to be a positive Flynn effect in logical/numeric and verbal abilities. This was larger if the father had a working-class background or many siblings. A Flynn effect was only visible in families where the father had low general cognitive ability at 18. We conclude that there is a general improvement in logical/numeric and verbal skills from one generation to the next, primarily based on improvement in disadvantaged families. The Flynn effect in Sweden during the later 20th century appears to represent a narrowing between social categories.
... Trends in scholastic assessments and on IQ tests can move in opposite directions during specific time periods in individual countries. This happened with SAT scores and IQ scores in the United States during the 1960s and 70s (Flynn, 1984;Rodgers, 1998), possibly as a results of changes implemented in the American school system at that time. However, both types of test measure manifest cognitive ability and reasoning skills, and both are intended to do so. ...
The aim of the study is to estimate the most recent trends of intelligence world-wide. We find that the most recent studies report mainly positive Flynn effects in economically less developed countries, but trivial and frequently negative Flynn effects in the economically most advanced countries. This is confirmed by an analysis of 48 countries in the 2000–2018 PISA tests, showing that high pre-existing IQ and school achievement are the best predictors of declining test scores. IQ gaps between countries are still large (e.g., 19 IQ points in PISA between East Asia and South Asia) but are diminishing world-wide. We predict that these trends, observed in adolescents today, will reduce cognitive gaps between the working-age populations of countries and world regions during coming decades. As is predicted by the well-established relationship between intelligence and economic growth, there is already evidence that the ongoing cognitive convergence is paralleled by global economic convergence. These developments raise questions as to how long this cognitive and economic convergence will continue, whether it will eliminate cognitive and economic gaps between countries entirely, and whether a condition with high levels of cognitive ability and economic prosperity is sustainable long-term.
The definition of second order interaction in a (2 × 2 × 2) table given by Bartlett is accepted, but it is shown by an example that the vanishing of this second order interaction does not necessarily justify the mechanical procedure of forming the three component 2 × 2 tables and testing each of these for significance by standard methods.*
Data from 14 nations reveal IQ gains ranging from 5 to 25 points in a single generation. Some of the largest gains occur on culturally reduced tests and tests of fluid intelligence. The Norwegian data show that a nation can make significant gains on a culturally reduced test while suffering losses on other tests. The Dutch data prove the existence of unknown environmental factors so potent that they account for 15 of the 20 points gained. The hypothesis that best fits the results is that IQ tests do not measure intelligence but rather a correlate with a weak causal link to intelligence. This hypothesis can also explain differential trends on various mental tests, such as the combination of IQ gains and Scholastic Aptitude Test losses in the United States.
This study was designed to provide information regarding the relationship between the WPPSI and the WISC-R, over a 1-yr. interval, for a sample of middle-class children of above average intelligence. There were 47 males and 43 females who were an average of 5.6 yr. at the time of first testing and 6.5 yr. at the time of second testing. The data, analyzed by a linear regression analysis, showed that the WISC-R Verbal, Peformance, and Full Scale IQs could be predicted with a reasonable degree of accuracy, i.e., standard errors of estimate: Verbal IQ = 8.6, Performance IQ = 8.15, Full Scale IQ = 7.14. The results were discussed in terms of the degree of similarity between cognitive skills measured by the two tests and the clinical utility of such information.