338 III. DEvELOPMENT
trajectories of Australians. In empirical re -
search, these higher-  level trajectories often 
represent different samples. When placed 
alongside one another, they can tell us if 
samples that are similar in other ways but 
simply contain different people show the 
same pattern of personality change. In this 
sense they can address issues of replicabil -
ity and reproducibility (Condon, Graham, 
& Mroczek, 2017; Graham et al., 2017; 
Graham et al., 2020). Longitudinal studies 
are not amenable to quick replication. One 
cannot simply read an original study that it -
self may have required only a few weeks of 
data collection, then reel off a replication in 
a short period of time. This is impossible for 
longitudinal studies with duration of 20 or 
30 years (longer in some case; the Terman 
sample was followed from the 1920s to the 
late 1990s by three generations of research -
ers; Friedman et al., 1993). Moreover, a sec -
ond study “does not a replication make.” 
With a minimum of four possible results for 
any hypothesis—  incorrectly rejecting the 
null (type 1 error), correctly rejecting the 
null, incorrectly retaining the null (type 2 
error), and correctly retaining the null—two 
studies seems quite inadequate.
However, at this interesting middle level 
of group- or sample-  level trajectories lies a 
potential solution to the vexing issue of rep -
lication in longitudinal studies. Coordinated 
analysis is a novel methodological approach 
designed to enhance replicability (Hofer & 
Piccinin, 2009), especially with respect to 
longitudinal research questions. It is a form 
of multistudy design that carries out identi -
cal (or near identical) modeling of a given 
research question within multiple (not just 
two or three) existing studies, then synthe -
sizes and integrates these models using meta- 
analytic tools such as forest plots, summary 
effect sizes, heterogeneity statistics, and so 
forth. This form of separate, but coordinat -
ed, modeling within a set of studies is a type 
of integrative data analysis (IDA; Curran & 
Hussong, 2009) and produces a group of 
replications that otherwise would be very 
difficult to obtain due to the nonconvenience 
nature of long-term longitudinal samples. In 
some cases, 15–30 studies can be included 
in such an IDA (Graham et al., 2017; van 
den Berg et al., 2014). Returning to our tra -jectories theme, we can imagine an array of 
sample-  level trajectories of a given trait that 
either heavily overlap with one another or 
are heterogeneous. In the former, different 
samples are congruent, indicating robust 
trajectories that appear regardless of sample. 
In the latter, sample-  level trajectories vary 
from each other (heterogeneity), indicating 
possible substantive or methodological dif -
ferences among samples. In either case, they 
can be illuminating. Heterogeneous trajec -
tories across samples (or even well-  defined 
subgroups; e.g., ethnic categories, age strata, 
or gender) raise the possibility that person -
ality change may look different for some 
people versus others, and that a particular 
form of change (e.g., declining neuroticism) 
may not hold for all groups, just as it does 
not hold for all individuals. As Kluckhohn 
et al. (1953) would put it, Oliver is like some 
other people.
Last, at a very high level, we can imag -
ine metatrajectories that define personality 
change (or stability, if the curve is flat) for 
a given characteristic for an entire popula -
tion. At this macro level, we can picture a 
world population metatrajectory for genera -
tivity or agreeableness. Many people are not 
described well by the metatrajectory. Even 
many samples and groups may diverge quite 
a bit from the metatrajectory (Graham et al., 
2020). Practically, there is no way to mea -
sure everybody in the world anyway, and 
even measuring everyone in a given country 
is unrealistic. Yet there is a metatrajectory 
for each of these large groups, even if there 
is no practical way of directly measuring it, 
and some people will track the population 
meta-  trajectory fairly well. However, it is 
here that Kluckhohn et al. (1953) metaphor 
breaks down. Those whose personal trajec -
tories overlie neatly with a metatrajectory 
can be thought of as being “like all other 
people.” Yet can the metatrajectory really 
be said to describe all people? Not quite. It 
abstracts a population in the way the arith -
metic mean characterizes a population or 
subgroup. It says nothing about the varia -
tion around it, and as individual differences 
researchers, we have an acute understanding 
that arithmetic means are limited in useful -
ness. Nonetheless, the idea of a metatrajec -
tory is still a valuable one.