734 vII. APPLICATIONS  AND  IMPLICATIONS
even after relatively long periods of time 
had elapsed, life satisfaction measures were 
moderately stable. For instance, the pre -
dicted 2-, 5-, and 10-year stabilities were ap -
proximately .60, .50, and .35, respectively.
More recently, Lucas and Donnellan 
(2007, 2012) used data from large-scale 
panel studies to estimate the stability of 
single-  item life satisfaction measure. They 
calculated that between 24 and 38% of the 
variance in single-  item life satisfaction mea -
sures is trait variance that is perfectly sta -
ble over time. Thus, these estimates predict 
that long-term stabilities should asymptote 
around .30. Anusic and Schimmack (2016) 
used meta-  analytic techniques to calculate 
similar estimates from a variety of con -
structs, including life satisfaction and affect, 
but also self-  esteem and personality. They 
found that although all constructs exhibited 
some degree of stability over time, a greater 
percentage of the reliable variance in per -
sonality traits was accounted for by a stable 
component as compared to the well-being 
variables. Measures of life satisfaction, in 
contrast, were influenced by reliable but 
changing sources of variance.
Most of the studies we described ear -
lier assess broad measures of SWB that are 
meant to tap general experiences of well-be -
ing over longer periods of time. As we noted 
earlier, however, it is also possible to assess 
well-being using more experiential mea -
sures that track affect as it is experienced 
over time. One disadvantage of such an ap -
proach is that it is relatively burdensome for 
researchers and participants, which means 
that it is often only feasible to assess experi -
enced affect over a short period of time. One 
question that emerges is whether the aggre -
gated reports of well-being that result from 
such short assessment periods are stable over 
time; one could imagine that experienced af -
fect could fluctuate substantially from day 
to day, which would mean that many days 
would need to be aggregated before a stable 
individual difference would emerge. How -
ever, large-scale panel studies such as the 
German Socio Economic Panel Study have 
started to include experiential measures like 
the day reconstruction method, and even 
these measures appear to be quite stable 
over time. For instance, Hudson, Lucas, and 
Donnellan (2017) examined day reconstruc -tion method measures across a 4-year period 
and found that stability was similar to that 
found in broader, global measures of affect.
Examination of stability coefficients pro -
vides one important piece of information 
about the extent to which personality can 
change. However, there are alternative tech -
niques for investigating change that go be -
yond simply assessing rank-order stability. 
For instance, stability coefficients alone can -
not distinguish stochastic change (in which 
a variety of factors causes random changes 
that accumulate over time) from system -
atic change resulting from major life events 
(Fraley & Roberts, 2005). To assess the rea -
sons for change, more sophisticated designs 
are required. For instance, the analysis of 
large-scale, long-  running, nationally repre -
sentative panel studies can be used to deter -
mine whether major life events have lasting 
effects on SWB. These studies can be used 
to examine long-term levels of SWB both be -
fore and after events occur. This means that 
preexisting differences between those who 
experienced a particular life event and those 
who did not can be separated from true lon -
gitudinal change. A number of important 
conclusions can be drawn from these studies 
(for reviews, see Diener, Lucas, & Scollon, 
2006; Lucas, 2007a; Luhmann, Hofmann, 
Eid, & Lucas, 2012).
First, life events have important associa -
tions with long-term levels of SWB. Divorce 
(Lucas, 2005), unemployment (Lucas, Clark, 
Georgellis, & Diener, 2003), and the onset 
of a long-term disability (Lucas, 2007b) are 
all associated with lasting changes in life 
satisfaction, and the effects can sometimes 
be quite large. For instance, onset of a last -
ing disability was associated with more than 
a half a standard deviation drop in life sat -
isfaction. The effects of severe disabilities 
were even larger, with effect sizes over a full 
standard deviation.
Second, there is no single answer to the 
question of whether people adapt to major 
life events. Very little adaptation occurred 
following the onset of disability, even over 
very long periods of time (Lucas, 2007b; 
though see Oswald & Powdthavee, 2008). 
For events such as unemployment and di -
vorce, on the other hand, some amount of 
adaptation occurred, but this adaptation 
was incomplete (Lucas, 2005; Lucas, Clark, 