Cognitive performance inconsistency: intraindividual change and variability.
ABSTRACT Although many studies have examined inconsistency of cognitive performance, few have examined how inconsistency changes over time. 91 older adults (age 52 to 79) were tested weekly for 36 consecutive weeks on a series of multitrial memory speed (i.e., letter recognition) tasks. A number of multivariate techniques were used to examine how individuals' level of inconsistency changed across weeks and how this change was related to interindividual differences in age and intelligence. Results indicated that (a) inconsistency of performance is a construct separate from the underlying performance ability (i.e., memory speed); (b) inconsistency reduces exponentially with practice; (c) individuals with higher scores on tests of fluid general intelligence (G-sub(f)) reached lower asymptotic levels of inconsistency compared to lower scorers; and (d) after controlling for the systematic effects of practice, variability in inconsistency from week-to-week was more pronounced for individuals with lower G-sub(f) scores compared to individuals with higher scores.
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ABSTRACT: As we age, our differences in cognitive skills become more visible, an effect especially true for memory and problem solving skills (i.e., fluid intelligence). However, by contrast with fluid intelligence, few studies have examined variability in measures that rely on one's world knowledge (i.e., crystallized intelligence). The current study investigated whether age increased the variability in text based global inference generation--a measure of crystallized intelligence. Global inference generation requires the integration of textual information and world knowledge and can be expressed as a gist or lesson. Variability in generating two global inferences for a single text was examined in young-old (62 to 69 years), middle-old (70 to 76 years) and old-old (77 to 94 years) adults. The older two groups showed greater variability, with the middle elderly group being most variable. These findings suggest that variability may be a characteristic of both fluid and crystallized intelligence in aging.PLoS ONE 01/2012; 7(5):e36161. · 4.09 Impact Factor
Cognitive Performance Inconsistency: Intraindividual Change
University of Virginia
University of Oxford
University of Bristol
John R. Nesselroade
University of Virginia
Although many studies have examined inconsistency of cognitive performance, few have examined how
inconsistency changes over time. 91 older adults (age 52 to 79) were tested weekly for 36 consecutive weeks
used to examine how individuals’ level of inconsistency changed across weeks and how this change was
related to interindividual differences in age and intelligence. Results indicated that (a) inconsistency of
performance is a construct separate from the underlying performance ability (i.e., memory speed); (b)
inconsistency reduces exponentially with practice; (c) individuals with higher scores on tests of fluid general
intelligence (Gf) reached lower asymptotic levels of inconsistency compared to lower scorers; and (d) after
controlling for the systematic effects of practice, variability in inconsistency from week-to-week was more
pronounced for individuals with lower Gfscores compared to individuals with higher scores.
Keywords: multivariate, memory speed, aging, growth curves, nonlinear
Intraindividual variability in cognitive performance has some-
times been conceptualized as inconsistency (e.g., Fuentes, Hunter,
Strauss, & Hultsch, 2001; Hultsch, MacDonald, Hunter, Levy-
Bencheton, & Strauss, 2000; Li & Lindenberger, 1999; Mac-
Donald, Hultsch, & Dixon, 2003; Stuss, Pogue, Buckle, & Bondar,
1994; Stuss, Stethem, Hugenholtz, Picton, & Richard, 1989). De-
fined as the variability of performance across occasions (e.g.,
Hultsch & MacDonald, 2004), inconsistency can be indexed or
measured using the intraindividual standard deviation (ISD) of
performance computed across occasions (Slifkin & Newell, 1998).
Individuals whose performance varies little from occasion to oc-
casion, regardless of level, are consistent (i.e., they have relatively
low ISDs), whereas those individuals whose performance varies
dramatically from occasion to occasion are inconsistent (i.e., they
have relatively high ISDs). In this manner, intraindividual vari-
ability across occasions can be indexed with a single score. This
inconsistency score can then be examined in the same manner as
any other intraindividual or interindividual difference variable.
At a theoretical level, inconsistency of performance on relatively
simple cognitive tasks has been considered a measure of variability in
central nervous system functioning (Hendrickson, 1982; Hultsch &
MacDonald, 2004; Li & Lindenberger, 1999). Consistent with this
hypothesis, inconsistency (or intraindividual variability) in cognitive
performance has been found to be related to age, injury, health, and
intelligence (Hultsch & MacDonald, 2004). Generally, older adults
(e.g., Anstey, 1999; Fozard, Vercruyssen, Reynolds, Hancock, &
Quilter, 1994; Hertzog, Dixon, & Hultsch, 1992; Hultsch, Mac-
Donald, Hunter, Maitland, & Dixon, 2002; West, Murphy, Armilio,
Craik, & Stuss, 2002), unhealthy or dysfunctional persons tend to be
more inconsistent than healthy or functional persons (e.g., Fuentes et
persons with lower levels of cognitive task performances (e.g., mea-
sures of Gfand Gc) tend to be more inconsistent than persons with
higher levels of performance (e.g., Hultsch et al., 2002; Li, Aggen,
Nesselroade, & Baltes, 2001; Rabbitt, Osman, Moore, & Stollery,
2001). In sum, on a variety of fronts, greater inconsistency seems to
be a marker of impending decline or low functionality (Hendrickson,
1982; Li & Lindenberger, 1999; Rowe & Kahn, 1985, 1997).
In most studies, inconsistency has been studied as the day-to-
day or week-to-week variability in performance. However, incon-
sistency can be measured across any time frame (Hultsch &
MacDonald, 2004; Slifkin & Newell, 1998). For instance, an ISD
calculated across multiple trials can be used to measure intraindi-
vidual variability or inconsistency in the moment-to-moment fluc-
tuations in performance over a short time period. Similarly an ISD
calculated across observations gathered days or weeks apart can be
used to measure inconsistency (or intraindividual variability) in
performance over a longer time period.
Nilam Ram and John R. Nesselroade, Department of Psychology, Uni-
versity of Virginia; Patrick Rabbitt, Department of Experimental Psychol-
ogy, University of Oxford; Brian Stollery, Department of Experimental
Psychology, University of Bristol.
Nilam Ram gratefully acknowledges the support provided by Grant T32
AG20500 from the National Institute on Aging in the preparation of this
article. Special thanks to those at the Institute for Developmental and
Health Research Methodology at the University of Virginia for helpful
comments on earlier versions of this work.
Correspondence concerning this article should be addressed to Nilam
Ram, Department of Psychology, P.O. Box 400400, University of Virginia,
Charlottesville, VA 22904-4400. E-mail: email@example.com
Psychology and Aging
2005, Vol. 20, No. 4, 623–633
Copyright 2005 by the American Psychological Association
In this study, we examined inconsistency in memory speed
performance in older adults using a series of multitrial letter
recognition tasks (Rabbitt et al., 2001). Inconsistency was opera-
tionalized as individuals’ intraindividual variability in perfor-
mance across trials (i.e., moment-to-moment or within-session
variability). We then examined within-person changes in inconsis-
tency across weeks (i.e., between-session changes in inconsistency).
A number of writers (e.g., Cattell, 1957; Horn, 1972; Hultsch &
MacDonald, 2004; Nesselroade & Featherman, 1997; Wohlwill,
1973) have discriminated between types of within-person changes.
Fiske and Rice (1955), for example, distinguished between reac-
tive or adaptive variability — those changes that are ordered in
some fashion (e.g., cycles and oscillations) and spontaneous vari-
ability — those changes that do not show any systematic trends
over time (i.e., the ordering of occasions is immaterial). Nessel-
roade (1991) distinguished between intraindividual change —
those changes that may or may not be reversible and that may or
may not be synchronous across individuals — and intraindividual
variability–those changes that are more or less reversible and that
may or may not be synchronous across individuals. Generally,
intraindividual change is conceptualized as long-term lasting
change, whereas intraindividual variability is conceptualized as
short-term or transient fluctuation.
Intraindividual change refers to within-person change that is
enduring and characterizes the types of changes seen in a construct
(e.g., cognitive ability) as a result of learning, development, or
aging (Nesselroade, 1991). For example, the development and
decline of fluid intelligence (Gf) over an individual’s life span,
characterized by a rapid increase in abilities during the first 20
years followed by a steady decline over the remainder of life,
would constitute a pattern of intraindividual change. Similarly, on
a shorter time scale, the learning that occurs with practice can also
be characterized as intraindividual change, which is usually best
described by exponential or power functions (e.g., Thurstone,
1919; Heathcote, Brown, & Mewhort, 2000).
One common method for studying intraindividual change is
growth curve analysis (e.g., Bryck & Raudenbush, 1987, 1992;
Rogosa & Willet, 1985; Singer & Willett, 2003; Wishart, 1938).
Generally, this type of analysis is used to describe, test hypotheses,
and make inferences about time-related phenomena, that is,
change. By allowing specific parameters in the growth expression
to vary between individuals, we can examine differences in per-
sons’ initial levels of performance (intercept), rates of improve-
ment over time, asymptotic levels of performance, and so forth. In
such manner, many researchers have fruitfully examined interin-
dividual differences in intraindividual change across a wide variety
of domains (see McArdle & Nesselroade, 2003, for a history).
In the present study, we hypothesized that as individuals gained
more and more exposure to the tasks the quality of their perfor-
mances would improve; they would become more consistent.
Other studies of inconsistency have found average levels of incon-
sistency to decrease across multiple testing sessions (e.g., Hultsch
et al., 2000; West et al., 2002). Our interest was in how inconsis-
tency changes over time at the individual level. Using growth
curve analysis methods, we examined the interindividual differ-
ences in intraindividual change in inconsistency and we investi-
gated what other personal characteristics were related to such
In addition to the systematic, long-term, lasting intraindividual
changes noted previously, individuals’ level of inconsistency may
also exhibit intraindividual variability or systematic transient fluc-
tuation. Intraindividual variability has been characterized in a
number of ways, including error, wobble, lability, instability, in-
consistency, and noise (Butler, Hokanson, & Flynn, 1994; Hultsch
& MacDonald, 2004; Hultsch et al., 2000; Li & Lindenberger,
1999; Nesselroade, 1988; Nesselroade & Ford, 1985; Shammi,
Bosman, & Stuss, 1998; Shoda, Mischel, & Wright, 1994; Slifkin
& Newell, 1998). For example, Nesselroade and Ford (1987)
suggested that the transient and relatively rapid changes that char-
acterize the variation within an individual might represent the
steady-state hum or base condition of daily functioning. Instead of
being considered as random errors in performance, an individual’s
variability in performance across time is a useful and informative
interindividual difference construct. For instance, levels of intra-
individual variability in performance have been found to be pre-
dictive of impending cognitive developmental transitions (see,
e.g., Siegler, 1994). Similarly, differences in the intraindividual
variability in infants’ heart rate are predictive of later differences
in temperament (Fox & Porges, 1985; Kagan, 1994). Among
adults, the amount of variability in self-esteem (i.e., self-esteem
lability) is predictive of depression proneness (Butler et al., 1994)
and, among older adults, the level of week-to-week variability in
internality beliefs is a risk factor for mortality some 5 years later
(Eizenman, Nesselroade, Featherman, & Rowe, 1997). This re-
search indicates that patterns or amounts of intraindividual vari-
ability may be related to age, health, vulnerability, and ultimately
death (e.g., Rowe & Kahn, 1985, 1997). Such findings highlight
the importance of analyzing interindividual differences in intrain-
The study of intraindividual variability usually proceeds along
two avenues. First, the intraindividual variability can be examined
for short-term patterns of change (Nesselroade, 2002; Slifkin &
Newell, 1998). In much the same manner that we extract patterns
of long-term intraindividual change from occasion-to-occasion
variance (e.g., using growth curve analysis), we can also extract
meaningful (i.e., interpretable) patterns of more transient changes.
For instance, Horn (1972) identified patterns of week-to-week
intraindividual variability that reflect the fluid-crystallized intelli-
gence distinction. Similarly, Hampson (1990) demonstrated the
existence of short-term patterns in performance fluctuations that
appear to be hormonally driven. Such findings illustrate that mean-
ingful and informative short-term statelike patterns of intraindi-
vidual variability can and do exist alongside the more traditional
long-term intraindividual change patterns. Second, after account-
ing for systematic short- and long-term changes, we can quantify
the amount of within-person occasion-to-occasion variability. This
can be done in a number of ways, such as calculating the ISD
across occasions, the distance between high and low scores, coef-
ficient of variation, and so forth (Hultsch & MacDonald, 2004;
Slifkin & Newell, 1998). Such scores are then examined for
RAM, RABBITT, STOLLERY, AND NESSELROADE
In this study, intraindividual variability across weeks was ex-
amined along both of the avenues outlined previously. First, the
structure of the intraindividual variability was examined for pat-
terns. Exploratory time-series analysis techniques were used to
obtain information about and to describe any systematic statelike
patterns that existed in the data. Second, the gross amount of
intraindividual variability across weeks was quantified and exam-
ined as an interindividual difference variable.
Note that our gross measure of intraindividual variability differs
from our measures of inconsistency in time frame only (week-to-
week vs. trial-to-trial). The mechanics of the calculations are
identical. In fact, inconsistency is intraindividual variability. Here,
however, we make a distinction between intraindividual variability
across trials (inconsistency) and intraindividual variability across
weeks (intraindividual variability) for the sake of readability. This
distinction highlights a key study design feature. In this study, data
were simultaneously collected along two time scales (Newell, Liu,
& Mayer-Kress, 2001) in a compressed measurement burst design
(Nesselroade, 1991). Within-burst assessments were obtained on a
moment-to-moment (i.e., trial-to-trial) time scale and were used to
construct a measure of inconsistency. Across-burst measurements
were obtained on a week-to-week time scale and were used to
examine how inconsistency changed and varied from occasion to
Summary of Purposes
In this article, we examine how individuals performed on a set
of memory speed tasks over successive trials and weeks to identify
and explain interindividual differences in the intraindividual
change and intraindividual variability of inconsistency. Our spe-
cific goals were the following:
1. Establish a multivariate measurement model of inconsistency.
2. Identify intraindividual change in inconsistency (i.e., practice
3. Identify interindividual differences in intraindividual change.
4. Identify intraindividual variability of inconsistency.
a. Identify any systematic statelike patterns in the intraindividual
variability of inconsistency.
b. Quantify the gross amount of intraindividual variability in
inconsistency (level of noise).
5. Identify interindividual differences in intraindividual variabil-
ity (i.e., differences in level of noise).
Ninety-one older adults (26 men, 65 women) who were 52 to 79 years
of age (M ? 65.9 years, SD ? 7.1) were recruited from a larger sample of
older adults in the Manchester Longitudinal Study of Cognitive Aging
(Rabbitt, 1993) to take part in an intensive training study. Participants were
selected on the basis of complete physical examinations by experienced
geriatric physicians so that all participants were free of pathology that
might impair their cognitive function and their ability to complete the long
and taxing involvement required by the intense repeated measures study
design. Furthermore, participants were matched on unadjusted AH4-1
Intelligence Test (Heim, 1970) scores (M ? 36.7, SD ? 9.6) such that there
were no mean differences between the three age groups of 51–60 years,
61–70 years, and 71–80 years. Across all ages, the sample participants
were in good health and had completed some higher education. Further
information can be obtained from an earlier description (see Rabbitt et al.,
Participants took part in a total of 36 weekly sessions (after an initial
familiarization session) and two follow-up sessions (3 and 6 months later).
Sessions took place at one of four times of day: 10 a.m., 12 p.m., 2 p.m.,
and 4 p.m. To balance circadian effects, age groups were matched for time
of day, and each individual attended at the same time and day each week.
With rest periods, each session lasted between 60 and 90 min during which
participants completed a substantial battery of cognitive measures. The
present study focuses on a series of multitrial letter search tasks that were
presented at each occasion.
Letter Search Tasks
Participants were presented with a set of 2, 4, or 6 letters to be
memorized (target letters). Their ability to remember these letters was then
tested by requiring them to select the presented letters from a larger set of
4, 8, or 12 letters shown on a computer screen. If the target letters were not
selected correctly, the letters were presented again; the cycle continued
until the participant was able to demonstrate recognition. After achieving
100% accuracy in target letter recognition, testing trials began. A sequence
of letters was presented, one at a time, on the screen. Participants were
asked to indicate if each letter was or was not one of the previously
memorized letters. As soon as a response was made, whether correct or not,
the next letter was presented. Letters were presented in 50 trial blocks, with
a short break between blocks.
Blocks of trials were constructed in two ways. In a varied mapping
condition, target and distractor letters were both selected from a single pool
of letters (i.e., C, W, Q, G, M, H, K, N, V, A, L, X). In a constant mapping
condition, target and distractor letters came from two separate pools of
letters (e.g., target letters selected from B, Z, E, R, F, T and distractor
letters selected from D, Y, P, U, J, S). In both conditions, letters were
chosen at random from the relevant pool such that half of the trials (letters)
were target letters and half were distractor letters.
In sum, there were six letter search task conditions: 3 (target load sizes:
2, 4, or 6 target letter memory sets) ? 2 (block conditions: variable or
constant mapping). All participants received all six conditions. Thus, in
each session participants completed six blocks (presented in random order)
of 50 trials each. Feedback (% accuracy) was given after every block and
at the end of the session (overall accuracy and mean reaction time).
number of milliseconds between letter presentation and the participant’s
indication on the keyboard of whether the letter was a target or nontarget
(i.e., distractor) letter. Two mean RTs were computed for each block by
separately averaging response times for target letters and for nontarget
letters. These measures, 12 for each session, were used as indicators of
In the same manner, two ISDs were computed across
trials on the within-block RTs, that is, for target letters and for nontarget
letters. These ISDs, 12 for each session, were used as indicators of
individuals’ within-occasion inconsistency.
Accuracy of response (i.e., correctly identifying letters as
target or nontarget letters) was computed as the percentage of correct
responses on target letter trials and, separately, the percentage of correct
responses on nontarget letter trials. Thus, matched to the memory speed
(RT) and inconsistency (ISD) measures, there were 12 accuracy measures
for each of 36 sessions. Participants were highly accurate with within-
person mean accuracy ranging from 96.9% to 99.4% across tasks. Because
Reaction time (RT) was recorded for each trial as the
CHANGE AND VARIABILITY OF INCONSISTENCY
the accuracy measures showed so little variation both within and between
participants, they provided little intraindividual or interindividual informa-
tion and so were not used in any of the following analyses.
In addition to the repeated measures, a number
of demographic and individual difference characteristics were obtained.
These included age (number of years since birth to first occasion of
measurement) and Cattell Culture Fair Intelligence Test scores (Cattell,
1973), an established measure of fluid intelligence (Gf).
Multivariate techniques were used to build a true score measure of
inconsistency and to model interindividual differences in intraindividual
change and intraindividual variability. First, factor analysis was used to
develop a parsimonious measurement model allowing for the precise,
error-free quantification of inconsistency. Second, growth models were
used to extract the intraindividual change in inconsistency that occurs with
practice across the 36 weekly sessions. Third, canonical correlation was
used to determine if and how interindividual differences in intraindividual
change were related to age and intelligence. Fourth, weekly measures of
practice-adjusted inconsistency were derived by removing the systematic
intraindividual changes.1The remaining intraindividual variability (resid-
ual) series were examined for evidence of systematic statelike patterns
using exploratory time-series methods (e.g., Ljung-Box test for random
noise and ARMA model fitting), and the gross amount of noise within them
was quantified. Finally, canonical correlation was again used to examine
how interindividual differences in the amount of intraindividual variability
were related to other available interindividual difference variables (i.e.,
age, intelligence). All models were fit to the data using either LISREL
(Version 8.54, Jo ¨reskog & So ¨rbom, 2003) or SAS. Incomplete data (?7%)
were treated as missing at random (Little & Rubin, 1987). Further descrip-
tion of the analysis techniques and results are presented in the next section
in stepwise fashion, with later analyses having been informed by earlier
Multivariate Measurement of Inconsistency
Individuals who perform inconsistently on one task may also
perform inconsistently on other tasks. That is, individuals may
have relatively inconsistent days or relatively consistent days. On
a “bad” day, they may be inconsistent on all the different tasks they
attempt. Alternatively, on a “good” day, they might perform con-
sistently on all tasks.
To examine these notions of task performance covariation
across occasions, the 12 inconsistency (ISD) measures were used
as multiple indicators of a single within-person inconsistency
process. Likewise, the 12 memory speed (RT) measures were used
as multiple indicators of a single within-person memory speed
process. We built and tested an appropriate multivariate measure-
ment model via a series of chain P-technique factor analyses.
Chain P-technique factor model.
R-technique factor analysis, a Persons ? Variables data matrix for
a single occasion of measurement is analyzed to identify patterns
in the relationships between variables that are defined across
persons. In P-technique factor analysis (Cattell, Cattell, & Rhymer,
1947), an Occasions ? Variables matrix for a single person is
analyzed to identify patterns in the relationships between variables
that are defined across occasions for one individual. Chain
P-technique (Cattell, 1963) combines these approaches by literally
chaining together the Multioccasion ? Variable data matrices of
In the commonly used
two or more persons. This composite matrix is then analyzed to
examine the relationships between variables that are defined across
both persons and occasions, thereby defining a common (or in-
variant) measurement space.
By design, chain P-technique analyses confound interindividual
and intraindividual differences by including multiple individuals
and multiple occasions in the same data matrix. Thus, to be sure
that the factor structure obtained in our analysis represented mea-
surement of within-person process, we took care to remove inter-
individual differences before combining persons into a single
chain P matrix. Specifically, the 24 observed measures (12 mean
RTs and 12 ISDs) were first standardized within person, thereby
removing all interindividual differences in mean and amount of
variation. Between-persons differences due to mean and variance
differences were thus minimized or eliminated.
On a first look at the data, exploratory chain P-technique factor
analyses indicated that one latent factor accounted for the common
variation between the 12 memory speed (RT) measures and that
one latent factor accounted for the common variation between the
12 inconsistency (ISD) measures. Then, in a confirmatory struc-
tural equation modeling framework, the 24 measures were mod-
eled simultaneously, with the 12 memory speed (RT) measures
indicating a single memory speed factor and the 12 inconsistency
(ISD) measures indicating a single inconsistency factor. This mea-
surement (factor) model, graphically presented in Figure 1, al-
lowed us to separate the “true” memory speed and inconsistency
variance from residual and error variance (u1to u24), thus afford-
ing us more reliable measurements of the phenomenon of interest,
The multivariate measurement model fit the data well (?2?
3746, df ? 239; RMSEA ? .067, CFI ? .95, Tucker-Lewis Index
[TLI] ? .94; see Table 1), providing support for our notions that
the 12 RT and 12 ISD measures could be represented parsimoni-
ously by two underlying factors. Although significant improve-
ments in fit could be achieved by expanding the model to include
a number of multimethod factors for the different task conditions
(e.g., Campbell & Fiske, 1959; Widaman, 1985, 1992), such
elaboration seemed only to detract from the current questions
regarding intraindividual change and variability. Thus the parsi-
monious model was retained.
Separability of memory and inconsistency factors.
tasks, the computed means (speed) and standard deviations (in-
consistency) are often highly related, with some argument as to if
and how they measure different processes (see, e.g., Hultsch &
MacDonald, 2004). Here, the separability of the two processes
(factors) was tested using hierarchically nested factor models (see
1Hultsch and colleagues have suggested that the calculation of intrain-
dividual variability be done after controlling for potential confounds such
as differing materials (tasks), mean differences in speed, and time-related
effects such as practice or learning to learn (Hultsch & MacDonald, 2004;
Hultsch et al., 2000). We also have considered these effects, but in a
different manner. Differing task effects were accommodated through the
measurement model. Our multivariate measure of inconsistency represents
only that portion of within-session intraindividual variability that was
common across all tasks. Mean differences and practice effects were
modeled using the exponential growth model. These effects were then
partialed (subtracted) from our subsequently derived measures of intrain-
dividual variability (across weeks).
RAM, RABBITT, STOLLERY, AND NESSELROADE
Table 1). In addition to the model shown in Figure 1, the data were
also modeled using a single factor indicated by all 24 measures
(?2? 4281, df ? 240, RMSEA ? .072, CFI ? .93, TLI ? .92).
Such a model would fit better if there were a single process driving
both speed and inconsistency. A chi-square difference test com-
paring the one- and two-factor models indicated that the two-factor
model fit the data significantly better (??2/?df ? 535/1). Thus, we
concluded that memory speed and inconsistency represent differ-
ent and separable constructs. Higher scores on the memory speed
factor indicate slower memory search, and lower scores indicate
faster memory search. Similarly, higher scores on the inconsis-
tency factor indicate greater variability in performances of multi-
ple tasks, that is, an erratic day; lower scores indicate consistency
of performance, that is, a steady day.
Inconsistency factor scores.
structure and theoretical basis for the latent factors, LISREL was
Having established a reasonable
used to compute estimates of inconsistency factor scores (Ander-
son & Rubin, 1956) for use in the subsequent analyses. These
factor scores are considered to be unbiased estimates of the factors.
Their sample covariance matrix is exactly equal to the estimated
covariance matrix of the reference variables. In other words, the
estimated factor scores are individuals’ scores on the error-free
latent construct to the extent that the factor model fits the data.
With issues concerning the indeterminacy of factor scores in mind
(e.g., Guttman, 1955), we examined the correlation between the
estimated factor scores and the unobserved latent factor for an
indication of the reliability of the estimates. A correlation of .86
indicated that factor indeterminacy was not problematic (i.e., r ?
.70; Grice, 2001). Thus, here, individuals’ estimated factor scores
are considered to be a reasonable (though by no means perfect)
representation of how the latent process underlying performance
inconsistency develops over time.
Note that the factor scores were obtained using the raw data, not
the standardized data used previously. Thus, these factor scores
contain the interindividual and intraindividual difference informa-
tion, some portions of which we will examine in the following
analyses. The 91 participants’ estimated scores for the 36 weekly
sessions are plotted in Figure 2. Each line represents the trajectory
of an individual’s inconsistency over time.
Intraindividual Change in Inconsistency (Practice Effects)
We hypothesized that the quality of individuals’ performances
would improve with increased exposure to the tasks. More specif-
ically, individuals’ performances would become more consistent
as they progressed through the 36 weeks of training. A series of
linear and nonlinear growth curve analyses were used to test this
hypothesis and to find the best description of such changes.
No-growth, linear, quadratic, and exponential growth models
were used to systematically model intraindividual change in in-
consistency. In line with our hypothesis, the models of change
(linear, quadratic, etc.) fit the data better than a no-growth model
(e.g., a linear model fit significantly better than a no-growth
model; ? -2 log likelihood [-2LL]/?df ? 1670/3; see Table 2). As
individuals became increasingly familiar with the task, they be-
came, on average, more consistent in their within-session perfor-
mances. As seen in Table 2, a comparison between a number of
different models of change (i.e., different mathematical expres-
sions of growth) indicated that, of the models fitted, a model of
exponential change provided the best overall representation of how
individuals’ level of inconsistency changed with practice.
inconsistency. The memory speed factor is indicated by 12 mean reaction
time (RT) measures and the inconsistency factor is indicated by 12 intra-
individual standard deviation (ISD) measures.
Factor (multivariate measurement) model of memory speed and
Factor Models of Memory Speed and Inconsistency
freedom; RMSEA ? root-mean-square-error of approximation; CFI ?
comparative fit index; TLI ? Tucker-Lewis index.
FIML ? full information maximum likelihood; df ? degree of
CHANGE AND VARIABILITY OF INCONSISTENCY
The exponential growth model was specified as:
Inconsistencyij ? ?0i ? ?1ie???2i*SESSIONij?? ?ij
The model uses three parameters to describe intraindividual
change. The ?0iparameter describes individuals’ asymptotic level
of performance and is interpreted as the limit of an individual’s
capability (i.e., the lowest level of inconsistency he or she can
attain). When combined with the intercept parameter, ?0i? ?1i
represents the level of inconsistency at which the person began the
training (i.e., level of inconsistency at time ? 0). Thus, ?1ion its
own represents an individual’s potential for improvement from his
or her initial level. Finally, ?2iindicates the rate at which an
individual’s consistency improved.
In sum, with week-to-week repetition, individuals generally
became exponentially more consistent. However, these improve-
ments eventually leveled off as an asymptotic level of consistency
was reached. From the (multilevel) exponential growth curve
analysis, we were able to derive predicted curves describing how
each individual’s inconsistency developed over the course of the
36 weeks of testing. These predicted growth curves of inconsis-
tency are shown in Figure 3.
Interindividual Differences in Intraindividual Change
(Age and Intelligence Effects)
In the plots (see Figure 3), it is clear that some individuals start
out more inconsistent than others, some individuals improve more
quickly than others, and some individuals reach a final level of
inconsistency that is higher than the level of others. In other words,
there are clear interindividual differences in intraindividual
change. We examined how these differences in intraindividual
change were related to age and intelligence.
A significant canonical correlation indicated that interindividual
differences in the parameters ?0i, ?1i, and ?2iwere related to
measures of age and intelligence (i.e., age in years and Cattell
Culture Fair Intelligence test scores; R2? .62), F(6, 156) ? 7.416,
p ? .0001. In follow-up tests, only the asymptotic level of incon-
sistency, ?0i, was significantly related to intelligence, t(89) ?
6.36, p ? .0001, ? ? ?.61. To summarize, ages and intelligence
scores were not systematically related either to the total amount by
which individuals’ consistency improved over the 36 weeks of
training or to the rates at which this improvement occurred. How-
ever, individuals with higher intelligence scores were more likely
connecting their week-to-week scores.
Estimated inconsistency factor scores across 36 weeks. Each individual is represented by a single line
Growth Curve Models of Intraindividual Change in Inconsistency
ModelGeneral equation No. of parm-2LL
Yij? ?0i? ?ij
Yij? ?0i? ?1i(tij) ? ?ij
Yij? ?0i? ?1i(tij) ? ?2i(tij
Yij? ?0i? ?1iexp(??2i(tij)) ? ?ij
2) ? ?ij
aImprovement in fit relative to no-growth model.
parm ? estimated parameters; ?2LL ? ?2 log likelihood; ??2LL/?df ? relative fit for nested models.
bImprovement in fit relative to linear model.
RAM, RABBITT, STOLLERY, AND NESSELROADE
to attain greater consistency than individuals with lower intelli-
Intraindividual Variability of Inconsistency
To separate intraindividual change from intraindividual variabil-
ity, the predicted inconsistency scores derived from the exponen-
tial growth model (intraindividual change) were subtracted from
actual inconsistency factor scores. This effectively removes the
systematic effects associated with practice or learning (Hultsch et
al., 2000). Residuals are plotted in Figure 4. Each residual noise
series represents individuals’ week-to-week fluctuations in incon-
sistency (intraindividual variability), after we controlled for sys-
tematic intraindividual changes. We proceeded to analyze these
residuals along two avenues.
Structure of intraindividual variability.
noted intraindividual variability in practice-adjusted inconsistency
for evidence of systematic statelike patterns. We began by using
Ljung-Box tests (Ljung & Box, 1978) to test whether individual
First, we examined the
individual is represented by a single line connecting the week-to-week scores, illustrating practice effects. The
prototypical trajectory is indicated as thick bold line.
Predicted exponential growth curves representing intraindividual change in inconsistency. Each
dividual variability in inconsistency is represented by a single line.
Residual (practice adjusted) inconsistency factor scores across 36 weeks. Each individual’s intrain-
CHANGE AND VARIABILITY OF INCONSISTENCY
time series were white noise. This statistical test indicates whether
or not the given time series can be considered a random white-
noise series. All but 3 of the 91 individuals’ intraindividual vari-
ability series were white noise (Ljung-Box Q ? critical ?2, p ? .01
for up to 12 lags).
Post hoc examinations of these three individuals’ performances
indicated that they were idiosyncratic in one way or another. For
two individuals, their intraindividual change with practice would
have been better identified by a linear rather than an exponential
pattern of change. The misidentification of the intraindividual
change model for these individuals left a systematic pattern in their
residual noise series. The other individual’s intraindividual change
exhibited a 4-week period of higher than expected inconsistency,
consecutive “outliers,” so to speak. These observations also led to
a misidentification of his exponential pattern of change and thus a
systematic pattern in his intraindividual variability series.
For all other individuals, the residual intraindividual variability
series were white noise. Thus, we concluded that, overall, there
was very little evidence of systematic patterns within the intrain-
dividual variability of inconsistency. There was no systematic
structure in the intraindividual variability that we could try to
model. We could, however, attempt to characterize the noise.
Level of intraindividual variability (noise).
of intraindividual variability in each individual’s residual noise
series was quantified by calculating the ISD across weeks (Slifkin
& Newell, 1998). This ISD was used as an index of the level of
noise or “hum” in individuals’ week-to-week inconsistency. Some
individuals exhibited large fluctuations, that is, lots of noise; others
exhibited only small fluctuations, that is, only a little noise.
The gross amount
Interindividual Differences in Intraindividual Variability
Across the 91 participants, the gross amount of intraindividual
variability (ISD) ranged from 0.135 to 0.751 (M ? 0.339, SD ?
.118). There were clear, relatively normally distributed, interindi-
vidual differences in amount of intraindividual variability (i.e.,
level of noise). Using multiple regression, we also found that level
of noise was significantly related to age and intelligence scores,
F(2, 79) ? 17.97, p ? .0001. Particularly, level of noise was
negatively related to Cattell Culture Fair Test scores, t ? 5.44, p ?
.0001, ? ? ?.51, and not related to age, t(89) ? 0.28, p ? .05. In
sum, individuals with lower levels of intelligence tended to exhibit
greater variability in consistency. On some occasions, they were
much more consistent than expected, and on others they were
much less consistent than expected. Individuals with higher intel-
ligence scores did not vary as much in their week-to-week levels
Our main purpose was to analyze a set of multivariable, multi-
occasion, multiperson, multi–time-scale data to extract and under-
stand interindividual differences in both intraindividual change
and intraindividual variability of inconsistency in memory speed
performance. Through five steps of data analysis, inconsistency
was shown to manifest systematic intraindividual changes, intra-
individual variability, and interindividual differences in these two
types of within-person change.
We used chain P-technique factor analysis to derive a model for
the multivariate measurement of inconsistency. We found good
convergence across multiple measures of inconsistency that al-
lowed for the reliable measurement of inconsistency. To our
knowledge, this is the first time that the advantages of multivariate
measurement models to nullify the intrusion of error have been
applied in the study of inconsistency.
Second, we identified intraindividual change in inconsistency
using growth curve analysis techniques. Exponential curves pro-
vided the best description of how an individual’s inconsistency
changes with practice and supported our hypothesis that inconsis-
tency changes with practice. These findings mirror a plethora of
research wherein learning processes have often been found to be
best described by curves of the exponential family (Heathcote et
al., 2000; Newell & Rosenbloom, 1981; Newell, et al. 2001;
Thorndike, 1913; Thurstone, 1919). Here, the evidence indicates
that consistency may be a product of learning as well.
The exponential model of change is particularly useful as a
descriptor of learning, because the estimated parameters can be
articulated in relation to theories of intellectual ability. The model
parameters, ?0i, ?1i, and ?2i, describe the final level of inconsis-
tency reached, the increment in improvement between initial level
and final level, and the rate of improvement over time. Provided
that the length of assessment is long enough for individuals to
reach an asymptotic level of performance, as appears to have been
the case in this study, the final level of inconsistency can be
interpreted as the limit of an individual’s capacity (e.g., testing the
limits, Kliegl, Smith, & Baltes, 1989; biological substrate, Thur-
stone, 1919). The individual cannot get any better. We have, in our
analysis of intraindividual change, not only described the learning
process, but also have been able to extract a measure of an
individual’s “true” ability level, a meaningful measure on which to
Third, we examined interindividual differences in intraindi-
vidual change. We found that individuals’ intelligence scores were
systematically related to their true ability levels (i.e., capacities)
but were not systematically related to either their potential for
improvement or the rate of their improvement over time. We only
found that individuals with higher intelligence scores were able to,
in the end, achieve lower levels of inconsistency. From a sampling
perspective, such results are as we would expect. Individuals’
previous experience with the task, familiarity with test taking, and
so forth are not known at the first occasion of measurement. We
expect such individual characteristics — characteristics that affect
task performance or inconsistency — to be randomly distributed
when participants enter into training. Therefore, we would not
expect these characteristics to be systematically related to intelli-
gence or age. Here they were not. However, after individuals have
progressed in their training and achieved a substantial level of
common experiences and familiarity with the tasks at hand, we
would expect those initial differences to be greatly reduced if not
erased. Any remaining interindividual differences would be ex-
pected to be related to true ability (i.e., intelligence). This was
indeed the case.
Additionally, age was not systematically related to any of these
three parameters. Within the context of these data and particular
sample, such a finding is to be expected. The participants were
specifically selected so that age was not related to intelligence
level. Participants across three decades of life were matched on
RAM, RABBITT, STOLLERY, AND NESSELROADE
intelligence scores (i.e., AH4-1). Thus, this sample does not ex-
hibit the usual negative age trends in intelligence scores that many
older samples exhibit (Rabbitt et al., 2001; Craik & Salthouse,
2000). We also found that the participants’ age is not related to
their pattern of intraindividual change in inconsistency.
Fourth, we identified intraindividual variability in inconsis-
tency. We examined the intraindividual variability in practice-
adjusted inconsistency for evidence of systematic statelike pat-
terns. Except for a few isolated cases, we found none. Individuals’
across-week variability in inconsistency was random noise. Al-
though we had not expected to arrive at this point so quickly, this
is what we had hoped to find at the end of the analysis. There were
no systematic patterns left to explain in the data. Thus, we found
within-person changes that were completely transient (i.e., true
intraindividual variability; Hultsh & Macdonald, 2004). Noise,
although qualitatively similar across all persons (i.e., all noise
series have no inherent structure), differed in quantity across
Finally, we examined these interindividual differences in intra-
individual variability, that is, noise. In line with previous research
(e.g., Li & Lindenberger, 1999), we found that level of noise was
related to level of intelligence. Individuals who exhibited higher
levels of noise in inconsistency tended to score lower on intelli-
gence tests. Thus, we provide new evidence that noise is a marker
for some inherent characteristic that is associated with a wide
variety of functional decrements. Recent research suggests that
noisy brain activation patterns are related to decrements in mem-
ory, intelligence, and so forth (see Raz, 2000, for a review). Our
findings suggest that future research in this area should also
include investigation of how neural activation relates to cognitive
performance inconsistency (see also Li & Lindenberger, 1999).
Previous research has indicated that inconsistency is related to
age, injury, health, and intelligence (Hultsch & MacDonald, 2004)
and is a marker of impending decline or of already low function-
ality (e.g., Li & Lindenberger, 1999; Rowe & Kahn, 1985). This
investigation indicates that inconsistency, itself, exhibits distinct
and meaningful patterns of change, some enduring and some
transient. In other words, inconsistency is not a stable interindi-
vidual difference construct. It changes from moment to moment.
Furthermore, these changes in inconsistency appear to contain
meaningful interindividual difference information (e.g., correlated
to intelligence). Implications of these results are that repeated
measurements of intraindividual variability (e.g., inconsistency)
may be warranted and that further examinations of the change and
variability of intraindividual variability (and change) may also
Rabbitt et al. (2001) examined the data used here for individual
differences in performance variability. They found that within-
session variability (what we have termed inconsistency) in task
performance was positively related to between-sessions variability
in mean task performance (which we did not examine). Similar
results have also been noted by Hultsch et al. (2000) and West et
al. (2002). To help place the results from this examination in
relation to this set of previous results, we noted a positive rela-
tionship (r ? .61) between individuals’ (asymptotic) within-
session variability, ?0i, and between-sessions variability in
(practice-adjusted) within-session variability (intraindividual vari-
ability). These results provide further evidence that there may be,
as Li and Lindenberger (1999) propose, a single neurobioloical
substrate underlying all kinds of intraindividual variability.
These previous studies have also noted that inconsistency makes
independent contributions to the prediction of other individual
differences (e.g., mental status, intelligence) in addition to those
made by level of performance. We noted a similar type of finding.
Both the (asymptotic) level of inconsistency and intraindividual
variability of inconsistency (i.e., level of noise) made independent
contributions to the prediction of intelligence scores (R2? .42,
?R2with addition of level of noise variable ? .06), F(2, 79) ?
29.08, p ? .0001. In sum, this study builds upon the previous
research by digging one level deeper into the characteristics of
inconsistency, with both the intraindividual changes and variability
of inconsistency seeming to hold some utility for furthering our
understanding of cognitive functioning.
In conclusion, we used a number of multivariate techniques to
analyze a set of longitudinal data that included two time scales of
measurement. Week-to-week measurements were taken across-
occasions, while second-to-second measurements were taken
within-session. Such a burst design provided a number of oppor-
tunities for analysis. Along the shorter time frame, we studied
intraindividual variability orinconsistency. Along the longer time
frame, we were able to examine both intraindividual change and
intraindividual variability in this construct. Without multiple time
scales of measurement, such an investigation would not have been
possible. Furthermore, if individuals’ actual trial-to-trial perfor-
mance information had been available (rather than only the within-
session summary statistics), we could have examined within-
inconsistency structures. We would encourage other researchers to
examine such design elaborations (see also Newell, Liu, & Mayer-
Kress, 2001). In the same way that much knowledge has been
gained over the years with the inclusion of multiple variables into
study designs, much might also be learned with the inclusion of
multiple time frames.
Anderson, R. D., & Rubin, H. (1956). Statistical inference in factor
analysis. Proceedings of the Third Berkeley Symposium of Mathematical
Statistics and Probability, 5, 111–150.
Anstey, K. J. (1999). Sensorimotor variables and forced expiratory volume
as correlates of speed, accuracy, and variability in reaction time perfor-
mance in late adulthood. Aging, Neuropsychology, and Cognition, 6,
Bryck, A. S., & Raudenbush, S. W. (1987). Application of hierarchical
linear models to assessing change. Psychological Bulletin, 101, 147–
Bryck, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models:
Applications and data analysis methods. Newbury Park, CA: Sage.
Butler, A. C., Hokanson, J. E., & Flynn, H. A. (1994). A comparison of
self-esteem lability and low trait self-esteem as vulnerability factors for
depression. Journal of Personality and Social Psychology, 66, 166–177.
Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant
validation by the multitrait-multimethod matrix. Psychological Bulletin,
Cattell, R. B. (1957). Personality and motivation: Structure and measure-
ment. New York: World Book.
Cattell, R. B. (1963). The structuring of change by P-technique and
incremental R-technique. In C. W. Harris (Ed.), Problems in measuring
change (pp. 167–198). Madison: University of Wisconsin Press.
Cattell, R. B. (1973). Cattell Culture Fair Intelligence Test. Champaign,
IL: Institute for Personality and Ability Testing.
CHANGE AND VARIABILITY OF INCONSISTENCY
Cattell, R. B., Cattell, A. K. S., & Rhymer, R. M. (1947). P-technique
demonstrated in determining psychophysical source traits in a normal
individual. Psychometrika, 12, 267–288.
Craik, F. I. M., & Salthouse, T. A. (Eds.) (2000). The handbook of aging
and cognition (2nd ed.). Mahwah, NJ: Erlbaum.
Eizenman, D. R., Nesselroade, J. R., Featherman, D. L., & Rowe, J. W.
(1997). Intra-individual variability in perceived control in an elderly
sample: The MacArthur Successful Aging Studies. Psychology and
Aging, 12, 489–502.
Fiske, D. W., & Rice, L. (1955). Intra-individual response variability.
Psychological Bulletin, 52, 217–250.
Fox, N. A., & Porges, S. W. (1985). The relationship between neonatal
heart period patterns and developmental outcome. Child Development,
Fozard, J. L., Vercruyssen, M., Reynolds, S. L., Hancock, P. A., & Quilter,
R. E. (1994). Age differences and changes in reaction time: The Balti-
more Longitudinal Study of Aging. Journal of Gerontology: Psycholog-
ical Sciences, 49, P179–P189.
Fuentes, K., Hunter, M. A., Strauss, E., & Hultsch, D. F. (2001). Intrain-
dividual variability in cognitive performance in persons with chronic
fatigue syndrome. The Clinical Neuropsychologist, 15, 210–227.
Grice, J. W. (2001). Computing and evaluating factor scores. Psycholog-
ical Methods, 6, 430–450.
Guttman, L. (1955). The determinacy of factor score matrices with appli-
cations for five other problems of common factor theory. British Journal
of Statistical Psychology, 8, 65–82.
Hampson, E. (1990). Variations in sex related cognitive abilities across the
menstrual cycle. Brain and Cognition, 14, 26–43.
Heathcote, A. Brown, S., & Mewhort, D. J. K. (2000). The power law
repealed: The case for an exponential law of practice. Psychonomic
Bulletin & Review, 7, 185–207.
Hendrickson, A. E., (1982). The biological basis of intelligence: I. Theory.
In H. J. Eysenck (Ed.), A model for intelligence (pp. 151–196). Berlin,
Hertzog, C. Dixon, R. A., & Hultsch, D. F. (1992). Intraindividual change
in text recall of the elderly. Brain and Language, 42, 248–269.
Horn, J. L. (1972). State, trait, and change dimensions of intelligence. The
British Journal of Educational Psychology, 42, 159–185.
Hultsch, D. F., & MacDonald, S. W. S. (2004). Intraindividual variability
in performance as a theoretical window onto cognitive aging. In R. A.
Dixon, L. Ba ¨ckman, & L.-G. Nilsson (Eds.), New frontiers in cognitive
aging (pp. 65–88). Oxford, England: Oxford University Press.
Hultsch, D. F., MacDonald, S. W. S., Hunter, M. A., Levy-Bencheton, J.,
& Strauss, E. (2000). Intraindividual variability in cognitive perfor-
mance in older adults: Comparison of adults with mild dementia, adults
with arthritis, and healthy adults. Neuropsychology, 14, 588–598.
Hultsch, D. F., MacDonald, S. W. S., Hunter, M. A., Maitland, S. B., &
Dixon, R. A. (2002). Sampling and generalisability in developmental
research: Comparison of random and convenience samples of older
adults. International Journal of Behavioral Development, 26, 345–359.
Jo ¨reskog, K. G., & So ¨rbom, D. LISREL (Version 8.54) [Computer pro-
gram]. Hillsdale, NJ: Scientific Software International.
Kagan, J. (1994). Galen’s prophecy. New York: Basic Books.
Kliegl, R., Smith, J., & Baltes, P. B. (1989). Testing-the-limits and the
study of adult age differences in cognitive plasticity of a mnemonic skill.
Developmental Psychology, 25, 247–256.
Li, S. C., Aggen, S. H., Nesselroade, J. R., & Baltes, P. B. (2001).
Short-term fluctuations in elderly people’s sensorimotor functioning
predict text and spatial memory performance: The MacArthur Success-
ful Aging Studies. Gerontology, 47, 100–116.
Li, S. C., & Lindenberger, U. (1999). Cross-level unification: A compu-
tational exploration of the link between deterioration of neurotransmitter
systems and dedifferentiation of cognitive abilitites in old age. In L. G.
Nilsson & H. J. Markowitsch (Eds.), Cognitive neuroscience of memory
(pp. 103–146). Seattle, WA: Hogrefe & Huber.
Little, R., & Rubin, D. (1987). Statistical analysis with missing data. New
Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time
series models. Biometrika, 65, 297–303.
MacDonald, S. W. S., Hultsch, D. F., & Dixon, R. A. (2003). Performance
variability is related to change in cognition: Evidence from the Victoria
Longitudinal Study. Psychology and Aging, 18, 510–523.
McArdle, J. J., & Nesselroade, J. R. (2003). Growth curve analysis in
contemporary psychological research. In J. Shinka, & W. Velicer (Eds.),
Comprehensive handbook of psychology: Vol. 2. Research methods in
psychology (pp. 447–480). New York: Wiley.
Nesselroade, J. R. (1988). Some implications of the trait-state distinction
for the study of development across the life span: The case of personality
research. In P. B. Baltes, D. L. Featherman, & R. M. Lerner (Eds.),
Life-span development and behavior (Vol. 8, pp. 163–189). Hillsdale,
Nesselroade, J. R. (1991). The warp and woof of the developmental fabric.
In R. Downs, L. Liben, & D. Palermo (Eds.), Visions of development, the
environment, and aesthetics: The legacy of Joachim F. Wohlwill (pp.
213–240). Hillsdale, NJ: Erlbaum.
Nesselroade, J. R. (2002). Elaborating the different in differential psychol-
ogy. Multivariate Behavioral Research, 37, 543–561.
Nesselroade, J. R., & Featherman, D. L. (1997). Establishing a reference
frame against which to chart age-related changes. In M. A. Hardy (Ed.),
Studying aging and social change: Conceptual and methodological
issues (pp. 191–205). Newbury Park, CA: Sage.
Nesselroade, J. R., & Ford, D. H. (1985). P-technique comes of age:
Multivariate, replicated, single-subject designs for research on older
adults. Research on Aging, 7, 46–80.
Newell, A., & Rosenbloom, P. S. (1981). Mechanisms of skill acquisition
and the law of practice. In J. R. Anderson (Ed.), Cognitive skills and
their acquisition (pp. 1–55). Hillsdale, NJ: Erlbaum.
Newell, K. M., Liu, Y. T., & Mayer-Kress, G. (2001). Time scales in motor
learning and development. Psychological Review, 108, 57–82.
Rabbitt, P. (1993). Crystal quest: An examination of the concepts of “fluid”
and “crystallised” intelligence as explanation for cognitive changes in
old age. In A. D. Baddeley & L. Weiskrantz (Eds.), Attention, selection,
awareness, and control. Oxford, England: Oxford University Press.
Rabbitt, P., Osman, P., Moore, B., & Stollery, B. (2001). There are stable
differences in performance variability, both from moment to moment
and from day to day. The Quarterly Journal of Experimental Psychol-
ogy, 54A, 981–1003.
Raz, N. (2000). Aging of the brain and its impact on cognitive perfor-
mance: Integration of structural and functional findings. In F. I. M. Craik
& T. A. Salthouse (Eds.), The handbook of aging and cognition (2nd ed.,
pp. 1–90). Mahwah, NJ: Erlbaum.
Rogosa, D., & Willet, J. B. (1985). Understanding correlates of change by
modeling individual differences in growth. Psychometrika, 50, 203–228.
Rowe, J. W., & Kahn, R. L. (1985). Human aging: Usual and successful.
Science, 237, 143–149.
Rowe, J. W., & Kahn, R. L. (1997). Successful aging. The Gerontologist,
Shammi, P., Bosman, E., & Stuss, D. T. (1998). Aging and variability in
performance. Aging, Neuropsychology, and Cognition, 5, 1–13.
Shoda, Y., Mischel, W., & Wright, J. C. (1994). Intraindividual stability in
the organization and patterning of behavior: Incorporating psychological
situations into the idiographic analysis of behavior. Journal of Person-
ality and Social Psychology, 67, 674–687.
Siegler, R. S. (1994). Cognitive variability: A key to understanding cog-
nitive development. Current Directions in Psychological Science, 3,
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis:
RAM, RABBITT, STOLLERY, AND NESSELROADE
Modeling change and event occurrence. New York: Oxford University
Slifkin, A. B., & Newell, K. M. (1998). Is variability in human perfor-
mance a reflection of system noise? Current Directions in Psychological
Science, 7, 170–177.
Spieler, D. H., Balota, D. A., & Faust, M. E. (1996). Stroop performance
in healthy younger and older adults and in individuals with dementia of
the Alzheimer’s type. Journal of Experimental Psychology: Human
Perceptions and Performance, 22, 461–479.
Stuss, D. T., Pogue, J. Buckle, L., & Bondar, J. (1994). Characterization of
stability of performance in patients with traumatic brain injury: Vari-
ability and consistency on reaction time tests. Neuropsychology, 8,
Stuss, D. T., Stethem, L. L., Hugenholtz, H. Picton, T., & Richard, M. T.
(1989). Reaction time after head injury: Fatigue, divided and focused
attention, and consistency of performance. Journal of Neurology, Neu-
rosurgery, and Psychiatry, 52, 742–748.
Thorndike, E. L. (1913). Educational psychology: The psychology of
learning (Vol. 2). New York: Teachers College Press.
Thurstone, L. L. (1919). The learning curve equation. Psychological Mono-
graphs, 26(3), 1–51.
West, R., Murphy, K. J., Armilio, M. L., Craik, F. I. M., & Stuss, D. T.
(2002). Lapses of intention and performance variability reveal age-
related increases in fluctuations of executive control. Brain and Cogni-
tion, 49, 402–419.
Widaman, K. F. (1985). Hierarchically nested covariance structure models
for multitrait-multimethod data. Applied Psychological Measurement, 9,
Widaman, K. F. (1992). Multitrait-multimethod models in aging research.
Experimental Aging Research, 18, 185–201.
Wishart, J. (1938). Growth rate determinations in nutrition studies with the
bacon pig, and their analyses. Biometrika, 30, 16–28.
Wohlwill, J. F. (1973). The study of behavioral development. New York:
Received November 3, 2004
Revision received May 31, 2005
Accepted June 10, 20005 ?
CHANGE AND VARIABILITY OF INCONSISTENCY