Allemand, M., Zimprich, D., & Hertzog, C. (2007). Cross-sectional
age differences and longitudinal age changes of personality in middle
adulthood and old age. Journal of Personality, 75(2), 323-358.
Abstract
The present study examines different aspects of personality continuity (or change) in
middle adulthood and old age both crosssectionally and longitudinally.
The sample comprised 445 middle-aged (42-46 years) and 420 older (60-64
years) participants, reassessed after a 4-year interval. Personality was
measured using the NEO-FFI personality inventory. After having
established strict factorial invariance, factor covariances were found
to be equal for both age groups and at both testing occasions,
indicating perfect structural continuity of personality. A number of age
differences in personality emerged at both measurement occasions.
Longitudinally, in both age groups, an average decline in Neuroticism
was observed. Longitudinal stability coefficients were around .80 in
middle-aged and old participants, implying high, but not perfect,
differential continuity. With respect to continuity of divergence,
statistically significant cross-sectional age differences were found for
the variance of Openness at both measurement occasions. Eventually,
concerning specific versus general continuity, a variety of medium
effect-sized correlated changes in the Big Five personality domains
across the 4-year period was established, implying that personality
changes share a certain amount of commonality.
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Hertzog, C., Lindenberger, U., Ghisletta, P., & Oertzen, T. v.
(2006). On the Power of Multivariate Latent Growth Curve Models to
Detect Correlated Change. Psychological Methods, 11(3), 244-252.
Abstract
We evaluated the statistical power of single-indicator latent growth curve models (LGCMs)
to detect correlated change between two variables (covariance of slopes)
as a function of sample size, number of longitudinal measurement
occasions, and reliability (measurement error variance). Power
approximations following the method of Satorra and Saris (1985) were
used to evaluate the power to detect slope covariances. Even with large
samples (N=500) and several longitudinal occasions (4 or 5), statistical
power to detect covariance of slopes was moderate to low unless growth
curve reliability at study onset was above .90. Studies using LGCMs may
fail to detect slope correlations because of low power rather than a
lack of relationship of change between variables. The present findings
allow researchers to make more informed design decisions when planning a
longitudinal study and aid in interpreting LGCM results regarding
correlated interindividual differences in rates of development.
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Hertzog, C., von Oertzen, T., Ghisletta, P., & Lindenberger, U. (2006).
Evaluating the Power of latent growth models to detect individual
differences in change. In press.
Abstract
We evaluated the statistical power of single-indicator latent growth curve models (LGCMs)
to detect individual differences in change (variances of latent slopes)
as a function of sample size, number of longitudinal measurement
occasions, and growth curve reliability (GCR). We recommend the 2
degree-of-freedom generalized test assessing loss of fit when both slope
related random effects, the slope variance and intercept-slope
covariance, are fixed to zero. Statistical power to detect individual
differences in change is low to moderate unless the residual error
variance is low, sample size is large, and there are more than four
measurement occasions. The generalized test has greater power than a
specific test isolating the hypothesis of zero slope variance, except
when the true slope variance is close to zero, and has uniformly
superior power to a Wald test based on the estimated slope variance.
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Small, B. J., Hertzog, C., Hultsch, D. F., & Dixon, R. A. (2003).
Stability and change in adult personality over 6 years: Findings from
the Victoria Longitudinal Study. Journal of Gerontology:
Psychological Sciences, 58B, 166-176.
Abstract
Data from the Victoria Longitudinal Study were
used to examine the 6-year longitudinal stability of personality in
older adults. Personality was measured with the NEO Personality
Inventory. The longitudinal sample consisted of 223 adults initially
ranging from 55 to 85 years of age. Longitudinal confirmatory factor
analyses were used to examine the stability of individual differences in
change over time, and the stability of the longitudinal factor
structure. The results indicated both substantial stability at the level
of individual differences in change, as well as significant individual
differences in change that were related to age and gender. Finally, the
factor structure of personality was invariant over time but did not
approximate simple structure for the five dimensions of personality. Our
study of 6-year personality development provided both (a) a confirmation
of early significant stability findings and (b) unique evidence for
significant individual differences in late adulthood.
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Hertzog, C., Dixon, R. A., Hultsch, D. F., & MacDonald, S. W.
S. (2003). Latent change models of adult cognition: Are
changes in processing speed and working memory associated with changes
in episodic memory? Psychology and Aging, 18, 755-769.
Abstract
The authors used 6-year longitudinal data from the
Victoria Longitudinal Study (VLS) to investigate individual differences
in amount of episodic memory change. Latent change models revealed
reliable individual differences in cognitive change. Changes in episodic
memory were significantly correlated with changes in other cognitive
variables, including speed and working memory. A structural equation
model for the latent change scores showed that changes in speed and
working memory predicted changes in episodic memory, as expected by
processing resource theory. However, these effects were best modeled as
being mediated by changes in induction and fact retrieval. Dissociations
were detected between cross-sectional ability correlations and
longitudinal changes. Shuffling the tasks used to define the Working
Memory latent variable altered patterns of change correlations.
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Hertzog, C., & Nesselroade, J. R. (2003). Assessing
psychological change in adulthood: An overview of methodological issues.
Psychology and Aging, 18, 639-657.
Abstract
This article reviews the current status of methods
available for the analysis of psychological change in adulthood and
aging. Enormous progress has been made in designing statistical models
that can capture key aspects of intraindividual change, as reflected in
techniques such as latent growth curve models and multilevel
(random-effects) models. However, the rapid evolution of statistical
innovations may have obscured the critical importance of addressing
rival explanations for statistical outcomes, such as cohort differences
or practice effects that could influence estimates of age-related
change. Choice of modeling technique and implementation of a specific
modeling approach should be grounded in and reflect both the theoretical
nature of the developmental phenomenon and the features of the sampling
design that selected persons, variables, and contexts for empirical
observation.
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Hultsch, D. F., Hertzog, C., Small, B., & Dixon, R. A. (1999).
Use it or lose it: Engaged lifestyle as a buffer of cognitive decline in
aging? Psychology and Aging, 14, 245-263.
Abstract
Data from the Victoria Longitudinal Study were
used to examine the hypothesis that maintaining intellectual engagement
through participation in everyday activities buffers individuals against
cognitive decline in later life. The sample consisted of 250 middle-aged
and older adults tested 3 times over 6 years. Structural equation
modeling techniques were used to examine the relationships among changes
in lifestyles variables and an array of cognitive variables. There was a
relationship between changes in intellectually related activities and
changes in cognitive functioning. These results are consistent with the
hypothesis that intellectually engaging activities serve to buffer
individuals against decline. However, an alternative model suggested the
findings were also consistent with the hypothesis that high-ability
individuals lead intellectually active lives until cognitive decline in
old age limits their activities.
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Hertzog, C., Hultsch, D. F., & Dixon, R. A. (1999). On
the problem of detecting effects of lifestyle on cognitive change in
adulthood: Reply to Pushkar et al. (1999). Psychology and Aging, 14,
528-534.
Abstract
The authors respond to issues raised about data
from the Victoria Longitudinal Study and further explain concerns
regarding evidence for the engagement hypothesis. Discussion focuses on
the use of social stratification variables such as occupational prestige
and educational attainment as measures of an engaged lifestyle. It is
argued that (a) tests of the hypothesis should focus on the relationship
of behaviors and activities thought to be proximal beneficial influences
on adult cognitive development; (b) persuasive evidence for engagement
effects from existing data require demonstration of effects of
intellectual activities that are statistically independent of
associations of social status with intellectual and cognitive
development; and (c) currently available longitudinal data do not
provide definitive evidence regarding the benefits of an engaged
lifestyle on cognitive change.
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Hertzog, C. (1990). On the utility of structural
equation models for developmental research. In P. B. Baltes, D. L.
Featherman, & R. M. Lerner (Eds.), Life-span development and
behavior, 10, 257-290. Hillsdale, NJ: Lawrence Erlbaum Associates.
Abstract
Structural equation models (SEM) have become an
increasingly popular technique for analysis of developmental research
questions. However, a number of unfortunate misconceptions can be found
in the literature regarding the nature, potential, and pitfalls of SEM.
It is fallacious to assume that use of SEM techniques guarantees sound
causal inference from correlational data: it is equally fallacious to
argue that use of SEM for purposes other than testing causal models is
an invalid misapplication of the method. In developmental research,
important descriptive research questions can be shown to be linked to
SEM models in two important ways: Alternative SEM models may be used to
provide direct statistical tests of important descriptive developmental
hypotheses, and SEM model parameters can be interpreted with respect to
fundamental issues in developmental analysis (e.g., estimating the
degree to which differential developmental patterns alter distributions
of individual differences). This paper develops the logic and procedures
for implementing longitudinal SEM techniques to address descriptive
developmental questions, with a brief illustration of the application of
SEM to longitudinal factor analysis.
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Hertzog, C., & Schaie, K. W. (1988). Stability and
change in adult intelligence: 2. Simultaneous analysis of longitudinal
means and covariance structures. Psychology and Aging, 3,
122-130.
Abstract
We analyzed data on psychometric intelligence from
the Seattle Longitudinal Study, simultaneously estimating longitudinal
factors, their covariance structure, and their mean levels. Data on five
Thurstone Primary Mental Abilities subtests were available for 412
adults, ages 22-70 at first test, who were tested three times at 7-year
intervals. A previous longitudinal factor analysis had shown high
stability of individual differences (covariance stability) in general
intelligence for three adult age groups. We extended that model to
estimate factor means. All three age groups showed high levels of
covariance stability, but differed sharply in their mean profiles. The
young group showed increasing levels of general intelligence, the
middle-aged group had stable levels of intelligence, and the old group
showed salient, approximately linear, decline. The patterns of stability
in middle-age, followed by mean decline and high covariance stability in
old age, suggest a normative developmental transition from a stability
pattern to a decline pattern of general intelligence, with the
inflection point occurring somewhere around age 60.
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Hertzog, C., & Nesselroade, J. R. (1987). Beyond
autoregressive models: Some implications of the trait-state distinction
for the structural modeling of developmental change. Child
Development, 58, 93-109.
Abstract
The use of structural modeling techniques to fit
change concepts, including developmental ones, to repeated-measurements
data has been rather firmly but uncritically wedded to autoregressive
model specifications. The uncritical application of an autoregressive
specification to repeated measures does not take into account subtleties
of conceptions of stability and change (e.g., the trait-state
distinction) that are now recognized in the behavioral research
literature. We review the basic distinction between trait and state and
examine the implications of the different possibilities for modeling
developmental phenomena. The arguments are illustrated with empirical
examples.
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Hertzog, C. (1987). Applications of structural
equation models in gerontological research. In K.W. Schaie (Ed.),
Annual review of gerontology and geriatrics, 7, 265-293. New York:
Springer.
Abstract
The past several years have been marked by an
accelerating rate of increase in sophisticated new methods for
conducting valid and informative empirical research on nonexperimental
data (e.g., Blalock, 1985a, b: Nesselroade & Baltes, 1979). Some of the
more important advances have been in the domain of structural
equation models (SEM). Traditionally, SEM usually refers to complex
regression models (e.g., path analysis) that analyze causal relations
among unobserved (latent) variables. An important component of SEM,
therefore, is that part of the model maps the latent variables onto
variables we actually measure empirically (the observed or manifest
variables). This part of SEM is usually termed the measurement model.
The SEM measurement model is, essentially, a confirmatory factor
analysis in which the observed variables are specified to be a linear
combination of latent variables (factors). The part of the SEM
specifying regression relationships among latent variables is the
structural regression model. In this chapter I describe SEM
applications, often consisting only of confirmatory factor analyses
without a structural regression model, that address research questions
of critical importance to gerontologists. Most of these applications are
in the domain of psychometric intelligence and cognition, but they
illustrate SEM techniques that can be used in other domains as well.
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