An experimental analysis of dynamic hypotheses about cognitive abilities and achievement from childhood to early adulthood

This study examined the dynamics of cognitive abilities and academic achievement from childhood to early adulthood. Predictions about time-dependent "coupling" relations between cognition and achievement based on R. B. Cattell's (1971, 1987) investment hypothesis were evaluated using linear dynamic models applied to longitudinal data (N=672). Contrary to Cattell's hypothesis, a first set of findings indicated that fluid and crystallized abilities, as defined by the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R; R. W. Woodcock & M. B. Johnson, 1989-1990), were not dynamically coupled with each other over time. A second set of findings provided support for the original predictions and indicated that fluid ability was a leading indicator of changes in achievement measures (i.e., quantitative ability and general academic knowledge). The findings of this study suggest that the dynamics of cognitive abilities and academic achievement follow a more complex pattern than that specified by Cattell's investment hypothesis.     

On structural models for analyzing change

The recommendation to base the analysis of multi-wave data upon explicit models for change is advocated. Several univariate and multivariate models are described, which emerge from an interaction between the classical test theory and the structural equation modeling approach. The resulting structural models for analyzing change reflect in some of their parameters substantively interesting aspects of intra- and interindividual change in follow-up studies. The models are viewed as an alternative to an ANOVA-based analysis of longitudinal data, and are illustrated on data from a cognitive intervention study of old adults (Bakes et al , 1986). The approach presents a useful means of analyzing change over time, and is applicable for purposes of (latent) growth curve analysis when analysis of variance assumptions are violated (e.g., Schaie & Hertzog, 1982; Morrison, 1976).     

Age changes in processing speed as a leading indicator of cognitive aging

Bivariate dual change score models were applied to longitudinal data from the Swedish Adoption/Twin Study of Aging to compare the dynamic predictions of 2-component theories of intelligence and the processing speed theory of cognitive aging. Data from up to 5 measurement occasions covering a 16-year period were available from 806 participants ranging in age from 50 to 88 years at the first measurement wave. Factors were generated to tap 4 general cognitive domains: verbal ability, spatial ability, memory, and processing speed. Model fitting indicated no dynamic relationship between verbal and spatial factors, providing no support for the hypothesis that age changes in fluid abilities drive age changes in crystallized abilities. The results suggest that, as predicted by the processing speed theory of cognitive aging, processing speed is a leading indicator of age changes in memory and spatial ability, but not verbal ability.     

Social participation attenuates decline in perceptual speed in old and very old age

Does an engaged and active lifestyle in old age alleviate cognitive decline, does high cognitive functioning in old age increase the possibility of maintaining an engaged and active lifestyle, or both? The authors approach this conundrum by applying a structural equation model for testing dynamic hypotheses, the dual change score model (J. J. McArdle & F. Hamagami, 2001), to 3-occasion longitudinal data from the Berlin Aging Study (Time 1: n=516, age range=70-103 years). Results reveal that within a bivariate system of perceptual speed and social participation, with age and sociobiographical status as covariates, prior scores of social participation influence subsequent changes in perceptual speed, while the opposite does not hold. Results support the hypothesis that an engaged and active lifestyle in old and very old age may alleviate decline in perceptual speed.     

Cognitive Development in Young-old Type-2 Diabetes Patients: A Longitudinal Analysis From The “Interdisciplinary Longitudinal Study of Aging”

In the present study, we investigated cognitive status and cognitive development in young-old Type-2 diabetes patients. Extending previous research, using the sample of the ILSE study (“Interdisciplinary Longitudinal Study of Aging”), we applied a comprehensive cognitive test battery to 38 Type-2 diabetes patients (mean age at T1: 63 years) and 421 control participants and tested both cognitive status and longitudinal changes after four years in several cognitive domains (crystallized intelligence, verbal memory, visual memory, executive functioning, psychomotor speed, and fluid intelligence). Results revealed no cross-sectional group differences in any cognitive domain at T1 nor at T2. However, testing group differences in intraindividual change over time, reliable group differences in the change of crystallized intelligence and fluid intelligence were revealed. While controls improved in crystallized intelligence as well as in fluid intelligence over time, diabetes patients’ performance decreased. The results demonstrate that in young-old adults the effects of diabetes on cognition may only be detected by analyzing within-person changes instead of mean level differences.

Placing inspection time, reaction time, and perceptual speed in the broader context of cognitive ability: The VPR model in the Lothian Birth Cohort 1936

The idea that information processing speed is related to cognitive ability has a long history. Much evidence has been amassed in its support, with respect to both individual differences in general intelligence and developmental trajectories. Two so-called elementary cognitive tasks, reaction time and inspection time, have been used to compile this evidence, but most studies have used either one or the other. Relations between speed and fluid intelligence have tended to be stronger than those between speed and crystallized intelligence, but studies testing this have confounded verbal abilities with crystallized intelligence and spatial/perceptual abilities with fluid intelligence. Questions have also been raised regarding whether speed contributes directly to general intelligence or to more specific cognitive abilities to which general intelligence also contributes. We used 18 ability and speed measures in the Lothian Birth Cohort 1936, assessed at approximately age 70, to construct alternative versions of the Verbal-Perceptual-Image Rotation (Johnson & Bouchard, 2005a) model of cognitive ability to test different hypotheses regarding these issues. Though differences in the extents to which our models fit the data were relatively small, they suggested that reaction and inspection time tasks were comparable indicators of information processing speed with respect to general intelligence, that verbal and spatial abilities were similarly related to information processing speed, and that spatial, verbal, and perceptual speed abilities were more directly related to information processing speed than was general intelligence. We discuss the theoretical implications of these results.     

On intelligence II: A neo-Spearman model to replace Cattell's theory of fluid and crystallized intelligence

While many empirical studies now support second-order simple structure factor analytic distinctions consistent with Cattell's theory of fluid and crystallized intelligence, the hierarchical order analyses carried out in the present study show that these factors are structured so as to suggest a neo-Spearman hierarchical model where fluid intelligence is equivalent to a general intelligence factor and broad group factors of verbal-educational knowledge, speediness, and visualization appear. The two models have different implications regarding the status of the fluid and crystallized "intelligence" factors and the relationship between them.     

Assessing Item Fit for Unidimensional Item Response Theory Models Using Residuals from Estimated Item Response Functions

Residual analysis (e.g. Hambleton & Swaminathan, Item response theory: principles and applications, Kluwer Academic, Boston, 1985; Hambleton, Swaminathan, & Rogers, Fundamentals of item response theory, Sage, Newbury Park, 1991) is a popular method to assess fit of item response theory (IRT) models. We suggest a form of residual analysis that may be applied to assess item fit for unidimensional IRT models. The residual analysis consists of a comparison of the maximum-likelihood estimate of the item characteristic curve with an alternative ratio estimate of the item characteristic curve. The large sample distribution of the residual is proved to be standardized normal when the IRT model fits the data. We compare the performance of our suggested residual to the standardized residual of Hambleton et al. (Fundamentals of item response theory, Sage, Newbury Park, 1991) in a detailed simulation study. We then calculate our suggested residuals using data from an operational test. The residuals appear to be useful in assessing the item fit for unidimensional IRT models.     

Latent growth curve analyses of accelerating decline in cognitive abilities in late adulthood

Latent growth models were applied to data from the Swedish Adoption/Twin Study of Aging to discover if the rate of change in cognitive performance increased from middle age to later adulthood. The sample included 590 participants aged 44 to 88 years at first measurement. Data were gathered at 2 follow-up occasions at intervals of 3 years. Cognitive ability was assessed through 11 tests that tapped crystallized, fluid, memory, and spatial abilities and perceptual speed. Results indicated stability for measures of crystallized ability, linear age changes for many cognitive abilities, and a significant acceleration in linear decline after age 65 for measures with a large speed component. Gender differences were found only in mean level, not in rate of decline.     

Well-being affects changes in perceptual speed in advanced old age: longitudinal evidence for a dynamic link

This study examined competing hypotheses about dynamic cross-domain associations between perceptual speed and well-being in advanced old age. We applied the bivariate dual change score model (J. J. McArdle & F. Hamagami, 2001) to 13-year incomplete longitudinal data from the Berlin Aging Study (P. B. Baltes & K. U. Mayer, 1999; N=516, 70-103 years at T1, M=85 years). Reports of well-being were found to influence subsequent decline in perceptual speed (time lags of 2 years). No evidence was found for a directed effect in the other direction. None of the potential covariates examined (initial health constraints, personality, and social participation) accounted for these differential lead-lag associations. Our results suggest that well-being is not only a consequence of but also a source for successful aging. The discussion focuses on conceptual implications and methodological considerations.     

The Getting of Wisdom: Fluid Intelligence Does Not Drive Knowledge Acquisition

The investment hypothesis proposes that fluid intelligence drives the accumulation of crystallized intelligence, such that crystallized intelligence increases more substantially in individuals with high rather than low fluid intelligence. However, most investigations have been conducted on adolescent cohorts or in two-wave data sets. There are few longitudinal representative studies with which to observe the growth of intelligence in young adulthood. A large community representative sample of 20- to 24-year-olds was recruited in 1999 and followed in 2003 and 2007. The Spot-the-Word Test Version A, a lexical decision task, was used to assess crystallized intelligence. The Symbol–Digit Modalities Test, a test of perceptual speed, was used as a measure of fluid intelligence. Latent growth models were used to examine whether growth in fluid intelligence drives the acquisition of crystallized intelligence. Fluid and crystallized intelligence are coupled; crystallized intelligence accrues in the 20s, but the rate of increase of crystallized intelligence is not associated with the level of fluid intelligence. Although young adulthood is associated with “the getting of wisdom,” accelerated development of crystallized intelligence in those with high fluid intelligence was not observed. Hence, no support was found for the investment hypothesis.     

Misspecification in Latent Change Score Models: Consequences for Parameter Estimation,Model Evaluation,and Predicting Change

Latent change score models (LCS) are conceptually powerful tools for analyzing longitudinal data (McArdle & Hamagami, 2001). However, applications of these models typically include constraints on key parameters over time. Although practically useful, strict invariance over time in these parameters is unlikely in real data. This study investigates the robustness of LCS when invariance over time is incorrectly imposed on key change-related parameters. Monte Carlo simulation methods were used to explore the impact of misspecification on parameter estimation, predicted trajectories of change, and model fit in the dual change score model, the foundational LCS. When constraints were incorrectly applied, several parameters, most notably the slope (i.e., constant change) factor mean and autoproportion coefficient, were severely and consistently biased, as were regression paths to the slope factor when external predictors of change were included. Standard fit indices indicated that the misspecified models fit well, partly because mean level trajectories over time were accurately captured. Loosening constraint improved the accuracy of parameter estimates, but estimates were more unstable, and models frequently failed to converge. Results suggest that potentially common sources of misspecification in LCS can produce distorted impressions of developmental processes, and that identifying and rectifying the situation is a challenge.     

Selecting a linear mixed model for longitudinal data: repeated measures analysis of variance, covariance pattern model, and growth curve approaches

With increasing popularity, growth curve modeling is more and more often considered as the 1st choice for analyzing longitudinal data. Although the growth curve approach is often a good choice, other modeling strategies may more directly answer questions of interest. It is common to see researchers fit growth curve models without considering alterative modeling strategies. In this article we compare 3 approaches for analyzing longitudinal data: repeated measures analysis of variance, covariance pattern models, and growth curve models. As all are members of the general linear mixed model family, they represent somewhat different assumptions about the way individuals change. These assumptions result in different patterns of covariation among the residuals around the fixed effects. In this article, we first indicate the kinds of data that are appropriately modeled by each and use real data examples to demonstrate possible problems associated with the blanket selection of the growth curve model. We then present a simulation that indicates the utility of Akaike information criterion and Bayesian information criterion in the selection of a proper residual covariance structure. The results cast doubt on the popular practice of automatically using growth curve modeling for longitudinal data without comparing the fit of different models. Finally, we provide some practical advice for assessing mean changes in the presence of correlated data.     

Parent–child interaction,task-oriented regulation,and cognitive development in toddlers facing developmental risks

The importance of supportive parent–child interactions and the development of regulatory abilities with regard to growth in children's cognitive abilities has been established. This study investigated the longitudinal relations among parental supportiveness, child task-oriented regulation, and cognitive development for low-income children facing developmental risks. Interlocking growth models were fit to these variables based on measurements made when the children were 14, 24, and 36 months of age. Results showed that growth rates of child cognition were related to baseline (14 months) parental supportiveness and changes in child task-oriented regulation. We additionally found that baseline levels of cognition were related to the baseline levels of child task-oriented regulation. This study suggests that both parental supportiveness during infancy and the concurrent development of child task-oriented regulation are related to cognitive development among children facing developmental risks.     

Analyzing psychopathology items: a case for nonparametric item response theory modeling

The authors discuss the applicability of nonparametric item response theory (IRT) models to the construction and psychometric analysis of personality and psychopathology scales, and they contrast these models with parametric IRT models. They describe the fit of nonparametric IRT to the Depression content scale of the Minnesota Multiphasic Personality Inventory--2 (J. N. Butcher, W. G. Dahlstrom, J. R. Graham, A. Tellegen, & B. Kaemmer, 1989). They also show how nonparametric IRT models can easily be applied and how misleading results from parametric IRT models can be avoided. They recommend the use of nonparametric IRT modeling prior to using parametric logistic models when investigating personality data.     

Which is the real intelligence? A reply to Robinson (1999)

Robinson's ([Robinson, D.L. (1999). The ‘IQ’ factor: implications for intelligence theory and measurement. Personality and Individual Differences, 27, 715–735]) arguments that crystallized intelligence represents the “one valid intelligence factor” are disputed. It is argued that Robinson seriously underestimated the relevance of fluid abilities to “intelligence”, by using an inappropriate criterion for assessing the intelligence-saturation of cognitive ability variables. The relevance of fluid and crystallized abilities to popular, psychometric, and biological conceptions of intelligence is discussed, and the issue of age-related changes in fluid and crystallized intelligence is also addressed.     

Modeling caregiver adaptation over time: the longitudinal impact of behavior problems

Although cross-sectional research has established the link between care demands and various indicators of caregiver adaptation, few studies have examined the impact of care recipients' problematic behavior over time. The present analysis determines the importance of behavior problems when predicting rates of change in subjective stressors (role overload and role captivity) and depression. Using 4-wave longitudinal data (N = 137) on dementia caregivers, the authors fit individual growth curve models for care demands (i.e., behavior problems, activities of daily living dependencies, and cognitive impairment), subjective stressors, and depression. Subsequent structural equation models found that increases in behavior problems were most likely to predict increases in role overload. The findings emphasize the deleterious long-term impact of behavior problems on individuals' emotional adaptation to caregiving.     

Typical Intellectual Engagement and Cognition in Old Age

ABSTRACT

Typical Intellectual Engagement (TIE) comprises the preference to engage in cognitively demanding activities and has been proposed as a potential explanatory variable of individual differences in cognitive abilities. Little is known, however, about the factorial structure of TIE, its relations to socio-demographic variables, and its influence on intellectual functioning in old age. In the present study, data of 364 adults (65–81 years) from the Zurich Longitudinal Study on Cognitive Aging (ZULU) were used to investigate the factorial structure of TIE and to examine the hypothesis that TIE is associated more strongly with crystallized intelligence than with fluid intelligence in old age. A measurement model of a second order factor based on a structure of four correlated first order factors (Reading, Problem Solving, Abstract Thinking, and Intellectual Curiosity) evinced an excellent fit. After controlling for age, sex, and formal education, TIE was more strongly associated with crystallized intelligence than with fluid intelligence, comparable to results in younger persons. More detailed analyses showed that this association is mostly defined via Reading and Intellectual Curiosity.     

Typical intellectual engagement and cognition in the ninth decade of life: The Lothian Birth Cohort 1921

Investment traits-the tendency to seek out and engage in cognitive activity-might affect intellectual growth across the life span, specifically the development from fluid to crystallized intelligence. Here we explore how childhood IQ at age 11 years, IQ at age 79, and the investment trait Typical Intellectual Engagement (TIE) at age 81 affect the mean level and change in verbal fluency scores, used as an indicator of crystallized intelligence, across the ages 79, 83, and 87 in the Lothian Birth Cohort 1921 (maximum N = 569; Deary, Whiteman, Starr, Whalley, & Fox, 2004). A first latent growth model showed significant variance in the mean level of verbal fluency and significant decline in verbal fluency from age 79 to age 87. The rate of change was invariant across study participants in the Lothian Birth Cohort 1921. A second model found that IQ at age 11 significantly predicted IQ at age 79 (β = .66; p < .001), which in turn predicted verbal fluency and TIE in the ninth decade of life with standardized path parameters of .46 and .15 (p < .001), respectively. TIE had a significant association with verbal fluency (β = .14, p = .002); together, IQ at age 11 and 79 and TIE accounted for 25.5% of the variance in verbal fluency. A final model identified the TIE subfactor of intellectual curiosity as a significant mediator of the effect of IQ on verbal fluency; the TIE subfactors abstract thinking, reading, and problem solving showed no significant associations. In summary, TIE-in particular, intellectual curiosity-significantly mediated the effects of IQ on crystallized intelligence in old age. Because there was no significant between-subjects variance in verbal fluency trajectories in the current study, neither TIE nor IQ were associated with individual differences in cognitive decline. (PsycINFO Database Record (c) 2012 APA, all rights reserved).