Modeling life-span growth curves of cognition using longitudinal data with multiple samples and changing scales of measurement

Tackling inequalities in political socialisation: A systematic analysis of access to and mitigation effects of learning citizenship at school

This article tackles the issue of social inequalities in voting and identifies how and when differences in learning political engagement are influenced by social background in the school environment between the ages of 11-16 in England. Using Latent Growth Curve Modelling and Regression Analysis on the Citizenship Education Longitudinal (CELS) data this research identifies two elements that influence the political socialisation process: access to political learning and effectiveness in the form of learning in reducing inequalities in political engagement. The results show that there is unequal access by social background to learning political engagement through political activities in school and through an open classroom climate for discussion. However, there is equal access by social background to Citizenship Education in schools and this method of learning political engagement is effective at the age of 15-16 in reducing inequalities in political engagement.

A Note on Recurring Misconceptions When Fitting Nonlinear Mixed Models

Nonlinear mixed-effects (NLME) models are used when analyzing continuous repeated measures data taken on each of a number of individuals where the focus is on characteristics of complex, nonlinear individual change. Challenges with fitting NLME models and interpreting analytic results have been well documented in the statistical literature. However, parameter estimates as well as fitted functions from NLME analyses in recent articles have been misinterpreted, suggesting the need for clarification of these issues before these misconceptions become fact. These misconceptions arise from the choice of popular estimation algorithms, namely, the first-order linearization method (FO) and Gaussian-Hermite quadrature (GHQ) methods, and how these choices necessarily lead to population-average (PA) or subject-specific (SS) interpretations of model parameters, respectively. These estimation approaches also affect the fitted function for the typical individual, the lack-of-fit of individuals' predicted trajectories, and vice versa.

Cognitive Aging: What Every Geriatric Psychiatrist Should Know

The authors of this review both served on the Institute of Medicine Committee, which produced the report "Cognitive Aging: Progress in Understanding and Opportunities for Action." In this review, the authors summarize portions of the report that are especially applicable to geriatric psychiatrists and other clinicians who work with the elderly. Cognitive aging is a universal phenomenon that must be better understood by clinicians, a trajectory across multiple cognitive functions upstream from mild neurocognitive and major neurocognitive disorders. The authors review the epidemiology, basic neurobiology, and evidence-based interventions for cognitive aging.

Data Harmonization in Aging Research: Not so Fast

BACKGROUND/STUDY CONTEXT

Harmonizing measures in order to conduct pooled data analyses has become a scientific priority in aging research. Retrospective harmonization where different studies lack common measures of comparable constructs presents a major challenge. This study compared different approaches to harmonization with a crosswalk sample who completed multiple versions of the measures to be harmonized.

METHODS

Through online recruitment, 1061 participants aged 30 to 98 answered two different depression scales, and 1065 participants answered multiple measures of subjective health. Rational and configural methods of harmonization were applied, using the crosswalk sample to determine their success; and empirical item response theory (IRT) methods were applied in order empirically to compare items from different measures as answered by the same person.

RESULTS

For depression, IRT worked well to provide a conversion table between different measures. The rational method of extracting semantically matched items from each of the two scales proved an acceptable alternative to IRT. For subjective health, only configural harmonization was supported. The subjective health items used in most studies form a single robust factor.

CONCLUSION

Caution is required in aging research when pooling data across studies using different measures of the same construct. Of special concern are response scales that vary widely in the number of response options, especially if the anchors are asymmetrical. A crosswalk sample that has completed items from each of the measures being harmonized allows the investigator to use empirical approaches to identify flawed assumptions in rational or configural approaches to harmonizing.

Application of Latent Variable Methods to the Study of Cognitive Decline When Tests Change over Time

BACKGROUND

The way a construct is measured can differ across cohort study visits, complicating longitudinal comparisons. We demonstrated the use of factor analysis to link differing cognitive test batteries over visits to common metrics representing general cognitive performance, memory, executive functioning, and language.

METHODS

We used data from three visits (over 26 years) of the Atherosclerosis Risk in Communities Neurocognitive Study (N = 14,252). We allowed individual tests to contribute information differentially by race, an important factor to consider in cognitive aging. Using generalized estimating equations, we compared associations of diabetes with cognitive change using general and domain-specific factor scores versus averages of equally weighted standardized test scores.

RESULTS

Factor scores provided stronger associations with diabetes at the expense of greater variability around estimates (e.g., for general cognitive performance, -0.064 standard deviation units/year, standard error = 0.015, vs. -0.041 standard deviation units/year, standard error = 0.014), which is consistent with the notion that factor scores more explicitly address error in measuring assessed traits than averages of standardized tests.

CONCLUSIONS

Factor analysis facilitates use of all available data when measures change over time, and further, it allows objective evaluation and correction for differential item functioning.

Measuring the development of inhibitory control: The challenge of heterotypic continuity

Inhibitory control is thought to demonstrate heterotypic continuity, in other words, continuity in its purpose or function but changes in its behavioral manifestation over time. This creates major methodological challenges for studying the development of inhibitory control in childhood including construct validity, developmental appropriateness and sensitivity of measures, and longitudinal factorial invariance. We meta-analyzed 198 studies using measures of inhibitory control, a key aspect of self-regulation, to estimate age ranges of usefulness for each measure. The inhibitory control measures showed limited age ranges of usefulness owing to ceiling/floor effects. Tasks were useful, on average, for a developmental span of less than 3 years. This suggests that measuring inhibitory control over longer spans of development may require use of different measures at different time points, seeking to measure heterotypic continuity. We suggest ways to study the development of inhibitory control, with overlapping measurement in a structural equation modeling framework and tests of longitudinal factorial or measurement invariance. However, as valuable as this would be for the area, we also point out that establishing longitudinal factorial invariance is neither sufficient nor necessary for examining developmental change. Any study of developmental change should be guided by theory and construct validity, aiming toward a better empirical and theoretical approach to the selection and combination of measures.

Reproducibility and differential item functioning of the alcohol dependence syndrome construct across four alcohol treatment studies: An integrative data analysis

BACKGROUND

The validity of the alcohol dependence syndrome has been supported. The question of whether different measures of the construct are comparable across studies and patient subgroups has not been examined. This study examined the alcohol dependence construct across four diverse large-scale treatment samples using integrative data analysis (IDA).

METHOD

We utilized existing data (n=4393) from the COMBINE Study, Project MATCH, the Relapse Replication and Extension Project (RREP), and the United Kingdom Alcohol Treatment Trial (UKATT). We focused on four measures of alcohol dependence: the Alcohol Dependence Scale (COMBINE and RREP), Alcohol Use Inventory (MATCH), the Leeds Dependence Questionnaire (UKATT), and the Diagnostic and Statistical Manual of Mental Disorders (COMBINE and MATCH). Moderated nonlinear factor analysis was used to create a measure of alcohol dependence severity that was moderated by study membership, gender, age, and marital status.

RESULTS

A commensurate measure of alcohol dependence severity was successfully created using 20 items available in four studies. We identified differential item functioning by study membership, age, gender, and/or marital status for 12 of the 20 items, indicating specific patient subgroups who responded differently to items based on their underlying dependence severity.

CONCLUSIONS

Alcohol dependence severity is a single unidimensional construct that is comparable across studies. The use of IDA provided a strong test of the validity of the alcohol dependence syndrome and clues as to how some items used to measure dependence severity may be more or less central to the construct for some patients.

Impact of Cancer and Its Treatments on Cognitive Function: Advances in Research From the Paris International Cognition and Cancer Task Force Symposium and Update Since 2012

CONTEXT

Although cognitive impairments have been identified in patients with non-central nervous system cancer, especially breast cancer, the respective roles of cancer and therapies, and the mechanisms involved in cognitive dysfunction remain unclear.

OBJECTIVES

To report a state-of-the-art update from the International Cognitive and Cancer Task Force conference held in 2012.

METHODS

A report of the meeting and recent new perspectives are presented.

RESULTS

Recent clinical data support that non-central nervous system cancer per se may be involved in cognitive dysfunctions associated with inflammation parameters. The role of chemotherapy on cognitive decline was confirmed in colorectal and testicular cancers. Whereas the impact of hormone therapy remains debatable, some studies support a negative impact of targeted therapies on cognition. Regarding interventions, preliminary results of cognitive rehabilitation showed encouraging results. The methodology of future longitudinal studies has to be optimized by a priori end points, the use of validated test batteries, and the inclusion of control groups. Comorbidities and aging are important factors to be taken into account in future studies. Preclinical studies in animal models highlighted the role of cancer itself on cognition and support the possible benefits of prevention/care during chemotherapy. Progress in neuroimaging will help specify neural processes affected by treatments.

CONCLUSION

Clinical data and animal models confirmed that chemotherapy induces direct cognitive deficit. The benefits of cognitive rehabilitation are still to be confirmed. Studies evaluating the mechanisms underlying cognitive impairments using advanced neuroimaging techniques integrating the evaluation of genetic factors are ongoing.

Effects of education and race on cognitive decline: An integrative study of generalizability versus study-specific results

The objective of the study was to examine variability across multiple prospective cohort studies in level and rate of cognitive decline by race/ethnicity and years of education. We compare data across studies, we harmonized estimates of common latent factors representing overall or general cognitive performance, memory, and executive function derived from the: (a) Washington Heights, Hamilton Heights, Inwood Columbia Aging Project (N = 4,115), (b) Spanish and English Neuropsychological Assessment Scales (N = 525), (c) Duke Memory, Health, and Aging study (N = 578), and (d) Neurocognitive Outcomes of Depression in the Elderly (N = 585). We modeled cognitive change over age for cognitive outcomes by race, education, and study. We adjusted models for sex, dementia status, and study-specific characteristics. The results found that for baseline levels of overall cognitive performance, memory, and executive function, differences in race and education tended to be larger than between-study differences and consistent across studies. This pattern did not hold for rate of cognitive decline: effects of education and race/ethnicity on cognitive change were not consistently observed across studies, and when present were small, with racial/ethnic minorities and those with lower education declining at faster rates. In this diverse set of datasets, non-Hispanic Whites and those with higher education had substantially higher baseline cognitive test scores. However, differences in the rate of cognitive decline by race/ethnicity and education did not follow this pattern. This study suggests that baseline test scores and longitudinal change have different determinants, and future studies to examine similarities and differences of causes of cognitive decline in racially/ethnically and educationally diverse older groups is needed.

Applying Biometric Growth Curve Models to Developmental Synchronies in Cognitive Development: The Louisville Twin Study

Biometric latent growth curve models were applied to data from the LTS in order to replicate and extend Wilson's (Child Dev 54:298-316, 1983) findings. Assessments of cognitive development were available from 8 measurement occasions covering the period 4-15 years for 1032 individuals. Latent growth curve models were fit to percent correct for 7 subscales: information, similarities, arithmetic, vocabulary, comprehension, picture completion, and block design. Models were fit separately to WPPSI (ages 4-6 years) and WISC-R (ages 7-15). Results indicated the expected increases in heritability in younger childhood, and plateaus in heritability as children reached age 10 years. Heritability of change, per se (slope estimates), varied dramatically across domains. Significant genetic influences on slope parameters that were independent of initial levels of performance were found for only information and picture completion subscales. Thus evidence for both genetic continuity and genetic innovation in the development of cognitive abilities in childhood were found.

A Hierarchical Multi-Unidimensional IRT Approach for Analyzing Sparse, Multi-Group Data for Integrative Data Analysis

The present paper proposes a hierarchical, multi-unidimensional two-parameter logistic item response theory (2PL-MUIRT) model extended for a large number of groups. The proposed model was motivated by a large-scale integrative data analysis (IDA) study which combined data (N = 24,336) from 24 independent alcohol intervention studies. IDA projects face unique challenges that are different from those encountered in individual studies, such as the need to establish a common scoring metric across studies and to handle missingness in the pooled data. To address these challenges, we developed a Markov chain Monte Carlo (MCMC) algorithm for a hierarchical 2PL-MUIRT model for multiple groups in which not only were the item parameters and latent traits estimated, but the means and covariance structures for multiple dimensions were also estimated across different groups. Compared to a few existing MCMC algorithms for multidimensional IRT models that constrain the item parameters to facilitate estimation of the covariance matrix, we adapted an MCMC algorithm so that we could directly estimate the correlation matrix for the anchor group without any constraints on the item parameters. The feasibility of the MCMC algorithm and the validity of the basic calibration procedure were examined using a simulation study. Results showed that model parameters could be adequately recovered, and estimated latent trait scores closely approximated true latent trait scores. The algorithm was then applied to analyze real data (69 items across 20 studies for 22,608 participants). The posterior predictive model check showed that the model fit all items well, and the correlations between the MCMC scores and original scores were overall quite high. An additional simulation study demonstrated robustness of the MCMC procedures in the context of the high proportion of missingness in data. The Bayesian hierarchical IRT model using the MCMC algorithms developed in the current study has the potential to be widely implemented for IDA studies or multi-site studies, and can be further refined to meet more complicated needs in applied research.

Contemporary Modeling of Gene × Environment Effects in Randomized Multivariate Longitudinal Studies

There is a great deal of interest in the analysis of Genotype × Environment interactions (G×E). There are some limitations in the typical models for the analysis of G×E, including well-known statistical problems in identifying interactions and unobserved heterogeneity of persons across groups. The impact of a treatment may depend on the level of an unobserved variable, and this variation may dampen the estimated impact of treatment. Some researchers have noted that genetic variation may sometimes account for unobserved, and hence unaccounted for, heterogeneity. The statistical power associated with the G×E design has been studied in many different ways, and most results show that the small effects expected require relatively large or nonrepresentative samples (i.e., extreme groups). In this article, we describe some alternative approaches, such as randomized designs with multiple measures, multiple groups, multiple occasions, and analyses, to identify latent (unobserved) classes of people. These approaches are illustrated with data from the Aging, Demographics, and Memory Study (part of the Health and Retirement Study) examining the relations among episodic memory (based on word recall), APOE4 genotype, and educational attainment (as a proxy for an environmental exposure). Randomized clinical trials (RCTs) and randomized field trials (RFTs) have multiple strengths in the estimation of causal influences, and we discuss how measured genotypes can be incorporated into these designs. Use of these contemporary modeling techniques often requires different kinds of data be collected and encourages the formation of parsimonious models with fewer overall parameters, allowing specific G×E hypotheses to be investigated with a reasonable statistical foundation.

What Is the Nature of Faking? Modeling Distinct Response Patterns and Quantitative Differences in Faking at the Same Time

Research trying to uncover the true nature of faking is currently dominated by two competing modeling approaches. One approach views faking as the manifestation of distinct and qualitatively different response patterns. Typically, mixed Rasch models are used within this approach. The alternative approach views faking as a continuous and quantitative variable resulting from the interaction between test taker personality and situational demands. Modeling techniques for this approach range from regression analyses to structural equation modeling. So far, there has been no study in which both modeling approaches have been applied within one data set. More importantly, so far there has been no methodological model in which both views of faking could have been modeled simultaneously. Within the present article such a modeling approach is introduced and applied to a data set of N = 497 applicants. By combining factor mixture modeling with a latent change score model, it was possible to test both views of faking within the same model. Findings support the view of faking mainly as a continuous and quantitative variable. Theoretical implications are discussed.

Measuring Constructs in Family Science: How Can Item Response Theory Improve Precision and Validity?

This article provides family scientists with an understanding of contemporary measurement perspectives and the ways in which item response theory (IRT) can be used to develop measures with desired evidence of precision and validity for research uses. The article offers a nontechnical introduction to some key features of IRT, including its orientation toward locating items along an underlying dimension and toward estimating precision of measurement for persons with different levels of that same construct. It also offers a didactic example of how the approach can be used to refine conceptualization and operationalization of constructs in the family sciences, using data from the National Longitudinal Survey of Youth 1979 (n = 2,732). Three basic models are considered: (a) the Rasch and (b) two-parameter logistic models for dichotomous items and (c) the Rating Scale Model for multicategory items. Throughout, the author highlights the potential for researchers to elevate measurement to a level on par with theorizing and testing about relationships among constructs.

Advantages of Integrative Data Analysis for Developmental Research

Amid recent progress in cognitive development research, high-quality data resources are accumulating, and data sharing and secondary data analysis is becoming an increasingly valuable tool. Integrative data analysis (IDA) is an exciting analytical framework that can enhance secondary data analysis in powerful ways. IDA pools item level data across multiple studies to make inferences possible both within and across studies and can be used to test questions not possible in individual contributing studies. Some of the potential benefits of IDA include the ability to study longer developmental periods, examine how the measurement of key constructs changes over time, increase subject heterogeneity, and improve statistical power and capability to study rare behaviors. Our goal in this paper is to provide a brief overview of the benefits and challenges of IDA in developmental research and to identify additional resources that provide more detailed discussions of this topic.

Statistical approaches to harmonize data on cognitive measures in systematic reviews are rarely reported

OBJECTIVES

To identify statistical methods for harmonization, the procedures aimed at achieving the comparability of previously collected data, which could be used in the context of summary data and individual participant data meta-analysis of cognitive measures.

STUDY DESIGN AND SETTING

Environmental scan methods were used to conduct two reviews to identify (1) studies that quantitatively combined data on cognition and (2) general literature on statistical methods for data harmonization. Search results were rapidly screened to identify articles of relevance.

RESULTS

All 33 meta-analyses combining cognition measures either restricted their analyses to a subset of studies using a common measure or combined standardized effect sizes across studies; none reported their harmonization steps before producing summary effects. In the second scan, three general classes of statistical harmonization models were identified (1) standardization methods, (2) latent variable models, and (3) multiple imputation models; few publications compared methods.

CONCLUSION

Although it is an implicit part of conducting a meta-analysis or pooled analysis, the methods used to assess inferential equivalence of complex constructs are rarely reported or discussed. Progress in this area will be supported by guidelines for the conduct and reporting of the data harmonization and integration and by evaluating and developing statistical approaches to harmonization.

Describing and predicting developmental profiles of externalizing problems from childhood to adulthood

This longitudinal study considers externalizing behavior problems from ages 5 to 27 (N = 585). Externalizing problem ratings by mothers, fathers, teachers, peers, and self-report were modeled with growth curves. Risk and protective factors across many different domains and time frames were included as predictors of the trajectories. A major contribution of the study is in demonstrating how heterotypic continuity and changing measures can be handled in modeling changes in externalizing behavior over long developmental periods. On average, externalizing problems decreased from early childhood to preadolescence, increased during adolescence, and decreased from late adolescence to adulthood. There was strong nonlinear continuity in externalizing problems over time. Family process, peer process, stress, and individual characteristics predicted externalizing problems beyond the strong continuity of externalizing problems. The model accounted for 70% of the variability in the development of externalizing problems. The model's predicted values showed moderate sensitivity and specificity in prediction of arrests, illegal drug use, and drunk driving. Overall, the study showed that by using changing, developmentally relevant measures and simultaneously taking into account numerous characteristics of children and their living situations, research can model lengthy spans of development and improve predictions of the development of later, severe externalizing problems.

Capturing Age-group Differences and Developmental Change with the BASC Parent Rating Scales

Estimation of age-group differences and intra-individual change across distinct developmental periods is often challenged by the use of age-appropriate (but non-parallel) measures. We present a short version of the Behavior Assessment System (Reynolds & Kamphaus, 1998), Parent Rating Scales for Children (PRS-C) and Adolescents (PRS-A), which uses only their common-items to derive estimates of the initial constructs optimized for developmental studies. Measurement invariance of a three-factor model (Externalizing, Internalizing, Adaptive Skills) was tested across age-groups (161 mothers using PRS-C; 200 mothers using PRS-A) and over time (115 mothers using PRS-C at baseline and PRS-A five years later) with the original versus short PRS. Results indicated that the short PRS holds a sufficient level of invariance for a robust estimation of age-group differences and intra-individual change, as compared to the original PRS, which held only weak invariance leading to flawed developmental inferences. Importance of test-content parallelism for developmental studies is discussed.

Directional relationships between alcohol use and antisocial behavior across adolescence

BACKGROUND

The co-occurrence of alcohol use and antisocial behavior is well established, but different hypotheses exist regarding the direction of effects between the 2 behaviors. We used longitudinal data to examine the directional relationship between the 2 behaviors across adolescence.

METHODS

A cross-lagged model was applied to longitudinal data from the Avon Longitudinal Study of Parents and Children. The sample used in the present study consisted of 4,354 females and 3,984 males. Alcohol use and antisocial behavior were measured with multiple items collected at 12, 13, 15, and 17 years of age.

RESULTS

Both alcohol use and antisocial behavior were highly stable, as evidenced by highly significant autoregressive paths. Regarding the cross-lagged paths, neither behavior was predictive of the other during early adolescence (between ages 12 and 13). During mid-to late adolescence (from ages 13 to 17), antisocial behavior was predictive of subsequent alcohol use. Alcohol use was predictive of antisocial behavior in late adolescence (between ages 15 and 17), although this relationship was mainly driven by males and was not significant in the female subgroup.

CONCLUSIONS

The result generally supported the direction from antisocial behavior to alcohol use, especially during mid- to late adolescence. However, there was also a suggestion that the direction of relationship between the 2 behaviors changes across adolescence. The results highlight the importance of considering developmental stages to understand the directional relationships between the 2 behaviors.

A Moderated Nonlinear Factor Model for the Development of Commensurate Measures in Integrative Data Analysis

Integrative data analysis (IDA) is a methodological framework that allows for the fitting of models to data that have been pooled across two or more independent sources. IDA offers many potential advantages including increased statistical power, greater subject heterogeneity, higher observed frequencies of low base-rate behaviors, and longer developmental periods of study. However, a core challenge is the estimation of valid and reliable psychometric scores that are based on potentially different items with different response options drawn from different studies. In Bauer and Hussong (2009) we proposed a method for obtaining scores within an IDA called moderated nonlinear factor analysis (MNLFA). Here we move significantly beyond this work in the development of a general framework for estimating MNLFA models and obtaining scale scores across a variety of settings. We propose a five step procedure and demonstrate this approach using data drawn from n=1972 individuals ranging in age from 11 to 34 years pooled across three independent studies to examine the factor structure of 17 binary items assessing depressive symptomatology. We offer substantive conclusions about the factor structure of depression, use this structure to compute individual-specific scale scores, and make recommendations for the use of these methods in practice.

Calibration and validation of an innovative approach for estimating general cognitive performance

OBJECTIVE

To evaluate a new approach for creating a composite measure of cognitive function, we calibrated a measure of general cognitive performance from existing neuropsychological batteries.

METHODS

We applied our approach in an epidemiological study and scaled the composite to a nationally representative sample of older adults. Criterion validity was evaluated against standard clinical diagnoses. Convergent validity was evaluated against the Mini-Mental State Examination (MMSE).

RESULTS

The general cognitive performance factor was scaled to have a mean of 50 and standard deviation of 10 in a nationally representative sample of older adults. A cutoff point of approximately 45, corresponding to an MMSE of 23/24, optimally discriminated participants with and without dementia (sensitivity = 0.94, specificity = 0.90, area under the curve = 0.97). The general cognitive performance factor was internally consistent (Cronbach's α = 0.91) and provided reliable measures of functional ability across a wide range of cognitive functioning. It demonstrated minimal floor and ceiling effects, which is an improvement over most individual cognitive tests.

CONCLUSIONS

The cognitive composite is a highly reliable measure, with minimal floor and ceiling effects. We calibrated it using a nationally representative sample of adults over the age of 70 in the USA and established diagnostically relevant cutoff points. Our methods can be used to harmonize neuropsychological test results across diverse settings and studies.

Planned missing designs to optimize the efficiency of latent growth parameter estimates

We examine the performance of planned missing (PM) designs for correlated latent growth curve models. Using simulated data from a model where latent growth curves are fitted to two constructs over five time points, we apply three kinds of planned missingness. The first is item-level planned missingness using a three-form design at each wave such that 25% of data are missing. The second is wave-level planned missingness such that each participant is missing up to two waves of data. The third combines both forms of missingness. We find that three-form missingness results in high convergence rates, little parameter estimate or standard error bias, and high efficiency relative to the complete data design for almost all parameter types. In contrast, wave missingness and the combined design result in dramatically lowered efficiency for parameters measuring individual variability in rates of change (e.g., latent slope variances and covariances), and bias in both estimates and standard errors for these same parameters. We conclude that wave missingness should not be used except with large effect sizes and very large samples.

Fighting for Intelligence: A Brief Overview of the Academic Work of John L. Horn

John L. Horn (1928-2006) was a pioneer in multivariate thinking and the application of multivariate methods to research on intelligence and personality. His key works on individual differences in the methodological areas of factor analysis and the substantive areas of cognition are reviewed here. John was also our mentor, teacher, colleague, and friend. We overview John Horn's main contributions to the field of intelligence by highlighting 3 issues about his methods of factor analysis and 3 of his substantive debates about intelligence. We first focus on Horn's methodological demonstrations describing (a) the many uses of simulated random variables in exploratory factor analysis; (b) the exploratory uses of confirmatory factor analysis; and (c) the key differences between states, traits, and trait-changes. On a substantive basis, John believed that there were important individual differences among people in terms of cognition and personality. These sentiments led to his intellectual battles about (d) Spearman's g theory of a unitary intelligence, (e) Guilford's multifaceted model of intelligence, and (f) the Schaie and Baltes approach to defining the lack of decline of intelligence earlier in the life span. We conclude with a summary of John Horn's unique approaches to dealing with common issues.

Modelling longitudinal changes in older adults' memory for spoken discourse: findings from the ACTIVE cohort

Episodic memory shows substantial declines with advancing age, but research on longitudinal trajectories of spoken discourse memory (SDM) in older adulthood is limited. Using parallel process latent growth curve models, we examined 10 years of longitudinal data from the no-contact control group (N = 698) of the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) randomised controlled trial in order to test (1) the degree to which SDM declines with advancing age, (2) the predictors of these age-related declines and (3) the within-person relationship between longitudinal changes in SDM and longitudinal changes in fluid reasoning and verbal ability over 10 years, independent of age. Individuals who were younger, were White, had more years of formal education, were male and had better global cognitive function and episodic memory performance at baseline demonstrated greater levels of SDM on average. However, only age at baseline uniquely predicted longitudinal changes in SDM, such that declines accelerated with greater age. Independent of age, within-person decline in reasoning ability over the 10-year study period was substantially correlated with decline in SDM (r = .87). An analogous association with SDM did not hold for verbal ability. The findings suggest that longitudinal declines in fluid cognition are associated with reduced spoken language comprehension. Unlike findings from memory for written prose, preserved verbal ability may not protect against developmental declines in memory for speech.

A gentle introduction to bayesian analysis: applications to developmental research

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are introduced using a simplified example. Thereafter, the advantages and pitfalls of the specification of prior knowledge are discussed. To illustrate Bayesian methods explained in this study, in a second example a series of studies that examine the theoretical framework of dynamic interactionism are considered. In the Discussion the advantages and disadvantages of using Bayesian statistics are reviewed, and guidelines on how to report on Bayesian statistics are provided.

A method for efficiently sampling from distributions with correlated dimensions

Bayesian estimation has played a pivotal role in the understanding of individual differences. However, for many models in psychology, Bayesian estimation of model parameters can be difficult. One reason for this difficulty is that conventional sampling algorithms, such as Markov chain Monte Carlo (MCMC), can be inefficient and impractical when little is known about the target distribution--particularly the target distribution's covariance structure. In this article, we highlight some reasons for this inefficiency and advocate the use of a population MCMC algorithm, called differential evolution Markov chain Monte Carlo (DE-MCMC), as a means of efficient proposal generation. We demonstrate in a simulation study that the performance of the DE-MCMC algorithm is unaffected by the correlation of the target distribution, whereas conventional MCMC performs substantially worse as the correlation increases. We then show that the DE-MCMC algorithm can be used to efficiently fit a hierarchical version of the linear ballistic accumulator model to response time data, which has proven to be a difficult task when conventional MCMC is used.

Growth charts of human development

This article reviews and compares two types of growth charts for tracking human development over age. Both charts assume the existence of a continuous latent variable, but relate to the observed data in different ways. The D-score diagram summarizes developmental indicators into a single aggregate score measuring global development. The relations between the indicators should be consistent with the Rasch model. If true, the D-score is a measure with interval scale properties, and allows for the calculation of meaningful differences both within and across age. The stage line diagram describes the natural development of ordinal indicators. The method models the transition probabilities between successive stages of the indicator as smoothly varying functions of age. The location of each stage is quantified by the mid- P-value. Both types of diagrams assist in identifying early and delayed development, as well as finding differences in tempo. The relevant techniques are illustrated to track global development during infancy and early childhood (0–2 years) and Tanner pubertal stages (8–21 years). New reference values for both applications are provided.

Modeling Intraindividual Change in Nonlinear Growth Models with Latent Change Scores

Three central goals of longitudinal research are the modeling of intraindividual change, the examination of interindividual differences in intraindividual change, and the evaluation of determinants of intraindividual change ( Baltes & Nesselroade, 1979 ). The latent growth model is a commonly fit statistical model to examine these goals. However, the latent growth model has difficulty in this examination when change trajectories are nonlinear with respect to time and multiple latent variables impact intraindividual change. We consider a latent growth modeling approach based upon latent change scores ( McArdle, 2001 , 2009 ), which yields information related to these goals of longitudinal research when change trajectories are nonlinear. We illustrate this approach with longitudinal data from the Berkeley Guidance Study regarding lifespan changes in verbal ability.

Integrating prospective longitudinal data: modeling personality and health in the Terman Life Cycle and Hawaii Longitudinal Studies

The present study used a collaborative framework to integrate 2 long-term prospective studies: the Terman Life Cycle Study and the Hawaii Personality and Health Longitudinal Study. Within a 5-factor personality-trait framework, teacher assessments of child personality were rationally and empirically aligned to establish similar factor structures across samples. Comparable items related to adult self-rated health, education, and alcohol use were harmonized, and data were pooled on harmonized items. A structural model was estimated as a multigroup analysis. Harmonized child personality factors were then used to examine markers of physiological dysfunction in the Hawaii sample and mortality risk in the Terman sample. Harmonized conscientiousness predicted less physiological dysfunction in the Hawaii sample and lower mortality risk in the Terman sample. These results illustrate how collaborative, integrative work with multiple samples offers the exciting possibility that samples from different cohorts and ages can be linked together to directly test life span theories of personality and health.

A new life-span approach to conscientiousness and health: combining the pieces of the causal puzzle

Conscientiousness has been shown to predict healthy behaviors, healthy social relationships, and physical health and longevity. The causal links, however, are complex and not well elaborated. Many extant studies have used comparable measures for conscientiousness, and a systematic endeavor to build cross-study analyses for conscientiousness and health now seems feasible. Of particular interest are efforts to construct new, more comprehensive causal models by linking findings and combining data from existing studies of different cohorts. Although methodological perils can threaten such integration, such efforts offer an early opportunity to enliven a life course perspective on conscientiousness, to see whether component facets of conscientiousness remain related to each other and to relevant mediators across broad spans of time, and to bolster the findings of the few long-term longitudinal studies of the dynamics of personality and health. A promising approach to testing new models involves pooling data from extant studies as an efficient and heuristic prelude to large-scale testing of interventions.

Distinguishing between-person and within-person relationships in longitudinal health research: arthritis and quality of life

BACKGROUND

Many health measures (e.g., blood pressure, quality of life) have meaningful fluctuation over time around a relatively stable mean level for each person.

PURPOSE

This didactic paper describes two closely related statistical models for examining between-person and within-person relationships between two or more sets of measures collected over time: the latent intercept model with correlated residuals (LI) in structural equation modeling framework and the multivariate multilevel model (MVML) in multilevel modeling framework.

RESULTS

We illustrated that the basic LI model and the MVML model are equivalent. We presented an illustrative example using a national arthritis data resource to examine between-person and within-person relationships of symptom status, functional health, and quality of life in arthritis patients.

DISCUSSION

Additional design and modeling issues for the treatment of missing data are considered. We discuss contexts in which one of the two models may be preferred. Mplus and SAS syntax are available.

Teacher's Corner: Latent Curve Models and Latent Change Score Models Estimated in R

In recent years the use of the Latent Curve Model (LCM) among researchers in social sciences has increased noticeably, probably thanks to contemporary software developments and to the availability of specialized literature. Extensions of the LCM, like the the Latent Change Score Model (LCSM), have also increased in popularity. At the same time, the R statistical language and environment, which is open source and runs on several operating systems, is becoming a leading software for applied statistics. We show how to estimate both the LCM and LCSM with the sem, lavaan, and OpenMx packages of the R software. We also illustrate how to read in, summarize, and plot data prior to analyses. Examples are provided on data previously illustrated by Ferrer, Hamagami, & McArdle, 2004. The data and all scripts used here are available on the first author's website.

Generating large-scale longitudinal data resources for aging research

OBJECTIVES

The need for large studies and the types of large-scale data resources (LSDRs) are discussed along with their general scientific utility, role in aging research, and affordability. The diversification of approaches to large-scale data resourcing is described in order to facilitate their use in aging research.

METHODS

The need for LSDRs is discussed in terms of (a) large sample size; (b) longitudinal design; (c) as platforms for additional investigator-initiated research projects; and (d) broad-based access to core genetic, biological, and phenotypic data.

DISCUSSION

It is concluded that a "lite-touch, lo-tech, lo-cost" approach to LSDRs is a viable strategy for the development of LSDRs and would enhance the likelihood of LSDRs being established which are dedicated to the wide range of important aging-related issues.

Nonlinear growth curves in developmental research

Developmentalists are often interested in understanding change processes, and growth models are the most common analytic tool for examining such processes. Nonlinear growth curves are especially valuable to developmentalists because the defining characteristics of the growth process such as initial levels, rates of change during growth spurts, and asymptotic levels can be estimated. A variety of growth models are described beginning with the linear growth model and moving to nonlinear models of varying complexity. A detailed discussion of nonlinear models is provided, highlighting the added insights into complex developmental processes associated with their use. A collection of growth models are fit to repeated measures of height from participants of the Berkeley Growth and Guidance Studies from early childhood through adulthood.

Nonlinear growth curves in developmental research

Developmentalists are often interested in understanding change processes, and growth models are the most common analytic tool for examining such processes. Nonlinear growth curves are especially valuable to developmentalists because the defining characteristics of the growth process such as initial levels, rates of change during growth spurts, and asymptotic levels can be estimated. A variety of growth models are described beginning with the linear growth model and moving to nonlinear models of varying complexity. A detailed discussion of nonlinear models is provided, highlighting the added insights into complex developmental processes associated with their use. A collection of growth models are fit to repeated measures of height from participants of the Berkeley Growth and Guidance Studies from early childhood through adulthood.

Personality disorders in later life: questions about the measurement, course, and impact of disorders

Lifespan perspectives have played a crucial role in shaping our understanding of many forms of psychopathology. Unfortunately, little attention has been given to personality disorders in middle adulthood and later life. Several issues are responsible for this deficiency, including difficulty applying the diagnostic criteria for personality disorders to older people and challenges in identifying appropriate samples of older participants. The goal of this review is to explore the benefits of considering older adults in the study of personality disorders. Later life offers a unique opportunity for investigators to consider links between personality pathology and consequential outcomes in people's lives. Many domains are relevant, including health, longevity, social adjustment, marital relationships, and the experience of major life events. We review each domain and consider ways in which the study of middle-aged and older adults challenges researchers to evaluate how personality disorders in general are defined and measured.

Joint modeling of longitudinal data in multiple behavioral change

Multiple behavioral change is an exciting and evolving research area, albeit one that presents analytic challenges to investigators. This manuscript considers the problem of modeling jointly trajectories for two or more possibly non-normally distributed dependent variables, such as marijuana smoking and risky sexual activity, collected longitudinally. Of particular scientific interest is applying such modeling to elucidate the nature of the interaction, if any, between an intervention and personal characteristics, such as sensation seeking and impulsivity. The authors describe three analytic approaches: generalized linear mixed modeling, group-based trajectory modeling, and latent growth curve modeling. In particular, the authors identify identify the strengths and weaknesses of these analytic approaches and assess their impact (or lack thereof) on the psychological and behavioral science literature. The authors also compare what investigators have been doing analytically versus what they might want to be doing in the future and discuss the implications for basic and translational research.

Group-based trajectory modeling in clinical research

Group-based trajectory models are increasingly being applied in clinical research to map the developmental course of symptoms and assess heterogeneity in response to clinical interventions. In this review, we provide a nontechnical overview of group-based trajectory and growth mixture modeling alongside a sampling of how these models have been applied in clinical research. We discuss the challenges associated with the application of both types of group-based models and propose a set of preliminary guidelines for applied researchers to follow when reporting model results. Future directions in group-based modeling applications are discussed, including the use of trajectory models to facilitate causal inference when random assignment to treatment condition is not possible.

Parent alcoholism impacts the severity and timing of children's externalizing symptoms

Although previous studies show that children of alcoholic parents have higher rates of externalizing symptoms compared to their peers, it remains unclear whether the timing of children's externalizing symptoms is linked to that of their parent's alcohol-related symptoms. Using a multilevel modeling approach, we tested whether children aged 2 through 17 showed elevated mother-, father- and child-reported externalizing symptoms (a) at the same time that parents showed alcohol-related consequences (time-varying effects), (b) if parents showed greater alcohol-related consequences during the study period (proximal effects), and (c) if parents had a lifetime diagnosis of alcoholism that predated the study period (distal effects). We used integrative data analysis to combine samples from two prospective studies to test these hypotheses. Distal effects of parent alcoholism on increased child externalizing symptoms were large and consistent. In addition, proximal and time-varying effects of parent alcohol symptoms were also found. Implications for preventing escalations in externalizing symptoms among this high-risk population are discussed.

Toward an integrative science of life-span development and aging

The study of aging demands an integrative life-span developmental framework, involving interdisciplinary collaborations and multiple methodological approaches for understanding how and why individuals change, in both normative and idiosyncratic ways. We highlight and summarize some of the issues encountered when conducting integrative research for understanding aging-related change, including, the integration of results across different levels of analysis; the integration of theory, design, and analysis; and the synthesis of results across studies of aging. We emphasize the necessity of longitudinal designs for understanding development and aging and discuss methodological issues that should be considered for achieving reproducible research on within-person processes. It will be important that current and future studies permit opportunities for quantitative comparison across populations given the extent to which historical shifts and cultural differences influence life-span processes and aging-related outcomes.

Non-linear Growth Models in Mplus and SAS

Non-linear growth curves or growth curves that follow a specified non-linear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this paper we describe how a variety of sigmoid curves can be fit using the Mplus structural modeling program and the non-linear mixed-effects modeling procedure NLMIXED in SAS. Using longitudinal achievement data collected as part of a study examining the effects of preschool instruction on academic gain we illustrate the procedures for fitting growth models of logistic, Gompertz, and Richards functions. Brief notes regarding the practical benefits, limitations, and choices faced in the fitting and estimation of such models are included.

The seemingly quixotic pursuit of a cumulative psychological science: introduction to the special issue

The goal of any empirical science is to pursue the construction of a cumulative base of knowledge upon which the future of the science may be built. However, there is mixed evidence that the science of psychology can accurately be characterized by such a cumulative progression. Indeed, some argue that the development of a truly cumulative psychological science is not possible with the current paradigms of hypothesis testing in single-study designs. The author explores this controversy as a framework to introduce the 6 articles that make up this special issue on the integration of data and empirical findings across multiple studies. The author proposes that the methods and techniques described in this set of articles can significantly propel researchers forward in their ongoing quest to build a cumulative psychological science.

Integrative data analysis through coordination of measurement and analysis protocol across independent longitudinal studies

Replication of research findings across independent longitudinal studies is essential for a cumulative and innovative developmental science. Meta-analysis of longitudinal studies is often limited by the amount of published information on particular research questions, the complexity of longitudinal designs and the sophistication of analyses, and practical limits on full reporting of results. In many cases, cross-study differences in sample composition and measurements impede or lessen the utility of pooled data analysis. A collaborative, coordinated analysis approach can provide a broad foundation for cumulating scientific knowledge by facilitating efficient analysis of multiple studies in ways that maximize comparability of results and permit evaluation of study differences. The goal of such an approach is to maximize opportunities for replication and extension of findings across longitudinal studies through open access to analysis scripts and output for published results, permitting modification, evaluation, and extension of alternative statistical models and application to additional data sets. Drawing on the cognitive aging literature as an example, the authors articulate some of the challenges of meta-analytic and pooled-data approaches and introduce a coordinated analysis approach as an important avenue for maximizing the comparability, replication, and extension of results from longitudinal studies.