John R. Nesselroade

Life-span developmental psychology : introduction to research methods

Contents: Part I:The Field of Developmental Psychology.Why Developmental Psychology? An Illustration of the Developmental Approach: The Case of Auditory Sensitivity. Part II:General Issues in Research Methodology.The Nature of Theories and Models. The Nature of Scientific Methods. The Internal Validity of Research Designs. The External Validity of Research Designs. Measurement. Data Analysis and Interpretation. Part III:Objectives and Issues of Developmental Research in Psychology.The Scope of Developmental Psychology. Targets of Developmental Analysis. Developmental Research Paradigms. Time and Change: The Basic Data Matrix. Part IV:Descriptive Developmental Designs.Simple Cross- Sectional and Longitudinal Methods. Sequential Cross-Sectional and Longitudinal Strategies. Developmental Design and Change in Subject Populations With Age. Change in Populations and Sampling: Assessment and Control. Selected Issues in Developmental Assessment. Modeling Change Over Time: From Description to Explanation. Part V:Explanatory-Analytic Developmental Research.Toward Explanation: The Simulation of Developmental Processes. Cross- Cultural and Comparative Developmental Psychology. Heredity- Environment Research and Development. Developmental Research on Learning: Group Designs. Developmental Research on Learning: Single-Subject Designs. Structural Models: The Continuing Search for Causes.

Emotional experience improves with age: Evidence based on over 10 years of experience sampling

Recent evidence suggests that emotional well-being improves from early adulthood to old age. This study used experience-sampling to examine the developmental course of emotional experience in a representative sample of adults spanning early to very late adulthood. Participants (N = 184, Wave 1; N = 191, Wave 2; N = 178, Wave 3) reported their emotional states at five randomly selected times each day for a one week period. Using a measurement burst design, the one-week sampling procedure was repeated five and then ten years later. Cross-sectional and growth curve analyses indicate that aging is associated with more positive overall emotional well-being, with greater emotional stability and with more complexity (as evidenced by greater co-occurrence of positive and negative emotions). These findings remained robust after accounting for other variables that may be related to emotional experience (personality, verbal fluency, physical health, and demographic variables). Finally, emotional experience predicted mortality; controlling for age, sex, and ethnicity, individuals who experienced relatively more positive than negative emotions in everyday life were more likely to have survived over a 13 year period. Findings are discussed in the theoretical context of socioemotional selectivity theory.

Reporting structural equation modeling results in Psychology and Aging: Some proposed guidelines.

Structural equation modeling (SEM) is now widely used in social and behavioral science research. SEM provides the possibility of fitting, and evaluating the fit, of well-specified, theoretical models to empirical data--more generally, of testing elaborated psychological theories. The options available to users of these approaches are many and varied. Popular SEM computational software packages, such as LISREL and EQS, provide a large amount of information, and there is some uncertainty as to what should be routinely reported. A series of guidelines are proposed for reporting SEM results in articles submitted to Psychology and Aging. The suggested guidelines ask authors using SEM methodology to provide important analysis information that will enable readers to evaluate the findings.

A Quantitative Genetic Analysis of Cognitive Abilities During the Second Half of the Life Span

Little is known about the importance of genetic effects on individual differences in cognitive abilities late in life. We present the first report from the Swedish Adoption/Twin Study of Aging (SATSA) for cognitive data, including general cognitive ability and 13 tests of specific cognitive abilities. The adoption/twin design consists of identical twins separated at an early age and reared apart (46 pairs), identical twins reared together (67 pairs), fraternal twins reared apart (100 pairs), and fraternal twins reared together (89 pairs); average age was 65 years. Heritability of general cognitive ability in these twins was much higher (about 80%) than estimates typically found earlier in life (about 50%). Consistent with the literature, heritabilities of specific cognitive abilities were lower than the heritability of general cognitive ability but nonetheless substantial. Average heritabilities for verbal, spatial, perceptual speed, and memory tests were, respectively, 58%, 46%, 58%, and 38%.

The Swedish Adoption Twin Study of Aging: An Update

The Swedish Adoption/Twin Study of Aging (SATSA) is a longitudinal program of research in gerontological genetics which is currently in its fifth year. The base population is comprised of 351 pairs of twins reared apart and 407 matched control pairs of twins reared together who responded to a questionnaire (Q1) in 1984. Two additional stages of SATSA have recently been completed: a longitudinal follow-up questionnaire mailed out in 1987 (Q2) and extensive in-person testing (IPT1) which included a health examination and cognitive battery. A second wave of IPT was started in January 1989. A summary of some of the major findings from Q1 and a description of IPT1 are reported.

Assessing psychological change in adulthood: an overview of methodological issues.

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.

Genetic influence on life events during the last half of the life span

Genetic influence on perceptions of major events later in life was assessed with a combination of twin and adoption designs as part of the Swedish Adoption/Twin Study of Aging (SATSA). The SATSA design includes 4 groups totaling 399 pairs of same-sex twins: identical and fraternal twins reared apart and matched twins reared together. The average age of the twins was 59 years. The results demonstrate significant genetic influence on reports of the occurrence of life events, especially for controllable events in which the individual can play an active role. Maximum likelihood model-fitting estimates of genetic influence indicate that 40% of the variance of the total life events score is due to genetic differences among individuals. How genetic factors can affect life experiences and directions for future research are discussed.

Optimism, pessimism and mental health: A twin/adoption analysis

Abstract Although recent research suggests links between optimism and mental health, little is known about the genetic and environmental origins of these links or of optimism itself. The Life Orientation Test of optimism and pessimism and various measures of self-reported mental health (depression, life satisfaction, paranoid hostility, and cynicism) were administered to over 500 same-sex pairs of middle-aged identical and fraternal twins, half of whom were reared together and half adopted apart early in life. Twin/adoption analyses yield significant heritability estimates of about 25% for both optimism and pessimism; shared rearing environmental influence was also significant for optimism but not for pessimism. Both optimism and pessimism contributed independently to the prediction of depression and life satisfaction; pessimism but not optimism predicted paranoid hostility and cynicism. These associations diminished little when neuroticism was controlled. Multivariate genetic analyses of the multiple correlations for the mental health variables suggest that genetic factors contribute appreciably to associations between optimism/pessimism and mental health.

Beyond Autoregressive Models: Some Implications of the Trait-State Distinction for the Structural Modeling of Developmental Change.

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.

The Relationship Between the Structure of Interindividual and Intraindividual Variability: A Theoretical and Empirical Vindication of Developmental Systems Theory

Proponents of the developmental systems theory (DST), like Gottlieb and Lerner, have questioned the relevance of behavior genetics for the study of developmental processes. In this chapter, the criticism of DST will be reformulated in a way that is consistent with Wohlwill’s thesis that the study of developmental processes requires analysis of intraindividual differences, not interindividual differences. The reasoning is straightforward: (1) behavior genetics is a branch of applied multivariate statistics, conjoined with simple and uncontroversial Mendelian laws of inheritance; (2) standard multivariate statistics, including (developmental) behavior genetics, is based on analysis of interindividual differences; (3) the results of an analysis of interindividual differences of a given phenotype may not be related at all to the structure of intraindividual differences of the same phenotype; (4) developmental processes give rise to intraindividual variation and also interindividual heterogeneity. From the above reasoning, the reformulated conclusion of DST follows.