Age differences in emotional experience over the adult life span were explored, focusing on the frequency, intensity, complexity, and consistency of emotional experience in everyday life. One hundred eighty-four people, age 18 to 94 years, participated in an experience-sampling procedure in which emotions were recorded across a 1-week period. Age was unrelated to frequency of positive emotional experience. A curvilinear relationship best characterized negative emotional experience. Negative emotions declined in frequency until approximately age 60, at which point the decline ceased. Individual factor analyses computed for each participant revealed that age was associated with more differentiated emotional experience. In addition, periods of highly positive emotional experience were more likely to endure among older people and periods of highly negative emotional experience were less stable. Findings are interpreted within the theoretical framework of socioemotional selectivity theory.

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.

Selecting indicators is as important for the generalizability of research designs as selecting persons or occasions of measurement. Elaborating on the extant knowledge base regarding indicator selection, the authors examine selection influences on the validity and reliability of multivariate representations. A simulation that systematically varied 4 key dimensions of indicator selection was used to investigate their effects on the fidelity of construct representations and the relative ability of exploratory and confirmatory analyses to recover within-and between-construct information. Confirmatory analyses, for example, yielded valid and unbiased estimates of the relations between constructs, even under conditions of very low internal consistency. Design, procedural, and analysis recommendations based on an expanded taxonomy of indicator selection and the simulation results are provided.Designing empirical research should oblige investigators to attend explicitly to the many measurement attributes of their expected data. Cattell (1952Cattell ( , 1996a, for example, explicated this point by identi-The contributions of the authors to this article were equivalent.We are very grateful to Paul Baltes, the other members of the Max Planck Institute for Human Development's Center for Lifespan Psychology, and to several colleagues, including Richard Ryan, Michael Shanahan, and Rolf Steyer, for their many helpful discussions and comments. We also thank Werner Scholtysik and Wolfgang Assmann for their computer resource management services.

The objective of this study is to determine the range of complex physical and cognitive abilities of older men and women functioning at high, medium and impaired ranges and to determine the psychosocial and physiological conditions that discriminate those in the high functioning group from those functioning at middle or impaired ranges. The subjects for this study were drawn from men and women aged 70-79 from 3 Established Populations for the Epidemiologic Study of the Elderly (EPESE) programs in East Boston MA, New Haven CT, and Durham County NC screened on the basis of criteria of physical and cognitive function. In 1988, 4030 men and women were screened as part of their annual EPESE interview. 1192 men and women met criteria for "high functioning". Age and sex-matched subjects were selected to represent the medium (n = 80) and low (n = 82) functioning groups. Physical and cognitive functioning was assessed from performance-based examinations and self-reported abilities. Physical function measures focused on balance, gait, and upper body strength. Cognitive exams assessed memory, language, abstraction, and praxis. Significant differences for every performance-based examination of physical and cognitive function were observed across functioning groups. Low functioning subjects were almost 3 times as likely to have an income of < or = $5000 compared to the high functioning group. They were less likely to have completed high school. High functioning subjects smoked cigarettes less and exercised more than others. They had higher levels of DHEA-S and peak expiratory flow rate. High functioning elders were more likely to engage in volunteer activities and score higher on scales of self-efficacy, mastery and report fewer psychiatric symptoms.

The term “growth curve” is used to describe data where: (1) the same entities are repeatedly observed, (2) the same procedures of measurement and scaling of observations are used, and (3) the timing of the observations is known. Growth curves are now common in many areas of psychological research, and some of these are presented here. The term “growth curve analysis” denotes the processes of describing, testing hypotheses, and making scientific inferences about the growth and change patterns in a wide range of time‐related phenomena. In this sense, growth curve analyses are a specific form of the larger set of developmental and longitudinal research methods, but the unique features of growth data permit unique kinds of analyses. Formal models for the analysis of growth curves which have been developed in many different substantive domains are described here in five sections: (1) An introduction to growth curves, (2) linear models of growth, (3) multiple groups in growth curve models, (4) aspects of dynamic theory for growth models, and (5) multiple variables in growth curve analyses. We conclude with a discussion of future issues raised by the current growth models.