1-15 of 15 Results

  • Keywords: longitudinal x
Clear all

Article

Erythocyte sedimentation rate (ESR) is one of the oldest measures of inflammation. It is used extensively in clinical medicine and has shown some utility in biomedical research. It is a nonspecific inflammation assay, and although it is less sensitive than more modern measures such as C-reactive protein, it is a useful measure in chronic illnesses. In general, ESR increases with age and appears to be a biomarker of aging in general. It predicts both cardiovascular disease (CVD) and cancer and is elevated in autoimmune disorders such as rheumatoid arthritis. Further, it predicts mortality both in the general population and in those with chronic illnesses such as CVD and cancer, independent of other indicators of illness severity. Interestingly, ESR is not associated with anxiety or general measures of distress but is consistently associated with measures of depression and suicidal ideation. Further, the effect of depressive symptoms on mortality appears to be mediated through increases in ESR. Studies of the relationship between stress and ESR have been less consistent, primarily because early studies were largely cross-sectional and in small samples. Studies using more modern, longitudinal analyses in larger samples may show more consistent results, especially if multilevel modeling was used that examined within-person changes in ESR in response to stress. Given that other large, longitudinal studies, such as the Baltimore Longitudinal Study on Aging, the Rotterdam Study, The Reykjavik Cohort Study, and Women’s Healthy Ageing Study have included ESR in their biomedical assays, it should be possible to analyze existing data to examine how psychosocial factors influence inflamm-aging in humans.

Article

Joseph E. Gaugler, Colleen M. Peterson, Lauren L. Mitchell, Jessica Finlay, and Eric Jutkowitz

Mixed methods research consists of collecting and analyzing qualitative and quantitative data within a singular study. The “methods” of mixed methods research vary, but the ultimate goal is to provide greater understanding and explanation via the integration of qualitative and quantitative data. Mixed methods studies have the potential to advance our understanding of complex phenomena over time in adult development and aging (e.g., depression following the death of a spouse), but the utility of this approach depends on its application. The authors systematically searched the literature (CINHAL, Embase, Ovid/Medline, PubMed, PsychInfo, and ProQuest) to identify longitudinal mixed methods studies focused on aging. They identified 6,351 articles published between 1994 and 2017, of which 174 met the inclusion criteria. The majority of mixed methods studies reported on the evaluation of interventions or educational programs. Non-interventional studies tended to report on experiences related to the progression of various health conditions, the needs and experiences of caregivers, and the lived experiences of older adults. About half (n = 81) of the mixed methods studies followed a sequential explanatory design where a qualitative component followed quantitative evaluation, and most of these studies achieved “integration” by comparing qualitative and quantitative data in Results sections. There was considerable heterogeneity across studies in terms of overall design (randomized trials, program evaluations, cohort studies, and case studies). As a whole, the literature suffered from key limitations, including a lack of reporting on sample selection methodology and mixed methods design characteristics. To maximize the value of mixed methods in adult development in aging research, investigators should conform to recommended guidelines (e.g., depict participant study flow and use recommended notation) and consider more sophisticated mixed methods applications to advance the state of the art.

Article

Michael J. Lyons, Chandra A. Reynolds, William S. Kremen, and Carol E. Franz

The rapidly increasing number of people age 65 and older around the world has important implications for public health and social policy, making it imperative to understand the factors that influence the aging process. Twin studies can provide information that addresses critical questions about aging. Twin studies capitalize on a naturally occurring experiment in which there are some pairs of individuals who are born together and share 100% of their segregating genes (monozygotic twins) and some pairs that share approximately 50% (dizygotic twins). Twins can shed light on the relative influence of genes and environmental factors on various characteristics at various times during the life course and whether the same or different genetic influences are operating at different times. Twin studies can investigate whether characteristics that co-occur reflect overlapping genetic or environmental determinants. Discordant twin pairs provide an opportunity for a unique and powerful case-control study. There are numerous methodological issues to consider in twin studies of aging, such as the representativeness of twins and the assumption that the environment does not promote greater similarity within monozygotic pairs than dizygotic pairs. Studies of aging using twins may include many different types of measures, such as cognitive, psychosocial, biomarkers, and neuroimaging. Sophisticated statistical techniques have been developed to analyze data from twin studies. Structural equation modeling has proven to be especially useful. Several issues, such as assessing change and dealing with missing data, are particularly salient in studies of aging and there are a number of approaches that have been implemented in twin studies. Twins lend themselves very well to investigating whether genes influence one’s sensitivity to environmental exposures (gene-environment interaction) and whether genes influence the likelihood that an individual will experience certain environmental exposures (gene-environment correlation). Prior to the advent of modern molecular genetics, twin studies were the most important source of information about genetic influences. Dramatic advances in molecular genetic technology hold the promise of providing great insight into genetic influences, but these approaches complement rather than supplant twin studies. Moreover, there is a growing trend toward integrating molecular genetic methods into twin studies.

Article

Multilevel modeling is a data analytic framework that is appropriate when analyzing data that are dependent due to the clustering of observations in higher-level units. Clustered data appear in a variety of disciplines, which makes multilevel modeling a necessary data analytic tool for many researchers. Longitudinal data are a special kind of clustered data as the repeated observations are clustered within individuals. Multilevel models can be applied to longitudinal data to examine how individuals change over time and how individuals differ in their change processes over time. For longitudinal data, linear multilevel models, where the fixed- and random-effects parameters enter the model in a linear fashion, and nonlinear multilevel models, where at least one fixed-and/or random-effect parameter enters the model in a nonlinear fashion are commonly estimated to examine different forms of the individual change process. Multilevel structural equation modeling is an extension of multilevel modeling that allows for multivariate outcomes, and this framework is very useful for modeling multivariate longitudinal data (e.g., multivariate growth models and second-order growth models).

Article

Catherine Compton-Lilly

In 1982, Denny Taylor coined the term “family literacy” to reference the ways young children and their parents interact around texts. Since then, the term family literacy has generally been applied to the practices that occur in homes to support young children as they become readers and writers. However, 30 years later, this definition negates the full scope of possibilities that might inform our understandings of the literacy practices that occur within home spaces and among family members. These possibilities reflect two important trends increasingly recognized within literacy research communities. First, technological advances have affected the ways people read and write and the multimodal literacy practices that have come to define literacy in families and homes. These developments are often the focus of New Literacy Studies as defined by the New London Group and others. Second, while generally not addressed in terms of family literacy, a substantial and growing body of research has documented the out-of-school literacy practices of adolescents and youth. Many of these literacy practices are enacted and displayed in home settings. While connections between out-of-school literacy practices and family literacy are generally not explicit, homes and families provide significant social contexts that are critical to engaging in technological, peer-informed, and popular culture practices. In short, family literacy does not end once children learn to read. In contrast, family literacy assumes new forms and involves new modalities that both echo and extend the literacy practices found within families. This is significant, as it challenges conceptions of adolescent and youth literacy as being separate from the literacy practices of families. To extend what is meant by family literacy, it is redefined as more than traditional activities that involve young children with texts. Instead, researchers are challenged to consider the full range of literacy practices that occur among family members and within homes across time. In doing this, family literacy and new literacy studies are brought together. Thus, the term family/home literacy is used to recognize not only the literacy practices that are enacted between children and parents, but the full range of literacy practices that occur among all family members—including siblings, extended family members, and friends. In short, family/home literacy practices are intertwined with home literacy affordances, which include the texts, opportunities, and technological resources that are available and used in homes. In order to explore family/home literacies over time, children’s literacy practices, including traditional and technological family/home literacy practices, are explored. Issues raised include parental mentoring of school-age children as they encounter new technologies at home, the adaptation of available resources by children as they move into and through adolescence, and transactions involving texts (both traditional and digital) among adolescents, young adults, and their parents.

Article

Gawon Cho, Giancarlo Pasquini, and Stacey B. Scott

The study of human development across the lifespan is inherently about patterns across time. Although many developmental questions have been tested with cross-sectional comparisons of younger and older persons, understanding of development as it occurs requires a longitudinal design, repeatedly observing the same individual across time. Development, however, unfolds across multiple time scales (i.e., moments, days, years) and encompasses both enduring changes and transient fluctuations within an individual. Measurement burst designs can detect such variations across different timescales, and disentangle patterns of variations associated with distinct dimensions of time periods. Measurement burst designs are a special type of longitudinal design in which multiple “bursts” of intensive (e.g., hourly, daily) measurements are embedded in a larger longitudinal (e.g., monthly, yearly) study. The hybrid nature of these designs allow researchers to address questions not only of cross-sectional comparisons of individual differences (e.g., do older adults typically report lower levels of negative mood than younger adults?) and longitudinal examinations of intraindividual change (e.g., as individuals get older, do they report lower levels of negative mood?) but also of intraindividual variability (e.g., is negative mood worse on days when individuals have experienced an argument compared to days when an argument did not occur?). Researchers can leverage measurement burst designs to examine how patterns of intraindividual variability unfolding over short timescales may exhibit intraindividual change across long timescales in order to understand lifespan development. The use of measurement burst designs provides an opportunity to collect more valid and reliable measurements of development across multiple time scales throughout adulthood.

Article

Victoria I. Michalowski, Denis Gerstorf, and Christiane A. Hoppmann

Aging does not occur in isolation, but often involves significant others such as spouses. Whether such dyadic associations involve gains or losses depends on a myriad of factors, including the time frame under consideration. What is beneficial in the short term may not be so in the long term, and vice versa. Similarly, what is beneficial for one partner may be costly for the other, or the couple unit over time. Daily dynamics between partners involving emotion processes, health behaviors, and collaborative cognition may accumulate over years to affect the longer-term physical and mental health outcomes of either partner or both partners across adulthood and into old age. Future research should move beyond an individual-focused approach to aging and consider the importance of and interactions among multiple time scales to better understand how, when, and why older spouses shape each other’s aging trajectories, both for better and for worse.

Article

Edda Humprecht and Linards Udris

The way news is produced and consumed has changed dramatically during the first two decades of the 21st century due to digitalization and economic pressures. In a globalized world, current events are reported in almost real time in various countries and are diffused rapidly via social media. Thus much scholarly attention is devoted to determining whether these developments have changed news content. Comparative research in the area of journalism focuses on whether news content across countries converges over time and to what degree national differences persist across countries. When studying the research on long-term trends in news content, three main observations can be made. First, theoretical assumptions are often rooted in different models of democracies, but they are rarely explicitly discussed. Second, many studies focus on the organizational level using theoretical concepts related to increased market orientation of news outlets, such as personalization, emotionalization, or scandalization. Furthermore, commercialization is associated with the effects of digitalization and globalization, namely, decreased advertising revenues and increased competition. A commonly expressed fear is that these changes have consequences for democracy and informed citizenship. Third, in recent years, there has been a steady increase of studies employing international comparisons as well as a growing standardization for measurements. These developments lead to more multicountry studies based on large samples but come at the expense of more fine-grained analysis of the way news content changes over time. Finally, the vast majority of cross-national and single-country studies focus on Western democracies. Thus our knowledge about recent changes in news content is limited to a small set of countries. Overall, many studies provide evidence for constant changes of news content driven by social, political, and economic developments. However, different media systems exhibit a sustained resilience toward transnational pressures reflected in a persistence of national differences in news content over time.

Article

Most studies conducted on the development of antisocial behavior focused on school children and attempted to understand how children learn to steal and aggress others. Results from longitudinal studies that were initiated in early childhood show that children do not learn to bully, physically aggress, and rob from their environment. These longitudinal studies show that antisocial behaviors are most frequent during early childhood and that children learn from their environment not to bully, not to aggress, and not to rob. In other words, young children are socialized by their environment. Those who do not learn well enough to control these natural tendencies are rejected very early in their development by their environment, unless they are living in an antisocial environment. The further advance of this research area will require that the next generation of researchers integrate theories and methods from the biological, psychological, and social sciences because the development of antisocial behavior involves complex interactions between biological, psychological and sociological causal factors. The lack of an integrated bio-psycho-social perspectives has been a major weakness of research in criminology up to now. Future research needs to concentrate on two central questions: (a) Why a minority of young children fail to learn to inhibit antisocial behaviors, and (b) how we can help these children learn alternatives to antisocial behavior. Valid and effective answers to these questions will come from randomized control trials which target at risk families with intensive and long term preventive interventions during early childhood, preferably at the start of a girl’s first pregnancy, with follow ups until the at risk children have become adults and are having their own children.

Article

Research methods in lifespan development include single-factor designs that either follow a single cohort of individuals over time or compare age groups at a single time point. The two basic types of studies involving the manipulation of the single factors of age, cohort, and time of measurement are longitudinal and cross-sectional. Each of these has advantages and disadvantages, but both are characterized by limitations because they cannot definitively separate the joint influences of age, cohort, and type of measurement. The third group of designs involves manipulation of two or more levels of each factor to permit inferences to be drawn that separate personal from social aging. The theoretical problems involved in both the single-factor and sequential designs combine with practical issues to present lifespan developmental researchers with a number of choices in approaching the variables of interest. The theoretical problems include the inevitable linking of personal with social aging, particularly evident in single-factor designs, and the fact that selective attrition leads to the differential availability of increasingly select older samples. Practical problems include the need to assign participants to appropriate age intervals and such clerical issues as the need to track participants in follow-up investigations. Researchers must also be aware of methodological issues related to task equivalence across individuals of different ages and the need to covary for potential confounds that could lead to differences across groups of participants due to such factors as education and health status. The increasing recognition of the need to address these issues is leading to a body of literature that reflects the growing sophistication of the field along with the more widespread availability of sophisticated analytic methods. As these improvements continue to raise the level of scholarship in the field, there will be a greater understanding of both ontogenetic change as well as the influence of context on development from childhood through later life.

Article

In the early 1990s business creation was receiving a great deal of attention after it was clear that new firms were a major source of job creation. There was not, however, reliable data on the prevalence of persons participating in firm creation, what they would do to implement new ventures, or the proportion of start-up efforts that became profitable businesses. This hiatus led to the development of longitudinal studies of the entrepreneurial process; 14 projects have now been implemented in 12 countries. The Panel Study of Entrepreneurial Dynamics (PSED) protocol was designed to provide estimates of the prevalence of individuals involved in business creation and the presence of pre-profit, start-up ventures; data on the major activities undertaken to implement a new firm; and track the proportion that completed the transition from start-up to profitable new firm. A number of challenges were involved in implementing the research program, including the development of efficient procedures for identifying representative samples of nascent entrepreneurs and criteria for determining the dates of entry into the start-up process, the transition to a profitable business, and disengagement from the initiative. Data collection is a three-stage process. The initial stage is identifying nascent entrepreneurs in a representative sample of adults. The second are detailed interviews on the start-up team and activity related to creating a new venture. The third stage is follow-up interviews completed to determine the outcome of the start-up efforts. A large number of scholars have been involved in development of the interviews and the PSED data sets have considerable information on the perspectives, activities, and strategies of those involved in the start-up process. Since the initial data sets were made available 15 years ago, there has been considerable research utilizing PSED data sets. One major finding, however, is that the firm creation process is much more diverse and complicated than had been expected. There are substantial research opportunities to be explored. A review of the major features of the PSED protocol and a summary of the existing data sets provides background that will facilitate additional analysis of the firm creation process. Four data sets (Australia, Sweden, and U.S. PSED I & II) are now in the public domain. Critical features of the start-up process have been consolidated and harmonized in a five-cohort, four country data set which is also available.

Article

Clive Beck, Clare Kosnik, and Elizabeth Rosales

The longitudinal study of teachers gives a time perspective on the life and work of teachers, instead of just a snapshot at a particular point. The time period in question may be just a few intense months, as in some ethnographic research, or several decades, as in some life-history research. Longitudinal research is useful in exploring such topics as how teachers change and grow over their careers, changes in teachers’ professional satisfaction over the years, patterns of teacher retention and drop-out, the impact of teachers on their students over time, and the influence of preservice and/or in-service teacher education on teachers. Continuous study of the same teachers over many years is challenging and accordingly not common. It is typically expensive and time-consuming, and extends beyond the time span of most research funding; moreover, many participants either leave the profession or move to other locations, making it difficult to keep in touch with them. Accordingly, additional ways to do longitudinal research need to be found: for example, studying teachers intensively for a shorter period; asking teachers to recall earlier phases in their life and/or career; or studying different cohorts of teachers at various career points (as in the classic Huberman study and parts of the U.K. VITAE research). Each of these methods has limitations but maintains the valuable outcome of providing a time perspective. Where it can be arranged, however, interviewing the same teachers at intervals over several years has the advantage of enabling researchers to get to know the participants well. As a result, the researchers are in a better position to understand what the participants are saying in the interviews, and assess the veracity of their self-reporting about their views and practices, past and present. Also, a degree of trust is established such that the teachers are more likely to be frank about their feelings, challenges, and concerns. But one danger of the emerging relationship is that the support the relationship it provides may positively impact the teachers’ experience (e.g., helping them fine-tune their practice and maintain their morale to an unusually high level). This limitation has to be weighed against the advantages in deciding whether or not to use this approach to the longitudinal study of teachers.

Article

Jeremy B. Yorgason, Melanie S. Hill, and Mallory Millett

The study of development across the lifespan has traditionally focused on the individual. However, dyadic designs within lifespan developmental methodology allow researchers to better understand individuals in a larger context that includes various familial relationships (husbands and wives, parents and children, and caregivers and patients). Dyadic designs involve data that are not independent, and thus outcome measures from dyad members need to be modeled as correlated. Typically, non-independent outcomes are appropriately modeled using multilevel or structural equation modeling approaches. Many dyadic researchers use the actor-partner interdependence model as a basic analysis framework, while new and exciting approaches are coming forth in the literature. Dyadic designs can be extended and applied in various ways, including with intensive longitudinal data (e.g., daily diaries), grid sequence analysis, repeated measures actor/partner interdependence models, and vector field diagrams. As researchers continue to use and expand upon dyadic designs, new methods for addressing dyadic research questions will be developed.

Article

Lifespan development is embedded in multiple social systems and social relationships. Lifespan developmental and relationship researchers study individual codevelopment in various dyadic social relationships, such as dyads of parents and children or romantic partners. Dyadic data refers to types of data for which observations from both members of a dyad are available. The analysis of dyadic data requires the use of appropriate data-analytic methods that account for such interdependencies. The standard actor-partner interdependence model, the dyadic growth curve model, and the dyadic dual change score model can be used to analyze data from dyads. These models allow examination of questions related to dyadic associations such as whether individual differences in an outcome can be predicted by one’s own (actor effects) and the other dyad member’s (partner effects) level in another variable, correlated change between dyad members, and cross-lagged dyadic associations, that is, whether one dyad member’s change can be predicted by the previous levels of the other dyad member. The choice of a specific model should be guided by theoretical and conceptual considerations as well as by features of the data, such as the type of dyad, the number and spacing of observations, or distributional properties of variables.

Article

Alexandre J.S. Morin and David Litalien

As part of the Generalized Structural Equation Modeling framework, mixture models are person-centered analyses seeking to identify distinct subpopulations, or profiles, of participants differing quantitatively and qualitatively from one another on a configuration of indicators and/or relations among these indicators. Mixture models are typological (resulting in a classification system), probabilistic (each participant having a probability of membership into all profiles based on prototypical similarity), and exploratory (the optimal model is typically selected based on a comparison of alternative specifications) in nature, and can take different forms. Latent profile analyses seek to identify subpopulations of participants differing from one another on a configuration of indicators and can be extended to factor mixture analyses allowing for the incorporation of latent factors to the model. In contrast, mixture regression analyses seek to identify subpopulations of participants’ differing from one another in terms of relations among profile indicators. These analyses can be extended to the multiple-group and/or longitudinal analyses, allowing researchers to conduct tests of profile similarity across different samples of participants or time points, and latent transition analyses can be used to assess probabilities of profiles transition over time among a sample of participants (i.e., within person stability and change in profile membership). Finally, growth mixture analyses are built from latent curve models and seek to identify subpopulations of participants following quantitatively and qualitatively distinct trajectories over time. All of these models can accommodate covariates, used either as predictors, correlates, or outcomes, and can even be extended to tests of mediation and moderation.