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Loneliness or perceived social isolation is a subjective experience relating to dissatisfaction with one’s social relationships. Most research has focused on the experience of loneliness in old age, but levels of loneliness are also known to be high among teenagers and young adults. While poor health may be associated with increased feelings of loneliness, there is now considerable evidence on the role of loneliness as a risk factor for poor mental and physical health. Studies show that loneliness is associated with an increased risk of developing dementia and chronic diseases, and also with a higher rate of mortality. Risky health behaviors, a poor cardiovascular profile and compromised immune functioning have all been proposed as potential pathways through which loneliness may affect health. However, much still remains to be understood about these mechanisms.

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.

Longitudinal structural equation modeling (LSEM) is used to answer lifespan relevant questions such as (a) what is the effect of one variable on change in and other, (b) what is the average trajectory or growth rate of some psychological variable, and (c) what variability is there in average trajectories and what predicts this variability. The first of these questions is often answered by a LSEM called an autoregressive cross-lagged (ACL) model. The other two questions are most typically answered by an LSEM called a latent growth curve (LGC). These models can be applied to a few time waves (measured over several years) or to many time waves (such as present in diary studies) and can be altered, expanded, or even integrated. However, decisions on what model to use must be driven by the research question. The right tool for the job is not always the most complex. And, more importantly, the right tool must be matched to the best possible research design. Sometimes in lifespan research the right tool is LSEM. However, researchers should prioritize research design as well as careful specification of the processes and mechanisms they are interested in rather than simply choosing the most complicated LSEM they can find.

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.

Mediator variables are variables that lie between the cause and effect in a causal chain. In other words, mediator variables are the mechanisms through which change in one variable causes change in a subsequent variable. The single-mediator model is deceptively simple because it has only three variables: an antecedent, a mediator, and a consequent. Determining that a variable functions as a mediator is a difficult process, however, because causation can be inferred only when many strict assumptions are met, including, but not limited to, perfectly reliable measures, correct temporal design, and no omitted confounders. Since many of these assumptions are difficult to assess and rarely met in practice, the significance of a statistical test of mediation alone usually provides only weak evidence of mediation.

New methodological approaches are constantly being developed to circumvent these limitations. Specifically, new methods are being created for the following purposes: (1) to assess the impact of violating assumptions (e.g., sensitivity analyses) and (2) to make fewer assumptions and provide more flexible analysis techniques (e.g., Bayesian analysis or bootstrapping) that may be more robust to assumption violations. Despite these advances, the importance of the design of a study cannot be overstated. A statistical analysis, no matter how sophisticated, cannot redeem a study that measured the wrong variables or used an incorrect temporal design.

Healthy aging is accompanied by decrements in episodic memory and working memory. Significant efforts have therefore been made to augment episodic and working memory in healthy older adults. Two principal approaches toward memory rehabilitation adults are restorative approaches and compensatory approaches. Restorative approaches aim to repair the affected memory processes by repeated, adaptive practice (i.e., the trained task becomes more difficult as participants improve), and have focused on recollection training, associative memory training, object-location memory training, and working memory training. The majority of these restorative approaches have been proved to be efficacious, that is, participants improve on the trained task, and there is considerable evidence for maintenance of training effects weeks or months after the intervention is discontinued. Transfer of restorative training approaches has been more elusive and appears limited to other tasks relying on the same domains or processes. Compensatory approaches to memory strive to bypass the impairment by teaching people mnemonic and lifestyle strategies to bolster memory performance. Specific mnemonic strategy training approaches as well as multimodal compensatory approaches that combine strategy training with counseling about other factors that affect memory (e.g., memory self-efficacy, relaxation, exercise, and cognitive and social engagement) have demonstrated that older adults can learn new mnemonics and implement them to the benefit of memory performance, and can adjust their views and expectations about their memory to better cope with the changes that occur during healthy aging. Future work should focus on identifying the personal characteristics that predict who will benefit from training and on developing objective measures of the impact of memory rehabilitation on older adults’ everyday functioning.

Metamemory is defined as cognitions about memory and related processes. Related terms in the literature include metacognition, self-evaluation, memory self-efficacy, executive function, self-regulation, cognitive control, and strategic behavior. Metamemory is a multidimensional construct that includes knowledge about how memory works, beliefs about memory (including beliefs about one’s own memory such as memory self-efficacy), monitoring of memory and related processes and products, and metacognitive control, in which adaptive changes in processing approaches and strategies may be contemplated if monitoring of memory processes (encoding, retention, retrieval) indicates that alternative strategies may be required. Older adults generally believe that their memory has declined and that, on average, they have less control over memory and lower memory self-efficacy than young and middle-aged adults. Many but not all aspects of online memory monitoring are well preserved in old age, such as the ability to discriminate between information that has been learned versus not learned. A major exception concerns confidence judgments concerning whether recognition memory decisions are correct; older adults are more prone to high-confidence memory errors, believing they are recognizing something they have not encountered previously. The evidence regarding metacognitive control is more mixed, with some hints that older adults do not use monitoring to adjust control behaviors (e.g., devoting more time and effort to studying items they believe have not yet been well-learned). However, any age deficits in self-regulation based on memory monitoring or adaptive strategy use can probably be addressed through instructions, practice, or training. In general, older adults seem capable of exerting metacognitive control in memory studies, although they may not necessarily do so without explicit support or prompting.

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.

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.

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.

Modeling and imagery are distinct but related psychological skills. However, despite sharing similar cognitive processes, they have traditionally been investigated separately. While modeling has shown similar psychological and physical performance benefits as imagery, it remains an understudied technique within applied sport psychology. Social cognitive and direct perception approaches remain often-used explanations for the effectiveness of modeling on skill acquisition; however, emergent neuropsychological explanations provide evidence to support these earlier theories and a link to the imagery literature.

With advances in technology and the development of applied frameworks, there is renewed interest in exploring modeling effects and how they parallel imagery use in applied settings. Specifically, modeling research has expanded beyond controlled laboratory settings to explore the effect of various theoretical models on motor performance and related cognitions within practice and competitive settings. The emergence of affordable video editing technology makes it easy for coaches and athletes to incorporate modeling into practice. The accessibility of video technology has sparked applied research on how various forms of modeling influence motor performance and cognitions, such as confidence and motivation. These applied investigations demonstrate the complementary nature of modeling and imagery in enhancing sport performance and skill acquisition, while highlighting the challenges in separating modeling and imagery effects. Both literatures offer possibilities for new methodological approaches and directions for studying these psychological skills in tandem as well as independently. Thus, there is much that imagery and modeling researchers can learn from each other in sport and other performance settings.

In behavioral, social, and developmental research, researchers often begin with a fundamental question that examines whether there is a significant relationship between an independent variable (IV; e.g., video games) and a dependent variable (DV; e.g., aggression). However, examining this simple IV-DV relationship is not sufficient in most research scenarios given that this relationship may differ across the levels of a third variable, which is known as a moderator. For example, researchers may examine the degree to which the relationship between an independent variable and a dependent variable differs across the levels of a moderator or moderators (e.g., gender, ethnicity, socioeconomic status, intervention) to provide a more complete picture of the IV-DV effect and how this effect is or is not applicable to certain groups of participants.

In lifespan developmental research, a key component lies in the study of change, growth, or trajectory of one’s life over time. Undoubtedly, not all individuals may follow the same developmental change or growth over time and examining moderators (e.g., gender, intervention, etc.) that may explain these individual changes is crucial for researchers to better understand the effects on their research investigation and for practical implications. The existing literature shows that conceptual and methodological strategies for moderation analysis have been developed and evolved in lifespan developmental psychology. In particular, researchers in lifespan developmental psychology have used various types of moderation analyses, including assessing whether moderators can explain the pretest and posttest difference based on the conventional analysis of variance (ANOVA) framework and evaluating whether moderators may explain how different individuals follow or deviate from the general growth and trajectory based on advanced latent growth curve modeling (LGCM). Researchers who study lifespan development have realized the importance of moderation effects in their work. In light of the complexity of current biological, psychological, and social factors embedded in lifespan developmental research, the trend of utilizing more sophisticated LGCM than ANOVA to understand the growth trajectories will receive more attention in the future.

Moderation occurs when the magnitude and/or direction of the relation between two variables depend on the value of a third variable called a moderator variable. Moderator variables are distinct from mediator variables, which are intermediate variables in a causal chain between two other variables, and confounder variables, which can cause two otherwise unrelated variables to be related. Determining whether a variable is a moderator of the relation between two other variables requires statistically testing an interaction term. When the interaction term contains two categorical variables, analysis of variance (ANOVA) or multiple regression may be used, though ANOVA is usually preferred. When the interaction term contains one or more continuous variables, multiple regression is used. Multiple moderators may be operating simultaneously, in which case higher-order interaction terms can be added to the model, though these higher-order terms may be challenging to probe and interpret. In addition, interaction effects are often small in size, meaning most studies may have inadequate statistical power to detect these effects.

When multilevel models are used to account for the nesting of individuals within clusters, moderation can be examined at the individual level, the cluster level, or across levels in what is termed a cross-level interaction. Within the structural equation modeling (SEM) framework, multiple group analyses are often used to test for moderation. Moderation in the context of mediation can be examined using a conditional process model, while moderation of the measurement of a latent variable can be examined by testing for factorial invariance. Challenges faced when testing for moderation include the need to test for treatment by demographic or context interactions, the need to account for excessive multicollinearity, and the need for care when testing models with multiple higher-order interactions terms.

Motivation is the largest single topic in psychology, with at least 32 theories that attempt to explain why people are or are not motivated to achieve. Within sport psychology research, there are a plethora of techniques of how to increase and sustain motivation (strategies to enhance agency beliefs, self-regulation, goal setting, and others). However, when explaining the conceptual undergirding of motivation in sport, the why of motivation, two theories predominate: Achievement Goal Theory (AGT) and Self-Determination Theory (SDT). Both theories predict the same outcomes, such as increased achievement striving, sustained behavior change, and perceptions of well-being, but they differ in why those outcomes occur. AGT assumes that individuals cognitively evaluate the competence demands and meaningfulness of the activity, and that those perceptions govern behavior. SDT assumes that individuals are driven by three basic needs, competence, autonomy, and relatedness, and the satisfaction of those needs govern behavior. The following discusses both theories and concludes that each has their strengths and weaknesses.

Multiculturalism is a broad term that encapsulates a number of idealistic constructs related to inclusion, understanding the diverse experiences of others, and creating equitable access to resources and opportunity in our society. Social justice activism is a core tenet of multiculturalism. In order to be optimally effective, multiculturalism needs to be an “action word” rather than a passive construct, one that is inextricably linked to the ability to commit to and engage in an agenda of social justice wherein the inclusive ideals of multiculturalism are actively sought out and fought for.

One such domain where the constructs of multiculturalism and social action are playing out in real time is within U.S. sport. U.S. athletes across all ranks (i.e., Olympic, professional, college, and youth sports) are actively engaging in social justice activism by using their platforms to advocate for equality and human rights. A recent display of activism that has garnered worldwide attention was the silent protest of former San Francisco 49ers quarterback Colin Kaepernick. During the National Football League (NFL) preseason games of the 2016 season, Kaepernick began kneeling during the playing of the U.S. national anthem as a means to protest racial injustice, police brutality, and the killing of African Americans. Since the start of his protest, athletes around the nation and the world have joined the activist–athlete movement, thereby raising awareness of the mistreatment of African Americans within U.S. society. The activist–athlete movement has amassed support and generated momentum, but consulting sport psychology professionals can adopt a more active role to better support athletes, thereby advancing the movement. Consulting sport psychologists can strive to better understand the nature of athlete-activism and aspire to help their athlete clients explore and express their opinions so they can work to effect meaningful societal change, using sport as the vehicle for their message.

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).

Human behavior takes place in different contexts (e.g., organizations, schools, families, sports teams, and communities) whose properties (e.g., climate, culture, cohesion, leadership, communication networks, and structure) influence human behavior. To estimate this influence, researchers need appropriate methods that avoid the problems associated with the application of standard Ordinary Least Square (OLS) regression.

Multilevel modeling methods offer researchers a way to estimate the aforementioned influence. These methods take into account that the variables involved reside at different levels. For instance, in the relationship between work unit climate and employee job satisfaction, the former variable resides at the work unit level (level 2) whereas the latter resides at the individual one (level 1). Moreover, multilevel modeling methods also take into account that the data analyzed to estimate this type of relationships have a nested structure in which individuals (e.g., employees) are nested into collectives (e.g., work units). Finally, these methods decompose variance into between-group and within-group components and allow researchers to model variability at the between and within levels.

Specifically, multilevel modeling methods allow researchers to test hypotheses that involve, among others: 1. A relationship between a higher-level predictor (e.g., work unit climate) and a lower-level outcome (e.g., employee job satisfaction); a so-called “direct cross-level effect”, and 2. An influence of a higher-level moderator (e.g., work unit climate) on an individual level relationship (e.g., the relationship between employee job stress and job satisfaction); a so-called “cross-level interaction”. Multilevel modeling methods can also be used to test more complex models involving mediation (e.g., 2-2-1, 2-1-1, or 1-1-1 models, depending on whether the antecedent and the mediator are level 1 or level 2 variables) and moderated mediation. We show how to test these models by presenting examples with real data and the corresponding SPSS syntax that readers can use to practice.

Brain organization can be measured across multiple spatial and temporal scales where each scale affects the other in the emergent functions that are known as cognition. As a complex adaptive system, the interplay of these scales in the brain represents the information that ultimately supports what one thinks and does. The dynamics of these multiscale operations can be quantified with measures of complexity, which are sensitive to the balance between information that is coded in local cell populations and that is captured in the network interactions between populations. This local versus global balance has its foundation in the structural connectivity of the brain, which is then realized through the dynamics of cell populations and their ensuing interactions with other populations. Considering brain function and cognition in this way enables a different perspective on the changes in cognitive function in aging.

Changes in brain signal complexity from childhood to adulthood were assessed in two independent studies. Both showed that maturation is accompanied by an overall increase in signal complexity, which also correlated with more stable and accurate cognitive performance. There was some suggestion that the maximal change occurs in medial posterior cortical areas, which have been considered “network hubs” of the brain. In extending to the study of healthy aging, a scale-dependent change in brain complexity was observed across three independent studies. Healthy aging brings a shift in local and global balance, where more information is coded in local dynamics and less in global interactions. This balance is associated with better cognitive performance and, interestingly, in a more active lifestyle. It also seems that the lack of this shift in local and global balance is predictive of worse cognitive performance and potentially predictive of additional decline indicative of dementia.