Search

This article serves as a practical guide to mediation design and analysis by evaluating the ability of mediation models to detect a significant mediation effect using limited data. The cross-sectional mediation model, which has been shown to be biased when the mediation is happening over time, is compared with longitudinal mediation models: sequential, dynamic, and cross-lagged panel. These longitudinal mediation models take time into account but bring many problems of their own, such as choosing measurement intervals and number of measurement occasions. Furthermore, researchers with limited resources often cannot collect enough data to fit an appropriate longitudinal mediation model. These issues were addressed using simulations comparing four mediation models each using the same amount of data but with differing numbers of people and time points. The data were generated using multilevel mediation models, with varying data characteristics that may be incorrectly specified in the analysis models. Models were evaluated using power and Type I error rates in detecting a significant indirect path. Multilevel longitudinal mediation analysis performed well in every condition, even in the misspecified conditions. Of the analyses that used limited data, sequential mediation had the best performance; therefore, it offers a viable second choice when resources are limited. Finally, each of these models were demonstrated in an empirical analysis. [For a related report, see ED588330.]

Intra-individual variability over a short period of time may contain important information about how individuals differ from each other. In this article we begin by discussing diverse indicators for quantifying intra-individual variability and indicate their advantages and disadvantages. Then we propose an alternative method that models inter-individual differences in intra-individual variability by separately considering both the amplitude of fluctuations and temporal dependency in the data. In the proposed model, temporal dependency and amplitude of fluctuations are both included as random effects. Parameter estimation is done with a multiple-step approach using maximum likelihood, or with a recommended 1-step approach using a Bayesian method. The similarities and differences between the proposed method and some existing methods are discussed and investigated using diary study data from older adults. The results from empirical data analysis revealed that temporal dependency and amplitude of fluctuations have different predictability of health outcomes and thus should be modeled and considered separately. (Contains 3 tables, 3 figures and 4 footnotes.)

Although daily social exchanges are important for well-being, it is unclear how different types of exchanges affect daily well-being, as well as which factors influence the way in which individuals react to their daily social encounters. The present study included a sample of 705 adults aged 31 to 91, and using Multilevel Modeling analyses investigated whether loneliness or age moderate the relationship between daily affect and daily social exchanges with family and friends. Results indicated differences between events involving family and those involving friends. Furthermore, lonelier individuals benefitted more from positive events than less lonely adults but were not more negatively reactive to negative events. Moreover, results suggested that older adults' affect is more independent of both positive and negative social events compared to younger people. Implications are discussed for the importance of daily social exchanges, daily social stress vulnerability, and the influences of loneliness across middle and later adulthood. (Contains 8 tables and 7 figures.)

The self-concept is often considered to be a personal resource that individuals may use to cope with life stressors, but little is known about how this entity might itself change in response to profound stress. The present study examines structural change in self-concept following conjugal loss in later life. Analyses were conducted on data collected from 57 widows every 4 months over the first 2 years post-loss. The first objective was to explore the adequacy of an operational definition of the self-concept as a latent construct lying at the confluence of self-esteem, perceived environmental mastery, and optimism. Because confirmatory factor analysis (CFA) supported this theoretically based conceptualization, the second objective of the study was to model stability and change in the structure of the self-concept over the 2-year study period. Results suggested that there is both stability and change in the self-system during the adjustment to major life stress. (Contains 2 tables, 2 figures and 2 footnotes.)

The study of intraindividual variability is central to the study of individuals in psychology. Previous research has related the variance observed in repeated measurements (time series) of individuals to traitlike measures that are logically related. Intraindividual measures, such as intraindividual standard deviation or the coefficient of variation, are likely to be incomplete representations of intraindividual variability. This article shows that the study of intraindividual variability can be made more productive by examining variability of interest at specific time scales, rather than considering the variability of entire time series. Furthermore, examination of variance in observed scores may not be sufficient, because these neglect the time scale dependent relationships between observations. The current article outlines a method of using estimated derivatives to examine intraindividual variability through estimates of the variance and other distributional properties at multiple time scales. In doing so, this article encourages more nuanced discussion about intraindividual variability and highlights that variability and variance are not equivalent. An example with simulated data and an example relating variability in daily measures of negative affect to neuroticism are provided. (Contains 4 footnotes and 4 figures.)

Among the many methods available for modeling intraindividual time series, differential equation modeling has several advantages that make it promising for applications to psychological data. One interesting differential equation model is that of the damped linear oscillator (DLO), which can be used to model variables that have a tendency to fluctuate around some typical, or equilibrium, value. Methods available for fitting the damped linear oscillator model using differential equation modeling can yield biased parameter estimates when applied to univariate time series. The degree of this bias depends on a smoothing-like parameter, which balances the need for increasing smoothing to minimize error variance but not smoothing so much as to obscure change of interest. This article explores a technique that uses surrogate data analysis to select such a parameter, thereby producing approximately unbiased parameter estimates. Furthermore the smoothing parameter, which is usually researcher-selected, is produced in an automated manner so as to reduce the experience required by researchers to apply these methods. Focus is placed on the damped linear model; however, similar issues are expected with other differential equation models and other techniques in which parameter estimates depend on a smoothing parameter. An example using affect data from the Notre Dame Longitudinal Study of Aging (2004) is presented, which contrasts the use of a single smoothing parameter for all individuals versus use of a smoothing parameter for each individual. (Contains 4 tables and 7 figures.)

The purpose of the present study was to examine dispositional resilience in the perceived stress-life satisfaction relation following conjugal loss. The sample included 55 widows, assessed on average, 1 month following the death of a spouse. Results supported dispositional resilience as a mediator (the initial relation between perceived stress and life satisfaction was significant, but reduced to a non-significant level once dispositional resilience was included in the model) and a moderator (the interaction between perceived stress and dispositional resilience significantly predicted life satisfaction) between perceived stress and life satisfaction. This research identifies personality characteristics that may be importantly involved in the adjustment to widowhood and discusses implications of mediators vs. moderators in this process. (Contains 1 figure and 3 tables.)

The present study examines the association between the self-concept and adaptation to conjugal loss; the primary aim was to explore whether those individuals high in self-esteem, environmental mastery, and optimism have more adaptive resources with which to ameliorate the detrimental sequelae of bereavement. Analyses were conducted on data collected from 58 widows every four months over a two-year period. One goal of the research was to explore the adequacy of the theoretically chosen operational definition of the self-concept; another goal was to analyze how changes in the level of self-concept components correlated with changes in levels of depression, health, and grief resolution as individuals adjusted to their losses. Analyses revealed that trajectories of depression and grief resolution were more highly related than health to changes in self-concept. (Contains 4 tables.)

We examined age differences in the within-person relationships among perceived stress (PS), perceived stress reactivity (PSR), and depressive affect (DA) as well as potential mechanisms of depression with a longitudinal moderated mediation model. Participants from the Notre Dame Study of Health & Wellbeing (N = 572) completed two to four waves of yearly assessments. Sequentially built multilevel models, in which year was nested within person, illustrated that only midlife adults experience an exacerbated effect of within-person fluctuations in PSR on the relationship between within-person PS and DA levels (γ41 = –.004, p < .01). Findings further suggest that PSR accounts for the PS–DA relationship. Furthermore, older adults illustrate successful emotion regulation strategies at the yearly level—resisting the negative ramifications of years of greater PS and PSR, whereas midlife adults who experience years of greater PSR would particularly benefit from stress management interventions and monitoring of DA levels. [ABSTRACT FROM AUTHOR]

Early work examining the impact of distributions on item measure estimates was demonstrated to be relatively low (Whitely and Dawis, 1974). More recently, as described in Embretson and Reise (2000), the authors state, "item parameter estimates are not much influenced by the trait distribution in the calibration sample" (p. 146). The extent to which varying trait distribution skewness impacts the item measure parameter estimates is explored in the present analysis. In this paper, we analyzed the Center for Epidemiologic Studies-Depression Scale (CES-D; Radlolf, 1977), used in the Notre Dame Study of Health and Well-Being (Bergeman and Deboeck, 2014), with the partial credit model (Masters, 1980) and response shift manipulations. Initial levels of skewness were observed in several items; varying the skewness was shown to greatly impact the item measures, calling into question the robustness of the invariance assumption that estimates are not much influenced by the trait distribution in the calibration sample. [ABSTRACT FROM AUTHOR]

Objective Exploration of development requires the use of research designs and process-oriented methodologies that can capture daily fluctuations within individuals, systematic changes within individuals, and differences between individuals. We examine the stress–affect relationship in this way to assess how the relationship between daily stress and negative affect (NA) as well as the relationship between daily stress and positive affect (PA) differs between individuals and changes over time depending on age and stress differences. Method Participants (N = 966) completed daily "burst" assessments of stress, NA, and PA. Three-level multilevel models depicted how cross-sectional age, within-person age changes, and global stress differences impact the daily stress–affect relationship. Results Findings illustrate that cross-sectional age and the aging process uniquely buffer the stress–NA relationship whereas global stress exacerbates it. Furthermore, older adults as well as adults with low global stress experience a weaker relationship between daily stress and PA as they age, but midlife adults and adults with high global stress experience a stronger relationship. Discussion These results depict differences in aging trajectories for both midlife and older adults and thus inform intervention and preventative care strategies aimed toward promoting stress regulation. [ABSTRACT FROM AUTHOR]

In the current study, extending from the cross-lagged panel models (CLPMs) in Cole and Maxwell (2003), we proposed the multilevel autoregressive mediation models (MAMMs) by allowing the coefficients to differ across individuals. In addition, Level-2 covariates can be included to explain the interindividual differences of mediation effects. Given the complexity of the proposed models, Bayesian estimation was used. Both a CLPM and an unconditional MAMM were fitted to daily diary data. The 2 models yielded different statistical conclusions regarding the average mediation effect. A simulation study was conducted to examine the estimation accuracy of Bayesian estimation for MAMMs and consequences of model mis-specifications. Factors considered included the sample size (N), number of time points (T), fixed indirect and direct effect sizes, and Level-2 variances and covariances. Results indicated that the fixed effect estimates for the indirect effect components (a and b) and the fixed effects of Level-2 covariates were accurate when N ≥ 50 and T ≥ 5. For estimating Level-2 variances and covariances, they were accurate provided a sufficiently large N and T (e.g., N ≥ 500 and T ≥ 50). Estimates of the average mediation effect were generally accurate when N ≥ 100 and T ≥ 10, or N ≥ 50 and T ≥ 20. Furthermore, we found that when Level-2 variances were zero, MAMMs yielded valid inferences about the fixed effects, whereas when random effects existed, CLPMs had low coverage rates for fixed effects. DIC can be used for model selection. Limitations and future directions were discussed. [ABSTRACT FROM AUTHOR]

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains. [ABSTRACT FROM AUTHOR]