Jonathan Rush

Differences in Within- and Between-Person Factor Structure of Positive and Negative Affect: Analysis of Two Intensive Measurement Studies Using Multilevel Structural Equation Modeling

The Positive and Negative Affect Schedule (PANAS) is a widely used measure of emotional experience. The factor structure of the PANAS has been examined predominantly with cross-sectional designs, which fails to disaggregate within-person variation from between-person differences. There is still uncertainty as to the factor structure of positive and negative affect and whether they constitute 2 distinct independent factors. The present study examined the within-person and between-person factor structure of the PANAS in 2 independent samples that reported daily affect over 7 and 14 occasions, respectively. Results from multilevel confirmatory factor analyses revealed that a 2-factor structure at both the within-person and between-person levels, with correlated specific factors for overlapping items, provided good model fit. The best-fitting solution was one where within-person factors of positive and negative affect were inversely correlated, but between-person factors were independent. The structure was further validated through multilevel structural equation modeling examining the effects of cognitive interference, daily stress, physical symptoms, and physical activity on positive and negative affect factors.

It is about time: Daily relationships between temporal perspective and well-being

This study examined the day-to-day relationships between temporal perspective and well-being. Temporal perspective has predominantly been measured with single-occasion measurement designs, which ignore the potential for within-person variations that may be important in accounting for fluctuations in well-being. A 14-day daily diary design was employed to examine the dimensions of temporal perspective (temporal focus, temporal attitude, and temporal distance) and their dynamic relationships with daily well-being. The results from multilevel analyses indicated that: (a) there is evidence of within-person variability in daily temporal perspective, and (b) this within-person variability in temporal perspective fluctuated systematically with fluctuations in daily well-being. Each temporal perspective dimension was useful in predicting daily well-being. Temporal perspective dimensions interacted with each other such that the daily relationships with well-being depended on both the temporal region (past, present, or future) and the nature of the thoughts (pleasant vs. unpleasant; near vs. far).

Trajectories of Personality Traits Preceding Dementia Diagnosis

Background Several retrospective studies using informant report have shown that individuals with dementia demonstrate considerable personality change. Two prospective studies, also using informant report, have shown that individuals who develop dementia show some personality changes prior to diagnosis. The current study is the first to assess personality trait change prior to dementia diagnosis using self-report measures from longitudinal data. Method This study used data from the Swedish OCTO-Twin Study, a longitudinal panel of 702 twins aged 80 and older. Analysis was restricted to 86 individuals who completed the Eysenck Personality Inventory and received a dementia diagnosis during follow-up occasions. Latent growth curve analyses were used to examine trajectories of extraversion and neuroticism preceding dementia diagnosis. Results Controlling for sex, age, education, depressive symptoms, and the interaction between age and education, growth curve analyses revealed a linear increase in neuroticism and stability in extraversion. Individuals who were eventually diagnosed with dementia showed a significant increase in neuroticism preceding diagnosis of dementia. Discussion Personality change, specifically an increase in neuroticism, may be an early indicator of dementia. Identification of early indicators of dementia may facilitate development of screening assessments and aid in early care strategies and planning.

The Identification of Regions of Significance in the Effect of Multimorbidity on Depressive Symptoms using Longitudinal Data: An Application of the Johnson-Neyman Technique

Background: The investigation of multimorbidity and aging is complex and highly intertwined with aging-related changes in physical and cognitive capabilities, and mental health and is known to affect psychological distress and quality of life. Under these circumstances it is important to understand how the effects of chronic conditions evolve over time relative to aging-related and end-of-life changes. The identification of periods in time where multimorbidity impacts particular outcomes such as depressive symptoms, versus periods of time where this is not the case, reduces the complexity of the phenomenon. Objective: We present the Johnson-Neyman (J-N) technique in the context of a curvilinear longitudinal model with higher-order terms to probe moderators and to identify regions of statistical significance. In essence, the J-N technique allows one to identify conditions under which moderators impact an outcome from conditions where these effects are not significant. Methods: To illustrate the use of the J-N technique in a longitudinal sample, we used data from the Health and Retirement Study. Analyses were based on time-to-death models including participants who died within the study duration of 12 years. Results: Multimorbidity differentially affects rates of change in depression. For some periods in time the effects are statistically significant while in other periods the same effects are not statistically different from zero. Conclusion: The J-N technique is useful to continuously probe moderating effects and to identify particular interactions with the model for time when certain effects are or are not statistically significant. In the context of multimorbidity this method is particularly useful for interpreting the complex interactions with differential change over time.

Increases in Neuroticism May Be an Early Indicator of Dementia: A Coordinated Analysis

Objectives Although personality change is typically considered a symptom of dementia, some studies suggest that personality change may be an early indication of dementia. One prospective study found increases in neuroticism preceding dementia diagnosis (Yoneda et al, 2017). The current study extends this research by examining trajectories of personality traits in additional longitudinal studies of aging. Method Three independent series of latent growth curve models were fitted to data from the Longitudinal Aging Study Amsterdam (LASA) and Einstein Aging Study (EAS) to estimate trajectories of personality traits in individuals with incident dementia diagnosis (Total N = 210), in individuals with incident Mild Cognitive Impairment (N = 135), and in individuals who did not receive a diagnosis during follow-up periods (Total N = 1740). Results Controlling for sex, age, education, depressive symptoms, and the interaction between age and education, growth curve analyses consistently revealed significant linear increases in neuroticism preceding dementia diagnosis in both datasets and in individuals with MCI. Analyses examining individuals without a diagnosis revealed non-significant change in neuroticism overtime. Discussion Replication of our previous work in two additional datasets provides compelling evidence that increases in neuroticism may be early indication of dementia, which can facilitate development of screening assessments.

Abstract: Power to Detect Within- and Between-Person Effects: A Comparison of Multilevel SEM and Unit-Weighted Scale Scores

Power to Detect Withinand Between-Person Effects: A Comparison of Multilevel SEM and Unit-Weighted Scale Scores Jonathan Rush, Philippe Rast, and Scott M. Hofer University of Victoria Intensive repeated measurement designs (e.g., daily diary) are frequently used to investigate within-person variation over relatively brief intervals of time. These designs allow variance to be partitioned into within-person (WP) and between-person (BP) sources of variability, enabling differential effects and factor structures to be estimated at the WP and BP level of analysis. The majority of research utilizing these designs relies on unit-weighted scale scores (Figure 1a), which assumes that the constructs are measured without error. Failing to account for such error has the potential to bias estimates and may decrease the sensitivity to detect WP and BP effects. An alternative approach makes use of multilevel SEM (Figure 1b), which permits the specification of latent variables at both WP and BP levels. These models disattenuate measurement error from systematic variance and should produce less biased WP and BP estimates and larger effects. However, factor models often result in poorer precision (increased standard errors) than observed score models, which can diminish power, despite the larger effect sizes. Differences in power, precision, and bias between multilevel unit-weighted and factor models were compared through a series of Monte Carlo simulations. First, an actual data set of 147 participants measured daily on a 10-item scale over 14 days with a single WP and BP covariate was used to derive population parameters. Second, hypothetical data were generated to examine the models under less desirable conditions (i.e., poor reliability, heterogeneous factor loadings, and fewer items). Both the unit-weighted and factor models performed comparably in power to detect WP and BP effects when population parameters were generated from actual data. Results based on simulated data revealed that precision was consistently poorer in the factor models than the unit-weighted models, particularly when reliability was Correspondence concerning this abstract should be addressed to Jonathan Rush, Department of Psychology, University of Victoria, P.O. Box 1700 STN CSC, Victoria, BC V8W 2Y2, Canada. E-mail: jrush@uvic.ca FIGURE 1 (a) Unit-weighted multilevel model with within-person and between-person covariate; (b) Multilevel factor model with within-person and between-person covariate. low. However, the degree of bias was considerably greater in the unit-weighted model than the factor model. Despite the unit-weighted model consistently underestimating the effect of a covariate, it still generally produced higher power than the factor models, due to the greater precision.