Fusing Biodiversity Metrics into Investigations of Daily Life: Illustrations and Recommendations With Emodiversity

Abstract

Objectives: Functionalist emotion and ecological systems theories suggest emodiversity-the variety and relative abundance of individuals' emotion experiences-is beneficial for psychological and physical health and may change with age. This paper examines and provides recommendations for operationalization of diversity-type intraindividual variability (IIV) constructs using intensive longitudinal data, and demonstrates the utility of emodiversity by examining its links to physical health moderated by mean levels of emotion and age.

Method: Using data from a daily diary study of 138 adults (age 40 to 65 years), we consider how item selection, response scale, choice of diversity index, and number of occasions enable/constrain mapping to theory, measurement reliability, and empirical inquiry.

Results: Item selection and response scale had limited influence on rank-order differences in diversity. Reliable measurement (r ≥ .8) required a minimum of 6 to 12 occasions depending on choice of index, theoretical conception, study design, and distribution of diversity scores. The empirical findings suggest mean level of negative affect, rather than age, moderates the relation between negative emodiversity and health.

Discussion: This study provides recommendations for the calculation of diversity-type IIV constructs and illustrates the potential for study of emodiversity to contribute to understanding of successful aging.

Keywords: Diversity; Emotion; Intraindividual variability; Longitudinal analysis.

Figure 1.

Self-reports of two individual’s emotion experiences obtained through end-of-day reports. The length and coloring of each “petal” indicates the number of occasions each emotion was experienced and the proportion of occasions the emotion was rated at low (= darker shades closer to center) to high (= lighter shades, closer to edges) intensities. Emodiversity is the variety and relative abundance of emotions that individuals experience within a given space and time frame. Person A (left) is relatively low in emodiversity compared to Person B (right). Mean levels of positive and negative emotions are approximately equal between the two individuals. We created all figures using the ggplot2 R package (Wickham, 2009).

Table 1.

Descriptives and Correlations for Emotion, Health, and Demographic Variables

Figure 2.

The Gini coefficient, richness diversity, Shannon’s entropy, and Simpson’s index were used to calculate emodiversity scores. Distributions of scores for each index are presented using positive valence (left column) and negative valence (right column) emotion items.

Figure 3.

Reliability for diversity scores calculated using the Gini coefficient (solid lines) and Simpson’s index (dashed lines) based on correlations among number of occasions of data included (ranging from T = 1 to T = 30) and the total number of occasions designated by the study protocol (T = 30). Horizontal dashed lines appearing at y-axis values .8 and .9 represent two reliability thresholds for each index: Gini positive emodiversity (circles; 3- and 7-occasions), Gini negative emodiversity (squares; 6- and 13-occasions), Simpson positive emodiversity (diamonds; 7- and 9-occasions), and Simpson negative emodiversity (triangles; 12- and 24-occasions). Horizontal dashed lines appearing at y-axis values .8 and .9 represent two reliability thresholds for each index: Gini positive emodiversity (circles; 3- and 7-occasions), Gini negative emodiversity (squares; 6- and 13-occasions), Simpson positive emodiversity (diamonds; 7- and 9-occasions), and Simpson negative emodiversity (triangles; 12- and 24-occasions).

Figure 4.

Simple slopes for the regression of physical health on negative emodiversity and mean negative emotion, with other covariates held constant. SF-36 PCS stands for SF-36 Physical Component Summary Score.