Psychology and Aging 2014. Vol. 29. No. 2, 284-296

© 2014 American Psychological Association 0882-7974/i4/$12.00 DOI: 10.1037/a0036302

Age-Related and Death-Related Differences in Emotional Complexity Yuval Palgi

Amit Shrira

University of Haifa

Bar-llan University

Menachem Ben-Ezra

Tal Spalter

Ariel University

University of Toronto

Gitit Kavé

Dov Shmotkin

The Open University

Tel Aviv University

The present study aimed to examine an aspect of emotional complexity as seen in covariation between retrospective judgments of positive and negative affects. We assume that individuals can experience positive affect independently of negative affect. Theories argue that emotional complexity increases in old age, but research shows mixed evidence. Additionally, emotional complexity has been shown to decrease in situations prevalent in old age, such as physical illness and disability. Integrating distinct effects of age and distance to death, we propose that emotional complexity may remain intact or even increase in old age, and yet it decreases in light of functional deterioration shortly before death. The current research examined whether emotional complexity decreases as a function of subjective perception of eloseness to death (subjective survival probability) or actual closeness to death. We used 3 large-scale databases: 2 cross-sectional (SHARE, N = 17,437, mean age = 64; HRS, N = 6,032, mean age = 67) and 1 longitudinal (CALAS, N = 1,310, mean age at baseline = 83). Hierarchical multiple regressions and multilevel models showed that respondents who perceived themselves as closer to death or were actually closer to death showed lower emotional complexity (a stronger negative correlation between positive and negative affects). Age and emotional complexity were unrelated or positively related, depending on the sample. Findings remained the same after controlling for demographic characteristics, as well as physical and cognitive functioning. The results indicate that both subjective and objective closeness to death are associated with lower emotional complexity. This death-related decrease in emotional complexity is discussed within current theories of aging. Keywords: positive affect, negative affect, emotional complexity, distance to death, old age

Some aspects of emotional functioning show a remarkable degree of resilience in old age (Charles & Carstensen, 2010). In contrast to the decline in physiological and cognitive functioning, positive affect (PA) is likely to endure and linger into late life.

decreasing only slightly, if at all, as individuals age (Charles et al., 2010; Pinquart, 2001). Additionally, negative affect (NA) decreases in early adulthood, levels off at young-old age, and slightly increases at very old age (Gerstorf et al., 2010; Pinquart, 2001).

Yuval Palgi, Department of Gerontology, University of Haifa, Israel; Amit Shrira, Interdisciplinary Department of Social Sciences, Bar-llan University, Israel; Menachem Ben-Ezra, School of Social Work, Ariel University, Israel; Tal Spalter, Factor-Inwentash Faculty of Social Work, University of Toronto, Canada; Gitit Kavé, Department of Education and Psychology, The Open University, Israel; Dov Shmotkin, School of Psychological Sciences and Herczeg Institute on Aging, Tel Aviv University, Israel. The first two waves of the CALAS were funded by grants from the U.S. National Institute on Aging (ROl-5885-03 and ROl-5885-06) and conducted at the Department of Clinical Epidemiology at the Sheba Medical Center, Israel. The third wave of data collection and continued work were conducted at the Herczeg Institute on Aging at Tel Aviv University, and were supported by the Israel Academy of Science (grant 1041-541), the Israel National Institute for Health Policy (grant R/17/2001), and a donation from the Ellern Foundation. The last collection of mortality data was funded by the Herczeg Institute on Aging at Tel Aviv University and by the

Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Israel. The SHARE data collection was primarily funded by the European Commission through its fifth and sixth framework programs (projects QLK6-CT-2001-00360; RII-CT- 2006-062193; CIT5-CT2005-028857). Additional funding came from the U.S. National Institute on Aging (grants UOl AG09740-13S2; POl AG005842; POl AG08291; P30 AG12815; Yl-AG-4553-01; OGHA 04-064). SHARE data collection in Israel was funded by the U.S. National Institute on Aging (R21 AG025169), by the German-Israeli Foundation for Scientific Research and Development, and by the National Insurance Institute of Israel. We are grateful to Professor Howard Litwin for facilitating our study with the SHARE data. The HRS is sponsored by the National Institute of Aging (UOl AG009740) and is conducted at the University of Michigan. Correspondence concerning this article should be addressed to Yuval Palgi, Department of Gerontology, Faculty of Social Welfare and Health Sciences, University of Haifa, 199 Aba Khoushy Avenue, Mount Carmel, Haifa 3498838, Israel. E-mail: [email protected] 284

DIFFERENCES IN EMOTIONAL COMPLEXITY Abundant evidence suggests that the predominance of PA over NA documented throughout the adult years also continues in late life, thus reflecting an apparent "well-being paradox" (Kunzmann, Little, & Smith, 2000). These interrelated trends of PA and NA presumably reflect the dynamic association between positive and negative aspects of one's well-being, and hence may be defined in terms of emotional complexity.

Emotional Complexity There is no agreement regarding either the definition of emotional complexity or the best way to operationalize it (Ong & Bergman, 2004; Ready, Carvalho, & Weinberger, 2008; Ready, Âkerstedt, & Mroczek, 2012; Reich, Zautra, & Davis, 2003). A recent study showed that different indices of emotional complexity load on different factors (Griihn, Lumley, Diehl, & Labouvie-Vief, 2013). According to the definition adopted here, emotional complexity reflects the self-perceived separability of PA and NA, as represented in self-ratings of affect. This separability is measured by the covariation of PA and NA. Low covariation suggests that a person can report positive and negative affects as independent of each other, and therefore is most likely to experience a greater emotional complexity (Reich et al., 2003). Indeed, covariation was recently found to be negatively correlated with granularity, namely the tendency to experience and report emotions in a discrete and differentiated fashion (Griihn et al., 2013). In this study, we specifically refer to the covariance between retrospective judgments that individuals make when evaluating their positive and negative experiences in the past (Bodner, Palgi, & Kaveh, 2012; Ready et al., 2012). This specific measurement of retrospective emotional complexity has received little empirical attention, but it is safe to say that it captures the fact that ratings of daily or weekly emotions generally contain summaries of emotional experience over time (Larsen & McGraw, 2011). Emotional complexity may be seen as one way in which individuals regulate their emotions in the face of internal or external events (Charles, 2010; Gross & Levenson, 1997). In contrast to the relatively consistent findings regarding the association between aging and affect level, empirical findings regarding the association between aging and emotional complexity are mixed (Carstensen, Pasupathi, Mayr, & Nesselroade, 2000; Carstensen et al., 2011; Hay & Diehl, 2011; Grühn et al., 2013; Ong & Bergman, 2004; Ready et al., 2008). The inconsistency in empirical findings is partly explained by the fact that studies vary in their definitions of emotional complexity, use different research designs (cross-sectional vs. longitudinal), focus on diverse time frames (requiring evaluations of affect that refer to a momentary mood, a single day, a week, or a month), use a variety of questionnaires, and investigate samples that differ in their age ranges (Ready et al, 2008; Schimmack, 2003). The diversity in means by which emotional complexity is evaluated may bias the results and must thus be taken into account in their interpretation (Schimmack, 2003). Nevertheless, emotional complexity has been associated with higher resilience and lower vulnerability (Ong & Bergman, 2004; Ong, Bergman, Bisconti, & Wallace, 2006; for an exception see Grühn et al., 2013). Emotional complexity can be explained through various theoretical frameworks. According to the socioemotional selectivity

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theory (SST), older adults show adaptive emotional regulation and high emotional complexity because they perceive their time as limited (Carstensen, Isaacowitz, & Charles, 1999). The sense that time is dwindling plays a fundamental role in motivation and goals. Awareness of the limited time available in late life produces a motivational shift from focusing on knowledge-related goals in young adulthood to adopting emotionally meaningful goals in older age (Carstensen, 1995; Carstensen et al., 1999). Older adults direct their attention away from negative stimuli and perceive fewer negative stitnuli relative to younger adults, a phenomenon termed as the positivity effect (Carstensen & Mikels, 2005; Isaacowitz, Allard, Murphy, & Schlangel, 2009). The theory further proposes that in the face of constrained time, older adults savor their moments but also appreciate their temporal fieetingness. They realize not only what they have but also what they cannot have forever. Thus, emotions deepen as life fragility comes more fully into awareness, and the report of mixed emotions should be more prevalent (Carstensen et al., 2000).

Functional Constraints That Result in Diminished Emotional Complexity SST seems to apply mostly to healthy older adults with adjusted social support. Recent literature reviews addressing emotional complexity (Charles & Carstensen, 2010), as well as the model of strength and vulnerability integration (SAVI; Charles, 2010), suggest that in situations in which older adults cannot avoid negative experiences, age-related improvements in emotional regulation will necessarily be attenuated and may even disappear completely (Charles & Piazza, 2009; Charies & Carstensen, 2010). These situations include times when individuals encounter loss of social belonging, continued exposure to chronic unrelenting Stressors, and neurobiological dysregulation (Charles, 2010). Consequently, the maintenance or increase of etnotional complexity in old age might be hampered under adverse circumstances. Providing another framework, Shmotkin's (2005) model of subjective well-being (SWB) in the face of adversity implies that since death is the ultimate failure in controlling the hostile confingencies of life, SWB regulation will be less functional when death approaches, thus reducing emotional complexity. According to Shmotkin's (2005) model, SWB is an agentic system designed to sustain a favorable psychological environment in the face of the hostile-world scenario (HWS). The HWS reflects one's concept of threats to one's life or to one's physical/mental integrity. Emotional complexity, along with associated adaptive systems, such as meaning in life, may play a role in protecting against the HWS (Shrira, Palgi, Ben-Ezra, & Shmotkin, 2011). Indeed, older adults resort to emotional complexity by experiencing happiness even during bad times as well as suffering even during good times (Shmotkin, Berkovich, & Cohen, 2006; Shmotkin & Shrira, 2012). At the same time, however, older people do not benefit from overly complex emotional situations, and in very old age clearly prefer simpler structures of SWB (Palgi & Shmotkin, 2010). We thus expect to find that chronological age will be related to higher emotional complexity, whereas actual or subjective distance to death will be related to lower emotional complexity.

PALGI ET AL.

286 Distance to Death and Emotional Complexity

Late-life changes in cognitive and affective processes are often infiuenced by factors associated with impending death. The terminal change phenomenon suggests that fundamental aspects of human ontogeny that occur at the very end of life may be more greatly related to distance to death than to chronological age (Backman & MacDonald, 2006). Birren and Cunningham (1985) made a distinction between primary, secondary, and tertiary aspects of aging. Whereas primary aging refers to biological and physical deterioration that most people experience with increasing age, secondary aging refers to changes related to disease and disability, and tertiary aging refers to accelerated functional deterioration, which is seen shortly before death. The relationship between distance to death and physiological and cognitive decline (Backman & MacDonald, 2006; Lunney, Lynn, Foley, Lipson, & Guralnik, 2003), as well as between distance to death and psychosocial deterioration, is stronger than the equivalent relationship between chronological age and decline in these domains (Gerstorf, Ram, Estabrook et al., 2008; Gerstorf, Ram, Röcke, Lindenberger, & Smith, 2008; Mroczek & Spiro, 2005; Palgi et al, 2010). In addition, subjective perception of approaching death increases as a function of objective closeness to death, and older adults seem to have reasonably accurate perceptions of their approaching death (Kotter-Grühn, Grühn, & Smith, 2010). Thus, self-perception that the time left to live is short may produce high stress as well (de Faye, Wilson, Chater, Viola, & Hall, 2006), and may generate processes related to actual closeness of death. Hence, we assume that when one becomes closer to one's actual or subjectively anticipated death, the well-being regulatory system may no longer be able to sustain a favorable psychological environment, as expressed in the failure of protective mechanisms, such as emotional complexity, to withstand the HWS (Shmotkin, 2005). According to the dynamic model of affect (DMA; Reich et al., 2003), affect does not operate in a vacuum. The DMA model postulates that stressful events place demands on the system, narrow the individual's attention span, reduce his or her processing capacity, and simplify the person's discriminative abilities. Consequently, generalizations are expanded into a strong fight-orflight reaction, and a negative correlation between PA and NA appears. In stressful situations in which death is either actually closer or subjectively evaluated as closer, PA and NA are expected to be highly inversed (Reich et al., 2003). In contrast, under moderate stress, PA and NA might remain independent in older age (Carstensen et al., 2000).

Overview of the Present Research The present research explores the relationship between chronological age and emotional complexity on the one hand, and between actual or perceived distance from death and emotional complexity on the other hand. It investigates three large-scale samples. Using data from the Survey of Health, Ageing, and Retirement in Europe (SHARE), the first study focuses on subjective evaluation of survival probability. Using data from tbe Health and Retirement Study (HRS), the second study examines both a subjective and an objective measure of distance to death. The third study uses data from the Cross-Sectional and Longitudinal Aging

Study (CALAS) to examine the within-person longitudinal covariance between actual distance to death and emotional complexity. We hypothesize that the limitation of time associated with perceived or actual closeness to death will be associated with reduced emotional complexity. We also examine the relationship between age and emotional complexity, although previous inconsistent findings make it hard to formulate a specific hypothesis regarding this relationship.

Study 1: Subjective Survival Probability and Emotional Complexity This study examines the relationship between subjective survival probability (SSP) and emotional complexity. Emotional complexity is defined here, as in previous studies (Ready et al., 2012; Reich et al., 2003), as the correlation between PA and NA. In line with the S AVI model (Charles, 2010), we hypothesize that the lower the SSP is, the stronger the negative relationship between PA and NA would be, indicating a lower level of emotional complexity. We also examine the relationship between age and emotional complexity, though we make no specific hypothesis regarding this relationship. Method Participants and procedure. We used data from the Survey of Health, Ageing, and Retirement in Europe (SHARE; BörschSupan et al., 2008). The SHARE includes persons aged 50 years and older from a dozen countries (Austria, Belgium, Denmark, France, Germany, Greece, Israel, Italy, The Netherlands, Spain, Sweden, and Switzerland) and their spouses of any age. Based on probabiUty samples of households in each participating country, SHARE represents the community-dwelling older population. In total, 31,115 persons were queried in the first wave ofthe SHARE project (2004-2006) by means of computer-assisted face-to-face interviews. A supplementary drop-off questionnaire was administered to selected participants. Wave 1 of SHARE obtained an overall household response rate of 62%, ranging from almost 40% in Belgium and Switzerland to 81% in Erance. The average within household response rate (i.e., the ratio between the number of responding individuals and the number of eligible persons in these households) was 85%, and ranged from over 70% in Spain to over 90% in Denmark, Belgium, France, and Greece. The final sample for the current analysis included 17,437 respondents aged 50 and older who completed the drop-off questionnaire, which included the measures of PA and NA studied here, and also completed the entire computer-assisted interview (M = 64.15 years, SD = 9.67, range = 50-100; 53.7% women). Measures. Positive and negative affects were measured by five items from the adapted version of the Center for Epidemiological Studies-Depression scale (Radloff, 1977). PA was measured by the items "happy" and "enjoyed life," and NA was measured by the items "depressed," "lonely," and "sad." These scales have been previously used to assess affect (e.g., Ostir, Ottenbacher, & Markides, 2004). Respondents were asked to refer to their experience during the last week and to rate the items from 1 (almost none ofthe time) to 4 (almost all ofthe time). PA and NA were computed as the mean ratings of the relevant items. Spearman-Brown split half r was .74 for PA and Cronbach's alpha was .77 for NA.

DIFFERENCES IN EMOTIONAL COMPLEXITY

Subjecdve survival probability (SSP) was assessed by one question from a block of probabilistic quesdons regarding various expectations (for previous use and validation of this measure see Hurd & McGarry, 2002; Smith, Taylor, & Sloan, 2001; van Doom & Kasl, 1998). At the beginning of this block, instructions were given to make sure that respondents understood the concept of probabilistic questions. Ten questions followed (for more information, see Winter, 2008). SSP was examined by the question: "What are the chances that you will live to be T or more?" Respondents were requested to rate their answer on a scale of 0 to 100. The target age T depended on the respondent's age and was 75 for respondents whose age was 50-65 at the time of the survey, 80 for those 66-70, 85 for those 71-75, 90 for those 76-80, 95 for those 81-85, 100 for those 86-95, 105 for those 96-100, 110 for those 101-105, and 120 for those 106-I-. The distance between the youngest age in each age category and the SSP target age varied between 9 and 25 years. Covariates included gender, educadon, marital status, medical conditions, and cognidve functiotiing. These variables were held constant because previous studies showed that they account for some age-related and death-related changes in well-being (e.g., Gerstorf et al., 2008). Education level was classified according to the Intemadonal Standard Classification of Educadonal Degrees (ISCED-97; United Nations Educational, Scientific and Cultural Organisation, 1997) that includes seven categories from "preprimary" (0) to "second stage tertiary education" (6). Medical conditions were assessed by a sum of listed illnesses that respondents reported to have been diagnosed with by a physician (e.g., heart problems, high blood pressure, high cholesterol, stroke or cerebral vascular disease; possible range was 0-14; for similar measures of health problems, see Ben-Ezra & Shmotkin, 2006). Cognidve funcdoning included three measures; (1) verbal recall was assessed by the number of words out of a 10-word list recalled 5 minutes after the list was read to the respondent; (2) word fluency was assessed by the number of correct animal names produced within 1 min; (3) arithmetic ability was assessed by the number of correct answers to four arithmedc quesdons. Following Kavé and colleagues (2012), who reported that these three cognitive domains load on a single latent factor, a composite score of all three cognitive domains was calculated. First, the scores in each domain were standardized and then the standardized scores were summed

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to a composite score, with a higher score representing better cognidve functioning. The difference between respondents' age and the SSP target age was also held as a covariate. As many spouses of respondents were included in the dataset (n = 10,772; 5,386 couples), a dichotomous variable was added to the analysis to control for the intercouple dependence ( 1 = having a spouse in sample; 0 = having no spouse in sample). We also controlled for country of origin by creating 11 dummy variables of each country versus the rest. Finally, to best disentangle age-related from deathrelated processes, age was included among the covariates when SSP was the predictor and vice versa. Thus, one model examined the main effect of age and its interacdon with PA, and the other model examined the main effect of SSP and its interacdon with PA. Data analysis. We used hierarchical multiple regression analyses to test our hypotheses. Because closeness to death is associated with stress-related variables (Gerstorf, Ram, Lindenbeerger, & Smith, 2013; Gerstorf et al., 2010; Griffin, Loh, & Hsketh, 2013), and stress is associated with negadve affect, we chose to use NA as the predicted variable in this study. NA was regressed on the covariates in Step 1, on PA and either age or SSP in Step 2, and on either the PA X Age or the PA X SSP interaction in Step 3. All continuous variables (except for cognitive functioning which was a total of standardized scores) were standardized and the interacdon term was computed as the product of muldplying the standardized scores.

Results Table 1 presents the descriptive stadstics and intercorrelations for all study variables. PA and NA correlated moderately and negadvely. Age showed weak correladons with PA and NA, and SSP showed slightly stronger correlations with PA and NA. Finally, age and SSP correlated moderately and negatively. When NA was regressed on age and PA, age (B = .07, SE = .00, p < .0001) and PA (5 = - . 4 8 , SE = .00, p < .0001) were significant predictors, but their interaction was not significant (B = - . 0 1 . SE = .00, p = .08). The model explained 25% of the variance in NA. When NA was regressed on SSP and PA (after controlling for the difference between the SSP target age and the respondents'

Table 1 Means, Standard Deviations, and Intercorrelations Among Study I Variables Variable

M

SD

1

1. PA 2. NA 3. Age 4. SSP 5. Gender (women)" 6. Education 7. Marital status (married)*" 8. Medical conditions 9. Cognitive functioning

2.81 1.51 64.15 61.96 53.7% 2.61 74.8% 1.49 .00

.74 .60 9.67 28.70 — 1.49 — 1.40 .76

— -.49 -.08 .21 -.07 .10 .13 -.15 .16

.12 -.24 .17 -.17 -.23 .20 -.24

— -.32 .00 -.25 -.22 .31 -.37

.00 .13 .12 -.24 .19

-.10 -.18 .07 -.06

.07 -.16 .48

-.11 .12

-.20

Note. N = 17,437. Absolute correlations .01 or higher are significant at the .0001 level. Pearson coefficients are presented for continuous variables. For correlations between dichotomous and continuous variables, point-biserial coefficients are presented. PA = positive affect; NA = negative affect; SSP = subjective survival probability. " Coded 1 = man, 2 = woman. '' Coded 1 = unmarried, 2 = married.

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PALGI ET AL.

age), SSP {B = - . 1 3 , SE = .00, p < .0001) and PA {B = -.46, SE = .00, p < .0001) were significant predictors, and their interaction was significant as well {B = .08, SE = .00, p < .0001). The model explained 27% of the variance in NA. When covariates were added to the model, the SSP X PA interaction remained significant (ß = .06, SE = .00, p < .0001), and the model explained 33% of the variance in NA. When adding country to the other covariates, the SSP X PA interaction remained significant [B = .07, SE = .00, p < .0001), and the model explained 34% of the variance in NA. Plotting the SSP X PA interaction showed that the relationship between PA and NA became stronger as SSP was lower. The PA-NA correlation was -.52 among those who rated their SSP as 0%-25% (n = 2,331) and -.44 among those who rated their SSP as 75%-100% (77 = 7,076). The difference between these two correlations was significant (z = 4.35, p < .0001). That is, emotional complexity was lower in people who predicted lower survival to a target age. In additional analyses, PA and NA were divided to low (below or at the median) and high levels (above the median; Md = 2.50 and 1.33 for PA and NA, respectively). Among those who rated their SSP as 0%-25%, 57.3% had a low PA level and 76.7% had a high NA level. In comparison, among those who rated their SSP as 75%-100%, 32.3% had a low PA level and 52.0% had a high NA level. This distribution differed significantly, x'^(2) = 507.78, p < .0001 for PA, and x^(2) = 543.57, p < .0001 for NA. Therefore, individuals who predicted lower survival to a target age reported a high NA level and a low PA level, demonstrating low emotional complexity. Finally, we examined changes in heterogeneity in variance of affect as a function of SSP. Because the variances in PA and NA were higher for individuals who reported low SSP, we rankordered the PA and NA scores for participants with low and high SSP so as to equalize the variances. In this procedure, previously used by Zautra, Reich, Davis, Potter, and Nicolson (2000), we identified the affect scores separating the quartiles in low and high SSP respondents. Then, the affect scores in each quartile were grouped and represented by one value. As a result of tbis procedure, scores in the lower quartile received the value 1, scores in the upper quartile received the value 4, and the intermediate quartiles received values 2 and 3, thus affect scores in each SSP quartile demonstrated similar variance. We then reran the regression analysis (including all the covariates). The SSP X PA interaction remained significant {p < .05), suggesting that the increased association between PA and NA when SSP was low was not due to changes in heterogeneity.

Discussion The findings support our hypothesis: lower SSP is associated with a stronger correlation between PA and NA, indicating that when SSP is low there is less emotional complexity. There was no association between age and emotional complexity. We suggest that in situations in which individuals perceive their death as close, they use simpler emotional processes. It seems that when the subjective evaluation of one's probability to survive is low, one finds it harder to savor time, or to see meaningfulness in the perceived limited future (Ersner-Hershfield, Mikels, Sullivan, & Carstensen, 2008; Zhang, Ersner-Hershfield, & Fung, 2010). It can

thus be concluded that some older adults do not exhibit high emotional complexity (Zhang et al., 2010). However, as this study examined SSP alone, rather than actual distance to death, it is possible that SSP depended on the assessor's affect, thus making it difficult to determine that lower emotional complexity was the result of the perceived dwindling time rather than the cause of perceiving survival that way. In order to better understand the impact of approaching death on emotional complexity. Study 2 analyzes the relation between PA and NA as a function of SSP along with an analysis of the relation between PA and NA as a function of actual distance to death in another large population sample.

Study 2: Subjective Survival Probability, Actual Distance to Death, and Emotional Complexity For the purpose of the second study we used data from the Health and Retirement Study (HRS). This study refers to subjective perception of distance to death (DtD), as well as to actual distance to death. Additionally, it aims to replicate Study 1 by using a different measure of affect. As in Study 1, we hypothesize that lower SSP will be associated with less emotional complexity. In addition, we predict that shorter DtD will be related to a stronger negative association between PA and NA (i.e., to lower emotional complexity). The relationship between age and emotional complexity is also examined. Method Participants and procedure. We used data from the Health and Retirement Study (HRS). The HRS is a longitudinal epidemiological survey of a nationally representative sample of individuals 50 years and older living in the United States. The HRS is sponsored by the National Institute of Aging and is conducted at the University of Michigan. The study was reviewed and approved by the University of Michigan's Health Sciences Institutional Review Board. The original HRS core sample design is a multistage area probability sample of households, with oversamples of Blacks, Latinos, and residents of Florida {N = 18,469). The target population of the HRS cohort includes all adults bom during the years 1931-1941 who reside in the United States. Participants take part in a biennial interview that covers a range of topics, including income, wealth, work, retirement, health, and health care utilization. The sample that was analyzed in the current study included 6,032 respondents who completed the 2006 self-report psychosocial leave-bebind questionnaire (M = 67.56 years, SD = 9.43, range = 50-89; 56.8% women). The self-report psychosocial questionnaire contained measures of positive and negative affect. We also analyzed data from a subsample of 279 respondents who completed the 2006 questionnaire and died by 2009 (M = 75.15 years, SD = 10.64, range = 50-101; 48.0% women). Measures. Positive and negative affects were measured by 12 items adapted from Mroczek and Kolarz (1998). PA was measured by six items (e.g., "calm and peaceful," "in good spirits") and NA was measured by six items (e.g., "nervous," "worthless"). Respondents rated these items from 1 {none of the time) to 5 (all of the time) in reference to their experience during the last month. PA and NA were computed as the mean ratings of the relevant items.

DIFFERENCES IN EMOTIONAL COMPLEXITY

Combach's a for positive and negative affect was .90 and .85, respectively. In the deceased subsample Combach's a for positive and negative affect was .90 and .87, respectively. Subjective survival probability (SSP) was assessed by one question from a block of probabilistic questions regarding various expectations. At the beginning of this block, instructions were given to ensure that respondents understood the concept of probabilistic questions. Various questions followed (for more information, see Hurd & McGarry, 2002). SSP was examined by the question: "What are the chances that you will live to be T or more?" Respondents were requested to rate their answer on a scale of 0 to 100. The target age T depended on the respondent's age and was 75 for respondents whose age was 50-64 at the time of the survey, 80 for those 65-69, 85 for those 70-74, 90 for those 75-79, 95 for those 80-84, and 100 for those 85-89. Individuals older than 89 were not asked about their SSP. The distance between the youngest age in each age category and the SSP target age varied between 9 and 25 years. Distance to death (DtD) was measured in months from the time of interview until the actual time of death. Month and year of death were available from 2006 (interview time) to 2009 based on the National Death Index (iV = 279, mean months to death = 14.16, SD = 7.29, range = 0-31). Covariates included gender, education, marital status, medical conditions, and cognitive functioning. Education was recorded by six categories, from "no schooling" to "graduate academic degree." Medical conditions were assessed by a sum of listed illnesses that respondents reported to have been diagnosed with by a physician (e.g., high blood pressure, diabetes, stroke; possible range was 0-8). Cognitive functioning was assessed in two domains: (1) verbal recall was assessed by the number of words out of a 10-word list recalled 5 minutes after the list was read to the respondent; (2) attention and calculation were assessed by the number of correct responses given when subtracting 7 from 100 up to five times. The tests were modeled after the Mini-Mental State Examination, a standard geriatric dementia screen (Folstein, Folstein, & McHugh, 1975). Following Ayalon and King-Kallimanis (2010), a composite score of both cognitive domains was calculated. First, the scores in each domain were standardized and then summed together, with a higher score representing better cognitive functioning.

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The difference between respondents' age and the SSP target age was also held as a covariate. To best disentangle age-related from death-related processes, age was included as a covariate when SSP or DtD were the predictors and vice versa. Data analysis. The same hierarchical multiple regression analyses that were used in Study 1 were performed in Study 2 as well. In the deceased subsample the same analyses were run, and DtD was entered to the analyses in addition to SSP.

Results Table 2 presents descriptive statistics and intercorrelations for all study variables. PA and NA correlated strongly and negatively. Age showed very weak correlations with PA and NA, and SSP showed moderate correlations with PA and NA. Age and SSP correlated weakly and negatively. When NA was regressed on age and PA, age was not a significant predictor {B = .02, SE = .01,/? = .05), PA was a significant predictor {B = -.57, SE = .01, p < .0001), and the interaction between age and PA was also significant (ß = .03, SE = .01, p < .0001). The model explained 32% of the variance in NA. When covariates were added to the model, the Age X PA interaction remained significant {B = .03, SE = .01, p < .0001), and the model explained 36% of the variance in NA. Plotting the Age X PA interaction showed that the relationship between PA and NA became weaker as age was higher. The PA-NA correlation was —.61 among those whose age was in the lower quartile (n = 1,594), and -.53 among those whose age was in the upper quartile (n = 1,479). The difference between these two correlations was significant (z = 3.28, p < .001). When NA was regressed on SSP and PA (after controlling for the difference between respondents' age and the SSP target age), SSP was a significant predictor of NA (B = -.07, SE = .01, p < .0001), PA was a significant predictor of NA (S = -.55, SE = .01, p < .0001), and so was their interaction (S = .08, SE = .01, p < .0001). The model explained 33% of the variance in NA. When covariates were added to the model, the SSP X PA interaction remained significant (B = .08, SE = .01, p < .0001), and the model explained 37% of the variance in NA. Plotting the SSP X PA interaction showed that the relationship between PA and NA became stronger as SSP was lower. The

Table 2 Means, Standard Deviations, and Intercorrelations Among Study 2 Variables Variable L 2. 3. 4. 5. 6. 7. 8. 9.

PA NA Age SSP Gender (women)" Education Marital status (married)"' Medical conditions Cognitive functioning

M

SD

1

3.60 1.57 67.56 46.50 56.8% 2.34 67.7% 1.89 .00

.67 .61 9.43 31.11 — 1.47 — 1.30 .78

— -.57 .04 .20 -.02 .05 .08 -.10 .04

— .00 -.19 .01 -.15 -.07 .17 -.16

— -.10 -.02 -.14 -.15 .33 -.26

.05 .11 .05 -.17 .08

-.07 -.21 .00 .02

.10 -.15 .33

-.08 .11

-.16

Note. N — 6,032. Absolute correlations .04 or higher are significant at the .01 level or above. Pearson coefficients are presented for continuous measures. For correlations between dichotomous and continuous variables, point-biserial coefficients are presented. PA = positive affect; NA = Negative affect; SSP = subjective survival probability. " Coded 1 = man, 2 = woman. ** Coded 1 = unmarried, 2 = married.

290

PALGI ET AL.

PA-NA correlation was —.58 among those who rated their SSP as 0%-25% (n = 1,976), and -.52 among those who rated their SSP as 75%-100% (n = 1,578). The difference between these two correlations was significant (z = 2.54, p < .01). That is, emotional complexity was lower in people who predicted lower survival to a target age. Because the variances in PA and NA were higher for individuals who report low SSP, we rank-ordered the PA and NA scores for participants with low and high SSP to equalize the variances, and then reran the regression analysis. The SSP X PA interaction remained significant (p = .002). Among the deceased subsample {N = 279, mean PA = 3.40, SD = .75, mean NA = 1.88, SD = .75), the correlation between PA and NA was strong and negative {r = - .48, p < .0001). There was no significant correlation between age and either PA or NA (r = .09 and —.10, respectively, p > .05), as well as between DtD and either PA or NA {r = .08 and —.11, respectively, p > .05). Age and DtD did not correlate with each other (r = -.05, p > .05). When NA was regressed on age and PA within the deceased subsample, age was not a significant predictor of NA {B = —.05, SE = .05), PA was a significant predictor of NA {B = -.48, SE = .05, p < .0001), and the interaction between age and PA was not a significant predictor of NA (ß = - . 0 1 , 5E = .05). The model explained 23% of the variance in NA. When NA was regressed on DtD and PA, DtD was not a significant predictor of NA (5 = —.07, 5^ = .05), PA was a significant predictor of NA {B = - . 4 8 , SE = .05, p < .0001), and the interaction between DtD and PA was a significant predictor of NA {B = .12, SE = .05, p < .05). The model explained 24% of the variance in NA. When covariates were added to the model, the DtD X PA interaction remained significant {B = .11, SE = .05, p < .05), and the model explained 34% of the variance in NA. Plotting the DtD X PA interaction showed that the relationship between PA and NA became stronger as DtD was shorter. The PA-NA correlation was —.67 among respondents whose DtD was in the lower quartile (n = 78) and —.43 among respondents whose DtD was in the upper quartile (n = 90). The difference between these two correlations was significant (z = 2.22, p < .05). That is, emotional complexity decreased as people approached their actual death. Because the variances in PA and NA were not significantly different for individuals who reported low and high SSP or for individuals who were close or far from death, the decreased association between PA and NA could not be attributed to a change in variance of affects as a function of either SSP or DtD.

Discussion Analyses conducted on a large population sample from the U.S. replicated and extended the results of the first analysis that examined an equivalent European sample. Specifically, when SSP or DtD were lower, the negative associations between PA and NA were stronger, indicating that emotional complexity was lower when individuals either predicted that they had little time to live or were actually close to their death as seen in objective mortality data. Moreover, age was weakly related to higher levels of emotional complexity (a weaker negative association between PA and NA). These findings suggest that age-related processes of affect

regulation might be different from processes that are related to either perceived or actual closeness to death.

Study 3: Actual Distance to Death and Emotional Complexity Study I and Study 2 examined our hypothesis that emotional complexity is lower when individuals approach death in very large population samples, but these analyses involved cross-sectional designs. Thus, these studies provide evidence only for differences among people who are closer or further from death or birth. To strengthen the developmental nature of this hypothesis. Study 3 extends the examination to an intraindividual longitudinal design. In this study we evaluate the relationship between actual DtD and emotional complexity in a third population sample in which individuals were followed up for over a decade, so that more extensive mortality data were available than those of the HRS. Due to the longitudinal nature of the study, we could hypothesize that as DtD decreased, emotional complexity would decrease as well. The longitudinal association between age and emotional complexity was also examined. Method Participants and procedure. We used data from the threewave Cross-Sectional and Longitudinal Aging Study (CALAS; Ben-Ezra & Shmotkin, 2006). The CALAS included a multidimensional assessment of a random sample of the older Jewish population in Israel (aged 75-94), stratified by age groups, gender, and place of birth. The sample was drawn from the National Population Registry (NPR) in January 1989. The CALAS interviews were carried out at the participant's home after the participant had signed an informed consent. Data collection and maintenance of information on the CALAS were conducted in accordance with institutional ethical requirements. For the purposes of the current analysis we used data from 1,310 respondents who were interviewed in person between 1989 and 1992 (Tl) and died by 2009 (M = 83.71 years, SD = 5.40, 46.0% women). Of those interviewed at Tl, 636 were also interviewed between 1993 and 1994 (T2 age: M = 85.89 years, SD = 5.10, 46.2% women), and 122 were also interviewed between 2001 and 2002 (T3 age: M = 92.52 years, SD = 4.48, 58.2% women). Attrition was mainly due to mortality or poor health. More details on the CALAS appear elsewhere (e.g., Ben-Ezra & Shmotkin, 2006; Palgi & Shmotkin, 2010). Measures. Positive and negative affects were measured by two subscales of the Center for Epidemiological StudiesDepression scale (Radloff, 1977). PA was measured by three of four items (e.g., "I felt happy") that comprised the well-being subscale. The fourth item ("I felt that I was just as good as other people") was omitted because it decreased internal reliability markedly. NA was measured by seven items (e.g., "I felt depressed") that comprised the depressed mood subscale. Respondents rated these items from 0 {not at all) to 3 {almost every day) in reference to their experience during the last month. PA and NA were computed as the mean ratings of the relevant items. Cronbach's alpha for PA was .71 in Tl, .69 in T2, and .66 in T3. Cronbach's alpha for NA was .83 in Tl, .84 in T2, and .77 in T3. Distance to death (DtD) was defined as the number of years from interview to death (mean years to death = 6.23, SD = 4.35,

DIFFERENCES IN EMOTIONAL COMPLEXITY

range = 0-20). Date of death was drawn from NPR records up to May, 2009. By that time, out of the 1,369 Tl Participants 1,310 had died and 50 were still alive. Mortality data were missing for nine participants. Covariates included gender, years of education, marital status, medical conditions, and cognitive functioning. Medical conditions were assessed by a sum of listed illnesses that respondents reported to have been diagnosed with by a physician (e.g., high blood pressure, diabetes, cancer; possible range was 0-26). Cognitive impairment was assessed by the Orientation-MemoryConcentration Test (Katzman et al., 1983). This test includes six items that address three cognitive functions of time orientation, memory, and concentration. Errors were multiphed by prefixed weights and summed up, and the raw score ranged from no impairment (0) to full impairment (28) (see Katzman et al., 1983). Cronbach's alpha in Tl, T2, and T3 was .73, .69, and .74, respectively. Data analysis. We fitted two separate growth curve models (Peugb & Enders, 2005) for NA (converted into T scores), once with chronological age and once with DtD in combination with PA. The models examined whether the observed covariation of PA-NA differed by respondents' age or by DtD at Tl. Eor these analyses, the Level 1 (within-person level) model was parameterized as; NA¡, =

ß„Time,-,

ß2i = 720 + 72i(Age/DtD,) -I- MJ;

Results Table 3 presents descriptive statistics and intercorrelations for all study variables in Tl. PA and NA correlated strongly and negatively. Age and DtD showed weak, mostly nonsignificant correlations with PA and NA. Age and DtD correlated strongly and negatively, indicating that older respondents were closer to death. Preliminary intraclass correlation for NA (the proportion of the between-individual variance due to the sum of the between- and within-individual variances) was .37, suggesting that there was substantial variability in within-person NA over time. Table 4 presents the fixed parameters and random effect estimates for the age and DtD models. When age was entered into the model, the estimate for age was not significant, estimate = -.03, p > .05, whereas the estimate for PA was significant, estimate = —6.81,p < .0001. The estimate for time was significant as well, estimate = .22, p < .01. There was a significant Time X PA interaction, estimate = .87, p < .0001, indicating that the relationship between PA and NA decreased with time. Importantly, this effect was not moderated by age, as indicated by the nonsignificant three-way interaction between time, age, and PA. The A pseudo-i?^ (Snijders & Bosker, 1999) showed that the age model (with the covariates) explained 10.4% of the variance in NA.

In this model, NA for person i at time r, A'A,,, is a function of (a) a random coefficient representing the intercept of NA for person i (across the t times for which the person provided data), ßo,; (b) slope parameters that represent linear rates of change over time for their respective variables for person /, ß, ,Time„, ß2,PA,.,; and (c) the within-individual random error, e¡,. The Level 2 (between-person level) model was parameterized so that the parameters from the within-person model were modeled as a function of age or DtD; ßo,- = 700 + 7oi(Age/DtD,) + «o;

(2)

ßi/ = 710 + 7ii(Age/DtD,) + «„•

(3)

(4)

In this model, 7oo represents the intercept of NA for a person with an average age or an average DtD (i.e., age or DtD equals the sample mean); 7oi represents the effect of age or DtD on the initial level of NA for person ¿, and MQ, represents the error of ßg,. In addition, y^ç, represents the time-NA slope for a person of average age or average DtD, 7,, represents the effect of age or DtD on the time-NA covariation, and M,, represents the error of ß,,. Finally, 720 represents the PA-NA slope for a person of average age or average DtD, 721 represents the effect of age or DtD on the PA-NA covariation, and w^, represents the error of ß2,. Substituting Equations 2 and 3 into Equation 1 allows the simultaneous estimation of individual and sample parameters of the PA-NA covariation. Incomplete data were treated as missing at random (Little & Rubin, 1987).

(1)

e¡,

291

and

Table 3 Means, Standard Deviations, and Intercorrelations Among Study 3 Time 1 Variables Variable

M

SD

1

1. PA 2. NA 3. Age 4. DtD (in years) 5. Gender (women)" 6. Education 7. Marital status (married)*" 8. Medical conditions 9. Cognitive impairment

1.08 .68 83.71 6.62 46.0% 7.77 41.8% 3.70 8.95

.56 .58 5.40 4.63 — 5.53

— -.42 -.07 .04 -.17 .14 .21 -.20 -.16

2.47 7.90

.02 -.04 .24 -.17 -.27 .30 .17

-.30 -.04 -.02 -.21 .00 .18

.10 -.03 .08 -.05 -.11

. -.22 -.48 .19 .20

.10 -.04 -.49

-.09 -.14

.04

Note. N = 1,310. Absolute correlations .06 or higher are significant at the .05 level or above. Pearson coefficients are presented for continuous variables. For correlations between dichotomous and continuous variables, point-biserial coefficients are presented. PA = positive affect; NA = Negative affect; DtD = distance to death. " Coded 1 = man, 2 = woman. '' Coded 1 = unmarried, 2 = married.

PALGI ET AL.

292 Table 4 Growth Curve Models for Negative Affect by Time, Positive Affect, and Either Chronological Age or Distance-to-Death (Study 3)

By distance-todeath

By age Parameter Fixed effect estimates Intercept Time Positive affect Age/Distance-to-death (Factor) Time X Positive affect Time X Factor Positive affect X Factor Time X Positive affect X Factor Random effect estimates Variance of intercept Variance of slope Covariance intercept, slope Residual variance Goodness-of-fit indices -2LL AIC

General Discussion

Estimate

SE

Estimate

SE

49.29*** 0.22** -6.81*** -0.03 0.87*** 0.00 -0.05 0.00

0.26 0.08 0.44 0.04 0.11 0.01 0.08 0.02

49.32*** 0.17* -6.77*** -0.04 0.69*** 0.00 0.21* -.10**

0.26 0.08 0.45 0.05 0.12 0.02 0.09 0.03

22.66*** 0.03 0.86 59.30***

4.07 0.00 0.57 3.27

23.14*** 0.03 0.91 58.54***

4.09 0.00 0.59 3.25

13143.37 13167.37

13133.00 13157.00

Note. N = 1,310 respondents who provided 2,068 observations. Unstandardized estimates and standard errors are presented. — 2LL = — 2 log likelihood. AIC = Akaike information criterion. ' p < .05. * * p £ . O l . * * * p < . 0 0 1 .

When DtD was analyzed, the esdmate for DtD was not significant, estimate = —.04, p > .05, the estimate for PA was significant, estimate = —6.77, p < .0001, and the estimate for time was significant as well, esdmate = .11, p < .05. The Time X PA esdmate = .69, p < .0001, and the PA X DtD esdmate = .21,p < .05, were significant. The significant relationship between PA and NA changed as a function of DtD, as indicated by the significant three-way interaction of Time X PA X DtD, estimate = —.10,p< .01. According to this interaction, the covariance between PA and NA (the Time X PA interaction) was stronger for individuals who were closer to death. Since a higher PA-NA association indicates decreased emotional complexity, emotional complexity was lower for people who were closer to death. After adding the covariates, the estimate for the Time X PA X DtD interaction remained significant, esdmate = -.09, p = .006. The A pseudo-i?^ showed that the DtD model (with the covariates) explained 11.3% of the variance in NA. To summarize, the analyses showed that the relationship between PA and NA increased as a funcdon of DtD but did not change as a function of chronological age.

Discussion Longitudinal analyses of a third population sample that looked at the covariance between PA and NA as a function of either age or DtD supported our hypothesis that emotional complexity decreases as death approaches. In this analysis we demonstrated that as DtD decreases, the negative correlation between PA and NA strengthens, indicating that DtD is associated with lower emotional complexity. The strength of the negative association between PA and NA did not change significantly as pardcipants were older.

The present research assumed that emodonal complexity, represented by self-perceived separability of PA and NA, relies on a regulatory system that can balance age-related negative threats. However, following previous findings regarding the limitations of this system, we examined whether both subjective distance-todeath and objecdve distance-to-death are related to lower emotional complexity. All three studies showed that as subjective or objective distance to death was shorter, there was a stronger relationship between PA and NA, which we interpreted as evidence of lower emotional complexity. Thus, when people approach their death, their presumed stress enhances a bipolar association between PA and NA, as predicted by the DMA (Reich et al., 2003). Although there are cases in which individuals are expecting their own death peacefully, most of the literature shows that approaching death represents a threat and increases stress levels, due primarily to preoccupation with existendal concerns or the welfare of family members (de Faye et al., 2006). Therefore, evidence shows a steeper decline in functioning than would be expected if death were not imminent among people of the same age (Gerstorf et al., 2010; Gerstorf et al., 2013; Griffin et al., 2013). Furthermore, in some cases emotional complexity may be lower due to purpose-driven emotional states such as approaching death. We believe that when one recruits all energies to deal with one ominous issue, such as illness or approaching death, emodonal complexity is reduced. Future studies should further examine under what circumstances individuals actually perceive their own approaching death as a stressful event. From a different perspecdve, it is possible that the SWB regulatory system gradually loses its ability to sustain the complex emotional environment required for adjustment vis-à-vis the HWS and its ultimate realization (Shmotkin, 2005). It seems that the tendency to use complex emotional regulation is related to greater distance from death, when there is enough dme or emodonal space to cultivate the approaching end or to imagine a meaningful ending (see Ersner-Hershfield et al., 2008). In other words, emotional complexity may be found when the individual succeeds in constitudng a regulatory emotional environment in the face of actual or potential threats, but not when death is close during tertiary aging, when the dying-related process is experienced more strongly. Note that lower emotional complexity, found in the present study to be related to actual or perceived closeness to death, was previously associated with lower resilience and higher vulnerability (Ong & Bergman, 2004; Ong et al, 2006). The current findings expand previous analyses according to which SWB shows a greater decline as a function of DtD than as a function of chronological age (Gerstorf et al., 2008; Mroczek & Spiro, 2005; Palgi & Shmotkin, 2010). Emodonal complexity, which is a derivative of SWB components, also shows a decline as a function of subjective and objective DtD reladve to the stability or the slight increase seen when emodonal complexity is predicted by age. It has been proposed that SWB might be linked to closeness to death through its relation to psychoneuroimmunological agents (Gerstorf, Ram, Estabrook, et al., 2008; Gerstorf, Ram, Röcke et al., 2008) or through its relation to a motivational component, such as the will to live (Carmel, Baron-Epel, & Shemy, 2007). It is still to be explored whether these mechanisms may also explain the death-related reduction in emotional com-

DIFFERENCES IN EMOTIONAL COMPLEXITY plexity. Our results suggest that when the subjective or objective distance to death becomes prominent in late life, a change in affect regulation may be taking place. Moreover, in accordance with Labouvie-Vief s (2003) theory and the findings according to which the deterioration in cognitive abilities is strongly related to DtD rather than to age (Backman & MacDonald, 2006), declining cognitive resources are expected to be related to the decrease in emotional complexity. This decline refiects the fact that emotional complexity requires demanding cognitive abilities (Labouvie-Vief & Medler, 2002). An elevation in emotional complexity as a function of age was found in Study 2, whereas the two other studies did not show a significant relationship between age and emotional complexity. This inconsistency has also been reported in previous research. For instance, Carstensen and her colleagues (2011) found an increase in emotional complexity as a function of age, whereas others (Gruhn et al., 2013; Hay & Diehl, 2011; Ong & Bergman, 2004; Ready et al., 2008 Study 1) found that the association between PA and NA did not change with age or that it even decreased with age (Ready et al., 2008 Study 2). As suggested, methodological considerations that characterized each study might account for this discrepancy (Griihn et al., 2013; Larsen & McGraw, 2011; Ready et al., 2008). Nevertheless, we note that Study 3 documented a decrease in the association between PA and NA as a function of time. Therefore, at the within-person level, there was an increase in emotional complexity as time passed. It is important to note that most previous studies that failed to find greater emotional complexity with age have examined age at the between-person level. A study that examined age at the within-person level did find greater emotional complexity as people grew older (Carstensen et al., 2011). It is possible that the effect of age on emotional complexity in later life becomes more salient when examining change in the PA-NA association over large segments of time, but future studies will have to confirm this hypothesis. The present study suggests that distance to death, rather than age, is more strongly associated with emotional complexity, and that emotional complexity has its limits when death approaches. Hence, our findings may help to clarify the elusive watershed between aging-related and dying-related process (Diehr, Williamson, Burke, & Psaty, 2002). Aging-related processes may include a richer understanding of our place in the world, a tapestry of awareness that is deeper and more profoundly accepting of complexity in our emotional lives, whereas dying-related processes might narrow one's emotional world and make it less integrative, less fiexible, and more bipolar. Emotional complexity may signal emotional maturity, a richer and more nuanced emotional experience, and a greater ability to experience and tolerate mixed emotions (Ready et al., 2012). Higher emotional complexity may enhance coping with agerelated losses and decline (Shmotkin & Shrira, 2012), yet it seems that the accumulated loss and decline shortly before death overpower successful emotional regulation, so that the ability to maintain high emotional complexity is undermined (Shmotkin, 2005). It is possible that the different effects of age and DtD reflect the different contributions of primary and secondary aging versus tertiary aging (Birren & Cunningham, 1985). Tertiary changes (Ram, Gerstorf, Fauth, Zarit, & Malmberg, 2010) and the focus on impending death seem to invert one's perspective on the time left. Therefore, experimental procedures that lead to the adoption of

293

limited future time perspective, such as graduation day or imagining a final experience (Ersner-Hershfield et al., 2008; Zhang et al., 2010) seem to be different from real impending death. When people cannot avoid negative experiences, as is the case when real death is near, age-related improvement in emotional complexity will be attenuated and eventually disappear completely. Our findings should be discussed within the context of the socioemotional selectivity theory (Carstensen et al., 1999; Charles & Carstensen, 2010), which postulates that as individuals grow older their sense of constricted time makes them focus on closer relationships within the srnall circuit of family and friends. This environment facilitates more positive experiences. The ability to feel both negative and positive emotions independently, as represented by high affect complexity, enables older people to preserve positive feelings when facing negative events. Our findings may allude to a more complicated relationship between tbe sense of constricted time and emotional regulation, pointing to the different effects of constricted time on emotional complexity due to changes in chronological age versus effects due to changes brought about by death. Our results are also relevant to the model of strength and vulnerability integration (Charles, 2010). According to this model, age-related appraisals, behaviors, and attentional strategies increase a person's ability to regulate everyday emotional experiences. However, in situations in which people cannot avoid negative experiences, age-related improvement in well-being will be attenuated and may even disappear completely (Charles & Carstensen, 2010). At such times, a decline in affect complexity is expected. We suggest that perceived and actual closeness to death are important factors that influence late-life emotional regulation. Note that emotional complexity decreased in our analyses as a function of perceived and actual closeness to death, even after adjusting for demographic characteristics, as well as for physical and cognitive functioning. Moreover, this effect was evident among community-based, relatively well-functioning older adults. Future studies should examine whether motivational components play a role in emotion regulation (e.g.. Carmel et al., 2007; Carmel, Shrira, & Shmotkin, 2013). It is also important to further explore how SSP mediates the association between DtD and emotional complexity.

Strengths and Limitations Some limitations of our research should be acknowledged. Studies 1 and 2 were cross-sectional in nature, and thus they confounded the effects of age, SSP, DtD, and cohorts. The samples also included respondents with varied age ranges, and this fact might have shaped the age- and death-related effects. However, similar results regarding age, DtD, and emotional complexity were found across studies, as well as in the longitudinal design in Study 3. Another limitation concerns the measures used to examine affect in Studies 1 and 3, which do not target specific emotions with high activation as does the most common measure of affect to date—the PANAS (Watson, Clark, & Tellegen, 1988). Note, though, that the measure used in Study 2 was stronger than our other measures and yet it generated the same pattern of results that was seen in the other two studies. Finally, we cannot rule out the possibility that our results are biased by a systematic measurement error that may characterize some studies in which individuals are asked to eval-

PALGI ET AL.

294

uate their affect (see Green, Goldman, & Salovey, 1993). We believe' that the use of symmetric as well as asymmetric response formats across studies has somewhat minimized this problem (Schimmack, 2003), though it can be further explored with the use of intraindividual longitudinal methods (Ong & Bergman, 2004). We also acknowledge that our concept of emotional complexity relies on retrospective ratings of affect, and thus might differ from assessments of momentary or daily emotional complexity. However, even when providing momentary or dally assessments, participants may report a summary of their emotional experience over time (Larsen & McGraw, 2011). Notwithstanding these limitations, our research sheds light on late-life emotional regulation, presenting a new outlook on the relationship between PA and NA during the second half of life in three large-scale national samples, involving different designs, tools, and countries. Aside from the abovementioned limitations, these studies had several strengths. The main finding was replicated along three different samples across the world. These were large-scale national samples with a wide age range from midlife to old-old age. The findings were also replicated across various measures administered in these national samples.

Conclusions The present study suggests that personal processes that are brought about by aging and by the perception that time is limited differ from emotional processes associated with the perception that death is actually around the corner. Whereas aging-related processes may lead to the acceptance of the complexities of life, death-related processes instigate a simpler emotional experience, possibly as a reaction to the particular stress associated with approaching death. The current findings are in line with Reich et al.'s (2003) DMA model and Shmotkin's (2005) model of SWB in the face of adversity, according to which emotional complexity (as represented by an association between PA and NA) can usually regulate the HWS as one ages. However, in the face of actual or potential threat of death, the association between PA and NA may be overpowered and may fail to constitute emotional complexity as an essential resource for maintaining a favorable psychological environment.

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