Self-Perceptions of Aging and Control of Life in Late Adulthood: Between-Person and Within-Person Associations

Abstract

Objectives: This study aimed to examine the longitudinal relationship between two central concepts in aging research—self-perceptions of aging (SPA) and perceived control of life (COL). Method: The data came from three measurement points over a 9-year period in the Health and Retirement Study (HRS). A random intercepts cross-lagged panel model (RI-CLPM) was estimated. Results: The covariations between SPA and COL across 9 years were evident at both the between-person level and the within-person within-time level. The results revealed a reciprocal relationship between SPA and COL: Higher than usual negative SPA predicted within-person decreases in COL 4 years later, and lower than usual COL predicted future within-person increases in negative SPA. Furthermore, SPA were found to have a somewhat larger effect on COL than the corresponding influence of COL on SPA. Discussion: This study enriches the stereotype embodiment theory and the practice by documenting a reciprocal interrelationship between SPA and COL.

Keywords

old age self-efficacy control beliefs attitude toward aging

Self-perceptions of aging (SPA) or older individuals’ beliefs about their own aging are one of the key concepts in studies of ageism and health (Levy et al., 2002). Ample evidence suggests that SPA are associated with aging outcomes such as physical functioning (Moser et al., 2011), cognitive performance (Robertson et al., 2016), psychological well-being (Bellingtier & Neupert, 2018), and health behavior (Sun & Smith, 2017). In terms of the directionality of these associations, prior research points to the causal dominance of perceptions of aging: Either SPA affect the abovementioned outcomes and not vice visa, or the effect of SPA is stronger than the reverse direction of causality (Levy, 2009; Levy et al., 2002; Sargent-Cox et al., 2012; Westerhof et al., 2014; Wurm et al., 2007).

Perceived control of life (COL; also known as control beliefs or the sense of control), which relates to personal mastery and environmental contingencies (Lachman et al., 2011), is another key psychosocial variable predicting aging-related outcomes. Prior research consistently suggests that a lack of sense of control contributes to age-related decline, such as cognitive decline, whereas a strong sense of control minimizes the negative consequences of declines and losses associated with aging (Lachman & Firth, 2004). For example, perceived COL can result in reduced negative emotions (Drewelies et al., 2020), reduced physical frailty (Mooney et al., 2018), greater positive affect (Imel & Dautovich, 2018), more social activity (Curtis et al., 2018), a higher level of well-being (Koffer et al., 2019), and better cognitive performance (Robinson & Lachman, 2020).

To date, few studies have directly examined the longitudinal associations between SPA and control beliefs. Indeed, prior research points out that surprisingly few studies have explored the antecedents of control beliefs among old adults (Lachman, 2006; Zhang et al., 2019), despite the existence of numerous studies exploring the consequences of control beliefs (e.g., aging-related outcomes).

Nonetheless, stereotype embodiment theory (Levy, 2009) provides another angle from which to understand the relationship between SPA and control beliefs. Stereotype embodiment theory (Levy, 2009) explains how SPA exert their influence along three pathways: psychological, behavioral, and physiological. The psychological pathway suggests that SPA generate age-related expectations which then become self-fulfilling. Perceived COL is one of the most frequently used proxies for the psychological pathway. Research has shown control beliefs to be a significant mediator between attitudes to aging and health outcomes (Levy et al., 2002; Siebert, Wahl, Degen, & Schröder, 2018; Wurm et al., 2007). For instance, one recent study (Siebert, Wahl, Degen, & Schröder, 2018) found that older German adults who were initially cognitively healthy and held more positive perceptions of aging at baseline were associated with fewer external control beliefs (e.g., chance, fate), which, in turn, led to a reduced risk of clinical diagnosis of mild cognitive impairment 12 years later.

Against this background, a considerable number of studies have examined the magnitude and direction of the interrelationships between SPA, control beliefs, and health outcomes, as well as the mechanisms of these associations, wherein control belief serves as, at most, a significant mediator. To the extent that negative SPA are associated with lower perceived COL, these studies document a unidirectional pathway from SPA to perceived COL. In other words, the stereotype embodiment theory and related empirical studies assume an a priori pathway from SPA to perceived COL while overlook a pathway from perceived COL to SPA or a reciprocal, bidirectional relationship between the two.

Therefore, although early studies have established a close connection between SPA and perceived COL among older adults, the directionality of this connection is unclear. To the best of our knowledge, to date, only one recent study has examined the directionality between SPA and control beliefs. Applying the cross-lagged autoregressive models to two waves of data, Tovel and colleagues (2019) explicitly examined the longitudinal relationship between SPA and physical functioning in late life and the mediating role of self-efficacy. They found that it was SPA that affected future control beliefs and not vice visa. We argue that this study is problematic because it fails to separate within- and between-person variances (discussed further in the “Method” section). As such, the relationship between the two might be biased.

We address previous limitations by analyzing longitudinal data from a large sample of older people (N = 2,989) with three measurement occasions over a 9-year period and applying the random intercepts cross-lagged panel model (RI-CLPM). Specifically, we address two questions. First, we examine the bidirectional relationship between SPA and perceived COL at both the between-person level and the within-person within-time level. Second, we examine which of the causal influences is stronger.

Method

Sample

We used data from the Health and Retirement Study (HRS). The HRS is a national representative survey of adults aged 50 years and above and their spouses of any age. In its ninth wave, collected in 2008, it randomly selected one half of the sample (N = 6,994) to answer the Participant Lifestyle Questionnaire. For the first time, it gathered information on adults’ SPA. This half of the sample was followed in 2012 and 2016.

Because the analytic method used in this study required at least three waves of data to obtain unbiased estimates, this study used the participants who took part in all three waves of the Participant Lifestyle Questionnaire (N = 2,989, n = 8,967). Over the 9-year investigation period from 2008 to 2016, 1,526 respondents died. Compared with the respondents who completed all three waves of investigation, the respondents who were alive but did not participate in all three waves were more likely to be older (M = 66.17 vs. 68.73, t = 10.22) and non-White (M = 0.78 vs. 0.73, t = −4.43); to have received fewer years of education (M = 13.00 vs.12.39, t = −7.45); to have poor self-rated health (M = 2.64 vs. 2.94, t = 10.11); to hold more negative perceptions of aging (M = 20.27 vs. 22.02, t = 8.87); and to perceive less control (M = 20.58 vs. 22.17, t = 6.33).

Measures

SPA

An 8-item scale derived from the Philadelphia Morale Scale and the Berlin Aging Study were used to measure SPA. Respondents were asked to report their feelings about their age and the things that happen as they get older on a 6-point scale (1 = strongly disagree to 6 = strongly agree). We dropped one item, “I have as much pep as I did last year,” because of its low correlations with other items. We then recoded three positive items and summed all seven items. Higher scores indicted more negative SPA for each wave. Internal consistency of this scale was good: Cronbach’s α is .80, .79, and .81 for 2008, 2012, and 2016, respectively. The intraclass correlation (ICC) across measurement occasions, which reflected the proportion of variance due to stable between-person differences, was moderately high (ICC = .61).

Perceived COL

The HRS used 10 items to examine perceived mastery and constraints on personal control (Lachman & Weaver, 1998; Pearlin & Schooler, 1978). Respondents were asked to report their sense of control in life on a 6-point scale (1 = strongly disagree to 6 = strongly agree). An example item was “I have little control over the things that happen to me.” To create an index of constraints on COL, we reverse coded the perceived mastery items and summed all 10 items so that higher scores indicted less perceived COL. Internal consistency of this measurement was good: Cronbach’s α is .87, .88, and .88 for 2008, 2012, and 2016, respectively. The ICC was also moderately high (ICC = .51).

Sociodemographic information

Prior research has shown that SPA and COL relationships are associated with age, gender, race/ethnicity, education, and self-rated health (Tovel et al., 2019); we, therefore, adjusted for these effects. Age was controlled for because there is an apparent decline in SPA and COL with age (Lachman, 2006; Siebert, Wahl, & Schröder, 2018). Gender was controlled for because women usually report a lower sense of general control (Lachman & Firth, 2004) and because there is gender difference in perceptions of aging (Sargent-Cox & Anstey, 2015). Prior research also suggests variations in control beliefs and age perceptions by race/ethnicity (Assari, 2017; Ayalon, 2018). We controlled for education because having more years of schooling is related to lower negative SPA and more sense of control (Gum & Ayalon, 2018; Mitchell et al., 2018). We also adjusted for self-rated health because better health is associated with more positive perceptions of aging (Beyer et al., 2015) and a higher sense of control (Lachman & Firth, 2004). The data on age (in years), gender (1 = female, 0 = male), race/ethnicity (1 = White, 0 = others), years of education, and self-rated health (assessed by a single question ranked on a 5-point scale ranging from 1 = excellent to 5 = poor) were gathered on the basis of self-report in 2008.

Analytic Strategy

Traditionally, the cross-lagged panel model (CLPM) has been regarded as a typical modeling approach to study causal influences between two (or more) variables in longitudinal panel data. Recent research shows that by including autoregressive parameters, the CLPM only accounts for the temporal stabilities of the variables and fails to adequately account for time-invariant, trait-like stable individual differences. That is to say, the estimates of lagged parameters are confounded by the relationship that exists at the between-person level. As a result, estimates may be biased (Mund & Nestler, 2019).

To address this issue, recent research proposed an alternative approach—the RI-CLPM. The RI-CLPM employs a multilevel perspective and distinguishes the within-person process from stable between-person differences. It allows the separation of within-person effects from between-person effects. By including a random intercept (i.e., a factor with all loadings constrained to 1), RI-CLPM accounts for trait-like, time invariant stability and thus partials out between-person variance to obtain the real within-person dynamics (Hamaker et al., 2015). In addition, the interpretations of RI-CLPM results differ from that of CLPM results.

Figure 1 shows the general illustration of the RI-CLPM examined in this study. The random intercepts reflect an individual’s average stable level of SPA and COL. The autoregressive parameters α₂, α₃, δ₂, and δ₃ relate to the degree of within-person carryover effects, thus showing whether deviations from one’s own expected SPA or COL score at one measurement occasion carry over to the next occasion. The cross-lagged regressions refer to relationships at the within-person level. Specifically, cross-lagged parameters (i.e., β₂, β₃, γ₂, and γ₃) can be interpreted as the extent to which changes in an individual’s deviation from the expected score of one variable (e.g., SPA) are predicted by deviations from the expected score of another variable (e.g., COL) on the previous measurement occasion, after adjusting for the carryover autoregression effects.

Figure 1.

Random intercept cross-lagged panel model (RI-CLPM) for the estimation of the reciprocal relationships between SPA and COL for three waves of data. Each observed score is decomposed into two parts: a within-person part and a between-person part. The cSPA and cCOL factors represent the within-person part of the outcomes. The two random intercepts, riSPA and riCOL, caputure the between-person part.

Structural equation modeling with Mplus 8.1 was used to estimate the models (Muthén & Muthén, 1998–2017). Model fit was evaluated on the basis of local and global fit indices, including the chi-square, the comparative fit index (CFI), the Tucker–Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). The commonly used criteria of a relatively good fit between the hypothesized model and the observed data are CFI values and TLI values greater than or equal to .95, SRMR values less than or equal to .08, and RMSEA values less than or equal to .06 (Hu & Bentler, 1999).

The analyses consisted of four steps. First, we estimated the RI-CLPM of the longitudinal relationships between SPA and perceived COL without covariates (M1). Next, as is common in cross-lagged analyses, we tested whether autoregressive parameters and cross-lagged parameters could be constrained to be equal over measurement occasions (M2). Third, we estimated the RI-CLPM with covariates (M3). Fourth, we tested which of the causal influences was dominant by constraining the cross-lagged parameters to be equal (M4) and compared the model fit with the freely estimated model fit (M3).

Results

Table 1 reports the descriptive statistics. Over the 9-year investigation period, SPA (higher scores indicate more negative) and perceived COL (higher scores indicate lower levels) increased between 2008 (T1) and 2016 (T3). From 2008 to 2016, older adults’ negative SPA increased 8.78% (from 20.27 to 22.05) and perceived lack of control increased 5.30% (from 20.58 to 21.67).

Table 1.

Sample Characteristics (n = 8,967).

Variables	M (%)	SD
Age	66.17	8.53
Gender (female = 1)	62
Race (White = 1)	78
Education (years)	13.00	2.87
Self-rated health	2.64	1.01
Longitudinal summaries by wave
Wave	M	SD
2008
SPA	20.27	7.37
COL	20.58	9.02
2012
SPA	20.58	9.02
COL	21.03	9.39
2016
SPA	22.05	7.50
COL	21.67	9.66

Note. SPA = Self-perceptions of aging; COL = Control of life.

Table 2 reports the Pearson’s correlations among the variables. At all measurement occasions, negative SPA were positively associated with lower perceived COL (r_s = .36–.55).

Table 2.

Correlation Matrix.

Variables	1	2	3	4	5	6	7	8	9	10
1. Age
2. Gender	−.07**
3. Race	.11**	−.04*
4. Education	−.07**	−.05**	.29**
5. Self-rated health	.00	.02	−.22**	−.31**
6. SPAT1	.05**	.01	−.06**	−.19**	.48**
7. SPAT2	.12**	.01	−.01	−.17**	.40**	.62**
8. SPAT3	.17**	−.01	.02	−.15**	.37**	.57**	.65**
9. COLT1	−.03	.04*	−.05**	−.15**	.30**	.51**	.42**	.36**
10. COLT2	.04*	.04*	−.04	−.16**	.28**	.42**	.49**	.40**	.53**
11. COLT3	.15**	.03	−.03	−.17**	.28**	.40**	.45**	.55**	.48**	.54**

Note. Cells report Pearson’s correlation coefficients. SPA = self-perceptions of aging; COL = Control of life.

p < .05. **p < .01.

To examine the relationship between SPA and COL, we first estimated a baseline model that estimated autoregressive and cross-lagged effects between SPA and COL without any covariates. The model showed appropriate fit: χ²(1) = 7.77, CFI = .99, TLI = .98, RMSEA = .05, and SRMR = .01. Constraining autoregressive and cross-lagged parameters to be equal across measurement occasions also resulted in an appropriate model fit: χ²(5) = 19.66, CFI = .99, TLI = .99, RMSEA = .03, and SRMR = .02. For ease of interpretability, we report the results of the constrained model.

The results revealed a reciprocal, bidirectional relationship between SPA and perceived COL. The positive within-person cross-lagged effects from SPA to COL (β_s = .11 and .12, p < .001) indicated that individuals’ deviations from expected COL were predicted by their SPA at the previous measurement occasion (i.e., individuals who scored higher than they typically would on SPA were more likely to score higher on COL than they typically would at the next assessment), after controlling for deviations from the expected COL score on the previous measurement occasion. The within-person cross-lagged effects from COL to SPA were also positive and significant (γ_s = .07 and .06, p < .001). In addition, the positive autoregressive effects suggested that within-person deviations from the expected SPA and COL scores predicted deviations from the expected SPA and COL scores at the next measurement occasion.

Next, we estimated another RI-CLPM with sociodemographic covariates as predictors of the random intercept factors of SPA and perceived COL. This model also showed appropriate fit: χ²(11) = 29.49, CFI = .99, TLI = .99, RMSEA = .02, and SRMR = .01. As shown in Figure 2, both cross-lagged parameters were robust after adjusting for these covariates. The model also showed positive autoregressions from baseline SPA and COL to their corresponding change over time (α = .22, p <. 001; δ = .15, p < .001). In addition, for the random intercept items, being older and White, receiving fewer years of schooling, and reporting lower self-rated health predicted having a more stable level of negative perceptions of aging and perceived lack of COL.

Figure 2.

Random intercept cross-lagged panel model (RI-CLPM) of the longitudinal relationship between SPA and COL among older adults. Standardized coefficients are reported. The solid lines indicate paths statistically significant at p < .05. This final model adjusted for age, race/ethnicity, education, and self-rated health.

As we found that SPA and COL influenced each other, we further tested which of the variables was causally dominant. We imposed equality constraints on the directional cross-lagged parameters and compared its model fit with the nonconstrained model fit. Constraining the estimates resulted in reduced model fit, Δχ²(2) = 8.19, p < .05, suggesting no equality between the two paths. The SPA was thus found to have a somewhat larger effect on perceived COL than the corresponding influence of COL on SPA.

Discussion

This study examined the longitudinal relationship between SPA and perceived COL over a 9-year period in a U.S. nationally representative sample of individuals aged 50 and above. We used the RI-CLPM to examine the theoretical models. We reach three main conclusions.

First, significant within-person autoregressive paths indicate adults who exhibit increases in the typical levels of their SPA at one measurement occasion are likely to experience increases in their SPA at the next measurement occasion. The same is true for the control belief: Lower than usual control beliefs in 2008 and 2012 imply an intraindividual increase in lack of control in 2012 and 2016, respectively. Underlying these findings is the idea that aging and control beliefs may have been developed or acquired in early life. Stereotype embodiment theory suggests that perceptions of aging start to develop in childhood, get internalized throughout the lifespan, and become self-relevant as people age (Levy, 2009). The life span developmental theory of control (Heckhausen & Schulz, 1995) implies that older adults’ control beliefs are inseparable from the control process developed in childhood and adolescence. Our analyses show that a higher stable level of negative SPA and a lack of perceived COL are associated with lower educational attainment and poor health status, suggesting that personal disadvantages might increase negative perceptions of aging and lower control beliefs.

Second, the analyses reveal a robust association between SPA and perceived COL at the between-person level and the within-person within-time level. For one thing, the positive covariation between the two random intercepts indicated that adults who hold a more negative attitude toward aging tend to experience a higher level of lack of control belief. For another, net of between-person differences, there is a significant within-person within-time covariation between SPA and perceived COL. At all measurement occasions, when adults’ deviation from their usual level of negative perceptions of aging was higher, their sense of lack of COL was also higher than usual. Altogether, covariations between perceptions of aging and control belief are evident both across people and within a person. This finding suggests the existence of individual differences in the shared propensity for perceptions of aging and sense of control beliefs in older adulthood. It could be that protecting motivational resource, one of the key elements in the motivational theory of life span development (Heckhausen et al., 2010), is insufficient in older adulthood to the extent that relevant control strategies of goal engagement and disengagement fail to be activated. Consequently, older adults lose control with increasing age and become less positive in their attitude toward aging.

Third, and more importantly, adjusting for stable, between-person differences, we find a bidirectional positive relationship between SPA and perceived COL, such that a respondent’s higher than usual negative attitude toward his or her own aging in 2008 and 2012 predicts intraindividual increases in a lower sense of COL in 2012 and 2016, respectively, and a lower than usual sense of COL in 2008 and 2012 both predicts increased negative SPA 4 years later. Moreover, we note that the effect of perceptions of aging on future COL is greater than the effect of COL on future perceptions of aging. This finding suggests that older people may face a “vicious cycle” in which holding more negative beliefs in one aspect will in turn exacerbate negative beliefs in another. It could be that negative SPA lead to impaired physical functioning (Sargent-Cox et al., 2012), which may further lead to lower self-esteem and sense of control. It could also be that a lower sense of control is associated with engaging in fewer health-promoting behaviors (Lachman & Firth, 2004), which may accelerate aging-related decline and negative perceptions of aging.

Selective panel attrition might cause underestimation of the association between SPA and perceived COL if the likelihood of attrition is larger for those holding a more negative attitude toward aging and perceiving less COL. We have reported that (in the “Method” section), compared with the respondents who completed all three waves of investigation, the respondents who were alive but did not participate in all three waves held more negative perceptions of aging (M = 20.27 vs. 22.02, t = 8.87) and perceived less COL (M = 20.58 vs. 22.17, t = 6.33). These excluded respondents were more likely to be older, non-White, less-educated, and have poor health, all of which were linked to more negative SPA and less sense of control. This suggests that the associations between SPA and perceived COL might be underestimated.

Our overall finding of the reciprocal relationship between SPA and perceived COL has important implications for both research and practice. On one hand, our main finding reveals the need to improve the stereotype embodiment theory (Levy, 2009), the psychological pathway of which assumes that older adults’ control beliefs mediate the effect of SPA on health outcomes. We find that control beliefs can also affect older adults’ SPA. Future research design should thus consider the bidirectionality between the two concepts. On the other hand, the dynamic, bidirectional relationship between attitudes toward aging and sense of control signals that interventions for older people who are experiencing a later adulthood should restore and strengthen the protective role of both perceptions of aging and control beliefs in the face of decrements.

This study has several limitations. First and foremost, prior research suggests that the development of negative attitudes toward aging might begin with early childhood experiences. This study could not explore the influence of early life on perceptions of aging and control beliefs and only reports the longitudinal associations over a 9-year span. Second, perceptions of aging and control beliefs were self-reported, which might have inflated the magnitude of the associations between the two variables. Third, although we found a reciprocal relationship between SPA and perceived COL, we do not offer clear answers on the mechanisms underlying this relationship.

Conclusion

This study contributes to a better understanding of the dynamic associations between SPA and control beliefs. It shows the reciprocal nature of this association: More negative perceptions of aging exacerbate lower control beliefs in later life, and lower control beliefs aggravate future negative perceptions of aging. Furthermore, the former effect is evidently larger than the latter, suggesting the need to reduce negative perceptions of aging as people age.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Meng Sha Luo

Lydia W. Li

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