Psychology and Aging 1992, Vol. 7, No. 4, 562-570
Copyright 1992 by the American Psychological Association. Inc. 0882-7974/92/S3.00
Functional \fersus Structural Social Support, Desirable Events, and Positive Affect in Older Adults Stanley A. Murrell
Fran H. Norris Georgia State University
University of Louisville
This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Quinn T. Chipley University of Louisville The independent contributions of structural social support versus functional support to positive affect in older adults were compared in this prospective design. Four social support models were translated from the stress-symptoms literature into promotional models for positive mental health and for relationships to desirable challenge events. A total of 1,031 older adults were interviewed 5 times at 6-month intervals, and events were measured across the middle 18-month period. The 2 support variables made quite different contributions. Functional support was directly related to positive affect 2 years later, independent of events; structural support had more transitory direct effects. Structural support, but not functional support, was related to the subsequent occurrence of challenge events and in turn was strengthened by challenge events, suggesting a dynamic interplay between the social network and desirable events.
The study of the mental health of older adults has seemed to concentrate on the gloomy aspects: symptoms, problems in functioning, and adaptation to unpleasant life experiences, such as poor health and bereavement. Life-event research has concentrated primarily on undesirable events, notwithstanding that desirable events are generally more frequent (Murrell, Norris, & Hutchins, 1984; Reich & Zautra, 1988). In a departure from this emphasis on the negative, our study was based on the premise that happiness is just as important as unhappiness in understanding later life mental health. Furthermore, social support was assumed to be an important resource for older adults not only for protecting them against stress and symptoms but also for enhancing their lives. In the past decade, there has been considerable research directed at the roles that social support can play in modifying the stressful effects of undesirable life events. However, as Barrera (1988) has noted, there have been no systematic attempts to test models that examine the ways that social support and desirable life events might be related and the consequences for positive mental health. Barrera emphasized the importance of moving beyond stress-buffering concepts to establish more completely the contribution of social support to psychological adaptation and well-being. Our study attempted to take models originally
described for social support in relation to stress and symptoms and translate those models into the various functions by which social support can contribute to growth and happiness in older adults. That is, this study examined possible promotional functions of social support. For life events as well, it is their contribution to positive mental health that is of interest here. Although early conceptions of life events emphasized degree of change, rather than undesirability as their etiologically important feature (e.g., B. S. Dohrenwend & Dohrenwend, 1974), subsequent studies found that negative life events were strongly related to symptoms but positive events were not (e.g., L. H. Cohen, McGowan, Fooskas, & Rose, 1984; Vinokur & Selzer, 1975). Reviews of these studies drew the conclusion that positive events were not related to negative mental health and therefore were not stressful but were related to positive affect and improved life satisfaction (Thoits, 1983; Zautra & Reich, 1983). Our study follows a trend in life-event research of studying specific types of events (rather than in the aggregate) and their relationship to particular resources (e.g., Hammen, Marks, de Mayo, & Mayol, 1985; Hobfoll, 1988; Thoits, 1987). We termed this type challenge events on their face content, denned as events that are evaluated positively by older adults, are related to an accomplishment, are to a large degree under the control of the person, and entail a possible degree of risk. Because these events were denned as being rewarding rather than stressful, the assumption was that they would not require a coping function from social support. Considerable conceptual attention also has been given to the disaggregation of the concept of social support into more strictly defined dimensions. S. Cohen and Wills (1985) hypothesized that structural measures of support, such as size or strength of the social network, would have direct effects on symptoms but would not serve the function of coping, with
This research was supported in part by National Institute of Aging Grant AG04551 and National Institute of Mental Health Grant MH33063. Portions of this study were presented at the 98th Annual Convention of the American Psychological Association, Boston, Massachusetts, in August 1990. We thank Suzanne Meeks and James S. Walker for their help in data analysis and in reviewing the manuscript of this article. Correspondence concerning this article should be addressed to Stanley A. Murrell, Department of Psychology, University of Louisville, Louisville, Kentucky 40292.
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SOCIAL SUPPORT AND POSITIVE AFFECT undesirable events. In contrast, functional measures, such as the perception of the adequacy of available support, were thought to be more relevant to the active coping with stress and were assumed to buffer the effects of events on well-being. We examined two different dimensions of social support: a functional measure termed anticipated help and a structural measure of the strength of the social network termed social integration. Anticipated help, which is similar to the measure and concept reliable alliance used with older adults by Russell and Cutrona (1991), reflects the expectation of receiving help from the social network when there is a need for help, that is, the support comes into play as a consequence of an undesirable occurrence. Thus, the broad assumption here is that although it is important for positive affect, anticipatory help will be less relevant in the face of desirable events than social integration. Social networks are assumed to be related both to the opportunity for desirable events to occur and to the psychological value of being able to share the desirable events with others.
Hypotheses for Differential Social Support Contributions to Positive Affect The broad assumption that resources would help in coping with stress and Cassel's (1976) more specific buffer hypothesis for social support have been extended and considerably explicated by Barrera (1986), Lin (1986), and Wheaton (1985) to include other ways or functions by which social support can counter distress and maintain well-being. Four of these functions are translated in this article into ways that social support can promote positive mental health and enhance the salubrious effects of desirable events. We used a prospective design in this study that provided for predicting change in variables from one measurement point to a later point.
Direct Promotion Function This is a translation of the distress deterrent function (Wheaton, 1985), which posits a direct negative relationship of the resource to distress: the stronger the social support the weaker the distress, independent of the level of stress. For the promotion function, the sense of security and attachment that can come from strong social support would lead directly to an older adult feeling good about one's self and one's life, even if nothing good in particular is happening in one's life. Direct effects of social support will be examined here as they operate independently of life events. S. Cohen and Wills (1985) hypothesized that structural social support would have direct effects on distress but functional support would not. However, the evidence did not consistently support this hypothesis (Barrera, 1988). Here it is hypothesized that both types of support will be related to positive affect over a 2-year period: Anticipated help provides a sense of interpersonal confidence and security from believing that others would help in an emergency, and this feeling leads to greater positive affect. Social integration provides a sense of attachment that would by itself promote positive affect.
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Event Enhancement Function This is a translation of an indirect effect of support on distress through undesirable events, referred to as stress deterrent (Wheaton, 1985) or stress prevention (Barrera, 1986). Social support is assumed to affect the appraisal and perhaps the occurrence of undesirable events. With the confidence of a strong support base the event is viewed less adversely, less stress is experienced, and less distress is experienced. More generally, strong resources may make the occurrence of undesirable events less likely, with ultimately less distress. For the promotion function, social integration at one point in time is assumed to be related to the occurrence or desirability of challenge events at a later point in time. A strong network may increase the opportunities for desirable events related to people in the network. The security based on a sense of attachment may make it more possible for the older person to take on the risk of challenge events; the sense of accomplishment from the challenge events may then lead to positive feelings about one's self and place in the social environment. Thus, there are two links to the hypothesis: First, social integration will be related to challenge events; second, challenge events will be related to positive affect. However, anticipated help, oriented to help in times of difficulty, would not be needed for challenge events.
Support Booster Function In the stress-symptom context, undesirable events may influence increases in social support; for example, a bereavement in the family may lead to increased social contacts for a time (Norris & Murrell, 1990). The increased support then reduces the distress. This has been called an additive buffer (Wheaton, 1985) or a support mobilization effect (Barrera, 1986) in which the event changes and improves the status of the resource. For the promotion function, we hypothesized that challenge events will be related to a change in social integration, in the direction of strengthening the social network. Challenge events, some of which have social aspects, may increase social opportunities (e.g., moving, going on trips, and going to church more often may increase friendships or deepen them) and may improve confidence in social interactions (e.g., receiving praise or getting a promotion may lead to greater initiative in social contacts). Strengthening the social network will increase the sense of attachment and sharing that will in turn lead to more positive feelings about one's self and one's life. Challenge events, because they do not engage help in time of trouble, will not change the level of anticipated help.
Interactive Booster Function This is a translation of the interactive buffer effect: It is assumed that when faced with high life-event stress, persons with strong social support will cope with the stress successfully and will experience little distress, whereas those with weak social support will cope less well and experience greater distress. Under conditions of low event stress, strong social support would not be needed and therefore would provide no adjustment advantage. For the interactive booster function (Murrell & Norris, 1984;
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Sandier, 1981), which operates under conditions of desirable events, we assumed that it is the sharing of the desirable event with others that further enhances the accomplishment experience; this, in turn, increases the older adult's positive affect. This reverses the buffer pattern: For those with weak social networks, the level of challenge events would not matter because there would be few persons with whom to share the accomplishment. For those with strong networks, the greater the challenge events the greater the positive affect. Statistically, this would be demonstrated in a synergistic interaction of social integration and challenge events. We hypothesized that anticipated help would not be relevant to desirable events. Wheaton (1985) emphasized that for an interactive buffer, the resource had to be available for use in coping during or following the stress of the undesirable event. For the booster function, it is assumed that it is the social integration that is available for sharing during or following the desirable event that is relevant here. Thus, we measured and analyzed social support and events at the same time periods for this hypothesis.
Method We used a prospective design in which five interviews were conducted of the same respondents in their homes with approximately 6-month intervals between interviews. Respondents were drawn from a probability sample of Kentuckians aged 55 years and older. Measures had been pretested on older adults and were found to be reliable (Himmelfarb & Murrell, 1983), appropriate, and comprehensible for older adults. The life events analyzed here were taken from the middle three waves (an 18-month period); positive affect was measured before and after these events, at the first and fifth waves. The social support measures were analyzed at different waves, depending on the hypothesis, to reduce possible confounding with events and affect. We used a series of hierarchical multiple regressions for data analysis. Demographic variables and positive affect on the first wave of interviews were included in all analyses as baseline controls. The procedure for the analysis of social support permitted estimates of effects of the two different dimensions independent of one another.
Sample and Sampling Procedure A stratified three-stage area probability design was used to select a statewide sample that reflected the three major geographic regions of Kentucky and several substrata within each region that varied in population density. The sampling procedures have been previously described in detail in Norris (1985); the characteristics of the first wave sample have also been described previously in detail (Murrell, Himmelfarb, & Wright, 1983). The first wave sample, when properly weighted, was quite similar in its age and sex distributions, marital status, and living arrangements to the United States population aged 55 years and older in 1980. The sample did have less education: Two thirds of the U.S. population of this age had greater than 8 years of education, whereas this was true for only one half of the Kentucky sample. Of the first-wave sample of 2,931 respondents, 1,174 had fewer than 5 completed valid interviews including 556 who had nonconsecutive multiple interviews, which made them ineligible for this design. The following are the completion rates for the sample existing at each wave: Wave 1,72%; Wave 2,73%; Wave 3,82%; Wave 4,83%; and Wave 5,88%. Of the first-wave sample, 2% terminated the interview after it began, 4% completed interviews that were later found to be invalid by quality control procedures, 5% were never contacted or were repeatedly rescheduled but still not contacted, and 17% refused to participate. Of
those who refused the interview, the reasons given were lack of interest, 68%; health reasons, 20%; and mental health reasons, 6%. A very similar pattern held across subsequent waves. Respondents were included in this study if they had complete interviews on each of the five waves, which yielded 1,307 respondents, and had complete measures on all variables analyzed here, which resulted in a total of 1,031 respondents aged 55 years and older with a mean age of 67.4 years. With respect to attrition, a study of thisfive-wavesample found it to be very similar to the original first-wave sample with the exception that this sample is somewhat healthier, physically and psychologically, than the surviving cohort (Norris, 1985). Another study (Norris, 1987) compared the five-wave sample with the first-wave sample on their initial correlation matrices and on regression equations predicting Wave 2 health and depression and found that attrition did not seriously affect relationships among variables, which is particularly relevant for this study. The demographic characteristics of these respondents are summarized in Table 1. A question could be raised regarding the effect of repeated interviews. A study (Himmelfarb & Norris, 1987) was done comparing a control group (randomly selected prior to Wave 1 but not interviewed until Wave 2) with the sample that had been interviewed on both of those waves. No evidence for testing effects was found.
Design The study variables here are intercorrelated, thus a cross-sectional design would be unable to determine direction of causality. Because this was a study of naturally occurring life events, an experimental design was not possible. Thus, a prospective design with statistical controls was the best feasible design because it temporally separated the variables that could be confounded and thus established the direction of causality. Moreover, each hypothesis had its own particular requirement for sequence of effects that specified the waves at which social support was measured and analyzed. For the study of the relationship of life events to psychological states, cross-sectional designs face serious potential confounding (B. P. Dohrenwend, 1974; Monroe, Imhoff, Wise, & Harris, 1983). Rather than the events causing changes in psychological states, it is equally plausible that the state is causing the events or the appraisal of the events. To avoid this possible confound, a measure of the psychological state prior to the event is required to control for the contribution of the state to the event or its appraisal. The prospective design of this study did control for positive affect prior to the occurrence of the challenge events. A measure of positive affect on the first measurement wave was included in all analyses; the dependent measure was positive affect on the fifth wave, thus the dependent measure was the residual, essentially the change in positive affect over a 2-year period. Because the measures of positive affect were taken on waves before and after the events being studied, the affect could not influence either the appraisal or judgment of occurrence of the event reported. The life-event measures analyzed were taken across the middle three waves (an 18month period) to increase their base rate and because a prior study suggested that brief episodes (6 months) of life events had weak effects on mental health (Murrell & Norris, 1984). Social support and life events could also be confounded. A mutual influence between characteristics of one's social network and the nature of the events that happen would seem to be part of the phenomena being studied. In fact, the hypotheses assume such influences. Thus, it is the separation in time that is essential to reduce possible methodological confounding. For the event enhancement hypothesis, the social support of interest is measured on the wave prior to the events being measured and analyzed; thus, the events could not have influenced Wave 1 support. In turn, for the support booster hypothesis, events that
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SOCIAL SUPPORT AND POSITIVE AFFECT Table 1 Characteristics of Sample Variable
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Variable Sex Male Female Age (years) 55-59 60-64 65-74 75+ Marital status Never married Married Widowed Separated or divorced
34.2 65.8 18.1 22.7 38.5 20.7 3.3 60.6 30.3 5.8
Urbanicity Urban Nonurban Social class I (lowest) II III IV V (highest) Number of challenge events8 0 1 2 3 4 5 6+
36.8 63.2 27.0 26.8 27.8 13.6 4.7 19.3 20.1 19.0 18.2 10.8 5.8 6.9
' The sum of events over Waves 2, 3, and 4.
occurred between Waves 1 and 4 are predicted to be related to residual social integration, a strengthening of the social network between Wave 1 and Wave 4 but not merely to the level of support at Wave 4. Social support and positive affect could also be confounded; therefore, in this design and analysis, their measures were taken on different waves. Also, the dependent measure was the residual in positive affect. A prior study (Norris & Murrell, 1987) found that life events are not random but are related to both person variables and environmental variables, which suggests that it is desirable to control for variables that are predisposing for events. This was done in these analyses by forcing a set of demographic variables into the regressions before the event measure. We recognized that the prospective design and analysis could not provide a guarantee that these study variables were independent of one another. Correlated relationships can exist even if not measured concurrently. However, the prospective design and analysis do greatly improve estimates of causality over cross-sectional designs. This would seem to argue that prospective designs should be the minimum standard in such studies.
Measures Demographic variables. These included age, sex, urbanicity of residence, marital status (primarily married vs. widowed), and social class (using the Hollingshead & Redlich, 1958, occupation and education categories and weights). In each multiple regression conducted, this set of demographic variables was forced into the regression first, with the rationale being that these variables had operated in the lives of respondents before the other variables under study. Positive affect. This was measured with the five positively worded items from the Bradburn Affect Balance Scale (Bradburn & Caplovitz, 1965; items were particularly excited or interested, proud, pleased about accomplishment, on top of the world, and feel things are going my way) and seven items from the General Well-Being Scale (Ware, Johnson, Davies-Avery, & Brook, 1979; items were excellent vs. low spirits; happy vs. dissatisfied; waking up fresh and rested; life full of interesting things; emotionally stable and sure of yourself; how much energy, pep, and vitality; and depressed vs. cheerful). This set of combined items had an alpha coefficient of .83. A high score reflected highly positive affect. The correlation between Wave 1 and Wave 5 was
.54. The Wave 1 measure was included in all analyses as a control for preevent level of positive affect. It was forced into the equation second, immediately after the set of demographic variables. Anticipated help. This was considered a functional social support measure. It consisted of five items adapted from the scale developed by Andrews, Tennant, Hewson, and Schanell (1978), which ask about the amount of help one could expect from different sources in an emergency. The sources include immediate family, relatives outside the home, friends, neighbors, and church. The items do not specify the type of help these sources would provide. The anticipated help measure has an alpha coefficient of .82. A high score indicated a perception of strong availability of support. The correlation between Wave 1 and Wave 4 was .48. Social integration. This was considered a structural social support measure. It consisted of seven items adapted from a scale by Phillips (1967), which reflect the extent, intimacy, and frequency of social contacts, or the strength of the social network (including but going beyond the size of the network). The social integration measure has an alpha coefficient of .67. The correlation between Wave 1 and Wave 4 was .58. A high score indicated a strong social network. None of these items referred to the function that members of the social network played; thus, the content seemed distinct from anticipated help. However, the two social support scales were correlated with one another at .47 (p < .001) at Wave 1. Because the two measures were significantly correlated, a rather conservative analytic procedure was used to estimate their effects independent of one another. Challenge events. The events measure was the Louisville Older Persons Event Scale (LOPES; Murrell et al., 1984), which includes 54 closeended and 2 open-ended event descriptions. The period of inquiry was the preceding 6 months for all events. Moreover, for each event that occurred, the respondent was to report the month in which it occurred, which permitted a check to ensure that the same event was not reported by the respondent on more than one wave. The 10 challenge events in this study were selected on the basis that they were judged as highly desirable by older adults on the first two waves and they appeared on their face as being feasibly within the control or responsibility of the respondent; that is, it was plausible for the respondent to feel some sense of accomplishment as a result. All 10 ranked in the top 14 most desirable events as evaluated by respondents in the first two waves (Murrell et al., 1984), with an average desirability
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S. MURRELL, F. NORRIS, AND Q. CHIPLEY
rating per event per interview of 1.80 compared with an average rating of -0.74 for the remaining 44 events (see the Concurrent events section). Thus, challenge events were normatively more desirable than the other events on the scale. Events that received equally high desirability ratings but did not appear to be as clearly within the control of the respondent and thus were not classed as challenge events were (desirability rank in parentheses) health improvement of self (54), birth of grandchild (52), health improvement of someone close (50), more money (44), and child marry (42). Events that received mean ratings that were desirable but weaker and were not clearly within the control of the respondent were family member moved in (39), change in work (38), spouse retired (37), small loan (36), and child left home (35). The remaining 34 events had mean ratings that were negative. Two event measures were used, frequency and desirability. The frequency measure was the count of challenge events occurring to the individual over the three waves, regardless of their evaluation. The desirability rating was the person's response to this follow-up probe of the event: "What kind of change was this for you? Was i t . . . very bad (—3), mostly bad (-2), slightly bad (-1), not much either way (0), very good (1), mostly good (2) or slightly good (3)." The desirability measure was the sum across all 10 challenge events of the responses to the three desirable alternatives (if the evaluation was not desirable, the score was zero), and these were summed over the three waves. The higher the score the greater the desirable impact of the events. For this sample the frequency of challenge events summed across 10 events and over three waves yielded a mean of 2.31 events per respondent, with a standard deviation of 1.90. The desirability of events summed across 10 events and across waves was a mean of 4.31 for this sample with a standard deviation of 4.30. The desirability measure includes an appraisal of the event, whereas the frequency measure does not. The desirability measure was used in the primary set of analyses. The frequency distribution of the challenge events is given in Table 1. The name of the event, the number of reports of the event in this sample over the three waves, and the percentage rating the event as either a very good or mostly good change are: went on a trip out of town, 1,488,70.2%; received an award or special praise, 378,67.3%; got a new pet, 157; 44.4%; started a new recreational activity, 137,68.4%; started going to church activities more often, 106,75.0%; moved to a new place to live, 78,63.9%; retired from job, 46,63.1%; had a promotion at work, 35, 55.3%; changed jobs, 24, 51.5%; and got married, 10,100%. Concurrent events. Because the challenge events were naturally occurring events, events other than challenge events could have been happening at the same time, could have been influenced by the challenge events, and could also have been related to positive affect. To better estimate the particular contribution of the challenge events, regressions were also conducted that added the desirability ratings (same as for challenge events) of the other 44 events on the LOPES summed over Waves 2,3, and 4. Among these events were the desirable events (but not challenge events) listed above, bereavements, illnesses of self and different family members, and various kinds of losses. The mean desirability score for respondents of the summed desirability ratings over the 44 events and three waves was 4.54 with a standard deviation of 4.71. Results Analyses The primary analyses consisted of hierarchical multiple regressions that used the desirability measure of events. All analyses were conducted on exactly the same respondents. In each regression, the set of demographic variables was forced as a block into the equation first, followed by the Wave 1 positive affect measure. Thus, all hypothesized effects would occur after
controls for these baseline measures had been entered. Each regression equation tested at least one hypothesis or one link of a hypothesis. The tables report the change in R2 at the step the variable was entered and its corresponding F value. The criterion for significance was set at p < .05; however, all results that were significant were at the p < .001 level. With respect to distinguishing the independent effects of the two social support measures, a rather conservative approach was taken. Where the effect of anticipated help was the focus, the Wave 1 social integration measure was entered into that equation in the block ahead of Wave 1 anticipated help. The reverse was done for the focus on social integration, in a separate analysis. Thus, the effects of either support measure were reported in the tables as those operating after the effects of the other preevent support measure have been controlled or taken into account. For purposes of clarity, the values for the Wave 1 control support measures are not reported in these tables. Using the frequency measure of events, we conducted secondary analyses that exactly duplicated the primary analyses. These results are described in the text but not in the tables or figures. This was done to estimate the part played by the appraisal of these events. Another secondary analysis examined the contribution of concurrent events other than the challenge events, using the desirability measure. Effects of Demographic Variables, Wave 1 Positive Affect, and Concurrent Events The block of demographic variables made a significant contribution in all of the analyses, indicating the advisability of their inclusion as controls. Inspection of the tables also shows that Wave 1 positive affect was strongly predictive of Wave 5 positive affect, challenge events, Wave 4 social integration, and Wave 4 anticipated help. As can be seen in Table 2, challenge Table 2
Hierarchical Regressions for Interactive Booster Hypothesis Wave 5 affect (DV) Independent variable
R2 change
Wave 1 demographics Wave 1 affect Wave 1 integration Wave 1 help Wave 234 challenge events Wave 234 concurrent events Wave 234 integration3 Wave 234 help" Wave 234 Challenge Events X Wave 234 Integration* Wave 234 Challenge Events X Wave 234 Help" Adjusted R2 ' Adjusted R2 "
.068 .243 .007 .015
15.00* 365.50* 10.00* 23.43*
5,999 6,998 7,997 7,997
.010
15.65*
8,996
.000 .023 .021
1.21 36.26* 33.00*
9,995 10,994 10, 994
.002
3.68
11,993
.001 .355* .349*
1.86
11,993
df
Note. Hypothesized values are in boldface. DV = dependent variable. * Values obtained after Wave 1 help was controlled. b Values obtained after Wave 1 integration was controlled. *p
Copyright 1992 by the American Psychological Association. Inc. 0882-7974/92/S3.00
Functional \fersus Structural Social Support, Desirable Events, and Positive Affect in Older Adults Stanley A. Murrell
Fran H. Norris Georgia State University
University of Louisville
This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Quinn T. Chipley University of Louisville The independent contributions of structural social support versus functional support to positive affect in older adults were compared in this prospective design. Four social support models were translated from the stress-symptoms literature into promotional models for positive mental health and for relationships to desirable challenge events. A total of 1,031 older adults were interviewed 5 times at 6-month intervals, and events were measured across the middle 18-month period. The 2 support variables made quite different contributions. Functional support was directly related to positive affect 2 years later, independent of events; structural support had more transitory direct effects. Structural support, but not functional support, was related to the subsequent occurrence of challenge events and in turn was strengthened by challenge events, suggesting a dynamic interplay between the social network and desirable events.
The study of the mental health of older adults has seemed to concentrate on the gloomy aspects: symptoms, problems in functioning, and adaptation to unpleasant life experiences, such as poor health and bereavement. Life-event research has concentrated primarily on undesirable events, notwithstanding that desirable events are generally more frequent (Murrell, Norris, & Hutchins, 1984; Reich & Zautra, 1988). In a departure from this emphasis on the negative, our study was based on the premise that happiness is just as important as unhappiness in understanding later life mental health. Furthermore, social support was assumed to be an important resource for older adults not only for protecting them against stress and symptoms but also for enhancing their lives. In the past decade, there has been considerable research directed at the roles that social support can play in modifying the stressful effects of undesirable life events. However, as Barrera (1988) has noted, there have been no systematic attempts to test models that examine the ways that social support and desirable life events might be related and the consequences for positive mental health. Barrera emphasized the importance of moving beyond stress-buffering concepts to establish more completely the contribution of social support to psychological adaptation and well-being. Our study attempted to take models originally
described for social support in relation to stress and symptoms and translate those models into the various functions by which social support can contribute to growth and happiness in older adults. That is, this study examined possible promotional functions of social support. For life events as well, it is their contribution to positive mental health that is of interest here. Although early conceptions of life events emphasized degree of change, rather than undesirability as their etiologically important feature (e.g., B. S. Dohrenwend & Dohrenwend, 1974), subsequent studies found that negative life events were strongly related to symptoms but positive events were not (e.g., L. H. Cohen, McGowan, Fooskas, & Rose, 1984; Vinokur & Selzer, 1975). Reviews of these studies drew the conclusion that positive events were not related to negative mental health and therefore were not stressful but were related to positive affect and improved life satisfaction (Thoits, 1983; Zautra & Reich, 1983). Our study follows a trend in life-event research of studying specific types of events (rather than in the aggregate) and their relationship to particular resources (e.g., Hammen, Marks, de Mayo, & Mayol, 1985; Hobfoll, 1988; Thoits, 1987). We termed this type challenge events on their face content, denned as events that are evaluated positively by older adults, are related to an accomplishment, are to a large degree under the control of the person, and entail a possible degree of risk. Because these events were denned as being rewarding rather than stressful, the assumption was that they would not require a coping function from social support. Considerable conceptual attention also has been given to the disaggregation of the concept of social support into more strictly defined dimensions. S. Cohen and Wills (1985) hypothesized that structural measures of support, such as size or strength of the social network, would have direct effects on symptoms but would not serve the function of coping, with
This research was supported in part by National Institute of Aging Grant AG04551 and National Institute of Mental Health Grant MH33063. Portions of this study were presented at the 98th Annual Convention of the American Psychological Association, Boston, Massachusetts, in August 1990. We thank Suzanne Meeks and James S. Walker for their help in data analysis and in reviewing the manuscript of this article. Correspondence concerning this article should be addressed to Stanley A. Murrell, Department of Psychology, University of Louisville, Louisville, Kentucky 40292.
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This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
SOCIAL SUPPORT AND POSITIVE AFFECT undesirable events. In contrast, functional measures, such as the perception of the adequacy of available support, were thought to be more relevant to the active coping with stress and were assumed to buffer the effects of events on well-being. We examined two different dimensions of social support: a functional measure termed anticipated help and a structural measure of the strength of the social network termed social integration. Anticipated help, which is similar to the measure and concept reliable alliance used with older adults by Russell and Cutrona (1991), reflects the expectation of receiving help from the social network when there is a need for help, that is, the support comes into play as a consequence of an undesirable occurrence. Thus, the broad assumption here is that although it is important for positive affect, anticipatory help will be less relevant in the face of desirable events than social integration. Social networks are assumed to be related both to the opportunity for desirable events to occur and to the psychological value of being able to share the desirable events with others.
Hypotheses for Differential Social Support Contributions to Positive Affect The broad assumption that resources would help in coping with stress and Cassel's (1976) more specific buffer hypothesis for social support have been extended and considerably explicated by Barrera (1986), Lin (1986), and Wheaton (1985) to include other ways or functions by which social support can counter distress and maintain well-being. Four of these functions are translated in this article into ways that social support can promote positive mental health and enhance the salubrious effects of desirable events. We used a prospective design in this study that provided for predicting change in variables from one measurement point to a later point.
Direct Promotion Function This is a translation of the distress deterrent function (Wheaton, 1985), which posits a direct negative relationship of the resource to distress: the stronger the social support the weaker the distress, independent of the level of stress. For the promotion function, the sense of security and attachment that can come from strong social support would lead directly to an older adult feeling good about one's self and one's life, even if nothing good in particular is happening in one's life. Direct effects of social support will be examined here as they operate independently of life events. S. Cohen and Wills (1985) hypothesized that structural social support would have direct effects on distress but functional support would not. However, the evidence did not consistently support this hypothesis (Barrera, 1988). Here it is hypothesized that both types of support will be related to positive affect over a 2-year period: Anticipated help provides a sense of interpersonal confidence and security from believing that others would help in an emergency, and this feeling leads to greater positive affect. Social integration provides a sense of attachment that would by itself promote positive affect.
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Event Enhancement Function This is a translation of an indirect effect of support on distress through undesirable events, referred to as stress deterrent (Wheaton, 1985) or stress prevention (Barrera, 1986). Social support is assumed to affect the appraisal and perhaps the occurrence of undesirable events. With the confidence of a strong support base the event is viewed less adversely, less stress is experienced, and less distress is experienced. More generally, strong resources may make the occurrence of undesirable events less likely, with ultimately less distress. For the promotion function, social integration at one point in time is assumed to be related to the occurrence or desirability of challenge events at a later point in time. A strong network may increase the opportunities for desirable events related to people in the network. The security based on a sense of attachment may make it more possible for the older person to take on the risk of challenge events; the sense of accomplishment from the challenge events may then lead to positive feelings about one's self and place in the social environment. Thus, there are two links to the hypothesis: First, social integration will be related to challenge events; second, challenge events will be related to positive affect. However, anticipated help, oriented to help in times of difficulty, would not be needed for challenge events.
Support Booster Function In the stress-symptom context, undesirable events may influence increases in social support; for example, a bereavement in the family may lead to increased social contacts for a time (Norris & Murrell, 1990). The increased support then reduces the distress. This has been called an additive buffer (Wheaton, 1985) or a support mobilization effect (Barrera, 1986) in which the event changes and improves the status of the resource. For the promotion function, we hypothesized that challenge events will be related to a change in social integration, in the direction of strengthening the social network. Challenge events, some of which have social aspects, may increase social opportunities (e.g., moving, going on trips, and going to church more often may increase friendships or deepen them) and may improve confidence in social interactions (e.g., receiving praise or getting a promotion may lead to greater initiative in social contacts). Strengthening the social network will increase the sense of attachment and sharing that will in turn lead to more positive feelings about one's self and one's life. Challenge events, because they do not engage help in time of trouble, will not change the level of anticipated help.
Interactive Booster Function This is a translation of the interactive buffer effect: It is assumed that when faced with high life-event stress, persons with strong social support will cope with the stress successfully and will experience little distress, whereas those with weak social support will cope less well and experience greater distress. Under conditions of low event stress, strong social support would not be needed and therefore would provide no adjustment advantage. For the interactive booster function (Murrell & Norris, 1984;
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S. MURRELL, E NORRIS, AND Q. CHIPLEY
Sandier, 1981), which operates under conditions of desirable events, we assumed that it is the sharing of the desirable event with others that further enhances the accomplishment experience; this, in turn, increases the older adult's positive affect. This reverses the buffer pattern: For those with weak social networks, the level of challenge events would not matter because there would be few persons with whom to share the accomplishment. For those with strong networks, the greater the challenge events the greater the positive affect. Statistically, this would be demonstrated in a synergistic interaction of social integration and challenge events. We hypothesized that anticipated help would not be relevant to desirable events. Wheaton (1985) emphasized that for an interactive buffer, the resource had to be available for use in coping during or following the stress of the undesirable event. For the booster function, it is assumed that it is the social integration that is available for sharing during or following the desirable event that is relevant here. Thus, we measured and analyzed social support and events at the same time periods for this hypothesis.
Method We used a prospective design in which five interviews were conducted of the same respondents in their homes with approximately 6-month intervals between interviews. Respondents were drawn from a probability sample of Kentuckians aged 55 years and older. Measures had been pretested on older adults and were found to be reliable (Himmelfarb & Murrell, 1983), appropriate, and comprehensible for older adults. The life events analyzed here were taken from the middle three waves (an 18-month period); positive affect was measured before and after these events, at the first and fifth waves. The social support measures were analyzed at different waves, depending on the hypothesis, to reduce possible confounding with events and affect. We used a series of hierarchical multiple regressions for data analysis. Demographic variables and positive affect on the first wave of interviews were included in all analyses as baseline controls. The procedure for the analysis of social support permitted estimates of effects of the two different dimensions independent of one another.
Sample and Sampling Procedure A stratified three-stage area probability design was used to select a statewide sample that reflected the three major geographic regions of Kentucky and several substrata within each region that varied in population density. The sampling procedures have been previously described in detail in Norris (1985); the characteristics of the first wave sample have also been described previously in detail (Murrell, Himmelfarb, & Wright, 1983). The first wave sample, when properly weighted, was quite similar in its age and sex distributions, marital status, and living arrangements to the United States population aged 55 years and older in 1980. The sample did have less education: Two thirds of the U.S. population of this age had greater than 8 years of education, whereas this was true for only one half of the Kentucky sample. Of the first-wave sample of 2,931 respondents, 1,174 had fewer than 5 completed valid interviews including 556 who had nonconsecutive multiple interviews, which made them ineligible for this design. The following are the completion rates for the sample existing at each wave: Wave 1,72%; Wave 2,73%; Wave 3,82%; Wave 4,83%; and Wave 5,88%. Of the first-wave sample, 2% terminated the interview after it began, 4% completed interviews that were later found to be invalid by quality control procedures, 5% were never contacted or were repeatedly rescheduled but still not contacted, and 17% refused to participate. Of
those who refused the interview, the reasons given were lack of interest, 68%; health reasons, 20%; and mental health reasons, 6%. A very similar pattern held across subsequent waves. Respondents were included in this study if they had complete interviews on each of the five waves, which yielded 1,307 respondents, and had complete measures on all variables analyzed here, which resulted in a total of 1,031 respondents aged 55 years and older with a mean age of 67.4 years. With respect to attrition, a study of thisfive-wavesample found it to be very similar to the original first-wave sample with the exception that this sample is somewhat healthier, physically and psychologically, than the surviving cohort (Norris, 1985). Another study (Norris, 1987) compared the five-wave sample with the first-wave sample on their initial correlation matrices and on regression equations predicting Wave 2 health and depression and found that attrition did not seriously affect relationships among variables, which is particularly relevant for this study. The demographic characteristics of these respondents are summarized in Table 1. A question could be raised regarding the effect of repeated interviews. A study (Himmelfarb & Norris, 1987) was done comparing a control group (randomly selected prior to Wave 1 but not interviewed until Wave 2) with the sample that had been interviewed on both of those waves. No evidence for testing effects was found.
Design The study variables here are intercorrelated, thus a cross-sectional design would be unable to determine direction of causality. Because this was a study of naturally occurring life events, an experimental design was not possible. Thus, a prospective design with statistical controls was the best feasible design because it temporally separated the variables that could be confounded and thus established the direction of causality. Moreover, each hypothesis had its own particular requirement for sequence of effects that specified the waves at which social support was measured and analyzed. For the study of the relationship of life events to psychological states, cross-sectional designs face serious potential confounding (B. P. Dohrenwend, 1974; Monroe, Imhoff, Wise, & Harris, 1983). Rather than the events causing changes in psychological states, it is equally plausible that the state is causing the events or the appraisal of the events. To avoid this possible confound, a measure of the psychological state prior to the event is required to control for the contribution of the state to the event or its appraisal. The prospective design of this study did control for positive affect prior to the occurrence of the challenge events. A measure of positive affect on the first measurement wave was included in all analyses; the dependent measure was positive affect on the fifth wave, thus the dependent measure was the residual, essentially the change in positive affect over a 2-year period. Because the measures of positive affect were taken on waves before and after the events being studied, the affect could not influence either the appraisal or judgment of occurrence of the event reported. The life-event measures analyzed were taken across the middle three waves (an 18month period) to increase their base rate and because a prior study suggested that brief episodes (6 months) of life events had weak effects on mental health (Murrell & Norris, 1984). Social support and life events could also be confounded. A mutual influence between characteristics of one's social network and the nature of the events that happen would seem to be part of the phenomena being studied. In fact, the hypotheses assume such influences. Thus, it is the separation in time that is essential to reduce possible methodological confounding. For the event enhancement hypothesis, the social support of interest is measured on the wave prior to the events being measured and analyzed; thus, the events could not have influenced Wave 1 support. In turn, for the support booster hypothesis, events that
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SOCIAL SUPPORT AND POSITIVE AFFECT Table 1 Characteristics of Sample Variable
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Variable Sex Male Female Age (years) 55-59 60-64 65-74 75+ Marital status Never married Married Widowed Separated or divorced
34.2 65.8 18.1 22.7 38.5 20.7 3.3 60.6 30.3 5.8
Urbanicity Urban Nonurban Social class I (lowest) II III IV V (highest) Number of challenge events8 0 1 2 3 4 5 6+
36.8 63.2 27.0 26.8 27.8 13.6 4.7 19.3 20.1 19.0 18.2 10.8 5.8 6.9
' The sum of events over Waves 2, 3, and 4.
occurred between Waves 1 and 4 are predicted to be related to residual social integration, a strengthening of the social network between Wave 1 and Wave 4 but not merely to the level of support at Wave 4. Social support and positive affect could also be confounded; therefore, in this design and analysis, their measures were taken on different waves. Also, the dependent measure was the residual in positive affect. A prior study (Norris & Murrell, 1987) found that life events are not random but are related to both person variables and environmental variables, which suggests that it is desirable to control for variables that are predisposing for events. This was done in these analyses by forcing a set of demographic variables into the regressions before the event measure. We recognized that the prospective design and analysis could not provide a guarantee that these study variables were independent of one another. Correlated relationships can exist even if not measured concurrently. However, the prospective design and analysis do greatly improve estimates of causality over cross-sectional designs. This would seem to argue that prospective designs should be the minimum standard in such studies.
Measures Demographic variables. These included age, sex, urbanicity of residence, marital status (primarily married vs. widowed), and social class (using the Hollingshead & Redlich, 1958, occupation and education categories and weights). In each multiple regression conducted, this set of demographic variables was forced into the regression first, with the rationale being that these variables had operated in the lives of respondents before the other variables under study. Positive affect. This was measured with the five positively worded items from the Bradburn Affect Balance Scale (Bradburn & Caplovitz, 1965; items were particularly excited or interested, proud, pleased about accomplishment, on top of the world, and feel things are going my way) and seven items from the General Well-Being Scale (Ware, Johnson, Davies-Avery, & Brook, 1979; items were excellent vs. low spirits; happy vs. dissatisfied; waking up fresh and rested; life full of interesting things; emotionally stable and sure of yourself; how much energy, pep, and vitality; and depressed vs. cheerful). This set of combined items had an alpha coefficient of .83. A high score reflected highly positive affect. The correlation between Wave 1 and Wave 5 was
.54. The Wave 1 measure was included in all analyses as a control for preevent level of positive affect. It was forced into the equation second, immediately after the set of demographic variables. Anticipated help. This was considered a functional social support measure. It consisted of five items adapted from the scale developed by Andrews, Tennant, Hewson, and Schanell (1978), which ask about the amount of help one could expect from different sources in an emergency. The sources include immediate family, relatives outside the home, friends, neighbors, and church. The items do not specify the type of help these sources would provide. The anticipated help measure has an alpha coefficient of .82. A high score indicated a perception of strong availability of support. The correlation between Wave 1 and Wave 4 was .48. Social integration. This was considered a structural social support measure. It consisted of seven items adapted from a scale by Phillips (1967), which reflect the extent, intimacy, and frequency of social contacts, or the strength of the social network (including but going beyond the size of the network). The social integration measure has an alpha coefficient of .67. The correlation between Wave 1 and Wave 4 was .58. A high score indicated a strong social network. None of these items referred to the function that members of the social network played; thus, the content seemed distinct from anticipated help. However, the two social support scales were correlated with one another at .47 (p < .001) at Wave 1. Because the two measures were significantly correlated, a rather conservative analytic procedure was used to estimate their effects independent of one another. Challenge events. The events measure was the Louisville Older Persons Event Scale (LOPES; Murrell et al., 1984), which includes 54 closeended and 2 open-ended event descriptions. The period of inquiry was the preceding 6 months for all events. Moreover, for each event that occurred, the respondent was to report the month in which it occurred, which permitted a check to ensure that the same event was not reported by the respondent on more than one wave. The 10 challenge events in this study were selected on the basis that they were judged as highly desirable by older adults on the first two waves and they appeared on their face as being feasibly within the control or responsibility of the respondent; that is, it was plausible for the respondent to feel some sense of accomplishment as a result. All 10 ranked in the top 14 most desirable events as evaluated by respondents in the first two waves (Murrell et al., 1984), with an average desirability
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rating per event per interview of 1.80 compared with an average rating of -0.74 for the remaining 44 events (see the Concurrent events section). Thus, challenge events were normatively more desirable than the other events on the scale. Events that received equally high desirability ratings but did not appear to be as clearly within the control of the respondent and thus were not classed as challenge events were (desirability rank in parentheses) health improvement of self (54), birth of grandchild (52), health improvement of someone close (50), more money (44), and child marry (42). Events that received mean ratings that were desirable but weaker and were not clearly within the control of the respondent were family member moved in (39), change in work (38), spouse retired (37), small loan (36), and child left home (35). The remaining 34 events had mean ratings that were negative. Two event measures were used, frequency and desirability. The frequency measure was the count of challenge events occurring to the individual over the three waves, regardless of their evaluation. The desirability rating was the person's response to this follow-up probe of the event: "What kind of change was this for you? Was i t . . . very bad (—3), mostly bad (-2), slightly bad (-1), not much either way (0), very good (1), mostly good (2) or slightly good (3)." The desirability measure was the sum across all 10 challenge events of the responses to the three desirable alternatives (if the evaluation was not desirable, the score was zero), and these were summed over the three waves. The higher the score the greater the desirable impact of the events. For this sample the frequency of challenge events summed across 10 events and over three waves yielded a mean of 2.31 events per respondent, with a standard deviation of 1.90. The desirability of events summed across 10 events and across waves was a mean of 4.31 for this sample with a standard deviation of 4.30. The desirability measure includes an appraisal of the event, whereas the frequency measure does not. The desirability measure was used in the primary set of analyses. The frequency distribution of the challenge events is given in Table 1. The name of the event, the number of reports of the event in this sample over the three waves, and the percentage rating the event as either a very good or mostly good change are: went on a trip out of town, 1,488,70.2%; received an award or special praise, 378,67.3%; got a new pet, 157; 44.4%; started a new recreational activity, 137,68.4%; started going to church activities more often, 106,75.0%; moved to a new place to live, 78,63.9%; retired from job, 46,63.1%; had a promotion at work, 35, 55.3%; changed jobs, 24, 51.5%; and got married, 10,100%. Concurrent events. Because the challenge events were naturally occurring events, events other than challenge events could have been happening at the same time, could have been influenced by the challenge events, and could also have been related to positive affect. To better estimate the particular contribution of the challenge events, regressions were also conducted that added the desirability ratings (same as for challenge events) of the other 44 events on the LOPES summed over Waves 2,3, and 4. Among these events were the desirable events (but not challenge events) listed above, bereavements, illnesses of self and different family members, and various kinds of losses. The mean desirability score for respondents of the summed desirability ratings over the 44 events and three waves was 4.54 with a standard deviation of 4.71. Results Analyses The primary analyses consisted of hierarchical multiple regressions that used the desirability measure of events. All analyses were conducted on exactly the same respondents. In each regression, the set of demographic variables was forced as a block into the equation first, followed by the Wave 1 positive affect measure. Thus, all hypothesized effects would occur after
controls for these baseline measures had been entered. Each regression equation tested at least one hypothesis or one link of a hypothesis. The tables report the change in R2 at the step the variable was entered and its corresponding F value. The criterion for significance was set at p < .05; however, all results that were significant were at the p < .001 level. With respect to distinguishing the independent effects of the two social support measures, a rather conservative approach was taken. Where the effect of anticipated help was the focus, the Wave 1 social integration measure was entered into that equation in the block ahead of Wave 1 anticipated help. The reverse was done for the focus on social integration, in a separate analysis. Thus, the effects of either support measure were reported in the tables as those operating after the effects of the other preevent support measure have been controlled or taken into account. For purposes of clarity, the values for the Wave 1 control support measures are not reported in these tables. Using the frequency measure of events, we conducted secondary analyses that exactly duplicated the primary analyses. These results are described in the text but not in the tables or figures. This was done to estimate the part played by the appraisal of these events. Another secondary analysis examined the contribution of concurrent events other than the challenge events, using the desirability measure. Effects of Demographic Variables, Wave 1 Positive Affect, and Concurrent Events The block of demographic variables made a significant contribution in all of the analyses, indicating the advisability of their inclusion as controls. Inspection of the tables also shows that Wave 1 positive affect was strongly predictive of Wave 5 positive affect, challenge events, Wave 4 social integration, and Wave 4 anticipated help. As can be seen in Table 2, challenge Table 2
Hierarchical Regressions for Interactive Booster Hypothesis Wave 5 affect (DV) Independent variable
R2 change
Wave 1 demographics Wave 1 affect Wave 1 integration Wave 1 help Wave 234 challenge events Wave 234 concurrent events Wave 234 integration3 Wave 234 help" Wave 234 Challenge Events X Wave 234 Integration* Wave 234 Challenge Events X Wave 234 Help" Adjusted R2 ' Adjusted R2 "
.068 .243 .007 .015
15.00* 365.50* 10.00* 23.43*
5,999 6,998 7,997 7,997
.010
15.65*
8,996
.000 .023 .021
1.21 36.26* 33.00*
9,995 10,994 10, 994
.002
3.68
11,993
.001 .355* .349*
1.86
11,993
df
Note. Hypothesized values are in boldface. DV = dependent variable. * Values obtained after Wave 1 help was controlled. b Values obtained after Wave 1 integration was controlled. *p
