INT'L. J. AGING AND HUMAN DEVELOPMENT, Vol. l O ( 2) . 1979-80
CORRELATES OF LIFE SATISFACTION: AN AID ANALYSIS
RON TOSELAND, PH.D. School of Social Welfare SUM Y, Albany
JOHN RASCH, PH.D. College of Social and Behavioral Sciences Rehabilitation Counseling Program University of South Florida
ABSTRACT
The Automatic Interaction Detector (AID3) was used to develop a model based on the interaction of predictors of life satisfaction. The sample consisted of 871 people over fifty-five years of age. Thirty-one potential predictors were used representing demographics, environmental variables, and social psychological variables. The findings indicate that nine predictors explained 22.1 per cent of the variance in life satisfaction scores. The most important predictors of life satisfaction were family life satisfaction, personal health satisfaction, and satisfaction with dwelling. The interactions between the predictors indicated that a simple linear-monotonic relationship between the predictors was too restrictive.
The examination and measurement of life satisfaction is as old as the field of gerontology itself. As early as 1933, Conkey became interested in measuring variables affecting the life satisfaction of older persons [l] Since that time, gerontologists have spent considerable effort attempting to define life satisfaction and identifying correlates of life satisfaction. The goal was to obtain knowledge about the human life cycle, and ultimately to use this knowledge to enhance the life satisfaction of older persons. Although progress has been made toward this goal, the measurement of life satisfaction and its correlates remains surrounded by controversy. As recently as 1975, Bloom questioned whether the available measures of life satisfaction were at all helpful in predicting important behaviors [2]. The question of
.
203 D 1979,Baywood Publishing Co.. Inc.
doi: 10.2190/N4HA-LQY4-RR3U-J800 http://baywood.com
204 / R. TOSELAND AND J. RASCH
whether to use a single self-reported measure of life satisfaction, a multiple item scale, or observer ratings of overt behavior has never been resolved. It is clear, however, that any measures should be based on dimensions which correlate highly with a respondent’s life satisfaction, rather than on dimensions of life satisfaction imposed on respondents by others who claim to know what makes older people satisfied. There is much evidence pointing to factors associated with the life satisfaction of older adults despite different measuring techniques. Of all the factors studied, self-assessedhealth status has consistently proven to be a significant predictor [3-51. Other important correlates of life satisfaction are socio-economic status [ 6 ] income and income adequacy [4]. Activity level and the extent of social interactions have also correlated consistently with life satisfaction [7]. Although there is a considerable amount of data, all of these studies have been designed to find primary and secondary predictors of life satisfaction. Little attention has been given to how much one predictor of life satisfaction contributes in relations to a set of predictor variables, and the interactions between these predictors. Interactions between predictors are of particular importance in explaining life satisfaction. An interaction effect implies that for different persons there are different predictors of the dependent variables. The relationship between the amount of social interaction within the family, for example, is not a simple one. Bell points out that older persons expect a certain level of contact with their family network [8]. Some older persons expect frequent contact, others do not. If this expectation is disconfirmed, either by more or less contact with the family, the life satisfaction of the older person is reduced. Thus, older persons may be effected differently by similar changes in family contact. This type of relationship between life satisfaction and famdy contact is an indication that statistical models attempting to relate life satisfaction to a linear-monotonic combination of predictor variables may be too restrictive. It is essential to examine a large number of predictors, and to consider the interaction between these predictors in order to develop a useful model. This study builds a model for life satisfaction, utilizing the most important predictors, without necessarily casting these predictors into a linear-monotonic relationship with the criterion.
METHOD A total of 871 persons, age fifty-five and over, were studied using thirty-one potential predictors. These people were originally a subset of a national sample of teenagers, adults and older persons living in twenty-eight communities across the United States who were selected for a study of planned community development [9]. Persons residing in the twenty-eight communities were chosen by a clustered area probability sample and received a 90 minute interview
CORRELATES O F LIFE SATISFACTION: A N A I D ANALYSIS I 205
in the spring of 1973. The response rate was 70.3 per cent for a total sample of 5, 51 1 persons. All persons who were fifty-five years of age or older were used in this research. Some basic demographic characteristics of the sample are presented in Table 1. As indicated, the sample of older persons is well above the national average for income, which restricts the generalizability of the findings. Given that socio-economic status and income contribute to overall life satisfaction, it is not surprising to find that many in this study rated their life satisfaction relatively high.
DESIGN The dependent variable utilized was the self-rating of older persons on a semantic differential life satisfaction scale. The respondents were asked to rate their overall life satisfaction. The independent or predictor variables are presented in Table 2. These can be grouped into three types: (1) demographics, (2) environmental characteristics, and (3) social-psychologicalvariables. The thirty-one predictors were chosen from research evidence identifying correlates of life satisfaction [7] and from environmental variables which have been identified as sources of satisfaction for residents of planned, unplanned and retirement communities [ 101.
DATA ANALYSIS The thirty-one predictor variables were evaluated by the Automatic Interaction Detector (AID3), which was originally developed in the Survey Research Center at the University of Michigan. This procedure has a resemblance to stepwise multiple regression, except that AID3 avoids the assumptions of linearity and additivity. The AID3 analysis uses the predictor variables to progressively partition the original sample on the levels of the independent variables into mutually exclusive subgroups, explaining the maximum variation in the dependent Table 1. Sex, Age, Marital Status and Income for the Sample of 871 Older Persons Sex
%
Male 44 Female 56
Age
%
Marital Status
%
Income
%
55-60 61-64 6570 71-75
34 17 27 12
Married Single Widowed Divorcedheparated and not ascertained
72 4 21 3
under 5,000 5,000 to 9,999 10,000 to 14,999 1 5,000 to 19,999
9 18 23 14
20,000 to 25,000 over 25,000 Not ascertained
11 11 14
206 / R. TOSELAND AND J. RASCH
-
Table 2. Independent Variables Used in the AID3 Analysis Demographic
1. Sex 2. Marital status 3. Education level 4. Employment status 5. Income 6. Age 7. Residential status 13. Type of dwelling 9. Private vehicle ownership 10. Number of children a t home
Community 11. Convenience 12. Density 13. Satisfaction with police force 14. Homogeneity of the neighborhood 15. Safety 16. Satisfaction with recreational facilities 17. Satisfaction with health facilities 18. Satisfaction with shopping facilities 19. Satisfaction with community 20. Availability of public transportation 21. Satisfaction with community association 22. Satisfaction with dwelling
Social-Psychological
23. Ease of making friends 24. Number of close friends 25. Number of relatives in the community 26. Relationship with neighbor 27. Community integration/ alienation 28. Level of racial prejudice 29. Personal health satisfaction 30. Religious participation 31. Satisfaction with family life
variable with each binary partition. Data sets with a large number of measurement points are required, so that the power of the analysis is not reduced due to small subgroup sample size [ 1 11 . The AID3 analysis is performed by a sequential one-way analysis of variance that is both simple and robust. The major advantage of model building using the AID3 program is the ability to determine interaction effects among the predictors, i.e. for different participants there may be different predictors of the dependent variable. The analysis also provides a transparent result, enabling the researcher to see the importance of each predictor variable at each stage of the analysis. In the analysis reported here, certain controls were set for the AID3 procedure. The analysis was not allowed to dichotomize an independent variable if the resulting partition led to a new group with less than twenty-five participants. Each binary partition was also required to explain at least 0.6 per cent of the dependent variable’s variance. These parameters were established to avoid spurious partitions of the sample. The final result, indicated by figure 1, shows: (1) the best predictors of the dependent variable, ( 2 ) the number of participants involved in each partition, (3) the variance explained by each partition, and (4) the interactions between predictor variables.
RESU LTS Table 3 presents a summary of the most important predictors of life satisfaction as determined by the AID3 analysis. The most important predictors
CORRELATES OF LIFE SATISFACTION: A N A I D ANALYSIS
/ 207
Table 3. Predictors of Life Satisfaction and The Amount of Variance ExDlained for 871 Older Persons Variable #
31 29 22 24
27 6 17 14
19
Predictor Variables
Family life satisfaction Personal health satisfaction Satisfaction with dwelling Number of close friends Community integrationlalienation Age Satisfaction with health facilities Homogeneity of the neighborhood Satisfaction with the community
Variance Explained 1%)
7.5 7.0 2.4 1.2 1.2 .9
.7 .6 -6
were family life satisfaction and personal health satisfaction. Both of these predictors have been significantly related to life satisfaction scores in previous studies [4,6, 121. These two predictors explain more than half of the variance in the dependent variable. Six other predictors, shown in Table 3, accounted for an additional 8.2 per cent of the variance in life satisfaction scores. The total variance explained (Between Sum of Squares/ Total Sum of Squares) equaled 22.1 per cent. Figure 1 delineates the interaction effects between the predictors. Family life satisfaction, at the base of the diagram, was selected as the most important predictor of life satisfaction, explaining 7.5 per cent of the variance in life satisfaction scores. For those persons high on family life satisfaction (left branch of figure one) the next most important predictor was personal health satisfaction, explaining 4.6 per cent of the variance. People with good family relationships and in good health have the number of personal friendships as the next best predictor of life satisfaction. The analysis end for those thirty-nine persons having high life satisfaction but low satisfaction with their health. As noted previously, the analysis terminates when the remaining sample is too small (N 25) or less than 0.6 per cent of the criterion variance is explainable. Contradictory evidence exits in the literature regarding the relationship of age to life satisfaction [7]. In this study, age explained relatively little of the variance in life satisfaction scores (.9%). Further, the analysis indicated that for those fifty-five to sixty years of age, homogeneity of the community was an important predictor. The community was also an important predictor of life satisfaction for people over sixty-one years of age, but no specific quality of the community was determined. For participants who were low on family life satisfaction (right branch of figure one), satisfaction with their personal dwelling was most important. For these people, material possessions and satisfaction with the immediate environment contributed most to their life satisfaction. The next best predictor
CORRELATES OF LIFE SATISFACTION: AN AID ANALYSIS
RON TOSELAND, PH.D. School of Social Welfare SUM Y, Albany
JOHN RASCH, PH.D. College of Social and Behavioral Sciences Rehabilitation Counseling Program University of South Florida
ABSTRACT
The Automatic Interaction Detector (AID3) was used to develop a model based on the interaction of predictors of life satisfaction. The sample consisted of 871 people over fifty-five years of age. Thirty-one potential predictors were used representing demographics, environmental variables, and social psychological variables. The findings indicate that nine predictors explained 22.1 per cent of the variance in life satisfaction scores. The most important predictors of life satisfaction were family life satisfaction, personal health satisfaction, and satisfaction with dwelling. The interactions between the predictors indicated that a simple linear-monotonic relationship between the predictors was too restrictive.
The examination and measurement of life satisfaction is as old as the field of gerontology itself. As early as 1933, Conkey became interested in measuring variables affecting the life satisfaction of older persons [l] Since that time, gerontologists have spent considerable effort attempting to define life satisfaction and identifying correlates of life satisfaction. The goal was to obtain knowledge about the human life cycle, and ultimately to use this knowledge to enhance the life satisfaction of older persons. Although progress has been made toward this goal, the measurement of life satisfaction and its correlates remains surrounded by controversy. As recently as 1975, Bloom questioned whether the available measures of life satisfaction were at all helpful in predicting important behaviors [2]. The question of
.
203 D 1979,Baywood Publishing Co.. Inc.
doi: 10.2190/N4HA-LQY4-RR3U-J800 http://baywood.com
204 / R. TOSELAND AND J. RASCH
whether to use a single self-reported measure of life satisfaction, a multiple item scale, or observer ratings of overt behavior has never been resolved. It is clear, however, that any measures should be based on dimensions which correlate highly with a respondent’s life satisfaction, rather than on dimensions of life satisfaction imposed on respondents by others who claim to know what makes older people satisfied. There is much evidence pointing to factors associated with the life satisfaction of older adults despite different measuring techniques. Of all the factors studied, self-assessedhealth status has consistently proven to be a significant predictor [3-51. Other important correlates of life satisfaction are socio-economic status [ 6 ] income and income adequacy [4]. Activity level and the extent of social interactions have also correlated consistently with life satisfaction [7]. Although there is a considerable amount of data, all of these studies have been designed to find primary and secondary predictors of life satisfaction. Little attention has been given to how much one predictor of life satisfaction contributes in relations to a set of predictor variables, and the interactions between these predictors. Interactions between predictors are of particular importance in explaining life satisfaction. An interaction effect implies that for different persons there are different predictors of the dependent variables. The relationship between the amount of social interaction within the family, for example, is not a simple one. Bell points out that older persons expect a certain level of contact with their family network [8]. Some older persons expect frequent contact, others do not. If this expectation is disconfirmed, either by more or less contact with the family, the life satisfaction of the older person is reduced. Thus, older persons may be effected differently by similar changes in family contact. This type of relationship between life satisfaction and famdy contact is an indication that statistical models attempting to relate life satisfaction to a linear-monotonic combination of predictor variables may be too restrictive. It is essential to examine a large number of predictors, and to consider the interaction between these predictors in order to develop a useful model. This study builds a model for life satisfaction, utilizing the most important predictors, without necessarily casting these predictors into a linear-monotonic relationship with the criterion.
METHOD A total of 871 persons, age fifty-five and over, were studied using thirty-one potential predictors. These people were originally a subset of a national sample of teenagers, adults and older persons living in twenty-eight communities across the United States who were selected for a study of planned community development [9]. Persons residing in the twenty-eight communities were chosen by a clustered area probability sample and received a 90 minute interview
CORRELATES O F LIFE SATISFACTION: A N A I D ANALYSIS I 205
in the spring of 1973. The response rate was 70.3 per cent for a total sample of 5, 51 1 persons. All persons who were fifty-five years of age or older were used in this research. Some basic demographic characteristics of the sample are presented in Table 1. As indicated, the sample of older persons is well above the national average for income, which restricts the generalizability of the findings. Given that socio-economic status and income contribute to overall life satisfaction, it is not surprising to find that many in this study rated their life satisfaction relatively high.
DESIGN The dependent variable utilized was the self-rating of older persons on a semantic differential life satisfaction scale. The respondents were asked to rate their overall life satisfaction. The independent or predictor variables are presented in Table 2. These can be grouped into three types: (1) demographics, (2) environmental characteristics, and (3) social-psychologicalvariables. The thirty-one predictors were chosen from research evidence identifying correlates of life satisfaction [7] and from environmental variables which have been identified as sources of satisfaction for residents of planned, unplanned and retirement communities [ 101.
DATA ANALYSIS The thirty-one predictor variables were evaluated by the Automatic Interaction Detector (AID3), which was originally developed in the Survey Research Center at the University of Michigan. This procedure has a resemblance to stepwise multiple regression, except that AID3 avoids the assumptions of linearity and additivity. The AID3 analysis uses the predictor variables to progressively partition the original sample on the levels of the independent variables into mutually exclusive subgroups, explaining the maximum variation in the dependent Table 1. Sex, Age, Marital Status and Income for the Sample of 871 Older Persons Sex
%
Male 44 Female 56
Age
%
Marital Status
%
Income
%
55-60 61-64 6570 71-75
34 17 27 12
Married Single Widowed Divorcedheparated and not ascertained
72 4 21 3
under 5,000 5,000 to 9,999 10,000 to 14,999 1 5,000 to 19,999
9 18 23 14
20,000 to 25,000 over 25,000 Not ascertained
11 11 14
206 / R. TOSELAND AND J. RASCH
-
Table 2. Independent Variables Used in the AID3 Analysis Demographic
1. Sex 2. Marital status 3. Education level 4. Employment status 5. Income 6. Age 7. Residential status 13. Type of dwelling 9. Private vehicle ownership 10. Number of children a t home
Community 11. Convenience 12. Density 13. Satisfaction with police force 14. Homogeneity of the neighborhood 15. Safety 16. Satisfaction with recreational facilities 17. Satisfaction with health facilities 18. Satisfaction with shopping facilities 19. Satisfaction with community 20. Availability of public transportation 21. Satisfaction with community association 22. Satisfaction with dwelling
Social-Psychological
23. Ease of making friends 24. Number of close friends 25. Number of relatives in the community 26. Relationship with neighbor 27. Community integration/ alienation 28. Level of racial prejudice 29. Personal health satisfaction 30. Religious participation 31. Satisfaction with family life
variable with each binary partition. Data sets with a large number of measurement points are required, so that the power of the analysis is not reduced due to small subgroup sample size [ 1 11 . The AID3 analysis is performed by a sequential one-way analysis of variance that is both simple and robust. The major advantage of model building using the AID3 program is the ability to determine interaction effects among the predictors, i.e. for different participants there may be different predictors of the dependent variable. The analysis also provides a transparent result, enabling the researcher to see the importance of each predictor variable at each stage of the analysis. In the analysis reported here, certain controls were set for the AID3 procedure. The analysis was not allowed to dichotomize an independent variable if the resulting partition led to a new group with less than twenty-five participants. Each binary partition was also required to explain at least 0.6 per cent of the dependent variable’s variance. These parameters were established to avoid spurious partitions of the sample. The final result, indicated by figure 1, shows: (1) the best predictors of the dependent variable, ( 2 ) the number of participants involved in each partition, (3) the variance explained by each partition, and (4) the interactions between predictor variables.
RESU LTS Table 3 presents a summary of the most important predictors of life satisfaction as determined by the AID3 analysis. The most important predictors
CORRELATES OF LIFE SATISFACTION: A N A I D ANALYSIS
/ 207
Table 3. Predictors of Life Satisfaction and The Amount of Variance ExDlained for 871 Older Persons Variable #
31 29 22 24
27 6 17 14
19
Predictor Variables
Family life satisfaction Personal health satisfaction Satisfaction with dwelling Number of close friends Community integrationlalienation Age Satisfaction with health facilities Homogeneity of the neighborhood Satisfaction with the community
Variance Explained 1%)
7.5 7.0 2.4 1.2 1.2 .9
.7 .6 -6
were family life satisfaction and personal health satisfaction. Both of these predictors have been significantly related to life satisfaction scores in previous studies [4,6, 121. These two predictors explain more than half of the variance in the dependent variable. Six other predictors, shown in Table 3, accounted for an additional 8.2 per cent of the variance in life satisfaction scores. The total variance explained (Between Sum of Squares/ Total Sum of Squares) equaled 22.1 per cent. Figure 1 delineates the interaction effects between the predictors. Family life satisfaction, at the base of the diagram, was selected as the most important predictor of life satisfaction, explaining 7.5 per cent of the variance in life satisfaction scores. For those persons high on family life satisfaction (left branch of figure one) the next most important predictor was personal health satisfaction, explaining 4.6 per cent of the variance. People with good family relationships and in good health have the number of personal friendships as the next best predictor of life satisfaction. The analysis end for those thirty-nine persons having high life satisfaction but low satisfaction with their health. As noted previously, the analysis terminates when the remaining sample is too small (N 25) or less than 0.6 per cent of the criterion variance is explainable. Contradictory evidence exits in the literature regarding the relationship of age to life satisfaction [7]. In this study, age explained relatively little of the variance in life satisfaction scores (.9%). Further, the analysis indicated that for those fifty-five to sixty years of age, homogeneity of the community was an important predictor. The community was also an important predictor of life satisfaction for people over sixty-one years of age, but no specific quality of the community was determined. For participants who were low on family life satisfaction (right branch of figure one), satisfaction with their personal dwelling was most important. For these people, material possessions and satisfaction with the immediate environment contributed most to their life satisfaction. The next best predictor
