Alternative Age Measures: a Research Agenda

ABSTRACT - Chronological age and five alternative age measures were assessed in an exploratory study in terms of interrelationship and association with established age correlates. The research relied on a 1983 self-report survey of female consumers, chronologically 30 to 69 years old. Multiple Regression models were tested to evaluate the relative impact of age-correlates on the six age measures. Results indicate that alternative age measures, particularly 'cognitive and discrepancy age,' may shed insight in various aspects of actual and ideal age-role self-concepts. Surprisingly, 'ideal age,' a measure of ideal age-role self-concept, does not (by itself) have much explanatory power.


Benny Barak and Steven Gould (1985) ,"Alternative Age Measures: a Research Agenda", in NA - Advances in Consumer Research Volume 12, eds. Elizabeth C. Hirschman and Moris B. Holbrook, Provo, UT : Association for Consumer Research, Pages: 53-58.

Advances in Consumer Research Volume 12, 1985      Pages 53-58


Benny Barak, Baruch College (CUNY)

Steven Gould, Baruch College (CUNY)


Chronological age and five alternative age measures were assessed in an exploratory study in terms of interrelationship and association with established age correlates. The research relied on a 1983 self-report survey of female consumers, chronologically 30 to 69 years old. Multiple Regression models were tested to evaluate the relative impact of age-correlates on the six age measures. Results indicate that alternative age measures, particularly 'cognitive and discrepancy age,' may shed insight in various aspects of actual and ideal age-role self-concepts. Surprisingly, 'ideal age,' a measure of ideal age-role self-concept, does not (by itself) have much explanatory power.


The aging-process is "coming of age" in the 1980's. Consumers chronologically over 30 have become a majority in the United States, and their number is still expanding rapidly. Research indicates that this population, while long ignored by marketers who prefer 'young' consumers, is well worth investigation. Consumers who are chronologically 30 to 69 years old have more discretionary income to spend than younger populations and dominate the consumer marketplace for a wide variety of product categories (e.g., cosmetics, and food consumed in the home) (Allen 1981; Conference Board and U.S. Bureau of the Census 1984; Newitt 1983; Underhill and Cadwell 1983; Robey 1981; Stolniz 1982). In addition, these consumers, for the first time, feel that it is O.K. to pay extra attention to oneself (e.g., by keeping healthy through exercise and diet), and to indulge in guilt free spending of money (Underhill and Cadwell 1983).

Special sessions concerned with aging issues have been presented at recent ACR conferences. It was pointed out at these sessions that most of our knowledge about aging is limited to information about age differences and not with the aging process, e.g., changes with age (Ross 1982). This paper discusses an empirical exploratory assessment of the aging process, in a population chronologically 30 to 69 years old, in terms of chronological age, and several new alternative measures.


Chronological age is an extremely useful measure--almost always included by researchers to describe population samples studied--and is considered by many to be one of the most relevant segmentation variables available to marketers (e.g., Phillips and Sternthal 1977; Vishabharathy 1982). Chronological age, however, is no longer presumed to be an automatic predictor of factors such as health, intellectual ability, and mental outlook (Zaltman, Alpert and Heffring, 1978). A variety of alternative age measures are readily available; for example: identity age (the most common self-perceived age measure in social gerontology--Cutler 1982), cognitive age (Barak and Schiffman 1981), age role/identity (George, Mutran and Pennybacker 1980; Mutran and George 1982), biological age (Bell 1972; Birren and Renner 1977), social age (Bengston, Kasschau and Ragan 1977; Rose 1972), personal age (Kastenbaum et al. 1972) and projected age (Puglesi 1983). This paper approaches age measurement from several alternative points of view. Five different age scales (all continuous, numerical and expressed in years) are considered in addition to chronological age. This study explores these six age measures in terms of interrelationship, as well as interaction. with other variables; theY are resPectively:

1. Chronological Age - the number of years a person has lived.

2. Cognitive Age - an individual's actual age-role self-concept, reflecting his/her age-identity in terms of four age dimensions (feel-age, look-age, do-age, and interest-age) expressed in years.

3. Ideal Age - an individual's ideal age-role self-concept--the age he/she considers to be a person's ideal age, expressed in years.

4. Youth Age - the number of years a person perceives him/herself to be younger (or older) than his/her chronological age, i.e., the discrepancy between a respondent's chronological and cognitive age.

5. Discrepancy Age - the number of years between a person's cognitive and ideal ages, i.e., discrepancy between actual and ideal age-role self-concepts.

6. Disparity Age - the number of years that separate a person's chronological and ideal age, i.e., discrepancy between chronological and ideal age.

Three of the above mentioned age-measures are congruent with the framework of self-concept theory (Sirgy 1982). The first, cognitive age (measured through self-identification with age-decade referent groups--Barak 1979; Barak and Schiffman 1981), is a measure of actual age-role self-concept. The second, ideal age, mirrors a person's ideal age-self concept. As Sirgy (1982) points out, discrepancy between actual and ideal self-concepts reflects a global self-attitude. Discrepancy age, the third age-self concept measure, therefore probably indicates the way someone feels about his/her subjective age (i.e., attitude towards age-self); accordingly a low discrepancy age designates a positive age-self attitude. The other three age-measures considered (chronological, youth and disparity age) are not obvious self-concept measures. Self-concept constructs tend to be concerned with 'subjectively perceived reality' and not with 'objectively measured reality,' as in the instance of chronological age. Both youth and disparity age reflect discrepancy between objective and subjective measures, and as such are not apparent self concept measures.


Data Base

Five hundred female residents of the New York metropolis participated in the study. To qualify, respondents had to specify their chronological age, and be chronologically 30 to 69 years old; this left 430 respondents in the study sample. Data utilized were gathered in Winter 1982/83, via self-report questionnaires, with a quota sampling procedure (chronologically 39 or younger and 40+). The questionnaire required about 45 minutes to be administered, and focused on a variety of leisure time and consumer behavior variables (e.g., psychographics). Table 1 presents chronological, cognitive, and ideal age distributions (expressed in decades) of the sample.




With the exception of chronological and self-perceived age measures, the age-measures under investigation have no prior research foundations. Due to its exploratory nature, this study therefore first considers interrelationships between the age measures this through the analysis of the following propositions:

PROPOSITION 1: Age measures which assess chronological, cognitive, and ideal age will be related (significant correlation) to each other; yet these same measures will not be similar--they measure different age-dimensions.

PROPOSITION 2: Age measures which assess the discrepancies between chronological, cognitive and ideal age will be related (significant correlation) to each other; yet these same measures will not be similar--they measure different age-dimensions.

The reasoning behind these two propositions relies- on the notion that if these age-measures are indeed age measures, then they ought to tap various dimensions of age; yet if they tap age-dimensions then they ought also to be significantly (pe.05) related to each other, while simultaneously being different (otherwise they would basically measure an identical age-dimension).

PROPOSITION 3: Age measures are significantly related to variables which past age-research (both chronological and non-chronological) regarded as age-correlates.

Since most of the new age-measures lack a research tradition, the last proposition assumes these measures will follow patterns similar to known age-measures.


Age researchers have paid particular attention to both chronological age (e.g., Meadow, Cosmas and Plotkin 1981; Palmore 1981; Phillips and Sternthal 1977), and self-perceived age (e.g., Peters 1971; Baum and Boxley 1982). These researchers have, over time, conducted many studies to assess the association of chronological and self-perceived age with numerous variables. A number of age-correlates have thus been established; the present study set out to assess these correlates (the independent research variables) in terms of their relationship with the six age measures. Since the intent of the study was to explore the nature of the relationship of the six age measures vis-a-vis age-correlates, it was decided to conduct this assessment through the research flow model shown in Figure 1.



A multiple regression technique was used to test this model. Only measures considered to be continuous in nature and potential age correlates were assessed. Accordingly the following variables were selected:

FAMILY DEMOGRAPHICS - Underhill and Cadwell (1983) report 'youthfulness' to be associated with children's age (teenage children seem to cause a greater sense of youthfulness than do children under six); this led to the inclusion of the variables measuring age of progeny (stated in terms of chronological age). These measures were respectively age of (1) youngest child, (2) oldest child, (3) youngest grandchild, and (4) oldest grandchild. Barak (1979) had found size of progeny (i.e., number of offspring) to be positively related to both chronological and cognitive age. This led to selection of four additional family measures -- (5) household size, (6) number of children, (7) number of grandchildren, and (8) progeny.

SEX-TRAITS (measured with the BSRI) - The BSRI (Bem Sex Role Inventory) is a self-descriptive survey instrument (utilizing 7-point true-untrue items) which assesses masculine and feminine sex-role traits (Bem 1974). As indicated by Puglesi (1983), prior age-research established a relationship (negative) between BSRI type measures of sex traits and both chronological and self-perceived age. This led to the inclusion of sex-traits in the present study. The traits considered were (1) femininity, and (2) masculinity; both 10-item scales determined with rotated factor analysis.

PSYCHOGRAPHICS (measured with Likert summation scales) respondents indicated agreement (or disagreement) with a wide range of Activities, Interests and Opinion Statements. Responses were then factor analyzed to establish specific AIO scales (Wells 1975). Social gerontological aging research employs several measures which are psychographic in nature, with life-satisfaction indexes probably the most prominent (Larson 1978). Research with self-perceived age measures indicates life-satisfaction, and its sub-component morale, to be inversely related with self-perceived age (Barak 1979; Bengston, Kasschau and Rags 1977; Mutran and Burke 1979; Peters 1971). The same research also reports self-confidence to have such an inverse relationship. Another factor, positively associated with aging, is traditionality (Bengston and Cutler 1976). Phillips and Sternthal (1977), in their review of the aging-process, indicate that an increase in age is often associated with greater price sensitivity. These findings led to the selection of the following six psychographic measures for the present study: (1) morale (e.g., agreement with "I am just as happy as when I was younger"), (2) self-confidence (e.g., agreement with "I think I have a lot of personal ability"), (3) homebody (e.g., agreement with "I am a homebody"), (4) old-fashioned (e.g., "a house should be dusted and polished three times a week"), (5) traditionality (e.g., agreement with "what young people need most of all is strict discipline by their parents"), and (6) price sensitivity (e.g., agreement with "I often deny myself something I want if I feel it costs too much").

LEISURE-TIME BEHAVIORS - Four restaurant-dining frequency measures were selected: dining frequency in restaurants perceived to be (1) expensive, (2) moderately priced, or (3) cheap. A fourth measure, restaurant dining frequency, summated the first three dining frequency measures. Leisure-time activities were also assessed in terms of time (hours/per day) spent on (1) eating out, (2) exercise, (3) watching TV, (4) listening to the radio, (5) reading magazines, (6) reading newspapers, and (7) reading books. An eighth leisure-time measure, reading, summarized the three reading measures. Reviews, of studies of elderly consumers, indicate that leisure time activities (like those considered in the present study) are likely to be age-correlates (Phillips and Sternthal 1977; Meadow. Cosmas and Plotkin 1981).


RELIABILITY - All multiple-item scales in the study were assessed for reliability; only measures with a reliability coefficient of .5 or greater (the rule-of-thumb suggested by Peter 1979) were included. Cognitive age, the only multiple-item age-scale, had a reliability coefficient ALPHA of .91.

PEARSON CORRELATION ANALYSES were performed to determine if relationships exist (1) between the age-measures (to assess Propositions 1 a Id 2) and/or (2) between the age-measures and the independent research variables (to assess Proposition 3, and to help determine which variables to utilize for further analysis). In addition, WITHIN SUBJECT ANALYSIS of VARIANCE (MANOVA) was utilized to test for differences between age measures (to test that aspect of Propositions 1 and 2). MULTIPLE REGRESSION ANALYSIS was utilized to test the model shown in Figure 1. To limit multi-collinearity, only measures with a PEARSON inter-correlation below .8 were included in the regression analysis (Lewis-Beck 1980).


PEARSON ANALYSES established that the age measures are related (p <.05) to each other in the manner suggested by Propositions 1 and 2 (see age-correlation matrix in Table 2). The WITHIN SUBJECT ANALYSIS established the direct age measures, as well as the discrepancy age measures, to be significantly different (see Table 3). Thus, both Propositions 1 and 2 could be accepted.





The WITHIN SUBJECT ANALYSIS also revealed that differences between direct, as well as between discrepancy age measures, held across various chronological age groupings (see Table 3). A look at the means of these age measures across the age groupings reveals interesting patterns (see Tables 1 and 3). Ideal age seems to operate in a narrow spectrum across chronological age groups. Older people tend to select an ideal age similar to that of younger people; therefore the disparity between ideal age and chronological age increases with age. Ideal age had been measured before by Needham, Harper and Steers (1980) annual life-style survey, with a similar pattern found for the relationship between ideal and chronological age.

PEARSON ANALYSES also established which independent research variables were significantly (p <.05) correlated with the six age measures (see Table 4).



MULTIPLE REGRESSION MODELS, based on the research flow model shown in Figure 1, were tested (for each of the six age measures) relative to established age-correlates (see Table 4). Results show significant (p<.000) MULTIPLE REGRESSION equations for all six dependent age variables on the basis of established age-correlates (Table 5). Profiles, based on the functions (equations) shown in Table 5, designate which of the age-correlates are particularly of prime significance in the determination of each type of age.



Table 5 therefore snows which variables formed (for each of the six alternative age measures) main age-correlates; direction of relationships is indicated by the sign in the equations (i.e., + - positive, and negative).


Progeny ages are significant in the aging process (age of progeny measures correlate with all six age measures; see Table 4); with 'age of youngest child' perhaps the most relevant age correlate all of the multiple regression models contained that particular progeny age. Promotions which use portrayals of family life with children may therefore be instrumental in the perception of the age associated with the various members of the portrayed household. These findings, of particular interest to promoters of age-sensitive products (e.g., cosmetics) aimed at older consumers, imply that children (portrayed as progeny of a certain age) may influence age-factors in advertisements (probably affecting perceptions of both models and/or products shown).

Age-linked behaviors, e.g., frequency of exercise and time spent dining out, are linked to both chronological and cognitive age, as well as to discrepancy and disparity age (see Table 4). Yet, time spent dining out was the only variable of a behavioral nature to enter the age-regression equations (those for chronological and cognitive age--see Table 5). This leads to the speculation that not all behavior is (as might be expected) obviously age related, even though the cognitive age scale contains a behavioral element (i.e., do-age).

Another finding is that advancement in chronological age is accompanied by increases in both discrepancy and disparity age. A majority (49.3%--Table 1) consider an age in the 30's as desirable. This helps explain why ideal age has so few correlates. On the other hand, discrepancy and disparity age have many correlates. Ideal age is therefore a relevant measure when considered in terms of its discrepant relationship with either chronological or cognitive age. In short, while ideal age (by itself) offers little insight into aging patterns (relative to the independent variables) discrepancy and/or disparity age are likely to provide understanding of the function of ideal age in the aging-process.

Discrepancy age is perhaps the most intriguing new measure. As a measure of the discrepancy between cognitive and ideal age, it is, when viewed within the framework of self-concept theory, also a measure of the discrepancy of a consumer's actual and ideal self-concept. As such, discrepancy age reflects attitude towards self-perceived age-role. The importance and relevance of this is further confirmed by the nature of the measure's many correlates. Of particular interest here are two findings. The first concerns the close relationship between discrepancy age and morale. The morale scale is basically a measure of happiness (and/or satisfaction) with one's age-status. The strength of the relationship between discrepancy age and morale (morale entered in the discrepancy age regression function--see Table 5) therefore confirms that both measures assess attitude towards aging. The second finding concerns the relationship between discrepancy and disparity age. This association highlights the importance and relevance of ideal age. Disparity age (like discrepancy age) reflects attitude towards one's own 'objective age,' i.e., chronological age. This notion is confirmed by the finding that the morale variable entered the disparity age regression function (see Table 5). As such, disparity age indicates how a person feels about his/her chronological age (cohort's age), e.g., a low disparity age implies high satisfaction with one's chronological age status. The study's findings infer that both discrepancy and disparity age tap different dimensions of attitude towards aging (see Table 3, as well as the regression equations in Table 5) notwithstanding their high correlation (r=81--see Table 2).

Further investigation is needed to determine when to use either of these two age-attitude scales. For instance, promotional researchers interested in evoking idealized age imagery in their promotional efforts could consider the magnitude of their target market's disparity age (since consumers with a high disparity age are likely to have a greater sensitivity for messages that cause them to feel closer to their idealized age-role) when they want consumers to aspire to seem like an idealized figure. If the promoters set out to have their target consumers 'identify' with models' age-roles, as portrayed in advertisements, measures such as cognitive and/or discrepancy age are likely to be of greater relevance.

Youth age, like disparity age, is a measure of the distance between chronological age and a subjective age measure. Surprisingly, youth age (unlike disparity age) has very few age correlates and its regression equation possesses a low R Square (.09--see Table 5). Youth age is therefore not as relevant as expected.

Overall, the most interesting new alternative age measures are cognitive, discrepancy, and disparity age. Researchers concerned with aspects of self-concept and reference groups will find these three age measures especially intriguing. Cognitive age, as an age self-image measure, focuses on identification with age-role reference groups. Thus marketers who would like consumers to identify with promotional models could select models that fit such age-role reference groups. More research is needed to help determine how marketers can best utilize the cognitive age variable. Present findings indicate that cognitive age might have special value as a psychographic segmentation variable since it has many correlates (see Table 4) and high explanatory power (R Square = .39--see equation in Table 5). Disparity age is likely to be of relevance when reference groups utilized in promotion are to reflect the ideal a consumer is to yearn for, rather than identify with. Discrepancy age may help to find the middle ground between these two types of notions. Consumers with high levels of discrepancy age will find it difficult to identify with reference groups (shown in commercials) who portray their ideal age. These consumers are therefore likely to be more sensitive, than chose with a low discrepancy age, to age-related cues provided by such reference groups.

This exploratory study attempted to provide insight into different aspects of the aging-process; implications are that a clear pattern of structural relationships exists between the various age-dimensions (tapped for the first time by the new alternative age measures). Additionally, the research functioned as a replication study (i.e., a reassessment of established age-correlates). The findings support and confirm the conclusions reached by past age-research--the ma; icy of independent variables assessed were age correlates. However, more study is needed of the nature of structural age relationships to find answers to questions such as: Is perception of age dependent on gender, i.e., do men experience age differently from women? What do the differences between chronological age and the new alternative age measures imply? Do alternative age measures help marketers to understand consumers' aging process better? And, how can such an understanding be beneficial to aging consumers?


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Benny Barak, Baruch College (CUNY)
Steven Gould, Baruch College (CUNY)


NA - Advances in Consumer Research Volume 12 | 1985

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