antecedents of cognitive age: a replication and extension

Antecedents of CognitiveAge: A Replication andExtensionAnil MathurHofstra University

George P. MoschisGeorgia State University

ABSTRACT

Cognitive age has been an important construct in studies of older con-sumers. The present study builds upon previous research by providingtheory-based antecedents of cognitive age. The results suggest that dif-ferences in cognitive age do not merely reflect differences in chrono-logical age, and that a person’s cognitive age is influenced by his or herexperiences of life events that serve as markers of transitions intosocial roles people are expected to enact at different stages in life. Inaddition, the experiences of health-related events, such as chronic con-ditions, make people aware of their aging, affecting their cognitive age.The influence of cognitive age on consumer-behavior variables is alsoexamined, and directions for future research are suggested. © 2005Wiley Periodicals, Inc.

Subjective or cognitive age1 has been an important construct in studiesof older consumers (e.g., Sherman, Schiffman, & Dillon, 1988; Stephens,1991; Szmigin & Carrigan, 2001; Wilkes, 1992). In 1992, Wilkes pub-lished a major study focusing on demographic antecedents and behavioral

Psychology & Marketing, Vol. 22(12): 969–994 (December 2005)Published online in Wiley InterScience (www.interscience.wiley.com)© 2005 Wiley Periodicals, Inc. DOI: 10.1002/mar.20094

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1 Although the term cognitive age is used to imply mental resources (processing, memory) in psy-chology and gerontology literature, many consumer researchers have used it to mean subjectiveage. To be consistent with the Wilkes (1992) usage, in the present research the term is used toimply subjective age.

consequences of cognitive age. Although he found a relatively high R2

for cognitive age antecedents used, he concluded by saying that, “otherpredictor variables need to be identified and tested” (p. 299).

However, studies focusing on antecedents of cognitive age have pro-duced contradictory results. Barak and Stern (1986) reviewed severalstudies and found that for variables such as retirement, widowhood, edu-cation, and social class, some studies report a significant relationshipwith measures of cognitive age, and others report no relationship. Also,they found that for variables such as gender, race, and marital status,all studies included in the review reported no relationship with meas-ures of cognitive age. Logan, Ward, and Spitze (1992) examined theimpact of life status variables, such as widowhood and retirement, oncognitive age, but could not find any significant relationship beyondthat produced by chronological age. Wilkes (1992) found that amongwomen aged 60–69, marital status and income were significant pre-dictors of cognitive age, whereas work status was not a significant pre-dictor. Similarly, Henderson, Goldsmith, and Flynn (1995) examinedthe relationship between cognitive age and demographic variables suchas gender, marital status, education, income, and race. However, theseresearchers did not find any significant relationships. Thus, despite anabundance of research examining cognitive age and its antecedents,there are inconsistencies in the findings reported by various researchers,there is a lack of research examining antecedents of cognitive age thatare based on theory, and there is a need to replicate previous researchto validate the findings.

In view of these gaps in previous studies and their suggestions forfuture research (e.g., Gwinner & Stephens, 2001; Wilkes, 1992), theresearch reported here replicates and extends the study conducted byWilkes (1992). It replicates previous research by (a) examining theeffects of cognitive age on the same variables as those investigated byWilkes, and (b) testing the same model with cognitive age as a media-tor of select antecedent variables and the same dependent variables.Furthermore, the present research extends previous research by (a)identifying and incorporating theory-based antecedents of cognitiveage, (b) using a much wider age range, and (c) samples of both menand women. Not all antecedent variables in the Wilkes (1992) model(marital status, income, chronological age) were included in the pres-ent study; only those that could be theoretically justified. There is alack of research examining life events as antecedents of cognitive age.In the present research theoretical justification for considering lifeevents as antecedents of cognitive age is presented and a model basedon that is empirically tested. Background information along with a con-ceptual framework is presented first, followed by a discussion of a modelof cognitive age and its antecedents. Next, the results of an empiricalstudy designed to test the model are presented. Finally, the implicationsof the findings are discussed.

MATHUR AND MOSCHIS970

THEORETICAL FRAMEWORK

A Life-Course Perspective

Over the past two decades or so, life-course research has begun developingas an interdisciplinary program for studying various phenomena. Thisapproach is reflected in recent theoretical formulations of biologists (e.g.,Cristofalo, 1988), anthropologists (e.g., Fry, 1988), psychologists (e.g.,Lerner, 1984; Perlmutter, 1988), sociologists (e.g., Hagestad & Neugarten,1985), as well as those who subscribe to humanistic approaches, whetherthese are dialectic (e.g., Reker & Wong, 1988) or critical in nature (Moody,1988). The life-course perspective is also seen by economists as potentiallyuseful in “the understanding of the labor market, the allocation of timeand goods, and the role of the future life time as individuals make eco-nomic and consumption decisions” (Baltes, Reese, & Lispitt, 1980, p. 100).

The definition of the life course varies with the disciplinary backgroundof the investigator. Psychologists usually use the term life-span devel-opment, focusing a great deal of attention on intrapsychic phenomena(e.g., George, 1982; Perlmutter, 1988). Sociologists, on the other hand,use the term life course in connection with role transitions, concentrat-ing on age-related transitions that are socially created, socially recog-nized, and shared (Hagestad & Neugarten, 1985). Abeles, Steel, and Wise(1980) have used the term in the specific context of adaptation, whichrefers to “. . . the process of meeting the organism’s biological, psycho-logical, and social needs under recurrently changing conditions” (Pfeif-fer, 1977, p. 650). In a similar vein, Clausen (1986) views adaptation asnecessary because of specific events or circumstances an individual mayexperience over the life course and due to developmental (biological) andsocial changes. As Clausen (1986) puts it:

One must adapt not only to the socially patterned demands of others, butalso to one’s growth and developmental problems, to changing life con-ditions and relationships, to frustration and losses, to illness, and if wesurvive long enough, to declining strength and abilities. (p. 17)

Because the life-course perspective is intricately related to age andaging, it provides an overarching conceptual framework for studying dif-ferences in cognitive age based on theories of aging. It is argued thatcognitive age is expected to change over the person’s life course, and dif-ferences in cognitive age can be understood in the context of theories ofaging. It is further suggested that cognitive age may influence relatedaspects of the person’s consumer behavior.

Conceptions and Theories of Aging

It is widely accepted that the study of aging and human behavior in gen-eral is inherently multidimensional (Moody, 1988). That is, people age asbiological beings, social beings, psychological beings, and even as spiri-

ANTECEDENTS OF COGNITIVE AGE 971

tual beings. Thus, definitions of, and explanations for, aging and age-related behaviors in later life are of multidimensional nature, and havecome from several disciplines.

Conceptions of aging generally fall into three categories: biological,psychological, and social aging. Biological aging (sometimes referred toas physiological or functional aging) refers to changes in cells and tissuesresulting in the physical deterioration of the biological system and its sus-ceptibility to disease and mortality. It is measured by the level of healthand functional performance of the main bodily systems (cardiovascular,musculoskeletal, etc.) (see, for example, Dean, 1988). Psychological agingrefers to the development and changes in cognition, personality, and self.Cognition refers to the psychological ability that accounts for all mentallife; it includes perception, memory, judgment, reasoning, and decision-making, which are commonly operationalized as cognitive capacities (e.g.,memory) and abilities to perform certain cognitive tasks, with variousscales (e.g., the Wechsler Adult Intelligence Scale) used as measures ofcognitive functioning (Dean, 1988; Salthouse, 1991). Personality refers tohow others see you, especially with respect to your attitudes and behav-ior, and self refers to how one sees oneself—what you think you are like,what you should be like, and the fit between the two. Personality is com-monly measured with personality inventories and scales familiar toresearchers (e.g., Kassarjian & Sheffet, 1991), whereas measures of self-concept often include self-perceptions in terms of age—that is, cognitiveage (e.g., Barak & Stern, 1986; Sirgy, 1982).

Finally, social aging refers to a changing composite of social lifestyles,attributes, and attitudes related to various social roles people are expectedto play at various stages in their lives such as “father,” “retiree,” and“grandparent” (Atchley, 1987; Riley, Fonner, Hess, & Toby, 1969). Socialaging (and its measures) is associated with the person’s eligibility andsuitability for various social roles across socially recognized turningpoints (e.g., stages in the life cycle) that provide road maps for human livesand outline life paths (e.g., Elder & Rockwell, 1979; Goslin, 1969; Hages-tad & Neugarten, 1985). Roles that people may enact at different stagesin life are determined to a large extent by age-graded events in connec-tion with specific roles, such as entry into work force, marriage, and birthof first child or grandchild. Many events are linked to the life cycle, suchas marriage and birth of children, and are known as transitional eventsbecause they involve experiences in moving between roles (Pearlin, 1982).For transitional events, there is a normative consensus among the mem-bers of a society about the optimum timing of such events (Pearlin, 1982).

The three conceptions of aging are interdependent, and knowledge aboutbiological and social aging may provide insight into the understanding ofcognitive aging. For example, biological aging can affect both psychologi-cal as well as social aging. Disease, especially those associated with laterlife (e.g., arthritis, incontinence), can have adverse effects on the person’sself-concept (including cognitive age) (Atchley, 1987) and could cause early


retirement (Smedley, 1976) and social withdrawal (Herzog, Brock, Fultz,Brouss, & Diokno, 1988), contributing to the aging person’s social aging.Similarly, social aging has implications for biological aging. For example,social isolation due to empty nest, retirement, and loss of spouse can alterfood consumption habits, directly affecting nutrition, which relates to dis-ease; and it may affect the person’s vulnerability to crime and perceivedcontrol over one’s environment, contributing to a declining self-concept(e.g., MacNeil & Teague, 1987; Natow & Heslin, 1980).

Because of wide variation in the various forms of aging across indi-viduals of the same age (Atchley, 1987; Birren & Renner, 1977; Breyt-spraak, 1984) and because aging is an ongoing process that occurs dif-ferently in different people, approaches to the study of aging andage-related behaviors are not limited to the study of people only at latestages in life. Rather, they are useful and applicable to the study of indi-viduals over their entire life span. For example, biophysical changesbegin very early in life (e.g., Cristofalo, 1988; Dean, 1988; Schewe, 1988;Schock, 1977), although models of psychological and human develop-ment (such as those advocated by Piaget, Flavell, Kohlberg, Baltes, andErikson) have recently been extended to apply over the person’s entirelife span. Similarly, theories of social aging that have been developed toexplain primarily age-related behaviors of older adults do not ignore sev-eral stages in earlier life; they recognize that (a) social aging is a grad-ual process that begins long before a person becomes eligible for a cer-tain social role in late life (i.e., anticipatory socialization), (b) there is agreat variability in social aging as in retirement for certain occupations(e.g., professional athletes versus actors), and (c) that early life experi-ences are of paramount importance in helping us understand behaviorin later stages in life (e.g., Atchley, 1987).

The focus of this research is on one type of psychological aging; thatis, the development and changes in the person’s age-identity dimensionof one’s self-concept. Differences in cognitive age beyond those accountedfor by chronological age are assumed to be influenced by biophysical andsocial aging over the person’s life course.

Changes in Self-Concept and Cognitive Age

Theory and research on self-concept form the underlying bases for thecognitive-age construct. Based on an extensive review, Shavelson, Hub-ner, and Stanton (1976) concluded that self-concept incorporates sevenkey characteristics: It is organized, multidimensional, hierarchical, sta-ble, developmental, evaluative, and differentiable from other constructs.Although some researchers have viewed self-concept in cognitive terms(e.g., Schouten, 1991), others have taken a behavioral approach (e.g.,Bracken, 1992), although there may be a close relationship betweenthe two. For example, Schouten (1991) defines self-concept as “the cog-nitive and affective understanding of who and what we are” (p. 412).


Bracken (1992) defines self-concept as “a pattern of behavior that issufficiently unique to an individual to be identified with that individ-ual” (p. 3).

Although most people may spend most of their lives without makingsignificant changes to their self-concept (Levinson, 1978) and maydescribe themselves in ageless terms (Kaufman, 1986), age identity isan important element of one’s self-concept that may or may not changewith age. Based on the notion that self-concept is a learned behavioralresponse pattern, it is argued that it is stable, like other learned orhabitual behaviors (Crain & Bracken, 1994). It could change, althoughslowly, if an individual’s environment changes or the person’s behaviorchanges within the environment (Crain & Bracken, 1994). When appliedto the concept of cognitive age, it would mean that although chrono-logical age changes at a steady rate, cognitive age may not change uni-formly because environments within which individuals live do notchange as much. Relatively stable environments may result in a slowerrate of change or no change in cognitive age. This may explain whymost people consider themselves to be younger than their chronologi-cal age (Peters, 1971; Underhill & Cadwell, 1983).

An individual’s actions or evaluations that change in response to aninternal or an external cue might bring about a change in one’s self-con-cept, a notion consistent with the social-breakdown model (Kuypers &Bengtson, 1973). As people go through life, they experience several lifeevents or transitions that reflect “changes in status that are discreteand bounded in duration (George, 1993, p. 358) and mark life changes(e.g., role acquisition, role relinquishment, occupation of a differenteconomic state) (Gierveld & Dykstra, 1993). As individuals performvarious roles and engage in certain behaviors within specific environ-mental contexts, they evaluate their actions and behavioral outcomes.These evaluations, in turn, affect one’s self-concept (Sirgy, 1982). Sim-ilarly, within the context of an aging individual, it has been found thatphysical and other changes associated with aging (due to illness or nor-mal aging) are associated with changes in self-concept (e.g., McCloskey,1976; McGlashan, 1988). Further, the experience of biological changes(events) such as menopause and the onset of chronic conditions such asarthritis, which are used as measures of physiological aging, make peo-ple aware of their aging bodies and may lead to shifts in their age iden-tity to an older age status (Moschis, 2000). Similarly, the experience ofevents that signify social aging, such as retirement, grandparenthood,and birthdays of social significance (e.g., eligibility for government ben-efits and marketing offerings), may cause shifts in the person’s ageidentity, since these events signify transitions into age-graded andsocially shared roles relevant to older people (Karp, 1988; Moschis,1994). Thus, cognitive age would be expected to change in response tothe person’s experiences of, and reactions to, various life-course or age-related changes.


HYPOTHESES

Based on these conceptual and theoretical notions, previously usedantecedents of cognitive age are recast into a theory-based conceptualframework, and hypotheses regarding the effects of these and otherantecedents of cognitive age are developed. Similarly, hypotheses arealso developed regarding the consequences of cognitive age on consumer-behavior variables used in previous research. A model of cognitive age,its antecedents, and its consequences is shown in Figure 1.

Although it is possible for cognitive age to influence biological and socialage, this research focuses on the effects of the latter two factors. Althoughit is rather difficult to disengage the reciprocal influences between the threetypes of age-related factors in survey data, Popper (1959) suggested that thevalue of survey data is in falsifying hypotheses rather than proving them.However, an effort is made to reduce the problem of reciprocal causality bydeveloping measures of biological and social aging based on earlier timeframes than that at which the person’s cognitive age was measured. Theselection of antecedent variables of cognitive age and specific consumerbehaviors are guided by previous theory and consumer research.

Biological Changes

One’s physical appearance plays an important role in creating and main-taining one’s self-concept. In fact, some research has focused exclusivelyon shape- and weight-based self-esteem (e.g., Altabe & Thompson, 1996;Geller, Johnston, & Madsen, 1997). However, of greater interest to the


Adapted from Wilkes (1992)

Figure 1. A model of cognitive age, its antecedents, and consequences.

present investigation is the relationship between biological aging andperceptions of one’s age. Biological aging is associated with a decline insensory systems such as vision and hearing, and the onset of many chronicconditions (e.g., blood pressure and arthritis). These, in turn, make peo-ple less able to do normal work and seek assistance with their day-to-dayactivities. Internal or external changes in one’s body and the onset ofdisease or functional disability provide messages that influence one’sage-related self-concept because they reflect a gradual “slowing down”(Karp, 1988). For example, experiencing a serious life-threatening ill-ness can often make people aware of their own mortality, and an eventassociated with biological change can trigger one’s thoughts or feelingsabout their own aging and mortality (Karp, 1988). More directly, age-related illnesses (e.g., arthritis) can negatively impact one’s self-concept(Atchley, 1987), and there is substantial evidence to suggest that healthstatus is a determinant of cognitive age (cf. Barak & Stern, 1986; Logan,Ward, & Spitze, 1992; Markides & Boldt, 1983; Gwinner & Stephens,2001). The preceding discussion suggests the following hypothesis:

H1: The number of biological changes experienced by an individual atany previous time is positively related to his or her cognitive age.

Transitional Life-Stage Changes

Over their life course, people experience several events or transitions (suchas getting married, having children, retirement) that can bring aboutchanges in their roles and social identity (Hagestad, 1988). Role theoristsview norms as cultural referents that are assigned to individuals or sub-groups (Parsons, 1951).According to role theory, role entry and exit are, bydefinition, transitions; they are normatively governed and predictable inboth occurrence and timing. Both role entry and role exit have been viewedas processes that include the creation of new identities as former and pres-ent role occupant (Ebaugh, 1988; Noble & Walker, 1997). An individual isgradually socialized into a role either before the occurrence of a normativeevent that serves as a marker of life transition (e.g., birth of first child into“parenthood”) or upon the occurrence of an unexpected life event (death ofspouse into “widowhood”). Similarly, the process of becoming an “ex” involvesstages that range from experiencing first doubts to the occurrence of aturning point (i.e., a specific event that triggers role exit) and the creationof a new identity as former role occupant (Ebaugh, 1988).

Adjustments to scheduled life events and role transitions result inadaptation to new life conditions, including changes in one’s self-identity.Often such adjustments take place far in advance of the normative eventor role through anticipatory socialization for the event or role transition.Adaptation to events that are considered to be normal or normative (e.g.,retirement) may occur over a relatively long period of time because theiroccurrence is anticipated and one prepares for them (Riley et al., 1969).Such an adaptation may also continue for a considerable time thereafter


(Murrell, Norris, & Grote, 1988). Through anticipatory socialization theperson also gradually changes his or her self-identity to fit the anticipatedrole (McAlexander, Schouten, & Roberts, 1993).

Sociologists have conceived the life course as being composed of a setof age-related interlocking roles (e.g., parenthood, grandparenthood, wid-owhood) (e.g., George, 1993; Hagestad & Neugarten, 1985). Because theexperience of such age-graded transition events requires adjustmentsin self-identity to fit age-related roles, it is expected that such eventswill also affect the person’s cognitive age.

H2: There is a positive relationship between the number of life-stage tran-sition events the person experiences and his or her cognitive age.

Chronological Age

Previous research has found chronological age to be the most important pre-dictor of cognitive age (e.g., Barak & Stern, 1986; Henderson et al., 1995;Wilkes, 1992). Being of a specific age does not necessarily mean that onehas to feel that old, yet being old has an important role to play in one’s feel-ing old. Not only does chronological age reflect the passage of time, theunderlying basis of the perception of age, but also has a context that cansignificantly influence such perceptions. A few specific birthdays repre-sent signposts in one’s life and are significant life-event markers by them-selves (e.g., eighteenth birthday, twenty-first birthday, fiftieth birthday)because they define eligibility for certain privileges and benefits (e.g., vot-ing, legal drinking, membership in AARP). Most importantly, such chrono-logical markers make people aware that they have joined an age-basedsubculture because marketers and nonprofit organizations (e.g., AARP)promote their common situation (e.g., eligibility for age-based benefits andmemberships), contributing to their group or status consciousness (Karp,1988). Such influences may facilitate the development of aged subculture,in line with Rose’s (1965) subculture theory, and may alter one’s cognitiveage as the person sees himself or herself as a member of the subculture.Thus, as hypothesized by previous researchers (e.g., Wilkes, 1992), it isexpected that chronological age will be associated with cognitive age.

H3: Chronological age is positively related to one’s cognitive age.

Consequences of Cognitive Age

The close relationship between self-concept and social behavior is well rec-ognized (cf. Bracken, 1992; Herzog, Franks, Markus, & Holmberg, 1998).Within the broader social structure, individuals not only get an oppor-tunity to express themselves (their self-image) through behavior, butalso, see their own reflection in the society. By maintaining a consistencybetween one’s own self-concept and behavior, the person is also able tovalidate his or her self-concept (Cross & Markus, 1990). The underlying


motivation for having this consistency could be self-enhancement (hav-ing a more positive image of oneself) or identity maintenance (keepingthe image one has) (Robinson & Smith-Lovin, 1992). For example, empir-ical evidence shows that people wear certain clothes to express their self-concept (Goldsmith, Flynn, & Moore, 1996; Miller, 1997). Further, Gold-smith and Stith (1990) found that there is a significant and negativerelationship between fashion innovativeness and the four dimensions ofcognitive age.

Wilkes’s (1992) research found that for older women (age 60–79), cog-nitive age was negatively associated with fashion interest, entertainmentactivity, and cultural activity. These findings are consistent with twoaging perspectives. First, they support the self-consistency motive, whichimplies a tendency to act in line with one’s self-concept. According toRosenberg (1979), the power and persistence of self-consistency motiveis strong enough to keep people from changing their self-views developedin early life, when such views would not be considered valid by others.Thus, people entering later years in life may try to hold on to self-imagesdeveloped in earlier life by continuing to engage in the same consump-tion-related activities that they engaged in in earlier life in their effortsto defend their self-concepts. With age, however, an increasing numberof them will not try to defend their self-image (Atchley, 1987), and theymay gradually move into a perceived degraded age status, engaging infewer “defensive-aging” consumption activities such as those examinedby Wilkes (1992). Second, the inverse relationships between cognitive ageand the three variables found by Wilkes suggest a possible decline of theperson’s self-concept in line with social breakdown theory (Passuth &Bengtson, 1988). A declining self-concept and possibly social withdrawalfurther suggest a consistency in the pattern of defensive-aging con-sumption activities. Thus, the aging person should engage in fewer othersimilar defensive-aging activities. The positive relationship betweenentertainment activity and cultural activity, and their negative rela-tionship to cognitive age found by Wilkes (1992), provide support forthis reasoning.

The preceding discussion suggests that with increasing cognitiveage, consumers are expected to show decreasing interest and propen-sity to engage in defensive-aging activities (such as those found byWilkes). Furthermore, the defensive-aging activities are expected tobe positively related. Thus, following Wilkes (1992) the followinghypotheses are presented:

H4: There is a negative relationship between cognitive age and (a) fash-ion interest, (b) frequency of entertainment activity, and (c) fre-quency of cultural activity.

H5: There is a positive relationship between frequency of entertain-ment activity and frequency of cultural activity.


METHOD

Sample

Data for the study were collected from a convenience sample. Studentsenrolled in graduate and undergraduate marketing classes of a universityin the northeastern region of the United States were asked to take surveyquestionnaires home and have them completed by their older family mem-bers (parents, uncles/aunts, or grandparents) or colleagues at their workplace.The only restrictions were that only one member of a family could com-plete the survey. Students were specifically instructed to seek out olderfamily members and ask them to complete the survey. This was designedto get a sample comprised of relatively older individuals, i.e., middle-agedand older. It was especially important to get a sample of relatively olderpeople (as compared to those in their teens or twenties) for this studybecause past research has shown that older people perceive themselves tobe younger in relation to their chronological age compared with younger agegroups (Lepisto, 1989; Underhill & Cadwell, 1983). Moreover, middle-agedand older (those in their fifties or older) individuals are more likely to haveexperienced a greater number of transitional life events compared withyounger individuals (Karp, 1988; Silvers, 1997). However, it was still desir-able to have some people in their forties or even thirties so as to get a highlevel of variance in the study variables. Three hundred fourteen peoplecompleted the questionnaires; 250 of the questionnaires were usable.Although a convenience sample collected this way does not enable one tomake any population-related estimates, nor would it allow one to general-ize the findings to other populations, the objective of this research was toreplicate a previous research with the use of a different sample and to testfor relationships between theory-driven concepts. The main concern wasin obtaining a great variability in study variables to be able to establish suchrelationships. The sample obtained in this study had sufficient variability.

The sample was almost evenly divided based on gender (males � 49.4%).Nearly one out of three (32.5%) of the respondents had high school or lesseducation, 22.9% had some college education, 30.1% had a college degree,and 14.4% had some postgraduate education or a postgraduate degree.Thesample also provided sufficient age variability. Almost one-fourth of therespondents (26.0%) were under 45 years of age, 36.8% were between 45and 54 years of age, 20.4% were between 55 and 64 years of age, 10.8%were between 65 and 74 years of age, and 6.0% were 75 years of age orolder. The age range was 18–92. Similarly, income of the respondentsshowed a wide variability. Nearly one-fifth (21.6%) of the respondentsearned less than $40,000 per year, 15.8% earned between $40,000 and$59,999, 22.4% earned between $60,000 and $79,999 per year, 19.5%earned between $80,000 and $99,999, and 20.7% earned more than$100,000 per year. The majority of the respondents were married (68.0%),17.6% were single (never married), 8.8% were divorced/separated, and5.6% were widowed. In terms of employment status, 65.7% of the respon-


dents were employed full-time and 12.2% were employed part-time. Inaddition, 16.3% of the respondents were retired, 3.3% identified them-selves as not currently employed, and 2.4% were never employed.

Measures

Cognitive age was measured with the scale developed by Barak andSchiffman (1981) and validated by Wilkes (1992). Respondents wereasked to identify themselves as members of self-referenced age decades(20s–90s) by responding to four statements: “I feel as though I am in my_________,” “I look as though I am in my _______,” “I do most things asthough I am in my ________,” and “My interests are mostly those of aperson in his/her ______” (the alpha reliability coefficient was 0.95).

Life-stage transitional events were measured by asking the respondentsto review a list of life events and indicate those events that they had per-sonally experienced “in the past 12 months,” “in the past 1–3 years,” “in thepast 3–5 years,” “more than 5 years ago,” or “have not experienced or doneit.” The list of events was based on previous research on life events (e.g.,Chiriboga, 1989; Cohen, 1988; Schewe & Balazs, 1992; Tausig, 1982).Although life-event research had focused on a wide range of events, forthis research only those life events that signify specific markers of tran-sition into new roles in the life course of an individual were selected. Forexample, life events such as marriage, birth of the first child, and the lastchild leaving home, indicate movement of an individual from one life stageto another. A total of 11 such events were selected.2 They were marriage,divorce, separation, birth/adoption of first child, first child moving out ofhousehold, last child moved out of household, marriage of other child, birthfirst grandchild, retirement (at own will), spouse retiring/ending work,and retirement (forced). For each event a positive response for any timeframe was coded as 1; otherwise it was coded as 0. Scores for all 11 eventswere summed to obtain a 0–11 point index of life-stage transitional events.

Life events associated with or marking an age-related decline in vari-ous bodily functions were selected as a measure of biological changes (cf.Dean, 1988; Moody, 1988). Respondents were asked to indicate if they hadpersonally experienced five biological/health events “in the past 12 months,”“in the past 1–3 years,” “in the past 3–5 years,” “more than 5 years ago,”or “have not experienced or done it.” For each event a positive response


2 Many of the life events examined in this research occur sequentially in temporal order (e.g., thebirth of the first child, first child entering school, and the first child leaving household), war-ranting at least an ordinal measure for life-event–based scales. However, a wide variety in thelife cycles experienced by different people makes it impossible to put all of the events studied intoa single order. In view of this, life-events studies tend to measure such experiences as the num-ber of events experienced rather than as an ordinal measure. Also, although using an inventoryof life events is a common practice, two other alternatives have become popular in recent times.The first approach is to use single events (e.g., retirement) and study its impact on other vari-ables. The second approach is to group theoretically relevant events (e.g., family events) andstudy their impact on other variables (George, 1993; Thoits, 1987). Partial aggregation (into bio-logical changes and transitional life-stage changes) reduces the inherent complexity associatedwith studying single events and does not mask the distinctive relationships, and was preferredfor developing and testing the model.

for any time frame was coded as 1; otherwise it was coded as 0. Responsesto five biological events were summed to form 0–5 point index of biologi-cal changes (lengthy hospitalization or rehabilitation, hearing impairment,needing assistance in day-to-day living, diagnosis of a chronic conditionor long-term illness, and eye problem that cannot be corrected with glasses).

Although there has been a considerable discussion on various dimen-sions of aging and its objective measurement (social, biological, and psy-chological aging) (e.g., Dean, 1988), the purpose of identifying life-eventexperiences and using them in this study was not to get comprehensivemeasures of social, psychological, or biological aging but to assess theirrole as markers signifying transitions in one’s life that trigger changesin self-concept (cognitive age).

Consistent with the Wilkes (1992) research, fashion interest was meas-ured with the use of four statements focusing on “generalized interest infashion or reaction to changing fashions in clothing” (p. 295) on a 7-pointscale (1 � strongly disagree, 7 � strongly agree) (alpha coefficient � 0.83).Similarly, frequency of participation in entertainment activity and culturalactivity were also measured with the use of Wilkes’s (1992) scales (alphacoefficients were 0.67 and 0.70, respectively). Respondents were asked toindicate the frequency of their participation in three entertainment-related activities (entertain at home, went out to dinner, and attended aparty) and three cultural activities (attended a lecture, went to a concert,and visited a gallery or museum) on a 5-point scale (never, less than aonce a month, once or twice a month, once or twice a week, and severaltimes a week or more). Demographic information was collected with theuse of standard demographic questions.

Analysis and Results

The first stage was to examine the relationship between cognitive age andchronological age. The mean chronological age for the sample was 51.14years (SD � 14.04); whereas the mean for cognitive age was 43.87 (SD �12.84). This finding is consistent with previous research suggesting thatpeople view themselves to be several years younger than their chronolog-ical age (Goldsmith & Heiens, 1992; Lepisto, 1989; Underhill & Cadwell,1983). An interesting finding was that the difference between chronologi-cal and cognitive age was positively related to chronological age (r � 0.440,p � .001), suggesting that the difference between cognitive age and chrono-logical age increases as people grow older. Moreover, the correlation betweenchronological and cognitive age was 0.799 (p � .001), suggesting that vari-ables other than age account for variation in cognitive age.

The next stage of the analysis was to test variables that could explainthe remaining variation in cognitive age. Structural-equation modelingwas used to test the entire model and the specific relationships suggestedby Hypotheses 1–5, similar to the approach used by Wilkes (1992). LIS-REL8 and associated program PRELIS2 were used for obtaining thecovariance matrix and for estimation of the parameters. Correlation


matrix, along with means and standard deviations for all variables usedfor testing the model, are given in Table 1. The first phase of the modeltesting involved testing the measurement model. Although the �2 for themeasurement model was significant (62.15, df �101, p value � .000),other indicators suggest a satisfactory fit of the model (GFI � 0.93, NFI� 0.93, NNFI � 0.96, standardized RMR � 0.044).

The next phase of the analysis involved testing the hypothesized struc-tural model. Assessment of the overall fit of the model was based on mul-tiple indicators as recommended by Bagozzi and Yi (1988). Although the�2 was significant (194.63, df � 112, p value � .000), other indicators ofthe overall fit suggest a good fit of the model (GFI � 0.92, NFI � 0.92,NNFI � 0.96, standardized RMR � 0.063) justifying further interpre-tation of the results. Maximum-likelihood estimates for all hypothesizedrelationships along with the structural model tested are given in Figure2. All parameter estimates are given in Table 2.

The model suggested that biological changes experienced by one havea positive influence on the person’s cognitive age (Hypothesis 1). Thishypothesis was supported by the data (maximum-likelihood estimate �0.09, t value � 1.98). Also, the model suggested that transition life-stageevents experienced by a person would positively influence his or her cog-nitive age (Hypothesis 2).This hypothesis was supported by the data (max-imum-likelihood estimate � 0.13, t value � 2.27). Finally, chronologicalage was found to positively influence cognitive age (maximum-likelihoodestimate � 0.69, t value � 10.53), providing support for Hypothesis 3.


Note: All parameters are significant at p < .05 level

Figure 2. Structural model of cognitive age, its antecedents, and consequences.


Tab

le1.

Cor

rela

tion

Mat

rix,

Mea

nan

dS

tan

dar

dD

evia

tion

sfo

rA

llV

aria

ble

sU

sed

inth

eA

nal

ysis

.

FE

EL

3.86

1.41

1.00

LO

OK

4.08

1.37

0.79

1.00

DO

3.87

1.36

0.85

0.82

1.00

INT

E3.

931.

400.

830.

770.

831.

00F

I14.

651.

74–0

.30

–0.3

0–0

.30

–0.3

21.

00F

I23.

621.

77–0

.25

–0.2

4–0

.19

–0.2

30.

551.

00F

I34.

321.

65–0

.14

–0.2

1–0

.13

–0.1

70.

540.

581.

00F

I44.

391.

95–0

.27

–0.3

2–0

.26

–0.2

60.

570.

530.

561.

00E

12.

791.

08–0

.24

–0.1

7–0

.16

–0.2

20.

230.

220.

160.

141.

00E

23.

451.

07–0

.31

–0.2

8–0

.31

–0.2

90.

200.

140.

250.

210.

351.

00E

32.

490.

96–0

.34

–0.2

8–0

.23

–0.3

20.

260.

280.

190.

160.

480.

401.

00C

11.

791.

04–0

.23

–0.2

0–0

.17

–0.2

50.

120.

070.

150.

090.

250.

290.

321.

00C

21.

860.

75–0

.35

–0.2

5–0

.31

–0.3

20.

100.

130.

090.

110.

300.

230.

340.

441.

00C

31.

800.

81–0

.19

–0.1

4–0

.16

–0.2

0–0

.01

0.01

0.06

0.05

0.22

0.12

0.20

0.40

0.54

1.00

BIO

0.88

1.20

0.41

0.40

0.41

0.40

–0.2

2–0

.19

–0.1

8–0

.18

–0.0

6–0

.14

–0.0

9–0

.02

–0.0

90.

121.

00A

GE

51.1

414

.05

0.72

0.80

0.72

0.74

–0.2

9–0

.22

–0.1

9–0

.26

–0.1

3–0

.23

–0.2

5–0

.14

–0.1

5–0

.08

0.44

1.00

TR

AN

3.69

2.95

0.58

0.64

0.56

0.56

–0.1

8–0

.13

–0.1

5–0

.19

–0.0

5–0

.17

–0.1

9–0

.06

–0.1

6–0

.04

0.44

0.69

1.00

Mea

nS

DF

EE

LL

OO

KD

OIN

TE

FI1

FI2

FI3

FI4

E1

E2

E3

C1

C2

C3

BIO

AG

ET

RA

N(Y

1)(Y

2)(Y

3)(Y

4)(Y

5)(Y

6)(Y

7)(Y

8)(Y

9)(Y

10)

(Y11

)(Y

12)

(Y13

)(Y

14)

(X1)

(X2)

(X3)

All relationships originally tested by Wilkes and included in this studywere also supported by the data [H4(a)–H4(c)]. Specifically, the results con-firm that cognitive age negatively influences fashion interest (maximum-likelihood estimate � –0.37, t value � –5.19), negatively influences enter-tainment activity (maximum-likelihood estimate � –0.44, t value � –5.24),and negatively influences cultural activity (maximum-likelihood estimate� –0.37, t value � –4.60). This suggests that as individuals think them-selves to be growing old their interest in fashion declines, frequency of theirparticipation in entertainment activities declines, and frequency of theirparticipation in various cultural activities also declines. Finally, the modelsuggested that entertainment activity would be positively related to culturalactivity (Hypothesis 5).This relationship was modeled by allowing the struc-


Table 2. Estimated Parameters from Structural Model.

Parameter Maximum Likelihood Estimate t Value**

λx1 1.20 22.32λx2 14.04 22.32λx3 2.95 22.32λy1 1.28*λy2 1.21 22.08λy3 1.25 24.51λy4 1.26 22.96λy5 1.31*λy6 1.30 10.57λy7 1.22 10.63λy8 1.46 10.75λy9 0.68*λy10 0.60 6.53λy11 0.71 7.22λy12 0.61*λy13 0.60 7.42λy14 0.52 7.22γ11 Biological changes → Cognitive age 0.09 1.98γ12 Chronological age → Cognitive age 0.69 10.53γ13 Transitional life-stage changes → Cognitive age 0.13 2.27β21 Cognitive age → Fashion interest –0.37 –5.19β31 Cognitive age → Entertainment activity –0.44 –5.24β41 Cognitive age → Cultural activity –0.37 –4.60ψ43 Entertainment activity → Cultural activity 0.40 4.06ϕ21 0.44 8.73ϕ31 0.44 8.57ϕ32 0.69 20.57R2 for cognitive age 0.68R2 for fashion interest 0.14R2 for entertainment activity 0.19R2 for cultural activity 0.14

Note: Error terms for single indicators of exogenous variables were fixed.* Error term fixed by LISREL for scaling of endogenous variables, t value not computed.** All parameters are significant at p � .05 level.χ2 � 194.63; df � 112; p value � 0.000; GFI � 0.92; NFI �0.92; NNFI � 0.96; standardized RMR � 0.053.

tural error terms for the two variables to correlate, implying that the twovariables are correlated, but no causal relationship is suggested.This hypoth-esis was supported by the data (maximum-likelihood estimate � 0.40, tvalue � 4.06). Although chronological age is the most important predictorof cognitive age, all the variables together account for a substantial variancein the cognitive age R2 � 0.68).These results help us increase understanding,because the demographic variables used as antecedents of cognitive age inthe model developed by Wilkes (1992) accounted for only 47% of the vari-ance in cognitive age and Henderson et al. (1995) found that age correlatedhighly with the four dimensions of cognitive age (ranging from 0.75 to 0.90),demographic variables other than age could not explain any variation in thefour dimensions of cognitive age when the effects of age had been controlledfor. In the model, all the independent variables were allowed to freely cor-relate with each other, suggesting a close relationship between biological andlife changes, but no implied causation.The finding provides support for thetheorized interdependence of biological and social aging discussed earlier.As shown in Table 1, all correlations were positive and significant.

Model Modification and Test for Alternative Models

To seek additional evidence in support of the model of the antecedentsof cognitive age and mediating role of cognitive age, several alternativemodels were also explored. First, the possibility that chronological agemay have a directional effect (perhaps causal) on the other two exogenousvariables was examined. To test for this possibility a modified model wastested with chronological age as the only exogenous variable and bio-logical changes and transitional life-stage changes as endogenous vari-ables. All other relationships were retained as depicted in the model.Test of this alternative model revealed that this alternative did not fitthe data as well, as revealed by a reduction in the overall fit statistics forthis alternative model (�2 � 205.31, df � 113, p � .000, GFI � 0.91, NFI� 0.91, NNFI � 0.95, standardized RMR � 0.63). This suggests that ageis not likely to have any causal effect on biological changes and life-stagechanges and the relationships depicted in the original model discussedearlier are better supported by the data.

A test of the possibility that the three exogenous variables (biologicalchanges, chronological age, and transitional life-stage changes) had somedirect effect on the three outcome variables (fashion interest, entertain-ment activity, and cultural activity) was also conducted.To test for this pos-sibility, first the path from biological changes to cognitive age was fixedand direct paths from biological changes to the three outcome variableswere set free. The overall fit of the model did not show a significantimprovement (��2 � 4.40, �df � 2). Moreover, only the path from biolog-ical changes to cultural activity was significant, and the other two wereinsignificant. Another variation (implying partial mediation by cognitiveage) was tested by setting the path from biological changes to cognitiveage free, in addition to setting free the direct paths from biological changes


to the three outcome variables. This change too did not make any signif-icant improvement in the overall fit of the model (��2 � 7.44, �df � 3).Similarly, alternative models were tested for direct effect of age and life-stage changes. In both the cases there was no improvement in the over-all fit for of the model. Moreover, only one additional direct path was sig-nificant (from chronological age to cultural activity). A final model wastested in which direct paths from biological changes to cultural activityand from chronological age to cultural activity (suggested by previousanalysis) were set free in addition to the complete theoretical model testedearlier.This modified model is shown in Figure 3.The final model depictedin Figure 3 had the best overall fit (�2 � 182.18, df � 110, GFI � 0.92, NFI� 0.92, NNFI � 0.96).All hypothesized relationships were statistically sig-nificant. In addition to that, only the direct path from chronological ageto cultural activity was also significant (maximum-likelihood estimate �0.33, t value � 2.70), implying partial mediation by cognitive age.

Impact of Individual Events

Although the above analysis implied that, collectively, experiences of lifeevents might make people revise their age-related self-concept or cognitiveage, another intriguing question emerged and needed to be answered.Whilethe model was being tested, indices for biological changes and life-stagetransition events were constructed by adding the total number of events


Note: All parameters are significant at p < .05 level

Figure 3. Revised structural model of cognitive age, its antecedents, and consequences.

experienced by the individual in the respective group. However, such indexcreation by adding the number of events automatically assigned equalweights or importance to each event.Although previous research has foundsufficient evidence to suggest that there are differences in how differentlife events affect individuals across different demographic groups and at theindividual level (e.g., Conger, Lorenz, Elder, Simons, & Ge, 1993; McLeod &Kessler, 1990; Thoits, 1991), the use of summated life-events scales hasbeen an acceptable procedure in psychological research for more than threedecades (e.g., Cohen, 1988; Tausig, 1982). Because no research was foundthat has systematically assigned weights to life events based on the degreeof their impact on self-concept in general and on cognitive age in particu-lar, it was desirable to know if there are any differences in the extent towhich individual events affected cognitive age. Exploratory analyses witht tests and stepwise regression were carried out to assess the impact ofindividual life events on cognitive age. First, a series of t tests were carried


Table 3. Mean Values of Cognitive Age for Respondents Who Experiencedand Those Who Did Not Experience Specific Events.

NotEvents Experienced Experienced t Value p Value

Transitional life-stage changesMarriage (first time) (N � 202) 45.73 36.06 –4.899 .000Divorce/separation (N � 40) 47.06 43.26 –1.723 .086Birth/adoption of the first child

(N � 166) 46.95 37.77 –5.664 .000The first child moving out of

household (N � 117) 49.95 38.52 –7.825 .000Last child moving out of

household (N � 72) 51.65 40.72 –6.596 .000Marriage of the first child (N � 80) 54.41 38.91 –10.758 .000*Marriage of other child (N � 59) 55.14 40.39 –8.820 .000*Birth of the first grandchild (N � 71) 54.04 39.84 –9.092 .000*Retirement (at own will) (N � 50) 56.00 40.84 –8.462 .000Spouse retiring/ending work (N � 52) 55.80 40.74 –8.548 .000Retirement (forced) (N � 14) 55.93 43.15 –3.708 .000

Biological changesLengthy hospitalization or

rehabilitation (N � 66) 49.84 41.73 –3.927 .000Hearing impairment (N � 37) 53.82 42.14 –5.389 .000Needing assistance in day-to-day

living (N � 17) 50.38 43.39 –1.487 .155Diagnosis of a chronic condition or

long-term illness (e.g., blood pressure) (N � 70) 51.43 40.93 –6.230 .000*

Eye problem that cannot be corrected with glasses (N � 31) 52.56 42.64 –4.158 .000

Note: N shows respondents who experienced the specific event.* The difference was significant even when chronological age was introduced in the model as a covariate.

out with cognitive age as the dependent variable and individual events asindependent variables (one at a time). Table 3 shows the results of thisanalysis. As shown in the table, mean cognitive age for those who experi-enced life events in the study was higher than that for those who did notexperience those life events. This suggests that experience of specific lifeevents is associated with a change in age-related self-concept or cognitiveage. Stepwise regression analysis was carried out with cognitive age as thedependent variable and 16 life-stage and biological events as independentvariables to find those events that had the greatest impact on cognitiveage. The final model out of this analysis had six events as significant pre-dictors of cognitive age.The overall model was significant (F � 35.120, p �.001) with R2 of 0.464. Table 4 lists regression parameters and associatedstatistics for selected significant independent life-event variables.As shownin the table, birth/adoption of the first child, marriage of the first child,retirement (at own will), and spouse retiring/ending work are the most sig-nificant life-stage changes that affect cognitive age. Similarly, lengthy hos-pitalization or rehabilitation and diagnosis of a chronic condition or long-term illness are the two biological events that affect cognitive age, findingsconsistent with those of previous research (Gwinner & Stephens, 2001).

A review of the significant life changes as well as others that were notin the final model reveals an interesting pattern. Certain life-stage changesmark major milestones in a person’s life (e.g., birth/adoption of the firstchild, retirement), and therefore may bring about a major change in one’sage-related self-concept or cognitive age. Other changes seem to be minormilestones, and therefore may have only a minor impact on one’s cogni-tive age. Similarly, certain biological experiences have a greater impacton cognitive age compared with other similar types of experiences.

DISCUSSION

An important requirement of the scientific discovery process is that theresults should be repeatable. As Popper (1959) said, “we do not take even


Table 4. Stepwise Regression Results.

UnstandardizedLife Changes Coefficient t Value p Value

Life-stage changesBirth/adoption the of first child 4.081 2.958 .003Marriage of the first child 8.501 5.388 .000Retirement (at own will) 5.237 2.324 .021Spouse retiring/ending work 4.489 2.020 .045

Biological changesLengthy hospitalization or rehabilitation 3.307 2.303 .022Diagnosis of a chronic condition or long-term

illness (e.g., blood pressure) 4.807 3.352 .001

our own observations quite seriously, or accept them as scientific obser-vations, until we have repeated and tested them” (p. 45). In this vein, oneof the objectives of this research was to replicate and extend the modeltested by Wilkes (1992) to find explanations for variations in cognitiveage in addition to those provided by chronological age. Life-event–basedvariables were used to explain change in the person’s cognitive age. Thepresent findings suggest that the reason people differ with respect totheir cognitive age may not be the mere experience of a birthday, but a hostof other biophysical and social changes that a person experiences duringhis or her life. The findings confirm the contention that biological changesand transitional life-stage changes experienced by a person may influ-ence his or her age-related self-concept or cognitive age.

Previous research had found limited conclusive evidence regarding theeffect of individual life events on cognitive age. One review of the litera-ture, for example (Barak & Stern, 1986), showed that although someresearchers found a significant relationship, others did not find any rela-tionship between measures of cognitive age and specific life events such asretirement. The present research found significant relationships betweenseveral life events and the person’s cognitive age. Furthermore, stepwiseregression results suggest that some of the events examined may be inter-dependent, suggesting caution in interpreting the findings.Therefore, anydefinitive conclusion regarding causal effects of the events examined inthis study cannot be sustained. For example, it is not clear whether thechange in cognitive age is due to marriage of the first child or the birth ofthe first grandchild. Furthermore, one does not know whether the out-come observed (change in cognitive age) is due to the measured life event(e.g., empty nest) or the result of a related (unmeasured) event (e.g., relo-cation). As research has shown, consumers may change their behavior inresponse to not just one but a series of interdependent events (Price &Curasi, 1996). This may explain the inconsistency in the finding of previ-ous research regarding the effects of specific events.

Although Wilkes (1992) used a sample composed of only females aged60–79, the sample used in the present study was composed of both malesand females. The range of ages for the sample used in the present researchwas much wider. Overall, the findings suggest that the model of cogni-tive age with chronological age, biological changes, and transitional life-stage changes as antecedents, and age-related consumer behaviors asconsequences of cognitive age, is applicable to a wide range of age groupsand both males and females.

The present study also found increasing discrepancy between chrono-logical age and cognitive age as one grows older, a finding consistentwith those of previous studies (Goldsmith & Heiens, 1992; Lepisto, 1989;Underhill & Cadwell, 1983). These findings suggest that the reason forthis discrepancy may be the experience of certain biological changes laterin life (due to healthier lifestyles) and transitions into age-graded roles(e.g., marriage, parenthood, grandparenthood) at later stages in life.Thus, to the extent a person’s cognitive age changes because of the expe-


rience of health concerns commonly associated with old age, as well asbecause of the experience of age-graded transition events, delay in expe-riencing such events may make a person hold on to an age-related selfconcept that does not reflect his or her chronological age.

Future research in this area should assess the impact of similar typesof antecedent variables derived from the theoretical frameworks pre-sented. Also, it would be useful to study the interaction effects of theseantecedent factors. It would be of immense value to know the conditionsunder which certain events have a positive effect on cognitive age and theconditions under which the same event might not have any effect on cog-nitive age. Future research might also focus on understanding the processof cognitive aging. For example, seeking answers to questions like howexternal events might trigger mental processes that eventually resultin a change in age-related self concept can enhance the understandingof cognitive aging. Longitudinal data would be most useful in assessingrelationships between cognitive age and consumer behavior, given the pos-sibility of reciprocal influence between age-related self-concept and con-sumer behavior (e.g., Sirgy, 1982).

Finally, the approach of examining the antecedents of cognitive agecan be extended to helping to understand the causes of cognitive aging,and how cognitive aging affects patterns of information processing.Such an investigation might help us understand age-related differ-ences in information processing represented in several consumer stud-ies (e.g., Moschis, 1992). One fruitful avenue is to examine how cog-nitive aging assessed with measures of cognitive functioning (e.g.,Salthouse, 1991) is affected by certain types of events or circumstancesa person has experienced earlier in life. There is a substantial amountof research in the field of psychology to suggest that the experience ofcertain events may contribute to or deter cognitive aging (e.g., Abeleset al., 1980; Salthouse, 1991; Turner & Avinson, 1992). For example,retirement may contribute to cognitive aging not because the personassumes the role of a retiree but primarily because the person ceasesto engage in cognitive activities that deter cognitive declines (Salt-house, 1991).

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This research was supported in part by a Summer Research Grant from the FrankG. Zarb School of Business, Hofstra University, to the first author.

Correspondence regarding this article should be sent to:Anil Mathur, Frank G. ZarbSchool of Business, 306 Weller Hall, 134 Hofstra University, Hempstead, NY 11549([email protected]).


antecedents of cognitive age: a replication and extension

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