Journal of Gerontology: SOCIAL SCIENCES
2007, Vol. 62B, No. 1, S36–S42

Copyright 2007 by The Gerontological Society of America

Functional Limitations and Changes in
Levels of Depression Among Older Adults:
A Multiple-Hierarchy Stratification Perspective
Scott Schieman and Gabriele Plickert
Department of Sociology, University of Toronto, Ontario, Canada.
Objectives. This study examined the effects of functional limitations on changes in levels of depression over time.
A multiple-hierarchy stratification perspective framed the analyses of potential stratification-based contingencies of
race, gender, and socioeconomic status.
Methods. We derived data from a longitudinal study of adults aged 65 and older in the Washington, DC, metropolitan
area over a 3-year period (2001–2003). We used ordinary least squares regression models in order to assess the focal
associations across a set of social status interaction terms.
Results. Changes in functional limitations were associated with changes in depression, but the patterns depended on
interactions among race, gender, and socioeconomic status.
Discussion. These observations contribute to research by blending ideas from double and triple jeopardy and differential vulnerability perspectives. Although analyses of multiple contingencies create analytical challenges, this
complexity is needed to accurately specify the mental health effects of functional limitations.

T

HE stress process perspective identifies limitations in activities of daily living as among the most pernicious stressors in late life (Pearlin & Skaff, 1996). Levels of functional
limitations are higher among older age groups (Long &
Pavalko, 2004) and are associated positively with depression
(Kelley-Moore & Ferraro, 2005; Yang & George, 2005).
Moreover, limitations are distributed unequally across statuses
such as gender, race, and socioeconomic status (SES; House,
Lantz, & Herd, 2005). Although most research has focused on
status variations in the levels of limitations, for this study we
asked the following question: Do dimensions of stratification
modify the effects of changes in limitations on changes in depression? In order to frame our analyses, we applied Clark and
Maddox’s (1992) multiple-hierarchy stratification perspective,
which asserts that ‘‘it is useful in understanding health
outcomes to consider income, education, gender, and race as
independent but potentially interactive influences on changes in
functional status over time’’ (p. S223).
Social scientists have long been interested in the ways in
which ascribed statuses intersect to influence health over the
life course (McLeod & Nonnemaker, 1999). Of those statuses,
race and gender are among the most influential because of their
association with racism, sexism, and socioeconomic disadvantages (Williams, 2005). For example, the double jeopardy thesis
posits that African Americans have worse health outcomes at
all ages (Ferraro & Farmer, 1996)—but being African American and old has compounding effects (Clark & Maddox,
1992). This draws upon the cumulative disadvantage thesis,
which posits that health disparities increase with age (Dannefer,
2003; O’Rand, 1996). Alternatively, some scholars contend that
age is a leveler because it erodes the harmful effects of
disadvantages, reducing the race-based health gap with age or
even creating a reversal among the oldest-old population

S36

(Dowd & Bengtson, 1978). In terms of health, some scholars
have identified elderly African Americans as a more robust
group than their younger peers (Gibson, 1991) or a survival
elite (Kelley-Moore & Ferraro, 2004). Others have described
a disability crossover in which African Americans in old age
surpass similarly aged White peers in health and physical
functioning (Clark & Maddox, 1992; Johnson, 2000).
According to Ferraro and Farmer (1996), ‘‘Another feature of
the literature that merits further investigation is the extension
of the concept of double jeopardy to triple jeopardy due to
sexism’’ (p. 29). That is, inequalities associated with gender
extend these ideas to the triple jeopardy hypothesis: African
American women are most likely to experience the sharpest
health declines during late life (Clark & Maddox, 1992). Yet,
Clark and Maddox found that African American women had
more modest physical health declines than African American
men. Evidence on this matter remains inconclusive. For
example, Manton (1980, 1988) contended that African American men who survive to old age do not have a lower life
expectancy than White men. After age 75, in fact, African
American men may have better life expectancy. Despite these
advances in knowledge, the role of SES in triple jeopardy
processes remains unclear.
Researchers have attributed race-linked health disparities
partly to SES disadvantages (Williams, 2005). We examined
the extent that SES—in combination with gender and race—
influences the association between changes in limitations and
depression. In order to frame our ideas, we propose the resource vulnerability versus resource erosion hypotheses. The
resource vulnerability hypothesis is derived from the differential vulnerability perspective, which implies that lower SES
groups tend to experience more deleterious effects of stressors
because they have fewer resources to avoid or manage stressors

FUNCTIONAL LIMITATIONS AND DEPRESSION

(McLeod & Nonnemaker, 1999). These ideas suggest that
limitations are associated with greater increases in depression
among low-SES elders. Moreover, the multiple-hierarchy
stratification perspective includes race and gender variants of
the SES-based resource vulnerability hypothesis. Clark and
Maddox (1992) asserted that ‘‘the greater probability of entering old age in poverty and with fewer years of formal
education exemplifies and may add to the impact of minority
status on the experience of aging, it has been argued, particularly for minority women’’ (p. S222). Applying these ideas,
the gender variant predicts that limitations are associated with
the largest increase in depression among low-SES women; the
race variant predicts that increased limitations have the strongest effect among low-SES African Americans.
In contrast to the resource vulnerability view, it is plausible
that high-SES groups have the most to lose with respect to
health status. For example, Pampel and Rogers (2004) contended that ‘‘high socioeconomic status groups are harmed the
most by unhealthy behaviors because, given their greater potential for good health, they have the most to lose from
damaging lifestyles’’ (p. 307). African Americans and low-SES
groups tend to experience lower life expectancy, higher morbidity and mortality, and worse health across the life course
(Kelley-Moore & Ferraro, 2004). If Whites and high-SES
groups are more likely to have experienced health advantages
over the life course, then late-life exposure to limitations may
be unexpected, unfamiliar, and challenging. By extension,
although socioeconomic resources may enhance coping and
reduce vulnerability to depression, limitations could undermine
these resource benefits (the resource erosion hypothesis).
Moreover, the resource erosion view includes race and gender
variants, which researchers must consider simultaneously
because White women have the highest life expectancy (80.0
years), followed by African American women (74.9), White
men (74.8), and African American men (68.2) (Centers for
Disease Control, 2002). Although women report poorer health
than men on some measures, men die at younger ages from
some conditions (Rieker & Bird, 2005). And although highSES Whites tend to have better health than other race/ethnic
groups over the life course, race may modify health patterns in
late life. If elderly African Americans are indeed a healthier
group than their White peers, then increased limitations are
likely to be more depressing for high-SES Whites and least
depressing for low-SES African American men.
In sum, we expected that increases in limitations would be
associated with increases in depression. The differential vulnerability hypothesis proposes that individuals with low status
are more vulnerable to limitations, whereas higher status is
protective against the distressing effects of increased limitations.
The multiple-stratification perspective underscores the intersections of race, gender, and SES. For example, race and gender
variants of the differential vulnerability thesis would predict
that the depressing effects of limitations would be strongest
among low-SES elders, especially African Americans and
women; we labeled this the resource vulnerability hypothesis
(the converse is the resource benefit, in which higher statuses
have protective effects). By contrast, the resource erosion
hypothesis would contend that increased limitations would
erode the resource benefits of those who have traditionally
enjoyed higher status advantages (i.e., high-SES Whites).

S37

METHODS

Sample
The data in this sample derived from in-person interviews
conducted in 2001–2002 with people 65 years and older residing in the District of Columbia and two adjoining Maryland
counties (Prince George’s and Montgomery). Sample selection
and recruitment began with the Medicare beneficiary files
for the three areas. In addition to the names of all people 65
years and older who are entitled to Medicare, the files provided
information about the race and gender of each beneficiary. The
next step entailed selection from the large pool of potential
participants. To maximize the social and economic diversity,
we randomly selected a total of 4,800 names equally divided
among the three locales, African Americans and Whites, and
women and men; this strategy created 12 groups, each containing 400 names. The goal was to enlist a sample of 1,200
people living independently, with approximately 100 in each of
the 12 groups. Approximately 65% of eligible respondents
(1,741) contacted agreed to participate, yielding 1,167 cases;
eligible participants spoke English, lived in the community, and
were able to independently complete the interview. Data
collection occurred in three waves, each separated by approximately 12 months. Wave 1 interviews occurred during 2001–
2002. For this article, we analyzed data from Waves 1 and 3
because of insufficient change in levels of depression between
Waves 1 and 2. At Wave 3, which occurred approximately 2
years after the first interview, we reinterviewed 925 individuals
(79%). We present results for individuals who were in Waves 1
and 3 and for whom we had complete responses to focal
measures (N ¼ 898).

Measures
The Appendix presents the specific items used to measure
depression, functional limitations, and diseases. Depression
items asked about symptoms in the past 7 days. We averaged
the items to create the index (aT1 ¼ .770; aT3 ¼ .764). We
selected these items from the longer version of the widely used
Hopkins Symptom Checklist (Derogatis, Lipman, Rickels,
Uhlenhuth, & Covi 1974). Studies document that the depression score is correlated with major depression as defined
by Diagnostic and Statistical Manual of Mental Disorders
(American Psychiatric Association, 1994). We subtracted T1
scores from T3 scores in order to create the change in
depression score.
The functional limitations items asked participants about
the extent to which they experienced difficulties performing
nine different activities of living (Katz, Ford, Moskowitz,
Jackson, & Jaffee, 1963). Three additional questions, selected
from well-known indices (see McDowell & Newell, 1996),
asked about scenarios that required physical effort. We standardized and averaged all items to create the limitations index;
higher scores indicate more limitations (aT1 ¼ .920, aT3 ¼ .898).
We subtracted T1 scores from T3 scores in order to assess
change in limitations.
In order to assess the joint effects of gender and race, we
created three dummy variables (African American women,
White women, and White men) with African American men as
the contrast code because, as we described above, the literature

S38

SCHIEMAN AND PLICKERT

identifies the experience of African American men in late life
as the most different (Clark & Maddox, 1992; Johnson, 2000;
Manton 1980, 1988).
We created an SES index by averaging standardized education and household income (a ¼ .700). We coded education
from low to high: eighth grade or less (1), some high school but
did not graduate, high school graduate or general equivalency
diploma, specialized training, some college but no degree, and
college graduate or more (6). Household income (before taxes
in the past year) included the participant’s best estimate of all
sources of income for household members from salaries, money
market funds, Social Security, pensions, real estate, or government entitlements. Eleven categories ranged from low (less
than $10,000) to high ($100,000 or more).
We coded marital status such that divorced, widowed, and
never married persons were contrasted with married persons.
We coded current/recent smoker as 1 if participants currently
smoked or had smoked in the past 10 years, and 0 otherwise.
We calculated body mass index as weight (kg) divided by
height squared (cm2) based on the self-reported measures of
weight (in pounds) and height (in inches). The disease index
summed the number of health problems in the 5 years prior
to the Wave 1 interview (see Appendix). In addition, the new
diseases index summed the number of new diseases that
occurred after the Wave 1 interview.

Plan of Analysis
Using ordinary least squares regression techniques, we tested
a series of models: (a) the stability model, which included only
Wave 1 depression as the sole predictor of change; (b) the
additive model, which included gender–race groups, baseline
and change in limitations, and controls (excluding the interaction terms); (c) the model including the Gender–race 3
Change in limitations terms; and (d) the inclusion of other health
measures. We included the fourth model to ensure that other
health statuses and conditions did not account for our focal
associations. In addition, we examined these models separately
for low (, median) and high (. median) SES groups. Separate
analyses (not shown) examined three-way interactions between
gender–race groups, limitations, and SES to confirm their
statistically significant effects; however, presenting all possible
interactions in one table is logistically complex. Thus we present
results only for the low- versus high-SES groups.
Following the procedures outlined by Mirowsky and Ross
(2001), we used the difference score as the dependent variable
(T3 depression minus T1 depression score) and adjusted for T1
depression in all models to control for regression to the mean. A
common problem of longitudinal data analysis is that sample
attrition may bias the results, especially if depression at baseline
affects attrition. In order to account for this, we adjusted for the
likelihood of attrition in all of our regression models. This
adjustment took into account the probability that the baseline
depression or any other of the T1 independent variables
predicted attrition (Winship & Mare, 1992). Using logistic
regression techniques, we found that baseline depression was
unrelated to the odds of attrition. Limitations, low SES, and
age, however, were associated positively with the odds of
attrition; body mass index and diseases, paradoxically, were
associated negatively with attrition. None of the other baseline variables were statistically significant. The model yielded

a pseudo r-square of .05, indicating that almost all attrition was
random with respect to the model’s baseline measures (results
available upon request).

RESULTS
Table 1 presents summary statistics for all of the focal
measures across SES and gender–race groups. Table 2 presents
the findings for the low-SES group. Model 1 presents the
stability model, showing that baseline levels of depression were
associated negatively with change in depression. Model 2
indicates that baseline levels and changes in functional limitations were associated with increases in depression, and levels
of depression increased more for White women compared to
African American men. However, we observed significant
Gender–race 3 Change in limitations interactions in Model 3,
indicating that increases in limitations were associated with
increases in depression. That pattern was stronger among White
women and men compared to African American men; tests for
model improvement showed that the inclusion of these
interactions improved model fit (F ¼ 2.99, p , .05). In sum,
increases in limitations were associated with concurrent
increases in depression for White women and men only. Moreover, as Model 4 shows, controlling for other health measures
only slightly altered the interactions.
Table 3 presents findings for the high-SES group. Model 1
indicates that baseline depression was associated negatively
with change in depression over time. Model 2 indicates that
baseline levels and changes in limitations were associated with
increases in depression. In Model 3, we observed significant
Gender–race 3 Change in limitations interactions that were
different from those among low-SES elders. The negative
White women 3 Change in limitations coefficient indicates that
increases in limitations were associated with increases in
depression more strongly among African American men compared to White women; however, tests for model improvement
showed that the inclusion of these interactions marginally
improved model fit (F ¼ 2.17, p ¼ .09). Adjustments for other
health measures in Model 4 had little effect.
In sum, we observed that increases in limitations were
associated with increases in depression; these patterns depended on gender, race, and SES. Among low-SES elders,
increases in limitations were associated with increases in
depression among White men and women only; among highSES elders, increases in limitations were associated with increases in depression among African American women, African
American men, and White men only. We have presented
observations separately by SES for ease of interpretation;
however, analyses (not shown) of three-way interactions supported the findings presented in Tables 2 and 3 (White men:
t ¼ 2.83, p , .01; White women: t ¼ 2.14, p , .05; full analyses
available upon request).
DISCUSSION
Although the patterning of levels of functional limitations
across dimensions of social stratification is well established, we
expanded the scope of research by documenting the statuscontingent effects of limitations on changes in depression.
Drawing upon stress process theory, we proposed that social
strata yield differential vulnerabilities with respect to the effects

FUNCTIONAL LIMITATIONS AND DEPRESSION

S39

Table 1. Means or Proportions for All Study Variables Across Socioeconomic Status and Race–Gender Groups
Socioeconomic Status , Median

Variable
Change in depression
Baseline depression
Baseline functional limitations
Change in functional limitations
Age
Married
Divorced/separated
Widowed
Never married
Current/recent smoker
Body mass index
Baseline diseases
New diseases

African
American
Women
(n ¼ 156)

African
American
Men
(n ¼ 119)

White
Women
(n ¼ 112)

À0.018
(0.552)
1.480
(0.526)
0.186c
(0.900)
0.088
(0.694)
73.750
(6.802)
0.244c
0.224c
0.481c
0.051
0.141
29.295b
(5.264)
2.256
(1.463)
1.686
(1.463)

À0.075
(0.634)
1.459
(0.603)
À0.057b
(0.803)
0.160
(0.831)
73.403
(5.419)
0.555a,b
0.168
0.244a,b
0.034
0.319a,b,c
27.200a
(4.551)
2.176
(1.516)
1.387
(1.491)

0.061
(0.472)
1.515
(0.610)
0.283c
(0.956)
0.132
(0.646)
76.152
(6.921)
0.330c
0.107
0.509c
0.054
0.143
26.697
(6.525)
2.357
(1.361)
1.616
(1.390)

Socioeconomic Status . Median
White
Men
(n ¼ 54)

African
American
Women
(n ¼ 62)

African
American
Men
(n ¼ 97)

White
Women
(n ¼ 117)

White
Men
(n ¼ 181)

0.089
(0.440)
1.349
(0.416)
À0.231
(0.756)
0.132
(0.589)
75.556
(5.971)
0.685
0.056
0.204
0.056
0.130
27.346
(3.852)
2.148
(1.535)
1.444
(1.436)

0.044
(0.512)
1.390
(0.476)
À0.037
(0.741)
0.081
(0.368)
72.661
(5.572)
0.435c
0.177c
0.371c
0.016
0.113
28.549b,c
(6.182)
2.274
(1.473)
1.290
(1.311)

0.065
(0.365)
1.253b
(0.375)
À0.345a
(0.752)
0.061
(0.589)
72.082c
(5.063)
0.814a,b
0.062
0.113a
0.010
0.082
27.451b
(3.826)
1.918
(1.441)
1.165
(1.336)

0.015
(0.532)
1.480c
(0.504)
À0.215
(0.556)
0.052
(0.462)
73.632
(5.757)
0.538c
0.120
0.248c
0.094
0.103
24.890
(4.581)
2.068
(1.400)
1.350
(1.347)

0.015
(0.442)
1.324
(0.403)
À0.294
(0.584)
À0.011
(0.508)
74.376
(6.551)
0.790
0.050
0.088
0.072
0.072
25.983
(3.342)
1.796
(1.413)
1.155
(1.255)

Notes: Standard deviations are shown in parentheses.
a
Significantly different from African American women ( p , .05).
b
Significantly different from White women ( p , .05).
c
Significantly different from White men ( p , .05).

of limitations. We also used the multiple-hierarchy stratification
perspective to examine race, gender, and SES contingencies.
This allowed us to identify resource vulnerability and resource
erosion variants of the SES-based differential vulnerability
thesis across race–gender groups.
Among low-SES elders, we observed that increases in
limitations were associated with concurrent increases in depression among White men and, to a lesser extent, White
women. These observations are consistent with the resource
erosion view in that Whites, irrespective of SES, may enjoy
better health and fewer limitations over the life course than
African Americans. With advancing age and increases in limitations, individuals may perceive threats to well-being as
unfamiliar and unexpected. Moreover, the fact that limitations
did not influence levels of depression among African Americans of low SES reinforces and extends the notion that elderly
African Americans may be an especially robust group in terms
of health and may reflect a survival elite. Our observations are
also consistent with the idea of the disability crossover, in
which African Americans in old age surpass similarly aged
White peers in terms of health and physical functioning.
According to Ferraro and Farmer (1996), ‘‘The advantages that
majority persons held over minority persons may cease to be as
important when all persons are confronted with the basic
challenges to health and functional ability’’ (p. 28). Here, we
identify the advantage with respect to the fact that, at the same
level of increase in limitations, African Americans (especially
men) reported lower levels of a concurrent increase in depression than White men and women. For lower SES individuals of minority status, being a member of the survivor elite

may yield psychosocial benefits that counter the depressing consequences of impairment.
We also found evidence consistent with the resource erosion
view of SES. This view implies that high SES should buffer
against the distressing effects of increasing limitations. The
resource erosion dimension of increasing levels of limitations,
however, may overwhelm the protective benefits of high
SES. Sooner or later, even people who have enjoyed health
advantages over the life course experience health problems or
impairment (House et al., 2005). The question becomes: At the
same level of limitations, what are the mental health effects for
individuals who are familiar with prior health adversities
(normative) compared to those who are unfamiliar with health
stressors (non-normative)? The stability of health inequalities
idea implies that the mental health impact of limitations is
worse for people who have enjoyed health advantages over the
life course—in this case, people with high SES. Thus, there
may be an advantage resource margin that compresses during
late-life in terms of morbidity and limitations (House et al.,
2005), as well as the deleterious mental health correlates of
limitations. Our findings among high-SES African American
women, African American men, and White men are consistent
with the resource erosion view: people with higher SES tend to
experience more advantages with respect to health over the life
course. For them, the sudden exposure to limitations may be
unexpected, unfamiliar, and challenging. By contrast, our
observations for high-SES White women are more consistent
with the resource benefits view: higher SES is protective
against the distressing effects of limitations.

SCHIEMAN AND PLICKERT

S40

Table 2. Regression of Change in Depression Among Individuals in
the Low-SES Group (n ¼ 441)
Variable

Model 1

Model 2

Model 3

Model 4

Focal associations
Baseline depression
African American womena
White womena
White mena
Baseline functional
limitations
Change in functional
limitations
African American
women 3 Change in
functional limitationsa
White women 3 Change in
functional limitationsa
White men 3 Change in
functional limitationsa

Variable

Model 1 Model 2 Model 3 Model 4

Focal associations
À.557*** À.611*** À.622***
(.038)
(.039)
(.039)
.044
.028
(.055)
(.055)
.138*
.115
(.059)
(.060)
.129
.089
(.072)
(.073)
.111***
.117***
(.027)
(.027)
.133***
.047
(.031)
(.048)
.085
(.070)

À.641***
(.040)
.025
(.057)
.111
(.060)
.086
(.073)
.087**
(.029)
.045
(.049)
.072
(.070)

.162*
.142
(.081)
(.081)
.298***
.258*
(.112)
(.112)

Basic control measures

À.398*** À.496*** À.499*** À.511***
(.045)
(.045)
(.045)
(.046)
African American womena
À.040
À.039
À.034*
(.068)
(.069)
(.068)
White womena
.021
.036
.029
(.057)
(.057)
(.058)
White mena
À.017
À.013
À.016
(.051)
(.051)
(.051)
Baseline functional limitations
.174*** .173*** .172***
(.033)
(.033)
(.035)
Change in functional limitations
.212*** .295*** .280***
(.040)
(.070)
(.071)
African American
À.028
À.016
women 3 Change in
(.156)
(.158)
functional limitationsa
White women 3 Change in
À.259* À.259*
functional limitationsa
(.106)
(.107)
White men 3 Change in
À.060
À.069
functional limitationsa
(.091)
(.091)
Baseline depression

Basic control measures
À.001
(.004)
.081
(.064)
.012
(.051)
.112
(.101)

Age
Divorcedb
Widowedb
Never marriedb

À.002
(.004)
.090
(.063)
.013
(.050)
.124
(.101)

À.001
(.004)
.107
(.064)
.025
(.051)
.135
(.101)

Health measures

Age

.005
(.004)
.198**
(.069)
.012
(.054)
.004
(.083)

Divorcedb
Widowedb
Never marriedb

.005
(.003)
.213**
(.069)
.005
(.054)
.008
(.083)

.005
(.004)
.202**
(.070)
.004
(.054)
À.001
(.083)

.730
.277

.039
(.068)
À.002
(.005)
À.016
(.015)
.039*
(.016)
.794
.288

Health measures
À.004
(.056)
À.003
(.004)
.019
(.017)
.037*
(.017)

Previous or recent smoker
Body mass index
Baseline diseases
New diseases
Constant
R2

Table 3. Regression of Change in Depression Among Individuals in
the High-SES Group (n ¼ 457)

.817
.325

.780
.382

.807
.395

.814
.409

Notes: Data are presented as unstandardized regression coefficients with
standardized coefficients in parentheses. Models show regression of change in
depression on baseline levels of depression (1); race–gender groups, functional
limitations, and basic controls (2); interactions (3); and other health statuses and
conditions (4).
a
Compared to African American men.
b
Compared to currently married.
*p , .05; **p , .01; ***p , .001 (two-tailed).

Several limitations of our study deserve brief mention. First,
the small sample sizes of specific subgroups suggest caution
in the interpretation of our estimates. Moreover, there may be
reporting biases across these subgroups that we are unable to
assess in the present study. The regional nature of the sample is
also a potential limitation. On the one hand, the metropolitan
District of Columbia area likely contains a slightly higher
number of well-educated, wealthier individuals. This is particularly the case for elderly African American women and men
in Prince George’s County. On the other hand, this is also
a potential strength of the data set, because it allows us to

Previous or recent smoker
Body mass index
Baseline diseases
New diseases
Constant
R2

.570
.146

.731
.266

Notes: Data are presented as unstandardized regression coefficients with
standardized coefficients in parentheses. Models show regression of change in
depression on baseline levels of depression (1); race–gender groups, functional
limitations, and basic controls (2); interactions (3); and other health statuses and
conditions (4).
a
Compared to African American men.
b
Compared to currently married persons.
*p , .05; **p , .01; ***p , .001 (two-tailed).

examine health and well-being processes among a group of
African American elders that represents a solid cross-section of
the socioeconomic spectrum. In addition, the low r-square
suggests that much of the variance in changes in depression
remains unexplained. This is hardly unique to this study, but it
deserves brief mention here and attention in future investigations. Finally, the short duration of time between interviews
limited the degree of change in focal measures. Future plans for
additional interviews of these participants, however, should
help to expand the scope of the present study to include growthcurve models of changes in functioning over a longer time
span.

FUNCTIONAL LIMITATIONS AND DEPRESSION

CONCLUSION
The stress process framework identifies functional limitations
as a stressor in late life. We drew upon an array of theoretical
and empirical views to propose that the increased limitations
have different psychological effects that vary across social
strata. Our observations underscore the necessity of using a
multiple-hierarchy stratification perspective to examine race,
gender, and SES contingencies to refine and extend current
knowledge about the effects of limitations.
ACKNOWLEDGMENTS
An NIA grant award AG17461 (Leonard I. Pearlin, P.I.) supports this
work. Address correspondence to Scott Schieman, PhD, University of
Toronto, Department of Sociology, 725 Spadina Avenue, Toronto, Ontario
M5S 2J4, Canada. E-mail: scott.schieman@utoronto.ca.
REFERENCES
American Psychiatric Association. (1994). Diagnostic and statistical
manual of mental disorders (4th ed.). Washington, DC: Author.
Centers for Disease Control. (2002). United States life tables, 2000.
National Vital Statistics Reports, 51(3).
Clark, D. O., & Maddox, G. L. (1992). Racial and social correlates of agerelated changes in functioning. Journal of Gerontology: Social
Sciences, 47B, S222–S232.
Dannefer, D. (2003). Cumulative advantage/disadvantage and the life
course: Cross-fertilizing age and social science theory. Journal of
Gerontology: Social Sciences, 58B, S327–S357.
Derogatis, L. R., Lipman, R., Rickels, K., Uhlenhuth, E. H., & Covi, L.
(1974). The Hopkins Symptom Checklist (HSCL): A self-report
symptom inventory. Behavioural Science, 19, 1–15.
Dowd, J. J., & Bengtson, V. L. (1978). Aging in minority populations: An
examination of the double jeopardy hypothesis. Journal of Gerontology, 33, 427–436.
Ferraro, K. F., & Farmer, M. M. (1996). Double jeopardy to health
hypothesis for African Americans: Analysis and critique. Journal of
Health and Social Behavior, 37, 27–43.
Gibson, R. C. (1991). Age-by-race differences in the health and functioning
of eldely persons. Journal of Aging and Health, 3, 335–351.
House, J. S., Lantz, P. M., & Herd, P. (2005). Continuity and change in the
social stratification of aging and health over the life course: Evidence
from a nationally representative longitudinal study from 1986 to 2001/
2002 (Americans’ Changing Lives Study). Journal of Gerontology:
Social Sciences, 60B, S15–S26.
Johnson, N. E. (2000). The racial crossover in comorbidity, disability, and
mortality. Demography, 37, 267–283.

S41

Katz, S., Ford, A. B., Moskowitz, R. W., Jackson, B. A., & Jaffee, M. W.
(1963). Studies of illness in the aged. The index of ADL: A standardized measure of biological and psychosocial function. Journal of
the American Medical Association, 185, 914–919.
Kelley-Moore, J. A., & Ferraro, K. F. (2004). The Black/White disability
gap: Persistent inequality in late-life? Journal of Gerontology: Social
Sciences, 59B, S34–S43.
Kelley-Moore, J. A., & Ferraro, K. F. (2005). A 3-D model of health
decline: Disease, disability, and depression among Black and White
older adults. Journal of Health and Social Behavior, 46, 376–391.
Long, J. S., & Pavalko, E. K. (2004). Comparing alternative measures of
functional limitations. Medical Care, 42, 19–27.
Manton, K. (1980). Sex and race specific mortality differentials in multicause of death data. The Gerontologist, 20, 480–493.
Manton, K. (1988). A longitudinal study of functional change and mortality in the United States. Journal of Gerontology: Social Sciences, 43,
S153–S161.
McDowell, I., & Newell, C. (1996). Measuring health: A guide to rating
scales and questionnaires. New York: Oxford University Press.
McLeod, J. D., & Nonnemaker, J. M. (1999). Social stratification and
inequality. In C. S. Aneshensel & J. C. Phelan (Eds.), Handbook of the
sociology of mental health (pp. 321–344). New York: Kluwer
Academic/Plenum.
Mirowsky, J., & Ross, C. E. (2001). Age and the effect of economic
hardship on depression. Journal of Health and Social Behavior, 42,
132–150.
O’Rand, A. M. (1996). The precious and the precocious: Understanding
cumulative disadvantage and cumulative advantage over the life course.
The Gerontologist, 36, 230–238.
Pampel, F. C., & Rogers, R. G. (2004). Socioeconomic status, smoking,
and health: A test of competing theories of cumulative advantage.
Journal of Health and Social Behavior, 45, 306–321.
Pearlin, L. I., & Skaff, M. M. (1996). Stress and the life course: A
paradigmatic alliance. The Gerontologist, 36, 239–247.
Rieker, P. P., & Bird, C. E. (2005). Rethinking gender differences in health:
Why we need to integrate social and biological perspectives. Journal
of Gerontology: Social Sciences, 60B, S40–S47.
Williams, D. R. (2005). The health of U.S. racial and ethnic populations.
Journal of Gerontology: Social Sciences, 60B, S53–S63.
Winship, C., & Mare, R. D. (1992). Models for sample selection bias.
Annual Review of Sociology, 18, 327–350.
Yang, Y., & George, L. K. (2005). Functional disability, disability
transitions, and depressive symptoms in late life. Journal of Aging and
Health, 17, 263–292.

Received December 15, 2005
Accepted July 20, 2006
Decision Editor: Kenneth F. Ferraro, PhD

SCHIEMAN AND PLICKERT

S42

APPENDIX
Survey Items
Item Wording

Response Categories

Depression Index
Lack enthusiasm for doing anything
Feel bored or have little interest in things
Cry easily or feel like crying
Feel downhearted or blue
Feel slowed down or low in energy
Blame yourself for everything that goes wrong
Have your feelings hurt easily

No days (1)
1 or 2 days (2)
3 or 4 days (3)
5 or more days (4)

Functional Limitations Index
Level of difficulty in:
Dressing and undressing
Getting in and out of bed
Taking a bath or shower
Getting to and using the toilet
Climbing up stairs
Keeping balance while walking
Going food shopping
Getting from your home to where you need to go
Figuring out your own monthly bills

Without difficulty (1)
With difficulty, but without help (2)
With a little help from someone (3)
Unable to do this without complete help from someone or special equipment (4)

Let’s suppose that you had to reach over your head to lower a bag of sugar.
What is the heaviest bag of sugar you could lower?

15 or more pound bag (1)
10 pound bag (2)
5 pound bag (3)
1 pound bag (4)
Not at all able to lower the bag (5)

Let’s suppose that you had to stand, without help, in a long line.
About how long could you stand?

1 hr or more (1)
45 min (2)
30 min (3)
15 min (4)
5 min (5)
Not at all (6)

How long are you able to walk without stopping to rest?

1 hr or more (1)
45 min (2)
30 min (3)
15 min (4)
5 min (5)
Not at all (6)

Disease Index
Asthma or emphysema
Arthritis
Diabetes
High blood pressure
Heart disease and/or heart attack
Stomach disorders
Stroke
Cancer of any kind
Osteoporosis
High cholesterol
Painful joints
Cataracts, glaucoma, detached retina, or any other condition of the retina

No (0)
Yes (1)