6210 Week 11 Discussion

  

Discussion – Week 11

Top of Form

Discussion: Systems Perspective and Social Change

As a social worker, when you address the needs of an individual client, you must also take into account the systems with which the client interacts. Obtaining information about these systems helps you better assess your client’s situation. These systems may provide support to the client, or they may contribute to the client’s presenting problem. Consider the example of a workplace; a client may get great satisfaction and sense of purpose from a career but the interpersonal relationships at the workplace itself are toxic. This system could be contributing both positively and negatively to the client’s well-being.

For this Discussion, you examine the systems perspective and its relevance and application to practice, in light of all you have learned about human behavior and the social environment.

To Prepare:

• Review the Learning      Resources on the systems perspective.
• Access the Social Work      Case Studies media and navigate to Lester.
• As you explore      Lester’s case, consider the systems with which Lester interacts. Think      about ways you might apply a systems perspective to his case. Also      consider the significance of the systems perspective for social work in      general.

By Day 02/09/2022
 

Post an explanation of how multiple systems within the social environment interact to impact individuals across the life span. Use Lester’s case as an example. Then explain how you as a social worker might apply a systems perspective to your work with Lester. Finally, explain how you might apply a systems perspective to social work practice in general.

Bottom of Form

Required Readings

Zastrow, C. H., & Kirst-Ashman, K. K. (2019). Understanding human behavior and the social environment (11th ed.). Cengage Learning.

• Review Chapter 1,      “Introduction to Human Behavior and the Social Environment” (pp.      1–44)

Wickrama, K. A. S., O’Neal, C. W., & Lee, T. K. (2020). Aging together in enduring couple relationships: A life course systems perspective. Journal of Family Theory and Review, 12(2), 238–263. https://doi.org/10.1111/jftr.12369

Required Media

Walden University, LLC. (2021). Social work case studies [Interactive media]. https://class.waldenu.edu

• Navigate to Lester.

Follow Rubric

Initial Posting: Content

14.85 (49.5%) – 16.5 (55%)

Initial posting thoroughly responds to all parts of the Discussion prompt. Posting demonstrates excellent understanding of the material presented in the Learning Resources, as well as ability to apply the material. Posting demonstrates exemplary critical thinking and reflection, as well as analysis of the weekly Learning Resources. Specific and relevant examples and evidence from at least two of the Learning Resources and other scholarly sources are used to substantiate the argument or viewpoint.

Readability of Postings

5.4 (18%) – 6 (20%)

Initial and response posts are clear and coherent. Few if any (less than 2) writing errors are made. Student writes with exemplary grammar, sentence structure, and punctuation to convey their message.

Discussion – Week 11

Top of Form

Discussion: Systems Perspective and Social Change

As a social worker, when you address the needs of an individual client, you must also take into account the systems with which the client interacts. Obtaining information about these systems helps you better assess your client’s situation. These systems may provide support to the client, or they may contribute to the client’s presenting problem. Consider the example of a workplace; a client may get great satisfaction and sense of purpose from a career but the interpersonal relationships at the workplace itself are toxic. This system could be contributing both positively and negatively to the client’s well-being.

For this Discussion, you examine the systems perspective and its relevance and application to practice, in light of all you have learned about human behavior and the social environment.

To Prepare:

· Review the Learning Resources on the systems perspective.

· Access the Social Work Case Studies media and navigate to Lester.

· As you explore Lester’s case, consider the systems with which Lester interacts. Think about ways you might apply a systems perspective to his case. Also consider the significance of the systems perspective for social work in general.

By Day 02/09/2022
Post an explanation of how multiple systems within the social environment interact to impact individuals across the life span. Use Lester’s case as an example. Then explain how you as a social worker might apply a systems perspective to your work with Lester. Finally, explain how you might apply a systems perspective to social work practice in general.

Bottom of Form

Required Readings

Zastrow, C. H., & Kirst-Ashman, K. K. (2019). Understanding human behavior and the social environment (11th ed.). Cengage Learning.

· Review Chapter 1, “Introduction to Human Behavior and the Social Environment” (pp. 1–44)

Wickrama, K. A. S., O’Neal, C. W., & Lee, T. K. (2020). Aging together in enduring couple relationships: A life course systems perspective. Journal of Family Theory and Review, 12(2), 238–263. https://doi.org/10.1111/jftr.12369

Required Media

Walden University, LLC. (2021). Social work case studies [Interactive media]. https://class.waldenu.edu

· Navigate to Lester.

Follow Rubric

Initial Posting: Content

14.85 (49.5%) – 16.5 (55%)

Initial posting thoroughly responds to all parts of the Discussion prompt. Posting demonstrates excellent understanding of the material presented in the Learning Resources, as well as ability to apply the material. Posting demonstrates exemplary critical thinking and reflection, as well as analysis of the weekly Learning Resources. Specific and relevant examples and evidence from at least two of the Learning Resources and other scholarly sources are used to substantiate the argument or viewpoint.

Readability of Postings

5.4 (18%) – 6 (20%)

Initial and response posts are clear and coherent. Few if any (less than 2) writing errors are made. Student writes with exemplary grammar, sentence structure, and punctuation to convey their message.

Chapter 1 Summary The following summarizes this chapter’s content as it relates to the learning objectives presented at the beginning of the chapter. Chapter content will help prepare students to do the following:

LO 1 Explain the importance of foundation knowledge for social work with an emphasis on assessment.
This book provides a knowledge base in preparation for social work practice. Social workers need knowledge in order to understand the dynamics of human behavior and conduct client assessments. The social work pro-cess then involves helping clients identify and evaluate available alternatives to select the best plan of action.
LO 2 Review the organization of this book that emphasizes lifespan development. This book is organized using a lifespan approach. The lifespan is divided into four phases: infancy and
childhood, adolescence, young and middle adult-hood, and later adulthood. Chapters on biological, psychological, and social
(bio-psycho-social) aspects of development portray common life events, normal developmental milestones, and relevant issues for each life phase.
LO 3 Describe important concepts for understand-ing human behavior (that are stressed throughout the book and include human diversity, cultural competency, oppression, populations-at-risk, em-powerment, the strengths perspective, resiliency, human rights, and critical thinking about ethical issues).
Human diversity is the vast range of human differ-ences among groups, including those related to “age, class, color, culture, disability and ability, ethnicity, gender, gender identity and expression, immigration status, marital status, political ideology, race, reli-gion/spirituality, sex, sexual orientation, and tribal sovereign status” (CSWE, 2015).
Chapter Summary The following summarizes this chapter’s content as it relates to the learning objectives presented at the beginning of the chapter. Chapter content will help prepare students to do the following:
LO 1 Explain the importance of foundation knowledge for social work with an emphasis on assessment.
This book provides a knowledge base in preparation for social work practice. Social workers need knowledge in order to understand the dynamics of human behavior and conduct client assessments. The social work pro-cess then involves helping clients identify and evaluate available alternatives to select the best plan of action.
LO 2 Review the organization of this book that emphasizes lifespan development. This book is organized using a lifespan approach. The lifespan is divided into four phases: infancy and
childhood, adolescence, young and middle adult-hood, and later adulthood. Chapters on biological, psychological, and social
(bio-psycho-social) aspects of development portray common life events, normal developmental milestones, and relevant issues for each life phase.
LO 3 Describe important concepts for understand-ing human behavior (that are stressed throughout the book and include human diversity, cultural competency, oppression, populations-at-risk, em-powerment, the strengths perspective, resiliency, human rights, and critical thinking about ethical issues).
Human diversity is the vast range of human differ-ences among groups, including those related to “age, class, color, culture, disability and ability, ethnicity, gender, gender identity and expression, immigration status, marital status, political ideology, race, reli-gion/spirituality, sex, sexual orientation, and tribal sovereign status” (CSWE, 2015).
One major goal of social work education is to
facilitate students’ attainment of the EPAS-designated nine core competencies and their 31 related behaviors so that students develop into competent practitioners. Students require knowledge in order to develop
skills and become competent. Our intent here is to specify what chapter content and knowledge coincides with the development of specific competencies and behaviors.

first cites the various Educational Policy (EP) core competencies and their related behaviors (which are alphabetized beneath competencies) that are relevant to chapter content. Note that most of the listing follows the order that competencies and behaviors are cited in the EPAS. We have established (See the Special Notes section at the end of this chapter) that “helping hands” icons such as that illustrated in this paragraph are interspersed throughout the chapter
indicating where relevant accompanying content is located. Page numbers noted below indicate where icons are placed in the chapter. Following the icon’s page number is a brief explanation of how the content accompanying the icon relates to the specified competency or practice behavior. EP1 Demonstrate Ethical and Professional Behavior (pp. 2, 46) Ethical questions are posed.
EP6a. Apply knowledge of human behavior and the social environment, person-in-environment, and other multidisciplinary theoretical frameworks to engage with clients and constituencies;
EP7b. Apply knowledge of human behavior and the social environment, person-in-environment, and other multidisciplinary theoretical frameworks in the analysis of assessment data from clients and constituencies;
EP8b. Apply knowledge of human behavior and the social environment, person-in-environment, and other multidisciplinary theoretical frameworks in interventions with
clients and constituencies (all of this chapter). Material on concepts and theories about human behavior and the social environment are presented throughout this chapter.
EP1a through EP 9d: All the competencies and behaviors of 2015 EPAS (pp. 57–61). This section reprints the knowledge, skills, values, and cognitive and affective processes needed for social work practice, as stated in 2015 EPAS.
WEB RESOURCES

1

© 2021 Walden University, LLC. Adapted from Plummer, S. -B., Makris, S., & Brocksen, S. M. (Eds.). (2014). Social

work case studies: Foundation year. Laureate International Universities Publishing.

Lester

Lester is a 59-year-old divorced African American male with two adult children. Four

months ago, he was a driver in a multiple vehicle crash while visiting his daughter in

another city and was injured in the accident, although he was not at fault. Prior to the

accident he was an electrician and lived on his own in a single-family home. He was an

active member in his church and a worship leader. He has a supportive brother and

sister-in-law who also live nearby. Both of his children have left the family home, and his

son is married and lives in a nearby large metropolitan area.

When he was admitted to the hospital, Lester’s CT showed some intracerebral

hemorrhaging, and the follow-up scans showed a decrease in bleeding but some

midline shift. He seemed to have only limited cognition of his hospitalization. When his

children came to visit, he smiled and verbalized in short words but could not

communicate in sentences; he winced and moaned to indicate when he was in pain. He

had problems with balance and could not stand independently nor walk without

assistance. Past medical history includes type 2 diabetes; elevated blood pressure; a

long history of smoking, with some emphysema; and a 30-day in-house treatment for

alcoholism 6 years ago.

One month ago, he was discharged from the hospital to a rehabilitation facility, and at

his last medical review it was estimated he will need an additional 2 months’ minimum

treatment and follow-up therapies in the facility.

As the social worker at the rehab center, I conducted a biopsychosocial assessment

after his admission to rehabilitation.

Biopsychosocial Assessment

At the time of the assessment, Lester was impulsive and was screened for self-harm,

which was deemed low risk. He did not have insight into the extent of his injury or

changes resulting from the accident but was frustrated and cried when he could not

manipulate his hands. Lester’s children jointly hold power of attorney (POA),but had not

expressed any interest to date in his status or care. His brother is his shared decision

making (SDM) proxy, but his sister-in-law seemed to be the most actively involved in

planning for his follow-up care. His son and daughter called but had not visited, but his

sister-in-law had visited him almost daily; praying with him at the bedside; and

managing his household financials, mail, and house security during this period. His

brother kept asking when Lester would be back to “normal” and able to manage on his

own and was eager to take him out of the rehabilitation center.

Lester seemed depressed, showed some flat affect, did not exhibit competency or show

interest in decision making, and needed ongoing help from his POA and SDM. His

medical prognosis for full recovery remains limited, with his Glasgow Coma Scale at

less than 9, which means his injury is categorized as catastrophic.

2

© 2021 Walden University, LLC. Adapted from Plummer, S. -B., Makris, S., & Brocksen, S. M. (Eds.). (2014). Social
work case studies: Foundation year. Laureate International Universities Publishing.

Lester currently has limited mobility and is continent, but he is not yet able to self-feed

and cannot self-care for cleanliness; he currently needs assistance washing, shaving,

cleaning his teeth, and dressing. He continues with daily occupational therapy (OT) and

physical therapy (PT) sessions.

He will also need legal assistance to apply for his professional association pension and

benefits and possible long-term disability. He will also need help identifying services for

OT and PT after discharge.

He will need assistance from family members as the determination is made whether he

can return to his residence with support or seek housing in a long-term care facility. He

will need long-term community care on discharge to help with basic chores of dressing

and feeding and self-care if he is not in a residential care setting.

A family conference is indicated to review Lester’s current status and short-term goals

and to make plans for discharge.

Kandauda (K. A. S.) Wickrama and Catherine Walker O’Neal University of Georgia

Tae Kyoung Lee University of Miami

Aging Together in Enduring Couple Relationships:

A Life Course Systems Perspective

This article introduces and demonstrates the use
of an integrated life course systems perspective
to advance the study of the aging processes of
couples in enduring relationships. This objec-
tive is accomplished by bridging the life course
and systems perspectives to conceptualize the
couple as a functioning system and to locate
couple dynamics within a longitudinal life
course context in order to identify multilevel
relational mechanisms that explain partners’
aging outcomes in their broader socioeco-
nomic and longitudinal context. Informed by
this integrated theoretical perspective, testable
hypotheses related to aging processes are
derived, and analytical methodologies that
can advance the research on couple aging
processes are demonstrated. Identifying these
relationship-health processes and contextual
considerations provides insight into leverage
points for the development and implementation
of prevention and intervention efforts to facil-
itate positive aging outcomes. Directions for
further theoretical and analytical advances in
the area of couple aging are discussed.

Minimal research has investigated aging in the
context of the couple relationship, even though

Department of Human Development and Family Science,
University of Georgia, 107 Family Science Center II
(House D), Athens, GA 30602 (cwalker1@uga.edu).

Key Words: Adult development, aging families, application
of theory and method, health, marriage.

intimate couple relationships are often among
the most salient relationships for older adults.
Thus, theoretical developments that inform
research on couples’ aging processes and
later-life outcomes in the context of enduring
but changing couple relationships are an impor-
tant task for family gerontologists. In particular,
such theoretical advances must acknowledge
continuity and change in experiences over the
life course. This article advances this direction
of theorizing in its specific focus on couples
in enduring relationships during the latter half
of the life course, beginning in their mid- to
later years (40 years of age and older), when
signs of the aging process typically begin to
appear. Although the specific focus on enduring
couple relationships means that this model
in its original conceptualization is specific
to couples entering their mid- to later years
with already-established relationships (e.g.,
those married in their 20s), the conceptual and
analytical models discussed may be extended
to various relationship types (e.g., same-sex
couples) and less established relationships (e.g.,
cohabiting couples) as well—a point to which
we return when considering the application to
other populations).

Gerontological research has frequently uti-
lized the successful aging model (Rowe & Kahn,
1998) to study aging outcomes, such as an indi-
vidual’s declines in mental and physical health
and cognition as well as social relations. Accord-
ing to the successful aging model, an individ-
ual’s attitudes, beliefs, and actions, as well as

238 Journal of Family Theory & Review 12 (June 2020): 238–263
DOI:10.1111/jftr.12369

Aging Together 239

physical and cognitive capacities, contribute to
established lifestyles, including health behav-
iors early in adulthood that continue into later
adulthood (Rowe & Kahn, 1998). However, the
successful aging model has been criticized for
its limited scope and lack of consideration of
contextual factors, including the accumulation
of, and changes in, life experiences over the
life course, that likely influence aging outcomes
(Stowe & Cooney, 2015). In particular, over-
looking stable and changing characteristics of
these long-term relationships as an influential
context is problematic given the salience of the
couple relationship at later life stages.

In addition to the successful aging model,
several social psychological theoretical perspec-
tives have been used to explain aging outcomes.
Among them, the life course perspective (Elder,
1998; Settersten, 2003; Stowe & Cooney, 2015)
and systems perspective (Broderick, 1993) have
been widely used by family and life course
researchers to explain aging outcomes. Both the
systems and the life course perspectives have
important strengths that can enhance the study of
aging couples, but they also have limitations. As
we will discuss in more detail, the systems per-
spective emphasizes the importance of relational
dependence and family dynamics in explaining
changes in health and well-being (Broderick,
1993). In this article, we consider enduring cou-
ples as a relatively stable dyadic system and
apply principles of the systems perspective to
effectively consider aging in a relational context.
However, the systems perspective lacks an ade-
quate focus on the continuity and accumulation
of life experiences over time (i.e., situating the
individual or couple in the context of previous
experiences). The systems perspective also fails
to adequately consider the influence of structural
socioeconomic context (e.g., historical time and
place, social class, community).

In contrast to the systems perspective, the life
course perspective emphasizes the continuity
and changes in individuals’ life experiences
over the life course (including the accumulation
of these experiences) while also highlighting
the impact of distal and structural environ-
ments as well as proximal social and economic
environments for health and well-being (Elder,
1998; Settersten, 2003). The life course per-
spective, however, lacks an adequate emphasis
on micro-level relationship dynamics, includ-
ing interindividual and individual-context
associations. Bridging these two perspectives

combines their strengths and ameliorates their
shortcomings while providing an integrated
theoretical framework with enhanced explana-
tory power for couple-focused aging research
(Utz, Berg, & Butner, 2016). Furthermore, the
combination of the two theories is consistent
with studies that have called for the integration
of the life course and systems perspectives when
studying the aging outcomes of adults nested
within families (Utz et al., 2016). We extend this
approach to inform future studies of the aging
process for individuals in long-term, enduring
couple relationships with the goal of developing
theoretical tenets and analytical guidelines for
the study of aging processes and outcomes in
the couple context. Accordingly, this article
has three main objectives: (a) to incorporate
the life course and systems perspectives into
an integrated life course systems perspective
that can advance knowledge of individuals’
aging process in the context of enduring cou-
ple relationships; (b) to demonstrate how an
integrated life course systems perspective can
inform hypotheses and to discuss advanced
analytical approaches that can be utilized to test
those hypotheses; and (c) to recommend future
directions to further strengthen the integrated
life course systems perspective and enhance
knowledge of individuals’ aging process.

An Integrated Life Course Systems
Perspective

The Life Course Perspective

Consistent with the life course perspective, aging
is not limited to a single life stage. Instead, it
is a process that unfolds across the life course,
characterized by trajectories of continuity and
change (Elder & Geile, 2009). Further, the
life course perspective contends that later-life
experiences are a product of an individual’s
experiences at previous life stages; that is, life is
a continuous chain of events and circumstances
influenced by multiple contextual, relational,
and individual factors. The theory specifically
emphasizes certain factors, including historical
place and time, social structure, continuity, and
parallel social and developmental pathways,
social and close relationships, and personal
agency (Elder, 1998; Settersten, 2003; Stowe
& Cooney, 2015). These factors influence life
experiences in various ways, including the pro-
vision of resources, the constraints exerted, and
individuals’ ability to make their own choices.

240 Journal of Family Theory & Review

Individuals’ lives are situated within his-
torical place and time, which influences the
aging process because the sociohistorical envi-
ronment has an impact on available resources
and also exerts constraints on individuals’ life
experiences. For example, the majority of older
adults today are members of the baby-boom
cohort, named for its large size in comparison
to previous generations. The range of resources
and constraints experienced vary by cohort. Fur-
thermore, in this cohort, those who lived in the
rural Midwest (historical place) and experienced
the rural farming crisis of the late 1980s (histor-
ical time) experienced particular resources and
constraints. Such individuals may have social
trajectories (e.g., relational, work, and economic
experiences over time) that vary from earlier and
later cohorts or even from members of their own
cohort who were not located in areas affected by
the farm crisis (Conger & Elder, 1994; Lorenz,
Elder, Bao, Wickrama, & Conger, 2000). These
distinct social trajectories stemming from his-
torical time and place may result in different
aging (health and well-being) trajectories.

Like historical place and time, social struc-
ture, as marked by characteristics such as social
class, race, and gender, also contributes to
available resources and constraints, thereby
influencing life experiences and exerting a
persistent influence on an individual’s aging
process over the life course. These character-
istics are largely ascribed to individuals from
birth, yet they are influential across the life span.
For instance, research has shown that character-
istics of social class in the family of origin and
related early socioeconomic adversities, such as
early family economic hardship, influence the
health and well-being outcomes of older adults
even after accounting for adult life experiences
(Moody-Ayers, Lindquist, Sen, & Covinsky,
2007; Wickrama, Mancini, Kwag, & Kwon,
2012).

In conceptualizing how early and accumulat-
ing life experiences come to influence later life,
the life course perspective recognizes the exis-
tence of parallel social and developmental path-
ways. That is, there are thought to be intercon-
nected, or parallel, trajectories of social circum-
stances (e.g., stressful experiences) and develop-
mental attributes within an individual. Changes
in social circumstances can reflect changes in
developmental attributes, and vice versa. In this
way, experiences (including cumulative experi-
ences) and development at each life stage are

sequentially linked to the next life stage. For
example, previous studies have shown that anxi-
ety symptom trajectories are influenced by work
insecurity trajectories, reflecting parallel trajec-
tories of changes in work or financial context and
mental health (Wickrama, O’Neal, & Lorenz,
2018). Moreover, physical health trajectories of
husbands and wives are influenced by marital
quality trajectories (Robles, 2014; Wickrama,
Lorenz, & Conger, 1997), and trajectories of
stressful experiences may be associated with tra-
jectories of physical health risks, as measured
by multiple biomarkers of metabolic syndrome,
inflammation, and epigenetics indicating level of
disease risk or accelerated aging (e.g., Arbeev
et al., 2018). Notably, the conceptualized social
pathway is not limited to continuous constructs
(e.g., marital quality), as it can also consist
of discrete events (e.g., children leaving home,
retirement). The timing and sequence of such life
events and transitions are important characteris-
tics that constitute the social pathway.

A relational component of the life course
perspective is the emphasis on social and close
relationships. In particular, the life course
perspective emphasizes the phenomenon of
“linked lives,” with the marital relationship
being the primary example. That is, partners’
daily life activities are intertwined with their life
trajectories, and each individual’s life trajecto-
ries influence his or her partner’s trajectories
(e.g., stress transfer; Milkie, 2010). Moreover,
couples’ shared life trajectories represent experi-
ences that are common to both partners, such as
family economic hardship (Elder, 1998; Stowe
& Cooney, 2015). These mutual influences may
operate at least in part through the provision, or
lack thereof, of social and emotional resources
in the couple’s relational context. For example,
previous studies have shown that individuals’
physical health trajectories are influenced by
their partner’s physical health trajectories as
well as by the couple’s shared experiences of
economic hardship over time (Cobb et al., 2015;
Kiecolt-Glaser & Wilson, 2017; Ledermann
& Kenny, 2012; Wickrama, O’Neal, & Neppl,
2019). Such influences are not limited to phys-
ical health. Research has provided evidence of
similar mutual influences for partners’ mental
health, such as husbands’ and wives’ depres-
sive symptom trajectories over the life course
(Kiecolt-Glaser & Wilson, 2017; Wickrama,
King, O’Neal, & Lorenz, 2019).

Aging Together 241

Last, although the life course perspective’s
emphasis on relationships and the broader
context is important for examining aging and
later-life outcomes in the context of enduring
couple relationships, the life course perspective
also recognizes that individuals are not solely a
product of their context. Individual agency rec-
ognizes the influence of personal choices. Both
positive characteristics (e.g., positive affect,
mastery, self-regulation, self-esteem) and nega-
tive characteristics (e.g., neuroticism, hostility)
of individuals play roles in life choices and, in
turn, affect continuity and change in an indi-
vidual’s life experiences. Studies have shown
that individual choices can shape, and even turn,
developmental trajectories. For example, joining
the military has been shown to positively turn
disadvantaged young adults’ developmental tra-
jectories in some instances (Gotlib & Wheaton,
1997). Moreover, early choices related to work,
marriage, and parenthood have been shown
to negatively influence youth developmental
outcomes (Koball et al., 2010; Lee, Wickrama,
O’Neal, & Prado, 2018). Later in life, decisions
often largely drive changes such as divorce,
remarriage, relocation, and timing of retirement
that may alter developmental trajectories. These
decisions have also been shown to influence
older adults’ health and well-being trajectories
in the context of structural constraints (Setter-
sten, 2003). In the present conceptualization, we
consider race/ethnicity and gender together with
agency as influential individual characteristics.

In summary, the life course perspective
provides a framework for understanding aging
processes in the couple context, giving consid-
eration to influences that stem from (a) specific
time periods (historical place and time); (b)
elements of social structure; (c) intraindividual
parallel trajectories of social and developmen-
tal trajectories; (d) social and relational factors,
including the linked lives of partners; and (e) per-
sonal characteristics (e.g., individual agency).

The Systems Perspective and Conceptualizing
Relational Systems

Consistent with the systems perspective, rela-
tionships can be conceptualized as systems
(i.e., an organized whole). The general systems
perspective (Von Bertalanffy, 1969) contends
that a system is comprised of interconnected,
dependent parts that mutually influence one
another. More importantly, the constituent

parts (i.e., individuals) are influenced by the
system (i.e., the relationship), and at the same
time, these parts influence the system, effecting
changes in the system as a whole. Notably,
system characteristics, particularly processes
within the system, are not merely the sum of
constituent parts but are higher-order properties
of the system. In addition, structural, or global,
system characteristics (e.g., size, number of
parts, composition, duration) can play a role in
how the system and its constituents function,
interact, and affect one another.

The family systems theory (Broderick, 1993;
Cox & Paley, 1997) was derived from the gen-
eral systems perspective by applying systems
principles to the family. Thus, family members
are interdependent parts of the family system,
in which interindividual influences exist among
family members and multilevel influences oper-
ate between members and the family system.
Because there is variation between families as
well as between members in a family, individ-
ual variations are decomposed into between and
within components (i.e., what varies between
families and what varies within families).

A smaller system in many families is the cou-
ple system, to which principles of the systems
perspective can also be applied. As previously
indicated, we consider an enduring couple to be
a relatively stable system and a system in which
these members have lived the majority of their
lives (Bookwala, 2016). That is, particularly in
enduring couple relationships, partners function
interdependently and their experiences occur in
a context of mutual influences and interactions
forming crossover, or partner, effects (e.g., one
individual influences his or her partner) and
contemporaneous associations between part-
ners (e.g., contagion of “sharing” experiences,
emotions, and so on). This conceptualization
expands on the life course notion of linked lives
by providing a more detailed exploration of
relationship dynamics.

Researchers have increasingly focused on
the couple as a dyadic system, noting the
existence of couple-level characteristics and
couple–individual dynamics. Drawing from
family systems, each individual contributes to
the couple context as reflected by couple-level
characteristics. Two examples that reflect couple
processes are joint activities between partners
(e.g., joint engagement in exercise, cooking,
leisure, eating) as a reflection of the couple’s
behavioral interaction and shared perceptions of

242 Journal of Family Theory & Review

the relationship (e.g., marital satisfaction) as an
indicator of marital quality. Another example
that likely reflects more structural elements
of the system is family economic hardship, to
which husbands’ and wives’ economic difficul-
ties both contribute (Lee, Wickrama, & O’Neal,
2019). In further conceptualizing couple-level
contexts, each partner’s individual characteris-
tics, such as health, can be utilized to assess lon-
gitudinal couple characteristics, such as health
synchrony, over time between partners (i.e.,
the degree to which health trajectories between
partners follow the same pattern over time).

As noted earlier, a key tenet of the systems
perspective is that the system (i.e., the couple
in this instance) can also affect individual mem-
bers. Couple research notes that couple-level
characteristics (e.g., economic hardship, mar-
ital quality) are related to individuals’ trajec-
tories of health and well-being. Furthermore,
family-focused biopsychosocial research sug-
gests that the family socioeconomic environ-
ment may influence relational processes in the
family and induce behavioral stress adaptation,
which can affect the biological processes of both
partners over time (Booth, McHale, & Lansdale,
2011; Papp, Pendry, Simon, & Adam, 2013).
Moreover, this adaptation is consistent with the
notion of common fate (Ledermann & Kenny,
2012), which posits that couple-level constructs
influence the outcomes of both partners. These
multilevel processes between the couple system
and individual may operate over the life course.

In addition to these multilevel processes,
the couple system modifies, adapts, and, more
generally, changes over time. When considering
the couple system and aging processes over the
life course, a particularly salient mechanism
for change is self-reorganization (Cox & Paley,
1997). Self-reorganization refers to adaptation
that occurs in response to changes in the envi-
ronment. Some examples of changes in the
environment around the couple system, particu-
larly changes in the proximal environment over
the life course, include changing relationships
with aging parents (e.g., death of parents),
increasingly independent children (e.g., chil-
dren leaving home), and changes in work quality
(e.g., change in work schedule) and/or status
(e.g., retirement). Different stages of life are
associated with specific age-graded roles with
varying salience (e.g., rearing children in early
midlife, launching adult children in late midlife,
becoming grandparents and retirement in later

years). Thus, these proximal environmental
changes are often dependent on life stage and
may influence the characteristics of the couple
system. Such changes may prompt reorgani-
zation within the couple and changes to the
couple system as a whole when partners’ roles
change or when partners acquire new roles.
These changes may influence the health and
well-being trajectories of both partners.

Integrating the Life Course and Systems
Perspectives

Consistent with socioemotional selectivity the-
ory (Carstensen, 1992), older adulthood is typi-
cally a stage of self-reflection, when older adults
begin to perceive time as limited, which results
in the increased importance of satisfying emo-
tional encounters. Social networks tend to shrink
to those that provide the most socially reward-
ing encounters, such as the marital relationship.
Thus, when focusing on aging partners in endur-
ing couple relationships, we posit that the couple
system is of increasing salience with advancing
age. Connecting this concept with the systems
perspective, within this enduring couple system,
each partner’s aging outcomes are closely tied to
the partner’s outcomes (e.g., partner effects and
dependencies) and are influenced by the couple
context in which they function (e.g., couple-level
characteristics such as family economic hard-
ship). As acknowledged by central tenets of
the life course perspective, although later life
involves key considerations of aging, including
increasing salience of the marital relationship,
aging is not limited to a single life stage. Instead,
it is a process that unfolds across the life course,
characterized by trajectories of continuity and
change, and it requires taking a long view while
considering the larger social context (e.g., his-
torical time and place, social structure, social
relations) (Stowe & Cooney, 2015). That is, part-
ners’ interlocking social and developmental or
aging trajectories are influenced by the larger
socioeconomic context and personal character-
istics, and these trajectories unfold within the
dynamic couple system over time.

Related Theoretical Perspectives

Ecological Model of Marriage

The proposed life course systems perspec-
tive is also informed by several other related

Aging Together 243

theoretical perspectives or models that focus
on marriage. First, the ecological perspective
(Helms, Supple, & Proulx, 2011; Huston, 2000)
contends that marital and parent–child relations
are nested within a multilayered ecological con-
text and that family relations and interactions
thus cannot be investigated in isolation. The
multilayered ecological context includes both
macro-socioeconomic context (e.g., sociohistor-
ical context, culture, socioeconomic conditions)
and proximal environment (e.g., community,
work). These ecological factors shape an indi-
vidual’s ability to sustain his or her marital
and/or parent–child relations over time. Further-
more, the ecological perspective contends that
individual characteristics (e.g., feelings, atti-
tudes, beliefs) have a direct additive influence on
partners’ marital behaviors. More importantly,
this perspective emphasizes not only the main
additive effects of individual characteristics
on dyadic relations but also the multiplica-
tive influences among ecological factors and
individual characteristics. That is, individ-
ual characteristics may intensify or weaken
the influence of ecological factors on dyadic
relations.

The proposed life course systems perspec-
tive accounts for important components of the
ecological perspective including multilayered
ecological factors, individual characteristics,
and dyadic relations. Specifically, we incorpo-
rated the element of multiplicative influences
(i.e., interactions) of ecological factors and
individual characteristics (e.g., individual
agency) in the proposed theoretical framework.
However, our proposed theoretical framework
extends beyond the ecological models in terms
of intraindividual dynamics (e.g., social and
developmental pathways) and interindividual
dyadic dynamics over the life course. Moreover,
unlike ecological models that focus on marriage,
the proposed model focuses on both social rela-
tionships and aging and health pathways over
the life course. The proposed perspective also
explicitly defines the couple context as an eco-
logical layer involved in upward and downward
influences with constituent members.

The Vulnerability–Stress–Adaptation Model

The vulnerability–stress–adaptation model
(VSAM; Karney & Bradbury, 1995) has been
extensively used by researchers to analyze
changes in the quality of marital relations over

time. The VSAM contends that individual
characteristics (early individual vulnerabilities
and strengths) and stressful life context (stress-
ful events and circumstances) additively and
interactively influence marital quality through
the couple’s adaptive processes. These dyadic
adaptive processes have a reciprocal relationship
with the stressful context. In addition, individual
vulnerabilities and strengths contribute to a
stressful context.

Although the VSAM provides an excellent
theoretical framework for studying change in
marriage, it has certain limitations, particularly
for the study of the aging and health of endur-
ing couples. These limitations largely stem from
the fact that the VSAM primarily focuses on
marriage. First, although individual background
or demographic characteristics, such as parents’
marital status, race/ethnicity, and educational
level, are considered influencing factors for per-
sonal enduring vulnerabilities (Karney & Brad-
bury, 1995), distal socioeconomic background
(e.g., the sociohistorical context from which
spouses come, including immigrant history and
circumstances) is not a separate construct in the
model. This lack of inclusion may limit exam-
inations of the interaction effect among per-
sonal vulnerabilities and distal socioeconomic
background factors. The proposed life course
systems perspective identifies distal socioeco-
nomic background factors as a separate con-
struct and conceptualizes that construct’s inter-
action effect with personal enduring vulnera-
bilities. Similarly, the VSAM does not identify
social structural factors (e.g., community socioe-
conomic adversity, work characteristics) as sep-
arate constructs that influence outcomes, which
may also limit the model’s ability to examine the
interaction among personal vulnerabilities and
social structural factors.

Second, the VSAM does not distinguish
acute stressors from chronic stressors (Karney
& Bradbury, 1995), which may limit the model’s
ability to examine the differential influences of
acute and chronic stressors, especially when
they act in concert with enduring vulnerabilities.
Moreover, some social structural conditions
(e.g., community and work adversities) may
operate as chronic stressors for spouses over
the life course. The present life course sys-
tems perspective proposes social structural
factors as a separate construct and conceptual-
izes that construct’s interaction with personal
vulnerabilities.

244 Journal of Family Theory & Review

Third, the VSAM does not lend itself to
considering distinctions between spouses,
which is addressed in the proposed life course
systems perspective’s conceptualization of
spouses’ distinct social and health or aging
pathways, which allows for examinations of
longitudinal interplay, comorbidity, and syn-
chrony between spouses. More generally, the
VSAM is a useful framework for research that
focuses on proximal determinants of marital
relations, whereas the proposed framework
largely focuses on the consequences of couple
experiences (S) for aging or health trajec-
tories (H) while incorporating the proximal
stressful context through the consideration
of more distal structural and socioeconomic
contexts.

The Proposed Theoretical Framework

In summary, the life course systems perspective
provides scaffolding for considering intraindi-
vidual, interindividual, and individual–context
associations (i.e., multilevel associations), as
well as the additive and multiplicative influences
of external and individual factors longitudinally
over the life course. This scaffolding can inform
research on relationships and aging across the
life course, including the incorporation of pre-
dictors and outcomes of continuity and change
in relationships during the aging process. That
is, an integrated perspective proposes multilevel
relational mechanisms that explain partners’
aging outcomes in the broader socioeconomic
and longitudinal couple context. Thus, bridging
the life course and systems perspectives both
hierarchically and longitudinally provides a
synthesized life course systems perspective
with enhanced explanatory power in relation to
partners’ aging process in the context of their
enduring couple relationships. Figure 1 provides
a graphical representation of the associations
highlighted for consideration within this inte-
grated life course systems perspective, and key
elements are described in the paragraphs that
follow drawing from panel data available in
the Later Adulthood Study (LAS) to inform
examples of these elements (see Wickrama
et al., 2017 for more on the study, including
survey measures). (In the proposed framework
and related discussion, heterosexual marriage is
used as the template; thus, the terms husband
and wife are employed. However, the model is
applicable to other populations. Readers could

use the terms partner or spouse instead.) The
key elements follow:

Intraindividual associations capture
within-individual dynamic associations over
time. These pathways include intraindividual
associations among social experiences (e.g.,
stressful experiences, S) and development over
time (e.g., health or aging outcomes, H) with
parallel developmental and social pathways
(actor effects denoted by continuous S and H
pathways in Figure 1). These pathways also
include intraindividual associations among dif-
ferent aging outcomes over time (longitudinal
comorbidity among aging outcomes signified
through contemporaneous correlations (e.g.,
mental health and physical health depicted by H
pathways in Figure 1).

Regarding these intraindividual associations,
the contemporaneous influence of social experi-
ences (S) on development outcomes (H) in the
longitudinal context may be reflected by parallel
intraindividual trajectories of social experiences
(e.g., marital quality, economic hardship) and
development (e.g., health), with changes in
one trajectory corresponding to changes in
another trajectory. It is also plausible for mutual
influences between intraindividual development
and social trajectories (S and H pathways) to
produce self-perpetuating life course processes
(e.g., accelerating work and health stress). Sim-
ilarly, the longitudinal comorbidity between
two developmental or aging attributes (e.g.,
depression and loneliness; body mass index,
or BMI, and depression) may be reflected by
parallel intraindividual trajectories (multiple H
pathways) when they demonstrate similar rates
of change (i.e., longitudinal comorbidity), which
has been shown to have a synergistic effect on
disease outcomes (Ladwig, Marten-Mittag,
Löwel, Döring, & Wichmann, 2006; Wickrama
et al., 2017).

Interindividual associations capture crossover
influences between partners over time (e.g., hus-
band to wife and wife to husband). These
pathways include interindividual associations
among aging outcomes over time (crossover or
contagion signified through individual–partner
contemporaneous correlations, such as between
H pathways for husbands and wives, and
partner effects, P, between husband and wife
pathways).

Regarding these interindividual associations,
drawing from research on stress crossover
between partners, the interdependence between

Aging Together 245

Figure 1. Integrated Life Course Systems Perspective to Study Couple Aging. H = husband; W = wife; HH,
HW = spouses’ developmental or aging trajectories; SH, SW = spouses’ social trajectories; HH, HW , SH, and SW
can be parallel or interlocking (intra- and interindividual) associations over time (T) with partner effects
(P). D = downward influences from context to individual; U = upward influences from individual to context;

R, Z, and Q = effects of distal socioeconomic factors, structural factors, and personal characteristics,
respectively, on individuals’ and couples’ social and aging processes.

partners in a longitudinal context can be
reflected by associations between interindividual
trajectories (Westman & Etzion, 1995), for
instance, a husband’s and wife’s parallel devel-
opmental trajectories (e.g., BMI trajectories)
or social trajectories (e.g., economic hard-
ship trajectories). Moreover, these crossover
associations may exist between individuals’
trajectories in one domain and their partner’s
trajectories in a different domain—for instance,
parallel trajectories between husbands’ devel-
opment and wives’ stressful work, or vice
versa. Reciprocal influences between husbands’
and wives’ trajectories are also possible. Par-
allel trajectories of an attribute (e.g., BMI)
between partners reflects the degree of syn-
chrony between partners, which has been shown
to have implications for the subsequent disease
outcomes of both partners (Wickrama, Lee, &
O’Neal, 2020).

Individual-couple context (multilevel) associ-
ations capture the influence of couple context on
individuals (downward effect, D) and the influ-
ence of individuals on the shared couple context
(upward effect, U). Other pathways included in
the framework consider

(a) the influence of distal environmental charac-
teristics (e.g., historical place and time) on
couples and individuals (R),

(b) the influence of social structure (e.g., social
class) and proximal socioeconomic environ-
ment (e.g., work conditions and community)
on couples and individuals (Z),

(c) the influence of personal characteristics and
choices (e.g., mastery, self-regulation, neu-
roticism, attitudes, race/ethnicity and gender)
on individuals (Q), and

(d) The interaction effect of Z and Q as well as R
and Q on social and developmental pathways,
which addresses the amplification or weaken-
ing of the influence of contextual factors by
individual characteristics.

In the theoretical model shown in Figure 1,
circles represent the couple system with varying
stability (or lack thereof) over the life course.
The continuities of social attributes (social
pathway, S) and health or aging attributes
(developmental pathway, H) are depicted by
intraindividual curved arrows over time that
account for cross-sectional and longitudinal
partner effects between partners (paths P).

246 Journal of Family Theory & Review

Multilevel influences between each individual
and the couple system are also illustrated (paths
D and U). Furthermore, downward arrows from
the upper box depict structural and historical
socioeconomic influences on individuals and the
couple system (direct additive effects, Z and R)
of the distal environment and social structure.
The upward arrows (paths Q) from the lower box
depict the influence of personal characteristics
(e.g., individual agency) on the individu-
als and the couple system (direct effects);
upward arrows also notate potential moderating
effects of these personal characteristics (R × Q
and Z × Q).

Specific Aging Hypotheses Derived From
the Life Course Systems Perspective

Our purpose in this section is to provide
examples of testable hypotheses and models
that can be derived from this life course sys-
tems perspective with an emphasis on better
understanding aging in the context of enduring
relationships. Again drawing from the LAS
panel data (see Wickrama et al., 2017, for more
on the study, including survey measures), we
conceptualize a repeated measure of physical
functioning (PF) as a valid indicator for aging,
where PF can be captured from a physical
impairment scale noting the range of impair-
ment for vigorous and moderate activities (e.g.,
running and carrying groceries, respectively;
RAND 36-Item Health Survey 1.0; Hays, Sher-
bourne, & Mazel, 1993). We conceptualize a
repeated measure of experiences of economic
hardship (EH) as an example of a social expe-
rience influenced by social structure (stressful
marital relations would be another example)
with implications for partners in enduring cou-
ple relationships, where EH can be captured by
summing yes responses to various items repre-
senting financial constraints and cutbacks (e.g.,
difficulty making ends meet, having a phone
disconnected). Both PF and EH are continuous
composite measures. Sample hypotheses, which
are not intended to be exhaustive of all possi-
bilities, capturing the aging process include the
following:

Hypothesis A: The level and change in individu-
als’ EH is related to the level and change in their
own PF over the life course. This hypothesis
examines an intraindividual influence, namely,
a longitudinal actor effect. (The covariate

predicting the level and change in PF can also
be time invariant, e.g., early EH).

Hypothesis B: The level and change in individu-
als’ EH is related to the level and change in their
partners’ PF over the life course. This hypothesis
examines an interindividual influence, namely,
a longitudinal partner effect. (The individuals’
covariate predicting the level and change in part-
ners’ PF can also be time invariant, e.g., their
partner’s personality).

Hypothesis C: PF is contemporaneously asso-
ciated (i.e., correlated) between partners. This
hypothesis examines interindividual contem-
poraneous associations and/or longitudinal
concordance. Similar contemporaneous associ-
ations may be hypothesized for EH.

Hypothesis D: There exists a couple-level con-
struct of EH (i.e., couple EH). The level and
change in couples’ EH is related to the level
and change in each individual’s PF over the life
course. This hypothesis examines a downward
contextual influence indicating how the couple
system influences its constituents (i.e., partners).

Hypothesis E: Proximal socioeconomic context
(e.g., work quality) influences both EH and PF
pathways (as depicted by Z in Figure 1).

Hypothesis F: Individuals’ sense of mastery
(reflecting agency) influences both EH and PF
pathways (as depicted by Q in Figure 1).

Hypothesis G: Proximal socioeconomic context
(e.g., work quality) and sense of mastery inter-
act to influence both EH and PF pathways (as
depicted by Z × Q in Figure 1).

Advanced Approaches for Testing
Hypotheses in the Couple Context

There are various methodological approaches,
both quantitative and qualitative, available to
test these example hypotheses. Given the goal
of presenting multiple quantitative analytical
approaches in extensive detail, space limitations,
unfortunately, do not allow for a discussion of
qualitative approaches to testing the hypotheses.
The quantitative approach best suited to address
the research hypotheses depends on the avail-
ability of data and the type of change process of
interest (e.g., residual change, absolute change).
We provide an overview of two broad method-
ological approaches and their extensions that are

Aging Together 247

particularly well suited to the study of the aging
process in the longitudinal context of enduring
couple relationships. We emphasize techniques
developed and enhanced relatively recently. The
two broad approaches are cross-lagged autore-
gressive modeling and latent growth curve mod-
eling. We review and discuss the assumptions,
applicability, strengths, and limitations of each
of these approaches.

A Cross-Lagged Autoregressive Approach

Cross-lagged autoregressive (CL-AR) modeling
(Jöreskog, 1970) is a useful tool for family
gerontologists who investigate time-sequential
processes of couple aging over the life course.
Figure 2 presents a simple CL-AR model detail-
ing cross-lagged and contemporaneous associ-
ations, which is also referred to as an intrain-
dividual cross-lagged contemporaneous model.
This model assesses a hypothesis connecting
the PF and EH of an individual across two
occasions. In Figure 2, autoregression refers to
regressing one variable on its lagged score (or
previous measurement of the same construct),
for example, regressing PF2 on PF1 or EH2 on
EH1. This is an example of Hypothesis A and
is denoted by the parallel lines between S and H
in Figure 1). In addition to traditional regression
assumptions, it is assumed that repeated mea-
sures of the same construct measure the same
attribute across occasions (i.e., time invariance)
and that the relationship between variables is lin-
ear. The regression coefficients b1 and b4 can be
interpreted as the extent to which the value of
an attribute at one time point predicts the value
of the same attribute at the later point in time
(e.g., how PF1 predicts or explains variation in
PF2). These effects (b1 and b4) describe the sta-
bility of individual differences over time or the
degree of “reshuffling” in the rank order of indi-
viduals (i.e., rank-order stability of PF and EH).
Small b1 and b4 coefficients suggest low stabil-
ity in the rank order over time. Furthermore, the
model depicts that EH1 predicts PF2 after con-
trolling for the effect of PF1 (b3). The model
also includes a similar cross-lagged effect testing
the influence of PF1 on EH2 (b2) (Hypothesis
A and denoted by the parallel lines in Figure 1).
Importantly, the strength of these cross-lagged
effects (b2 and b3) depends on the strength of the
stabilities (b1 and b4). For a more detailed dis-
cussion on how cross-lagged effects in CL-AR
models are influenced by correlations among the

Figure 2. Intra- and Interindividual Cross-Lagged
Auto-Regressive (CL-AU) Approach with Repeated
Measures. Stability and cross-lagged paths for

intraindividual associations are in Gray; H and W
superscripts represent husband and wife,
respectively. PF = physical functioning,

EH = economic hardship, and T = time point.

four variables comprising the model, see Lorenz,
Conger, Simons, and Whitbeck (1995).

Even with longitudinal data, cross-lagged
effects (b2 and b3) do not firmly establish the
causal order of attributes because data come
from a passive design rather than a design in
which predictors are experimentally manipu-
lated. However, if the CL-AR model is based
on a strong theory and a proper temporal design
(e.g., appropriate time lags between measure-
ment occasions), the model can provide some
evidence for the causal order. Importantly,
this approach allows for an examination of
the relative strength of the mutual influences
of these attributes on each other (e.g., b2 and
b3) (Kearney, 2016). The primary weakness

248 Journal of Family Theory & Review

of a CL-AR model is that it is not sensitive to
intra- or within-individual changes over time.
Furthermore, when a construct has high stability
over time, it is impossible to predict residual
change in the construct even when there is some
degree of absolute change.

Figure 2 also presents an extended CL-AR
model with three time points. For instance, this
example model includes measures of PF and EH
at three occasions: early midlife, middle midlife,
and later life (where early midlife overlaps with
the aftermath of rural farm crisis). It is also pos-
sible to incorporate direct paths from Time 1 to
Time 3, representing the delayed influence of
early experiences on later outcomes. An advan-
tage of this approach is that researchers can
examine whether the effect between PF and EH
is stable. For example, this model allows for
researchers to test whether associations between
EH1 and PH2 are similar in magnitude to the
associations between EH2 and PH3.

Figure 2 further extends this CL-AR
approach to incorporate interindividual influ-
ences (Hypothesis B), adding data from partners
rather than relying solely on data from one
individual. Regarding the incorporation of
partner data, this model considers intraindi-
vidual associations for husbands and wives
while simultaneously assessing possible spousal
crossover (i.e., partner effects between couple
members, Hypotheses B and C noted as P in
Figure 1), for example, the influence of hus-
bands’ PF1 on wives’ PF2 and vice versa, or,
similarly, the influence of husbands’ EH1 on
wives’ PF2 and vice versa.

The inclusion of data from both partners
also allows researchers to examine whether
these processes have a similar magnitude over
time, which is known as the dyadic invari-
ance assumption (Peugh, DiLillo, & Panuzio,
2013). For example, two of the more common
constraint patterns with distinguishable dyad
members (e.g., husband and wife) involve a
model that constrains husbands’ and wives’
intraindividual processes to be equal and/or
interindividual processes to be equal. These
equality assumptions allow for investigations of
whether inter- and intraindividual processes are
significantly different between husbands and
wives.

When the dyadic model contains repeated
measures with more than two occasions, the
dyadic invariance assumption becomes more
complex and allows for an investigation of

whether intra- and interindividual processes
are similar in magnitude between dyad mem-
bers and across time. This assumption is
known as the longitudinal dyadic invariance
assumption (Whittaker, Beretvas, & Falbo,
2014). For example, researchers can test
whether intraindividual processes are equal
over time and between dyad members (e.g.,
b(HH)21 = b(HH)32 = b(WW)21 = b(WW)32 in
Figure 4, presented in more detail below).
In the same manner, researchers can test
whether interindividual processes are equal
over time and between dyad members (e.g.,
b(WH)21 = b(WH)32 = b(HW)21 = b(HW)32; see
Figure 4). Although two invariance assump-
tions (across time and dyadic members) can be
tested simultaneously, they could also be tested
consecutively (i.e., longitudinally followed by
dyadic invariance or vice versa; Whittaker et al.,
2014).

Figure 3 presents an extension of the CL-AR
approach with couple-level constructs (Hypoth-
esis D). Here, the couple context of EH is
defined using a latent construct derived from the
husband’s and wife’s EH measures. Incorpo-
rating the couple context in this manner allows
for an assessment of hypotheses related to
couple-level continuity over time (Hypothesis D
and noted as D and U in Figure 1). In addition
to intra- and interindividual associations, this
model includes multilevel associations between
each partner and the couple context. In sum, this
model can be utilized to locate couple dynamics
within a life course context with a long view.
To test Hypotheses E–G, it is possible to add
variables capturing the socioeconomic con-
text, structural characteristics, and/or personal
characteristics and their interactions for incor-
poration into the models shown in Figures 2 and
3 to predict EH and PF constructs.

A Random-Intercept Extension of the CL-AR
Approach (RI-CL-AR)

The CL-AR models allow researchers to exam-
ine dynamic processes (Hypotheses A and B)
related to couple aging over time. However,
longitudinal data can also be considered a
form of multilevel data in which measurement
occasions are nested within individuals. Fol-
lowing this conceptualization, it is possible,
and perhaps preferable in some instances, to
separate within-person (within-level) vari-
ability from between-person (between-level)

Aging Together 249

Figure 3. Intra- and Inter-Individual Cross-Lagged Contemporaneous Model Considering the Couple
Context. PF = physical functioning, EH = economic hardship, and T = time point. Paths for intra- and

inter-individual associations are shown in gray. Factor loadings are shown with dashed lines. H, W, and C
superscripts represent husband, wife, and couple, respectively.

variability, which can be accomplished using
the random-intercept CL-AR (RI-CL-AR)
model proposed by Hamaker, Kuiper, and
Grasman (2015). A minimum of three mea-
surement waves is required (Hamaker et al.,
2015). An example is shown in Figure 4, where
random-intercept latent variables are defined
for husbands’ and wives’ PF. Because these
random-intercept factors are simply added to
a standard CL-AR, the CL-AR is statistically
nested within the RI-CL-AR. The inclusion
of a random-intercept factor accounts for the
time-invariant, traitlike stability of the given
attribute within each individual across measure-
ment occasions (Berry & Willoughby, 2017). In
this example, the random-intercept variable for
husbands’ PF estimates the average intercept of
husbands’ PF considering each time point (T1,
T2, and T3). The mean of this random-intercept
variable represents the estimated average PF for
husbands across the sample, and the variance
of this variable represents the between-person
variability in average PF for husbands. Using
this type of model in a structural equation
modeling (SEM) framework allows researchers
to examine the between-person association
of husbands’ and wives’ PF by specifying a
covariance between their random intercept

constructs (see the double-arrowed line in
Figure 4).

After accounting for between-person pro-
cesses, observed indicators have specific
residuals at each time point. At a given time,
the residual represents the individual’s deviation
from his or her own average (i.e., within-person
variability) (e.g., Hoffman, 2015). In an SEM,
these residuals can be estimated as another
set of latent variables. For example, Figure 4
shows six latent variables representing the
residuals of husbands’ and wives’ PF at Times
1, 2, and 3. These latent variables estimate
time-specific residuals of husbands’ and wives’
PF, indicating within-person variations in their
PF. These residuals can be used in the RI-CL-AR
model to examine cross-lagged autoregres-
sive associations in PF between husbands
and wives.

In the RI-CL-AR example shown in Figure 4,
the contemporaneous correlation in PF between
husbands and wives (noted as path c) reflects
the association between husbands’ and wives’
within-person deviations in PF. The autore-
gressive paths between the residuals can
be interpreted similarly to the paths in the
traditional CL-AR model for actor and part-
ner effects. An example actor path among

250 Journal of Family Theory & Review

Figure 4. Random-Intercept Cross-Lagged Autoregressive (RI-CL-AR) Approach. RI = random intercept,
RE = residual, PF = physical functioning, and T = time point. H and W superscripts represent husband and

wife, respectively. Factor loadings are shown with dashed

lines.

these residuals is the path from husbands’
Time 1 residual to husbands’ Time 2 resid-
ual (labeled b(HH)21). Similarly, an example
partner path among the residuals is the path
from husbands’ Time 1 residual to wives’
Time 2 residual (labeled b(WH)21). Here,

the associations involve only within-person
deviations of PF, which means that the
parameters reflect intra- and interindividual
processes with time-specific deviation scores
(i.e., residual scores) after accounting for the
time-invariant, traitlike stability component

Aging Together 251

of each individual across the time span
investigated.

A Latent Growth Curve Modeling Approach

Latent growth curve modeling (LGCM; Mered-
ith & Tisak, 1990) is another analysis approach
that can prove useful for testing Hypotheses A,
B, and C involving time-varying attributes. Panel
a of Figure 5 presents a model with two LGCs
created from three repeated measures of PF and
EH for an individual with latent variables calcu-
lated from the repeated measures to indicate the
initial level (I) and slope (S), or rate of change.
At the conceptual level, growth curve model-
ing in an SEM framework can be considered a
two-stage process. At the first stage, the goal
is to describe change in construct(s) over time
for each individual in the study. Conceptually,
a regression line with an intercept and slope(s)
(i.e., a growth curve) is estimated to plot each
individual’s change over time for the construct
of interest (capturing intraindividual change). In
the example in Figure 5, LGCs are shown for PF
and EH. At the second stage, individual-specific
intercepts and slopes are estimated as latent
constructs with a mean and a variance, where
the mean of the intercept represents the average
of the variable of interest at the first time point
and the mean slope represents the average rate
of change over time. The variance calculations
identify the interindividual differences in these
initial level and slope factors.

Assuming that there is sufficient variation in
the growth parameters (indicated by statistically
significant variance statistics for the intercept
and/or slope), theoretically driven covariates, or
predictors, can then be incorporated into the
LGCM to explain the variation in initial levels
(intercepts) and slopes among individuals. For
example, using time-invariant covariates (e.g.,
mastery), this type of model could identify why
the initial level of PF is higher for some individ-
uals than others and/or why the rate of change
in PF is steeper for some individuals than oth-
ers. Thus, in addition to the simple descrip-
tion of change, LGCM allows for the system-
atic explanation of interindividual differences in
both the level and the change of study constructs
such as PF and/or EH. Furthermore, this LGCM
approach estimates residuals for each time point
(unexplained by the systematic growth), which
can also be used to estimate residual change over
time using external factors.

Figure 5. Latent Growth Curve Models (LGCMs).
PF = physical functioning, EH = economic hardship,
I = initial level, and S = slope. The H, W, and C

superscripts represent husbands, wives, and couples,
respectively.

As shown in Figure 5, the predictors can
be growth parameters of another time-varying
variable, which is the case when parallel growth
curves are modeled in an SEM framework
(Hypothesis A). In this example, with growth
curves for PF and EH, the level and slope of EH
are shown to predict the level and slope of PF,
respectively (Hypothesis A, noted by the parallel
lines between S and H in Figure 1). In addition,
consistent with the cumulative disadvantage
notion, within an individual, the level of early
EH also can predict the slope of PF (Hypothesis
A, not shown in Figure 1). Alternatively, as
also shown in Figure 5, associations between
growth curves’ parameters of both partners’ PF
or EH can be modeled simultaneously to assess

252 Journal of Family Theory & Review

partner effects (Hypothesis B, noted as P in
Figure 1).

With the LGCM approach, it is also pos-
sible to incorporate a growth curve for the
time-varying couple context (e.g., couple EH).
Figure 5 shows that the growth parameters
of couple EH can be defined as second-order
growth parameters (i.e., a factor-of-curves
model) using growth parameters of both part-
ners’ EH (Wickrama, Lee, O’Neal, & Lorenz,
2016). This analysis produces a comprehensive
multilevel model of (a) intraindividual asso-
ciations (e.g., associations in growth factors
between husbands’ EH and PF, or Hypothesis
A), (b) interindividual associations (e.g., associ-
ations in growth factors between husbands’ EH
and wives’ PF, or Hypothesis B), and (c) couple
contextual associations (e.g., associations in
growth factors between the couple-level slope
of EP and each partner’s level and slope of PF,
or Hypothesis D).

This LGCM approach to investigating aging
processes has three clear advantages over the
CL-AR approach. First, although CL-AR mod-
els can incorporate more than two time points,
they can only consider change in two repeated
measurements of a variable at a time, which
means that systematic growth patterns over time
cannot be assessed. That is, changes across all
three time points are not considered in relation to
one another in a comprehensive and systematic
manner, thereby largely failing to conceptualize
time as an ongoing process (Coyne & Downey,
1991). This assumption is particularly problem-
atic when change follows a nonlinear trajectory
(Willet & Sayer, 1994). Consequently, a growth
curve approach might be more appropriate for
examining systematic patterns of change in con-
structs over time.

A second strength of LGCM relates to
the inability of CL-AR models to estimate
intraindividual change and explain its variation.
For example, a slope parameter of PF in a growth
curve captures the intraindividual change in PF
with a mean and a variance statistic. As shown in
Figure 4, this PF slope variance can be explained
by EH growth parameters (i.e., intercept and
slope). Unlike a CL-AR model, LGCM is not
constrained by the stability of PF over time. At
the same time, the PF-level parameter captures
interindividual differences (i.e., rank order) in
PF with a mean and variance statistic. This level
variance of PF can also be explained by the level
parameter of EH. That is, to understand and

comprehensively investigate the aging process
in couples (e.g., physical limitations, psycho-
logical symptoms, behaviors), it is important to
account for multiple facets of change. The level
and rate of change (slope) capture elements of
a multifaceted process of aging, namely, the
intensity or severity (level) and the amount of
growth or decline (rate of change, or slope).
The CL-AR approach is not sensitive enough
to capture, or distinguish, distinct courses of
aging characteristics (i.e., physical limitations),
although these courses may have particular
antecedents and/or consequences.

Third, distinct courses of aging (as evidenced
by the level and change growth parameters) may
predict key outcomes, such as the onset of severe
health problems. It is important to understand
the relative contributions of different growth
parameters of an attribute (e.g., level and rate
of change) to an outcome (e.g., a specific dis-
ease of interest). For example, the level and
change in day-to-day memory failure may inde-
pendently predict the onset of dementia in later
years. Using a CL-AR approach, only the level
of an attribute (or attributes) can be used to pre-
dict subsequent aging outcomes.

Furthermore, a latent change score (LCS)
approach can also be used to estimate latent
growth curve models. In these models, in addi-
tion to modeling the constant change traditional
LGCMs estimate (systematic change or matu-
ration), proportional change can also be spec-
ified and estimated. Proportional change is the
change attributed to a lagged outcome variable,
whereas systematic change unfolds over time.
For specifics on LCS growth curves, refer to
Grimm, Ram, and Estabrook (2017).

Combining LGCM and CL-AR Approaches

With an SEM approach, LGCM and CL-AR
models can be combined to examine dynamic
processes of changes among repeated measures.
We describe two specific approaches: an autore-
gressive latent trajectory with structured residu-
als (ALT-SR) approach and a latent change score
approach.

An autoregressive latent trajectory with struc-
tured residuals approach. An example of the
ALT-SR approach (Berry & Willoughby, 2017;
Curran, Howard, Bainter, Lane, & McGinley,
2014) is shown in Figure 6. By disaggregat-
ing the two levels of inference (within-person

Aging Together 253

Figure 6. Autoregressive Latent Trajectory With
Structured Residuals (ALT-SR) Approach. I = initial

level, SL = slope, RE = residual, PF = physical
functioning, and T = time point. H and W
superscripts represent husband and wife,

respectively. Gray arrows represent intraindividual
processes. Factor loadings are shown with dashed

lines.

processes and between-person processes), the
ALT-SR latent factors are modeled to simulta-
neously estimate between-person variations in
trajectories and within-person, dynamic CL-AR
associations. This extension can be used to test
Hypotheses B and C in a comprehensive manner
that considers both growth factors and residuals.

As in a traditional LGCM, an ALT-SR model
estimates two components of change using
repeated measures of PF for both husbands and
wives: (a) initial level and (b) slope. Variances of
the latent growth variables are estimated and rep-
resent between-person variations in trajectories.
Longitudinal associations between husbands’
and wives’ PF can be estimated by specifying
covariances among growth factors (see gray
double arrows in Figure 6). These associations
represent between-person processes in couple
PF change over time. In addition, traditional
CL-AR associations can be specified in this
model using time-specific latent residual vari-
ables. These CL-AR associations are analogous
to those in the RI-CL-AR model described in the

previous section, where all CL-AR associations
are estimated based on within-person deviations
in husbands’ and wives’ PF.

RI-CL-AR and ALT-SR models are sim-
ilar in that both approaches simultaneously
estimate between-person and within-person
processes. However, there are some impor-
tant differences in the processes. Regarding
between-person processes, the intercept latent
factors of ALT-SR are different from the ran-
dom intercept factors of RI-CL-AR. In an
RI-CL-AR model, random-intercept factors
estimate the time-invariant, traitlike stability
of outcomes (i.e., estimated average scores
of husbands’ or wives’ PF across Times 1, 2,
and 3). In an ALT-SR model, intercept fac-
tors indicate estimated scores of PF at Time
1 (or at any specified time). In addition, the
estimation of within-person CL-AR processes
also differs across the models. ALT-SR models
estimate CL-AR associations on the basis of
the time-specific deviation from the individual’s
trajectory (residual scores after accounting for
the initial levels and slopes of PF). In contrast,
RI-CL-AR models estimate CL-AR associa-
tions based on time-specific deviations from
the individual’s averages (residual scores after
accounting for the average of PF across Times
1, 2, and 3).

The question of whether to include latent
variables for examining between-person pro-
cesses can be addressed empirically by compar-
ing model fit across models with and without
random intercepts and slopes and by comparing
direct tests (i.e., likelihood ratio tests) of whether
the latent variances are statistically significant.
If a model with a slope fits the data substan-
tially better than a model without a slope does,
then substantive interpretations of the model
with the slope fit are likely more valid (Ehm,
Hasselhorn, & Schmiedek, 2019). Theoretical
justifications should accompany such empirical
considerations.

A latent change score approach (LCS). The
LCS approach (Hamagami & McArdle, 2007) is
shown in Supplemental Figure 1. In LCS mod-
els, cross-lagged effects between dyad mem-
bers involving scores can be examined after
incorporating both constant and proportional
change into the model. External predictors can
also be included, as in ALT-SR models (Jajo-
dia, 2012). Based on a true score model, an
LCS model operates on true scores of PFs (i.e.,

254 Journal of Family Theory & Review

latent variables, L-PF1 to L-PF3) by separat-
ing true scores from measurement errors. In
an LCS model, this true score of PF can be
used to express the state of PF at a given time
(e.g., L-PF2) as a function of its previous state
(e.g., L-PF1) and the true change scores of PF
between two successive measurement occasions
(e.g., ΔPF1-2), which are estimated as latent
change scores.

These latent change scores (e.g., ΔPF1-1,
ΔPF1-2, ΔPF1-3. ..) can then be used to esti-
mate two dynamic processes (within-person and
between-person processes) of PF: (a) constant
change of PF across two successive time points
(Time Points 1–2, Time Points 2-3 … ) and
(b) proportional change of PF between succes-
sive time points. Using repeated latent change
scores, a constant changes model estimates two
components of changes: initial level and slope
(see L and G in Supplemental Figure 1), corre-
sponding to the level and slope growth factors
in traditional LGCM. The variances of these
growth factors represent between-person vari-
ations in trajectories. In addition, proportional
change of PF can be modeled by specifying
regression paths between true scores of PF (e.g.,
L-PF1) and latent change scores (ΔPF1-2) (see
𝜋 coefficients in Supplemental Figure 1). These
proportional change coefficients represent each
individual’s change in PF across two successive
time points proportional to his or her previous
true state (e.g., change between L-PF1 and
L-PF2—denoted as ΔPF1-2—is influenced by
L-PF1). Typically, these coefficients can be con-
strained to be equal over time. In this example
of PF, constant change and proportional change
processes can be estimated simultaneously
in a dual change model (see Supplemental
Figure 1). A researcher can select the optimal
change model through model comparison tests
(e.g., Bayesian information criterion values,
likelihood ratio tests).

This dual change model of PF can be extended
to the dyadic latent change score model (see
Supplemental Figure 2). This dyadic latent
change score model accounts for the dynamic
processes of change between dyad members
(e.g., interdependent associations in change
scores of PF between husbands and wives).
In the same manner as an ALT-SR model,
longitudinal associations between husbands’
and wives’ PF across time can be estimated by
specifying covariances among growth factors. In
addition, CL-AR associations can be specified

in the dyadic latent change score model using
coupling effect regression paths (see 1 and 2
coefficients in Supplemental Figure 2). These
coupling coefficients are useful for family
researchers examining how an individual’s prior
true status predicts subsequent changes (e.g., an
individual’s prior true status of PF predicting
changes in his or her partner’s PF).

For testing some hypotheses, this LCS
approach is thought to be an improvement over
LGCM. With an LCS model, in addition to
constant change (G), proportional change (the
change component proportional to the previous
state) is also estimated. The estimation of these
two components of change may be important
for family gerontology research. For instance,
using the current example, the constant change
component may correspond to persistent change
in PF due to maturation, and the proportional
change component may correspond to incre-
mental deterioration due to the severity of PF. In
addition, a dyadic LCS model allows for the esti-
mation of cross-lagged paths between spouses,
which is not possible in a traditional LGCM.

Growth Mixture Model Extensions of LGCM

The previously discussed models (e.g., CL-AR,
RI-CL-AR, LGCM, ALT-SR, LCS) are all vari-
able centered, meaning that they assume all indi-
viduals (or trajectories) are drawn from a sin-
gle population for which a single set of “aver-
aged” parameters can be estimated. However,
this assumption may be inaccurate when the
sample comprises multiple unknown subpopula-
tions of trajectories (i.e., population heterogene-
ity), because each subpopulation may be best
characterized by a different set of parameters.
For example, some individuals may show high
and decreasing PF trajectories, whereas another
subgroup of individuals may exhibit low and
increasing PF trajectories over time.

A person-centered approach can be utilized
to identify subpopulations of similar individuals
using latent class variables in an SEM frame-
work (Wickrama et al., 2016). These latent class
models are often referred to as finite growth
mixture models (GMMs). With this analytical
approach, trajectory class membership is not
known but is inferred from the data on the
basis of posterior class membership probabil-
ity (Muthén, 2004). The antecedents and conse-
quences of the identified subgroups of individu-
als can be examined. Subgroups can be identified

Aging Together 255

at the individual level (e.g., groups of husbands)
or the couple level (groups of couples), which
can be particularly useful for dyadic analysis of
couples in enduring relationships.

Parallel process growth mixture model
(PP-GMM). One specific type of couple-level
GMM that can identify similar groups of cou-
ples with the same dyadic trajectory pattern
is a parallel process growth mixture model
(PP-GMM). A PP-GMM provides a test of
Hypothesis C regarding the contemporaneous
association in PF between partners by identify-
ing groups of similar couples. A hypothesized
path diagram for this model within a latent
growth curve framework is shown in Figure 7.
As shown, husbands’ and wives’ PF trajectories
can be specified as factors of a latent grouping
variable, C (= 1, 2, 3, . . ., K). In this model, all
growth parameters of husbands and wives (i.e.,
the initial levels and slopes for husband and wife
trajectories of PF) are modeled as simultaneous
contributors to the empirical identification of
couple-level latent classes with similar patterns
of PF trajectories. This model identifies dis-
tinct patterns of longitudinal changes in couple
PF, thereby enabling researchers to examine
couples’ longitudinal comorbidity of PF. For
family science research, the identification of
co-occurring attributes in the couple context is
important for understanding mutual influences
and dependencies across partners.

After identifying unobserved subgroups of
couples’ PF trajectories with distinct patterns,
covariates can be specified into the PP-GMM.
For instance, following previous research not-
ing that early couple-level financial strain can
have long-term effects on couple-level finan-
cial strain in later adulthood through PF (Lee
et al., 2019), Figure 7 demonstrates a model
specifying family-level EH as a concurrent event
(i.e., predictor) and consequence (i.e., outcome)
of couple-level classes of FP. Another example
is a recent study that identified groups of cou-
ples with similar dyadic patterns of marital
trajectories over 25 years, including socioeco-
nomic background characteristics as predictors
of these trajectories and later mental and phys-
ical health as consequences of those trajecto-
ries (Wickrama, Klopack, & O’Neal, 2020). In
an SEM framework, there are multiple stepwise
approaches for specifying predictors and out-
comes (e.g., one- and three-step approaches) in
a mixture model. Detailed descriptions of these

stepwise approaches can be found in Wickrama
et al. (2016).

Latent transition growth mixture modeling
(LT-GMM). Another extension of GMM that
can be helpful for investigating complex couple
aging processes is latent transition growth mix-
ture modeling (LT-GMM). LT-GMM allows
researchers to investigate transition patterns
(or discontinuous trajectories) over time (Lee,
Wickrama, Kwon, Lorenz, & Oshri, 2017). One
example is conjoint class trajectories between
husbands’ and wives’ PF (shown in Figure 7).
For example, a PP-GMM can identify couples’
conjoint class trajectories of PF from midlife to
later adulthood, but a PP-GMM also assumes
that the subgroups are fixed. That is, develop-
mental continuity is assumed, with subgroup
members expected to follow the same growth
trajectory across times (i.e., from midlife to
later adulthood). However, classification of
couple PF trajectories may change depending
on couples’ life experiences and their responses
to life transitions (e.g., timing of retirement or
relocation), which suggests developmental dis-
continuity in couples’ PF trajectories during the
transition period from midlife to later adulthood.
This scenario is consistent with Hypothesis E,
signifying the influence of proximal contexts,
including the retirement context, and thus, a
PP-GMM may not be appropriate. Instead,
as shown in Figure 7, a LT-GMM specifies
two separate latent classes in the model: (a)
classes for couple PF trajectories in midlife
and (b) classes for couple PF trajectories in
later adulthood. An LT-GMM also estimates
transition probabilities from classes of PF
trajectories in midlife to classes of PF trajec-
tories in later adulthood, which allows for a
comparison of movers and stayers. Movers are
those couples who transition from one class
to another across time. Stayers are those who
remain in the same class across time. With this
comparative capability, life transition experi-
ences, such as early retirement, can be specified
as predictors or outcomes of these transition
patterns.

Future Directions

We sought in this article to derive testable
hypotheses and demonstrate analytical method-
ologies that can advance research on aging
processes in the context of couple relationships.

256 Journal of Family Theory & Review

Figure 7. Growth Mixture Modeling (GMM). C = latent class, k = number of latent class, I = initial level,
SL = slope, PF = physical functioning, and EH = economic hardship. H and W superscripts represent husband
and wife, respectively. Gray arrows represent intraindividual processes. Factor loadings are shown with

dashed lines.

In completing this, directions for future research
in the area of couple aging are evident. We
highlight two specific areas: (a) incorporating
psychosocial, behavioral, and biological pro-
cesses and (b) incorporating recent advances in
studies of couple dynamics into the life course
systems perspective.

Incorporating Psychosocial, Behavioral,
and Biological Processes

The study of family gerontology can be
advanced considerably by conceptualizing
stress as a process that is encountered over the
life course. For instance, stressors stemming
from socioeconomic adversity may multiply
and accumulate through various stress pro-
cesses, including stress proliferation, stress
accumulation, and stress potentiation (Pearlin,
Schieman, Fazio, & Meersman, 2005). Accord-
ing to past research, stressors stemming from
earlier socioeconomic contexts and, relatedly,
social class may proliferate in the socioeco-
nomic domain as well as across other domains,

and the accumulation of stressors can exert
particular influences on individuals (Elder &
Geile, 2009; Pearlin et al., 2005). Furthermore,
individuals’ exposure to stressors early in life
may increase their vulnerability to stressful
life experiences later in life (stress potentia-
tion) (Dich et al., 2015). The elucidation of
these stress processes in the life course systems
perspective will enhance understanding of the
formation and continuation of chains of stressful
circumstances.

One way these chains may exist is through
psychosocial and cognitive mechanisms that
connect stress exposure to aging outcomes.
Consistent with the life course systems per-
spective, partners’ stressful life experiences
may stem in part from issues in the larger con-
text (e.g., early and proximal socioeconomic
environment) and may continue as stressful
social pathways within the couple system,
with detrimental consequences for health and
aging outcomes. For instance, recent research
has focused on psychological schema, includ-
ing hostility and negative and positive affect,

Aging Together 257

as mediating constructs connecting stressful
experiences and health outcomes (Gibbons
et al., 2014; Luecken & Roubinov, 2012; Wick-
rama, Lee, Klopack, & Wickrama, 2019).
The identification of these types of modifi-
able micromechanisms would inform health
promotion policies and programs for aging
couples.

In addition to psychosocial and cognitive
mechanisms, there is also a need to consider
behavioral processes such as an unhealthy
lifestyle, which may represent a proximal health
risk. An unhealthy lifestyle can refer to multiple
risk behaviors, such as lack of exercise, poor
diet, and smoking. Engaging in an unhealthy
lifestyle has been shown to result in cumulative
physiological dysregulation and elevated disease
risk, as reflected by biomarkers of allostatic load
and inflammation (Lee, Wickrama, & O’Neal,
2018). In particular, the timing of chronic dis-
ease onset is a determining factor of accelerated
biological aging (Maggio, Guralnik, Longo, &
Ferrucci, 2006; Pischon et al., 2008). Although
health behaviors and aging have been researched
to some extent, less research has situated these
behaviors and their aging consequences within
the life course systems perspective. Research
has shown that partners’ shared unhealthy
lifestyle has an impact on their aging process
(Umberson, Williams, Powers, Liu, & Need-
ham, 2006). Thus, incorporating constructs that
capture health behavior for research rooted
in the life course systems perspective would
enhance understanding of partners’ accelerated
aging.

In recent years, aging research has increas-
ingly focused on the degree of biological
system dysregulation (often referred to as
biological aging, early disease risk, or acceler-
ated aging). Beyond the behavioral pathways,
the dysregulation of biological systems may
also reflect chronic direct exposure to stressors,
including a stressful socioeconomic context
(“weathering”; Geronimus, Hicken, Keene, &
Bound, 2006). That is, research suggests that
the effects of stress can occur at a more basic,
almost cellular level with notable consequences
for later health. These health consequences
often go undetected until they reach a thresh-
old that results in disease onset. Biological
aging can be assessed by various molecular
markers, including epigenetic, inflammatory,
and metabolic syndrome markers (Maggio
et al., 2006; Pischon et al., 2008; Xia, Chen,

McDermott, & Han, 2017). The incorporation of
markers of biological aging or accelerated aging
into the life course systems perspective would
enhance our understanding of the influences
of chronic stressful environments on the aging
process.

Furthermore, research is needed to evaluate
more extensively the interconnections among
behavior, cognition, and psychosocial mecha-
nisms. As one example, with advancing age,
partners often become increasingly dependent
on each other for a variety of needs from
basic activities of daily living (e.g., dressing,
meal preparation) to social interaction and
stimulating conversation. As such, caregiving
can be physically, mentally, and emotionally
demanding (Godfrey et al., 2018). More com-
prehensive research can examine how these
caregiving trajectories relate to previous life
experiences and influence the health and rela-
tional outcomes of both couple members. In
turn, research can identify how these health
impacts of caregiving vary depending on indi-
vidual and couple characteristics as well as
surrounding context and available resources.
Moreover, in identifying key transitions of the
later life course, the transition to “caregiver” and
“one being cared for” is a sizable change with
implications that ripple throughout the couple
and family. Not only would this knowledge in
a long-term context advance our understand-
ing of aging in the couple context, it would
also provide clear implications for programs
and policies by identifying which resources
(both distal and proximal, structural and rela-
tional) are most strongly connected to caregiver
well-being and position programs to exert
the maximum impact on supporting couples
in later life.

Incorporating Recent Advances in the Study
of Couple Dynamics

Less is known about partner–partner health risk
resemblance in a longitudinal context and its
health consequences for each partner. That is,
although individuals can have distinct health
risk trajectories, these trajectories are interre-
lated for many partners and may combine to
influence health outcomes. More recently, aging
research has focused on this comorbidity of
health or aging outcomes between spouses (i.e.,
“lovesick”; Kiecolt-Glaser & Wilson, 2017).
For partners in enduring couple relationships,

258 Journal of Family Theory & Review

longitudinal comorbidity or synchrony of health
risks (e.g., trajectories of husbands’ and wives’
BMI) has been shown to amplify the health risk
of both partners (Wickrama, Lee, & O’Neal,
2020). Furthermore, synchronized health risk
trajectories may exist within individuals (e.g.,
individuals’ trajectories of BMI and depressive
symptoms), and there is evidence that these
synergies explain more variation in health risks
than examinations of individual risk trajecto-
ries (Wickrama et al., 2017). Thus, examining
partners’ health risk trajectories simultane-
ously within the life course systems perspective
can shed light on the joint influences of part-
ners’ health risk trajectories on their health
outcomes.

Within the study of couple dynamics, ana-
lytical advances have increasingly demonstrated
that there is no single health risk trajectory
for couples. Instead, there are likely homoge-
neous groups of couples whose members share
similar trajectory patterns, and these patterns
may vary significantly among groups of cou-
ples. This couple clustering may be attributed to
underlying social processes stemming from their
socioeconomic background, including early and
distal socioeconomic factors, social class, and
race/ethnicity. Thus, within a dyadic longitudi-
nal context, health risk trajectory patterns are
influenced and stratified by couples’ socioe-
conomic background, and these couple trajec-
tory patterns are expected to exert differential
influences on partners’ aging outcomes. Utiliz-
ing analytic approaches such as growth mix-
ture modeling that are sensitive to the potential
underlying groups of homogeneous couples (i.e.,
unobserved heterogeneity) will further identify
how the aging process of couples can be socially
stratified.

Beyond Traditional Marriage

We recognize that the focus in this article on
enduring couples and the utilization of panel
data from husbands and wives available in the
Later Adulthood Study for conceptualizing
hypotheses and analyses could be seen as a
limitation in applying the perspective to other
populations. However, there are two primary
reasons the perspective could prove fruitful
in research with more diverse couples (e.g.,
same-sex couples, couples with racial/ethnic
diversity, cohabiting couples, remarried couples
who are less established). First, the domains and

experiences (e.g., social trajectories, socioeco-
nomic factors) encompassing this life course
systems perspective are relatively global experi-
ences and occur in large part across populations.
As an example, economic hardship is a rela-
tively universal stressor that is not specific to a
single population, although economic hardship
can certainly be greater for certain populations
(which the perspective captures as R × Q and
Journal of Marriage and Family. Z × Q inter-
actions). Second, although purposefully broad
in nature, the perspective was also created to
enable flexibility by distinguishing constructs
at a macro level (e.g., socioeconomic factors),
which encourages researchers to focus on spe-
cific characteristics of these constructs that are
most salient to the study population and focus.
For instance, discrimination can be concep-
tualized as a characteristic of social structure
(Z), and research on racial/ethnic minority
couples that addresses discrimination or health
inequality as elements of social structure (Z)
with implications for couple context and aging
(S and H) is poised to advance the field of family
gerontology. In this manner, this life course sys-
tems perspective provides a theoretical scaffold
to inform the work of family gerontologists and
proposes quantitative methods that may fit well
with the identified research hypotheses without
being overly prescriptive.

A related point we acknowledge is the chang-
ing landscape of later-life relationships. One
change is the rising number of divorces in
the second half of life (known as the “gray
divorce revolution”) (Brown & Lin, 2012). Of
the analytical approaches identified, transition
analyses may be most appropriate for studies
of gray divorces. Cohabitation has also become
more prevalent and accepted in the general
population, including for older adults (particu-
larly given that older adults may see structural
and relational disadvantages of legal marriage,
such as loss of benefits connected to a former
spouse or simply a desire to maintain indepen-
dence). This changing demographic for later
adults can have implications for the salience
of the pathways proposed in the life course
systems perspective. For instance, couple-level
constructs, such as couple-level stress, may
be less salient for cohabiting couples. How-
ever, it is also plausible that the salience for
couple-level constructs depends on the reason
for cohabitation, and the more determining fac-
tor of the salience of couple-level factors may be

Aging Together 259

couple connectedness or closeness rather than
technical marital status. In contrast, couple-level
constructs, such as couple stress, may be more
salient for same-sex couples and racial/ethnic
minority couples because of shared experiences
of discrimination. These are important areas
for examination in future research. Although
the life course systems perspective proposed
cannot fully account for all nuances of intimate
relationships across the life course, it provides a
scaffold for initiating research to address a large
number of topics.

Last, it must be noted that the current perspec-
tive, and particularly the analyses highlighted
here, focuses specifically on understanding
longitudinal changes over extended periods of
times (i.e., multiple years or even decades).
Nevertheless, this focus does not eliminate the
need for research on aging that examines shorter
longitudinal processes, which are often best
examined using intensive longitudinal designs,
such as daily diaries. These designs may be
particularly helpful when examining the aging
process for couples during acute times of tran-
sition (e.g., death of a partner; critical illness
that immediately and substantially shifts daily
dynamics). Although these research designs and
analyses take a different form from those high-
lighted here, sizable portions of the perspective
proposed are still very much applicable.

Conclusion

In the current article, the life course and systems
perspectives were described and integrated
into a life course systems perspective that
can advance knowledge of the aging process
of partners in enduring couple relationships.
Recognizing the necessity for longitudinal
analytical techniques that can appropriately
address hypotheses derived from theory, we
then demonstrated how an integrated life course
systems perspective can inform empirically
testable hypotheses utilizing advanced ana-
lytical approaches, particularly CL-AR and
LGCM approaches. Theory development and
analytical advances should be an iterative pro-
cess in which theory informs the analytical
approaches utilized and analytical advances
also contribute to the creation and revision of
theories. That is, analytical approaches are a
scientific tool allowing researchers, including
family gerontologists, to test their own theories
and revise extant theories as needed. To this end,

the current article utilizes the life course sys-
tems perspective and the analytical approaches
described to recommend future directions for
strengthening the integrated life course systems
perspective to enhance knowledge of couples’
aging process.

Through these theoretical and analytical
advancements, family gerontologists are poised
to contribute knowledge that can be utilized to
improve the well-being of families, particularly
as it relates to aging in the context of relation-
ships. For instance, this knowledge can inform
prevention and intervention programs for older
adults as well as policy changes to improve
well-being in later life by enacting a long view
that considers how structural and social charac-
teristics contribute to aging processes over time.
Specifically, this article emphasizes several
broad aspects of couple aging to be consid-
ered by prevention and intervention programs.
First, programs must conceptualize couples as
systems in which social and aging (i.e., develop-
mental) trajectories unfold, thereby recognizing
the need to enact a “long view” when seeking
to implement change. Second, these trajectories
are influenced by intra- and interindividual
(between-partners) forces, which emphasizes
the need for couple-focused programs. Third,
developmental and social trajectories are influ-
enced by various external factors (particularly
socioeconomic factors and structural factors
in their environment), which emphasizes the
need for policy interventions. Finally, because
existing research suggests that characteristics
representing elements of individuals’ agency
(e.g., mastery) shape their developmental and
social trajectories and moderate the influence
of external factors, the perspective highlights
the role of agency and supports the need for
prevention and intervention efforts to assist
in the development of individuals’ resilience
factors.

Author Note

This research is currently supported by a
grant from the National Institute on Aging
(AG043599, Kandauda A. S. Wickrama, PI).
The content is solely the responsibility of the
authors and does not necessarily represent
the official views of the funding agencies.
Support for earlier years of the study also
came from multiple sources, including the
National Institute of Mental Health (MH00567,

260 Journal of Family Theory & Review

MH19734, MH43270, MH59355, MH62989,
MH48165, MH051361), the National Insti-
tute on Drug Abuse (DA05347), the National
Institute of Child Health and Human Devel-
opment (HD027724, HD051746, HD047573,
HD064687), the Bureau of Maternal and Child
Health (MCJ-109572), and the MacArthur
Foundation Research Network on Successful
Adolescent Development Among Youth in
High-Risk Settings.

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Supporting Information

Additional supporting information may be
found online in the Supporting Information
section at the end of the article.

Supplemental Figure 1 Univariate Latent
Change Score (LCS) Model. L = latent vari-
able, Δ = change score, L = initial level,
G = constant change latent factor, PF = physical
functioning. Observed repeated measures wer-
aree not shown in Panels b, c, and d. 𝜀 =
measurement error.

Supplemental Figure 2. Dyadic Latent
Change Score Model. L = latent vari-
able, Δ = change score, L = initial level,
G = constant change latent factor, PF = physical
functioning. H and W superscripts represent hus-
band and wife, respectively. Observed repeated
measures are not shown.

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