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. Author manuscript; available in PMC: 2026 Feb 18.
Published in final edited form as: Fam Relat. 2025 Feb 18;74(4):1424–1440. doi: 10.1111/fare.13151

Partner strain and support associations with affective reactions to daily stressors

Dakota D Witzel 1, Kelly D Chandler 2, John G Geldhof 2, Karen Hooker 2, Robert S Stawski 3
PMCID: PMC12439855  NIHMSID: NIHMS2059812  PMID: 40964048

Abstract

Objective:

We aimed to understand how marital quality and gender influence affective responses to daily stressors (i.e., affective reactivity and residue).

Background:

Affective reactions are putative mechanisms through which daily stressors impact long-term health and well-being. Understanding which resilience or vulnerability factors (e.g., marital quality, gender) are associated with affective reactions is therefore crucial to promote healthy lives and aging.

Method:

Data came from a subsample of married adults (N = 1,335) who participated in both the Midlife in the United States (MIDUS) Study and the associated National Study of Daily Experiences (NSDE). Participants rated their levels of strain and support with their partners in the MIDUS survey. As part of the NDSE, participants subsequently reported their affect and experience of daily stressors over 8 consecutive days.

Results:

Partner strain significantly exacerbated associations between previous-day stressors and negative affect (i.e., affective residue), but not same-day stressor–affect associations (i.e., affective reactivity).

Conclusion:

Findings from this study highlight the importance of marital quality for shaping daily stress and affective well-being.

Implications:

Cultivating couple relationship skills—including decreasing partner strain—in adulthood may provide a means to decrease affective reactions to daily stress and promote short- and long-term health and well-being.

Keywords: affect/emotions, daily stress, marriage, relationship quality

Daily stressors (e.g., being stuck in traffic, arguing with friends, or dealing with a deadline at work) are detrimental to overall health and well-being (Almeida, 2005). Indeed, affective responses to daily stressors influence longer term physical and mental health outcomes (e.g., Piazza et al., 2013; Stawski et al., 2023). Affective responses to daily stressors, however, are moving targets (Smyth et al., 2018, 2022) that vary as a function of relationship-specific (Cichy et al., 2012), situation-specific (Witzel & Stawski, 2021), and person-specific (Almeida, 2005; Witzel et al., 2023) factors. Thus, effective interventions related to stress and health must consider which factors buffer or exacerbate affective reactions to stressors and during specific periods of the life course. In particular, stress–health associations are especially salient across midlife and adulthood (Infurna et al., 2020). Adults often have more responsibilities and multiple social roles compared with those in other developmental periods (Antonucci et al., 2014). These pressures manifest as hassles and stressors in daily life (Almeida et al., 2020). Understanding situation-, person-, and relationship-specific factors that contribute to or buffer daily affective reactions across the adult lifespan is therefore an important avenue of research. Three such factors are perceptions of partner support, perceptions of partner strain, and gender.

Marital relationships are a potentially crucial source of support (and/or strain) when coping with stressors in daily life (Cohen et al., 1983). For example, marital strengths (e.g., partner support) buffer the impacts of daily stress on health, whereas partner strain (e.g., criticism or demands from a partner) intensifies them (Slatcher, 2010). Although researchers note biological mechanisms through which marriage impacts health (e.g., Kiecolt-Glaser & Newton, 2001), less is known about the psychological mechanisms through which marriage impacts health (Slatcher, 2010). As such, the first aim of this study was to examine how aspects of marital quality, including partner strain and support, modify associations between daily stressors and affect—affective responses to daily stressors that contribute to long-term health risk (Almeida et al., 2024; Stawski et al., 2023).

Additionally, gender is a critical factor for understanding how marital quality may affect individuals’ affective reactions in daily life. Compared with men, women often report stronger affective reactions to daily stressors (Almeida & Kessler, 1998; Birditt et al., 2005; Birditt & Fingerman, 2003; Witzel et al., 2023). Although changing, traditional gender roles are still salient in U.S. culture and remain powerful influences on marital dynamics (Carreiro, 2021; Ridgeway, 2011). Thus, a second aim of this study was to explore whether gender interacts with marital quality (i.e., partner support and strain) to modify affective reactions to daily stressors.

THEORETICAL PERSPECTIVES

Two theoretical perspectives offer a lens through which to understand why marital quality may inform daily stress, and gender differences therein. First, Almeida’s (2005; Almeida et al., 2024) daily stress process (DSP) model provides a framework for understanding the associations between daily stressors and health and well-being. The DSP model posits that daily stress processes have implications for short- and long-term health and well-being through affective reactions to daily stress. Importantly, characteristics of daily stressors, such as exposure, feelings of control, or resolution, modify affective responses to daily stressors. For example, the resolution of a daily stressor may relate to dampened or even extinguished affective responses (e.g., Witzel & Stawski, 2021), which may, in turn, reduce the likelihood mental health disorders in the future. Notably, the DSP model clarifies that individual differences, such as gender or marital quality, additionally modify associations among daily stress, affect, and health and well-being. The effects of stressor exposure on indices of daily well-being (e.g., affect) depend on daily stress processes or the qualifying characteristics that define the daily stressor (e.g., type of stressor, who was involved). Indeed, the DSP model provides a conceptual framework to understand how individual difference characteristics, such as marital quality, modify affective reactions daily stressors. For example, feelings of marital strain may exacerbate negative affective responses to an argument when they occur with a spouse; further, low feelings of marital support may increase negative affective responses to other daily stressors because the partner may not be an avenue for social support to buffer the effects of daily stressors.

Second, Slatcher’s (2010) strength and strain conceptual model focuses specifically on how one individual difference characteristic—marital quality—informs health through stress responses. Slatcher’s model suggests that marital quality influences health through psychological mechanisms (e.g., affective responses to stressors). Additionally, marital quality impacts health indirectly by moderating the impact of stressors in two ways. First, partner support is operationalized as how much people perceive their partners to engage in positive partner behaviors (e.g., their partner listens to them). Partner support (e.g., perceived support, responsiveness) is theorized to buffer stress (including daily stressors) effects. Second, partner strain, is operationalized as one’s perception of how often the partner engages in negative marital behaviors, such as criticism. Furthermore, partner strain (e.g., hostility, criticism) exacerbates stress effects.

Slatcher and Selcuk (2017) expanded the strength and strain model to include individual difference characteristics, suggesting that individual differences—specifically gender—directly impact marital quality, as well as indirectly impact marital quality and psychological mechanisms. Notably, marital relationships provide a way to enact gender roles (e.g., Ridgeway, 2011), and traditional gender roles may assume differences in daily stress reactivity (e.g., Almeida & Kessler, 1998; Matud, 2004). Thus, gender and marital quality may influence health through moderating stressor–affect associations.

AFFECTIVE REACTIONS TO DAILY STRESSORS

Researchers indicate affective reactivity, defined as same-day increases in negative affect associated with the experience of daily stressors, is related to increased inflammation (Sin et al., 2015), worse cognitive health (Stawski et al., 2019), and poorer sleep (Seluck et al., 2017). Moreover, less is known about more prolonged affect reactivity, such as changes in affect into the next day(s) after a stressor (i.e., affective residue; Almeida et al., 2024; Witzel & Stawski, 2021). There has, however been associations between affective residue and increased reports of chronic health conditions (Piazza et al., 2013), poorer mental health (Charles et al., 2013; Stawski et al., 2023), and increased mortality risk (Chaing et al., 2018).

Importantly, research also suggests that affective residue is moderated by situation-, person-, and relationship-specific factors (Cichy et al., 2012; Witzel & Stawski, 2021). For example, when focusing only on daily stressors involving participants’ families, Cichy and colleagues (2012) found that daily arguments involving family member and network stressors (events that happen to family members but not the respondent) were associated with increased affective residue. Further, Witzel and Stawski (2021) found the magnitude of affective residue depends on the type of stressor experienced (significance regarding daily arguments but not avoided arguments). Given potential associations between daily stressors and health through affective reactions, we examined how marital quality and gender differentially related to both negative affective reactivity and negative affective residue.

MARITAL QUALITY AND AFFECTIVE REACTIONS TO DAILY STRESSORS

Most research pertaining to partner support and partner strain has examined these constructs separately. For example, Bertera (2005) found that social negativity (i.e., partner strain) and positive support (i.e., partner support) were related to more mood and anxiety disorder episodes, whereas positive partner support was not associated with either type of disorder episode. Schuster et al. (1990) found similar associations with depressed mood. However, researchers acknowledge that partner strain and support may work in tandem (e.g., ambivalence; Rook et al., 2012; Uchino et al., 2013). Ambivalence, defined as both strain and support within a relationship, has been associated with a host of poor health outcomes (for review, see Ross et al., 2019). Ross and colleagues (2019) determined that the ability to characterize a relationship (through the interaction of positive and negative relationship attributes) as high quality (high support/low strain), low quality (low support/high strain), or ambivalent (high support/high strain) may be particularly useful for relationship researchers. However, only one study to our knowledge has directly tested the interaction between partner support and strain on negative affect as a means to test ambivalence.

DeLongis et al. (2004) examined interactions between daily partner support and strain on same-day and next-day negative affect, within persons, across 1 week using daily diaries. They found that partner support and strain did not interact to predict same-day negative affect but that each exhibited unique and significant main effects. By contrast, partner support and strain interacted to predict next-day negative affect. Specifically, people with higher partner support reported lower next-day negative affect when they also reported low partner strain, compared with high partner strain. Although their study provided evidence that partner strain and support are associated with negative affect, DeLongis and colleagues (2004) only covaried for daily hassles rather than testing whether partner strain and support moderated the effect of daily hassles. Thus, the combined influence of partner support and strain on the association between daily stressors and affective reactivity and residue remains unclear.

It is notable that some research examining daily stress processes and marital quality (e.g., Slatcher et al., 2015; Stanton et al., 2019) focused on one specific aspect of partner support: partner responsiveness. Both Slatcher et al. (2015) and Stanton et al. (2019) examined how partner responsiveness was related to affective reactivity 10 years later and how affective reactivity 10 years later mediated associations between partner responsiveness and health outcomes 20 years later. Slatcher and colleagues (2015) found that more partner responsiveness was associated with lower negative affective reactivity 10 years later. Conversely, in the same sample, Stanton and colleagues (2019) did not find significant associations between partner responsiveness and negative affective reactivity 10 years later.

Importantly, although the previous studies captured potential long-term associations, a 10-year interval may not capture how marital quality influences affective reactions over shorter periods of time. If affective responses to daily stressors are reflective of poorer marital quality, then examining how indices of marital quality are associated with affective responses to daily stressors across shorter timescales is a pertinent avenue of research. To date, however, no previous research has explored how both partner support and partner strain may be associated with subsequent and more proximal assessments of affective responses to daily stressors.

GENDER DIFFERENCES

Traditional gender roles remain salient in U.S. culture and influence marital dynamics (Carreiro, 2021; Ridgeway, 2011). Ridgeway (2011) posited that gendered roles are enacted through social relations (e.g., marriage) and in daily life. Examining gender differences in the associations between partner support and partner strain, and affective reactions to daily stressors, then, is crucial for understanding how gender and marital norms from society manifest in daily life.

Although gender differences in exposure to daily stressors are clearly evidenced (Almeida & Kessler, 1998; Matud, 2004), gender differences in affective reactions are mixed and highly focused on affective reactivity (Almeida & Kessler, 1998; Birditt et al., 2005; Sin et al., 2015; Stawski et al., 2023; Witzel et al., 2023). Some research, for example, has suggested that women experience stronger affective reactions to daily stressors than do men (Almeida & Kessler, 1998; Birditt et al., 2005; Stawski et al., 2023; Witzel et al., 2023). Other studies reported no gender differences (e.g., Sin et al., 2015).

Only one study to date has examined gender differences in the associations between daily stressors and both affective reactivity and residue (Witzel et al., 2023). Witzel and colleagues (2023) found that women reported fewer arguments and avoided arguments; however, when arguments and avoided arguments did occur, women were more affectively reactive than men. That study focused on interpersonal daily stressors rather than including all daily stressors and was not restricted to married people. No studies to date have examined the roles of marital quality (i.e., partner support and strain) and gender in daily stressor–affect associations. Because marriage is an important context for adult development (e.g., Kiecolt-Glaser, 2018; Umberson et al., 2009), exploring how these associations vary by marital quality adds valuable information for understanding systematic variation in affective reactions to daily stressors.

CURRENT STUDY

We aimed to understand the roles of marital quality and gender as they relate to variability in affective responses to daily stressors. Specifically, we examined whether partner support and partner strain modified associations between daily stressors and negative affect on the same (affective reactivity) and the following day (affective residue). Finally, we examined the extent to which these associations differed between men and women. The following were the research questions and hypotheses for this study:

RQ1: How do partner support and strain moderate affective reactivity and residue to daily stressors?

H1: People with low partner support or high partner strain will exhibit greater affective reactivity and residue compared to people with high partner support or low partner strain.

RQ2: How do partner support and strain interact to moderate affective reactivity and residue to daily stressors?

H2: People with low partner support and high partner strain will exhibit the greatest affective reactivity and residue compared with people with high partner support and low partner strain compared with other compositions (e.g., high partner support, high partner strain).

RQ3: Because of the mixed empirical evidence, we ask the following exploratory research question in lieu of a hypothesis: Does gender moderate the effects of partner support and partner strain on affective reactivity and residue to daily stressors?

METHOD

Transparency and openness

Most data and codebooks for the publicly available data used in the current study are available on the open data sharing network: colectica (https://midus.colectica.org), which can be accessed by creating an account. Data were analyzed using SAS, version 9.4 (SAS Institute, 2013) and the PROC MIXED function was used for primary data analysis. This study’s design and its analysis were not preregistered. Code for all analyses are available by emailing the corresponding author.

Participants and procedure

This study used data from the second wave of the Midlife in the United States (MIDUS) Study and the National Study of Daily Experiences (NSDE). Data collection for Wave 2 of MIDUS began in 2005, and the NSDE assessment occurred approximately 9 months after the MIDUS assessment. The NSDE included a subset of MIDUS participants who completed measures of daily stress processes and health across 8 days. More information pertaining to the NSDE and MIDUS studies can be found at https://midus.wisc.edu. Although the total NSDE sample is 2,022, we restricted the sample to individuals who reported being married during Wave 2 (N = 1,335). Demographic statistics of these participants can be found in Table 1.

TABLE 1.

Descriptive statistics for sample demographic characteristics and study variables

n M (SD) % Range Skew Kurtosis
Age 1,335 55.98 (11.94) 33–83 0.21 –0.77
Race
  White
  Other
1,246
89
93.33
6.67
Gender
  Men
  Women
644
691
48.24
51.76
Highest educational level
  High school diploma or lower
  Some college
  Bachelor’s or higher
380
696
259
28.46
52.13
19.40
More than one child living in home
  Yes
  No

563
772

42.17
57.83
No. of living children
  0
  1–3
  ≥4
84
941
310
6.29
70.48
23.23
First marriage duration (years) 1,299 34.53 (13.83) 1.42–66.75 –0.09 –0.71
Neuroticism 1,279 2.01 (0.61) 1–4 0.49 0.05
General health 1,281 7.50 (1.49) 0–10 –1.10 1.73
Variables of interest
  Negative affect (daily average) 1,335 0.19 (0.24) 0–2.54 3.15 14.76
  Any stressor (proportion of days) 1,335 0.34 (0.25) 0–1
  Partner support 1,267 2.63 (0.53) 0–3 –1.99 4.07
  Partner strain 1,264 2.86 (0.59) 1–4 0.51 0.25
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Measures

Marital Quality and Gender Measures from MIDUS

Partner support

Partner support was measured using six items related to how much an individual feels supported by their partner. People were asked, “How much does your spouse or partner really care about you?” or “How much can you open up to [them] if you need to talk about your worries?” Responses were indicated on a 4-point scale from 1 (a lot) to 4 (not at all). We reverse-coded and averaged the items, such that higher responses indicated greater support (α = .90). We then converted the average values to z-scores.

Partner strain

Partner strain was measured using six items focusing on how often the person felt their partner strained their relationship. Questions included, “How often does your spouse or partner make too many demands on you?” or “How often does [your spouse/partner] criticize you?” For the purposes of this study, we dropped one question, “How often do [they] argue with you?” given the high content overlap between this item and exposure to arguments. Responses were recoded from 1 (often) to 4 (never), and items were averaged so that higher scores indicated higher strain (α6-item = 0.86; α5-item = 0.83).

Because the support and strain measures are noted as one measure within the MIDUS publicly available codebook, and given inconsistency for how literature uses these subscales, we ran an exploratory factor analysis (EFA) with a geomin rotation in Mplus to determine whether using one measure with 11 items or two measures with five and six items, respectively, would be more appropriate. Eigenvalues plotted from the EFA in a scree plot suggested that the best solution was a two-factor solution (eigenvalue 1 = 5.90, eigenvalue 2 = 1.28, eigenvalue 3 = 0.66), and model fit statistics suggested that a two-factor solution was a better fit for these 12-items (see Supplemental Table 1). A total of 53.64% of the variance was explained by the first eigenvalue, and 11.64% was explained by the second eigenvalue.

The factor loadings in Table 1 represent the partial regression coefficients. In Supplemental Table 1, Items 1 through 6, which represent the items for spousal support indicate partial regression coefficients ranging from −0.12 to 0.13 for Factor 1, and partial regression coefficients ranging from 0.70 to .87 for Factor 2. Items 7 through 11, or the items representing spousal strain, indicate partial regression coefficients ranging from 0.38 to 0.83 for Factor 1, and partial regression coefficients ranging from −0.14 to .38 for Factor 2. Item 10, “How often does he or she let you down when you are counting on him or her?” is the only item to report similar partial regression coefficients across factors (.38 and .37, respectively). As such, we used each measure of strain and support as unique measures. The correlation between Factors 1 and 2 was 0.68, indicating that although related, the two measures are not collinear. From this two-factor model, we derived scores by averaging across items and then standardizing averages into z-scores.

Gender

Participants indicated whether they identified as a 1 (man) or 0 (woman) in response to a question asking what their gender is.

Daily measures from NSDE

Any daily stressor

The Daily Inventory of Stressful Experiences (DISE; Almeida et al., 2002) measures daily stressor exposure in the NSDE through stem questions for seven types of daily stressors. Participants answered whether arguments, avoided arguments, work or school, home, network, or other stressors occurred that day and responded with either yes (1) or no (0). We created an “any daily stressor” variable to indicate whether any stressor was reported on a given day or not; we coded this variable as at least one stressor reported (1) or no stressors reported (0).

Negative affect

To measure negative affect, we used a 14-item modified version of the Positive and Negative Affect Scales (PANAS; Watson et al., 1988). Items asked, “How much of the time today did you feel …” and included feelings such as “angry” or “hopeless.” Responses were indicated from 0 (none of the time) to 4 (all of the time). We averaged items with higher levels indicating higher negative affect (αwithin-persons = .77, αbetween-persons = .97).

Time-invariant covariates

We leveraged age, race, marital duration (years married in most recent marriage), previous-day affect, education, neuroticism, and health status as covariates based on previous research noting significant associations with marital quality, daily stress processes, or affect (Almeida, 2005; Charles et al., 2013; Piazza et al., 2013; Stawski et al., 2013). Race was binary representing 0 (White) or 1 (racialized as another race). Education was measured in three categories, 1 (high school or less), 2 (some college), or 3 (bachelor’s degree or higher). Neuroticism was the average of four items; participants were asked how much certain qualities (e.g., moody, nervous) described them from 0 (not at all), to 4 (a lot; Rossi, 2001). Subjective health status was indicated on a scale from 0 (poor health) to 10 (great health).

Analytic plan

We used two-level general linear models given the nested structure of the data (i.e., days within individuals; Hoffman & Stawski, 2009). For the main analyses examining marital quality and daily stressors, both current-day and previous-day daily stressors were included. Therefore, the outcomes of affective reactivity and residue were represented by the time-varying slopes between current-day and/or previous-day stressors predicting negative affect. Reactivity slopes, then, indicated the difference in negative affect on a day when a stressor occurred compared to a day when no stressor occurred. Residue slopes indicated the difference in negative affect when a stressor occurred the previous day compared with when no stressor occurred the previous day. The intercept reflected the sample average level of negative affect on days when no daily stressors occurred, no stressors were reported on the previous day, and when all covariates are zero.

To examine the effect of partner support and partner strain on affective reactivity and residue, we used two-level linear multilevel models with random intercepts and random slopes for current-day and previous-day stressors. Models included both the same-day and previous-day stressors, partner support and strain, and interactions between support, strain, and same- and previous-day stressors as well as individual differences in the frequency of stressor exposure (i.e., person means; Hoffman & Stawski, 2009). We ran two models for each research question; one was unadjusted for covariates and the second model covaried for age, race, marital duration, education, neuroticism, and health status. We examined the Bayesian information criterion (BIC) and −2 log likelihood (−2LL) as means to evaluate model fit, with lower values indicating better fit, and likelihood ratio chi-square tests to evaluate change in −2LL to compare nested models in each table (Hoffman, 2015).

RESULTS

Table 1 provides descriptive statistics for our primary study variables. Correlations among demographics and variables of interest can be found in Table 2. The intraclass correlation coefficient from an unconditional model showed 47.93% of the variance in negative affect was reflected in between-person differences, with the remaining 52.07% reflecting within-person variation. Table 3 reports model fit and parameter estimates for the covariate-adjusted models examining all three research questions.

TABLE 2.

Correlation among study variables

1 2 3 4 5 6 7 8 9
1. Support —
2. Strain –.65*** –
3. Any stressor –.15*** .17*** —
4. Negative affect –.20*** .21*** .30*** —
5. Age .10** –.11*** –.16*** –.15*** —
6. Gender .14*** –.07* –.12*** –.05 .10** —
 7. First marriage duration .07* –.10** –.14*** –.13*** .92*** .004 —
8. General health .09** –.06* –.03 –.18*** –.003 –.001 –.04 —
9. Neuroticism –.14*** .21*** .09** –.33*** –.21*** –.12*** –.17*** –.25*** —
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Note. N = 1,245–1,335. Variables 5 and 6 represent the average person mean of the stressor and negative affect.

*

p < .05.

**

p < .01.

***

p < .001.

TABLE 3.

Model fit and parameter estimates of general linear mixed models exploring partner support and strain, daily stressors, and negative affect

Model 1 Model 2 Model 3 Model 4
Model fit
Parameters 26 29 34 40
  –2LL –2259.30 –2262.60 –2265.50 –2273.00
  BIC –2081.50 –2063.40 –2030.70 –1995.6
Fixed effects parameter estimates b (SE) b (SE) b (SE) b (SE)
Intercept 0.05 (0.04) 0.06 (0.04)*** 0.04 (0.04) 0.05 (0.04)
Between-person effects
  Any stressor (person mean) 0.11 (0.02)*** 0.11 (0.02)*** 0.11 (0.02)*** 0.11 (0.02)***
  Gender –0.004 (0.01) –0.003 (0.01) –0.003 (0.01) 0.003 (0.01)
  Support (z-score) –0.001 (0.01) –0.01 (0.01) –0.01 (0.01) –0.01 (0.01)
  Strain (z-score) 0.01 (0.01) 0.01 (0.01) –0.01 (0.01) –0.01 (0.01)
  Support * gender — — –0.02 (0.01) –0.001 (0.01)
  Strain * gender — — 0.02 (0.01) 0.021 (0.01)
  Support * strain — –0.01 (0.004) — 0.003 (0.01)
  Support * strain * gender — — — 0.01 (0.01)
Within-person effects
  Reactivity (same-day effects)
   Any stressor 0.16 (0.01) *** 0.16 (0.01) *** 0.17 (0.01)*** 0.17 (0.01)***
   Any stressor * support –0.015 (0.008)* –0.018 (0.01) –0.011 (0.01) –0.011 (0.01)
   Any stressor * strain 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01)
   Any stressor * gender — — –0.01 (0.01) –0.01 (0.02)
   Any stressor * support * gender — — –0.01 (0.02) –0.01 (0.02)
   Any stressor * strain * gender — — –0.002 (0.02) –0.003 (0.02)
   Any stressor * support * strain — 0.01 (0.01) — 0.0004 (0.01)
   Any stressor * support * strain * gender — — — 0.003 (0.01)
  Residue (previous-day effects)
   Any stressor 0.01 (0.005)* 0.01 (0.006) 0.01 (0.007) 0.006 (0.008)
   Any stressor * support –0.005 (0.01) –0.01 (0.01) –0.003 (0.009) 0.005 (0.01)
   Any stressor * strain 0.01 (0.007)* 0.01 (0.007)* 0.015 (0.009) 0.015 (0.009)
   Any stressor * gender — — –0.003 (0.01) 0.01 (0.01)
   Any stressor * support * gender — — –0.004 (0.01) –0.02 (0.02)
   Any stressor * strain * gender — — –0.002 (0.02) –0.004 (0.01)
   Any stressor * support * strain — 0.003 (0.01) — –0.006 (0.006)
   Any stressor * support * strain * gender — — — 0.019 (0.01)
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Note. N = 1,228, Nobservations = 7,956. Both models covaried for age, gender (0 = men, 1 = women), race, education, marital duration, health, and neuroticism. Partner strain and support have both been recoded to indicate higher scores represented higher strain or support and have been z-scored. −2LL = −2 log likelihood. BIC = Bayesian information criterion.

*

p ≤ .05.

**

p < .01.

***

p < .001.

Main effects of partner support and strain

Model 1 in Table 3 reports the estimates and standard errors for the multilevel model associated with Hypothesis 1.

Negative affective reactivity

On days when a daily stressor occurred, affective reactivity was significantly higher for people with average levels of partner support and strain compared to days when no daily stressor occurred. This increase in negative affective reactivity was moderated by partner support. Shown in Figure 1, on days when any daily stressor occurred, for people with lower partner support (i.e., 1 SD below the sample average), there was a larger increase in negative affective reactivity (reactivity estimate = 0.17, SE = 0.01, p < .001) compared to people with higher partner support (i.e., 1 SD above the sample average; reactivity estimate = 0.14, SE = 0.01, p < .001). Partner strain did not significantly modify stressor–affective reactivity associations.

FIGURE 1.

FIGURE 1

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Affective reactivity and residue slopes associated with any daily stressor moderated by partner support and strain

Note. Error bars represent standard errors. **p < .05. *** p < .001.

Negative affective residue

Although partner support did not significantly interact with exposure to any previous-day stressor, partner strain did. As can be seen in Figure 1, individuals who reported high partner strain (i.e., 1 SD above the mean) reported more negative affect when any daily stressors occurred on the previous day (residue slope estimate = 0.03, SE = 0.01, p = .003), compared with people with low partner strain (i.e., 1 SD below the mean), who showed no evidence of residue (residue slope estimate = −0.003, SE = 0.01, p = .71).

Interactive associations between partner support and strain

Model 2 in Table 3 reports the estimates and standard errors for the multilevel model associated with Hypothesis 2.

Negative affective reactivity

On days when any daily stressor occurred, affective reactivity was higher for people with average levels of partner support and strain compared with days when no daily stressor occurred. Neither partner support nor strain significantly interacted with daily stressors independently to impact negative affect, nor did partner support and strain interact with daily stressors to predict negative affect in the covariate-adjusted model (see Model 2, Table 3).

Negative affective residue

Following the inclusion of partner support and strain in the models, the average level of negative affect associated with having any previous-day daily stressor was not significant. Partner support and strain did not interact with previous-day daily stressors to predict negative affect (see Model 2, Table 3).

Gender differences across associations

Model 3 in Table 3 reports the estimates and standard errors for the multilevel model associated with Research Question 1.

Partner support and strain

Gender did not moderate associations between partner support and strain and daily stressors for either same-day or previous-day negative affect (see Models 3 and 4 in Table 3).

DISCUSSION

In this study, we aimed to examine how aspects of marital quality buffered or exacerbated negative affective reactions associated with daily stressors. Researchers have previously examined how some aspects of marital quality impact health through affective reactions to daily stressors over extended periods of time (10 or more years), and the current study adds to the literature by providing insight into how marital quality and gender moderate more nuanced dimensions of affective responsivity to daily stressors. Moreover, these findings highlight understanding stress and well-being in daily life offers information into how marital quality may be represented in daily life. Our findings indicate that marital quality is a key factor for understanding affective reactivity and residue to daily stressors in married adults. Although partner support and partner strain did not significantly interact to moderate affective responses to daily stressors, we did find evidence that partner support buffered the effect of daily stressor exposure on negative affect (i.e., decreased affective reactivity), whereas partner strain exacerbated the effect of a previous-day daily stressor on negative affect (i.e., increased affective residue).

Partner support, partner strain, and affective reactions

We hypothesized that married individuals with low partner support and high partner strain would exhibit greater affective reactivity and residue compared with those with high partner support and low partner strain; this hypothesis was not supported. Testing ambivalence through the interaction of partner support and strain, we found that partner support and partner strain did not interact significantly to predict affective responses to daily stressors. DeLongis and colleagues (2004) found that there was a significant interaction between partner strain and support on negative affect; however, DeLongis and colleagues did not include stress as a moderator for associations. Importantly, Slatcher and Selcuk’s strength and strain model suggests that support and strain inform health through affective responses to stress, but uniquely rather than interactively. As such, although there is evidence to suggest that these phenomena interact to inform affect broadly, the current study extended this line of questioning to find that partner strain and support did not interact to inform a person’s affective reactions to daily stress. Moreover, it is notable that DeLongis and colleagues (2004) examined daily assessments of partner support and strain, in addition to affect, whereas the current study examined time-invariant measurements of partner support and strain. Therefore, the temporal differences in marital quality may account for differences in associations between studies.

Although partner support and partner strain did not interact significantly to predict affective responses to daily stressors, both partner support and partner strain emerged as unique moderators of daily stressor–affect associations. Specifically, partner support buffered the effect of same-day stressor exposure on negative affect, suggesting that partner support was related to a decrease in negative affective reactivity. Previous research has shown that daily partner support and partner strain were uniquely associated with negative affect into the next day (DeLongis et al., 2004). Within the context of daily stressors, the effect of partner support may be more effective for initial emotional downregulation of reactions to stress as seen by the current finding that high partner support was related to lower affective reactivity but was not affective residue.

Moreover, previous research has shown that daily partner support and partner strain were uniquely associated with negative affect into the next day (DeLongis et al., 2004). The current research extended this to test whether marital quality extended the affective reactions of daily stressors into the next day, finding that people with higher partner strain had higher residue than people with lower. This finding partially echoes DeLongis et al.’s (2004) finding but highlights the detrimental effects of daily stressors when coupled with high partner strain. Research shows that negative events may have a prolonged impact on well-being (e.g., affect) compared with positive events (Baumeister et al., 2001; Taylor, 1991), and this may be uniquely reflected in partner strain given the potentially prolonged negative context of strain in a marital relationship. That associations between partner strain and negative affect were only significant for previous-day stressor exposure (i.e., residue) was unexpected. Both reactivity and residue reflect proximal effects of daily stressors, and partner strain may impact negative affective residue through less proximal mechanisms. For example, higher partner strain represents how often partners engage in behaviors such as criticism, letting them down, or getting on their nerves. These items may represent a conceptually more negative or problematic relationship. Furthermore, people with more partner strain may be primed to ruminate or perseverate on their daily experiences, about their days and experiences, exacerbating more prolonged (i.e., residue), but not more immediate (i.e., reactivity) affective responses. Such a pattern is consistent with research showing that aspects of marital quality, specifically higher marital partner withdrawal, was related to more rumination on daily stressors (King & DeLongis, 2014).

Gender differences

Results revealed that gender did not significantly inform associations between partner support, strain, any daily stressor and negative affect. Research suggests associations between some aspects of marital quality such as marital satisfaction (Ng et al., 2009) and well-being are moderated by gender; however, a meta-analysis examining marital quality and health (Robles et al., 2014) found little evidence of gender differences. Further, the lack of gender differences is in line with some other research suggesting a lack of gender differences in daily stress, negative affect, and inflammation associations (Sin et al., 2015). Of note, Sin and colleagues (2015) found that there were gender differences between negative affective reactivity and inflammation, but only when not accounting for health conditions. As such, had we chosen not to include general health status, we may have found different results.

The lack of gender associations may additionally underscore changes in gender norms and expectations. The results of this study suggest that men and women with average levels of marital quality report similar levels of negative affect on nonstressor days and that men and women may exhibit similar affective reactions to daily stressors. Although this is an initial step into understanding associations among marital quality, gender, and daily stress processes, more work is needed to determine the role marital quality may have in the impact of gender roles and norms surrounding daily stressors and affective reactions.

Limitations

First, we did not disambiguate by stressor type or who was involved (if anyone) in the daily stressor. Given that Slatcher (2010) and Slatcher and Selcuk’s (2017) strength and strain model conceptualizes how marital quality buffers or exacerbates the impact of stressors outside of marriage on psychological, biological, or cognitive health, disambiguating between whether spouses were involved in daily stressors may be important. Moreover, researchers have noted that daily stressor–affect associations vary by stressor type (e.g., Cichy et al., 2012; Witzel & Stawski, 2021) and family involvement (Witzel et al., 2023).

Although the current study provides strengths in its large national dataset, it is limited in that we focus on individual-level, rather than dyadic-level, factors. MIDUS does not include dyadic-level data and, as such, it will be imperative for future work to explore these associations within couples to gain a better understanding of how daily stressors and marital quality may influence affective reactions on a couple level. Additionally, the use of data collected in 2005 reflect associations at that point in time. Indeed, societal and economic differences between 2005 and the current data may preclude the ability to generalize to people today and may mask potential cohort and period effects. Future directions should evaluate such possibilities to understand how associations vary or maintain under differential circumstances. Understanding more about the family context of these marital relationships is an important direction deserving future attention. Finally, the gender associations were limited to men and women. The participants in the NSDE only selected from two gender identity options (man or woman), limiting potential comparisons outside of the gender binary. Similarly, although the current study focused on married individuals, it is unclear whether these marital relationships vary by gender and/or sexual orientation.

Implications

The present study provides additional evidence that marital ties can be leveraged to support short-term population health. Married adults report better health outcomes (Kiecolt-Glaser, 2018) and live longer than single people; however, when married people are unhappy or have poorer quality marriages, they have poorer well-being than unmarried people (Lawrence et al., 2019). Although health and well-being can be impacted by marital quality, the current study extends previous research by examining how marital quality may impact and be reflected in people’s experiences and affective well-being in daily life.

Previous research has noted that social ties, such as marital relationships, are a resource to be harnessed to promote public health (Umberson & Montez, 2010). For example, in line with Umberson and Montez (2010), marital education interventions (DeMaria, 2005) may focus on ways to decrease partner strain and risk to improve affective reactivity and residue. Marital education is often used by married people who are already in distressed marriages (DeMaria, 2005). Educational training such as ELEVATE for couples (Futris et al., 2020) are aimed at teaching the physiology behind human interaction and practical skills for relationships. The focus of programs like ELEVATE may provide a space to decrease partner strain for some couples and thus may benefit from the current research.

Future directions

This study presents several future research directions. First, other aspects of marital quality, such as marital satisfaction, may also moderate associations between stressor exposure and negative affect. Daily stressors are associated with marital satisfaction and health and well-being (Ong et al., 2020; Slatcher et al., 2015). Furthermore, Slatcher’s (2010) marital strength and strain model suggests that marital strengths and strains are not limited to partner support and partner strain. Future research should examine additional indicators of marital quality to address this limitation and more comprehensively examine the dimensionality of, and diverse processes by which, marital quality may impact health through stress, affect, and other mechanisms. Finally, stressor events and affect were measured 9 months after measures of partner support and strain, which may affect results. Although partner support and strain represent perceived trait-like support and strain from a partner, these feelings may fluctuate across months and by event (e.g., DeLongis et al., 2004). For example, after an argument, the respondent may feel more partner strain because they did not feel supported during the time and would likely report different levels of strain compared with when an argument had not occurred that day. It will be necessary for future work to measure partner support and strain across days and months to gain a better understanding of whether fluctuating patterns of these phenomena coincide with daily events (e.g., stress).

Conclusion

The current study highlights the importance of marital quality for supporting affective well-being in daily life. Marital quality, and particularly the negative ramifications for poor marital quality, undermines health and well-being, potentially through negative affective reactivity and/or residue associated with stressors people experience in their daily lives. Marriage is an important social relationship, and the current study suggests that marital quality may be considerably important for daily stress processes such as affective reactions to daily stress. We found that partner strain may inform affective residue to daily stressors for married adults. Given these findings, it is likely that cultivating couple relationship skills—including promoting partner support and decreasing partner strain—in adulthood may provide a means to decrease affective reactions to daily stressors and promote short- and long-term health and well-being of individuals and their marital relationships.

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Acknowledgments

Publicly available data from the MIDUS study was used for this research. Since 1995, the MIDUS study has been funded by the John D. and Catherine T. MacArthur Foundation Research Network, and National Institute on Aging (P01-AG020166; U19-AG051426). Preparation of this manuscript was partially supported through funding by the National Institute of Health (T32 AG049697).

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