Hearts Correspond, Love Follows: Couple Similarity and Marital Satisfaction

SU Wei¹, FANG Xiaoyi², HOU Juan¹
(¹ School of Philosophy, Anhui University, Hefei 230039, China)
(² Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China)

Abstract

Similarity constitutes an essential component of marital satisfaction, yet current research has yielded inconsistent conclusions regarding the relationship between the two. Specifically, in what dimensions are couples actually similar, and how does such similarity influence marital satisfaction? This study examined 638 Chinese couples using a couple-centered approach (CCA), integrating profile-level similarity (PCS) and attribute-level difference (ADS) indicators to simultaneously examine actor effects, partner effects, similarity effects, and difference effects. The study investigated how similarity in individual, interaction, and family-of-origin variables relates to marital satisfaction across different marital stages. Results indicated that: (1) real couples are more similar than randomly paired couples; (2) compared with individual traits and interaction patterns, couples show greater similarity in the family-of-origin dimension; (3) the influence of couple similarity on marital satisfaction varies by gender across different marital stages; and (4) although similarity affects marital satisfaction, actor effects remain the strongest predictor of marital satisfaction.

Keywords: marital satisfaction, similarity, APIM, couple-centered approach, family life cycle

1. Introduction

Marital relationships are widely regarded as the most important and optimal social form for fulfilling individuals' emotional needs (Zakhirehdari et al., 2019), with marital quality significantly impacting personal well-being, work performance, and interpersonal relationships (Tavakol et al., 2017). According to assortative mating theory, individuals tend to select partners who are similar to themselves—essentially, "birds of a feather flock together" (Fehr, 2008). For instance, people prefer to marry those with similar personality traits (Botwin et al., 1997). Empirical research has confirmed that real couples exhibit significantly greater personality similarity than randomly paired couples (Luo & Klohnen, 2005; Thiessen et al., 1997), with substantial similarity in age, religious beliefs, and political orientation (Luo & Klohnen, 2005; Watson et al., 2004), and moderate similarity in education, emotional intelligence, and values (Watson et al., 2004). However, does similarity necessarily guarantee happiness, and does dissimilarity inevitably lead to unhappiness? Current research presents contradictory findings. Some studies indicate that similar couples experience higher relationship satisfaction; for example, partners similar in personality openness report greater intimacy satisfaction (Luo & Klohnen, 2005; Weidmann et al., 2017). Conversely, other research suggests that personality similarity does not necessarily predict happiness (Chopik & Lucas, 2019; Gattis et al., 2004), and that partners with less similar values may actually be happier (Luo et al., 2008). Some studies have even found no relationship between similarity and relationship satisfaction (Abe & Oshio, 2018; Brauer et al., 2022; Dyrenforth et al., 2010). For instance, Abe and Oshio (2018) found that similarity had no effect on marital satisfaction among couples with longer marriage durations.

Why have previous studies failed to reach consistent conclusions? We propose four primary reasons. First, prior research has inadequately considered marriage duration, resulting in a lack of dynamic adaptability in findings. For example, Shiota and Levenson (2007) found that similarity in personality openness had no effect on marital satisfaction among middle-aged couples with an average marriage duration of 21.4 years, whereas among newlyweds with an average marriage duration of only 5 months, greater similarity in personality openness predicted higher marital satisfaction (Luo & Klohnen, 2005). Gottman (1999) argued that couples' shared experiences render their similarity more pronounced over time, as partners become increasingly similar in emotional responsiveness (Anderson et al., 2003). However, existing studies vary substantially in their treatment of marriage duration: some treat it as a control variable while focusing on other predictors (Wang et al., 2018), and others examine its interaction with similarity (Abe & Oshio, 2018) without specifically analyzing how similarity affects marital satisfaction across different marital stages. In reality, marital status changes throughout the family life cycle in response to shifting family environments and spousal interaction patterns (Bühler & Orth, 2022), with events such as marriage, childbirth, children's schooling, and children leaving home serving as critical markers of family transitions (Glick, 1977). Therefore, this study integrates family life cycle criteria to specifically examine how couple similarity influences marital satisfaction across different marital stages.

Second, research content limitations have rendered findings incomparable. Previous similarity research has primarily focused on individual traits such as personality, attitudes, and values (Arránz Becker, 2013; Luo & Klohnen, 2005; Štěrbová et al., 2021). However, according to Karney and Bradbury's (2020) theoretical framework, marital satisfaction predictors should encompass three domains: individual, interaction, and environment. Individual traits refer to partners' inherent characteristics (Zhang, 2020), including not only personality and attitudes but also attachment styles and stress perception. For example, Conradi et al. (2021) found that similarity in insecure attachment affects relationship stability but not satisfaction, whereas Ben-Ari and Lavee (2005) found that both partners being insecurely attached was associated with lower marital satisfaction. Thus, research on individual trait similarity and marital satisfaction has also yielded inconsistent results.

Interaction patterns, defined as specific modes of engaging with one's partner (Karney & Bradbury, 2020), are considered crucial for marital satisfaction (Gottman, 1994). According to the interpersonal process model of intimacy, interaction forms the foundation for developing and maintaining close relationships (Reis & Shaver, 1988). Gottman (1999) found that similarity in conflict resolution strategies positively impacts relationship outcomes when partners understand each other's emotion regulation methods. However, other research indicates that similarity in expressive suppression correlates positively with wives' but not husbands' marital satisfaction, and that this association disappears with marriage duration (Velotti et al., 2016). Thus, the influence of interaction pattern similarity is closely tied to gender and marriage duration. Finally, the family of origin, as a critical environmental factor affecting individual development, is closely related to marital satisfaction (Wu et al., 2019). Chinese marriage has long emphasized the tradition of "matching doors and windows" (status matching), stressing equivalence in family economic and social status (Li, 2017). Similar patterns exist in Western countries, where Americans tend to marry individuals whose parents possess similar wealth (Charles et al., 2013). However, Wen and Yang (2020) found that matching family backgrounds had no significant effect on marital satisfaction, and that women having higher personal and family economic conditions than men actually significantly increased happiness (Wang & Li, 2014). Previous research has focused on similarity in socioeconomic status of couples' families of origin while neglecting important factors such as parental marital quality. Therefore, the effects of similarity in individual traits, interaction patterns, and family-of-origin variables on marital satisfaction require further investigation.

Third, different studies have employed varying similarity indices, leading to divergent conclusions. Current methods for assessing similarity in dyadic data fall into two categories: similarity and difference measures (Gray & Coons, 2017). Previous research has commonly used PCS (Pearson correlation score) as an indicator of profile similarity (Furler et al., 2013; Luo et al., 2008; Wang et al., 2018), as PCS is more sensitive to changes in dyadic relationships and reflects pattern consistency between spouses (Wang et al., 2018). Difference scores, typically represented by the absolute difference between partners' scores (Absolute Difference Score, ADS), capture the degree of difference between husbands and wives on a given attribute (Luo & Klohnen, 2005). However, ADS only reflects differences on specific characteristics, whereas PCS indicates similarity across multiple features (McCrae, 2008). For instance, Wang et al. (2018) found using PCS that overall personality consistency was more important for husbands' marital quality, while ADS results revealed that different personality traits varied in their importance for explaining marital quality. Therefore, combining PCS and ADS can capture both overall similarity and attribute-level differences, yielding more precise conclusions.

Fourth, previous similarity research has predominantly employed a variable-centered approach (VCA), combining actor effects (individual's influence on self), partner effects (individual's influence on partner), and similarity effects (couple similarity's influence on self) (Dyrenforth et al., 2010; Großmann et al., 2019; Luo & Klohnen, 2005). The variable-centered approach calculates correlations between husbands' and wives' scores on a single characteristic across all couples in a sample (Luo & Klohnen, 2005). For example, although Wang et al. (2018) used different indices to examine personality similarity, their variable-centered approach could only investigate similarity on a single characteristic and provided no information about any specific couple's similarity (Luo & Klohnen, 2005). Few studies have used the couple-centered approach (CCA) in similarity research (Brauer et al., 2022; Luo & Klohnen, 2005), which explicitly focuses on couples and examines the degree of similarity between each husband and wife across multiple items (Luo & Klohnen, 2005). Only by analyzing each couple individually can we obtain more complete and comprehensive information about partner characteristics (Rogers et al., 2018).

In summary, this study examined couples at different marital stages using a couple-centered approach, integrating PCS and ADS indicators to simultaneously examine actor effects, partner effects, similarity effects, and difference effects (the influence of couple differences on self) (Štěrbová et al., 2021), investigating the relationship between similarity in individual, interaction, and family-of-origin variables and marital satisfaction. Using dyadic data, we addressed three questions: (1) Are real couples more similar than random couples? (2) On which dimensions are couples at different marital stages more similar? (3) How do these similarities affect marital satisfaction?

2. Method

2.1 Participants

We recruited 638 Chinese couples through online advertisements, community outreach, and personal referrals. All participants were in their first marriage, had at least a junior high school education, and resided in Beijing or Tianjin, China. Based on the family life cycle, couples were divided into four marital stages (Wang et al., 2025). Stage 1 comprised newlywed couples (n=109; no children; marriage duration=0.89±0.64 years). Stage 2 comprised parents of infants or preschoolers (n=190; children were infants or kindergarten students; marriage duration=4.73±2.88 years). Stage 3 comprised parents of primary or secondary school students (n=144; children were primary or secondary school students; marriage duration=13.32±4.14 years). Stage 4 comprised empty-nest couples (n=195; adult children had left home or there were no children; marriage duration=32.74±8.37 years). Additional demographic variables are presented in Table 1 [TABLE:1].

Table 1 Descriptive Statistics of Demographic Variables

Variable Stage 1 (n=109) Stage 2 (n=190) Stage 3 (n=144) Stage 4 (n=195) Age (years) Income (10,000 yuan)

Note: For education level, "1"=elementary school or below; "2"=junior high; "3"=high school (including technical/vocational); "4"=college (including correspondence/adult education); "5"=bachelor's; "6"=graduate (master's or doctoral).

2.2 Measures

Marital Satisfaction. We measured marital satisfaction using the Quality of Marriage Index (QMI) developed by Norton (1983), which assesses marital satisfaction from the perspective of individuals' subjective feelings and overall evaluation of their marriage. The scale consists of 6 items requiring both spouses to evaluate their marital satisfaction subjectively. The first 5 items use a 7-point scale from "1" (strongly disagree) to "7" (strongly agree); the 6th item uses a 10-point scale from "1" (very dissatisfied) to "10" (very satisfied), with higher scores indicating greater marital satisfaction. In this study, the Cronbach's α coefficient for the total scale was 0.967, 0.972 for husbands, and 0.965 for wives. The mean marital satisfaction score was 6.30 (SD=1.37) for husbands and 6.03 (SD=1.52) for wives.

There were 12 independent predictor variables categorized into individual (n=5), interaction (n=5), and family-of-origin (n=2) dimensions (summarized in Figure 1 [FIGURE:1]; detailed scale information is provided in the supplementary materials). For dyadic data analysis, predictors included both partners' scores.

2.3 Procedure

Figure 1. Schematic Diagram of Variable Selection

First, we conducted descriptive statistics and paired-sample t-tests on predictor variables and marital satisfaction across individual, interaction, and family-of-origin dimensions using SPSS. Second, we calculated ADS for real couples using SPSS and PCS for real couples using RStudio, then constructed random pairings to calculate PCS for randomly matched couples. After performing Fisher's r-to-z transformation, we computed the means and standard deviations for transformed real and random couples, followed by one-sample t-tests comparing real and random couples using SPSS. Third, we conducted descriptive statistics on untransformed PCS and ADS for real couples by marital stage using SPSS. Finally, we tested the model using Mplus with an improved Actor-Partner Interdependence Model (APIM) that includes ADS and PCS (Luo et al., 2008) to assess the effects of husbands' and wives' individual, interaction, and family-of-origin variables and their similarity on marital satisfaction (see Figure 2 [FIGURE:2]).

2.4 Data Analysis

Figure 2. Actor-Partner Interdependence Model

First, to calculate PCS for each variable using the couple-centered approach, we recorded couples' scores on all items of each scale, then used RStudio to compute correlation coefficients between each column of data (these correlation coefficients represent PCS). To avoid duplicate calculations, we computed only the upper triangular matrix (with real couples' correlations on the diagonal and random pairings elsewhere). To obtain highly reliable random couple pairings, we calculated correlations for all possible combinations, yielding up to 203,203 random pairings. However, due to some scales having few items or identical scores on certain items, the actual numbers varied by variable, with real couple PCS ranging from 420 to 635 and random couple PCS ranging from 113,055 to 202,413. We then performed Fisher's r-to-z transformation on these PCS values using SPSS and computed transformed means and standard deviations. Finally, to determine whether real couples were more similar than random couples, we conducted one-sample t-tests for each variable across three dimensions, comparing real couples' similarity means against random couples' similarity means (using random pairings' mean as the population mean) and calculated effect sizes (Cohen's d).

Second, to identify dimensions where couples were more similar across different marital stages, we first calculated the absolute difference between partners' scores on each variable to obtain ADS, then compared untransformed PCS means and ADS means across marital stages.

Third, to examine the relationship between couple similarity and marital satisfaction across marital stages, we used Mplus to test the model. Figure 2 presents the APIM linking husbands' scores, wives' scores, PCS, and ADS to both partners' marital satisfaction. Specifically, husbands' satisfaction was predicted by their own scores (actor effect; path a), their wives' scores (partner effect; path b), PCS (similarity effect; path c), and ADS (difference effect; path d). Wives' corresponding paths were labeled a' (actor effect), b' (partner effect), c' (similarity effect), and d' (difference effect). The model included covariances among the four independent variables and correlated error terms between the two dependent variables. Analyses used untransformed PCS r-values, with missing PCS values imputed using the Expectation-Maximization algorithm in SPSS (Dempster et al., 1977).

3. Results

3.1 Common Method Bias Test

We conducted Harman's single-factor test to assess common method bias. The test on all variables except demographics revealed 101 factors with eigenvalues greater than 1, with the first factor explaining 14.44% of variance—far below the 40% threshold—indicating no significant common method bias (Zhou & Long, 2004).

3.2 Descriptive Statistics and t-Test Results for Real Couples

Table 2 [TABLE:2] presents means, standard deviations, and paired-sample t-test results for husbands and wives across all variables. Results showed no gender differences in interaction support (t=﹣0.12, p=0.905), conflict resolution (t=1.03, p=0.302), perceived partner responsiveness (t=0.65, p=0.519), or family-of-origin interference (t=0.17, p=0.862), but significant gender differences in all other variables.

Table 2. Descriptive Statistics and t-Test Results

Variable Husband Mean (SD) Wife Mean (SD) t-value Marital Satisfaction 6.30 (1.37) 6.03 (1.52) 5.75***

p<0.05, p<0.005, *p<0.001 (same below).

3.3 t-Test Results Comparing Real and Random Couple Similarity

Table 3 [TABLE:3] presents descriptive statistics for couple similarity (PCS) after Fisher's r-to-z transformation and missing value deletion. Real couples ranged from 347 to 635 pairs, with PCS means ranging from ﹣0.03 to 0.58; random couples ranged from 111,158 to 202,413 pairs, with PCS means ranging from 0.07 to 0.89. Real couples showed considerable variability in similarity (SD range: 0.30 to 0.79), indicating that some couples were very similar while others were quite dissimilar. One-sample t-tests revealed significant differences between real and random couples across all variables, with real couples being more similar than random couples in all variables except family-of-origin dimensions.

Table 3. t-Test Results Comparing Real and Random Couple Similarity

Variable Real Couples (n) Real PCS Mean (SD) Random PCS Mean (SD) t-value Family-of-Origin Interference ﹣7.21***

3.4 Descriptive Statistics of Real Couple Similarity and Difference

Tables 4 [TABLE:4] and 5 [TABLE:5] present descriptive statistics for couple similarity (PCS) and difference (ADS) across marital stages. For similarity, except for Stage 4 couples who were most similar on interaction dimensions, couples in all other stages were most similar on family-of-origin dimensions. For difference, couples across all stages showed the smallest differences on family-of-origin dimensions.

Table 4. Descriptive Statistics of Couple Similarity (PCS) Across Marital Stages

Dimension Stage 1 Stage 2 Stage 3 Stage 4 Family-of-Origin 0.31 (0.31) 0.31 (0.31) 0.31 (0.30) 0.32 (0.31)

Table 5. Descriptive Statistics of Couple Difference (ADS) Across Marital Stages

Dimension Stage 1 (n=109) Stage 2 (n=190) Stage 3 (n=144) Stage 4 (n=195) Family-of-Origin

3.5 Similarity Effect Test Results

We used the improved APIM to simultaneously examine the unique contributions of husbands' scores, wives' scores, PCS, and ADS to both partners' marital satisfaction. We allowed the four independent variables to covary and correlated the error terms of the two dependent variables. Because paired-sample t-tests revealed significant gender differences, we did not constrain path coefficients. Results are presented in Tables 6 [TABLE:6] through 13 [TABLE:13] (Tables 8-13 are in supplementary materials), with all models being saturated.

Regarding similarity effects, husbands' marital satisfaction was most strongly influenced by family-of-origin similarity in Stage 1, whereas individual trait similarity had the greatest effect in all other stages. For wives, individual trait similarity was most influential in Stages 1 and 4, while family-of-origin similarity dominated Stages 2 and 3. Regarding difference effects, only husbands in Stage 2 and wives in Stage 4 were most affected by individual trait and interaction pattern differences, respectively; in all other stages, family-of-origin differences most significantly impacted marital satisfaction. The relative contribution of different variables to marital satisfaction varied across marital stages and effects. However, across all stages and for both husbands and wives, actor effects were the strongest predictors.

Table 6. Standardized Path Coefficients Predicting Husbands' Marital Satisfaction in Stage 1 from Actor, Partner, Similarity, and Difference Effects

Variable Actor Effect (p) Partner Effect (p) Similarity Effect (p) Difference Effect (p) R² (p) Attachment Anxiety 0.194 (0.098) 0.067 (0.523) 0.257 (0.039) ﹣0.031 (0.760) 0.126 (0.076) Attachment Avoidance ﹣0.366 (0.000) ﹣0.231 (0.024) 0.132 (0.107) 0.055 (0.643) 0.262 (0.001) Perceived Partner Responsiveness 0.617 (0.000) 0.192 (0.045) 0.056 (0.448) ﹣0.008 (0.900) 0.566 (0.000) Parental Marital Quality ﹣0.279 (0.046) 0.001 (0.997) ﹣0.167 (0.109) ﹣0.035 (0.745) 0.072 (0.159) Family-of-Origin Interference ﹣0.231 (0.128) ﹣0.248 (0.010) ﹣0.112 (0.171) 0.150 (0.351) 0.111 (0.045)

Table 7. Standardized Path Coefficients Predicting Wives' Marital Satisfaction in Stage 1 from Actor, Partner, Similarity, and Difference Effects

Variable Actor Effect (p) Partner Effect (p) Similarity Effect (p) Difference Effect (p) R² (p) Attachment Anxiety 0.252 (0.011) ﹣0.027 (0.763) 0.229 (0.050) 0.052 (0.555) 0.144 (0.014) Attachment Avoidance ﹣0.309 (0.005) ﹣0.276 (0.014) 0.214 (0.013) 0.034 (0.794) 0.290 (0.000) Perceived Partner Responsiveness 0.677 (0.000) 0.143 (0.044) 0.089 (0.113) ﹣0.018 (0.764) 0.645 (0.000) Parental Marital Quality ﹣0.149 (0.222) ﹣0.045 (0.748) ﹣0.174 (0.140) ﹣0.161 (0.072) 0.066 (0.217) Family-of-Origin Interference ﹣0.301 (0.001) ﹣0.175 (0.161) ﹣0.065 (0.431) 0.185 (0.136) 0.111 (0.066)

4. Discussion

Based on Karney and Bradbury's (2020) theoretical framework, this study selected individual traits, interaction patterns, and family-of-origin variables to examine the influence of similarity on marital satisfaction from multiple dimensions, expanding the breadth and depth of similarity research. Additionally, drawing on the family life cycle, we investigated how couple similarity and marital satisfaction change across marital stages. Methodologically, this study employed a couple-centered approach, integrating profile-level similarity and attribute-level difference indicators to more precisely quantify couple similarity.

4.1 Real Couples Are More Similar Than Random Couples

The findings that real couples are more similar than random couples further validate assortative mating theory, which posits that individuals tend to select similar partners for intimate relationships (Fehr, 2008). On one hand, people are attracted to individuals similar to themselves in attitudes and behaviors (Baumeister & Leary, 1995) and choose partners with similar characteristics for long-term romantic relationships (Chopik & Lucas, 2019; Fehr, 2008). On the other hand, similarity in intimate relationships is not static; initially similar partners may become increasingly alike over time (Gonzaga et al., 2007) as shared experiences influence both partners (Mejía & Gonzalez, 2017). To maintain relationship harmony, partners may consciously or unconsciously adjust themselves to accommodate each other, further intensifying their similarity. Therefore, it is unsurprising that real couples are more similar than random couples across numerous variables.

4.2 Real Couples Show Greater Similarity in Family-of-Origin

Similarity and difference results revealed that real couples are more similar in family-of-origin dimensions compared with individual traits and interaction patterns. From a sociocultural perspective, China's deeply rooted tradition of "matching doors and windows" leads people to select partners with equivalent conditions (Dong, 2020), making family-of-origin characteristics such as parental marital quality natural criteria in mate selection. Unlike Western countries, however, grandparent involvement in child-rearing is common in Chinese families (Guo, 2014), making interference from the family of origin more prevalent in Chinese society (Yuan & Fang, 2022). This widespread parental interference further strengthens similarity in the family-of-origin dimension. From an intergenerational transmission perspective, socialization theory posits that parents serve as role models for children, with their marital relationship patterns (e.g., divorce or remarriage) transmitted through modeling, shaping children's templates for intimate relationships (Ryan et al., 2009) and leading them to select partners with similar family-of-origin patterns. Consequently, real couples exhibit greater similarity in family-of-origin dimensions.

4.3 Gender Differences in Family-of-Origin Similarity Effects

Results indicated that both similarity and difference in family-of-origin significantly influence marital satisfaction, with wives being more affected than husbands. This gender difference stems primarily from wives bearing greater child-rearing pressure (Hou et al., 2019). In traditional Chinese culture, marriage represents not just a union of two individuals but a connection between two families (Yuan et al., 2015), making it difficult for adult children to separate completely from their families of origin, with grandparents frequently participating in child-rearing (Chen et al., 2011). Family-of-origin involvement can even create triangulation with children, affecting their marriages through interference (Song et al., 2022; Yuan, 2019). According to the family life cycle and the U-shaped curve of marital quality, Stages 2 and 3 represent critical periods for raising minor children, with wives typically bearing primary child-rearing responsibilities (Breton et al., 2025; Kellerman & Katz, 1978). Due to the interdependence of family parenting roles, mothers' parenting stress is closely related to grandmother-child relationships (Zou et al., 2020). This means wives interact more frequently with grandparents during child-rearing and are thus more influenced by the family of origin than husbands. Additionally, wives face greater role expectations within the family, while husbands focus more on external social roles, making wives more susceptible to long-term stress (Hou et al., 2019) and more sensitive to family-of-origin factors. Therefore, wives' marital satisfaction is more strongly influenced by family-of-origin similarity throughout the child-rearing process.

When children gradually become independent and leave home, the influence of the family of origin diminishes, and wives' marital satisfaction becomes more aligned with husbands', being most strongly influenced by individual trait similarity. As children move out, couples have more time together, and their core roles shift from parents back to partners (Gorchoff et al., 2008), refocusing their attention on each other. An individual's own characteristics are important factors affecting relationships (Badr et al., 2001) and influence behavior patterns in intimate relationships (Asendorpf & Wilpers, 1998; Auhagen & Hinde, 1997). For example, partners with similar attachment styles can achieve greater emotional resonance and better understand each other's emotions (Murray et al., 2002). When couples share high similarity in individual characteristics, they tend to have similar reactions and behavioral patterns when facing life situations, thereby affecting marital satisfaction. Therefore, in Stage 4, individual trait similarity is an important factor influencing marital satisfaction for both husbands and wives.

4.4 Limited Influence of Similarity Effects on Marital Satisfaction

Examining actor effects, partner effects, similarity effects, and difference effects simultaneously revealed that across all marital stages, actor effects were the strongest predictors of marital satisfaction, followed by partner effects, with similarity effects being weaker. This aligns with previous findings (Dyrenforth et al., 2010; Großmann et al., 2019). For example, in representative samples from Australia, the UK, and Germany, actor effects accounted for approximately 6% of variance in relationship satisfaction, partner effects accounted for 1% to 3%, and similarity effects accounted for less than 0.5% after controlling for actor and partner effects (Dyrenforth et al., 2010). The stronger actor effect suggests that couple similarity is not a core factor in marital relationships but merely a "bonus." Essentially, similarity arises from people's belief that shared attributes are driven by deep-seated essence, but individual traits have more significant effects than similarity itself (Chu & Lowery, 2023). We also found that PCS had stronger effects on marital satisfaction than ADS, consistent with previous research (Luo & Klohnen, 2005; Luo et al., 2008). This is because PCS captures consistency in couples' responses across items, reflecting similar (positive), unrelated (zero), or opposite (negative) relationships, whereas ADS only reflects differences on a specific characteristic and can only indicate degree of similarity from high to low (Luo & Klohnen, 2005), failing to capture the complex patterns of couple similarity. Moreover, ADS is a linear transformation of self and partner scores (absolute difference), making it often non-significant in APIM analyses. This confirms that PCS is a more sensitive and effective indicator of similarity than ADS (Gaunt, 2006).

4.5 Limitations and Future Directions

This study used dyadic data from couples at different marital stages to examine the relationship between similarity and marital satisfaction across multiple dimensions while considering actor, partner, similarity, and difference effects. This holds important significance for future research on similarity and marital satisfaction mechanisms. However, several limitations exist. First, the data were entirely self-reported. Future research could incorporate physiological measures to directly record physiological consistency during interactions (Li et al., 2022) for more precise investigation of the similarity-satisfaction relationship. Second, although the sample from Beijing and Tianjin allowed for effective research, participants lived in first-tier cities, requiring caution when generalizing nationally. Furthermore, this study focused solely on the Chinese cultural context; future cross-cultural research could compare dimensional differences in couple similarity and their effects on marital satisfaction across cultures to enhance generalizability. Finally, although this study examined relationships between similarity and marital satisfaction across marital stages, cross-sectional design precludes causal conclusions. Future longitudinal research could investigate whether similarity leads to higher marital satisfaction or whether satisfaction renders partners more similar.

5. Conclusion

Overall, real couples exhibit greater similarity than randomly paired couples, but this similarity has relatively limited explanatory power for marital satisfaction, with actor effects remaining the primary predictor. Notably, compared with individual traits and interaction patterns, real couples show significantly greater similarity in family-of-origin dimensions, and this similarity has a substantially greater impact on wives than husbands during child-rearing stages.

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Supplementary Materials

1. Empathy

We used the Interpersonal Reactivity Index for Couples (IRI) (Péloquin & Lafontaine, 2010), comprising two dimensions: empathic concern (7 items) and cognitive perspective-taking (6 items), totaling 13 items. Using a 5-point scale from 1 (strongly disagree) to 5 (strongly agree), with 4 items (2, 6, 7, 8) reverse-scored, higher scores indicate greater perspective-taking and empathic concern. In this study, Cronbach's α coefficients for husbands ranged from 0.627 to 0.734 across subscales and total scale; for wives, from 0.606 to 0.735.

The following statements describe thoughts and feelings about what happens between you and your loved one. Please respond based on your actual situation, circling the appropriate option.

(1) When I see my loved one is less fortunate than me, I feel very heartbroken.
(2) Sometimes when my loved one is in trouble, I don't feel that sympathetic toward him/her.
(3) Before making decisions, I stand in my loved one's shoes to view our disagreements/conflicts.
(4) When I see my loved one being taken advantage of, I want to protect him/her.
(5) Sometimes I stand in my loved one's shoes to better understand him/her.
(6) My loved one's misfortunes usually don't bother me much.
(7) If I'm sure I'm right about something, I won't waste much time listening to his/her opinion.
(8) When I see my loved one treated unfairly, sometimes I don't sympathize with him/her.
(9) Some things that happen between my loved one and me touch me deeply.
(10) I believe any problem between us has two sides, and I try to view them from both perspectives.
(11) In my relationship with my loved one, I consider myself a particularly soft-hearted person.
(12) When angry with my loved one, I usually think about what I would do if I were him/her.
(13) Before criticizing my loved one, I imagine how I would feel if I were him/her.

2. Attachment Style (Attachment Anxiety and Avoidance)

Based on the Experiences in Close Relationships (ECR) scale, Fraley et al. (2000) revised it to ECR-R, comprising two dimensions—attachment anxiety and attachment avoidance—with 36 items total. Couples responded using a 7-point scale from 1 (completely disagree) to 7 (completely agree), with 10 items (3, 15, 19, 25, 27, 29, 31, 33, 35, 22) reverse-scored. Higher scores indicate higher attachment avoidance or anxiety. In this study, Cronbach's α coefficients for husbands ranged from 0.791 to 0.824 across dimensions and total scale; for wives, from 0.836 to 0.852.

Attachment Style Questionnaire

Please read the following statements and consider your feelings in your current romantic relationship, circling the appropriate option.

(1) Generally, I don't like to let my loved one know my deep-down feelings.
(2) I worry I will be abandoned.
(3) I find it pleasant to be close to my loved one.
(4) I worry a lot about my relationship with my loved one.
(5) When my loved one starts to get close to me, I find myself pulling away.
(6) I worry my loved one won't care about me as much as I care about him/her.
(7) When my loved one wants to be very close to me, I feel uncomfortable.
(8) I somewhat worry about losing my loved one.
(9) I find it uncomfortable to be open and honest with my loved one.
(10) I often hope my loved one's feelings for me are as strong as mine for him/her.
(11) I want to be close to my loved one but always hold back.
(12) I often want to be inseparable from my loved one, but sometimes this scares him/her away.
(13) When my loved one is overly intimate with me, I feel inner tension.
(14) I worry about being alone.
(15) I'm willing to tell my loved one my inner thoughts and feelings; I find this comfortable.
(16) My desire to be very close to my loved one sometimes scares him/her away.
(17) I try to avoid becoming too close to my loved one.
(18) I need my loved one to repeatedly reassure me that he/she loves me.
(19) I find it relatively easy to be close to my loved one.
(20) I feel I'm demanding that my loved one show more feelings and investment in our relationship.
(21) I find it difficult to let myself depend on my loved one.
(22) I don't often worry about being abandoned by my loved one.
(23) I tend not to be overly intimate with my loved one.
(24) If I can't get my loved one's attention and care, I get upset or angry.
(25) My loved one and I talk about everything.
(26) I find my loved one isn't as willing to be close to me as I'd like.
(27) I often discuss my problems and concerns with my loved one.
(28) Without my loved one, I feel somewhat anxious and uneasy.
(29) I find it comfortable to depend on my loved one.
(30) If my loved one can't be with me as much as I'd like, I feel discouraged.
(31) I don't mind seeking comfort, advice, or help from my loved one.
(32) If my loved one isn't by my side when I need him/her, I feel frustrated.
(33) Asking my loved one for help when needed is very useful.
(34) When my loved one doesn't spend time with me, I feel resentful.
(35) I turn to my loved one for help with many things, including seeking comfort and commitment.
(36) When my loved one disagrees with me, I feel it's really my fault.

3. Stress Perception

We used the Perceived Stress Scale (PSS) developed by Cohen et al. (1983). PSS-14 includes two dimensions—distress perception and coping perception—with 14 items total. Individuals assessed their stress levels over the past month using a 5-point scale from 1 (never) to 5 (always), with 7 items (4, 5, 6, 7, 9, 10, 13) reverse-scored. Higher scores indicate higher perceived stress levels. In this study, Cronbach's α coefficients for husbands ranged from 0.804 to 0.845 across dimensions and total scale; for wives, from 0.796 to 0.828.

Stress Perception Questionnaire

The following items concern some feelings and thoughts that stress in your life may have caused you over the past month. Please respond based on your actual situation, circling the appropriate option.

(1) Some unexpected things made you feel upset and confused.
(2) Felt unable to control important things in your life.
(3) Felt nervous and stressed.
(4) Successfully handled troublesome life problems.
(5) Effectively coped with important changes in life.
(6) Felt confident in your ability to handle problems.
(7) Felt things were going your way.
(8) Felt unable to handle things you had to do.
(9) Felt you had ways to cope with annoying things in life.
(10) Felt you were in control of things.
(11) Felt angry because things happened beyond your control.
(12) Thought about things you had to accomplish.
(13) Felt able to control your time schedule without interference.
(14) Felt difficulties were piling up so high you couldn't overcome them.

4. Emotional Expression

The Trait Affection Given Scale (TAS–G) measures affection expression toward partners and others. TAS–G assesses individual levels of affection expression with 10 items, 5 of which are reverse-scored. Based on Horan and Booth-Butterfield's (2010) revision of the target population, we changed "people" to "romantic partner" in item 2. Couples rated their own affection expression using a 7-point scale from 1 (completely disagree) to 7 (completely agree), with 5 items (4, 5, 6, 8, 10) reverse-scored. Higher scores indicate higher affection expression levels. In this study, Cronbach's α coefficients were 0.826 for husbands and 0.859 for wives.

Emotional Expression Scale

The following are descriptions about affection expression. Please respond based on your actual situation, circling the appropriate option.

(1) I consider myself an emotionally rich person.
(2) I often express my care to my loved one.
(3) When I like someone, I usually express it.
(4) Expressing love or care to others is difficult for me.
(5) I'm not very good at expressing feelings.
(6) I'm not an emotionally rich person.
(7) I like to hug or put my arm around others.
(8) I don't often express emotions to others.
(9) People who know me say I'm emotionally rich.
(10) Expressing emotions to others makes me feel uncomfortable.

5. Conflict Resolution

We selected the conflict resolution strategies subscale from Kerig's (1996) Conflict and Problem-Solving Scales (CPS). The subscale measures conflict resolution strategies in partner relationships across 6 dimensions with 44 items total: cooperation (9 items), avoidance-surrender (9 items), stalemate-obstruction/resistance (5 items), verbal aggression (8 items), physical aggression (8 items), and child involvement (5 items). This study used only the cooperation dimension, with a 4-point scale from 1 (never) to 4 (often) to assess frequency of different problem-solving strategies during conflicts. Higher scores indicate more frequent use of that strategy. Cronbach's α coefficients were 0.803 for husbands and 0.795 for wives.

Conflict Resolution Questionnaire

The following are common practices after marital conflicts. When you and your loved one have conflicts or disagreements, what do you usually do? Please circle the appropriate number.

(1) Discuss with loved one.
(2) Openly express thoughts and feelings.
(3) Listen to loved one's perspective.
(4) Try hard to understand loved one's true feelings.
(5) Try hard to reason with loved one.
(6) Try to find solutions that equally satisfy both parties' needs.
(7) Find solutions with help from counselors or friends.
(8) Compromise and find middle-ground solutions.
(9) Try hard to settle disputes.

Never Rarely Occasionally Often

6. Emotion Regulation

We used the Emotion Regulation Questionnaire (ERQ) (Gross & John, 2003), comprising two dimensions—cognitive reappraisal (5 items) and expressive suppression (5 items)—with 10 items total. Couples rated their own emotion regulation strategy use on a 7-point scale from 1 (completely disagree) to 7 (completely agree). Higher scores indicate higher levels of cognitive reappraisal and expressive suppression. In this study, Cronbach's α coefficients for husbands ranged from 0.721 to 0.830 across dimensions and total scale; for wives, from 0.749 to 0.827.

Emotion Regulation Questionnaire

The following are ways people handle emotions. Please respond based on your actual situation, circling the appropriate option.

(1) When I want to feel more positive emotions (like happiness or pleasure), I change my thoughts.
(2) I control my emotions.
(3) When I want to feel fewer negative emotions (like sadness or anger), I change my thoughts.
(4) When I'm in a positive mood, I'm careful not to show it.
(5) When facing stressful situations, I think about problems in ways that help me stay calm.
(6) I control my emotions by not expressing them.
(7) When I want to feel more positive emotions, I change how I think about the situation.
(8) I control my emotions by changing how I think about the situation.
(9) When I feel negative emotions, I make sure not to show them.
(10) When I want to reduce negative emotional experiences, I change how I think about the situation.

7. Communication Patterns

We used Navran's (1967) revised Primary Communication Inventory (PCI). The original scale had 25 items; the current version has 19 items, with couples rating each description on a 5-point scale from 1 (never) to 5 (always). It comprises two dimensions: verbal communication (13 items) and nonverbal communication (6 items). Two items (7, 13) are reverse-scored, with higher scores indicating higher communication quality. In this study, Cronbach's α coefficients for husbands ranged from 0.696 to 0.881 across dimensions and total scale; for wives, from 0.779 to 0.906.

Communication Patterns Questionnaire

The following are ways couples communicate and interact. Please respond based on your actual communication with your loved one, circling the appropriate option.

(1) I talk with my loved one about happy things that happened that day.
(2) I talk with my loved one about unhappy things that happened that day.
(3) I discuss things where we disagree or have difficulties with my loved one.
(4) I talk with my loved one about things we're both interested in.
(5) My loved one adjusts what he/she says based on my current feelings.
(6) My loved one knows what I want to say before I ask.
(7) My loved one and I avoid certain topics in conversation.
(8) My loved one expresses his/her thoughts and feelings to me through eye contact or gestures.
(9) I discuss important decisions with my loved one before making them.
(10) Even without asking, my loved one can sense how my day went.
(11) My loved one discusses sexual matters with me.
(12) My loved one and I use special meanings only we two understand.
(13) My loved one often gives me the silent treatment.
(14) When chatting, if a friend's words make my loved one and I exchange glances, we understand each other's meaning.
(15) My loved one and I discuss each other's personal problems.
(16) I feel that most of the time, my loved one understands what I'm trying to express.
(17) I prefer discussing private matters with my loved one rather than others.
(18) I can read my loved one's facial expressions.
(19) Generally, my loved one and I agree on most things in marriage.

8. Interaction Support

We used the Support in Intimate Relationships Rating Scale (SIRRS) (Barry et al., 2009), comprising four dimensions: informational support (8 items), physical comfort (4 items), esteem/emotional support (8 items), and instrumental support (5 items). Using a 5-point scale from 1 (never) to 5 (often) to measure partner support frequency, higher scores indicate more support. In this study, Cronbach's α coefficients for husbands ranged from 0.907 to 0.953 across dimensions and total scale; for wives, from 0.917 to 0.957.

Interaction Support Questionnaire

When you encounter problems or difficulties, how often does your loved one do the following for you? Please circle the appropriate number.

(1) Provides advice for coping with problems and difficulties.
(2) Tells me how to do things.
(3) Helps me think about difficulties from new perspectives.
(4) Teaches or demonstrates how to do things.
(5) Shares his/her experiences in similar situations and how he/she handled them.
(6) Analyzes details and relevant information about problems.
(7) Explains problems or difficulties from another angle.
(8) Tells me what I might be feeling in this situation.
(9) Hugs me or cuddles with me.
Never Rarely Occasionally Often Frequently
(10) Kisses me.
(11) Holds my hand.
(12) Pats me affectionately.
(13) Tells me everything will be okay.
(14) Tells me he/she thinks I've handled difficulties well before.
(15) Expresses confidence in my ability to handle situations.
(16) Says praising and encouraging words to me.
(17) Tells me it's okay to have my current feelings.
(18) Agrees with or supports my viewpoint when discussing my situation.
(19) Tells me he/she would do the same if he/she were me.
(20) Tells me it's not my fault in this situation.
(21) Is willing to do things to directly help me.
(22) Does practical things to directly help me.
(23) Is willing to do practical things to indirectly help me (like housework).
(24) Does practical things to indirectly help me (like housework).
(25) Is willing to do practical things to make me feel better.

9. Perceived Partner Responsiveness

The Perceived Partner Responsiveness Scale (PPRS) is a self-report measure assessing perceptions of partner responsiveness (Reis et al., 2017). It comprises three dimensions: perceived understanding (6 items), perceived support and validation (6 items), and general partner responsiveness (6 items), totaling 18 items. Couples rated perceived partner responsiveness on a 9-point scale from 1 (completely uncharacteristic) to 9 (completely characteristic), with higher scores indicating greater perceived responsiveness. In this study, Cronbach's α coefficients for husbands ranged from 0.847 to 0.974 across dimensions and total scale; for wives, from 0.836 to 0.969.

Perceived Partner Responsiveness Questionnaire

The following describes some possible views or behaviors your loved one may have toward you. 1 means "completely uncharacteristic," 9 means "completely characteristic." Please circle the number that best describes your loved one's actual situation.

My loved one...
(1) Evaluates my personality very accurately.
(2) Understands the real me.
(3) Can see my strengths and weaknesses as I see them.
(4) Always knows about me without error.
(5) Respects me greatly, accepting everything about me including my flaws.
(6) Knows me very well.
(7) Respects and values all aspects of me, allowing me to be myself.
(8) Usually focuses more on my good side.
(9) Can know my thoughts and feelings.
(10) Understands me very well.
(11) Is good at listening to me.
(12) Expresses liking and encouragement to me.
(13) Seems very interested in my thoughts and feelings.
(14) Enjoys doing things with me.
(15) Values my abilities and ideas greatly.
(16) Always agrees with my views.
(17) Respects me greatly.
(18) Responds to my needs.

10. Parental Marital Quality

We adapted Zhang's (2009) Parental Marital Quality Questionnaire, based on Olson's (1999) marital quality questionnaire, to evaluate one's own and one's partner's parents' marriages. The scale has 4 items assessing marital quality of self and partner's biological parents, with higher scores indicating higher marital quality. In this study, Cronbach's α coefficients were 0.587 for husbands and 0.531 for wives.

Parental Marital Quality Questionnaire

The following describes your parents' marital situation. Please respond based on actual circumstances.

(1) Your biological parents' marriage is: ①First marriage ②Remarriage ③Reconciliation ④Divorced ⑤Other (please specify)
(2) How do you think your biological parents' marital relationship is: ①Very good ②Relatively good ③Average ④Relatively poor ⑤Very poor
(3) Your partner's biological parents' marriage is: ①First marriage ②Remarriage ③Reconciliation ④Divorced ⑤Other (please specify)
(4) How do you think your partner's biological parents' marital relationship is: ①Very good ②Relatively good ③Average ④Relatively poor ⑤Very poor

11. Family-of-Origin Interference

We selected items from the Family-of-Origin Involvement Scale (Yuan et al., 2015), which adapted Li's (2011) mother-in-law interaction scale to measure interference from the family of origin. It includes two dimensions: family life interference (13 items) and personal life interference (7 items). Due to some duplicate items, we selected 5 aspects from family life interference (marital conflict, household division, finances, daily family life, child-rearing) and 4 aspects from personal life interference (personal work, interpersonal relationships, entertainment, values), totaling 9 items. Couples rated the degree of interference from both sets of parents in family and personal life on a scale from 1 (never) to 4 (often), with higher scores indicating more interference. If one or both parents have passed away, respondents marked 0. In this study, Cronbach's α coefficients for husbands ranged from 0.977 to 0.987 across dimensions and total scale; for wives, from 0.974 to 0.984.

Family-of-Origin Interference Questionnaire

In the past year, have your and your partner's parents interfered in your relationship and family life? 1=Never; 2=Occasionally; 3=Sometimes; 4=Often. Please circle the appropriate number based on actual circumstances. (If parents have passed away, please mark 0.)

(1) Marital arguments and conflicts
(2) Household division between spouses
(3) Financial expenses and management
(4) Daily living habits and diet
(5) Child-related matters (e.g., whether to have children)
(6) Personal study and work
(7) Interpersonal relationships
(8) Entertainment activities
(9) Values

Your parents' interference | Your partner's parents' interference

12. Marital Satisfaction

Marital Quality Questionnaire

The following describes marital situations. "1" means "strongly disagree," "7" means "strongly agree." Please circle the number that best describes your actual situation.

(1) We have a good marriage.
(2) My relationship with my loved one is very stable.
(3) Our marriage is strong.
(4) My relationship with my loved one makes me happy.
Strongly disagree························Strongly agree
(5) I truly feel that my loved one and I are a whole.
(6) Considering all factors, how happy do you think your marriage is? Full score is 10 points. Please circle the appropriate number.

Very unhappy Happy Very happy

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