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. 2026 Mar 17;44(3):641–656. doi: 10.1111/bjdp.70040

Profiles of sibling relationships among Chinese adolescents: The contribution of parental differential treatment of siblings

Fanlong Wang 1, Huiguang Ren 2,✉, Junsheng Liu 1, Xiaohua Bian 3,✉, Yan Wang 4
PMCID: PMC13452003  PMID: 41845590

Abstract

Since the implementation of multi‐child policies in China, the proportion of multi‐child families has increased, making sibling relationships a critical issue. The present study used latent profile analysis to identify distinct profiles of sibling relationships among Chinese adolescents. The associations between maternal and paternal differential treatment and children's membership in specific profiles were also examined. A sample of 1553 Chinese adolescents (M age = 13.28, 48.1% boys) with at least one sibling revealed five subgroups: positive, conflicting, average, moderately negative, and highly negative groups. Results also showed that adolescents experiencing a high degree of maternal differential treatment were more likely to belong to the highly negative group, while paternal differential treatment had no significant associations. These findings highlighted the heterogeneity of sibling relationships and the important contribution of maternal differential treatment in shaping sibling dynamics, which offered valuable insights for enhancing the well‐being of multi‐child families.

Keywords: Chinese adolescents, latent profile analysis, parental differential treatment, sibling relationships

Statement of Contribution.

What is already known on this subject

  • Using a variable approach, parental differential treatment correlated with poor sibling relationships.

  • Person‐centered studies in non‐Chinese cultures identified sibling relationship types.

  • Preliminary evidence from the U.S. showed maternal treatment links to sibling relationship quality.

What the present study adds

  • Latent profile analysis identifies five sibling relationship types within Chinese cultures.

  • Only maternal differential treatment significantly increased the odds of belonging to the highly negative group.

INTRODUCTION

Since the implementation of the multi‐child policy in China in 2016, the proportion of second‐born infants in total births has increased (National Bureau of Statistics of China, 2022). This demographic shift has precipitated novel family dynamics particularly in sibling relationships among millions of Chinese adolescents. Sibling relationships represent individuals' most enduring social bonds, exerting long‐term influences on individual development (McHale et al., 2012). A critical review suggested that positive sibling relationships correlate with increased well‐being and psychological adjustment, while negative relationships show strong associations with aggressive behaviours and emotional maladjustment (Jensen et al., 2023). Most research has predominantly employed variable‐centered approaches that assume population homogeneity in sibling relationship patterns. However, existing evidence has revealed distinct types of sibling dynamics (Killoren et al., 2017; McHale et al., 2007), underscoring the necessity of using person‐centered approaches to identify meaningful subgroups and explore factors that may contribute to this heterogeneity.

Within multi‐child families, parental challenges in ensuring equitable treatment across each child emerge as a critical factor that may shape sibling relationships. A meta‐analysis identified that greater parental differential treatment is related to poor sibling relationships (Jiang et al., 2025). Nevertheless, the existing literature remains limited in examining how parental differential treatment is associated with specific sibling relationship patterns and fails to distinguish between the distinct contribution of maternal and paternal differential treatment, particularly within the Chinese cultural context. To address these limitations, the present study employed latent profile analysis to (a) identify latent subgroups of sibling relationships within the Chinese sociocultural context and (b) investigate the associations between maternal and paternal differential treatment and adolescent subgroup membership.

Profiles of sibling relationships

While previous studies have predominantly employed variable‐centered approaches to examine specific dimensions of sibling relationships, these methods are limited in capturing the complex interaction between multiple relational dimensions. Gamble and Yu (2014) contended that simultaneous examination of multiple interaction dimensions provides a more comprehensive understanding of sibling dynamics, offering more nuanced insights into how specific dimension combinations affect individual development. Person‐centered approaches, such as latent profile analysis, offer a more nuanced and comprehensive understanding of sibling relationship diversity, highlighting their particular value for investigating specific relational configurations.

Using person‐centered approaches, distinct sibling relationship profiles have been identified based on warmth, conflict, and control dimensions across ethnic groups, including African American and Mexican American adolescents (Killoren et al., 2017; McHale et al., 2007). Notably, the positive (high warmth, low conflict and control) and negative (high conflict and control, low warmth) sibling relationship patterns have been universally identified across samples. However, cross‐cultural specificity emerged, with the uninvolved or distant (low warmth, low conflict, and low control) pattern being absent in Mexican American adolescents (Killoren et al., 2017). These differences may reflect a cultural orientation where familism values promote frequent sibling interactions among Mexican‐origin adolescents, thereby preventing distant dynamics (Killoren et al., 2017). These findings underscore the critical role of cultural norms in shaping sibling relationship prototypes, highlighting the need for culturally specific investigations.

Despite these advancements, the literature remains unclear regarding sibling relationship profiles in the Chinese context. The Confucian concept of siblinghood establishes older siblings as quasi‐parental figures holding higher power and status, a role embodied in the proverb zhangxiong rufu (长兄如父, ‘eldest siblings are like parents’). Within Chinese family systems, eldest siblings are granted higher authority and status but are also expected to take on the responsibility of guiding and caring for their younger siblings. In return, younger siblings are socialized to show obedience and deference (Chen & Tan, 2021). These social expectations may create a unique authoritative‐support dynamic, where control behaviours (e.g., arranging what younger siblings should do) toward younger siblings are culturally viewed as part of the eldest sibling's family responsibilities. Consequently, culturally distinct relationship patterns (e.g., control‐care) may emerge. However, scant research has examined these culturally specific dynamics in the context of Chinese sibling relationships. Our study therefore aimed to address this critical gap by identifying sibling relationship profiles among Chinese adolescents.

In our framework, sibling relationship profiles are identified based on four key dimensions: warmth, conflict, control, and jealousy. Among these, warmth and conflict represent universal constructs consistently examined in sibling relationships research across cultures. Warmth captures positive emotional bonds and supportive interactions, while conflict reflects overt quarrels and aggressive behaviours. As mentioned earlier, control, as a culturally salient dimension, reflects the hierarchical nature of Chinese sibling relationships rooted in Confucian values (Chen & Tan, 2021). Given that control behaviours among siblings have not been adequately differentiated or empirically examined (e.g., adaptive or maladaptive forms of sibling control) in the existing literature, this study thus aligns with prior work by focusing on general control behaviours. Additionally, we introduce jealousy as a novel dimension due to its prevalence in sibling interactions. Shared family environments and overlapping developmental needs create fertile ground for social comparison and resource competition, leading to near‐universal experiences of sibling jealousy (Qian, 2024; Thompson & Halberstadt, 2008). By integrating these four dimensions, our analytical framework enhanced ecological validity while capturing both universal and culturally specific aspects of sibling relationships.

Parental differential treatment and sibling relationship profiles

In multi‐child families, parents often treat each child differently, a phenomenon known as parental differential treatment (PDT; Jensen & Thomsen, 2024). Family system theory emphasizes the family as a complex system of interconnected and interdependent subsystems (Cox & Paley, 2003). The parent–child subsystem (e.g., parental differential treatment) would influence the sibling subsystem (e.g., sibling relationships). Equity theory explains inequity as a cause of tension and conflict (Walster et al., 1973). In the context of PDT, parents who treat their children differently foster conflict within the family (Jensen & Thomsen, 2024). When children perceive differential treatments, this perception triggers negative emotional and behavioural reactions within siblings. Existing evidence consistently demonstrated that greater PDT was negatively associated with sibling relationship quality (e.g., Jiang et al., 2025; Shi et al., 2022). Importantly, the impact of PDT can be more impactful than that of parenting styles (Jensen & Jorgensen‐Wells, 2025). That is, the negative outcomes associated with PDT stem not from whether the parenting is good or bad, but from the experience of being treated differently. These findings positioned PDT as a significant risk factor for adverse sibling dynamics. However, extant variable‐centered approaches are limited in identifying distinct subgroups of adolescents' sibling relationships characterized by unique combinations of multiple interaction dimensions, potentially obscuring how PDT contributes to divergent relational patterns across these subgroups. This methodological constraint leaves a key question unanswered: which patterns are most sensitive to PDT? Person‐centered approaches address this by examining how PDT predicts membership in these distinct subgroups.

While previous studies have shown incongruence in differential treatment between parents (Kan et al., 2008), little attention has been devoted to exploring differences between mothers and fathers. Some preliminary evidence from the U.S. suggests that maternal differential treatment is negatively associated with sibling intimacy, whereas paternal differential treatment does not (Jensen et al., 2013). However, no studies have yet examined this critical parental differentiation within the specific context of Chinese families. The Chinese caregiving paradigm, rooted in the traditional yan'fu ci'mu (严父慈母) concept, views fathers as disciplinarians maintaining hierarchical distance and mothers as caregivers fostering emotional closeness (Chen et al., 2019). This parenting model highlights the important role of mothers' greater involvement in parenting, relative to those of fathers in Chinese culture. This dynamic may result in a closer tie between mothers and children and a more distant relationship between children and fathers (Xu et al., 2024). Given children's heightened emotional dependence on their mothers within this cultural framework, maternal differential treatment was more likely to shape sibling dynamics compared with paternal influences. Therefore, our second goal was to examine the potential differences in the associations between maternal and paternal differential treatment with sibling relationship subgroups among Chinese adolescents.

Sibling research in the context of China

Due to the implementation of the one‐child policy (1979–2015), sibling relationship is an understudied area in Chinese family research. Today, the shifts in population policy provide a valuable opportunity to explore sibling dynamics in China. The uniqueness of Chinese culture is reflected in its familism values that emphasize interdependence and harmony within families (Chen & Tan, 2021). The concept of shouzu (手足, meaning hands‐feet siblinghood) metaphorically positions siblings as inseparable bodily extensions, with an expectation to maintain interdependence (Wang et al., 2022). Simultaneously, Chinese parents socialize their children to value the family over individual desires and encourage them to restrain personal desires to prioritize their siblings' needs (Wang et al., 2022). Growing up immersed in this socialization context, Chinese children may be more tolerant when they experience sibling differences in resources, attention and treatment approaches from their parents compared with their Western counterparts. Therefore, differential treatment may exert more negative implications in individualistic than in collectivistic cultures (McHale et al., 2005). Given these cultural scripts contrast sharply with Western values that emphasize individual independence and maximization, conducting research within the Chinese context is particularly meaningful for gaining valuable insights into PDT and its associations with sibling relationships.

The present study

Prior research lacks person‐centered investigations into the heterogeneity of sibling relationships within China's transition to a multi‐child policy. Furthermore, the distinct roles of maternal and paternal differential treatment remain underexplored, particularly in Chinese culture, which emphasizes gendered parenting roles. To fill these gaps, the present study had two purposes. The first was to identify distinct sibling relationship profiles among Chinese adolescents focusing on four salient features as observed variables (i.e., warmth, conflict, jealousy, and control). Based on previous studies, we hypothesized the existence of at least three distinct subgroups marked by either high levels of warmth, conflict, or jealousy between siblings. The second was to examine the differential associations of maternal and paternal differential treatment with profile membership. We hypothesized that (a) the higher degree of PDT would increase the probability of being categorized into more adverse profiles (i.e., profiles marked with higher levels of conflict, jealousy, and control, and lower levels of warmth); (b) maternal differential treatment would exhibit a stronger association with adverse profiles compared with paternal treatment.

METHOD

Participants

The original sample included 2378 students (46.6% boys) from fifth to ninth grades in a public school in Xingyang, Henan Province, P. R. China. From among this original sample, 825 students who were only children in their families were excluded from subsequent analyses. The final sample consisted of 1553 students with at least one sibling (M age = 13.28, SD age = 1.30, 48.1% boys). The ratio between multi‐child and single‐child families was comparable to the proportion of multi‐child families in the area (Henan Province Bureau of Statistics, 2019). There were no differences in demographic characteristics between adolescents with and without siblings. The students were from 30 classes with approximately 50 students in each class. Sibling demographic characteristics revealed a mean age of 11.76 years (SD = 5.86), with an average age gap of 4.86 years between siblings (SD = 3.12). In our sample, 72.6% of adolescents had one sibling, and 27.4% of adolescents had two or more siblings. For those adolescents who had two or more siblings, we referred to Chen's (2023) practice to ask them to choose the sibling closest to their age to consider when completing the questionnaires. Birth order indicated that 38% of adolescents were younger siblings, 59.3% of adolescents were older siblings, and 2.7% of adolescents were not identified. The sibling gender composition included 19.4% brother pairs, 25.2% older brother/younger sister pairs, 34.1% older sister/younger brother pairs, and 17.3% sister pairs (4.0% siblings' gender was not identified). Regarding family background, 32.8% of fathers and 32.3% of mothers had attained senior high school or higher levels of education. In terms of monthly family income, compared with the local average (approximately 6000 RMB), most students came from low‐to‐middle income households, with 71% of families earning a monthly income between 2001 and 5000 RMB. The majority of students (70%) reported that both parents served as their primary caregivers in their daily lives.

Procedure

The current study was reviewed and approved by the East China Normal University review board before data collection. Written informed consent was obtained from both participating students and their parents through the school. During data collection, students completed questionnaires about sibling relationships, PDT, and demographic variables. The data collection process was carried out by a team of psychology students.

Measures

Sibling relationships

Warmth, conflict, and jealousy were measured using the Chinese version of Sibling Relationship Questionnaire (Wang et al., 2022). Control was measured by the adapted Relative Power subscale from the Sibling Relationship Questionnaire (Furman & Buhrmester, 1985). The Warmth subscale contains nine items, such as ‘My siblings and I help each other’. The Conflict subscale contains four items, such as ‘My sibling and I would push each other or fight’. The Jealousy subscale contains five items, such as ‘I feel jealous when my sibling has more parental attention’. The Power subscale comprises four items to capture general mutual control behaviours between siblings, such as ‘I decide what my sibling should do’, ‘My sibling decides what I should do’, ‘I order my sibling what to do’, and ‘My sibling orders me what to do’. The items were rated on a 5‐point scale (1 = never, 5 = always). We calculated mean scores for each subscale, with higher scores indicating greater manifestation of the respective dimension. In the current study, Cronbach's alphas for warmth, conflict, jealousy, and control were .94, .91, .95, and .73, respectively.

Parental differential treatment

Parental differential treatment was measured by the Sibling Inventory of Differential Experience‐Chinese Version (SIDE‐C; Ong & Tan, 2018). The measure contains nine items with two dimensions: control and affection. Specifically, students were asked to separately rate their mothers' and fathers' treatment. The items were rated on a 5‐point scale (1 = much less, 2 = a bit less, 3 = equal, 4 = a bit more, 5 = much more). The representative items of control and affection were ‘Compared with my sibling, my mother/father was strict with me’ and ‘Compared with my sibling, my mother/father was proud of things I have done’. We used absolute scores to capture the degree of PDT. This approach is necessary because disfavored participants may rate their treatment as ‘much less’ (scores 1), whereas favoured participants may rate it as ‘much more’ (scores 5). While scores 1 and 5 may seem to reflect opposite degree of parental treatment, they essentially represent the same high magnitude of PDT. If analysed directly as raw values, the low and high scores would statistically cancel out, thereby masking the true degree of PDT and its effects in the sample. Therefore, following previous studies that have validated this scoring method (e.g., Shi et al., 2022; Zhou & Woszidlo, 2024), each response was recoded on a 3‐point absolute scale: scores 1 (much less) and 5 (much more) were recoded as 2 (much inequality), scores 2 (less) and 4 (more) as 1 (moderate inequality) and scores 3 (equal) as 0 (no inequality). Given the high correlation between control and affection (mother: r = .82, father: r = .85), the final scores aggregated two domains of differential treatment, with high scores representing a greater degree of differential treatment. In the current study, Cronbach's alphas for was .93 for maternal treatment and .95 for paternal treatment.

Analytic plan

The primary analyses were conducted using Mplus version 8.3. For our first goal, latent profile analysis was conducted to identify subgroups across the four observed variables: warmth, conflict, jealousy, and control. The scores of the four observed variables were standardized for easier interpretation. Two through seven profile solutions were sequentially estimated. The following fit indices were used to determine the appropriate number of profiles (Nylund et al., 2007): Akaike information criterion (AIC), Bayesian information criterion (BIC), sample‐size adjusted Bayesian information criterion (a‐BIC), adjusted Lo–Mendell–Rubin likelihood ratio test (aLMR), bootstrap likelihood ratio test (BLRT), and entropy. Models with smaller AIC, BIC, and a‐BIC values suggest better fitting solutions. Significant p‐values of aLMR and BLRT indicate that a model with k profiles has a better fit than a model with k‐1 profiles. High entropy values (greater than 0.80) represent greater precision and classification quality of models. In addition, we also considered whether the smallest subgroup consisted of at least 5% of the sample because small groups are difficult to interpret (Nylund et al., 2007).

For our second goal, we conducted multinomial logistic regression to test whether maternal and paternal differential treatment differentially contribute to sibling relationship profiles. This analysis controlled for demographic variables (gender, birth order, age, and age gap), which are established correlates of sibling relationships (Jensen et al., 2023). In this step, we used the three‐step approach to compare group differences in predictors. As Asparouhov and Muthén (2014) suggested, compared with the one‐step approach, the three‐step approach did not change profile membership after adding predictors.

The amount of missing values on four observed variables and PDT ranged from 0.0% to 1.6%. The Little's MCAR test's result showed that the data were missing completely at random (Little, 1988), χ 2(38) = 44.75, p = .210. Missing data were handled by using full information maximum likelihood (FIML).

RESULTS

The correlations, means and standard deviations of the study variables are reported in Table S1 (see Supplementary Materials). The direction of correlations between variables was consistent with our expectations.

Profiles of sibling relationships

A series of two‐to seven‐latent profile models was estimated. As shown in Table 1, given that the smallest subgroup of six‐ and seven‐profile solutions (n < 5%) was too small to interpret, these two solutions were excluded. Among 2‐ to 5‐ solutions, AIC, BIC, and aBIC continued to decrease with each additional profile, indicating the five‐profile solution achieved better data fit. The entropy values were similar and the aLMR and BLRT maintained statistical significance across solutions, confirming that each additional profile significantly improved model fit compared with the previous one. Collectively, these metrics converge to support the five‐profile solution. More importantly, the five‐profile solution further identified a unique group not observed in the previous solutions, characterized by high levels of sibling conflict yet unexpectedly low jealousy scores. We deemed this group unique because pure conflict without jealousy may reflect greater differences in individual traits and characteristics (e.g., opinions, temperaments, habits), whereas higher levels of conflict and jealousy in another group may result from large differences in resources, treatment, and evaluation from the environment. Therefore, based on both statistical indicators and theoretical considerations, we retained the five‐profile solution as the optimal result.

TABLE 1.

Fit statistics for latent profile analyses.

Model LL FP AIC BIC aBIC Entropy aLMR BLRT Proportions min (%)
2 −8142.96 13 16,311.92 16,381.44 16340.14 0.935 <.001 <.001 13.2
3 −7835.73 18 15,707.46 15,803.72 15746.54 0.922 <.001 <.001 5.9
4 −7553.53 23 15,153.07 15,276.07 15203.01 0.956 <.001 <.001 5.3
5 −7411.55 28 14,879.11 15,028.86 14939.91 0.933 <.001 <.001 5.3
6 −7201.75 33 14,469.49 14,645.97 14541.14 0.952 <.001 <.001 2.9
7 −7119.54 38 14,315.08 14,518.30 14397.59 0.952 .001 <.001 2.9
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Note: Bold line indicates selected model.

Abbreviations: aBIC, sample‐size adjusted BIC; AIC, Akaike information criterion; aLMR, adjusted Lo–Mendell–Rubin test; BIC Bayesian information criterion; BLRT, bootstrap likelihood ratio test; FP, free parameters; LL, log likelihood.

The standardized and raw mean values of the indicator variables for five profiles are reported in Table 2. The significant F‐tests for all indicators confirmed substantial differences across the profiles. Profile 1 (58.6%, n = 910) was characterized by the highest levels of warmth than all other groups, and was labelled as the positive group. Profile 2 (5.8%, n = 90) was characterized by the highest levels of conflict than all other groups, and was labelled as the conflicting group. Profile 3 (20.4%, n = 317) was characterized by scores on four indicators close to the average levels, and was labelled as the average group. Profile 4 (5.3%, n = 82) was characterized by the highest levels of jealousy and control, and relatively higher levels of conflict, and was labelled as the highly negative group. Profile 5 (9.9%, n = 154) was characterized by moderate levels of conflict, jealousy, and control, which were significantly higher than those in the average group but lower than the highly negative group, and was labelled as the moderately negative group. The standardized mean scores of four observed variables for the five‐profile solution are graphically illustrated in Figure 1.

TABLE 2.

Means of indicators and parental differential treatment by latent profile membership.

Indicators Positive Conflicting Moderately negative Average Highly negative F
Warmth 0.27c (3.64) −0.72a (2.58) −0.39ab (2.93) −0.17b (3.17) −0.65a (2.65) 45.58***
Conflict −0.52a (1.66) 1.75e (4.15) 0.74c (3.04) 0.28b (2.54) 1.37d (3.74) 391.64***
Jealousy −0.59a (1.07) −0.55a (1.10) 1.41c (3.00) 0.34b (1.96) 3.10d (4.61) 3453.10***
Control −0.31a (1.53) 0.57c (2.26) 0.53c (2.22) 0.20b (1.95) 1.05d (2.65) 60.36***
MDT −0.18a (0.49) 0.25b (0.77) 0.09ab (0.66) 0.04ab (0.63) 0.90c (1.15) 15.85***
FDT −0.15a (0.51) 0.25b (0.78) 0.15ab (0.71) −0.04ab (0.58) 0.69c (1.06) 10.18***
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Note: Raw scores are presented in brackets. Means within the same row that do not share a subscript differ significantly at p < .05. a < b < c < d < e.

Abbreviations: FDT, fathers' differential treatment; MDT, mothers' differential treatment.

***

p < .001.

FIGURE 1.

FIGURE 1

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Latent profile indicator means for the five‐profile solution.

Parental differential treatment and sibling relationship profiles

As shown in Table 3, the results of the three‐step approach revealed distinct patterns of associations between maternal and paternal differential treatment for profile membership. Maternal differential treatment demonstrated significant associations across multiple profile comparisons. Specifically, compared with the positive, moderately negative, and average groups, adolescents experiencing higher maternal differential treatment were more likely to belong to the highly negative group, indicating the prominent contribution of maternal differential treatment to adverse sibling relationship patterns. Furthermore, compared with the positive group, adolescents experiencing greater maternal differential treatment had an increased likelihood of being classified into the average group. In contrast, no significant result was found for paternal differential treatment.

TABLE 3.

Associations between demographic characteristics, parental differential treatment, and sibling relationship profiles.

Predictor Conflicting vs. positive (ref.) Moderately negative vs. positive (ref.) Average vs. positive (ref.) Highly negative vs. positive (ref.)
b (SE) OR 95% CI b (SE) OR 95% CI b (SE) OR 95% CI b (SE) OR 95% CI
Gender −0.01 (0.38) 0.99 [0.47, 2.07] 0.32 (0.25) 1.38 [0.84, 2.25] 0.29 (0.20) 1.34 [0.91, 1.98] −0.41 (0.35) 0.67 [0.33, 1.32]
BO 1.10 (0.50)* 2.99 [1.13, 7.95] 0.87 (0.25)** 2.39 [1.45, 3.93] 0.67 (0.21)** 1.95 [1.30, 2.92] 1.65 (0.46)*** 5.18 [2.12, 12.6]
Age 0.28 (0.19) 1.32 [0.90, 1.92] 0.16 (0.13) 1.17 [0.92, 1.50] 0.14 (0.10) 1.16 [0.95, 1.40] −0.01 (0.19) 0.99 [0.68, 1.45]
Age gap −0.88 (0.27)** 0.42 [0.24, 0.71] −0.37 (0.14)** 0.70 [0.53, 0.91] −0.27 (0.10)** 0.77 [0.63, 0.93] −0.23 (0.18) 0.72 [0.56, 1.14]
MDT 0.45 (0.25) 1.57 [0.96, 2.58] 0.19 (0.20) 1.21 [0.82, 1.79] 0.41 (0.14)** 1.51 [1.14, 2.01] 0.94 (0.23)*** 2.55 [1.62, 4.02]
FDT 0.21 (0.25) 1.23 [0.75, 2.02] 0.24 (0.10) 1.27 [0.87, 1.85] −0.13 (0.15) 0.88 [0.66, 1.17] 0.10 (0.24) 1.10 [0.69, 1.76]
Predictor Moderately negative vs. conflicting (ref.) Average vs. conflicting (ref.) Highly negative vs. conflicting (ref.) Average vs. moderately negative (ref.)
b (SE) OR 95% CI b (SE) OR 95% CI b (SE) OR 95% CI b (SE) OR 95% CI
Gender 0.33 (0.41) 1.39 [0.63, 3.08] 0.31 (0.39) 1.35 [0.63, 2.89] −0.40 (0.47) 0.67 [0.27, 1.68] −0.03 (0.29) 0.97 [0.56, 1.70]
BO −0.23 (0.52) 0.80 [0.29, 2.20] −0.43 (0.50) 0.65 [0.24, 1.75] 0.55 (0.63) 1.73 [0.51, 5.93] −0.20 (0.29) 0.82 [0.46, 1.45]
Age −0.11 (0.21) 0.89 [0.60, 1.33] −0.13 (0.19) 0.88 [0.60, 1.28] −0.28 (0.25) 0.75 [0.46, 1.22] −0.02 (0.14) 0.98 [0.75, 1.29]
Age gap 0.51 (0.29) 1.67 [0.95, 2.93] 0.61 (0.28)* 1.84 [1.07, 3.18] 0.65 (0.30)* 1.92 [1.06, 3.48] 0.10 (0.16) 1.10 [0.81, 1.50]
MDT −0.26 (0.28) 0.77 [0.44, 1.33] −0.04 (0.25) 0.96 [0.59, 1.56] 0.49 (0.30) 1.63 [0.91, 2.91] 0.22 (0.21) 1.25 [0.82, 1.89]
FDT 0.03 (0.28) 1.04 [0.60, 1.79] −0.34 (0.26) 0.72 [0.43, 1.19] −0.11 (0.31) 0.90 [0.49, 1.65] −0.37 (0.21) 0.69 [0.46, 1.05]
Predictor Highly negative vs. moderately negative (ref.) Highly negative vs. average (ref.)
b (SE) OR 95% CI b (SE) OR 95% CI
Gender −0.73 (0.40) 0.48 [0.22, 1.06] −0.70 (0.37) 0.50 [0.24, 1.02]
BO 0.77 (0.50) 2.17 [0.82, 5.75] 0.98 (0.47)* 2.66 [1.06, 6.67]
Age −0.17 (0.22) 0.84 [0.55, 1.29] −0.15 (0.20) 0.86 [0.58, 1.26]
Age gap 0.14 (0.21) 1.15 [0.76, 1.73] 0.04 (0.19) 1.04 [0.72, 1.50]
MDT 0.75 (0.28)** 2.11 [1.23, 3.64] 0.53 (0.24)* 1.69 [1.06, 2.68]
FDT −0.14 (0.28) 0.87 [0.51, 1.50] 0.23 (0.25) 1.26 [0.78, 2.03]
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Note: Gender: 0 = boys, 1 = girls; birth order: 0 = younger sibling, 1 = older sibling. Bold indicates significant odds ratios 95% CIs.

Abbreviations: BO, birth order; FDT, fathers' differential treatment; MDT, mothers' differential treatment; OR, odds ratio; Ref, reference group.

*

p < .05.

**

p < .01.

***

p < .001.

Birth order had significant associations with profile membership. Compared with the other four groups, older siblings were less likely to belong to the positive group. Additionally, older siblings were more likely to belong to the highly negative group than to the average group. A larger age gap between siblings was associated with a higher likelihood of belonging to the positive profile relative to the conflicting, moderately negative, and average profiles. Conversely, a smaller age gap between siblings increased the likelihood of belonging to the conflicting group relative to the average and highly negative groups.

Supplementary analyses

Given prior findings that child gender and birth order moderate the associations between PDT and sibling relationships (Shi et al., 2022), we examined whether the associations between PDT and sibling relationship profile membership would differ by gender and birth order. Specifically, we examined the moderating effects of children's gender and birth order. Supplementary analyses revealed no statistically significant moderation effects for either gender or birth order, as detailed in Table S2. Additionally, we examined whether sibling gender composition predicted profile membership and found no significant associations. The specific results regarding gender composition are detailed in Tables S3 and S4.

DISCUSSION

The present study advanced sibling relationships research through two key contributions. First, in the context of the shift in China's population policies and the cultural emphasis on family interdependence and hierarchical structures, we addressed critical limitations of variable‐centered approaches that cannot identify qualitatively distinct subgroups within a population by employing latent profile analysis. Second, our investigation provided novel insights into the differential roles of maternal and paternal differential treatment, which are crucial yet underexplored distinctions in Chinese cultures that emphasize gendered parenting roles. Using latent profile analysis, the present study identified five distinguishable groups: positive group, conflicting group, average group, moderately negative group, and highly negative group. In addition, our results further revealed that adolescents experiencing a greater magnitude of maternal differential treatment are more likely to belong to the highly negative and average groups, but we did not find significant associations for paternal differential treatment.

Profiles of sibling relationships

In the present study, a large proportion of students (58.6%) fell into the positive group, characterized by high warmth and closer interactions with siblings. This predominance aligns with Confucian values of siblinghood, where maintaining familial cohesion and relational harmony constitutes both a moral imperative and a social expectation (Chen & Tan, 2021). Additionally, we identified the conflicting group, characterized by the highest levels of conflict with siblings, comprising the lower prevalence (5.8%) in our sample. Both of these groups were also identified in previous studies (Killoren et al., 2017; McHale et al., 2007), suggesting that these two subgroups were prevalent across cultural contexts. We also identified the average group (20.4%), characterized by a lack of positive interactions and fewer negative incidents with siblings. It is possible that these adolescents increasingly allocate time to peers and academic pursuits, potentially reducing engagement in shared sibling activities (Tucker et al., 2008). Finally, we identified two quantitatively distinct yet qualitatively similar groups: the moderately negative group (9.9%) and the highly negative group (5.3%). Both groups exhibit notable conflict, jealousy, and control, with the key difference being the intensity of these experiences. Adolescents in the highly negative group experienced greater intensity across these three domains compared with those in the moderately negative group. This may reflect more pervasive and intense relational challenges for the sibling dyads in the highly negative group than the moderately negative group.

Notably, what distinguished the highly negative group from the conflicting group was the central theme of jealousy. This critical distinction stemmed from the different relational mechanisms of conflict and jealousy. Sibling conflict refers to the mutual opposition in behaviour, goals, or activities between siblings, manifested as mutual aggression or arguments, primarily stemming from mismatched temperaments or preferences (Qu & Cao, 2021). These dyadic tensions exist from daily interactions among themselves and may fade with reduced contact (Jensen et al., 2013). In contrast, sibling jealousy arises from a more complex triangular dynamic (parent, adolescent, and sibling), in which adolescents perceive themselves at a disadvantage (Volling et al., 2002). Jealousy indicates deeper relational erosion within the family, which, in turn, exacerbates further conflict (Kolak & Volling, 2011). This may explain why conflict and jealousy co‐occur in the highly negative group.

While our results showed some variation compared with previous studies, likely due to differences in measurement indicators and sample characteristics, several profiles identified in our study align with those reported in earlier research (Killoren et al., 2017; McHale et al., 2007). The consistency across diverse cultural contexts strengthened the validity of person‐centered approaches for capturing the complexity of sibling relationships.

Parental differential treatment and sibling relationship profiles

According to the family system theory (Cox & Paley, 2003), the effects of negative dynamics in parent–child subsystems (i.e., parental differential treatment) tend to spill over into the sibling subsystem, intensifying the tension of sibling relationships. Unequal parental treatment may foster a sense of injustice and emotional insecurity among siblings that may engender destructive sibling relational patterns, such as persistent competition and jealousy (Jensen & Thomsen, 2024). As Volling et al. (2010) suggested, parental differential treatment actually is an inherent family dynamic associated with intense jealousy among adolescents. Our findings similarly found that maternal differential treatment significantly increased the probability of children belonging to the highly negative group. For the highly negative group, the intense jealousy further fuels conflict and rivalry, pushing the sibling relationship into an adverse trajectory (Kolak & Volling, 2011). This may also help explain why maternal differential treatment only significantly differentiated adolescents' membership between the positive group and the highly negative group, yet failed to distinguish between the positive group and the conflicting group. As mentioned earlier, the quarrels and aggressive behaviours of conflicting group typically stem from mismatched individual traits at the dyadic level (Qu & Cao, 2021). Given that parental differential treatment is a primary trigger for sibling jealousy during adolescence (Qian, 2024), it may lack the specificity to differentiate the conflicting group from the positive group. Furthermore, higher levels of maternal differential treatment were significantly associated with an increased likelihood of belonging to the average group rather than the positive group. Perceptions of unequal treatment may lead to emotional withdrawal (Barth, 2011), which may further result in sibling relationships that are neither very positive nor very negative.

Notably, no significant association was found between maternal differential treatment and the moderately negative profile in the present study. We found that the level of maternal differential treatment of the moderately negative group showed no significant difference from the other three profiles, except for the highly negative group. This pattern may reflect that the moderate level of jealousy observed in the moderately negative group may arise from factors other than PDT. This is consistent with recent evidence that other factors (e.g., parenting stress, paternal depression) can contribute to jealousy development (Chen et al., 2023). When comparing the moderately negative and highly negative profiles, maternal differential treatment served as a significant predictor. This result supports that maternal differential treatment may be an exacerbating factor for high levels of sibling jealousy. Overall, these results underscore the critical role that maternal differential treatment plays in intensifying jealousy. This critical distinction would be obscured without person‐centered approaches, which allowed us to identify the unique subgroups and to determine which subgroups are most sensitive to PDT. However, it is important to interpret these findings with caution due to the cross‐sectional nature of the present study. We cannot definitively establish the direction of causality, whether maternal differential treatment leads to intense jealousy or whether sibling jealousy triggers maternal differential treatment. This limitation highlights the need for longitudinal research to disentangle the directional pathways.

Our findings further revealed culturally specific dynamics in how parental differential treatment operates within Chinese sibling relationships. Notably, we examined paternal and maternal differential treatment simultaneously. After controlling for paternal differential treatment, maternal differential treatment still showed a significant association with membership of sibling relationship profiles. Conversely, paternal differential treatment demonstrated no significant links to any profile. This difference may reflect cultural scripts regarding Chinese parental roles. Mothers are viewed as primary caregivers, who are typically more involved in daily nurturing and emotional communication, resulting in closer ties between mothers and children (Xu et al., 2024). In contrast, fathers predominantly fulfil disciplinary and authority functions, which may lead to alienated father‐child relationships (Xu et al., 2024). Due to the close emotional bonds between mothers and children, perceiving maternal differential treatment directly threatened children's sense of family belonging and led to a greater emotional reaction (e.g., intense jealousy; Volling et al., 2002).

Besides, our results also revealed that older siblings were less likely to belong to the positive group and more likely to fall into the highly negative group compared with younger siblings. In Chinese families, older siblings often face greater social expectations to serve as role models (Chen & Tan, 2021). To meet these expectations, Chinese parents often used strict disciplinary practices to train older siblings (Fung & Chen, 2001). This may create great pressure for older siblings, potentially reducing positive siblings' interactions and fostering jealousy toward their younger counterparts (Qian et al., 2024). Moreover, consistent with existing research (Buist & Vermande, 2014), larger sibling age gaps were associated with a higher likelihood of positive group membership. This may reflect natural mentoring dynamics in larger age gap dyads, where older siblings assume quasi‐parental roles that provide caring and nurturing (Chen & Tan, 2021). Conversely, compared with those with larger age gaps, adolescents with smaller sibling age gaps were more likely to belong to the conflicting group rather than the average and highly negative groups. This pattern aligns with prior research indicating that age proximity intensifies conflict through increased daily interaction and friction (Chen, 2019a). It is worth noting that although adolescents with larger age gaps experience fewer conflicts, this does not necessarily imply positive relationship quality. On the contrary, larger age gaps may shift obvious conflict into more covert jealousy or lead to generally neutral, averaged relationships. A larger age gap often means siblings have fewer shared life experiences and social circles (Campione & Killoren, 2019), which may result in generally neutral relationships. Additionally, for firstborns, the transition from having long monopolized parental resources as an only child to sharing those resources with a sibling may constitute a significant stressor, heightening their vulnerability to jealousy (Volling, 2012). Secondborns may experience similar feelings, as larger age gaps often involve distinct developmental stages with differing needs, prompting parents to adopt different parenting approaches. Younger siblings may not grasp this reasoning, resulting in feelings of jealousy (Kowal & Kramer, 1997). These unique experiences of first‐ and second‐borns with large age gaps may account for the emergence of jealousy and alienation between them.

Limitations and future directions

Several limitations of our study should be noted. First, the cross‐sectional design prevents drawing conclusions about causal inferences. Future longitudinal studies should examine profile stability and transitions over time, as well as the temporal dynamics between PDT and profiles. Second, reliance on self‐reported data introduces potential biases, such as social desirability. However, evidence from meta‐analyses indicates that child‐reported PDT shows stronger associations with adjustment than parental reports (Jensen & Thomsen, 2024). Nevertheless, subsequent studies would benefit from multi‐informant designs incorporating parent reports and behavioural observations to provide more comprehensive insights into family dynamics. Third, this study examined sibling relationships and PDT from individual perspectives, but siblings within the same family often hold divergent perceptions of their relationship quality and parental treatment (Steffgen et al., 2025). Therefore, future research may consider examining sibling relationships at the sibling dyadic level to refine our findings. Related to this issue, in addition to the perception‐based measures utilized in this research, PDT is frequently operationalized through computed difference scores based on parental accounts of their behaviour toward each child. These methodological approaches add to our understanding and capture unique aspects of PDT (Jensen & Whiteman, 2014). However, perception‐based measures are almost always the report of one individual. Future research should employ both perception‐based and difference scores measures to provide more nuanced insights into PDT. Fourth, regarding control behaviours, some forms (e.g., monitoring) can be potentially supportive, whereas other forms (e.g., punitive control) may be detrimental. Future studies would benefit from distinguishing between these different forms of sibling control to better understand how specific controlling behaviours contribute to sibling relationship quality. Fifth, this study did not include information on parents' sibling status (i.e., having a sibling or not). Given that emerging evidence has indicated parents' sibling status may shape their parenting behaviours and children's sibling relationships (e.g., Chen, 2019b), future research may consider examining how parents' sibling status influences parental differential treatment and sibling dynamics in multi‐child families.

Implications

Our study offers two key theoretical contributions. First, it enhances our understanding of sibling relationship heterogeneity within the Chinese cultural context and the implementation of multi‐child policies. Second, it deepens theoretical insights into the contribution of PDT to sibling relationship heterogeneity, as well as the underlying parental gender differences. Specifically, maternal differential treatment predicts more undesirable sibling relationship profiles, whereas paternal PDT shows no significant effects. These findings align with the gender‐expected parenting paradigms in traditional Chinese culture and highlight the need to integrate cultural parenting roles into family dynamics.

Additionally, conducted in the context of Chinese population policies, this study provides insights for improving sibling relationships and parenting in multi‐child families. Tailored family education programs may be provided to improve sibling dynamics for siblings falling in the conflicting group. Specifically, interventions should emphasize social–emotional learning and conflict‐resolution skills, including: (a) sibling training in constructive communication and problem‐solving (Updegraff et al., 2016); (b) parental guidance to coach children and mediate disputes; and (c) structured shared activities to foster mutual understanding. Special attention is required for the highly negative group due to its strong connection to maternal differential treatment. Parents should aim to minimize overt differential treatment. When differential treatment is unavoidable, clear explanations should be given to help children understand the rationale behind it. Importantly, firstborns should not be neglected. Parents should adjust their expectations and reduce firstborns' caregiving burden to foster a more supportive sibling dynamic.

Conclusions

Using latent profile analysis, this study identified five distinct sibling relationship profiles among Chinese adolescents. Maternal differential treatment was a significant predictor, increasing the likelihood of adolescents belonging to the highly negative profile relative to the positive, average, and moderately negative profiles. These findings underscore the value of person‐centered approaches for understanding sibling relationship heterogeneity and its link to maternal differential treatment, which can provide practical aid for family education, especially for Chinese parents adapting to multi‐child parenting after the one‐child policy.

AUTHOR CONTRIBUTIONS

Fanlong Wang: Formal analysis; writing – original draft; conceptualization. Huiguang Ren: Conceptualization; supervision; writing – review and editing·. Junsheng Liu: Conceptualization; supervision; writing – review and editing. Xiaohua Bian: Investigation; funding acquisition; writing – review and editing. Yan Wang: Writing – review and editing.

CONFLICT OF INTEREST STATEMENT

The authors declared no conflicts of interest.

Supporting information

Data S1:

BJDP-44-641-s001.docx (58.2KB, docx)

ACKNOWLEDGEMENTS

We are grateful to the adolescents for their participation. This work was supported by the Talent Project of the Ministry of Science and Technology of China (G2023026012L), the National Education Science Planning of China (CEA250282), and the Talent Project of the Science and Technology Department of Henan Province (HNGD2023037).

Contributor Information

Huiguang Ren, Email: hgren@dep.ecnu.edu.cn.

Xiaohua Bian, Email: bxh1999@163.com.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1:

BJDP-44-641-s001.docx (58.2KB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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