Measurement Invariance of the Spanish Brief Version of the Experiences in Close Relationships for Assessing Adult Attachment in Neurodivergent and Neurotypical Chilean Emerging Adults

Abstract

Despite growing interest in attachment research within neurodivergent populations, limited attention has been paid to how attachment is assessed in these groups. Addressing this gap, the present study examines the factorial structure and measurement invariance of the Spanish brief version of the Experiences in Close Relationships Scale (SB-ECR-12) in neurotypical and neurodivergent (autistic and/or with attention deficit) emerging adults. The sample consisted of 443 Chilean emerging adults (60.9% female) aged 18–29 years, including 169 neurodivergent and 274 neurotypical individuals. Confirmatory factor analysis revealed good fit to a two-factor model and supported measurement invariance (configural, metric, scalar, strict) of the SB-ECR-12 across neurodivergent and neurotypical. However, latent mean comparisons indicated significant group differences: neurodivergent individuals reported higher levels of both attachment anxiety and avoidance. These findings provide evidence of the SB-ECR-12’s validity and reliability (attachment anxiety and avoidance: α and ω > .85) for assessing attachment insecurity in neurodivergent populations.

Keywords

adult attachment attachment anxiety attachment avoidance neurodivergence emerging adulthood

Introduction

Attachment theory accounts for the human need to establish meaningful bonds to obtain security throughout the life (Bowlby, 1969, 1973, 1980). The quality of these connections influences the development of Internal Working Models (IWMs), which comprise representations of the self and others (Bowlby, 1980). IWMs include perceptions of one’s ability to foster affection and care for others (model of self) and views of the availability and responsiveness of significant figures (model of others) (Bartholomew & Horowitz, 1991). These models are expressed in patterns called attachment styles, comprising expectations, needs, emotions, and social behavior shaping how individuals perceive and respond to relational dynamics based on their past experiences (Collins & Read, 1990). Attachment security is characterized by comfort with closeness and interdependence, reflecting positive self and other views, while insecurity involves excessive or insufficient involvement in relationships and negative self and/or other views (Brennan et al., 1998). Adult attachment insecurity can be conceptualized through two dimensions. The first, attachment anxiety entails fear of abandonment, excessive concern about partner availability, and a heightened need for reassurance. The second, attachment avoidance is marked by discomfort with closeness and intimacy, emotional distancing, and a reliance on self-sufficiency (Brennan et al., 1998).

Emerging adulthood is a period of significant changes in multiple areas of life, including close relationships (Arnett, 2024). During this stage, romantic relationships often become a central source of emotional support and well-being (Adamczyk & Bookwala, 2013; Arnett, 2015; Beckmeyer & Cromwell, 2019; Bohn et al., 2020), with attachment figures extending beyond parents to include friends and romantic partners (Bohn et al., 2020). This shift in the social domain can be particularly challenging for neurodivergent individuals, who may experience specific challenges in social interaction and communication. These include differences in interpretating nonverbal social cues, variations in social reciprocity, and unique ways of developing and maintaining interpersonal relationships (Alaghband-rad et al., 2023).

In this context, Autism Spectrum Condition (ASC) and Attention Deficit Disorder (ADD) are increasingly recognized as overlapping conditions along a neurodevelopmental continuum, rather than as entirely distinct diagnoses. Research highlights substantial co-occurrence between these conditions, with shared challenges in social interaction, emotional regulation, and executive functioning (Antshel & Russo, 2019; Hours et al., 2022). This overlapping nature suggests that individuals with ASC and ADD often face similar relational difficulties, even if these challenges may manifest in varying degrees or through different mechanisms. Addressing attachment in neurodivergent populations, therefore, benefits from an integrated perspective that considers these shared traits alongside condition-specific factors. While ASC and ADD have traditionally been viewed as disorders, a paradigm shift towards the neurodiversity perspective reframes them as natural variations in brain functioning and identity (Cherewick & Matergia, 2024; Hunt & Procyshyn, 2024; Kapp et al., 2013; Turner & Smith, 2023). This approach emphasizes that not all differences in brain functioning and human behavior are pathological but rather a reflection of human diversity, as highlighted by these and other authors.

Furthermore, autistic adults and individuals with ADD often employ camouflaging strategies (efforts to mask or modify behaviors to align with social norms), which add complexity to their interactions and can impact emotional and social well-being (Alaghband-rad et al., 2023). For example, autistic individuals may struggle with interpreting emotional and social cues, which influences their ability to initiate and maintain conversations and appropriately express emotions (Davidson et al., 2023; Douglas & Sedgewick, 2024). Similarly, individuals with ADD often face difficulties in emotion regulation, influencing their capacity to adapt to social dynamics and attend to others’ emotional cues (Jaisle et al., 2023; Walter et al., 2023). Hence, the need to navigate new relational dynamics during emerging adulthood, combined with differences in processing social and emotional information, could significantly shape how neurodivergent individuals experience and express attachment during this critical stage.

Although attachment has been extensively studied in various populations with different conditions and mental health problems (Alessandri et al., 2014; Herstell et al., 2021), research focusing specifically on neurodivergent individuals remains limited, particularly in adult samples (Al-Yagon et al., 2020; Jin et al., 2020; Lau & Peterson, 2011; Rüfenacht et al., 2021; Taylor et al., 2008). In studies with non-clinical samples, autistic traits have been associated with both attachment anxiety and avoidance (Beffel et al., 2021; Jin et al., 2020; Lamport & Turner, 2014; Sörnyei et al., 2024), although one study found an association only with avoidance, but not with anxiety (Gallitto & Leth-Steensen, 2015). Similarly, studies involving adults diagnosed with ASC have reported a higher prevalence of insecure attachment (Lau & Peterson, 2011; Taylor et al., 2008). Similarly, ADD has been associated with higher rates of attachment insecurity, contributing to emotional instability and relational difficulties (Al-Yagon et al., 2020; Koemans et al., 2015).

Despite the growing research on attachment in neurodivergent populations, little attention has been given to how attachment is assessed in these groups. Traditionally, the Experiences in Close Relationships (ECR) Scale (Brennan et al., 1998) has been one of the most widely used tools for measuring attachment (Crowell, 2021; Mikulincer & Shaver, 2016) validated for its use in multiples languages, including Spanish (Guzmán-González et al., 2020; Spencer et al., 2013). However, given that the ECR was originally developed for neurotypical individuals, it is essential to question whether this scale is equally valid for neurodivergent populations. Differences in sensory processing, social communication, and social interaction may lead neurodivergent individuals to interpret certain SB-ECR items differently. For instance, some items related to emotional intimacy or closeness may be interpreted differently for autistic individuals who often experience challenges in understanding and expressing emotions. Similarly, the broader cognitive and emotional variability characteristic of neurodivergence could influence how individuals engage with the scale’s constructs, potentially affecting their responses. Hence, before applying the SB-ECR in neurodivergent populations, it is crucial to evaluate whether the instrument functions equivalently across groups. Establishing measurement invariance is crucial to ensure that any potential differences in attachment means between neurodivergent and neurotypical individuals reflect true variations rather than measurement bias, thereby providing a valid foundation for cross-group comparisons (Boateng et al., 2018).

Therefore, the objective of this study is to examine the factor structure of de SB-ECR-12 in neurodivergent emerging adults, and its measurement invariance in comparison with neurotypical adults. Although the ECR and its various versions have demonstrated solid psychometric properties, fewer studies have focused specifically on its measurement invariance across different populations. For instance, some research has shown that the ECR is invariant across gender (e.g., Brauer & Proyer, 2025; Calvillo et al., 2020; Gray & Dunlop, 2019; Guzmán-González et al., 2020; Sarling et al., 2021; Siroňová et al., 2020), age (Guzmán-González et al., 2023; Sarling et al., 2021; Wongpakaran et al., 2023), sexual orientation (Calvillo et al., 2020), and relationship status (Brauer & Proyer, 2025; Gray & Dunlop, 2019). However, findings tend to indicate partial or non-invariance when it comes to clinical populations (e.g., Wongpakaran et al., 2023) and cross-cultural comparisons (Hao et al., 2019; Mastrotheodoros et al., 2015), with the exception of Siroňová et al. (2020), who reported full invariance across cultures. To date, no study has evaluated the measurement invariance of the ECR in neurodivergent populations. This gap is especially relevant given the unique cognitive, emotional, and interpersonal characteristics that may influence how individuals from these populations interpret and respond to ECR items. By addressing these aspects, the study aims to enhance the understanding of attachment in neurodivergent populations, particularly in autistic individuals and those with ADHD. This information is expected to provide a more accurate tool for assessing attachment variations, enabling the development of targeted interventions and supports to improve mental health and overall well-being in these groups.

Given the robust psychometric evidence supporting the two-factor structure of the SB-ECR-12 (Brennan et al., 1998; Raby et al., 2021), it is expected this structure comprising attachment anxiety and avoidance, will be replicated in both neurotypical and neurodivergent groups. However, due to the lack of prior research on measurement invariance of attachment instruments in neurodivergent populations, no specific hypotheses were formulated regarding whether configural, metric, or scalar invariance would be achieved. Instead, the examination of measurement invariance was approached as an exploratory aim, guided by the theoretical and empirical considerations outlined above.

Method

Design and Participants

This study employed a psychometric research design (Ato et al., 2013) and was part of a broader investigation into attachment in emerging adults. The sample consisted of 443 emerging adults (60.9% female) aged 18–29 years (M = 22.43, SD = 2.65), recruited from various regions of Chile (see Supplemental Table 1 for details). Participants were classified into two groups: 169 were identified as neurodivergent, defined as individuals with a clinical diagnosis of Autism Spectrum Condition (ASC) and/or Attention Deficit Disorder (ADD), while 274 participants formed a comparison group of neurotypical emerging adults without neurodevelopmental disorders. Neurodivergent status was determined based on participants’ responses to a binary question asking whether they had been diagnosed with autism spectrum condition (ASC) and/or attention deficit disorder (ADD) by a medical professional (e.g., physician or psychiatrist). Self-identification without a formal diagnosis was not sufficient for inclusion.

Among the neurodivergent group, 38.46% reported both ASC and ADD, 38.46% reported only ASC, and 23.08% reported only ADD. Neurotypical participants were screened using ad hoc questions to confirm the absence of any neurodivergent conditions, including ASC, ADD, or other diagnoses. Participants were excluded if they did not fall within the 18–29 age range, provided incomplete responses, or, in the case of the neurodivergent group, reported a self-diagnosis without formal confirmation by a medical professional.

Procedure

The ethical aspects of this study were reviewed and approved by the Ethics Committee of the Universidad Católica del Norte [No. 021/2023]. Participants accessed an online survey hosted on the SurveyMonkey platform. Before beginning, they were required to read and agree to an informed consent form, which outlined the study’s objectives, the voluntary nature of participation, and the measures taken to ensure anonymity and confidentiality of their responses. Only participants who provided informed consent could proceed with the survey.

A monetary reimbursement equivalent to 8 USD (six thousand Chilean pesos) was offered for participation. Participants retained the right to withdraw from the study at any time. The average time to complete the questionnaires was approximately 30 minutes. While the percentage of individuals who chose not to participate was not tracked, measures were taken to ensure that only participants willing to complete the survey proceeded beyond the consent stage.

Instruments

Sociodemographic Data

Information was collected on sex assigned at birth, clinical diagnosis, gender identity, sexual orientation, age, main activity, educational level, and current partner status.

Attachment

Attachment insecurity was assessed using the Experiences in Close Relationships Questionnaire (ECR) (Brennan et al., 1998), specifically its brief Spanish (SB-ECR-12; Guzmán-González et al., 2020). This scale measures bonding patterns in romantic relationships. The SB-ECR-12 consists of 12 items rated on a 7-point Likert scale, distributed across two subscales of 6 items each: attachment anxiety (e.g., “I feel frustrated when my partner does not pay as much attention to me as I would like”) and attachment avoidance (e.g., “I try to avoid getting too intimate with my partner”). Higher scores indicate greater levels of attachment anxiety and/or avoidance. This brief version was derived from the full ECR scale that was linguistically and culturally adapted for the Chilean context (Spencer et al., 2013). The SB-ECR-12 showed a good fit to a two-dimensional structure and was found to be invariant across sex assigned at birth and sexual orientation. Additionally, it exhibited adequate internal consistency, with Cronbach’s alpha values exceeding .72 for both subscales (Guzmán-González et al., 2020). In the present sample, the SB-ECR-12 showed high internal consistency, with Cronbach’s α = .86 and McDonald’s ω = .86 for attachment anxiety, and Cronbach’s α = .85–.87 and McDonald’s ω = .86–.88 for attachment avoidance across groups.

Data Analysis

The data analysis included a preliminary descriptive examination of the items, assessing their mean, standard deviation, skewness, and kurtosis to verify univariate normality. Univariate normality was considered violated if skewness or kurtosis values exceeded the range of ±1.5 (Cain et al., 2017). Additionally, multivariate normality was evaluated using Mardia’s test (Mardia, 1970), with non-compliance indicated by a statistically significant Mardia’s coefficient (p < .05).

The factor structure of the scale was examined by Confirmatory Factor Analysis (CFA) using the robust maximum likelihood estimator (MLR), which is appropriate when multivariate normality assumptions are not met. Analyses were performed in R software (Rosseel, 2012). Model fit was assessed using multiple goodness-of-fit indices. Specifically, the Comparative Fit Index (CFI) and the Tucker-Lewis Index (TLI) were interpreted as indicating an adequate fit when values were ≥.90 and an excellent fit when values exceeded .95. Additionally, values of Root Mean Square Error of Approximation (RMSEA) ≤ .08 and Standardized Root Mean Square Residual (SRMR) ≤ .08 were considered acceptable (Browne & Cudeck, 1993; Ferrando & Anguiano-Carrasco, 2010). These thresholds are consistent with widely accepted guidelines for evaluating model fit in CFA using robust estimators, and are appropriate when using MLR in the context of non-normal data (Browne & Cudeck, 1993; Marsh & Hau, 1996).

A multigroup factorial invariance analysis was subsequently conducted to compare neurotypical and neurodivergent emerging adults. Invariance testing was conducted using the same fit criteria described above (CFI/TLI ≥.90, RMSEA/SRMR ≤.08). Configural, metric, scalar, and strict invariance levels were evaluated. Model transitions were deemed acceptable if the change in chi-square (Δχ²) was not statistically significant (p > .05) (Asparouhov & Muthén, 2014) and changes in fit indices met the following criteria: ΔCFI > −0.01 and ΔRMSEA ≤0.01 (Chen, 2007). Once strict invariance was established, we proceeded to examine latent mean differences in attachment anxiety and attachment avoidance between neurotypical and neurodivergent emerging adults. To evaluate whether significant group differences existed, this model was compared to the strict invariance model using a chi-square difference test. A p-value <.05 in this comparison indicates that at least one latent mean differs significantly between groups. Subsequently, a latent means comparison model was estimated, in which the latent means for the neurotypical group were fixed to zero (serving as the reference group), while those for the neurodivergent group were freely estimated. To determine the specific sources of difference, we examined the critical ratio (CR) for each latent mean, calculated by dividing the parameter estimate by its standard error. A CR greater than 1.96 was interpreted as indicating a statistically significant latent mean difference (Byrne, 2013), with positive values reflecting higher latent means in the neurodivergent group compared to the neurotypical reference group. Finally, the reliability of the SB-ECR-12 was assessed using Cronbach’s alpha (α) and McDonald’s Omega (ω) coefficients (McDonald, 1999). All analyses were conducted using R version 4.3.2, with the “psych”, “lavaan,” and “semPlot” packages (R Core Team, 2022).

Results

Descriptive Analysis

The results of the socio-demographic characteristics of the sample are presented in Supplemental Table 1. In addition, in Supplemental Table 2, the SB-ECR-12 items revealed higher mean scores for attachment anxiety, indicating concerns about abandonment. Attachment avoidance items had lower mean scores, suggesting less discomfort with emotional intimacy. The data also exhibited skewness, particularly for anxiety items, as well as positive kurtosis in the distributions. On the other hand, the results of Mardia’s test (Mardia, 1970), indicating significant deviations from multivariate normality. The skewness (1574.63; p < .01) and kurtosis (23.26 < .01) values for the items fall outside the acceptable range, suggesting that multivariate normality is not met.

In Table 1 displays the mean scores for attachment anxiety and avoidance across neurotypical, neurodivergent, and the total sample. Across both groups, attachment anxiety scores are higher than avoidance scores, indicating that fear of abandonment and heightened need for reassurance were more salient features of attachment insecurity than discomfort with closeness and intimacy.

Table 1.

Descriptive Analysis of Global SB-ECR-12 Scores Among Neurotypical, Neurodivergent, and Total Samples of Emerging Adults

	Attachment anxiety		Attachment avoidance
	M	SD	M	SD
Neurotypical	4.22	1.47	2.16	1.83
Neurodivergent	4.54	1.43	2.44	1.39
Total sample	4.34	1.46	2.27	1.22

Note. M = Mean; SD = Standard Deviation.

Confirmatory Factor Analysis

The model tested for the sample corresponds to a two-factor oblique structure, with six items loading onto each factor. The factors represent attachment anxiety and attachment avoidance, and the correlations between these factors were freely estimated across all groups.

Table 2 presents the fit indices for the two-dimensional model of the SB-ECR-12 across neurotypical, neurodivergent, and total samples. The results indicate an adequate fit for all main indices (CFI, TLI, SMRS, and RMSEA) in each sample. The reliability analysis reveals that the internal consistency parameters are satisfactory for both subscales, as shown by Cronbach’s alpha (α) and McDonald’s omega (ω). The minimal differences between the two reliability coefficients further support the instrument’s appropriateness for assessing attachment dimensions in neurodivergent participants.

Table 2.

Fit Indices Analysis for the SB-ECR-12 Across Neurotypical, Neurodivergent, and Total Sample

Model	ꭓ2	df	CFI	TLI	SRMR	RMSEA (IC, 90%)
Neurotypical	146.733	53	.928	.910	.060	.087 (.070 - .104)
Neurodivergent	100.389	53	.945	.932	.074	.077 (.054 - .100)
Total	186.182	53	.938	.922	.060	.082 (.069 - .095)

Note. Neurotypical emerging adults = 274; Neurodivergent (N = 169), total (N = 443).

Figure 1 presents the factor loadings (λ) and estimation errors (ε) for the total sample, neurotypical participants, and neurodivergent participants. In general terms, the majority of factor loadings exceed the recommended threshold of .40 (Kline, 2015), indicating acceptable item performance. However, item 11, the only reverse-coded item, exhibits loadings below .40 in the neurotypical sample, suggesting potential issues with its performance. The covariance (Φ) between the latent variables (anxiety and avoidance) is also shown, with nonsignificant results in all samples.

Internal Consistency Reliability

Table 3 presents the internal consistency estimates for the SB-ECR-12 in the neurotypical, neurodivergent, and total samples, using Cronbach’s alpha (α) and McDonald’s omega (ω) for both dimensions: attachment anxiety and attachment avoidance. The results indicate good internal consistency for both attachment anxiety and avoidance across all groups, with values of α and ω consistently above .85. These findings support the reliability and stability of the SB-ECR-12 scores in both neurotypical and neurodivergent populations.

Table 3.

Internal Consistency Reliability of the SB-ECR-12 Across Neurotypical, Neurodivergent, and Total Samples

	Attachment anxiety		Attachment avoidance
	α	ω	α	ω
Neurotypical	.86	.86	.85	.86
Neurodivergent	.86	.86	.85	.86
Total	.86	.86	.87	.88

Note. α = Cronbach’s alpha, ω = McDonald’s omega.

Metric Invariance of the SB-ECR-12 Across Neurotypical and Neurodivergent Emerging Adults

Table 4 presents the results of the multigroup confirmatory factor analysis (MGCFA) using the robust maximum likelihood (MLR) estimation method to evaluate the measurement invariance of the ECR-12 scale across neurotypical and neurodivergent individuals. The invariance analysis was conducted progressively through the following stages: (1) configural invariance, that verifies whether the factor structure is equivalent across groups; (2) metric invariance that assesses the equality of factor loadings, ensuring items load similarly in both groups; (3) scalar invariance that examines whether item intercepts are consistent across groups; (4) Strict invariance that evaluates the equivalence of measurement errors between groups; and, (5) Latent mean invariance that tests the equivalence of latent factors means across groups (Cheung & Rensvold, 2009).

Table 4.

Results of the Multigroup Confirmatory Factor Analysis Evaluating Measurement Invariance of the SB-ECR-12 Across Neurotypical and Neurodivergent Individuals

Model	χ²	df	CFI	TLI	RMSEA (CI⁹⁰)	Δχ 2	Δdf	Δp	ΔCFI	ΔRMSEA
Model	χ²	df	CFI	TLI	RMSEA (CI⁹⁰)	Δχ 2	Δdf	>.05	<.01	<.01
Configural	247.37*9	106	.935	.919	.083 (.070-.097)
Metric	260.478*	116	.933	.924	.080 (.067-.094)	13.492	10	.197	.002	.003
Scalar	273.717*	126	.932	.929	.078 (0.65-.090)	12.122	10	.277	.001	.002
Strict	284.569*	138	.929	.932	.076 (.064-.089)	16.520	12	.168	.004	.002
Latent means	294.158*	140	.925	.929	.077 (.065-.090)	10.834	2	.004	.004	.001

Note. χ2 = Chi-square; df = Degrees of freedom; CFI = Comparative Fit Index; RMSEA = Root Mean Square Error of Approximation; Δ = Change. *p < .05.

The results confirm the measurement invariance of the SB-ECR-12 scale across neurotypical and neurodivergent individuals, with metric, scalar, and strict invariance achieved. These findings suggest that the attachment dimensions of anxiety and avoidance are measured consistently across both groups. Additionally, all goodness-of-fit indices remained within acceptable ranges, further supporting the model’s validity across the two groups. Specifically, the changes in the fit indices (CFI, RMSEA) across the different invariance models (configural, metric, scalar, strict) were minimal and within the thresholds for acceptable invariance, confirming that the factor structure, item loadings, intercepts, and measurement errors are equivalent across neurotypical and neurodivergent emerging adults. All goodness-of-fit indices remained within acceptable ranges, further supporting the validity of the model across both groups.

However, the latent mean invariance model showed a significant difference in the chi-square statistic (Δχ² = 10.834, p = .004), suggesting that the latent means may not be fully equivalent. Despite this, the changes in fit indices (ΔCFI = 0.004, ΔRMSEA = 0.001) were minimal. In the latent mean analysis, neurotypical were used as the reference group (fixed to zero), while latent means for neurodivergent group were freely estimated. Based on the strict invariance model, results revealed the neurodivergent individuals (M = 4.54, SD = 1.43) reported significantly higher levels of attachment anxiety compared to neurotypical individuals (M = 4.22, SD = 1.47), ΔM = 0.249, SE = 0.104, CR = 2.39, p < .016. Similarly, neurodivergent individuals (M = 2.44, SD = 1.39) reported significantly higher levels of attachment avoidance than neurotypical individuals (M = 2.16, SD = 1.83), ΔM = 0.276, SE = 0.125, CR = 2.21, p < .028. These findings suggest that neurodivergent emerging adults report higher levels of both attachment anxiety and attachment avoidance compared to their neurotypical counterparts. Because strict invariance was established, these latent mean differences can be interpreted as reflecting true group-level variations in attachment insecurity rather than measurement bias.

Discussion

The primary aim of this study was to examine the factorial structure and measurement invariance of the Spanish brief version of the Experiences in Close Relationships (SB-ECR-12) scale in a sample of neurodivergent emerging adults in comparison with neurotypical adults. The findings provide evidence of the SB-ECR-12’s validity and reliability for assessing attachment insecurity, supporting its applicability without significant bias in neurodivergent populations.

Regarding the factorial structure, the two-dimensional model demonstrated a good fit, clearly distinguishing between attachment anxiety and avoidance among the two groups. This supports Brennan et al. (1998) framework, which posits that attachment individual differences can be described through these two dimensions. The replication of this structure in the neurodivergent sample further reinforces the SB-ECR-12’s relevance for assessing attachment insecurity in this population. Regarding the local fit, item 11 showed a slightly lower-than-expected factor loading in the neurotypical sample. This may be attributed to its reverse wording, which can introduce cognitive complexity or response errors, particularly in brief instruments. Prior research has shown that reverse-coded items may reduce internal consistency and distort factor structure due to misunderstanding or inattentive responding (Sonderen et al., 2013; Swain et al., 2008). Alternatively, it is possible that item 11 captures aspects beyond the intended construct of attachment avoidance, such as trust, support-seeking behaviors, or openness to communication. In contrast, among neurodivergent emerging adults, the factor loading of item 11 fell within the expected range. This difference may reflect the tendency of some neurodivergent individuals to process language more literally, potentially reducing the confusion or inattentive responding that reverse-worded items often elicit in neurotypical samples. In any case, these findings highlight the need for further examination of this item to determine whether its inclusion accurately reflects attachment avoidance. It would also be valuable to assess whether rewording or removing item 11 improves the scale’s psychometric performance, particularly among neurotypical respondents.

In terms of internal consistency, the SB-ECR-12 demonstrated good reliability in the studied sample, confirming its suitability as a measurement instrument. These values are consistent with those reported in previous studies conducted with neurotypical populations (Wei et al., 2011) and suggest that the ECR-12 provides reliable scores in neurodivergent emerging adults as well.

The multigroup confirmatory factor analysis demonstrated that the SB-ECR-12 exhibits measurement invariance across neurotypical and neurodivergent individuals. This indicates that the scale measures attachment dimensions in a comparable manner across these groups and captures the constructs of attachment anxiety and avoidance equivalently. The confirmation of metric, scalar, and strict invariance also ensures that the SB-ECR-12 can be used to make valid comparisons between neurotypical and neurodivergent groups, while taking into account potential differences in their lived experiences.

The lack of full latent mean invariance, indicated by the significant chi-square difference, suggests that the neurodivergent and neurotypical groups differ in their absolute levels of attachment anxiety and avoidance. Given that the SB-ECR-12 met metric, scalar, and strict invariance criteria, these differences cannot be attributed to measurement bias. Rather, they should be interpreted as meaningful group-level variation in attachment insecurity between neurodivergent and neurotypical emerging adults. More specifically, neurodivergent emerging adults reporting higher levels of attachment insecurity, both anxiety and avoidance, compared to their neurotypical peers. This finding is consistent with the limited existing evidence suggesting a higher prevalence of insecure attachment among neurodivergent adults (e.g., Gallitto & Leth-Steensen, 2015; Lau & Peterson, 2011). These findings may reflect the interpersonal difficulties often associated with neurodivergence, along with experiences of rejection that can hinder the formation and preservation of secure attachment bonds. However, these are tentative explanations that warrant further investigation in future studies.

These results can have meaningful implications for both research and clinical practice. From a research perspective, having an instrument with evidence of validity is essential for advancing our understanding of attachment patterns in diverse populations. This is particularly important given that attachment insecurity has been consistently linked to less satisfying couple relationships (Guzmán-González et al., 2024; Mikulincer & Shaver, 2016), numerous mental health problems (Dagan et al., 2018; Zhang et al., 2022), lower well-being (Calvo et al., 2022; Valarezo-Bravo et al., 2024), and greater emotion regulation difficulties (Domic-Siede et al., 2024a, 2024b). Clinically, the reliable measurement of attachment anxiety and avoidance in neurodivergent individuals also offers practitioners a valuable tool for identifying attachment-related difficulties, which have been recognized as a transdiagnostic factor across several mental health problems (Ein-Dor et al., 2016; Herstell et al., 2021).

Considering also the increased prevalence of mental health problems (Auerbach et al., 2018; Gustavson et al., 2018; Martínez et al., 2021) and the higher attachment insecurity observed in the neurodivergent group or our sample, promoting secure attachment in autistic individuals and those with ADHD has the potential to reduce the risk of mental health difficulties and significantly improve their quality of life during this critical developmental period. In this sense, having an instrument that captures individual differences in attachment and whose functioning has been empirically examined is a first step in that direction.

Furthermore, the higher attachment insecurity found for neurodiverse emerging adults highlights the relevance of interventions that are tailored to the particularities of this population. Given that attachment anxiety was more prominent that attachment avoidance, interventions with neurodivergent emerging adults, if this finding is replicated in future studies, to focus more directly on anxiety-related attachment concerns, such as fear of abandonment, hypervigilance to rejection, dependence, and difficulties in emotion regulation, rather than on avoidance-related issues.

Incorporating attachment-informed frameworks into such interventions may help improve relational functioning and emotional well-being, and may also facilitate a more detailed recognition of their special needs. For example, Emotion-Focused Individual Therapy (EFIT; Johnson & Campbell, 2021) is focused on the development of awareness, expression, and reorganization of emotion experiences. These interventions can be incorporated as part of both individual and group programs aimed at autistic and ADHD individuals, with a focus on enhancing socioemotional competence, relational pattern awareness, and the development of meaningful connections Inclusive support programs in educational or workplace settings may also benefit from incorporating an attachment perspective, as understanding attachment styles can enhance relational adjustment and well-being among neurodivergent emerging adults. Recent studies also emphasize the relevance of digital and interactive approaches for strengthening youth socioemotional competencies in various contexts (Mancone et al., 2024), which could be adapted to support attachment literacy in neurodivergent populations. Furthermore, the development of valid and brief instruments to assess relational processes aligns with broader psychophysiological approaches aimed at improving emotional functioning and interpersonal dynamics (Tosti et al., 2024).

In addition to its utility in traditional clinical settings, the SB-ECR-12 may serve as a valuable tool in experimental or psychophysiological research aimed at understanding and modifying the neural and physiological correlates of attachment-related processes. For instance, recent studies have begun to explore how attachment dimensions modulate brain dynamics during emotion regulation tasks using EEG (Domic-Siede et al., 2024a, 2024b) and pupillometry (Domic-Siede et al., 2025). Other research has shown that neurofeedback-based interventions can enhance emotion regulation and reduce anxiety in university students, including those with neurodivergent traits (Diotaiuti et al., 2024). These findings highlight the potential of integrating brief attachment measures, such as the SB-ECR-12, into multimodal research designs or intervention protocols targeting emotion regulation and relational functioning.

In methodological terms, this study contributes to the growing body of research applying measurement invariance techniques to instruments in populations characterized by neurodevelopmental diversity (McKernan & Russo, 2024; Moore et al., 2025; Murray et al., 2014). While measurement invariance has been explored extensively in cross-cultural adaptations of the ECR (Deveci-Şirin & Şen-Doğan, 2021; Lee et al., 2023; Mikulincer & Shaver, 2016), its application in neurodivergent groups remains limited. Methodological literature emphasizes the importance of testing invariance when comparing latent constructs across such diverse populations, to ensure valid and unbiased interpretations of group differences (Boateng et al., 2018; Byrne & van de Vijver, 2010; Diotaiuti et al., 2021; Putnick & Bornstein, 2016). By confirming configural, metric, scalar, and strict invariance, the present study offers evidence that supports the scale’s cross-group comparability in neurotypical and neurodivergent emerging adults.

Despite the strengths of this study, some limitations must be acknowledged. First, the study employed a non-probability convenience sampling method, which limits the generalizability of the findings to the broader population of emerging adults with ASD and/or ADHD. While this approach facilitated access to a typically underrepresented and hard-to-reach population (Brice et al., 2021), it may have introduced sampling biases, particularly with respect to participants who are more engaged, self-aware, or better resourced (Nicolaidis et al., 2013). Future research should prioritize more systematic recruitment strategies, including probabilistic or stratified sampling, and ensure the inclusion of individuals with varying levels of support needs and from diverse socioeconomic and regional backgrounds.

Second, the identification of neurodivergent individuals relied on self-report of prior diagnoses made by a medical professional. While this procedure ensured that only participants with a formal diagnosis (e.g., by a physician or psychiatrist) were included, it lacks the additional clinical confirmation provided by standardized diagnostic evaluations conducted by multidisciplinary teams. Although this approach is commonly used in psychological research with large community samples, it may still involve some degree of diagnostic imprecision (Brugha et al., 2016). Future studies could incorporate verification procedures (e.g., clinical interviews, standardized tools such as the ADOS-2 and ADI-R) to further enhance diagnostic accuracy and explore how specific diagnostic features (e.g., age at diagnosis, co-occurring conditions, support needs) relate to attachment patterns (Lord et al., 2018, 2022).

Additionally, the use of financial compensation may have introduced a self-selection bias, whereby individuals with greater economic need or motivation to participate in paid research might be overrepresented. This could influence the characteristics of both neurodivergent and neurotypical groups, potentially limiting the generalizability of the findings. Future studies should consider strategies to mitigate this bias, such as varying recruitment channels or assessing participants’ motivations for enrollment (Hsieh & Kocielnik, 2016).

Third, all data were collected through self-report measures, which are inherently susceptible to biases such as social desirability, acquiescence, or limited introspective accuracy, factors that may be particularly relevant in neurodivergent populations given documented differences in metacognition and emotional insight (Livingston & Happé, 2017). Incorporating multimethod approaches, such as behavioral observations, reports from close others, or clinician-rated interviews, would provide a more comprehensive and ecologically valid assessment of attachment representations in these groups (Crowell, 2021; Fraley & Roisman, 2019; Lord et al., 2022). Fourth, although the present study provides evidence of factorial validity and internal consistency, it did not assess other forms of construct validity, such as convergent or discriminant validity, nor test-retest reliability. These are important components of the overall validation process and should be addressed in future research. Fifth, due to limited subgroup sizes (e.g., ASC only, ADD only, or both), it was not feasible to examine measurement invariance separately by diagnosis type or to evaluate invariance by sex or age within these neurodivergent profiles. Finally, given the cross-sectional design of the study, it is not possible to draw causal conclusions regarding the association between neurodivergence and attachment insecurity. Longitudinal research is needed to examine this link over time.

For future studies, it would be valuable to include larger and more diverse samples that encompass different age groups to enhance the generalizability of findings. Additionally, exploring the relationship between attachment and other relevant variables in neurodivergent individuals, such as social functioning, psychological well-being, and quality of life, could provide a more comprehensive understanding of attachment patterns in this population. Longitudinal studies are also recommended to investigate attachment trajectories in individuals with ASD and ADHD. Such research could offer insights into developmental trajectories and identify critical periods for intervention. Furthermore, examining the effects of therapeutic interventions on attachment patterns could inform the design of tailored strategies to improve relational and emotional outcomes in neurodivergent individuals. Finally, as the sample consisted exclusively of Chilean emerging adults, the findings may not be generalizable to other Spanish-speaking or cultural contexts. Therefore, future research should examine if similar findings are replicated diverse cultural samples.

Supplemental Material

Supplemental Material - Measurement Invariance of the Spanish Brief Version of the Experiences in Close Relationships for Assessing Adult Attachment in Neurodivergent and Neurotypical Chilean Emerging Adults

Supplemental Material for Measurement Invariance of the Spanish Brief Version of the Experiences in Close Relationships for Assessing Adult Attachment in Neurodivergent and Neurotypical Chilean Emerging Adults by Omayck Valarezo-Bravo, Eduardo Méndez-Fuentes, Diego Atencio-Quevedo, Camila Manriquez, Mónica Guzmán-González, Karla Tay-Karapas, Marcos Domic-Siede in Emerging Adulthood

Footnotes

Acknowledgments

To the Subdirectorate of Human Capital - ANID, for funding the National Doctorate studies for Valarezo-Bravo, O; with file number 21230106.

ORCID iDs

Omayck Valarezo-Bravo

Eduardo Méndez-Fuentes

Diego Atencio-Quevedo

Mónica Guzmán-González

Karla Tay-Karapas

Marcos Domic-Siede

Ethical Considerations

The ethical aspects of this study were reviewed and approved by the Ethics Committee of the Universidad Católica del Norte [No. 021-2023].

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the National Fund for Scientific and Technologic Development of Chile, FONDECYT 1230800.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Transparency and Openness Statement

The datasets and the analysis code used during the current study are available from the corresponding author on reasonable request. No aspects of the study were pre-registered.

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Omayck Valarezo-Bravo (M.Sc.) is a Clinical Psychologist and Forensic Psychology Specialist. He is currently pursuing a Ph.D. in Psychology at Universidad Católica del Norte and an associate researcher at Universidad Nacional de Loja. His research focuses on adult attachment to romantic partners, friends, and parents, as well as mental health in emerging adults. He is currently involved in national and international research projects.

Eduardo Méndez-Fuentes (M.Sc.) is a physical therapist and specialist in chronic musculoskeletal pain management, currently working at KinEvidence, a multidisciplinary center in Antofagasta, Chile. His work focuses on evaluating and treating biomechanical and psychosocial variables related to persistent pain, integrating evidence-based interventions from physical therapy, neuroscience, and behavioral sciences. Eduardo is also involved in academic teaching and international collaboration, having delivered lectures and workshops in Latin America on pain education and non-pharmacological management.

Diego Atencio-Quevedo (M.Sc.) is pursuing his doctoral degree at the Universidad de Tarapacá. He has worked on research related to indicators of child and adolescent mental health in ethnic minorities and factors associated with quality of life in people with Schizophrenia and their families. Currently, his work focuses on the identification of areas of intervention for the diagnosis and treatment of people with Schizophrenia from the first outbreak.

Camila Manríquez-Fuenzalida (M.Sc.) is a psychologist and Ph.D. candidate in Psychology at the Universidad de Tarapacá, Chile. She holds a Master’s degree in Higher Education Teaching from Universidad Andrés Bello and is currently a lecturer at the Universidad de Tarapacá. Her doctoral research examines the quality and quantity of maternal caregiving time and its association with early childhood socioemotional and cognitive development.

Mónica Guzmán González (Ph.D.) is a psychologist with a Ph.D. in Psychology from the Pontificia Universidad Católica de Chile. She is a full professor at the School of Psychology, Universidad Católica del Norte, where she teaches undergraduate and graduate courses. Her research focuses on adult attachment, romantic relationships, and mental health, including sexual and gender minorities. She has led multiple research projects and published widely in high-impact journals. From 2021 to 2024, she served as Dean of the Faculty of Humanities.

Karla Tay-Karapas (Ph.D.) is a Clinical Psychologist with a Ph.D. in Psychology from the Universidad del País Vasco. Academic and researcher at the Universidad Católica del Norte, with experience in undergraduate and postgraduate teaching. Her areas of interest include attachment, parental divorce and child and adolescent mental health. She has published in scientific journals, presented at international conferences and worked as a child and adolescent therapist. Her approach combines teaching, research and clinical intervention with a systemic and evidence-based perspective.

Marcos Domic-Siede (Ph.D.) is an Associate Professor and researcher at Universidad Católica del Norte, Chile, where he leads the Cognitive Neuroscience Laboratory. His research focuses on cognitive and affective neuroscience, particularly emotion regulation and attachment, using electroencephalography (EEG) and eye-tracking. He is Principal Investigator of nationally funded research projects and supervises undergraduate and doctoral theses in cognitive neuroscience and psychology.

References

Adamczyk

Bookwala

(2013). Adult attachment and single vs. partnered relationship status in Polish university students. Psihologijske Teme, 22(3), 481–500. https://psycnet.apa.org/record/2014-00812-006

Alaghband-rad

Hajikarim-Hamedani

Motamed

(2023). Camouflage and masking behavior in adult autism. Frontiers in Psychiatry, 14,1-9. Article 1108110. https://doi.org/10.3389/fpsyt.2023.1108110

Alessandri

Fagnani

Di Gennaro

Meldolesi

G. N.

Pasquini

Caroppo

Martinotti

Toni

Fabi

Picardi

(2014). Measurement invariance of the experiences in close relationships questionnaire across different populations. Spanish Journal of Psychology, 17, Article E22. https://doi.org/10.1017/sjp.2014.19

Al-Yagon

Lachmi

Shalev

(2020). Coping strategies among adults with ADHD: The mediational role of attachment relationship patterns. Research in Developmental Disabilities, 102(1), Article 103657. https://doi.org/10.1016/j.ridd.2020.103657

Antshel

K. M.

Russo

(2019). Autism spectrum disorders and ADHD: Overlapping phenomenology, diagnostic issues, and treatment considerations. Current Psychiatry Reports, 21(5), 34. https://doi.org/10.1007/s11920-019-1020-5

Arnett

(2015). The Oxford handbook of emerging adulthood. Oxford University Press.

Arnett

(2024). Emerging adulthood. Oxford University Press. https://doi.org/10.1093/oso/9780197695937.001.0001

Asparouhov

Muthén

(2014). Auxiliary variables in mixture modeling: Three-step approaches using M Plus. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 329–341. https://doi.org/10.1080/10705511.2014.915181

Ato

López-García

J. J.

Benavente

(2013). A classification system for research designs in psychology. Anales de Psicología, 29(3), 1038–1059. https://doi.org/10.6018/analesps.29.3.178511

10.

Auerbach

R. P.

Mortier

Bruffaerts

Alonso

Benjet

Cuijpers

Demyttenaere

Ebert

D. D.

Green

J. G.

Hasking

Murray

Nock

M. K.

Pinder-Amaker

Sampson

N. A.

Stein

D. J.

Vilagut

Zaslavsky

A. M.

Kessler

R. C.

WHO WMH-ICS Collaborators . (2018). WHO World Mental Health Surveys International College Student Project: Prevalence and distribution of mental disorders. Journal of Abnormal Psychology, 127(7), 623–638. https://doi.org/10.1037/abn0000362

11.

Bartholomew

Horowitz

L. M.

(1991). Attachment styles among young adults: A test of a four-category model. Journal of Personality and Social Psychology, 61(2), 226–244. https://doi.org/10.1037/0022-3514.61.2.226

12.

Beckmeyer

J. J.

Cromwell

(2019). Romantic relationship status and emerging adult well-being: Accounting for romantic relationship interest. Emerging Adulthood, 7(4), 304–308. https://doi.org/10.1177/2167696818772653

13.

Beffel

J. H.

Cary

K. M.

Nuttall

A. K.

Chopik

W. J.

Maas

M. K.

(2021). Associations between the broad autism phenotype, adult attachment, and relationship satisfaction among emerging adults. Personality and Individual Differences, 168(2021), Article 110409. https://doi.org/10.1016/j.paid.2020.110409

14.

Boateng

G. O.

Neilands

T. B.

Frongillo

E. A.

Melgar-Quiñonez

H. R.

Young

S. L.

(2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer. Frontiers in Public Health, 6, 1–18. Article 149. https://doi.org/10.3389/fpubh.2018.00149

15.

Bohn

Holtmann

Luhmann

Koch

Eid

(2020). Attachment to parents and well-being after high school graduation: A study using self- and parent ratings. Journal of Happiness Studies, 21(7), 2493–2525. https://doi.org/10.1007/s10902-019-00190-y

16.

Bowlby

(1969). Attachment and loss: Attachment. Basic books (Vol. 1). Basic Books.

17.

Bowlby

(1973). Attachment and loss. Separation: Anxiety and anger (Vol. 2). The Hogarth Press.

18.

Bowlby

(1980). Attachment and loss. Loss: Sadness and depression (Vol. 3). Hogarth.

19.

Brauer

Proyer

R. T.

(2025). A study of the measurement invariance of the Experiences in Close Relationships (ECR) questionnaire across relationship status, romantic partners, and gender. European Journal of Psychological Assessment. Advance online publication. https://doi.org/10.1027/1015-5759/a000902

20.

Brennan

Clark

Shaver

(1998). Self-report measurement of adult attachment: An integrative overview. In Rholes

J. A.

(Ed.), Attachment theory and close relationships (pp. 46–76).S. & W. S.

21.

Brice

Rodgers

Ingham

Mason

Wilson

Freeston

Le Couteur

Parr

J. R.

(2021). The importance and availability of adjustments to improve access for autistic adults who need mental and physical healthcare: Findings from UK surveys. BMJ Open, 11(3), Article e043336. https://doi.org/10.1136/bmjopen-2020-043336

22.

Browne

M. W.

Cudeck

(1993). Alternative ways of assessing model fit. In Bollen

K. A.

Long

J. S.

(Eds.), Testing structural equation models (pp. 136–162). Sage.

23.

Brugha

T. S.

Spiers

Bankart

Cooper

S.-A.

McManus

Scott

F. J.

Smith

Tyrer

(2016). Epidemiology of autism in adults across age groups and ability levels. British Journal of Psychiatry, 209(6), 498–503. https://doi.org/10.1192/bjp.bp.115.174649

24.

Byrne

B. M.

(2013). Structural equation modeling with mplus. Routledge. https://doi.org/10.4324/9780203807644

25.

Byrne

B. M.

van de Vijver

F. J. R.

(2010). Testing for measurement and structural equivalence in large-scale cross-cultural studies: Addressing the issue of nonequivalence. International Journal of Testing, 10(2), 107–132. https://doi.org/10.1080/15305051003637306

26.

Cain

M. K.

Zhang

Yuan

K.-H.

(2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49(5), 1716–1735. https://doi.org/10.3758/s13428-016-0814-1

27.

Calvillo

Sánchez-Fuentes

Carreño

Sierra

(2020). Validación de la versión breve de la Experiences in Close Relationship en adultos con pareja del mismo sexo [Validation of the short version of the Experiences in Close Relationship in adults with a same sex partner]. Revista Iberoamericana de Diagnóstico y Evaluación – e Avaliação Psicológica, 55(2), 107–119. https://doi.org/10.21865/RIDEP55.2.08

28.

Calvo

D’Aquila

Rocco

Carraro

(2022). Attachment and well-being: Mediatory roles of mindfulness, psychological inflexibility, and resilience. Current Psychology, 41(5), 2966–2979. https://doi.org/10.1007/s12144-020-00820-2

29.

Chen

(2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834

30.

Cherewick

Matergia

(2024). Neurodiversity in practice: A conceptual model of autistic strengths and potential mechanisms of change to support positive mental health and wellbeing in autistic children and adolescents. Advances in Neurodevelopmental Disorders, 8(3), 408–422. https://doi.org/10.1007/s41252-023-00348-z

31.

Cheung

G. W.

Rensvold

R. B.

(2009). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5

32.

Collins

N. L.

Read

S. J.

(1990). Adult attachment, working models, and relationship quality in dating couples. Journal of Personality and Social Psychology, 58(4), 644–663. https://doi.org/10.1037/0022-3514.58.4.644

33.

Crowell

J. A.

(2021). Measuring the security of attachment in adults. In Thompson

R. A.

Simpson

J. A.

Berlin

L. J.

(Eds.), Attachment: The fundamental questions (pp. 86–92). The Guilford Press.

34.

Dagan

Facompré

C. R.

Bernard

(2018). Adult attachment representations and depressive symptoms: A meta-analysis. Journal of Affective Disorders, 236(2018), 274–290. https://doi.org/10.1016/j.jad.2018.04.091

35.

Davidson

Russo-Ponsaran

van Rest

M. M.

Scarpa

(2023). Editorial: Emotion processing in autism spectrum disorders. Frontiers in Psychology, 14, Article 1141824. https://doi.org/10.3389/fpsyg.2023.1141824

36.

Deveci-Şirin

Şen-Doğan

(2021). Psychometric properties of the Turkish version of the Experiences in Close Relationships–Relationship Structures Questionnaire (ECR-RS). Sage Open, 11(1), 1–15. https://doi.org/10.1177/21582440211006056

37.

Diotaiuti

Valente

Corrado

Tosti

Carissimo

Di Libero

Cerro

Rodio

Mancone

(2024). Enhancing working memory and reducing anxiety in university students: A neurofeedback approach. Brain Sciences, 14(6), 578. https://doi.org/10.3390/brainsci14060578

38.

Diotaiuti

Valente

Mancone

Grambone

Chirico

(2021). Metric goodness and measurement invariance of the Italian brief version of interpersonal reactivity index: A study with young adults. Frontiers in Psychology, 12, Article 773363. https://doi.org/10.3389/fpsyg.2021.773363

39.

Domic-Siede

Guzmán-González

Sánchez-Corzo

Álvarez

Araya

Espinoza

Zenis

Marín-Medina

(2024a). Emotion regulation unveiled through the categorical lens of attachment. BMC Psychology, 12(1), 240. https://doi.org/10.1186/s40359-024-01748-z

40.

Domic-Siede

Guzmán-González

Sánchez-Corzo

Granillo

Salazar

Silva

Ortiz

Ossandón

(2025). Pupil responses to emotion regulation strategies: The role of attachment orientations. Cognition and Emotion, 1–18. Advance online publication. https://doi.org/10.1080/02699931.2025.2512886

41.

Domic-Siede

Sánchez-Corzo

Guzmán-González

(2024b). Brain oscillations during emotion regulation and the two-dimensional model of adult attachment. Biological Psychology, 189(2024), Article 108793. https://doi.org/10.1016/j.biopsycho.2024.108793

42.

Douglas

Sedgewick

(2024). Experiences of interpersonal victimization and abuse among autistic people. Autism, 28(7), 1732–1745. https://doi.org/10.1177/13623613231205630

43.

Ein-Dor

Viglin

Doron

(2016). Extending the transdiagnostic model of attachment and psychopathology. Frontiers in Psychology, 7, 484. https://doi.org/10.3389/fpsyg.2016.00484

44.

Ferrando

P. J.

Anguiano-Carrasco

(2010). Factor analysis as a research technique in psychology. Papeles Del Psicólogo, 31(1), 18–33.

45.

Fraley

Roisman

G. I.

(2019). The development of adult attachment styles: Four lessons. Current Opinion in Psychology, 25, 26–30. https://doi.org/10.1016/j.copsyc.2018.02.008

46.

Gallitto

Leth-Steensen

(2015). Autistic traits and adult attachment styles. Personality and Individual Differences, 79, 63–67. https://doi.org/10.1016/j.paid.2015.01.032

47.

Gray

J. S.

Dunlop

W. L.

(2019). Structure and measurement invariance of adult romantic attachment. Journal of Personality Assessment, 101(2), 171–180. https://doi.org/10.1080/00223891.2017.1391274

48.

Gustavson

Knudsen

A. K.

Nesvåg

Knudsen

G. P.

Vollset

S. E.

Reichborn-Kjennerud

(2018). Prevalence and stability of mental disorders among young adults: Findings from a longitudinal study. BMC Psychiatry, 18(1), 65. https://doi.org/10.1186/s12888-018-1647-5

49.

Guzmán-González

Calderón

Domic-Siede

Rivera-Ottenberger

Spencer

Garrido-Rojas

(2023). Propuesta de valores de referencia para el Cuestionario de Evaluación del Apego Adulto: Experiences in Close Relationships (ECR-12), en población adulta chilena. Terapia Psicologica, 41(1), 39–62. https://doi.org/10.4067/S0718-48082023000100039Propuesta

50.

Guzmán-González

Rivera-Ottenberger

Brassard

Spencer

Lafontaine

M.-F.

(2020). Measuring adult romantic attachment: Psychometric properties of the brief Spanish version of the experiences in close relationships. Psicologia: Reflexão e Crítica, 33(1), 9. https://doi.org/10.1186/s41155-020-00145-w

51.

Guzmán-González

Valarezo-Bravo

Casu

(2024). Are we okay…am I okay? Romantic attachment, couple relationship quality and depressive symptoms in a Chilean sample. Current Psychology, 43, 25247–25256. https://doi.org/10.1007/s12144-024-06214-y

52.

Hao

Chan

R. C. K.

Wilkinson

R. B.

(2019). A cross-cultural examination of the Experiences in Close Relationships — Revised — General Short Form (ECR-R-GSF) in an Australian and a Chinese sample. Journal of Relationships Research, 10, 1-10. Article e11. https://doi.org/10.1017/jrr.2019.8

53.

Herstell

Betz

L. T.

Penzel

Chechelnizki

Filihagh

Antonucci

Kambeitz

(2021). Insecure attachment as a transdiagnostic risk factor for major psychiatric conditions: A meta-analysis in bipolar disorder, depression and schizophrenia spectrum disorder. Journal of Psychiatric Research, 144(2021), 190–201. https://doi.org/10.1016/j.jpsychires.2021.10.002

54.

Hours

Recasens

Baleyte

J.-M.

(2022). ASD and ADHD comorbidity: What are we talking about? Frontiers in Psychiatry, 13, Article 837424. https://doi.org/10.3389/fpsyt.2022.837424

55.

Hsieh

Kocielnik

(2016). You get who you pay for. Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing (pp. 823–835). https://doi.org/10.1145/2818048.2819936

56.

Hunt

A. D.

Procyshyn

T. L.

(2024). Changing perspectives on autism: Overlapping contributions of evolutionary psychiatry and the neurodiversity movement. Autism Research, 17(3), 459–466. https://doi.org/10.1002/aur.3078

57.

Jaisle

E. M.

Groves

N. B.

Black

K. E.

Kofler

M. J.

(2023). Linking ADHD and ASD symptomatology with social impairment: The role of emotion dysregulation. Research on Child and Adolescent Psychopathology, 51(1), 3–16. https://doi.org/10.1007/s10802-022-00982-6

58.

Jin

Chen

Zhao

(2020). Autistic traits and social skills in Chinese college students: Mediating roles of adult attachment styles and empathy. Current Psychology, 41(4), 2408–2417. https://doi.org/10.1007/s12144-020-00751-y

59.

Johnson

S. M.

Campbell

T. L.

(2021). A primer for emotionally focused individual therapy (EFIT). Routledge.

60.

Kapp

S. K.

Gillespie-Lynch

Sherman

L. E.

Hutman

(2013). Deficit, difference, or both? Autism and neurodiversity. Developmental Psychology, 49(1), 59–71. https://doi.org/10.1037/a0028353

61.

Kline

R. B.

(2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

62.

Koemans

R. G.

van Vroenhoven

Karreman

Bekker

M. H.

(2015). Attachment and autonomy problems in adults with ADHD. Journal of Attention Disorders, 19(5), 435–446. https://doi.org/10.1177/1087054712453170

63.

Lamport

Turner

L. A.

(2014). Romantic attachment, empathy, and the broader autism phenotype among college students. The Journal of Genetic Psychology, 175(3), 202–213. https://doi.org/10.1080/00221325.2013.856838

64.

Lau

Peterson

C. C.

(2011). Adults and children with Asperger syndrome: Exploring adult attachment style, marital satisfaction and satisfaction with parenthood. Research in Autism Spectrum Disorders, 5(1), 392–399. https://doi.org/10.1016/j.rasd.2010.06.001

65.

Lee

Kim

Y.-K.

Shin

Y.-J.

(2023). Validation of the Korean version of culturally responsive experiences in close relationships–Short form. International Journal for the Advancement of Counselling, 45(1), 57–81. https://doi.org/10.1007/s10447-023-09503-6

66.

Livingston

L. A.

Happé

(2017). Conceptualising compensation in neurodevelopmental disorders: Reflections from autism spectrum disorder. Neuroscience & Biobehavioral Reviews, 80(2017), 729–742. https://doi.org/10.1016/j.neubiorev.2017.06.005

67.

Lord

Charman

Havdahl

Carbone

Anagnostou

Boyd

Carr

de Vries

P. J.

Dissanayake

Divan

Freitag

C. M.

Gotelli

M. M.

Kasari

Knapp

Mundy

Plank

Scahill

Servili

Shattuck

McCauley

J. B.

(2022). The lancet Commission on the future of care and clinical research in autism. The Lancet, 399(10321), 271–334. https://doi.org/10.1016/S0140-6736(21)01541-5

68.

Lord

Elsabbagh

Baird

Veenstra-Vanderweele

(2018). Autism spectrum disorder. The Lancet, 392(10146), 508–520. https://doi.org/10.1016/S0140-6736(18)31129-2

69.

Mancone

Corrado

Tosti

Spica

Diotaiuti

(2024). Integrating digital and interactive approaches in adolescent health literacy: A comprehensive review. Frontiers in Public Health, 12, Article 1387874. https://doi.org/10.3389/fpubh.2024.1387874

70.

Mardia

K. V.

(1970). Measures of multivariate kewness and Kurtosis with applications. Biometrika, 57(3), 519. https://doi.org/10.2307/2334770

71.

Marsh

H. W.

Hau

K.-T.

(1996). Assessing goodness of fit. The Journal of Experimental Education, 64(4), 364–390. https://doi.org/10.1080/00220973.1996.10806604

72.

Martínez

Jiménez-Molina

Á.

Mac-Ginty

Martínez

Rojas

(2021). Salud mental en estudiantes de educación superior en Chile: una revisión de alcance con meta-análisis. Terapia Psicológica, 39(3), 405–426. https://doi.org/10.4067/S0718-48082021000300405

73.

Mastrotheodoros

Chen

B. B.

Motti-Stefanidi

(2015). Experiences in close relationships-revised (ECR-R): Measurement (non-) invariance across Chinese and Greek samples. European Journal of Developmental Psychology, 12(3), 344–358. https://doi.org/10.1080/17405629.2015.1021327

74.

McDonald

(1999). Test theory: A unified treatment. Lawrence Erlbaum Associates, Inc.

75.

McKernan

E. P.

Russo

(2024). Investigating the structure of the CESD-R and DASS-21 across adults with low and high levels of autistic traits. Journal of Autism and Developmental Disorders, 54(5), 2026–2039. https://doi.org/10.1007/s10803-023-05944-5

76.

Mikulincer

Shaver

P. R.

(2016). Attachment in adulthood. Structure, dynamics, and change. In Paper knowledge. Toward a media history of documents (second edi). The Guilford Press. https://www.guilford.com

77.

Moore

H. L.

Freeston

Rodgers

Cassidy

(2025). A measurement invariance analysis of the anxiety scale for autism-adults in a sample of autistic and non-autistic men and women. Journal of Autism and Developmental Disorders, 55(3), 981–996. https://doi.org/10.1007/s10803-024-06260-2

78.

Murray

A. L.

Booth

McKenzie

Kuenssberg

O'Donnell

(2014). Are autistic traits measured equivalently in individuals with and without an autism spectrum disorder? An invariance analysis of the autism spectrum quotient short form. Journal of Autism and Developmental Disorders, 44(1), 55–64. https://doi.org/10.1007/s10803-013-1851-6

79.

Nicolaidis

Raymaker

McDonald

Dern

Boisclair

W. C.

Ashkenazy

Baggs

(2013). Comparison of healthcare experiences in autistic and non-autistic adults: A cross-sectional online survey facilitated by an academic-community partnership. Journal of General Internal Medicine, 28(6), 761–769. https://doi.org/10.1007/s11606-012-2262-7

80.

Putnick

D. L.

Bornstein

M. H.

(2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41(2016), 71–90. https://doi.org/10.1016/j.dr.2016.06.004

81.

Raby

K. L.

Fraley

R. C.

Roisman

G. I.

(2021). Categorical or dimensional measures of attachment? Insights from factor-analytic and taxometric research. In Thompson

R. A.

Simpson

J. A.

Berlin

L. J.

(Eds.), Attachment: The fundamental questions (pp. 70–77). The Guilford Press.

82.

R Core Team . (2022). R: A language and environment for statistical computing (version 4.1). https://cran.r-project.org/

83.

Rosseel

(2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02

84.

Rüfenacht

Pham

Nicastro

Dieben

Hasler

Weibel

Perroud

(2021). Link between history of childhood maltreatment and emotion dysregulation in adults suffering from attention deficit/hyperactivity disorder or borderline personality disorder. Biomedicines, 9(10), 1469. https://doi.org/10.3390/biomedicines9101469

85.

Sarling

Jansson

Englén

Bjärtå

Rondung

Sundin

Ö.

(2021). Psychometric properties of the Swedish version of the experiences in close relationships – Relationship structures questionnaire (ECR-RS global nine-item version). Cogent Psychology, 8(1), Article 1926080. https://doi.org/10.1080/23311908.2021.1926080

86.

Siroňová

Cígler

Ježek

Lacinová

(2020). Psychometric characteristics of the ECR-RS, structure of the relationship between global and specific attachment–cross-validation of the results using the Czech and original versions of the ECR-RS. Studia Psychologica, 62(4), 291–313. https://doi.org/10.31577/sp.2020.04.806

87.

Sonderen

E. V.

Sanderman

Coyne

J. C.

(2013). Ineffectiveness of reverse wording of questionnaire items: Let’s learn from cows in the rain. PLoS One, 8(7), Article e68967. https://doi.org/10.1371/journal.pone.0068967

88.

Sörnyei

Vass

Á.

Németh

Farkas

(2024). Autistic and schizotypal traits exhibit similarities in their impact on mentalization and adult attachment impairments: A cross-sectional study. BMC Psychiatry, 24(1), 654. https://doi.org/10.1186/s12888-024-06048-9

89.

Spencer

Guzmán-González

Fresno

Ramos

(2013). Validación chilena del cuestionario de evaluación del apego romántico Experiences in Close Relationships (ECR): Análisis de la validez de criterio. Terapia Psicológica, 31(3), 313–324. https://doi.org/10.4067/S0718-48082013000300006

90.

Swain

S. D.

Weathers

Niedrich

R. W.

(2008). Assessing three sources of misresponse to reversed likert items. Journal of Marketing Research, 45(1), 116–131. https://doi.org/10.1509/jmkr.45.1.116

91.

Taylor

E. L.

Target

Charman

(2008). Attachment in adults with high-functioning autism. Attachment & Human Development, 10(2), 143–163. https://doi.org/10.1080/14616730802113687

92.

Tosti

Corrado

Mancone

Di Libero

Rodio

Andrade

Diotaiuti

(2024). Integrated use of biofeedback and neurofeedback techniques in treating pathological conditions and improving performance: A narrative review. Frontiers in Neuroscience, 18, Article 1358481. https://doi.org/10.3389/fnins.2024.1358481

93.

Turner

N. E.

Smith

H. H.

(2023). Supporting neurodivergent talent: ADHD, autism, and dyslexia in physics and space sciences. Frontiers in Physics, 11, Article 1223966. https://doi.org/10.3389/fphy.2023.1223966

94.

Valarezo-Bravo

Guzmán-González

Włodarczyk

Ubillos-Landa

Casu

(2024). Parental attachment, attachment to friends, and well-being among Chilean adolescents and emerging adults. PLoS One, 19(10), Article e0312777. https://doi.org/10.1371/journal.pone.0312777

95.

Walter

Martz

Weibel

Weiner

(2023). Tackling emotional processing in adults with attention deficit hyperactivity disorder and attention deficit hyperactivity disorder + autism spectrum disorder using emotional and action verbal fluency tasks. Frontiers in Psychiatry, 14, Article 1098210. https://doi.org/10.3389/fpsyt.2023.1098210

96.

Wei

Liao

K. Y. H.

T. Y.

Shaffer

P. A.

(2011). Attachment, self-compassion, empathy, and subjective well-being among college students and community adults. Journal of Personality, 79(1), 191–221. https://doi.org/10.1111/j.1467-6494.2010.00677.x

97.

Wongpakaran

Lerttrakarnnon

Jiraniramai

Saisavoey

Tantrarungroj

Satthapisit

DeMaranville

Myint

K. M.

Wedding

(2023). Psychometric assessment of the 10-item, revised experience of close relationship (ECR-R-10) in nonclinical and clinical populations of adults and older adults in Thailand. Scientific Reports, 13(1), Article 14969. https://doi.org/10.1038/s41598-023-41306-y

98.

Zhang

Xie

Chen

Hudson

N. W.

(2022). The relationship between adult attachment and mental health: A meta-analysis. Journal of Personality and Social Psychology, 123(5), 1089–1137. https://doi.org/10.1037/pspp0000437

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