Psychometric Properties of the Helicopter Parenting Behaviour Questionnaire (HPBQ) Among Emerging Adults in India

Psychometric Properties of the Helicopter Parenting Behaviour Questionnaire (HPBQ) Among Emerging Adults in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Psychometric Properties of the Helicopter Parenting Behaviour Questionnaire (HPBQ) Among Emerging Adults in India Mahadevaswamy M, Dr. Sneha Nathawat, Komal Yadav, Shelja Kumar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9106639/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Helicopter Parenting Behavior Questionnaire (HPBQ; Schiffrin et al., 2014) is a well-validated self-report tool for assessing parental control in the lives of emerging adults, that has typically been used within Western contexts. Given the pervasiveness of such disruptive parenting styles within India, the prevelance of which ranges between 48% to 83%, there is value in systematically investigating the utility of the HPBQ in an Indian context. This study examined the HPBQ from the perspectives of both the mother's and father's behavior within a sample of 438 emerging adults (Mage = 21.53, SDage = 2.74; 225 females) using combined classical test theory and item response theory. Key findings include: (1) the original two-factor structure of [i] helicopter parenting, and [ii] autonomy supportive behavior, were confirmed with acceptable factor loadings and good internal consistency; (2) strong reliability, convergent validity, and divergent validity were observed; (3) the HPBQ functioned similarly across male and female responders; and (4) most items were easily endorsed by respondents. These results indicate that the HPBQ is valid for use within emerging adults in India and thus, this scale has potential to inform empirical research on helicopter parenting in this context as well as serving as a tool for those seeking to understand the impact of such parenting styles. Psychology Psychiatry Helicopter Parenting Behaviour Classical Test Theory Item Response Theory India Mother Form Father Form Figures Figure 1 Introduction Parental involvement shapes children's emotional, social, and academic growth (Schiffrin et al., 2014). While appropriate parental involvement offers benefits (e.g., increased support, intimacy, and compassion), excessive or developmentally inappropriate parental involvement can adversely impact psychological well-being and problem-solving capabilities (Hadiwijaya et al., 2020; Schiffrin et al., 2014). One such maladaptive parenting style is helicopter parenting , a term that describes a parental pattern of "hovering" over their children, overly shielding them from stress, not allowing them to address their own barriers and/or issues, and offering constant support and validation (Cline & Fay, 2020; Hirsch & Goldberger, 2010). In India, rates of helicopter parenting range from 48% (Saranya et al., 2021) to 83% (Shaki et al., 2022), suggesting that such parenting styles in this population are particularly pervasive. From a developmental perspective, excessive use of helicopter parenting styles under the guise of love and protection may result in poor parent-child communication and children being ill-prepared for life's challenges (Cline & Fay, 2020; Segrin et al., 2012; Srivastav & Lal Mathur, 2020). Such impacts also negatively influence the well-being of young adults and are associated with lower self-determination (Schiffrin et al., 2013; 2019), poorer academic performance (Saranya et al., 2021), higher narcissistic traits and entitlement (Segrin et al., 2012), and ineffective emotion regulation strategies, such as withholding emotions and distancing oneself from others (Segrin et al., 2013). In turn, these effects may contribute to heightened levels of anxiety and stress (Segrin et al., 2013; Srivastav & Lal Mathur, 2020). Thus, there is a clear need to examine how helicopter parenting shapes children’s growth, so as to best advise practitioners in understanding parental behaviors, as well as any associated psychological impact on their children. In turn, this knowledge has potential to aid the creation of interventions that seek to improve mental health (Srivastav & Lal Mathur, 2020). Several instruments have been developed to measure helicopter parenting in general population samples, including the Parental Bonding Instrument (PBI; Parker et al., 1979; Parker, 1989), the Helicopter Parenting Scale (HPS; LeMoyne & Buchanan, 2011), the Overparenting Scale (Bradley-Geist & Olson-Buchanan, 2014), the Helicopter Parenting Instrument (Odenweller et al., 2014), and the Helicopter Parenting Behaviors Questionnaire (HPBQ; Schiffrin et al., 2014). These measures were primarily developed and validated in Western contexts, meaning that in the most part, their utility for assessing helicopter parenting within Indian populations, specifically, remains underexplored. The consequence of this is an inability to both internationally generalise the utility of these instruments, and adequately explore this phenomenon outside of the West. Where research on helicopter parenting within Indian contexts exists, this is restricted to the development of the Minor Teen Helicopter Parenting Scale (Sood & Buchanan, 2023), used within teenagers aged 13–17 years. Although this scale addresses parental behaviors to some degree (e.g., “My parents feel bad/ashamed when I do something wrong or perform poorly in academics or extra-curricular activities”), it overlooks the experiences and needs of individuals subjected to these parenting styles as they enter adulthood. Padilla-Walker and Nelson (2012) emphasize that autonomy is especially critical during early adulthood; further highlighting the importance of understanding helicopter parenting at this later developmental stage. In addition to providing broader societal knowledge, this topic has relevance for institutions, such as universities, who invest significant resources into managing the (inter)personal and well-being challenges faced by emerging adults (LeMoyne & Buchanan, 2011; Vigdal & Brønnick, 2022). The focal measure in this manuscript is the Helicopter Parenting Behavior Questionnaire (HPBQ; Schiffrin et al., 2014); a 15-item self-report measure developed to assess parental behaviors that involve excessive control over college students’ activities and parental intervention on behalf of the child. The HPBQ reliably captures behavioral control within the parent–child relationship, and was originally developed to assess perceived maternal Helicopter Parenting Behaviors (HPB), with nine items (Items 1, 3, 4, 7, 9, 10, 11, 13, 14 e.g., “My mother had/will have a say in what major I chose/will choose”, α = .77), and Autonomy Supportive Behaviors (ASB), with six items (Items 2, 5, 6, 8, 12, 15 e.g., “My mother encourages me to discuss any academic problems I am having with my professor”, α = .71). Responses are recorded on a 6-point Likert scale from 1 (strongly disagree) to 6 (strongly agree), with higher scores indicating greater endorsement of the behavior. Since, the HPBQ has been adapted to differentiate between fathers’ and mothers’ behaviors (Schiffrin et al., 2019), which is not the case with other measures that solely index overall, or mother-specific helicopter parenting. This distinction is important given that college students typically report higher levels of helicopter parenting from mothers than from fathers (Schiffrin et al., 2019), which, in turn, might differently influence well-being outcomes such as anxiety and depression (Vigdal & Brønnick, 2022). The HPBQ has been used in Western populations (Reilly & Semkovska, 2018) and has been translated and validated in Turkish (Kömürcü-Akik & Alsancak-Akbulut, 2023). However, despite its assessment efficacy, its psychometric properties have not yet been examined in an Indian context. Validating the HPBQ in India is crucial given cross-cultural differences in parenting practices, including physical care, cognitive engagement, emotional warmth, behavioral control, and disciplinary strategies (Lansford, 2022). Specifically, compared to UK mothers, Indian mothers tend to make fewer mind-minded comments, (i.e., it is the caregiver's ability to interpret the mental states underlying their infant's behaviors, which can be assessed by counting appropriate mind-minded comments during interactions with their children) about their children and issued more instructions and controlling comments (Bozicevic et al., 2023). Moreover, and on a statistical level, there is scope to further elevate the extant depth to which the HPBQ has been validated. Prior research using the HPBQ has heavily relied on Classical Test Theory (CTT) for establishing scale properties (Kömürcü-Akik & Alsancak-Akbulut, 2023; Schiffrin et al., 2014). However, Item Response Theory (IRT) is commonly viewed as superior for change assessment (Jabrayilov et al., 2016; Magno, 2009), with advantages in: (1) providing item difficulty estimates that remain consistent regardless of the sample; (2) evidencing stability in said estimates across different forms of the same construct; (3) producing stable internal consistency measures across samples; and (4) producing significantly less measurement error (Magno, 2009). The Current Study Taken together, the present study aims to rigorously evaluate the psychometric properties of both the father and mother forms of the HPBQ among Indian emerging adults through the combined use of CTT and IRT. In doing so, it aims to derive new knowledge and means to understanding the manifestations of helicopter parenting within an Indian context, which has potential to inform future academic research and professional intervention. Methods Procedure This study received approval from an institutional ethics committee (No./MGMC&H/IEC/JPR/2025/4961, 03/10/2025) and adhered to the Declaration of Helsinki ethical standards. Data were collected via an anonymous online Google Form distributed through social media platforms between October 2025 and January 2026. The survey provided information about the study, the objectives of it, and explanations of confidentiality, withdrawal, and data retention. The ‘request response’ function was used throughout to attenuate incomplete responses. Digital informed consent was obtained prior to participation. An attention check item was added (“I have never used a computer”) with a specific response instructed “Please select Moderately Inaccurate for this item” (Curran, 2015). Participants who selected the prompted response were considered correct; those who select otherwise (e.g., very inaccurate, slightly inaccurate, slightly accurate, very accurate) were incorrect. The questionnaire was administered in English and no monetary compensation was provided. Participants With the HPBQ comprising 15 items per parental form (30 total), the sample size followed Nunnally and Bernstein's (1994) recommended 10:1 participant-to-item ratio, yielding a minimum of 300 participants for reliable factor analysis and scale development. Participants were recruited using snowball and convenience sampling; an effective method for reaching hard-to-access populations and fostering participation through existing social networks (Ting et al., 2025). A total of 465 emerging adults initially participated in this study. After excluding 27 invalid responses (underage, non-students, failure in attention check item, or incomplete demographics), the final sample comprised n = 438 emerging adults aged 18–29 years (M age = 21.53, SD age = 2.74). This included 213 males (M age = 21.34, SD age = 2.86) and 225 females (M age = 21.72, SD age = 2.61), all of whom were proficient in English, were raised by and currently residing with their parents (rather than grandparents or other relatives, as specified by wording of the HPBQ), and resided in India. Of the 438 participants, 77.8% were college students, 52.5% residing in urban areas, 45.2% belonged to middle socioeconomic status and the majority (88%) were drawn from the north region. Measures 1. Helicopter Parenting Behaviour Questionnaire (HPBQ; Schiffrin et al., 2014) : For comprehensive details about the HPBQ’s development and theoretical background, please refer to the Introduction section. 2. Depression, Anxiety, Stress Scale − 21 (DASS-21; Lovibond & Lovibond, 1995) : The DASS-21 is a 21-item self-report questionnaire with three subscales: depression (7 items; “I couldn’t seem to experience any positive feeling at all”), anxiety (7 items; “I was worried about situations in which I might panic and make a fool of myself”) and stress (7 items; “I found it hard to wind down”), each with 7 items. Each item is rated on a 4-point Likert scale (0–3), and subscale scores range from 0 to 21. The scale has adequate psychometric properties. For this study, only depression and anxiety subscales were included. In a previous Indian study among university students, Cronbach's alpha was.834 for depression and.895 for anxiety (Vinayak et al., 2025). In the current sample, internal consistency was good for depression (α = .725, ω = .730) and anxiety (α = .798, ω = .800). 3. Helicopter Parenting Inventory (HPI; Odenweller et al., 2014) : The HPI is a 15-item measure that assesses young adults’ perceptions of their parents’ current developmentally inappropriate helicopter parenting. Each item (e.g., “my parent tries to make all of my major decisions”) is rated on a 5-point Likert scale from strongly disagree (1) to strongly agree (5). Total scores range from 15 to 75, with higher scores indicating greater helicopter parenting, after reverse-scoring Items 5 and 14. The HPI has shown adequate psychometric properties, with Cronbach’s α values of .78. In this sample, internal consistency was good (α = .800, ω = .804). Statistical Analysis and Parameters Data were analyzed using IBM SPSS Statistics for Windows (Version 22.0), IBM AMOS (Version 22.0), and Jamovi (Version 2.7.15). Initially, data were screened for missing data, and descriptive statistics (mean, SD, frequency) were computed wherever applicable. Following Byrne’s (2016) guidelines, normality was assessed to determine whether the data met the assumption of univariate and multivariate normality, and multivariate outliers were evaluated using Mahalanobis distance (Collier, 2020). The Harman single-factor test was conducted to assess the presence of common method bias in the study data (Collier, 2020; Podsakoff et al., 2003), and floor and ceiling effects were deemed to be present if more than 15% of participants attained the lowest score (floor effect), or the highest score (ceiling effect) (Lim et al., 2015). The following guidelines was used to assess the reliability: Cronbach's α and McDonald's ω values of .70–.90 were classified as good, and > .90 as excellent, composite reliability > .70, inter-item correlations between .15–.85, average inter-item correlation .20–.40, and corrected item-total correlations > .30 (Collier, 2020; Paulsen & BrckaLorenz, 2017; Piedmont & Hyland., 1993, Silva Castillo et al., 2025). Pearson’s r correlation was performed to assess the convergent/divergent validity, with strength of these relationship interpreted per Cohen (1988): r = .10–.30 (small), .30–.50 (medium), ≥ .50 (large). Divergent validity was assessed by calculating Heterotrait-Monotrait (HTMT) values between the subscales in both parental forms, and values below .85 (Henseler et al., 2015) were considered suggestive of divergent validity. Confirmatory factor analysis (CFA) was performed to assess the internal structure of the HPBQ. The model fit was evaluated using established criteria (Bentler, 1990, 1992; Byrne, 2016; Collier, 2020; Hu & Bentler, 1999; MacCallum et al., 1996), where: CFI, TLI, and GFI values ≥ .90 indicate acceptable fit and ≥ .95 indicate good fit; RMSEA values < .05 indicate good fit, < .08 adequate fit, and .08–.10 mediocre fit; and SRMR values < .05 indicate good fit and .05–.09 indicate adequate fit. Given χ²'s sensitivity to sample size, the relative χ² test (χ²/df) was used instead. Values < 3 to 5 indicate acceptable fit (Collier, 2020). Factor loadings (λ) ≥ .30 were considered acceptable (Boyle, 1985), and squared multiple correlations (R²) were examined, with values ≥ .20 indicating sufficient explained variance (Hooper et al., 2008). Subsequently, we conducted measurement invariance (MI) testing through a stepwise process. First, we compared models with progressively increasing restrictions to assess four types of MI: (a) configural invariance; (b) metric invariance; (c) scalar invariance; and (d) strict invariance. The sample included 213 males and 225 females, which is considered adequate for measurement invariance testing (Kim & Willson, 2014). MI was considered supported if changes in Comparative Fit Index (ΔCFI), Root Mean Square Error of Approximation (ΔRMSEA), and Standardized Root Mean Square Residual (ΔSRMR) remains within recommended thresholds: ΔCFI < .01, ΔRMSEA < .015, and SRMR of .030 (for metric) or .015 (for scalar/residual invariance) (Chen, 2007; Cheung & Rensvold, 2002; Putnick & Bornstein, 2016). Subsequently, the Rasch Rating Scale Model (RSM) was conducted as the HPBQ employs a 6-point Likert scale appropriate for ordered-category response data (von Davier, 2014). The polytomous model was estimated using the eRm package (Mair et al., 2021) within snowIRT (Seol, 2026). Analyses included item difficulty estimates, person (Pearson) reliability with a cut-off of ≥ .70 (Abdulmajid et al., 2024), and local independence assessed via Yen’s Q3 statistic, with values < .36 indicating adequate local independence (Flens et al., 2016). Item fit was evaluated using infit and outfit MnSq statistics, with acceptable values ranging from 0.60 to 1.40 (Kook & Varni, 2008). Additionally, differential item functioning (DIF) was examined using the difNLR package (Hladka et al., 2022) in snowIRT, employing generalized logistic regression models. Results Preliminary Analysis Kurtosis values ranged from − 1.49 to 0.02 for the mother form (MF-HPBQ), and from − 1.46 to 0.56 for the father form (FF-HPBQ), all within the acceptable range of ± 7, indicating univariate normality (Byrne, 2016). The Z-statistics for kurtosis were 14.72 (MF-HPBQ) and 18.45 (FF-HPBQ), both exceeding the cut-off of 5.00, suggesting multivariate non-normality (Byrne, 2016). Assessment for multivariate outliers using the Mahalanobis distance found no observation with both p 1 < .001 and p 2 < .001, indicating the absence of multivariate outliers (Collier, 2020). Harman's single-factor test extracted multiple factors, with the first factor accounting for 27.13%, which is below the 40% threshold, indicating the absence of common method bias (Collier, 2020; Podsakoff et al., 2003). Finally, a few participants scored at the minimum or maximum values on the study variables (≤ 3.65%), confirming the absence of floor or ceiling effects (Lim et al., 2015). Internal Structure: Confirmatory Factor Analysis (CFA) Two CFAs were performed to assess the internal structural validity of the MF-HPBQ and FF-HPBQ using maximum likelihood estimation. To address multivariate non-normality, bootstrap resampling (5,000 samples) was applied (Collier, 2020). The first CFA (see Fig. 1a) on the MF-HPBQ showed acceptable fit for all indices except TLI. Modification indices guided sequential addition of two error covariances (e7-e11, e1-e4, e2-e6, e12-e13). Each modification improved fit indices toward acceptable thresholds via iterative re-evaluation (Byrne, 2010; Collier, 2020; Hooper et al., 2008). Following this procedure, the model demonstrated improved fit across multiple indices: χ² (85) = 197.327, p < .001; χ²/ df = 2.321; GFI = .943; CFI = .931; TLI = .915; RMSEA = .055 [.045-.065]; SRMR = .0564 (see Table 1 ). These values collectively indicate good model fit, supporting the MF-HPBQ's construct validity. All factor loadings (λ) were above 0.30 (Boyle, 1985), ranging from 0.386 to 0.733. Additionally, except for Item 4 in the MF-HPBQ, which had a R² value of 0.149, falling below the recommended minimum of 0.20 (Hooper et al., 2008) all other observed variables demonstrated satisfactory R² values ranging from 0.234 to 0.538 (see Table 2 ). The second CFA (see Fig. 1b) on the FF-HPBQ showed adequate initial fit for all indices except TLI (mirroring MF-HPBQ results). Using the same modification procedure, two error covariances were added based on modification indices (e10-e14, e7-e11), with iterative re-evaluation confirming improved fit after each addition. Following this iterative refinement, the model demonstrated improved fit: χ² (85) = 230.872, p < .001; χ²/ df = 2.716; GFI = .935; CFI = .939; TLI = .925; RMSEA = .063 [.053-.072]; SRMR = .0598. These indices collectively indicate good model fit, supporting FF-HPBQ construct validity (see Table 1 ). All factor loadings (λ) ranged from .502 to .761, with R² values from .252 to .579, indicating strong item-factor associations and adequate explained variance for the FF-HPBQ (see Table 2 ). In both FF-HPBQ and MF-HPBQ, all factor loadings and R² estimates fell within 95% confidence intervals that did not include zero, indicating the validity of the estimates based on bootstrapping to normalize the data (Collier, 2020). Table 1 Model Fit Indices for the Mother and Father Forms of the HPBQ Mother χ2/df Relative χ² GFI CFI TLI RMSEA SRMR 197.327/85 *** 2.321 .943 .931 .915 .055 [.045-.065] .0564 Father 230.872/85 *** 2.716 .935 .939 .925 .063 [.053-.072] .0598 Note: ***Significant at .001; GFI: Goodness of Fit Index; CFI: Comparative Fit Index; TLI: Tucker Lewis Index; RMSEA: Root Mean Square Error of Approximation; SRMR: Standardized Root Mean Squared Residual Table 2 Standardized Factor Loadings and Squared Multiple Correlations (R²) for the Mother and Father Forms of the HPBQ MOTHER FORM FATHER FORM Standardized Regression Weights (λ) Squared Multiple Correlations (R²) Standardized Regression Weights (λ) Squared Multiple Correlations (R²) Helicopter Parenting Behaviour (HPB) HPB_HPBQ1 0.501 0.251 0.622 0.387 HPB_HPBQ3 0.647 0.419 0.644 0.415 HPB_HPBQ4 0.386 0.149 0.502 0.252 HPB_HPBQ7 0.531 0.282 0.646 0.417 HPB_HPBQ9 0.492 0.242 0.688 0.474 HPB_HPBQ10 0.733 0.538 0.652 0.425 HPB_HPBQ11 0.702 0.493 0.761 0.579 HPB_HPBQ13 0.682 0.466 0.666 0.443 HPB_HPBQ14 0.483 0.234 0.618 0.382 Autonomy Supportive Behaviour (ASB) ASB_HPBQ2 0.603 0.364 0.635 0.403 ASB_HPBQ5 0.547 0.299 0.611 0.373 ASB_HPBQ6 0.576 0.332 0.716 0.513 ASB_HPBQ8 0.520 0.271 0.592 0.350 ASB_HPBQ12 0.620 0.384 0.648 0.419 ASB_HPBQ15 0.537 0.288 0.742 0.550 Note: HPBQ = Helicopter Parenting Behaviour Questionnaire, HPB = Helicopter Parenting Behaviour, ASB = Autonomy Supportive Behaviour Measurement Invariance Measurement invariance was assessed to evaluate the psychometric equivalence of MF-HPBQ and FF-HPBQ across genders. Invariance criteria, including configural, metric, scalar, and strict invariance were met (see Table 3 ). Changes in the ΔCFI, ΔRMSEA, and ΔSRMR remained within recommended cut-offs: ΔCFI < .01, ΔRMSEA < .015, and SRMR of .030 (for metric) or .015 (for scalar/residual invariance) (Chen, 2007; Cheung & Rensvold, 2002; Putnick & Bornstein, 2016). Table 3 Measurement Invariance of HRS-SR Across Gender CFI ΔCFI RMSEA ΔRMSEA SRMR ΔSRMR Mother Form Configural .924 - .041 (.033-.049) - .0505 - Metric .918 − .006 .041 (.034-.049) .000 .0594 .0089 Scalar .907 − .011 .044 (.036-.051) .003 .0784 .0190 Strict .909 .002 .041 (.034-.048) − .003 .0845 .0061 Father Form Configural .915 - .053 (.046-.061) - .0651 - Metric .913 − .002 .052 (.045-.059) − .001 .0685 .0034 Scalar .909 − .004 .053 (.046-.060) .001 .0779 .0094 Strict .900 − .009 .053 (.046-.059) .000 .0830 .0051 Note: CFI: Comparative Fit Index; RMSEA: Root Mean Square Error of Approximation; SRMR: Standardized Root Mean Squared Residual Reliability Analysis The internal consistency of the HPBQ was assessed using Cronbach’s α, McDonald’s ω, and composite reliability. Cronbach's α indicated good internal consistency for both forms: MF-HPBQ form = .82 [HPB], .75 [ASB]); FF-HPBQ (α = .87 [HPB], .81 [ASB]). McDonald ω values were comparable: MF-HPBQ (.82 [HPB], .75 [ASB]); FF-HPBQ (.87 [HPB], .81 [ASB]) (see Table 4 ). Composite reliability was adequate for both parental forms: maternal ASB = 0.74 and HPB = 0.82; paternal ASB = 0.82 and HPB = 0.87. Inter-item correlations for the FF-HPBQ ranged from .321–.566 (ASB) and .249–.643 (HPB), and for the MF-HPBQ .186–.529 (HPB) and .232–.479 (ASB), all within the acceptable range of .15–.85 (Paulsen & BrckaLorenz, 2017) (see Supplementary Table S1 and S2). Additionally, Average Inter-Item Correlation values across both forms ranged from .328–.421. Furthermore, the corrected item-total correlations for the FF-HPBQ ranged from .440 to .691 for the HPB domain and .484 to .665 for the ASB domain. For the MF-HPBQ, the values ranged from .371 to .646 for HPB and .425 to .576 for ASB domains of the HPBQ. All values exceeded .30 (Silva Castillo et al., 2025), indicating that all items demonstrated good discrimination abilities. Moreover, deleting any item did not increase Cronbach's α, supporting the reliability of the MF-HPBQ and FF-HPBQ. Convergent, Divergent and Discriminant Validity Convergent validity was evaluated through correlations between the HPBQ and the established measure of helicopter parenting (HPI). Divergent validity was assessed via correlations with theoretically unrelated constructs of depression and anxiety (DASS-21). We expected that the correlations between both forms of the HPBQ and the HPI would be stronger, reflecting convergent validity, whereas the correlations between the HPBQ and depression and anxiety would be weaker or negligible, thereby supporting divergent validity. As shown in Table 4 , HPB domains for both MF-HPBQ ( r = .558, p < .001) and FF-HPBQ ( r = .555, p .50), supporting excellent convergent validity of the HPBQ across parental forms. Similarly, the ASB domain of the MF-HPBQ ( r = .111, p < .05) and FF-HPBQ ( r = .213, p < .001) also showed statistically significant positive correlations with the HPI (see Table 2 ). The HPB domains for both MF-HPBQ and FF-HPBQ showed nonsignificant relationships with anxiety (maternal: r = .021, p > .05; paternal: r = .029, p > .05) and depression (maternal: r = .036, p > .05; paternal: r = .080, p > .05). In contrast, the ASB domain showed small but significant negative associations with depression for MF-HPBQ ( r = –.206, p < .001) and FF-HPBQ ( r = –.181, p < .001), and similarly small negative associations with anxiety for both MF-HPBQ ( r = –.115, p < .05) and FF-HPBQ ( r = –.146, p < .01). These nonsignificant (HPB) and weak (ASB) associations confirm the HPBQ's divergent validity from anxiety/depression constructs for both parental forms (see Table 4 ). Discriminant validity was established using HTMT correlation ratios, yielding values of .58 (MF-HPBQ: HPB vs. ASB) and .59 (FF-HPBQ: HPB vs. ASB). Both fell substantially below the .85 threshold (Henseler et al., 2015), confirming the theoretical distinctiveness of ASB from HPB across parental forms. Table 4 Findings of the Pearson Correlation Coefficients among study variables (n = 438) MF_HBP MF_HBP MF_ASB FF_HBP FF_ASB HPI DASS-D DASS-A 1 MF_ASB .441 *** 1 FF_HBP .666 ** .277 ** 1 FF_ASB .371 ** .480 ** .542 ** 1 HPI .558 ** .111 * .555 ** .213 ** 1 DASS-D .036 − .206 ** .080 − .181 ** .196 ** 1 DASS-A .021 − .115 * .029 − .146 ** .109 * .601 ** 1 α .819 .746 .867 .811 .800 .725 .798 ω .822 .748 .868 .812 .804 .730 .800 AIC .335 .328 .419 .421 .214 .276 .361 Note: MF = Mother Form, FF = Father Form, HPB = Helicopter Parenting Behaviour, ASB = Autonomy Supportive Behaviour, HPI = Helicopter Parenting Inventory, DASS – D = Depression, Anxiety and Stress Scale – Depression, DASS – A = Depression, Anxiety and Stress Scale – Anxiety, AIC = Average Inter-item Correlation Item Response Theory (IRT) Assumptions IRT requires unidimensionality and local independence (Magno, 2009). CFA results confirmed unidimensionality for both the MF-HPBQ and FF-HPBQ. The Yen’s Q3 residual correlations provided evidence of local independence, with all values .70 (Abdulmajid et al., 2024) indicated adequate reliability for both MF-HPBQ (HPB = .840; ASB = .751) and FF-HPBQ (HPB = .863; ASB = .797). Item Fit Statistics Table 5 shows that item fit statistics for both forms of HPBQ. The HPB Infit ranged from 0.832 to 1.324 and Outfit from 0.809 to 1.361, while ASB showed Infit from 0.940 to 1.116 and Outfit from 0.938 to 1.114 for the MF-HPBQ. For the FF-HPBQ, MNSQ Infit values of 0.849 to 1.416 with Outfit values of 0.793 to 1.517 (HPB subscale) and Infit values of 0.934 to 1.158 and Outfit values of 0.905 to 1.195 (ASB subscale). All values fell within the .6 to 1.4 range (Kook & Varni, 2008), indicating acceptable model fit. No substantial underfit (> 1.4) or severe overfit (< 0.6) was observed, confirming that each item contributed adequately to its domain. Item Difficulty Item difficulty estimates in the father form ranged from − 1.37 to -1.87 logits (SE = 0.0381–0.0436) for the ASB subscale and − 1.67 to -1.01 logits (SE = 0.0375–0.0390) for the HPB subscale. Mother form items difficulties spanned − 1.743 to -0.918 logits (SE = 0.0355–0.0393) for HPB and − 1.73 to -1.44 logits (SE = 0.0397–0.0444) for ASB (see Table 5 ). Table 5 Item fit statistics (MNSQ infit and outfit) and item difficulty for the MF-HPBQ and FF-HPBQ. MF-HPBQ FF-HPBQ Measure S.E.Measure Infit Outfit Measure S.E.Measure Infit Outfit HPB_HPBQ1 -1.291 .0356 1.071 1.109 -1.55 .0381 1.125 1.117 HPB_HPBQ3 -1.199 .0355 .958 .946 -1.3 .0375 .963 .96 HPB_HPBQ4 -1.486 .0366 1.324 1.361 -1.67 .039 1.416 1.517 HPB_HPBQ7 -1.743 .0393 1.078 1.062 -1.54 .0381 1.069 1.144 HPB_HPBQ9 − .991 .036 1.04 1.067 -1.18 .0378 .946 .936 HPB_HPBQ10 -1.508 .0368 .887 .874 -1.32 .0375 .937 .942 HPB_HPBQ11 -1.363 .0358 .832 .809 -1.4 .0376 .849 .793 HPB_HPBQ13 -1.508 .0368 .911 .894 -1.48 .0378 .999 1.019 HPB_HPBQ14 − .918 .0365 .961 .981 -1.01 .0387 1.007 1.039 ASB_HPBQ2 -1.57 .0415 1.064 1.114 -1.6 .04 1.062 1.078 ASB_HPBQ5 -1.57 .0416 1.049 1.085 -1.37 .0381 1.158 1.195 ASB_HPBQ6 -1.73 .0444 1.116 1.067 -1.87 .0436 .945 .935 ASB_HPBQ8 -1.44 .0397 .988 1.028 -1.59 .0399 1.065 1.182 ASB_HPBQ12 -1.52 .0408 1.028 1.055 -1.78 .0423 1.071 1.051 ASB_HPBQ15 -1.62 .0424 .940 .938 -1.83 .043 .934 .905 Note: MF = Mother Form, FF = Father Form, HPBQ = Helicopter Parenting Behaviour Questionnaire, HPB = Helicopter Parenting Behaviour, ASB = Autonomy Supportive Behaviour Differential Item Functioning (DIF) DIF analysis across gender confirmed measurement equivalence for nearly all items on both MF-HPBQ and FF-HPBQ. Except for a few items, likelihood ratio tests (adjusted p > .05 via multiple comparisons) showed no evidence of uniform or non-uniform DIF, indicating unbiased item responses. Notably, HPB subscale Item 4 ("When I am home with my mother/father, I have a curfew” [a certain time that I must be home by every night]) exhibited uniform DIF across both forms, while non-uniform DIF was absent. Additionally, father form HPB Item 10 ("My father monitors my diet") showed uniform DIF while absence of non-uniform DIF (see Table 6). MF-HPBQ FF-HPBQ Uniform Non-Uniform Uniform Non-Uniform Statistic p Adj. p Statistic p Adj. p Statistic p Adj. p Statistic p Adj. p Helicopter Parenting Behaviour HPBQ1 .08 .78 1.00 6.70 .01 .09 .52 .47 1.00 .00 1.00 1.00 HPBQ3 .42 .52 1.00 .03 .86 1.00 3.97 .05 .42 3.49 .06 .56 HPBQ4 13.82 < .001 .00 3.36 .07 .60 23.00 < .001 < .001 3.97 .05 .42 HPBQ7 .83 .36 1.00 .58 .45 1.00 1.80 .18 1.00 5.76 .02 .15 HPBQ9 1.94 .16 1.00 .00 1.00 1.00 1.18 .28 1.00 4.45 .04 .31 HPBQ10 6.50 .01 .10 .07 .80 1.00 9.39 .00 .02 .41 .52 1.00 HPBQ11 1.64 .20 1.00 4.46 .04 .31 .61 .43 1.00 3.23 .07 .65 HPBQ13 .57 .45 1.00 .65 .42 1.00 .07 .80 1.00 .57 .45 1.00 HPBQ14 .15 .70 1.00 1.08 .30 1.00 6.51 .01 .10 1.23 .27 1.00 Autonomy Supportive Behaviour HPBQ2 2.16 .14 .85 1.03 .31 1.00 .82 .36 1.00 1.37 .24 1.00 HPBQ5 1.16 .28 1.00 4.18 .04 .25 6.57 .01 .06 2.00 .16 .94 HPBQ6 1.58 .21 1.00 3.33 .07 .41 .07 .79 1.00 .37 .54 1.00 HPBQ8 .68 .41 1.00 .91 .34 1.00 1.71 .19 1.00 1.94 .16 .99 HPBQ12 2.54 .11 .67 .07 .80 1.00 .09 .76 1.00 1.07 .30 1.00 HPBQ15 5.82 .02 .10 .30 .58 1.00 .00 .97 1.00 4.76 .03 .18 Note: MF = Mother Form, FF = Father Form, HPBQ = Helicopter Parenting Behaviour Questionnaire Discussion The present study assessed psychometric properties of both the maternal and paternal forms of the HPBQ among emerging adults in India using complementary CTT and IRT approaches. Classical Test Theory Based Parameters The CFA confirmed the two-factor models (HPB and ASB) for the MF-HPBQ and FF-HPBQ in our Indian sample. This structure was initially proposed by Schiffrin et al. (2014) for the maternal form and was later extended to the paternal form (Schiffrin et al., 2019), before being validated in Turkish (Kömürcü-Akik & Alsancak-Akbulut, 2023), meaning our data are consistent across multiple countries. In both parental forms, the standardized factor loadings indicated that each unobservable variable had an adequate direct effect on its respective factor. These factor loadings, which measure the correlation between each variable and factor, indicate the degree of correspondence wherein higher loadings indicate that the variable is more representative of the factor (Hair et al., 2019). Additionally, except for one item in the mother form (HPB Item 4), all other squared multiple correlations suggested that each indicator explained an adequate proportion of the variance. Nevertheless, this item was retained in the final model because the other fit indices remained adequate. The findings supported measurement invariance at the configural, metric, scalar, and strict levels (Putnick & Bornstein, 2016) for both MF-HPBQ and FF-HPBQ. This demonstrates the psychometric equivalence of helicopter parenting across groups, such as gender, in this study, indicating that the helicopter parenting construct does not differ in structure or meaning between genders. This suggests that both genders responded to the MF-HPBQ and FF-HPBQ items similarly. Thus, any gender differences observed in the construct are unlikely to arise from measurement bias and are more likely to reflect actual gender-based differences. The present study is the first to report measurement invariance for both parental forms of HPBQ. Internal consistency was evaluated using multiple reliability indices owing to known limitations of Cronbach’s α, wherein artificial inflation can be introduced when a construct includes many indicators and assumes tau-equivalence (i.e., equal factor loadings across items; Collier, 2020; Park et al., 2022). Therefore, McDonald’s ω was also adopted to ensure accurate reliability estimates even when items vary in how strongly they relate to the underlying construct (Park et al., 2022). Across all forms, and subscales therein, good internal consistency was evident; mirroring reliability estimates reported elsewhere (Kömürcü-Akik & Alsancak-Akbulut, 2023; Schiffrin et al., 2014; 2019). Such consistency highlights the HPBQ’s stability and reliability across contexts, and adds evidence for the measure’s cross-cultural applicability and psychometric strength. Inter-item correlations across both parental forms supported the notion that the items adequately captured the constructs underpinning helicopter parenting behaviour. These results show a shared construct without redundancy, affirming each item's unique but related role. A homogeneous test measures a single construct. Greater homogeneity leads to higher inter-item consistency, especially in narrowly defined behavioral domains (Cohen & Swerdlik, 2005). In the helicopter parenting measures, the mean inter-item correlations for both forms were adequate. This shows optimal homogeneity. It suggests the total score reflects the complexity of helicopter parenting behaviors, without items being excessively redundant. Thus, the measured construct captures the intended breadth of behaviors without being too narrow or repetitive. All corrected item-total correlation values were adequate, indicating each item correlated well with its respective factor total score (Silva Castillo et al., 2025). This adequate item-total correlation indicates that each item effectively distinguishes between respondents with different levels of helicopter parenting traits, thereby ensuring good discrimination. In terms of validity, both the MF-HPBQ and FF-HPBQ demonstrated strong positive correlations with the HPI (Odenweller et al., 2014); indicating convergent validity. In contrast, both forms negatively correlated with anxiety, and shared non-significant correlations with depression, suggesting - as expected - that the HPBQ does not measure anxiety or depression, thus, providing evidence for its divergent validity. This somewhat mirrors Schiffrin et al. (2014), wherein the HPB facet had a small correlation with depression and a non-significant relationship with anxiety, and in Kömürcü-Akik and Alsancak-Akbulut (2023), wherein both parental forms of the Turkish version showed weak or non-significant correlations with depression, anxiety, and stress, but a significant, moderate-to-large positive correlation with HPI. Together, this supports both convergent and divergent validity of the scale across cultural contexts. Recently, discriminant validity assessment has become a generally accepted prerequisite for analyzing true relationships between latent variables (Henseler et al., 2015). In the present study, the HPB and ASB domains were considered distinct from one another demonstrating that these two constructs can be empirically differentiated within the HPBQ. This is true because helicopter parenting can cause negative mental health outcomes, whereas autonomy supportive parenting can lead to greater well-being and life satisfaction by fostering a sense of autonomy, relatedness, and competence (Schiffrin et al., 2014). Item Response Theory Based Parameters The Rasch Rating Model findings provided evidence of unidimensionality indicating that all items are functionally dependent upon only one underlying continuum and local independence indicating that each respondent has a certain probability of giving a predefined response to each item, and that this probability is independent of the answers given to the preceding items (Magno, 2009). These locally independent findings develop our CTT results, where all items demonstrated adequate inter-item correlations within each factor. Item fit statistics indicated that all items fell within our pre-defined recommended range (Kook & Varni, 2008) for both parental forms, further supporting the validity of the HPBQ in that each item seemingly contributes appropriately to measuring the construct. Item difficulty estimates indicated that all the items functioned with relative ease; an indicator of where each item lies on the ability scale. For example, an easy item functions among low-ability participants, in this case, participants who report experiencing fewer HPB and ASB from their parents and a hard item function among high-ability individuals, for example, participant who reports experiencing a higher degree of helicopter parenting (Ashraf & Jaseem, 2020). This finding indicates that in future iterations of the HPBQ, there is scope to further strengthen the scale’s psychometric rigor and ensure comprehensive assessment wherein future revisions incorporate more difficult items that target higher levels of helicopter parenting and autonomy-supportive behavior. Finally, the study found that with few exceptions, most items showed no evidence of uniform or nonuniform differential item functioning (DIF), which refers to cases in which participants from different groups (e.g., gender) with the same level of the latent trait (i.e., helicopter parenting) have different probabilities of responding to a particular item (Chen & Revicki, 2023). Specifically, uniform DIF appears when ability level and group membership do not interact, and nonuniform DIF appears when they do interact (Narayanon & Swaminathan, 1996). This finding suggests items function equivalently regardless of gender. However, HPB subscale item 4 ("When I am home with my mother/father, I have a curfew") exhibited uniform DIF across both MF-HPBQ and FF-HPBQ. Nonuniform DIF was absent. Additionally, FF-HPBQ subscale HPB item 10 ("My father monitors my diet") showed uniform DIF, while non-uniform DIF was again absent. As this is the first study to assess DIF across gender for both parental forms, additional research is necessary to confirm these findings. Should subsequent studies also indicate DIF for the identified items, future revisions will be required to adapt them to ensure equivalent functioning across genders. Limitations and Future Directions The present study is among the first South Asian studies to provide evidence on the psychometric properties of the HPBQ using both CTT and IRT approaches. Additionally, including an attention item to check whether participants read survey questions carefully helped reduce careless or insufficient-effort responses, which are common in online self-report data collection (Curran, 2015). However, our findings should be interpreted in the context of several study-specific limitations. First, the presence of additional psychiatric conditions among participants was not considered, which could have affected the findings. Not only are 7.5% of young adults in India aged 18 to 29 years (the same age group as the current study) diagnosed with one or more mental disorders (Murthy, 2017), but children and adolescents raised by overprotective parents more frequently report having anxiety-related disorders stemming from their parents reducing their autonomy and displaying harsh or inconsistent control (Yaffe, 2021). Second, while internal consistency was assessed, the study did not evaluate test-retest reliability and so we cannot be confident that results remain consistent over time (Raykov & Marcoulides, 2011). Given that previous studies using the HPBQ have also not reported test-retest reliability (Kömürcü-Akik & Alsancak-Akbulut, 2023; Schiffrin et al., 2014; Schiffrin et al., 2019), this indicates a more general need to investigate the HPBQ’s stability. Third, this validation in an Indian context is restricted to English-speaking populations, and thus, future studies should consider translating the HPBQ into the various vernacular languages used in India. This is especially relevant since the Census of India lists 22 official languages in the Eighth Schedule of the Constitution (Census of India, 2011). Fourth, the study used the HPI (Odenweller et al., 2014) to assess convergent validity, however, the validity and reliability of the HPI in the Indian context were not explored. Though we indicated good internal consistency, the comprehensive psychometric properties of the HPI require further evaluation. Finally, consistent with previous research, this study utilizes student self-reports, which may provide a limited perspective. Incorporating both student and parent responses could yield a broader, more comparable view of the parent-child relationship (Kömürcü-Akik & Alsancak-Akbulut, 2023; Schiffrin et al., 2014), as it would allow a direct examination of how each group's perceptions align or differ. Clinical Implications Considering the detrimental impacts of helicopter parenting on individuals' well-being, which primarily stem from the perceived infringement on individuals fundamental psychological needs for autonomy and competence (Schiffrin et al., 2014), the HPBQ can be effectively used to assesses parental behaviors, such as excessive control over college students’ activities and parental intervention on their behalf. This is particularly important among emerging adults in India, who are highly vulnerable to the negative effects of overparenting (Padilla-Walker & Nelson, 2012). Using the HPBQ may help provide targeted interventions to reduce the negative impact of helicopter parenting on emerging adults' mental health. A meta-analysis (Ntoumanis et al., 2021) found that self-determination theory-informed interventions, such as increasing need support and fostering autonomous motivation, promote positive changes in health behaviors and improve overall health and psychological well-being, highlighting their mental health benefits. In practice, the HPBQ can be applied in parenting programs to systematically identify overparenting behaviors, inform parents about their potential effects, and tailor interventions to support positive physical, cognitive, social, and emotional development (Lansford, 2022). Through, educational programs parents can be encouraged to engage in authoritative parenting, as it is associated with positive child outcome while, authoritarian parenting is associated with negative outcome (Pinquart & Kauser, 2018). Theoretically, this validation can enable exploration of helicopter parenting within Indian contexts, across age groups, and in the context of comorbidities that might influence data. Conclusions This study provides the comprehensive psychometric evaluation of both maternal and paternal forms of the HPBQ among emerging adults in India through employing both CTT and IRT approaches. The study findings provided evidence that both parental forms of HPBQ are reliable and valid tools for assessing helicopter parenting among emerging adults in India. The findings also showed that the HPBQ items function equivalently across gender. Declarations Conflicts of Interest: The authors declare that there are no conflicts of interest. Ethical Statement This study received ethical approval from the Office of the Institute Ethics Committee (No./MGMC&H/IEC/JPR/2025/4961, 03/10/2025). Digital Informed consent was obtained from all the participants prior to participation. Consent for publication: Not applicable. Contributions Conceptualization = MM, SN, DF; Methodology = MM, SN, SM, SK, KY, DF; Data Collection = SK, KY, SM; Data Analysis = MM; Writing-Original Draft Preparation = MM, SM; Writing-Review & Editing = DF, SN, SK, KY. All authors have read and approved the manuscript. Funding: No external agencies provided funding for this study. Data Availability Statement: Data are available from the corresponding author upon reasonable request, provided the institute's ethical approval has been obtained. References Abdulmajid, A. A., & Khalid, K. A. (2024). Using Rasch Analysis for Validation of Knowledge and Perception of Orthopedic Workplace-Based Assessment among Postgraduate Orthopedic Trainees’ Questionnaire. Journal of Pharmacy and Bioallied Sciences , 16 (4), 126–129. https://doi.org/10.4103/jpbs.jpbs_629_24 Ashraf, Z. A., & Jaseem, K. (2020). Classical and modern methods in item analysis of test tools. International Journal of Research and Review , 7 (5), 397–403. Bentler P. M. (1990). Comparative fit indexes in structural models. Psychological bulletin , 107 (2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238 Bentler, P. M. (1992). On the fit of models to covariances and methodology to the Bulletin. . Psychological Bulletin, 112 (3), 400–404. https://doi.org/10.1037/0033-2909.112.3.400 Boyle, G. J. (1985). A reanalysis of the higher-order factor structure of the Motivation Analysis Test and the Eight State Questionnaire. Personality and Individual Differences , 6 (3), 367–374. https://doi.org/10.1016/0191-8869(85)90061-3 Bozicevic, L., Hill, J., Chandra, P. S., Omirou, A., Holla, C., Wright, N., & Sharp, H. (2023). Cross-cultural differences in early caregiving: levels of mind-mindedness and instruction in UK and India. Frontiers in Child and Adolescent Psychiatry , 2 , 1124883. https://doi.org/10.3389/frcha.2023.1124883 Bradley-Geist, J. C., & Olson-Buchanan, J. B. (2014). Helicopter parents. An examination of the correlates of over-parenting of college students, Education+ Training, 56(4), 314–328. https://doi.org/10.1108/ET-10-2012-0096. Byrne, B. M. (2016). Structural equation modeling with AMOS. Basic concepts, applications, and programming (Third ed.). Routledge Census of India https://censusindia.gov.in Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural equation modeling: a multidisciplinary journal , 14 (3), 464–504. Chen, WH., Revicki, D. (2023). Differential Item Functioning (DIF). In: Maggino, F. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Cham. https://doi.org/10.1007/978-3-031-17299-1_728 Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural equation modeling , 9 (2), 233–255. Cline, F., & Fay, J. (2020). Parenting with love and logic: Teaching children responsibility (Updated and expanded ed.). NavPress. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587 Cohen, R. J., & Swerdlik, M. E. (2005). Psychological testing and assessment: An introduction to tests and measurement (6th ed.). New York: McGraw-Hill. Collier, J.E. (2020). Applied Structural Equation Modeling using AMOS: Basic to Advanced Techniques (1st ed.). Routledge. https://doi.org/10.4324/9781003018414 Curran, P. G. (2016). Methods for the detection of carelessly invalid responses in survey data. Journal of Experimental Social Psychology , 66 , 4–19. http://dx.doi.org/10.1016/j.jesp.2015.07.006 Flens, G., Smits, N., Carlier, I., van Hemert, A. M., & de Beurs, E. (2016). Simulating computer adaptive testing with the Mood and Anxiety Symptom Questionnaire. Psychological assessment , 28 (8), 953–962. https://doi.org/10.1037/pas0000240 Hadiwijaya, H., Klimstra, T. A., Darling, N., Vermunt, J. K., Branje, S., & Meeus, W. H. J. (2020). The family context as a foundation for romantic relationships: A person-centered multi-informant longitudinal study. Journal of family psychology: JFP: journal of the Division of Family Psychology of the American Psychological Association (Division 43) , 34 (1), 46–56. https://doi.org/10.1037/fam0000601 Hair, J. F., Babin, B. J., Anderson, R. E., & Black, W. C. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science , 43 (1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 Hirsch, D., & Goldberger, E. (2010). Hovering practices in and outside the classroom. About Campus , 14(6), 30–32. https://doi.org/10.1002/abc.20007 Hladka, A., Martinkova, P., & Zvara, K. (2022). difNLR: DIF and DDF Detection by Non-Linear Regression Models . (Version 1.4.1) [R package]. URL https://CRAN.R-project.org/package=difNLR. Hooper D, Coughlan J, Mullen MR. Structural equation modelling: guidelines for determining model fit. Electron J Bus Res Methods. 2008;6(1):53–60. Available from: https://academic-publishing.org/index.php/ejbrm/article/view/1224/1187. Accessed 2nd February 2026 Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118. Jabrayilov, R., Emons, W. H. M., & Sijtsma, K. (2016). Comparison of Classical Test Theory and Item Response Theory in Individual Change Assessment. Applied psychological measurement , 40 (8), 559–572. https://doi.org/10.1177/0146621616664046 Kim, E. S., & Willson, V. L. (2014). Measurement invariance across groups in latent growth modeling. Structural Equation Modeling: A Multidisciplinary Journal , 21 (3), 408–424. http://dx.doi.org/10.1080/10705511.2014.915374 Kömürcü-Akik, B., & Alsancak-Akbulut, C. (2023). Assessing the psychometric properties of mother and father forms of the helicopter parenting behaviors questionnaire in a Turkish sample. Current Psychology , 42 (4), 2980–2992. Kook, S. H., & Varni, J. W. (2008). Validation of the Korean version of the pediatric quality of life inventory™ 4.0 (PedsQL™) generic core scales in school children and adolescents using the Rasch model. Health and quality of life outcomes , 6 (1), 41. https://doi.org/10.1186/1477-7525-6-41 Lansford J. E. (2022). Annual Research Review: Cross-cultural similarities and differences in parenting. Journal of child psychology and psychiatry, and allied disciplines , 63 (4), 466–479. https://doi.org/10.1111/jcpp.13539 Lansford J. E. (2022). Annual Research Review: Cross-cultural similarities and differences in parenting. Journal of child psychology and psychiatry, and allied disciplines , 63 (4), 466–479. https://doi.org/10.1111/jcpp.13539 LeMoyne, T., & Buchanan, T. (2011). Does “hovering” matter? Helicopter parenting and its effect on well-being. Sociological Spectrum, 31(4), 399–418. https://doi.org/10.1080/02732173. 2011.574038 Lim, C. R., Harris, K., Dawson, J., Beard, D. J., Fitzpatrick, R., & Price, A. J. (2015). Floor and ceiling effects in the OHS: an analysis of the NHS PROMs data set. BMJ open , 5 (7), e007765. https://doi.org/10.1136/bmjopen-2015-007765. Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the depression anxiety stress scales (DASS) with the Beck depression and anxiety inventories. Behaviour Research and Therapy, 33(3), 335–343. https://doi.org/10.1016/0005-7967(94)00075-U MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power Analysis and Determination of Sample Size for Covariance Structure Modeling. Psychological Methods, 1, 130–149. https://doi.org/10.1037/1082-989X.1.2.130 Magno, C. (2009). Demonstrating the difference between classical test theory and item response theory using derived test data. The International Journal of Educational and Psychological Assessment, 1 (1), 1–11. Mair, P., Hatzinger, R., Maier, M., Rusch, T., & Debelak R. (2021). eRm: Extended Rasch Modeling . (Version 1.0.2) [R package]. URL https://CRAN.R-project.org/package=eRm. Murthy R. S. (2017). National Mental Health Survey of India 2015–2016. Indian journal of psychiatry , 59 (1), 21–26. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_102_17 Narayanon, P., & Swaminathan, H. (1996). Identification of items that show nonuniform DIF. Applied Psychological Measurement , 20 (3), 257–274. https://doi.org/10.1177/014662169602000306 Ntoumanis, N., Ng, J. Y. Y., Prestwich, A., Quested, E., Hancox, J. E., Thøgersen-Ntoumani, C., … Williams, G. C. (2021). A meta-analysis of self-determination theory-informed intervention studies in the health domain: effects on motivation, health behavior, physical, and psychological health. Health Psychology Review , 15 (2), 214–244. https://doi.org/10.1080/17437199.2020.1718529 Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill. Odenweller, K. G., Booth-Butterfield, M., & Weber, K. (2014). Investigating helicopter parenting, family environments, and relational outcomes for Millennials. Communication Studies, 65(4), 407–425. https://doi.org/10.1080/10510974.2013.811434. Padilla-Walker, L. M., & Nelson, L. J. (2012). Black hawk down?: Establishing helicopter parenting as a distinct construct from other forms of parental control during emerging adulthood. Journal of Adolescence, 35(5), 1177–1190. https://doi.org/10.1016/j. adolescence.2012.03.007 Park, J., van den Broek, K. L., Bhullar, N., Ogunbode, C. A., Schermer, J. A., Doran, R., ... & Yadav, R. (2022). Comparison of the inter-item correlations of the Big Five Inventory-10 (BFI-10) between Western and non-Western contexts. Personality and Individual Differences , 196 , 111751. https://doi.org/10.1016/j.paid.2022.111751 Parker G. (1989). The Parental Bonding Instrument: psychometric properties reviewed. Psychiatric developments , 7 (4), 317–335. Parker, G., Tupling, H., & Brown, L. B. (1979). A Parental Bonding Instrument. British Journal of Medical Psychology, 52, 1–10. http://dx.doi.org/10.1111/j.2044-8341.1979.tb02487.x Paulsen, J. & BrckaLorenz, A. (2017). Internal consistency. FSSE Psychometric Portfolio. Retrieved from fsse.indiana.edu. Piedmont, R. L., & Hyland, M. E. (1993). Inter-item correlation frequency distribution analysis: A method for evaluating scale dimensionality. Educational and psychological measurement , 53 (2), 369–378. https://doi.org/10.1177/0013164493053002006 Pinquart, M., & Kauser, R. (2018). Do the associations of parenting styles with behavior problems and academic achievement vary by culture? Results from a meta-analysis. Cultural Diversity & Ethnic Minority Psychology, 24(1), 75–100. https://doi.org/10.1037/cdp0000149 Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology , 88 (5), 879. https://doi.org/10.1037/0021-9010.88.5.879. 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 : DR , 41 , 71–90. https://doi.org/10.1016/j.dr.2016.06.004 Raykov, T., & Marcoulides, G. A. (2011). Introduction to psychometric theory. New York, NY: Routledge. Reilly, S., & Semkovska, M. (2018). An examination of the mediatory role of resilience in the relationship between helicopter parenting and severity of depressive symptoms in Irish university students. Adolescent Psychiatry , 8 (1), 32–47. https://doi.org/10.2174/2210676608666180508130224 Saranya, J., Nirmala, S. V. S. G., Reddy, E. S., Challa, R., & Nuvvula, S. (2021). Prevalence of helicopter parenting and its effect on academic performance and oral hygiene status in adolescents: A cross-sectional study. International Journal of Current Research and Review, 13(15), 122–126. https://doi.org/10.31782/IJCRR.2021.131523 Schiffrin, H. H., Erchull, M. J., Sendrick, E., Yost, J. C., Power, V., & Saldanha, E. R. (2019). The effects of maternal and paternal helicopter parenting on the self-determination and well-being of emerging adults. Journal of Child and Family Studies, 28(12), 3346–3359. https://doi.org/10.1007/s10826-019-01513-6. Schiffrin, H. H., Liss, M., Miles-McLean, H., Geary, K. A., Erchull, M. J., & Tashner, T. (2014). Helping or hovering? The effects of helicopter parenting on college students’ well-being. Journal of Child and Family Studies , 23(3), 548–557. https://doi.org/10.1007/s10826-013-9716-3 Segrin, C., Woszidlo, A., Givertz, M., & Montgomery, N. (2013). Parent and child traits associated with overparenting. Journal of Social and Clinical Psychology , 32 (6), 569–595. Segrin, C., Woszidlo, A., Givertz, M., Bauer, A., & Murphy, M. T. (2012). The association between overparenting, parent-child communication, and entitlement and adaptive traits in adult children. Family Relations, 61, 237–252. DOI: 10.1111/j.1741-3729.2011.00689.x Seol, H. (2026). snowIRT: Item Response Theory for jamovi . (Version 5.1.8) [jamovi module]. URL https://github.com/hyunsooseol/snowIRT. Shaki, O., Gupta, G. K., Yadav, P., & Faisal, F. A. (2022). Helicopter parenting, from good intentions to poor outcomes. What parents needs to know?. Journal of family medicine and primary care , 11 (8), 4753–4757. https://doi.org/10.4103/jfmpc.jfmpc_2474_21 Silva Castillo, L. H., Hernández-Rosas, E., Silas-Casillas, J. C., Cazares, S. N., & Fonseca León, L. C. (2025). Cross-sectional psychometric validation, convergent validity, and measurement invariance of the DASS-21 in Mexican university students. Frontiers in Psychology , 16 , 1707786. https://doi.org/10.3389/fpsyg.2025.1707786 Sood, M., & Buchanan, T. (2024). Helicopter parenting of minor teenagers in India: Scale development and consequences. The Family Journal , 32 (2), 292–303. https://doi.org/10.1177/10664807231215429 Srivastav, D., & Mathur, M. L. (2020). Helicopter parenting and adolescent development: from the perspective of mental health. In Parenting . IntechOpen. Available from: http://dx.doi.org/10.5772/intechopen.93155 Ting, H., Memon, M. A., Thurasamy, R., & Cheah, J. H. (2025). Snowball sampling: A review and guidelines for survey research. Asian Journal of Business Research , 15 (1). Vigdal, J. S., & Brønnick, K. K. (2022). A Systematic Review of "Helicopter Parenting" and Its Relationship With Anxiety and Depression. Frontiers in psychology , 13 , 872981. https://doi.org/10.3389/fpsyg.2022.872981 Vinayak, P. L., Nathawat, S., Maresha, N., Mahadevaswamy, M., & Parashurama, K. G. (2025). Impact of Adverse Childhood Experience on Internet Addiction: The Mediating Role of Depression and Anxiety among University Students. Indian Journal of Social Psychiatry , 41 (4), 387–395. https://doi.org/10.4103/ijsp.ijsp_324_24 von Davier, M. (2014). Rasch Analysis. In: Michalos, A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_2411 Yaffe, Y. (2021). A narrative review of the relationship between parenting and anxiety disorders in children and adolescents. International Journal of Adolescence and Youth , 26 (1), 449–459. https://doi.org/10.1080/02673843.2021.1980067 Additional Declarations The authors declare no competing interests. Supplementary Files Supplimentaryfiles.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9106639","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605189326,"identity":"efb1d9c3-c563-4a24-99bb-337fd57141fa","order_by":0,"name":"Mahadevaswamy 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version.\u003c/p\u003e","description":"","filename":"placeholderimage.png","url":"https://assets-eu.researchsquare.com/files/rs-9106639/v1/fcbd6eba2ef7d72c9e759b5d.png"},{"id":104835419,"identity":"608943ee-5d2e-488d-afc0-7ab5085d5022","added_by":"auto","created_at":"2026-03-17 17:44:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1580426,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9106639/v1/521f9d29-014d-44c3-8641-fc4714e5d2b8.pdf"},{"id":104795493,"identity":"c4b1c412-f476-4754-a092-6d6953758754","added_by":"auto","created_at":"2026-03-17 09:27:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24035,"visible":true,"origin":"","legend":"","description":"","filename":"Supplimentaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-9106639/v1/d48ce516c0c059c26f90b2c4.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePsychometric Properties of the Helicopter Parenting Behaviour Questionnaire (HPBQ) Among Emerging Adults in India\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParental involvement shapes children's emotional, social, and academic growth (Schiffrin et al., 2014). While appropriate parental involvement offers benefits (e.g., increased support, intimacy, and compassion), excessive or developmentally inappropriate parental involvement can adversely impact psychological well-being and problem-solving capabilities (Hadiwijaya et al., 2020; Schiffrin et al., 2014). One such maladaptive parenting style is \u003cem\u003ehelicopter parenting\u003c/em\u003e, a term that describes a parental pattern of \"hovering\" over their children, overly shielding them from stress, not allowing them to address their own barriers and/or issues, and offering constant support and validation (Cline \u0026amp; Fay, 2020; Hirsch \u0026amp; Goldberger, 2010). In India, rates of helicopter parenting range from 48% (Saranya et al., 2021) to 83% (Shaki et al., 2022), suggesting that such parenting styles in this population are particularly pervasive.\u003c/p\u003e \u003cp\u003eFrom a developmental perspective, excessive use of helicopter parenting styles under the guise of love and protection may result in poor parent-child communication and children being ill-prepared for life's challenges (Cline \u0026amp; Fay, 2020; Segrin et al., 2012; Srivastav \u0026amp; Lal Mathur, 2020). Such impacts also negatively influence the well-being of young adults and are associated with lower self-determination (Schiffrin et al., 2013; 2019), poorer academic performance (Saranya et al., 2021), higher narcissistic traits and entitlement (Segrin et al., 2012), and ineffective emotion regulation strategies, such as withholding emotions and distancing oneself from others (Segrin et al., 2013). In turn, these effects may contribute to heightened levels of anxiety and stress (Segrin et al., 2013; Srivastav \u0026amp; Lal Mathur, 2020). Thus, there is a clear need to examine how helicopter parenting shapes children\u0026rsquo;s growth, so as to best advise practitioners in understanding parental behaviors, as well as any associated psychological impact on their children. In turn, this knowledge has potential to aid the creation of interventions that seek to improve mental health (Srivastav \u0026amp; Lal Mathur, 2020).\u003c/p\u003e \u003cp\u003eSeveral instruments have been developed to measure helicopter parenting in general population samples, including the Parental Bonding Instrument (PBI; Parker et al., 1979; Parker, 1989), the Helicopter Parenting Scale (HPS; LeMoyne \u0026amp; Buchanan, 2011), the Overparenting Scale (Bradley-Geist \u0026amp; Olson-Buchanan, 2014), the Helicopter Parenting Instrument (Odenweller et al., 2014), and the Helicopter Parenting Behaviors Questionnaire (HPBQ; Schiffrin et al., 2014). These measures were primarily developed and validated in Western contexts, meaning that in the most part, their utility for assessing helicopter parenting within Indian populations, specifically, remains underexplored. The consequence of this is an inability to both internationally generalise the utility of these instruments, and adequately explore this phenomenon outside of the West.\u003c/p\u003e \u003cp\u003eWhere research on helicopter parenting within Indian contexts exists, this is restricted to the development of the Minor Teen Helicopter Parenting Scale (Sood \u0026amp; Buchanan, 2023), used within teenagers aged 13\u0026ndash;17 years. Although this scale addresses parental behaviors to some degree (e.g., \u0026ldquo;My parents feel bad/ashamed when I do something wrong or perform poorly in academics or extra-curricular activities\u0026rdquo;), it overlooks the experiences and needs of individuals subjected to these parenting styles as they enter adulthood. Padilla-Walker and Nelson (2012) emphasize that autonomy is especially critical during early adulthood; further highlighting the importance of understanding helicopter parenting at this later developmental stage. In addition to providing broader societal knowledge, this topic has relevance for institutions, such as universities, who invest significant resources into managing the (inter)personal and well-being challenges faced by emerging adults (LeMoyne \u0026amp; Buchanan, 2011; Vigdal \u0026amp; Br\u0026oslash;nnick, 2022).\u003c/p\u003e \u003cp\u003eThe focal measure in this manuscript is the Helicopter Parenting Behavior Questionnaire (HPBQ; Schiffrin et al., 2014); a 15-item self-report measure developed to assess parental behaviors that involve excessive control over college students\u0026rsquo; activities and parental intervention on behalf of the child. The HPBQ reliably captures behavioral control within the parent\u0026ndash;child relationship, and was originally developed to assess perceived \u003cem\u003ematernal\u003c/em\u003e Helicopter Parenting Behaviors (HPB), with nine items (Items 1, 3, 4, 7, 9, 10, 11, 13, 14 e.g., \u0026ldquo;My mother had/will have a say in what major I chose/will choose\u0026rdquo;, α\u0026thinsp;=\u0026thinsp;.77), and Autonomy Supportive Behaviors (ASB), with six items (Items 2, 5, 6, 8, 12, 15 e.g., \u0026ldquo;My mother encourages me to discuss any academic problems I am having with my professor\u0026rdquo;, α\u0026thinsp;=\u0026thinsp;.71). Responses are recorded on a 6-point Likert scale from 1 (strongly disagree) to 6 (strongly agree), with higher scores indicating greater endorsement of the behavior. Since, the HPBQ has been adapted to differentiate between fathers\u0026rsquo; and mothers\u0026rsquo; behaviors (Schiffrin et al., 2019), which is not the case with other measures that solely index overall, or mother-specific helicopter parenting. This distinction is important given that college students typically report higher levels of helicopter parenting from mothers than from fathers (Schiffrin et al., 2019), which, in turn, might differently influence well-being outcomes such as anxiety and depression (Vigdal \u0026amp; Br\u0026oslash;nnick, 2022).\u003c/p\u003e \u003cp\u003eThe HPBQ has been used in Western populations (Reilly \u0026amp; Semkovska, 2018) and has been translated and validated in Turkish (K\u0026ouml;m\u0026uuml;rc\u0026uuml;-Akik \u0026amp; Alsancak-Akbulut, 2023). However, despite its assessment efficacy, its psychometric properties have not yet been examined in an Indian context. Validating the HPBQ in India is crucial given cross-cultural differences in parenting practices, including physical care, cognitive engagement, emotional warmth, behavioral control, and disciplinary strategies (Lansford, 2022). Specifically, compared to UK mothers, Indian mothers tend to make fewer mind-minded comments, (i.e., it is the caregiver's ability to interpret the mental states underlying their infant's behaviors, which can be assessed by counting appropriate mind-minded comments during interactions with their children) about their children and issued more instructions and controlling comments (Bozicevic et al., 2023).\u003c/p\u003e \u003cp\u003eMoreover, and on a statistical level, there is scope to further elevate the extant depth to which the HPBQ has been validated. Prior research using the HPBQ has heavily relied on Classical Test Theory (CTT) for establishing scale properties (K\u0026ouml;m\u0026uuml;rc\u0026uuml;-Akik \u0026amp; Alsancak-Akbulut, 2023; Schiffrin et al., 2014). However, Item Response Theory (IRT) is commonly viewed as superior for change assessment (Jabrayilov et al., 2016; Magno, 2009), with advantages in: (1) providing item difficulty estimates that remain consistent regardless of the sample; (2) evidencing stability in said estimates across different forms of the same construct; (3) producing stable internal consistency measures across samples; and (4) producing significantly less measurement error (Magno, 2009).\u003c/p\u003e\n\u003ch3\u003eThe Current Study\u003c/h3\u003e\n\u003cp\u003eTaken together, the present study aims to rigorously evaluate the psychometric properties of both the father and mother forms of the HPBQ among Indian emerging adults through the combined use of CTT and IRT. In doing so, it aims to derive new knowledge and means to understanding the manifestations of helicopter parenting within an Indian context, which has potential to inform future academic research and professional intervention.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eThis study received approval from an institutional ethics committee (No./MGMC\u0026amp;H/IEC/JPR/2025/4961, 03/10/2025) and adhered to the Declaration of Helsinki ethical standards. Data were collected via an anonymous online Google Form distributed through social media platforms between October 2025 and January 2026. The survey provided information about the study, the objectives of it, and explanations of confidentiality, withdrawal, and data retention. The \u0026lsquo;request response\u0026rsquo; function was used throughout to attenuate incomplete responses. Digital informed consent was obtained prior to participation. An attention check item was added (\u0026ldquo;I have never used a computer\u0026rdquo;) with a specific response instructed \u0026ldquo;Please select \u003cem\u003eModerately Inaccurate\u003c/em\u003e for this item\u0026rdquo; (Curran, 2015). Participants who selected the prompted response were considered correct; those who select otherwise (e.g., very inaccurate, slightly inaccurate, slightly accurate, very accurate) were incorrect. The questionnaire was administered in English and no monetary compensation was provided.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eWith the HPBQ comprising 15 items per parental form (30 total), the sample size followed Nunnally and Bernstein's (1994) recommended 10:1 participant-to-item ratio, yielding a minimum of 300 participants for reliable factor analysis and scale development. Participants were recruited using snowball and convenience sampling; an effective method for reaching hard-to-access populations and fostering participation through existing social networks (Ting et al., 2025). A total of 465 emerging adults initially participated in this study. After excluding 27 invalid responses (underage, non-students, failure in attention check item, or incomplete demographics), the final sample comprised \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;438 emerging adults aged 18\u0026ndash;29 years (M\u003csub\u003eage\u003c/sub\u003e = 21.53, SD\u003csub\u003eage\u003c/sub\u003e = 2.74). This included 213 males (M\u003csub\u003eage\u003c/sub\u003e = 21.34, SD\u003csub\u003eage\u003c/sub\u003e = 2.86) and 225 females (M\u003csub\u003eage\u003c/sub\u003e = 21.72, SD\u003csub\u003eage\u003c/sub\u003e = 2.61), all of whom were proficient in English, were raised by and currently residing with their parents (rather than grandparents or other relatives, as specified by wording of the HPBQ), and resided in India. Of the 438 participants, 77.8% were college students, 52.5% residing in urban areas, 45.2% belonged to middle socioeconomic status and the majority (88%) were drawn from the north region.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMeasures\u003c/b\u003e \u003c/p\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003e1. Helicopter Parenting Behaviour Questionnaire (HPBQ; Schiffrin et al., 2014)\u003c/b\u003e: For comprehensive details about the HPBQ\u0026rsquo;s development and theoretical background, please refer to the \u003cem\u003eIntroduction\u003c/em\u003e section.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003e2. Depression, Anxiety, Stress Scale\u0026thinsp;\u0026minus;\u0026thinsp;21 (DASS-21; Lovibond \u0026amp; Lovibond, 1995)\u003c/b\u003e: The DASS-21 is a 21-item self-report questionnaire with three subscales: depression (7 items; \u0026ldquo;I couldn\u0026rsquo;t seem to experience any positive feeling at all\u0026rdquo;), anxiety (7 items; \u0026ldquo;I was worried about situations in which I might panic and make a fool of myself\u0026rdquo;) and stress (7 items; \u0026ldquo;I found it hard to wind down\u0026rdquo;), each with 7 items. Each item is rated on a 4-point Likert scale (0\u0026ndash;3), and subscale scores range from 0 to 21. The scale has adequate psychometric properties. For this study, only depression and anxiety subscales were included. In a previous Indian study among university students, Cronbach's alpha was.834 for depression and.895 for anxiety (Vinayak et al., 2025). In the current sample, internal consistency was good for depression (α\u0026thinsp;=\u0026thinsp;.725, ω\u0026thinsp;=\u0026thinsp;.730) and anxiety (α\u0026thinsp;=\u0026thinsp;.798, ω\u0026thinsp;=\u0026thinsp;.800).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003e3. Helicopter Parenting Inventory (HPI; Odenweller et al., 2014)\u003c/b\u003e: The HPI is a 15-item measure that assesses young adults\u0026rsquo; perceptions of their parents\u0026rsquo; current developmentally inappropriate helicopter parenting. Each item (e.g., \u0026ldquo;my parent tries to make all of my major decisions\u0026rdquo;) is rated on a 5-point Likert scale from strongly disagree (1) to strongly agree (5). Total scores range from 15 to 75, with higher scores indicating greater helicopter parenting, after reverse-scoring Items 5 and 14. The HPI has shown adequate psychometric properties, with Cronbach\u0026rsquo;s α values of .78. In this sample, internal consistency was good (α\u0026thinsp;=\u0026thinsp;.800, ω\u0026thinsp;=\u0026thinsp;.804).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis and Parameters\u003c/h3\u003e\n\u003cp\u003eData were analyzed using IBM SPSS Statistics for Windows (Version 22.0), IBM AMOS (Version 22.0), and Jamovi (Version 2.7.15). Initially, data were screened for missing data, and descriptive statistics (mean, SD, frequency) were computed wherever applicable. Following Byrne\u0026rsquo;s (2016) guidelines, normality was assessed to determine whether the data met the assumption of univariate and multivariate normality, and multivariate outliers were evaluated using Mahalanobis distance (Collier, 2020). The Harman single-factor test was conducted to assess the presence of common method bias in the study data (Collier, 2020; Podsakoff et al., 2003), and floor and ceiling effects were deemed to be present if more than 15% of participants attained the lowest score (floor effect), or the highest score (ceiling effect) (Lim et al., 2015). The following guidelines was used to assess the reliability: Cronbach's α and McDonald's ω values of .70\u0026ndash;.90 were classified as good, and \u0026gt; .90 as excellent, composite reliability \u0026gt; .70, inter-item correlations between .15\u0026ndash;.85, average inter-item correlation .20\u0026ndash;.40, and corrected item-total correlations \u0026gt; .30 (Collier, 2020; Paulsen \u0026amp; BrckaLorenz, 2017; Piedmont \u0026amp; Hyland., 1993, Silva Castillo et al., 2025). Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e correlation was performed to assess the convergent/divergent validity, with strength of these relationship interpreted per Cohen (1988): \u003cem\u003er\u003c/em\u003e = .10\u0026ndash;.30 (small), .30\u0026ndash;.50 (medium), \u0026ge; .50 (large). Divergent validity was assessed by calculating Heterotrait-Monotrait (HTMT) values between the subscales in both parental forms, and values below .85 (Henseler et al., 2015) were considered suggestive of divergent validity.\u003c/p\u003e \u003cp\u003eConfirmatory factor analysis (CFA) was performed to assess the internal structure of the HPBQ. The model fit was evaluated using established criteria (Bentler, 1990, 1992; Byrne, 2016; Collier, 2020; Hu \u0026amp; Bentler, 1999; MacCallum et al., 1996), where: CFI, TLI, and GFI values \u0026ge; .90 indicate acceptable fit and \u0026ge; .95 indicate good fit; RMSEA values \u0026lt; .05 indicate good fit, \u0026lt; .08 adequate fit, and .08\u0026ndash;.10 mediocre fit; and SRMR values \u0026lt; .05 indicate good fit and .05\u0026ndash;.09 indicate adequate fit. Given χ\u0026sup2;'s sensitivity to sample size, the relative χ\u0026sup2; test (χ\u0026sup2;/df) was used instead. Values\u0026thinsp;\u0026lt;\u0026thinsp;3 to 5 indicate acceptable fit (Collier, 2020). Factor loadings (λ) \u0026ge; .30 were considered acceptable (Boyle, 1985), and squared multiple correlations (R\u0026sup2;) were examined, with values \u0026ge; .20 indicating sufficient explained variance (Hooper et al., 2008). Subsequently, we conducted measurement invariance (MI) testing through a stepwise process. First, we compared models with progressively increasing restrictions to assess four types of MI: (a) configural invariance; (b) metric invariance; (c) scalar invariance; and (d) strict invariance. The sample included 213 males and 225 females, which is considered adequate for measurement invariance testing (Kim \u0026amp; Willson, 2014). MI was considered supported if changes in Comparative Fit Index (ΔCFI), Root Mean Square Error of Approximation (ΔRMSEA), and Standardized Root Mean Square Residual (ΔSRMR) remains within recommended thresholds: ΔCFI \u0026lt; .01, ΔRMSEA \u0026lt; .015, and SRMR of .030 (for metric) or .015 (for scalar/residual invariance) (Chen, 2007; Cheung \u0026amp; Rensvold, 2002; Putnick \u0026amp; Bornstein, 2016).\u003c/p\u003e \u003cp\u003eSubsequently, the Rasch Rating Scale Model (RSM) was conducted as the HPBQ employs a 6-point Likert scale appropriate for ordered-category response data (von Davier, 2014). The polytomous model was estimated using the eRm package (Mair et al., 2021) within snowIRT (Seol, 2026). Analyses included item difficulty estimates, person (Pearson) reliability with a cut-off of \u0026ge; .70 (Abdulmajid et al., 2024), and local independence assessed via Yen\u0026rsquo;s Q3 statistic, with values \u0026lt; .36 indicating adequate local independence (Flens et al., 2016). Item fit was evaluated using infit and outfit MnSq statistics, with acceptable values ranging from 0.60 to 1.40 (Kook \u0026amp; Varni, 2008). Additionally, differential item functioning (DIF) was examined using the difNLR package (Hladka et al., 2022) in snowIRT, employing generalized logistic regression models.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePreliminary Analysis\u003c/h2\u003e \u003cp\u003eKurtosis values ranged from \u0026minus;\u0026thinsp;1.49 to 0.02 for the mother form (MF-HPBQ), and from \u0026minus;\u0026thinsp;1.46 to 0.56 for the father form (FF-HPBQ), all within the acceptable range of \u0026plusmn;\u0026thinsp;7, indicating univariate normality (Byrne, 2016). The Z-statistics for kurtosis were 14.72 (MF-HPBQ) and 18.45 (FF-HPBQ), both exceeding the cut-off of 5.00, suggesting multivariate non-normality (Byrne, 2016). Assessment for multivariate outliers using the Mahalanobis distance found no observation with both \u003cem\u003ep\u003c/em\u003e1 \u0026lt; .001 and \u003cem\u003ep\u003c/em\u003e2 \u0026lt; .001, indicating the absence of multivariate outliers (Collier, 2020). Harman's single-factor test extracted multiple factors, with the first factor accounting for 27.13%, which is below the 40% threshold, indicating the absence of common method bias (Collier, 2020; Podsakoff et al., 2003). Finally, a few participants scored at the minimum or maximum values on the study variables (\u0026le;\u0026thinsp;3.65%), confirming the absence of floor or ceiling effects (Lim et al., 2015).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInternal Structure: Confirmatory Factor Analysis (CFA)\u003c/h3\u003e\n\u003cp\u003eTwo CFAs were performed to assess the internal structural validity of the MF-HPBQ and FF-HPBQ using maximum likelihood estimation. To address multivariate non-normality, bootstrap resampling (5,000 samples) was applied (Collier, 2020). The first CFA (see Fig.\u0026nbsp;1a) on the MF-HPBQ showed acceptable fit for all indices except TLI. Modification indices guided sequential addition of two error covariances (e7-e11, e1-e4, e2-e6, e12-e13). Each modification improved fit indices toward acceptable thresholds via iterative re-evaluation (Byrne, 2010; Collier, 2020; Hooper et al., 2008). Following this procedure, the model demonstrated improved fit across multiple indices: χ\u0026sup2; (85)\u0026thinsp;=\u0026thinsp;197.327, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; χ\u0026sup2;/\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.321; GFI = .943; CFI = .931; TLI = .915; RMSEA = .055 [.045-.065]; SRMR = .0564 (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These values collectively indicate good model fit, supporting the MF-HPBQ's construct validity. All factor loadings (λ) were above 0.30 (Boyle, 1985), ranging from 0.386 to 0.733. Additionally, except for Item 4 in the MF-HPBQ, which had a R\u0026sup2; value of 0.149, falling below the recommended minimum of 0.20 (Hooper et al., 2008) all other observed variables demonstrated satisfactory R\u0026sup2; values ranging from 0.234 to 0.538 (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe second CFA (see Fig.\u0026nbsp;1b) on the FF-HPBQ showed adequate initial fit for all indices except TLI (mirroring MF-HPBQ results). Using the same modification procedure, two error covariances were added based on modification indices (e10-e14, e7-e11), with iterative re-evaluation confirming improved fit after each addition. Following this iterative refinement, the model demonstrated improved fit: χ\u0026sup2; (85)\u0026thinsp;=\u0026thinsp;230.872, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; χ\u0026sup2;/\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.716; GFI = .935; CFI = .939; TLI = .925; RMSEA = .063 [.053-.072]; SRMR = .0598. These indices collectively indicate good model fit, supporting FF-HPBQ construct validity (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All factor loadings (λ) ranged from .502 to .761, with R\u0026sup2; values from .252 to .579, indicating strong item-factor associations and adequate explained variance for the FF-HPBQ (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In both FF-HPBQ and MF-HPBQ, all factor loadings and R\u0026sup2; estimates fell within 95% confidence intervals that did not include zero, indicating the validity of the estimates based on bootstrapping to normalize the data (Collier, 2020).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit Indices for the Mother and Father Forms of the HPBQ\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMother\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ2/df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelative χ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197.327/85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.321\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.943\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.931\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.915\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.055 [.045-.065]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.0564\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFather\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e230.872/85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e.063 [.053-.072]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.0598\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: ***Significant at .001; GFI: Goodness of Fit Index; CFI: Comparative Fit Index; TLI: Tucker Lewis Index; RMSEA: Root Mean Square Error of Approximation; SRMR: Standardized Root Mean Squared Residual\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized Factor Loadings and Squared Multiple Correlations (R\u0026sup2;) for the Mother and Father Forms of the HPBQ\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMOTHER FORM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFATHER FORM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized Regression Weights (λ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSquared Multiple Correlations (R\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized Regression Weights (λ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSquared Multiple Correlations (R\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHelicopter Parenting Behaviour (HPB)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAutonomy Supportive Behaviour (ASB)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASB_HPBQ2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASB_HPBQ5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASB_HPBQ6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASB_HPBQ8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASB_HPBQ12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASB_HPBQ15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: HPBQ\u0026thinsp;=\u0026thinsp;Helicopter Parenting Behaviour Questionnaire, HPB\u0026thinsp;=\u0026thinsp;Helicopter Parenting Behaviour, ASB\u0026thinsp;=\u0026thinsp;Autonomy Supportive Behaviour\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMeasurement Invariance\u003c/h3\u003e\n\u003cp\u003eMeasurement invariance was assessed to evaluate the psychometric equivalence of MF-HPBQ and FF-HPBQ across genders. Invariance criteria, including configural, metric, scalar, and strict invariance were met (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Changes in the ΔCFI, ΔRMSEA, and ΔSRMR remained within recommended cut-offs: ΔCFI \u0026lt; .01, ΔRMSEA \u0026lt; .015, and SRMR of .030 (for metric) or .015 (for scalar/residual invariance) (Chen, 2007; Cheung \u0026amp; Rensvold, 2002; Putnick \u0026amp; Bornstein, 2016).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement Invariance of HRS-SR Across Gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eΔSRMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eMother Form\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eConfigural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.041 (.033-.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMetric\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.041 (.034-.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.0089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eScalar\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.044 (.036-.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.0190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eStrict\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.041 (.034-.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.0061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eFather Form\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eConfigural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.053 (.046-.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMetric\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.052 (.045-.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.0034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eScalar\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.053 (.046-.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.0094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eStrict\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.053 (.046-.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.0051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: CFI: Comparative Fit Index; RMSEA: Root Mean Square Error of Approximation; SRMR: Standardized Root Mean Squared Residual\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eReliability Analysis\u003c/h2\u003e \u003cp\u003eThe internal consistency of the HPBQ was assessed using Cronbach\u0026rsquo;s α, McDonald\u0026rsquo;s ω, and composite reliability. Cronbach's α indicated good internal consistency for both forms: MF-HPBQ form = .82 [HPB], .75 [ASB]); FF-HPBQ (α\u0026thinsp;=\u0026thinsp;.87 [HPB], .81 [ASB]). McDonald ω values were comparable: MF-HPBQ (.82 [HPB], .75 [ASB]); FF-HPBQ (.87 [HPB], .81 [ASB]) (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Composite reliability was adequate for both parental forms: maternal ASB\u0026thinsp;=\u0026thinsp;0.74 and HPB\u0026thinsp;=\u0026thinsp;0.82; paternal ASB\u0026thinsp;=\u0026thinsp;0.82 and HPB\u0026thinsp;=\u0026thinsp;0.87. Inter-item correlations for the FF-HPBQ ranged from .321\u0026ndash;.566 (ASB) and .249\u0026ndash;.643 (HPB), and for the MF-HPBQ .186\u0026ndash;.529 (HPB) and .232\u0026ndash;.479 (ASB), all within the acceptable range of .15\u0026ndash;.85 (Paulsen \u0026amp; BrckaLorenz, 2017) (see Supplementary Table S1 and S2). Additionally, Average Inter-Item Correlation values across both forms ranged from .328\u0026ndash;.421. Furthermore, the corrected item-total correlations for the FF-HPBQ ranged from .440 to .691 for the HPB domain and .484 to .665 for the ASB domain. For the MF-HPBQ, the values ranged from .371 to .646 for HPB and .425 to .576 for ASB domains of the HPBQ. All values exceeded .30 (Silva Castillo et al., 2025), indicating that all items demonstrated good discrimination abilities. Moreover, deleting any item did not increase Cronbach's α, supporting the reliability of the MF-HPBQ and FF-HPBQ.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConvergent, Divergent and Discriminant Validity\u003c/h2\u003e \u003cp\u003eConvergent validity was evaluated through correlations between the HPBQ and the established measure of helicopter parenting (HPI). Divergent validity was assessed via correlations with theoretically unrelated constructs of depression and anxiety (DASS-21). We expected that the correlations between both forms of the HPBQ and the HPI would be stronger, reflecting convergent validity, whereas the correlations between the HPBQ and depression and anxiety would be weaker or negligible, thereby supporting divergent validity. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, HPB domains for both MF-HPBQ (\u003cem\u003er\u003c/em\u003e = .558, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and FF-HPBQ (\u003cem\u003er\u003c/em\u003e = .555, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) demonstrated strong positive correlations with the HPI (\u003cem\u003er\u003c/em\u003e \u0026gt; .50), supporting excellent convergent validity of the HPBQ across parental forms. Similarly, the ASB domain of the MF-HPBQ (\u003cem\u003er\u003c/em\u003e = .111, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05) and FF-HPBQ (\u003cem\u003er\u003c/em\u003e = .213, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) also showed statistically significant positive correlations with the HPI (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The HPB domains for both MF-HPBQ and FF-HPBQ showed nonsignificant relationships with anxiety (maternal: \u003cem\u003er\u003c/em\u003e = .021, \u003cem\u003ep\u003c/em\u003e \u0026gt; .05; paternal: \u003cem\u003er\u003c/em\u003e = .029, \u003cem\u003ep\u003c/em\u003e \u0026gt; .05) and depression (maternal: \u003cem\u003er\u003c/em\u003e = .036, \u003cem\u003ep\u003c/em\u003e \u0026gt; .05; paternal: \u003cem\u003er\u003c/em\u003e = .080, \u003cem\u003ep\u003c/em\u003e \u0026gt; .05). In contrast, the ASB domain showed small but significant negative associations with depression for MF-HPBQ (\u003cem\u003er\u003c/em\u003e = \u0026ndash;.206, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and FF-HPBQ (\u003cem\u003er\u003c/em\u003e = \u0026ndash;.181, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and similarly small negative associations with anxiety for both MF-HPBQ (\u003cem\u003er\u003c/em\u003e = \u0026ndash;.115, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05) and FF-HPBQ (\u003cem\u003er\u003c/em\u003e = \u0026ndash;.146, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01). These nonsignificant (HPB) and weak (ASB) associations confirm the HPBQ's divergent validity from anxiety/depression constructs for both parental forms (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Discriminant validity was established using HTMT correlation ratios, yielding values of .58 (MF-HPBQ: HPB vs. ASB) and .59 (FF-HPBQ: HPB vs. ASB). Both fell substantially below the .85 threshold (Henseler et al., 2015), confirming the theoretical distinctiveness of ASB from HPB across parental forms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFindings of the Pearson Correlation Coefficients among study variables (n\u0026thinsp;=\u0026thinsp;438)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMF_HBP\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMF_HBP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMF_ASB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFF_HBP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFF_ASB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHPI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDASS-D\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDASS-A\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMF_ASB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.441\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFF_HBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.666\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.277\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFF_ASB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.371\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.480\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.542\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.558\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.111\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.555\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.213\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDASS-D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.206\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.181\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.196\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDASS-A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.115\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.146\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.109\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.601\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eα\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eω\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: MF\u0026thinsp;=\u0026thinsp;Mother Form, FF\u0026thinsp;=\u0026thinsp;Father Form, HPB\u0026thinsp;=\u0026thinsp;Helicopter Parenting Behaviour, ASB\u0026thinsp;=\u0026thinsp;Autonomy Supportive Behaviour, HPI\u0026thinsp;=\u0026thinsp;Helicopter Parenting Inventory, DASS \u0026ndash; D\u0026thinsp;=\u0026thinsp;Depression, Anxiety and Stress Scale \u0026ndash; Depression, DASS \u0026ndash; A\u0026thinsp;=\u0026thinsp;Depression, Anxiety and Stress Scale \u0026ndash; Anxiety, AIC\u0026thinsp;=\u0026thinsp;Average Inter-item Correlation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eItem Response Theory (IRT) Assumptions\u003c/h2\u003e \u003cp\u003eIRT requires unidimensionality and local independence (Magno, 2009). CFA results confirmed unidimensionality for both the MF-HPBQ and FF-HPBQ. The Yen\u0026rsquo;s Q3 residual correlations provided evidence of local independence, with all values \u0026lt; .36 (Flens et al., 2016): MF-HPBQ (HPB: \u0026minus;.239 to .131; ASB: \u0026minus;.261 to .039) and FF-HPBQ (HPB: \u0026minus;.280 to .263; ASB: \u0026minus;.303 to .001). Person reliability values \u0026gt; .70 (Abdulmajid et al., 2024) indicated adequate reliability for both MF-HPBQ (HPB = .840; ASB = .751) and FF-HPBQ (HPB = .863; ASB = .797).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eItem Fit Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that item fit statistics for both forms of HPBQ. The HPB Infit ranged from 0.832 to 1.324 and Outfit from 0.809 to 1.361, while ASB showed Infit from 0.940 to 1.116 and Outfit from 0.938 to 1.114 for the MF-HPBQ. For the FF-HPBQ, MNSQ Infit values of 0.849 to 1.416 with Outfit values of 0.793 to 1.517 (HPB subscale) and Infit values of 0.934 to 1.158 and Outfit values of 0.905 to 1.195 (ASB subscale). All values fell within the .6 to 1.4 range (Kook \u0026amp; Varni, 2008), indicating acceptable model fit. No substantial underfit (\u0026gt;\u0026thinsp;1.4) or severe overfit (\u0026lt;\u0026thinsp;0.6) was observed, confirming that each item contributed adequately to its domain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eItem Difficulty\u003c/h2\u003e \u003cp\u003eItem difficulty estimates in the father form ranged from \u0026minus;\u0026thinsp;1.37 to -1.87 logits (SE\u0026thinsp;=\u0026thinsp;0.0381\u0026ndash;0.0436) for the ASB subscale and \u0026minus;\u0026thinsp;1.67 to -1.01 logits (SE\u0026thinsp;=\u0026thinsp;0.0375\u0026ndash;0.0390) for the HPB subscale. Mother form items difficulties spanned\u0026thinsp;\u0026minus;\u0026thinsp;1.743 to -0.918 logits (SE\u0026thinsp;=\u0026thinsp;0.0355\u0026ndash;0.0393) for HPB and \u0026minus;\u0026thinsp;1.73 to -1.44 logits (SE\u0026thinsp;=\u0026thinsp;0.0397\u0026ndash;0.0444) for ASB (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eItem fit statistics (MNSQ infit and outfit) and item difficulty for the MF-HPBQ and FF-HPBQ.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eMF-HPBQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eFF-HPBQ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.Measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInfit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOutfit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS.E.Measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInfit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOutfit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPB_HPBQ14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASB_HPBQ2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASB_HPBQ5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASB_HPBQ6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASB_HPBQ8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASB_HPBQ12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASB_HPBQ15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: MF\u0026thinsp;=\u0026thinsp;Mother Form, FF\u0026thinsp;=\u0026thinsp;Father Form, HPBQ\u0026thinsp;=\u0026thinsp;Helicopter Parenting Behaviour Questionnaire, HPB\u0026thinsp;=\u0026thinsp;Helicopter Parenting Behaviour, ASB\u0026thinsp;=\u0026thinsp;Autonomy Supportive Behaviour\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Item Functioning (DIF)\u003c/h2\u003e \u003cp\u003eDIF analysis across gender confirmed measurement equivalence for nearly all items on both MF-HPBQ and FF-HPBQ. Except for a few items, likelihood ratio tests (adjusted \u003cem\u003ep\u003c/em\u003e \u0026gt; .05 via multiple comparisons) showed no evidence of uniform or non-uniform DIF, indicating unbiased item responses. Notably, HPB subscale Item 4 (\"When I am home with my mother/father, I have a curfew\u0026rdquo; [a certain time that I must be home by every night]) exhibited uniform DIF across both forms, while non-uniform DIF was absent. Additionally, father form HPB Item 10 (\"My father monitors my diet\") showed uniform DIF while absence of non-uniform DIF (see Table\u0026nbsp;6).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eMF-HPBQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eFF-HPBQ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUniform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eNon-Uniform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eUniform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eNon-Uniform\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdj.\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdj.\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAdj.\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAdj.\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c13\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHelicopter Parenting Behaviour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPBQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPBQ3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPBQ4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e 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colname=\"c11\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPBQ13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPBQ14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c13\" namest=\"c2\"\u003e 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align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPBQ5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPBQ6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPBQ8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPBQ12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHPBQ15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote: MF\u0026thinsp;=\u0026thinsp;Mother Form, FF\u0026thinsp;=\u0026thinsp;Father Form, HPBQ\u0026thinsp;=\u0026thinsp;Helicopter Parenting Behaviour Questionnaire\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study assessed psychometric properties of both the maternal and paternal forms of the HPBQ among emerging adults in India using complementary CTT and IRT approaches.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eClassical Test Theory Based Parameters\u003c/h2\u003e \u003cp\u003eThe CFA confirmed the two-factor models (HPB and ASB) for the MF-HPBQ and FF-HPBQ in our Indian sample. This structure was initially proposed by Schiffrin et al. (2014) for the maternal form and was later extended to the paternal form (Schiffrin et al., 2019), before being validated in Turkish (K\u0026ouml;m\u0026uuml;rc\u0026uuml;-Akik \u0026amp; Alsancak-Akbulut, 2023), meaning our data are consistent across multiple countries. In both parental forms, the standardized factor loadings indicated that each unobservable variable had an adequate direct effect on its respective factor. These factor loadings, which measure the correlation between each variable and factor, indicate the degree of correspondence wherein higher loadings indicate that the variable is more representative of the factor (Hair et al., 2019). Additionally, except for one item in the mother form (HPB Item 4), all other squared multiple correlations suggested that each indicator explained an adequate proportion of the variance. Nevertheless, this item was retained in the final model because the other fit indices remained adequate.\u003c/p\u003e \u003cp\u003eThe findings supported measurement invariance at the configural, metric, scalar, and strict levels (Putnick \u0026amp; Bornstein, 2016) for both MF-HPBQ and FF-HPBQ. This demonstrates the psychometric equivalence of helicopter parenting across groups, such as gender, in this study, indicating that the helicopter parenting construct does not differ in structure or meaning between genders. This suggests that both genders responded to the MF-HPBQ and FF-HPBQ items similarly. Thus, any gender differences observed in the construct are unlikely to arise from measurement bias and are more likely to reflect actual gender-based differences. The present study is the first to report measurement invariance for both parental forms of HPBQ.\u003c/p\u003e \u003cp\u003eInternal consistency was evaluated using multiple reliability indices owing to known limitations of Cronbach\u0026rsquo;s α, wherein artificial inflation can be introduced when a construct includes many indicators and assumes tau-equivalence (i.e., equal factor loadings across items; Collier, 2020; Park et al., 2022). Therefore, McDonald\u0026rsquo;s ω was also adopted to ensure accurate reliability estimates even when items vary in how strongly they relate to the underlying construct (Park et al., 2022). Across all forms, and subscales therein, good internal consistency was evident; mirroring reliability estimates reported elsewhere (K\u0026ouml;m\u0026uuml;rc\u0026uuml;-Akik \u0026amp; Alsancak-Akbulut, 2023; Schiffrin et al., 2014; 2019). Such consistency highlights the HPBQ\u0026rsquo;s stability and reliability across contexts, and adds evidence for the measure\u0026rsquo;s cross-cultural applicability and psychometric strength.\u003c/p\u003e \u003cp\u003eInter-item correlations across both parental forms supported the notion that the items adequately captured the constructs underpinning helicopter parenting behaviour. These results show a shared construct without redundancy, affirming each item's unique but related role. A homogeneous test measures a single construct. Greater homogeneity leads to higher inter-item consistency, especially in narrowly defined behavioral domains (Cohen \u0026amp; Swerdlik, 2005). In the helicopter parenting measures, the mean inter-item correlations for both forms were adequate. This shows optimal homogeneity. It suggests the total score reflects the complexity of helicopter parenting behaviors, without items being excessively redundant. Thus, the measured construct captures the intended breadth of behaviors without being too narrow or repetitive. All corrected item-total correlation values were adequate, indicating each item correlated well with its respective factor total score (Silva Castillo et al., 2025). This adequate item-total correlation indicates that each item effectively distinguishes between respondents with different levels of helicopter parenting traits, thereby ensuring good discrimination.\u003c/p\u003e \u003cp\u003eIn terms of validity, both the MF-HPBQ and FF-HPBQ demonstrated strong positive correlations with the HPI (Odenweller et al., 2014); indicating convergent validity. In contrast, both forms negatively correlated with anxiety, and shared non-significant correlations with depression, suggesting - as expected - that the HPBQ does not measure anxiety or depression, thus, providing evidence for its divergent validity. This somewhat mirrors Schiffrin et al. (2014), wherein the HPB facet had a small correlation with depression and a non-significant relationship with anxiety, and in K\u0026ouml;m\u0026uuml;rc\u0026uuml;-Akik and Alsancak-Akbulut (2023), wherein both parental forms of the Turkish version showed weak or non-significant correlations with depression, anxiety, and stress, but a significant, moderate-to-large positive correlation with HPI. Together, this supports both convergent and divergent validity of the scale across cultural contexts. Recently, discriminant validity assessment has become a generally accepted prerequisite for analyzing true relationships between latent variables (Henseler et al., 2015). In the present study, the HPB and ASB domains were considered distinct from one another demonstrating that these two constructs can be empirically differentiated within the HPBQ. This is true because helicopter parenting can cause negative mental health outcomes, whereas autonomy supportive parenting can lead to greater well-being and life satisfaction by fostering a sense of autonomy, relatedness, and competence (Schiffrin et al., 2014).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eItem Response Theory Based Parameters\u003c/h2\u003e \u003cp\u003eThe Rasch Rating Model findings provided evidence of unidimensionality indicating that all items are functionally dependent upon only one underlying continuum and local independence indicating that each respondent has a certain probability of giving a predefined response to each item, and that this probability is independent of the answers given to the preceding items (Magno, 2009). These locally independent findings develop our CTT results, where all items demonstrated adequate inter-item correlations within each factor.\u003c/p\u003e \u003cp\u003eItem fit statistics indicated that all items fell within our pre-defined recommended range (Kook \u0026amp; Varni, 2008) for both parental forms, further supporting the validity of the HPBQ in that each item seemingly contributes appropriately to measuring the construct. Item difficulty estimates indicated that all the items functioned with relative ease; an indicator of where each item lies on the \u003cem\u003eability\u003c/em\u003e scale. For example, an easy item functions among low-ability participants, in this case, participants who report experiencing fewer HPB and ASB from their parents and a hard item function among high-ability individuals, for example, participant who reports experiencing a higher degree of helicopter parenting (Ashraf \u0026amp; Jaseem, 2020). This finding indicates that in future iterations of the HPBQ, there is scope to further strengthen the scale\u0026rsquo;s psychometric rigor and ensure comprehensive assessment wherein future revisions incorporate more difficult items that target higher levels of helicopter parenting and autonomy-supportive behavior.\u003c/p\u003e \u003cp\u003eFinally, the study found that with few exceptions, most items showed no evidence of uniform or nonuniform differential item functioning (DIF), which refers to cases in which participants from different groups (e.g., gender) with the same level of the latent trait (i.e., helicopter parenting) have different probabilities of responding to a particular item (Chen \u0026amp; Revicki, 2023). Specifically, uniform DIF appears when ability level and group membership do not interact, and nonuniform DIF appears when they do interact (Narayanon \u0026amp; Swaminathan, 1996). This finding suggests items function equivalently regardless of gender. However, HPB subscale item 4 (\"When I am home with my mother/father, I have a curfew\") exhibited uniform DIF across both MF-HPBQ and FF-HPBQ. Nonuniform DIF was absent. Additionally, FF-HPBQ subscale HPB item 10 (\"My father monitors my diet\") showed uniform DIF, while non-uniform DIF was again absent. As this is the first study to assess DIF across gender for both parental forms, additional research is necessary to confirm these findings. Should subsequent studies also indicate DIF for the identified items, future revisions will be required to adapt them to ensure equivalent functioning across genders.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eThe present study is among the first South Asian studies to provide evidence on the psychometric properties of the HPBQ using both CTT and IRT approaches. Additionally, including an attention item to check whether participants read survey questions carefully helped reduce careless or insufficient-effort responses, which are common in online self-report data collection (Curran, 2015).\u003c/p\u003e \u003cp\u003eHowever, our findings should be interpreted in the context of several study-specific limitations. First, the presence of additional psychiatric conditions among participants was not considered, which could have affected the findings. Not only are 7.5% of young adults in India aged 18 to 29 years (the same age group as the current study) diagnosed with one or more mental disorders (Murthy, 2017), but children and adolescents raised by overprotective parents more frequently report having anxiety-related disorders stemming from their parents reducing their autonomy and displaying harsh or inconsistent control (Yaffe, 2021). Second, while internal consistency was assessed, the study did not evaluate test-retest reliability and so we cannot be confident that results remain consistent over time (Raykov \u0026amp; Marcoulides, 2011). Given that previous studies using the HPBQ have also not reported test-retest reliability (K\u0026ouml;m\u0026uuml;rc\u0026uuml;-Akik \u0026amp; Alsancak-Akbulut, 2023; Schiffrin et al., 2014; Schiffrin et al., 2019), this indicates a more general need to investigate the HPBQ\u0026rsquo;s stability. Third, this validation in an Indian context is restricted to English-speaking populations, and thus, future studies should consider translating the HPBQ into the various vernacular languages used in India. This is especially relevant since the Census of India lists 22 official languages in the Eighth Schedule of the Constitution (Census of India, 2011). Fourth, the study used the HPI (Odenweller et al., 2014) to assess convergent validity, however, the validity and reliability of the HPI in the Indian context were not explored. Though we indicated good internal consistency, the comprehensive psychometric properties of the HPI require further evaluation. Finally, consistent with previous research, this study utilizes student self-reports, which may provide a limited perspective. Incorporating both student and parent responses could yield a broader, more comparable view of the parent-child relationship (K\u0026ouml;m\u0026uuml;rc\u0026uuml;-Akik \u0026amp; Alsancak-Akbulut, 2023; Schiffrin et al., 2014), as it would allow a direct examination of how each group's perceptions align or differ.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications\u003c/h2\u003e \u003cp\u003eConsidering the detrimental impacts of helicopter parenting on individuals' well-being, which primarily stem from the perceived infringement on individuals fundamental psychological needs for autonomy and competence (Schiffrin et al., 2014), the HPBQ can be effectively used to assesses parental behaviors, such as excessive control over college students\u0026rsquo; activities and parental intervention on their behalf. This is particularly important among emerging adults in India, who are highly vulnerable to the negative effects of overparenting (Padilla-Walker \u0026amp; Nelson, 2012). Using the HPBQ may help provide targeted interventions to reduce the negative impact of helicopter parenting on emerging adults' mental health. A meta-analysis (Ntoumanis et al., 2021) found that self-determination theory-informed interventions, such as increasing need support and fostering autonomous motivation, promote positive changes in health behaviors and improve overall health and psychological well-being, highlighting their mental health benefits. In practice, the HPBQ can be applied in parenting programs to systematically identify overparenting behaviors, inform parents about their potential effects, and tailor interventions to support positive physical, cognitive, social, and emotional development (Lansford, 2022). Through, educational programs parents can be encouraged to engage in authoritative parenting, as it is associated with positive child outcome while, authoritarian parenting is associated with negative outcome (Pinquart \u0026amp; Kauser, 2018). Theoretically, this validation can enable exploration of helicopter parenting within Indian contexts, across age groups, and in the context of comorbidities that might influence data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides the comprehensive psychometric evaluation of both maternal and paternal forms of the HPBQ among emerging adults in India through employing both CTT and IRT approaches. The study findings provided evidence that both parental forms of HPBQ are reliable and valid tools for assessing helicopter parenting among emerging adults in India. The findings also showed that the HPBQ items function equivalently across gender.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical Statement\u003c/h2\u003e \u003cp\u003eThis study received ethical approval from the Office of the Institute Ethics Committee (No./MGMC\u0026amp;H/IEC/JPR/2025/4961, 03/10/2025). Digital Informed consent was obtained from all the participants prior to participation.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eContributions\u003c/strong\u003e \u003cp\u003eConceptualization\u0026thinsp;=\u0026thinsp;MM, SN, DF; Methodology\u0026thinsp;=\u0026thinsp;MM, SN, SM, SK, KY, DF; Data Collection\u0026thinsp;=\u0026thinsp;SK, KY, SM; Data Analysis\u0026thinsp;=\u0026thinsp;MM; Writing-Original Draft Preparation\u0026thinsp;=\u0026thinsp;MM, SM; Writing-Review \u0026amp; Editing\u0026thinsp;=\u0026thinsp;DF, SN, SK, KY. All authors have read and approved the manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNo external agencies provided funding for this study.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eData are available from the corresponding author upon reasonable request, provided the institute's ethical approval has been obtained.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdulmajid, A. A., \u0026amp; Khalid, K. A. (2024). Using Rasch Analysis for Validation of Knowledge and Perception of Orthopedic Workplace-Based Assessment among Postgraduate Orthopedic Trainees’ Questionnaire. \u003cem\u003eJournal of Pharmacy and Bioallied Sciences\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4), 126–129. https://doi.org/10.4103/jpbs.jpbs_629_24\u003c/li\u003e\n\u003cli\u003eAshraf, Z. A., \u0026amp; Jaseem, K. (2020). Classical and modern methods in item analysis of test tools. \u003cem\u003eInternational Journal of Research and Review\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(5), 397–403.\u003c/li\u003e\n\u003cli\u003eBentler P. M. (1990). Comparative fit indexes in structural models. \u003cem\u003ePsychological bulletin\u003c/em\u003e, \u003cem\u003e107\u003c/em\u003e(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238\u003c/li\u003e\n\u003cli\u003eBentler, P. M. (1992). On the fit of models to covariances and methodology to the \u003cem\u003eBulletin.\u003c/em\u003e. \u003cem\u003ePsychological Bulletin, 112\u003c/em\u003e(3), 400–404. https://doi.org/10.1037/0033-2909.112.3.400\u003c/li\u003e\n\u003cli\u003eBoyle, G. J. (1985). A reanalysis of the higher-order factor structure of the Motivation Analysis Test and the Eight State Questionnaire. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3), 367–374. https://doi.org/10.1016/0191-8869(85)90061-3\u003c/li\u003e\n\u003cli\u003eBozicevic, L., Hill, J., Chandra, P. S., Omirou, A., Holla, C., Wright, N., \u0026amp; Sharp, H. (2023). Cross-cultural differences in early caregiving: levels of mind-mindedness and instruction in UK and India. \u003cem\u003eFrontiers in Child and Adolescent Psychiatry\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, 1124883. https://doi.org/10.3389/frcha.2023.1124883\u003c/li\u003e\n\u003cli\u003eBradley-Geist, J. C., \u0026amp; Olson-Buchanan, J. B. (2014). Helicopter parents. An examination of the correlates of over-parenting of college students, Education+ Training, 56(4), 314–328. https://doi.org/10.1108/ET-10-2012-0096.\u003c/li\u003e\n\u003cli\u003eByrne, B. M. (2016). Structural equation modeling with AMOS. Basic concepts, applications, and programming (Third ed.). Routledge\u003c/li\u003e\n\u003cli\u003eCensus of India https://censusindia.gov.in\u003c/li\u003e\n\u003cli\u003eChen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. \u003cem\u003eStructural equation modeling: a multidisciplinary journal\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3), 464–504.\u003c/li\u003e\n\u003cli\u003eChen, WH., Revicki, D. (2023). Differential Item Functioning (DIF). In: Maggino, F. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Cham. https://doi.org/10.1007/978-3-031-17299-1_728\u003c/li\u003e\n\u003cli\u003eCheung, G. W., \u0026amp; Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. \u003cem\u003eStructural equation modeling\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 233–255.\u003c/li\u003e\n\u003cli\u003eCline, F., \u0026amp; Fay, J. (2020). Parenting with love and logic: Teaching children responsibility (Updated and expanded ed.). NavPress.\u003c/li\u003e\n\u003cli\u003eCohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587\u003c/li\u003e\n\u003cli\u003eCohen, R. J., \u0026amp; Swerdlik, M. E. (2005). \u003cem\u003ePsychological testing and assessment: An introduction to tests and measurement\u003c/em\u003e (6th ed.). New York: McGraw-Hill.\u003c/li\u003e\n\u003cli\u003eCollier, J.E. (2020). Applied Structural Equation Modeling using AMOS: Basic to Advanced Techniques (1st ed.). Routledge. https://doi.org/10.4324/9781003018414\u003c/li\u003e\n\u003cli\u003eCurran, P. G. (2016). Methods for the detection of carelessly invalid responses in survey data. \u003cem\u003eJournal of Experimental Social Psychology\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e, 4–19. http://dx.doi.org/10.1016/j.jesp.2015.07.006\u003c/li\u003e\n\u003cli\u003eFlens, G., Smits, N., Carlier, I., van Hemert, A. M., \u0026amp; de Beurs, E. (2016). Simulating computer adaptive testing with the Mood and Anxiety Symptom Questionnaire. \u003cem\u003ePsychological assessment\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(8), 953–962. https://doi.org/10.1037/pas0000240\u003c/li\u003e\n\u003cli\u003eHadiwijaya, H., Klimstra, T. A., Darling, N., Vermunt, J. K., Branje, S., \u0026amp; Meeus, W. H. J. (2020). The family context as a foundation for romantic relationships: A person-centered multi-informant longitudinal study. \u003cem\u003eJournal of family psychology: JFP: journal of the Division of Family Psychology of the American Psychological Association (Division 43)\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(1), 46–56. https://doi.org/10.1037/fam0000601\u003c/li\u003e\n\u003cli\u003eHair, J. F., Babin, B. J., Anderson, R. E., \u0026amp; Black, W. C. (2019). \u003cem\u003eMultivariate Data Analysis\u003c/em\u003e (8th ed.). Cengage Learning.\u003c/li\u003e\n\u003cli\u003eHenseler, J., Ringle, C. M., \u0026amp; Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. \u003cem\u003eJournal of the academy of marketing science\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8\u003c/li\u003e\n\u003cli\u003eHirsch, D., \u0026amp; Goldberger, E. (2010). Hovering practices in and outside the classroom. \u003cem\u003eAbout Campus\u003c/em\u003e, 14(6), 30–32. https://doi.org/10.1002/abc.20007\u003c/li\u003e\n\u003cli\u003eHladka, A., Martinkova, P., \u0026amp; Zvara, K. (2022). \u003cem\u003edifNLR: DIF and DDF Detection by Non-Linear Regression Models\u003c/em\u003e. (Version 1.4.1) [R package]. URL https://CRAN.R-project.org/package=difNLR.\u003c/li\u003e\n\u003cli\u003eHooper D, Coughlan J, Mullen MR. Structural equation modelling: guidelines for determining model fit. Electron J Bus Res Methods. 2008;6(1):53–60. Available from: https://academic-publishing.org/index.php/ejbrm/article/view/1224/1187. Accessed 2nd February 2026\u003c/li\u003e\n\u003cli\u003eHu, L. T., \u0026amp; Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.\u003c/li\u003e\n\u003cli\u003eJabrayilov, R., Emons, W. H. M., \u0026amp; Sijtsma, K. (2016). Comparison of Classical Test Theory and Item Response Theory in Individual Change Assessment. \u003cem\u003eApplied psychological measurement\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(8), 559–572. https://doi.org/10.1177/0146621616664046\u003c/li\u003e\n\u003cli\u003eKim, E. S., \u0026amp; Willson, V. L. (2014). Measurement invariance across groups in latent growth modeling. \u003cem\u003eStructural Equation Modeling: A Multidisciplinary Journal\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(3), 408–424. http://dx.doi.org/10.1080/10705511.2014.915374\u003c/li\u003e\n\u003cli\u003eKömürcü-Akik, B., \u0026amp; Alsancak-Akbulut, C. (2023). Assessing the psychometric properties of mother and father forms of the helicopter parenting behaviors questionnaire in a Turkish sample. \u003cem\u003eCurrent Psychology\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(4), 2980–2992.\u003c/li\u003e\n\u003cli\u003eKook, S. H., \u0026amp; Varni, J. W. (2008). Validation of the Korean version of the pediatric quality of life inventory™ 4.0 (PedsQL™) generic core scales in school children and adolescents using the Rasch model. \u003cem\u003eHealth and quality of life outcomes\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 41. https://doi.org/10.1186/1477-7525-6-41\u003c/li\u003e\n\u003cli\u003eLansford J. E. (2022). Annual Research Review: Cross-cultural similarities and differences in parenting. \u003cem\u003eJournal of child psychology and psychiatry, and allied disciplines\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e(4), 466–479. https://doi.org/10.1111/jcpp.13539\u003c/li\u003e\n\u003cli\u003eLansford J. E. (2022). Annual Research Review: Cross-cultural similarities and differences in parenting. \u003cem\u003eJournal of child psychology and psychiatry, and allied disciplines\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e(4), 466–479. https://doi.org/10.1111/jcpp.13539\u003c/li\u003e\n\u003cli\u003eLeMoyne, T., \u0026amp; Buchanan, T. (2011). Does “hovering” matter? Helicopter parenting and its effect on well-being. Sociological Spectrum, 31(4), 399–418. https://doi.org/10.1080/02732173. 2011.574038\u003c/li\u003e\n\u003cli\u003eLim, C. R., Harris, K., Dawson, J., Beard, D. J., Fitzpatrick, R., \u0026amp; Price, A. J. (2015). Floor and ceiling effects in the OHS: an analysis of the NHS PROMs data set. \u003cem\u003eBMJ open\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(7), e007765. https://doi.org/10.1136/bmjopen-2015-007765.\u003c/li\u003e\n\u003cli\u003eLovibond, P. F., \u0026amp; Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the depression anxiety stress scales (DASS) with the Beck depression and anxiety inventories. Behaviour Research and Therapy, 33(3), 335–343. https://doi.org/10.1016/0005-7967(94)00075-U\u003c/li\u003e\n\u003cli\u003eMacCallum, R. C., Browne, M. W., \u0026amp; Sugawara, H. M. (1996). Power Analysis and Determination of Sample Size for Covariance Structure Modeling. Psychological Methods, 1, 130–149. https://doi.org/10.1037/1082-989X.1.2.130\u003c/li\u003e\n\u003cli\u003eMagno, C. (2009). Demonstrating the difference between classical test theory and item response theory using derived test data. \u003cem\u003eThe International Journal of Educational and Psychological Assessment, 1\u003c/em\u003e(1), 1–11.\u003c/li\u003e\n\u003cli\u003eMair, P., Hatzinger, R., Maier, M., Rusch, T., \u0026amp; Debelak R. (2021). \u003cem\u003eeRm: Extended Rasch Modeling\u003c/em\u003e. (Version 1.0.2) [R package]. URL https://CRAN.R-project.org/package=eRm.\u003c/li\u003e\n\u003cli\u003eMurthy R. S. (2017). National Mental Health Survey of India 2015–2016. \u003cem\u003eIndian journal of psychiatry\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(1), 21–26. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_102_17\u003c/li\u003e\n\u003cli\u003eNarayanon, P., \u0026amp; Swaminathan, H. (1996). Identification of items that show nonuniform DIF. \u003cem\u003eApplied Psychological Measurement\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(3), 257–274. https://doi.org/10.1177/014662169602000306\u003c/li\u003e\n\u003cli\u003eNtoumanis, N., Ng, J. Y. Y., Prestwich, A., Quested, E., Hancox, J. E., Thøgersen-Ntoumani, C., … Williams, G. C. (2021). A meta-analysis of self-determination theory-informed intervention studies in the health domain: effects on motivation, health behavior, physical, and psychological health. \u003cem\u003eHealth Psychology Review\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 214–244. https://doi.org/10.1080/17437199.2020.1718529\u003c/li\u003e\n\u003cli\u003eNunnally, J. C., \u0026amp; Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.\u003c/li\u003e\n\u003cli\u003eOdenweller, K. G., Booth-Butterfield, M., \u0026amp; Weber, K. (2014). Investigating helicopter parenting, family environments, and relational outcomes for Millennials. Communication Studies, 65(4), 407–425. https://doi.org/10.1080/10510974.2013.811434.\u003c/li\u003e\n\u003cli\u003ePadilla-Walker, L. M., \u0026amp; Nelson, L. J. (2012). Black hawk down?: Establishing helicopter parenting as a distinct construct from other forms of parental control during emerging adulthood. Journal of Adolescence, 35(5), 1177–1190. https://doi.org/10.1016/j. adolescence.2012.03.007\u003c/li\u003e\n\u003cli\u003ePark, J., van den Broek, K. L., Bhullar, N., Ogunbode, C. A., Schermer, J. A., Doran, R., ... \u0026amp; Yadav, R. (2022). Comparison of the inter-item correlations of the Big Five Inventory-10 (BFI-10) between Western and non-Western contexts. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e196\u003c/em\u003e, 111751. https://doi.org/10.1016/j.paid.2022.111751\u003c/li\u003e\n\u003cli\u003eParker G. (1989). The Parental Bonding Instrument: psychometric properties reviewed. \u003cem\u003ePsychiatric developments\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(4), 317–335.\u003c/li\u003e\n\u003cli\u003eParker, G., Tupling, H., \u0026amp; Brown, L. B. (1979). A Parental Bonding Instrument. British Journal of Medical Psychology, 52, 1–10. http://dx.doi.org/10.1111/j.2044-8341.1979.tb02487.x\u003c/li\u003e\n\u003cli\u003ePaulsen, J. \u0026amp; BrckaLorenz, A. (2017). Internal consistency. FSSE Psychometric Portfolio. Retrieved from fsse.indiana.edu.\u003c/li\u003e\n\u003cli\u003ePiedmont, R. L., \u0026amp; Hyland, M. E. (1993). Inter-item correlation frequency distribution analysis: A method for evaluating scale dimensionality. \u003cem\u003eEducational and psychological measurement\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(2), 369–378. https://doi.org/10.1177/0013164493053002006\u003c/li\u003e\n\u003cli\u003ePinquart, M., \u0026amp; Kauser, R. (2018). Do the associations of parenting styles with behavior problems and academic achievement vary by culture? Results from a meta-analysis. Cultural Diversity \u0026amp; Ethnic Minority Psychology, 24(1), 75–100. https://doi.org/10.1037/cdp0000149\u003c/li\u003e\n\u003cli\u003ePodsakoff, P. M., MacKenzie, S. B., Lee, J. Y., \u0026amp; Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. \u003cem\u003eJournal of applied psychology\u003c/em\u003e, \u003cem\u003e88\u003c/em\u003e(5), 879. https://doi.org/10.1037/0021-9010.88.5.879.\u003c/li\u003e\n\u003cli\u003ePutnick, D. L., \u0026amp; Bornstein, M. H. (2016). Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research. \u003cem\u003eDevelopmental review : DR\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e, 71–90. https://doi.org/10.1016/j.dr.2016.06.004\u003c/li\u003e\n\u003cli\u003eRaykov, T., \u0026amp; Marcoulides, G. A. (2011). Introduction to psychometric theory. New York, NY: Routledge.\u003c/li\u003e\n\u003cli\u003eReilly, S., \u0026amp; Semkovska, M. (2018). An examination of the mediatory role of resilience in the relationship between helicopter parenting and severity of depressive symptoms in Irish university students. \u003cem\u003eAdolescent Psychiatry\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 32–47. https://doi.org/10.2174/2210676608666180508130224\u003c/li\u003e\n\u003cli\u003eSaranya, J., Nirmala, S. V. S. G., Reddy, E. S., Challa, R., \u0026amp; Nuvvula, S. (2021). Prevalence of helicopter parenting and its effect on academic performance and oral hygiene status in adolescents: A cross-sectional study. International Journal of Current Research and Review, 13(15), 122–126. https://doi.org/10.31782/IJCRR.2021.131523\u003c/li\u003e\n\u003cli\u003eSchiffrin, H. H., Erchull, M. J., Sendrick, E., Yost, J. C., Power, V., \u0026amp; Saldanha, E. R. (2019). The effects of maternal and paternal helicopter parenting on the self-determination and well-being of emerging adults. Journal of Child and Family Studies, 28(12), 3346–3359. https://doi.org/10.1007/s10826-019-01513-6.\u003c/li\u003e\n\u003cli\u003eSchiffrin, H. H., Liss, M., Miles-McLean, H., Geary, K. A., Erchull, M. J., \u0026amp; Tashner, T. (2014). Helping or hovering? The effects of helicopter parenting on college students’ well-being. \u003cem\u003eJournal of Child and Family Studies\u003c/em\u003e, 23(3), 548–557. https://doi.org/10.1007/s10826-013-9716-3\u003c/li\u003e\n\u003cli\u003eSegrin, C., Woszidlo, A., Givertz, M., \u0026amp; Montgomery, N. (2013). Parent and child traits associated with overparenting. \u003cem\u003eJournal of Social and Clinical Psychology\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(6), 569–595.\u003c/li\u003e\n\u003cli\u003eSegrin, C., Woszidlo, A., Givertz, M., Bauer, A., \u0026amp; Murphy, M. T. (2012). The association between overparenting, parent-child communication, and entitlement and adaptive traits in adult children. Family Relations, 61, 237–252. DOI: 10.1111/j.1741-3729.2011.00689.x\u003c/li\u003e\n\u003cli\u003eSeol, H. (2026). \u003cem\u003esnowIRT: Item Response Theory for jamovi\u003c/em\u003e. (Version 5.1.8) [jamovi module]. URL https://github.com/hyunsooseol/snowIRT.\u003c/li\u003e\n\u003cli\u003eShaki, O., Gupta, G. K., Yadav, P., \u0026amp; Faisal, F. A. (2022). Helicopter parenting, from good intentions to poor outcomes. What parents needs to know?. \u003cem\u003eJournal of family medicine and primary care\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(8), 4753–4757. https://doi.org/10.4103/jfmpc.jfmpc_2474_21\u003c/li\u003e\n\u003cli\u003eSilva Castillo, L. H., Hernández-Rosas, E., Silas-Casillas, J. C., Cazares, S. N., \u0026amp; Fonseca León, L. C. (2025). Cross-sectional psychometric validation, convergent validity, and measurement invariance of the DASS-21 in Mexican university students. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 1707786. https://doi.org/10.3389/fpsyg.2025.1707786\u003c/li\u003e\n\u003cli\u003eSood, M., \u0026amp; Buchanan, T. (2024). Helicopter parenting of minor teenagers in India: Scale development and consequences. \u003cem\u003eThe Family Journal\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(2), 292–303. https://doi.org/10.1177/10664807231215429\u003c/li\u003e\n\u003cli\u003eSrivastav, D., \u0026amp; Mathur, M. L. (2020). Helicopter parenting and adolescent development: from the perspective of mental health. In \u003cem\u003eParenting\u003c/em\u003e. IntechOpen. Available from: http://dx.doi.org/10.5772/intechopen.93155\u003c/li\u003e\n\u003cli\u003eTing, H., Memon, M. A., Thurasamy, R., \u0026amp; Cheah, J. H. (2025). Snowball sampling: A review and guidelines for survey research. \u003cem\u003eAsian Journal of Business Research\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1).\u003c/li\u003e\n\u003cli\u003eVigdal, J. S., \u0026amp; Brønnick, K. K. (2022). A Systematic Review of \"Helicopter Parenting\" and Its Relationship With Anxiety and Depression. \u003cem\u003eFrontiers in psychology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 872981. https://doi.org/10.3389/fpsyg.2022.872981\u003c/li\u003e\n\u003cli\u003eVinayak, P. L., Nathawat, S., Maresha, N., Mahadevaswamy, M., \u0026amp; Parashurama, K. G. (2025). Impact of Adverse Childhood Experience on Internet Addiction: The Mediating Role of Depression and Anxiety among University Students. \u003cem\u003eIndian Journal of Social Psychiatry\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(4), 387–395. https://doi.org/10.4103/ijsp.ijsp_324_24\u003c/li\u003e\n\u003cli\u003evon Davier, M. (2014). Rasch Analysis. In: Michalos, A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_2411\u003c/li\u003e\n\u003cli\u003eYaffe, Y. (2021). A narrative review of the relationship between parenting and anxiety disorders in children and adolescents. \u003cem\u003eInternational Journal of Adolescence and Youth\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(1), 449–459. https://doi.org/10.1080/02673843.2021.1980067\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Helicopter Parenting Behaviour, Classical Test Theory, Item Response Theory, India, Mother Form, Father Form","lastPublishedDoi":"10.21203/rs.3.rs-9106639/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9106639/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Helicopter Parenting Behavior Questionnaire (HPBQ; Schiffrin et al., 2014) is a well-validated self-report tool for assessing parental control in the lives of emerging adults, that has typically been used within Western contexts. Given the pervasiveness of such disruptive parenting styles within India, the prevelance of which ranges between 48% to 83%, there is value in systematically investigating the utility of the HPBQ in an Indian context. This study examined the HPBQ from the perspectives of both the mother's and father's behavior within a sample of 438 emerging adults (Mage\u0026thinsp;=\u0026thinsp;21.53, SDage\u0026thinsp;=\u0026thinsp;2.74; 225 females) using combined classical test theory and item response theory. Key findings include: (1) the original two-factor structure of [i] helicopter parenting, and [ii] autonomy supportive behavior, were confirmed with acceptable factor loadings and good internal consistency; (2) strong reliability, convergent validity, and divergent validity were observed; (3) the HPBQ functioned similarly across male and female responders; and (4) most items were easily endorsed by respondents. These results indicate that the HPBQ is valid for use within emerging adults in India and thus, this scale has potential to inform empirical research on helicopter parenting in this context as well as serving as a tool for those seeking to understand the impact of such parenting styles.\u003c/p\u003e","manuscriptTitle":"Psychometric Properties of the Helicopter Parenting Behaviour Questionnaire (HPBQ) Among Emerging Adults in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 09:25:02","doi":"10.21203/rs.3.rs-9106639/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2eb65b22-0c31-47cf-b5e3-0e2e0e18c170","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64407051,"name":"Psychology"},{"id":64407052,"name":"Psychiatry"}],"tags":[],"updatedAt":"2026-03-17T14:16:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 09:25:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9106639","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9106639","identity":"rs-9106639","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}