Psychometric Properties of the Hoarding Rating Scale-Self-Report (HRS-SR): Evidence from Classical Test Theory and Item Response Theory Based on Secondary Data

Psychometric Properties of the Hoarding Rating Scale-Self-Report (HRS-SR): Evidence from Classical Test Theory and Item Response Theory Based on Secondary Data | 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 Hoarding Rating Scale-Self-Report (HRS-SR): Evidence from Classical Test Theory and Item Response Theory Based on Secondary Data MAHADEVASWAMY M, Dr. Sneha Nathawat, Dr. Dean Fido This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8924690/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 Hoarding Rating Scale-Self Report (HRS-SR; Tolin et al., 2010 ) is a well-established and internationally-recognised tool used to assess hoarding severity, which has been validated and translated across countries and cultures. However, to date, its psychometric properties have not been systematically examined within an Indian context where hoarding behaviour is a known issue. This study assessed the psychometric properties of the HRS-SR among 357 Indian college students (M age = 21.47, SD age = 2.24; 67.8% female) using classical test theory (CTT) and more robust item response theory (IRT). The HRS-SR was found to have a unidimensional factor structure and showed acceptable factor loadings, with good internal consistency (α = .825, ω = .828) and composite reliability (.817). Findings also indicated similar response patterns across male and female responders. Taken together, this study validates and supports the utility of the HRS-SR within Indian samples, with discussions focused around how academic researchers and clinicians can and should capitalise on this tool and build upon our findings. Psychology Psychiatry Hoarding Classical Test Theory Item Response Theory India Figures Figure 1 INTRODUCTION Hoarding disorder is defined as the acquisition of possessions and the failure to discard them, regardless of their actual or perceived value, which frequently results in emotional discomfort and distress (Frost & Hartl, 1996 ). Consequently, possessions accumulate, leading to congestion in active living areas and substantially limiting their intended use, such as cooking in the kitchen and sleeping in their beds (APA, 2022; WHO, 2019). Prior to the DSM-5, hoarding disorder was considered a form of obsessive-compulsive disorder (OCD) and a diagnostic criterion for obsessive compulsive personality disorder (OCPD), however, has since been reclassified as obsessive compulsive and related disorder due to key differences from OCD (Mataix-Cols et al., 2010 ; Ricci et al., 2023 ). Such differences include (a) OCD symptoms (e.g., checking, rituals, and contamination) intercorrelating strongly with one another, but only moderately correlating with hoarding symptoms, specifically; (b) OCD patients reporting more frequent and severe OCD, but not hoarding symptoms, relative to either clinical outpatient or nonclinical groups; and (c) OCD, but not hoarding symptoms being consistently associated with negative affect (Wu & Watson, 2005 ). Reported prevalence of hoarding disorder vary substantially with rates ranging from 1.5% to 6% among general adult populations (Postlethwaite et al., 2019 ), to 1.02% among Indian primary care patients (Jaisoorya et al., 2020 ) and 16% in a sample of Iranian university students (Barahmand & Hooshmand, 2014 ), which may be explained by cultural, diagnostic, sample size or methodological differences. Indeed, the methodological design of prevalence research (e.g., modes of data collection and access to clinical samples) can introduce bias, leading to over- or underestimation of a disease or disorder's true prevalence in a population (Postlethwaite et al., 2019 ). For some individuals, hoarding symptoms develop in early adulthood and persist as a chronic trait, whereas in others, hoarding starts later in life in response to stress or loss (Grisham et al., 2006 ; Woerner et al., 2017 ). The ongoing accumulation of possessions can impact the maintenance of a healthy lifestyle and quality of life, and may seriously impact mental health, including increasing the risk of anxiety, depression, and suicidal thoughts (Sekhon & Leontieva, 2023 ; Woerner et al., 2017 ) as well as physical health difficulties such as cardiovascular or metabolic conditions, chronic pain, and sleep apnoea (Nutley et al., 2021 ). Moreover, the shame and humiliation resulting from hoarding and the familial and societal perceptions thereof often discourage hoarders from seeking help, which perpetuates the cycle of hoarding (Sekhon & Leontieva, 2023 ). Frost and Hristova ( 2011 ) emphasized that assessing the key features and behaviours associated with hoarding is essential for understanding and formulating treatment plans, and in doing so, identified several self-report, interview, and observational tools for evaluating hoarding and related issues. These tools include the Saving Inventory-Revised (SI-R; Frost et al., 2004 ), Hoarding Rating Scale-Interview (HRS-I; Tolin et al., 2007 , 2010 ), UCLA Hoarding Severity Scale (UHSS; Saxena et al., 2007 ), Hoarding Assessment Scale (HAS; Schneider et al., 2008 ), Clutter Image Rating (CIR; Frost et al., 2008 ), Activities of Daily Living in Hoarding Scale (ADL-H; Frost et al., 2013 ), Saving Cognitions Inventory (SCI; Steketee et al., 2003 ), Compulsive Acquisition Scale (CAS; Frost et al., 2009 ), and Clutter Hoarding Scale (NSGCD, 2003). Such a comprehensive landscape therefore requires measures to be selected based on a subject’s openness and insight, with self-report instruments only being feasible for individuals who possess awareness about their condition (Frost & Hristova, 2011 ). Of note, the 24-item SI-R provides a detailed assessment of hoarding symptoms, but is time-consuming, whereas the 5-item Hoarding Rating Scale-Self-Report (HRS-SR; Tolin et al., 2010 ) provides a means to assess and diagnose hoarding severity across the facets of ‘clutter’, ‘difficulty discarding’, ‘excessive acquisition’, ‘distress’, and impairment’ on 9-point Likert scale, where higher total scores indicate more severe hoarding behavior. Since the publication of the HRS-SR, it has demonstrated adequate psychometric properties and strong internal consistency with the interview version (Tolin et al., 2010 ), and has been widely used and validated in countries such as the United States (Nutley et al., 2020 ), and has been translated into Arabic (Hussain et al., 2023 ), Chinese (Liu et al., 2020 ), Korean (Lee et al., 2021 ), Japanese (Tsuchiyagaito et al., 2017 ), and Spanish (Stamatis et al., 2021 ) as a means of examining cultural differences. Nevertheless, the psychometric properties of the HRS-SR have not to date been systematically examined within an Indian context; important given variation in Eastern beliefs pertaining to emotion and responsibility, which are stronger and more culturally engrained than in the West (Choi, 2024 ). Such understanding has potential to facilitate the effective planning of culturally sensitive treatment approaches in India. Moreover, most existing studies that have examined the HRS-SR have exclusively used classical test theory (CTT; Hussain et al., 2023 ; Nutley et al., 2020 ; Lee et al., 2021 ; Liu et al., 2020 ; Tsuchiyagaito et al., 2017 ), with few using item response theory (IRT; Stamatis et al., 2021 ). IRT, also known as latent trait theory, is a contemporary psychometric framework that offers notable advantages over CTT (Gao & Liu, 2024 ), including its capacity to [1] translate item responses into scale-free measures of latent traits, [2] assess item-level parameters that remain consistent across different samples, [3] identify items that function differently across groups, and [4] estimate the standard error of measurement for participants by combining information from items at various levels of the latent trait (Gao & Liu, 2024 ). Thus, the inclusion of IRT in this study, allows us to systematically assess the HRS-SR psychometric properties within an Indian cohort, whilst producing accurate ability estimates, and ensuring fairness in administration and valid interpretations thereof (Eghan et al., 2026 ). METHODS Ethical approval was obtained by the Office of the Institute Ethics Committee (No./MGMC&H/IEC/JPR/2026/5130). We performed a secondary psychometric analysis to assess the psychometric properties of HRS-SR; data previously collected for an investigation into the mediating role of emotional attachment in the association between loneliness and hoarding disorder among college students (Mahadevaswamy, 2026 ). The sample comprised data from 357 students aged 18 to 25 years (M age = 21.47, SD age = 2.24, n male = 115, n female = 242) who were enrolled in college at the time and who were fluent in English. Data were collected using an online Google Form distributed across social media platforms between October to December 2025. Informed consent was obtained from all participants, and confidentiality was assured. The sample size is adequate for the present proposed analysis based on Comrey and Lee’s ( 1992 ) guidelines for factor analysis. Measures : 1. Hoarding Rating Scale-Self Report (HRS-SR; Tolin et al., 2010 ): The detailed description of HRS-SR is provided in the Introduction. 2. The UCLA 3-Item Loneliness Scale ( Hughes et al., 2004 ): The UCLA 3-Item Loneliness Scale is a self-report instrument comprising three items that assess relational connectedness, social connectedness, and self-perceived isolation (e.g., "How often do you feel that you lack companionship?"). Response categories are coded as 1 (hardly ever), 2 (some of the time), and 3 (often). Scores for each item are summed to yield a total score ranging from 3 to 9, with higher scores indicating greater loneliness. The scale demonstrates adequate psychometric properties. In the current study, the scale exhibited good internal consistency (α = .81; ω = .81). 3. Saving Cognition Inventory (SCI; Steketee et al., 2003 ): The SCI is a self-report tool comprising 24 items that assesses participants' beliefs about saving or possessions across four subscales: Emotional Attachment (10 items, e.g., “Throwing away this possession is like throwing away a part of me”), Memory (5 items, e.g., “My memory is so bad I must leave this in sight or I’ll forget about it.”), Control (3 items, e.g., “It upsets me when someone throws something of mine away without my permission.”), and Responsibility (6 items, e.g., “Throwing this away means wasting a valuable opportunity”). Each item is rated on a 7-point Likert scale (1 = not at all, 4 = sometimes, 7 = very much) to indicate the extent to which they experienced each thought when deciding whether to throw something away during the past week. Scores for each individual's subscale are calculated by summing the item scores within each domain. The SCI demonstrated good to excellent internal consistency in the original study: Emotional Attachment (.95), Memory (.89), Control (.86), Responsibility (.90), and the 24-item total score (.96) (Steketee et al., 2003 ). For the present study, we considered only the Emotional Attachment domain to provide evidence of the HRS-SR's convergent validity, which exhibited excellent internal consistency (α = .912; ω = .913). Data Analysis Data were analysed using IBM SPSS (v. 22), IBM AMOS (v. 22), and JAMOVI (v. 2.7.15). Initially, descriptive statistics were calculated, before conducting a confirmatory factor analysis (CFA) to test the unidimensional factor structure of the HRS-SR, and applying a polytomous Rasch Rating Scale Model (RSM) to investigate Likert scale functioning in the context of hoarding behaviour. Parameters for these approaches are outlined below. Classical Test Theory To address the issues of multivariate non-normality, we employed bootstrap analysis with 5000 resamples and a 95% confidence interval. Whilst applying the Bollen–Stine bootstrap to provide further evidence of model fit with the bootstrap samples. We expected the Bollen-Stine bootstrap to be non-significant ( p > .05), indicating an adequate model fit across bootstrap samples (Coolier, 2020). Model fit was assessed using the following indices: goodness of fit index (GFI > .95), comparative fit index (CFI > .95), Tucker-Lewis Index (TLI > .95), root mean square error of approximation (RMSEA < .08), and standardized root mean square residual (SRMR < .06), where these values indicate excellent fit (Bentler, 1992; Byrne, 2010 ; Collier, 2020 ; Hu & Bentler, 1995; 1999 ; MacCallum et al., 1996 ). Given the sensitivity of the chi-square value to sample size, we used relative chi-square values of 3–5 to assess fit (Collier, 2020 ). Standardized factor loadings were considered adequate if they met the .50 cutoff (Hair et al., 2019 ). Squared multiple correlations were required to be above .20 (Hooper et al., 2008 ). Measurement invariance across gender was also tested. Configural, metric, scalar, and strict invariance were established if changes between models fit indices were within the acceptable limits (ΔRMSEA < .015, ΔCFI < .010, ΔTLI < .010; Chen, 2007 ; Cheung & Rensvold, 2002 ). The internal consistency was assessed using Cronbach's alpha, McDonald's omega, and composite reliability. For Cronbach’s alpha, values above 0.7, 0.8, and 0.9 were considered acceptable, good, and excellent, respectively (George & Mallery, 2021 ). For McDonald's omega, a value above 0.80 was considered acceptable (Cervin et al., 2022 ). Composite reliability above 0.70, based on factor loadings, indicated good internal consistency (Collier, 2020 ). Inter-item correlation values ranging from 0.15 to 0.85, and average inter-item correlation between 0.15 and 0.50, were deemed acceptable (Paulsen & BrckaLorenz, 2017 ). The item discriminant index was considered acceptable if it exceeded 0.50 (Paulsen & BrckaLorenz, 2017 ). We used Spearman correlation analysis to assess the convergent and divergent validity of the HRS-SR by examining its strength of the relationship with loneliness and emotional attachment, following Cohen’s (1998) guidelines (small = .10, medium = .30, large = .50). To further evaluate discriminant validity, we calculated HTMT using a CFA on a correlated three-factor model that included HRS-SF, Emotional Attachment, and Loneliness. HTMT values below .85 (Henseler et al., 2015 ; Kline, 2011 ) indicate adequate discriminant validity. Item Response Theory: Rasch Rating Model After confirming the assumption of Unidimensionality through CFA, the assumption of Local independence was evaluated using Q3 residual correlations, with a cut off of 0.50 (Ten Klooster et al., 2008 ). The polytomous RSM was conducted using the JAMOVI (v. 2.7.15) package. The eRm R package (Mair et al., 2021 ) within the snowIRT program (Seol, 2026 ) was used to estimate item difficulty parameters and to assess item fit using Infit and Outfit mean-square (MnSq) statistics. Acceptable Infit and Outfit MnSq values ranged from 0.70 to 1.30 (Wright & Linacre, 1994 ), facilitating the identification of underfitting or overfitting items. Reliability was determined using person reliability, with a cut-off of 0.70 indicating acceptable reliability (Abdulmajid & Khalid, 2024 ). Both uniform and non-uniform Differential Item Functioning (DIF) across gender was evaluated using the difNLR::difORD package of R via the snowIRT program (Hladka et al., 2022 ). Adjusted p -values > .05 indicate no significant difference in item functioning between genders, showing that items are interpreted similarly by males and females. RESULTS Data Distribution Skewness values ranged from 0.70 to 1.04, and kurtosis values ranged from − 0.04 to 0.63; well within the recommended cut-offs to determine univariate normality (skewness < ± 2, kurtosis < ± 4; Collier, 2020 ). However, the critical ratio was 25.680, which exceeds the recommended cut-off of 5.0 (Byrne, 2010 ), indicating multivariate non-normality. When all items (UCLA 3-item loneliness scale, HRS-SR, emotional attachment domain of SCI) were modelled, the principal axis factoring method using an unrotated factor solution extracted three factors, and the first factor explained 39.583% of the total variance, which is less than 50%, indicating the absence of common method bias (Collier, 2020 ; Howard et al., 2024). No significant floor or ceiling effects were observed; only 10% had the lowest score, and none had the highest, both below the recommended 15% threshold (Gulledge et al., 2019 ). Psychometric Properties using Classical Test Theory Confirmatory Factor Analysis The CFA was conducted to examine the structural validity of the HRS-SR. The initial unidimensional model (Model 1) showed a poor fit (χ²/df = 36.494/5, p < .001; GFI = .960; TLI = .896; CFI = .948; RMSEA = .133, 95% CI [.095, .175]; SRMR = .045). Modification indices indicated that allowing a covariance between error terms for Items 2 and 3 would improve the model. This covariance was freely estimated in Model 2, which in turn showed a very good fit (χ²/df = 9.812/4, p = .044; GFI = .989; TLI = .976; CFI = .990; RMSEA = .064, 95% CI [.010, .116]; SRMR = .022) (see Table 1 ); supporting the unidimensional structure (See Fig. 1 ) of the HRS-SR as proposed by Tolin et al. ( 2010 ). Standardised factor loadings exceeded .50 (Hair et al., 2019 ), ranging from .602 (Item 2) to .827 (Item 4). All squared multiple correlations were above .20 (Hooper et al., 2008 ), ranging from .362 (Item 2) to .685 (Item 4), showing that all items were strongly associated with the latent construct. All factor loadings and squared multiple correlations were statistically significant ( p < .05) with 95% confidence intervals not including zero, supporting parameter estimates based on bootstrapping (Collier, 2020 ) (see Table 2 ). Finally, the Bollen–Stine bootstrap was non-significant ( p = .205), further indicating adequate fit across bootstrap samples (Collier, 2020 ). Table 1 Goodness-of-fit indexes of the unidimensional model of the HRS-SR ( n = 357) χ²/df CMIN/DF GFI TLI CFI RMSEA (90% CI) SRMR Model 1 36.494/5 *** 7.299 .960 .896 .948 .133 [.095-.175] .045 Model 2 9.812/4 * 2.453 .989 .976 .990 .064 [.010-.116] .022 Note: GFI – Goodness-of-Fit Index, TLI – Tucker–Lewis Index, CFI – Comparative Fit Index, RMSEA (90% CI) – Root Mean Square Error of Approximation, SRMR – Standardized Root Mean Square Residual Table 2 Standardized Factor Loading and Squared Multiple Correlations Items λ LLCI ULCI p R 2 LLCI ULCI p HRS1 0.643 0.520 0.740 .001 0.413 0.270 0.547 .001 HRS2 0.602 0.508 0.679 .001 0.362 0.258 0.461 .001 HRS3 0.605 0.505 0.698 < .001 0.366 0.255 0.487 < .001 HRS4 0.827 0.753 0.897 < .001 0.685 0.567 0.804 < .001 HRS5 0.741 0.636 0.821 .001 0.549 0.404 0.673 .001 Note: HRS = Hoarding Rating Scale, LLCI – Lower Limit Confidence Interval, ULCI – Upper Limit Confidence Interval Measurement Invariance Measurement invariance across gender was tested using constrained models. Results showed configural invariance, as indicated by acceptable model fit indices (TLI = .905, CFI = .952, SRMR = .042, RMSEA = .090, 95% CI [.061, .121]). Although the RMSEA value indicated only a reasonable fit, it should be interpreted with caution given RMSEA can overestimate lack of fit in models with few degrees of freedom (e.g., df < 10; Kenny et al., 2015 ). Furthermore, changes in fit indices across models met recommended thresholds (ΔRMSEA < .015, ΔCFI < .010, ΔTLI < .010) (Chen, 2007 ; Cheung & Rensvold, 2002 ), supporting metric, scale, and strict invariance across gender (see Table 3 ). Table 3 Measurement Invariance across gender TLI ΔTLI CFI ΔCFI RMSEA ΔRMSEA Configural 0.905 - 0.952 - 0.090 [.061-.121] - Matric 0.919 .014 0.943 − .009 0.083 [.058-.109] − .007 Scaler 0.926 .007 0.945 .002 0.079 [.055-.105] − .004 Strict 0.933 .007 0.933 − .012 0.076 [.055-.098] − .003 Note: TLI – Tucker–Lewis Index, CFI – Comparative Fit Index, RMSEA – Root Mean Square Error of Approximation, SRMR – Standardized Root Mean Square Residual Reliability The internal consistency of the HRS-SR was good (α = .825, ω = .828). Composite reliability was also acceptable (CR = .817). Inter-item correlations ranged from .363 to .617 (M = .485), within the accepted correlation range of .15 to .85 and mean range of.15 to .50 (Paulsen & BrckaLorenz, 2017 ). All five HRS-SR items showed high discrimination indices (.571 to .703); exceeding the .50 cut-off (Paulsen & BrckaLorenz, 2017 ). Item 4 (“To what extent do you experience emotional distress because of clutter, difficulty discarding, or problems with buying or acquiring things?”) had the highest index, and Item 1 (“Because of the clutter or number of possessions, how difficult is it for you to use the rooms in your home?”) had the lowest. Deleting any item did not improve Cronbach’s alpha (see Table 4 ). Table 4 Descriptive Statistics, Inter-Item and Item–Total Correlations, and Internal Consistency of the HRS-SR Items ( n = 357) Mean SD HRS1 HRS2 HRS3 HRS4 HRS5 Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted HRS1 1.681 1.630 1 .571 .804 HRS2 1.924 1.731 .435 1 .606 .794 HRS3 2.003 1.840 .363 .559 1 .595 .798 HRS4 1.924 1.852 .507 .501 .526 1 .703 .765 HRS5 1.804 1.845 .507 .409 .425 .617 1 .626 .789 Note: Average Inter-Item Correlation = .485, HRS = Hoarding Rating Scale, SD = Standard Deviation Validity Spearman correlation analyses were performed to assess the convergent and divergent validity of the HRS-SF, wherein we expected a stronger association between HRS-SF and the Emotional Attachment domain of the SCI-R than between HRS-SF and Loneliness, thus providing evidence for convergent and divergent validity, respectively. As shown in Table 5 , the HRS-SF demonstrated a strong, significant positive association with the Emotional Attachment domain ( r = .636, p < .001), supporting convergent validity. Meanwhile, its significant but moderate positive association with Loneliness provides support for divergent validity. To further assess discriminant validity, we calculated HTMT using a CFA on a correlated three-factor model including HRS-SF, Emotional Attachment, and Loneliness (3-item UCLA). Notably, the CFA findings indicated that this three-factor model demonstrated adequate fit indices (x2/df = 371.011/132, p < .001, TLI = .911, CFI = .924, RMSEA = .071 [.063-.080], SRMR = .058). Additionally, HTMT values supported discriminant validity, with scores of .71 for HRS-SR–Emotional Attachment and .46 for HRS-SR–Loneliness, both below the recommended threshold of .85 (Henseler et al., 2015 ; Kline, 2011 ). Table 5 Descriptive Statistics, Spearman Correlations Coefficient and Internal Consistency HRS-SR EA Loneliness HRS-SR 1 EA .636 ** 1 Loneliness .410 ** .349 ** 1 Mean 9.34 28.18 5.40 SD 6.83 12.10 2.22 Skewness .338 .454 .294 Kurtosis − .755 .134 -1.431 α .825 .912 .810 ω .828 .913 .810 Note: HRS-SR = Hoarding Rating Scale – Self Report, EA = Emotional Attachment, SD = Standard Deviation, α = Cronbach Alpha, ω = McDonald Omega. **Significant at the level of < .01 Psychometric Properties using Item Response Theory Assumption Analysis: Unidimensionality and Local Independence After confirming the unidimensionality assumption through CFA, local independence was assessed using Yen’s Q3 correlation matrix with a cut-off value of .36 (Flens et al., 2016 ). The results indicated that the residual correlations ranged from − .389 to .002. Only two item pairs exceeded the threshold: Item 2 and Item 5 (-.389), and Item 1 and Item 3 (-.378). Since few studies use a cut-off as high as .50 (Ten Klooster et al., 2008 ), these values suggest no item redundancies and support local independence. The person reliability index was .727, indicating adequate reliability. Table 6 Item statistics of the rating scale model Measure S. E. Measure Infit Outfit HRS1 1.22 0.0446 1.079 1.054 HRS2 1.06 0.0426 1.022 0.987 HRS3 1.01 0.0420 1.113 1.058 HRS4 1.06 0.0426 0.935 0.851 HRS5 1.14 0.0435 1.132 1.014 Note: HRS = Hoarding Rating Scale Item Statistics of the Rating Scale Model The difficulty levels of the items varied from 1.01 (item 3) to 1.22 (item 1). Furthermore, the Infit values ranged from 0.935 (item 4) to 1.132 (item 5), while the Outfit values spanned from 0.851 (item 4) to 1.058 (item 3). All values fell within the recommended fit range of 0.7 to 1.3 (Wright et al., 1994), suggesting no signs of overfitting or underfitting items aligning with the expectations of the RSM (see Table 6 ). Differential Item Functioning DIF analysis examined whether individuals with the same hoarding severity, but different genders, responded differently to HRS-SR items. Both uniform and non-uniform DIF showed no statistically significant differences after adjustment ( p > .05), suggesting HRS-SR items function equally across genders (see Table 7 ). Table 7 Differential Item Functioning across gender Uniform Non-Uniform Items Statistic p Adj.p Statistic p Adj. p HRS1 0.42189 .516 1.000 5.513 .019 0.094 HRS2 2.11003 .146 0.732 0.166 .684 1.000 HRS3 0.55764 .455 1.000 1.603 .206 1.000 HRS4 0.18817 .664 1.000 0.105 .745 1.000 HRS5 0.00915 .924 1.000 1.255 .263 1.000 Note. Adj. p = The adjusted p -values by likelihood ratio test using multiple comparison; HRS = Hoarding Rating Scale DISCUSSION This study assessed the psychometric properties of the HRS-SR among a sample of Indian college students using robust classical test theory (CTT) in addition to more robust item response theory (IRT). Findings verified that the HRS-SR is a valid and reliable tool to assess the severity of hoarding disorder among college students in India. Classical Test Theory Consistent with prior studies, the CFA supported the unidimensional factor structure of HRS-SR originally proposed by Tolin et al. ( 2010 ), indicating that the HRS-SR adequately measures the core features of hoarding disorder, including clutter, difficulty discarding, excessive acquisition, distress, and impairment. Such unidimensionality has also been found to be the case across countries and cultures (Hussain et al., 2023 ; Lee et al., 2021 ; Liu et al., 2020 ; Tsuchiyagaito et al., 2017 ; Stamatis et al., 2021 ). Furthermore, all standardized factor loadings exceeded .602, demonstrating sufficient correspondence (see Hair et al., 2019 ) between the distinct items and the composite hoarding disorder factor. Additionally, all squared multiple correlations exceeded .362; indicating adequate variance explained by the latent factor in accordance with Hooper et al.’s ( 2008 ) proposed cut-off of .20. Findings also demonstrated measurement invariance across gender at the configural, metric, scalar, and strict levels (Chen, 2007 ; Cheung & Rensvold, 2002 ), indicating that the HRS-SR assesses hoarding symptoms equivalently in both male and female participants; further boasting its utility. The HRS-SR showed good reliability, with both Cronbach's alpha and McDonald's omega exceeding .80 (Cervin et al., 2022 ; George & Mallery, 2003). These findings are consistent with the scale’s original conceptualisation (Tolin et al., 2010 ; α = .83) and more recent validations, which report acceptable to excellent internal consistency (Cronbach's α = .78 − .93; Lee et al., 2021 ; Liu et al., 2020 ; Stamatis et al., 2021 ; Tsuchiyagaito et al., 2017 ). According to Hair et al. ( 2019 ), although Cronbach’s alpha is widely used to assess reliability, it does not weigh individual indicators, which are instead addressed by composite reliability, making it the preferred method. The composite reliability based on standardized factor loadings was .817; further validating its internal consistency. Additionally, the HRS-SR showed adequate inter-item correlation and acceptable average inter-item correlation, indicating that scores on each item positively related to scores on other scale items. In practice, this means that the items measure a common construct (homogeneity) but still have sufficient unique characteristics to ensure they are not redundant or identical to one another (i.e., not isomorphic) (Piedmont, 2014 ). Furthermore, all items had a corrected item-total correlation greater than .50 (Paulsen & BrckaLorenz, 2017 ), providing strong evidence of each item's consistency with the total HRS-SR score and demonstrating high discriminant validity. The HRS-SR was strongly and positively associated with emotional attachment, meaning that students with greater hoarding severity within our sample were more likely to hold maladaptive beliefs about their possessions. This supports the scale's convergent validity. The cognitive behavioural model of hoarding disorder conceptualizes hoarding as a multifaceted problem arising from information processing deficits, difficulties in forming emotional attachments, behavioural avoidance, and erroneous beliefs regarding the nature of possessions (Frost & Hartl, 1996 ). In contrast, the HRS-SR showed a positive correlation with loneliness, which itself is related, but not identical to, hoarding. This association supports the scale's divergent validity by indicating that the HRS-SR measures a construct distinct from social/emotional relatedness. Furthermore, the HRS-SR demonstrated adequate discriminant validity, as evidenced by HTMT values below .85 (Henseler et al., 2015 ; Kline, 2011 ) with emotional attachment and loneliness. Together, this data evidences the utility of the HRS-SR within Indian cohorts. Item Response Theory: Rasch Rating Scale Model In the present study, despite demonstrating the strong psychometric properties of the HRS-SR using CTT, we also applied IRT principles to further establish its psychometric properties. Specifically, we used the Rasch Rating Scale Model, a form of IRT, which is suitable for modelling dichotomous responses and calculates the probability that an individual will provide a correct answer on a dichotomous item (Magno, 2009 ). The CFA demonstrated unidimensionality, indicating that the HRS-SR measures a single latent construct with local independence, which, in practice, means that the participants’ responses across items were not statistically related once the latent trait is accounted for (Yang & Kao, 2014 ). These findings help us to understand that only one underlying characteristic is measured and that responses to one item are not contingent on responses to another (Yang & Kao, 2014 ). Person-specific reliability was .727, reflecting a high level of consistency across items (Abdulmajid & Khalid, 2024 ), with all five items indicating a high degree of difficulty; suggesting that students generally need higher ability levels to have at least a 50% chance of answering correctly. This suggests that only students with stronger trait ability are likely to answer these items correctly (Yang & Kao, 2014 ). Surprisingly, all five items demonstrated high discriminant indices, with each corrected total-item correlation exceeding .50, indicating strong distinction among the items based on CTT-based parameters (Paulsen & BrckaLorenz, 2017 ). Furthermore, regarding item fit, all observed infit and outfit values fell within the cut-offs of 0.7, which may indicate item redundancy, and 1.3, which may indicate a given item does not measure hoarding disorder akin to others within the scale (Wright et al., 1994). Thus, the results show no overfitting or underfitting, supporting the scale's unidimensionality and construct validity. Finally, the findings indicated that students having the same underlying ability have the same probabilities of getting an item correct regardless of gender (Narayanon, & Swaminathan, 1996 ) as both uniform and non-uniform DIF were non- statistically significant after adjustment ( p > .05). In the present study, however, all five HRS-SR items showed equivalence across male and female responders. This equivalence was evident in both DIF analyses and CTT-based parameters, both of which demonstrated adequate measurement invariance across gender. To our knowledge, in only one study, an IRT approach was used to assess DIF between the English and Spanish versions of HRS-SR (Stamatis et al., 2021 ), which that the HRS item pertaining to clutter was the only item to exhibit DIF, indicating that Spanish-speaking participants with similar hoarding symptoms were slightly less likely to endorse cluttering behaviour. However, the impact of this DIF was minimal, even at the item level, and had little effect on the overall test characteristic curves (Stamatis et al., 2021 ). Strength and Limitations Results should be discussed in light of six limitations and considerations. First, data were derived from a single sample of Indian college students. As such, findings cannot be generalized to other populations and clinical samples, and so would benefit from further replication to assess consistency. Second, owing to this study utilising secondary data, we were unable to assess the test-retest reliability of the HRS-SR, which is essential for establishing temporal reliability over time. Third, despite establishing convergent and divergent validity, future studies may be wise to include other hoarding disorder scales, such as the SI-R (Frost et al., 2004 ), to provide further evidence of convergent validity. Fourth, we did not account for the presence of comorbid psychological disorders and so could not include such information within our models, which may have influenced the findings as studies indicated a high comorbidity rate for major depressive disorder and acquisition-related impulse control disorders, including compulsive buying, kleptomania, and acquiring free items (Frost et al., 2011 ). Additionally, Abouzed et al. ( 2024 ) reported a significant positive association between hoarding tendencies and autism traits. Similarly, Woerner et al. ( 2017 ) found that hoarding symptoms correlated positively with anxiety, depression, and ADHD symptoms. Fifth, it would be advantageous for future studies to include a more robust Graded Response Model. Zein and Akhtar (2024) note that the RSM accounts only for varying item difficulty and assumes equal item discrimination. In contrast, the Graded Response Model both estimates discrimination and threshold parameters for each item and is suited for modelling ordinal data with more than two response categories (i.e., Likert-style). Finally, future studies need to conduct focus groups with practitioners and researchers to assess the utility of such a measure in order for us to better understand its utility moving forward. Conclusions The present study represents the first validation of the HRS-SR with an Indian sample using both CTT and more contemporary IRT. The CTT-based parameters indicated adequate reliability and a valid unidimensional construct measuring hoarding disorder components, including clutter, difficulty discarding, excessive acquisition, distress, and impairment. The findings also support measurement invariance across gender. Similarly, IRT-based parameters demonstrated high consistency across items, with all five items fitting the unidimensional construct supporting both unidimensionality and construct validity without evidence of misfit. Additionally, all five HRS-SR items function equivalently across male and female responders. Together, this data evidences utility of the HRS-SR within Indian samples, and we call on practitioners and researchers within this area to further explore the utility of it. 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/2026/5130). Informed consent was not required as the study used secondary data. Consent for publication: Not applicable. Contributions Conceptualization, MM, SN, and DF; Methodology, MM, SN, and DF; Data Analysis, MM; Writing-Original Draft Preparation, MM; Writing-Review & Editing, SN and DF. All authors have read and approved the manuscript. Funding: No external funding was received for this study. Data Availability Statement: Data are available upon reasonable request from the corresponding author, subject to the institute's ethical approval. References Abdulmajid AA, Khalid KA (2024) Using Rasch Analysis for Validation of Knowledge and Perception of Orthopedic Workplace-Based Assessment among Postgraduate Orthopedic Trainees’ Questionnaire. 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World Health Organization. https://icd.who.int Wright BD, Linacre JM (1994) Reasonable mean-square fit values. Rasch Meas Trans 8:370 Wu KD, Watson D (2005) Hoarding and its relation to obsessive–compulsive disorder. Behav Res Ther 43(7):897–921. https://doi.org/10.1016/j.brat.2004.06.013 Yang FM, Kao ST (2014) Item response theory for measurement validity. Shanghai archives psychiatry 26(3):171–177. https://doi.org/10.3969/j.issn.1002-0829.2014.03.010 Zein RA, Akhtar H (2025) Getting started with the graded response model: an introduction and tutorial in R. Int J Psychol 60(1):e13265. https://doi.org/10.1002/ijop.13265 Additional Declarations The authors declare no competing interests. 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. 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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-8924690","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594388956,"identity":"7aed9fc6-0947-473c-bbe2-cd3a339148e1","order_by":0,"name":"MAHADEVASWAMY M","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYFACHjBiYONvPmDwAcRgJ1YLn8SxhMIZIC3MxGqRY8gx+AxiMBDSott+9uCHNxWH5dkYzhhutvm1TZ6PmYHxw8cc3FrMzuQlS845c9iwjbmt2Di37zaQwcAsOXMbHi0HcgykedvSGNsYDm8zzu25zQjUwsbMi0/L+TfGv3n/pdm3MSSY/7bsuW1PWMuNHDNp3gabxDaGFANjhh+3E4nQ8sbMcs4xm+Q2YCAb9jbcTm5jZmzG75fzOcY33tRI2M7vB0bljz+3bee3Nx/88BGPFlQADAQQ2UCsehD4Q4riUTAKRsEoGCkAAJaYUmsnqt20AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-6250-9100","institution":"Central Institute of Psychiatry (CIP)","correspondingAuthor":true,"prefix":"","firstName":"MAHADEVASWAMY","middleName":"","lastName":"M","suffix":""},{"id":594388957,"identity":"8387c6cf-61e5-4276-b52d-8534b7f2cc5f","order_by":1,"name":"Dr. Sneha Nathawat","email":"","orcid":"https://orcid.org/0000-0003-4408-267X","institution":"Mahatma Gandhi Medical College and Hospital","correspondingAuthor":false,"prefix":"Dr.","firstName":"Sneha","middleName":"","lastName":"Nathawat","suffix":""},{"id":594388958,"identity":"56f818d5-b45c-4fd6-9a4b-9db12106b41c","order_by":2,"name":"Dr. Dean Fido","email":"","orcid":"https://orcid.org/0000-0001-8454-3042","institution":"University of Derby, Derby, UK","correspondingAuthor":false,"prefix":"Dr.","firstName":"Dean","middleName":"","lastName":"Fido","suffix":""}],"badges":[],"createdAt":"2026-02-20 10:38:09","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8924690/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8924690/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103189341,"identity":"55922aa9-a021-40d1-8d1f-6e9fe9f782a8","added_by":"auto","created_at":"2026-02-23 00:44:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUnidimensional Structure of HRS-SR\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8924690/v1/ddc92b6428b7e31a0f31be8b.png"},{"id":104397246,"identity":"e2c66d51-c085-4ee3-b24e-4361eefab29f","added_by":"auto","created_at":"2026-03-11 11:45:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1164394,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8924690/v1/c2b2f4c5-7130-4ca4-b241-3bfb79bd7ae6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePsychometric Properties of the Hoarding Rating Scale-Self-Report (HRS-SR): Evidence from Classical Test Theory and Item Response Theory Based on Secondary Data\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eHoarding disorder is defined as the acquisition of possessions and the failure to discard them, regardless of their actual or perceived value, which frequently results in emotional discomfort and distress (Frost \u0026amp; Hartl, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Consequently, possessions accumulate, leading to congestion in active living areas and substantially limiting their intended use, such as cooking in the kitchen and sleeping in their beds (APA, 2022; WHO, 2019). Prior to the DSM-5, hoarding disorder was considered a form of obsessive-compulsive disorder (OCD) and a diagnostic criterion for obsessive compulsive personality disorder (OCPD), however, has since been reclassified as obsessive compulsive and related disorder due to key differences from OCD (Mataix-Cols et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ricci et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such differences include (a) OCD symptoms (e.g., checking, rituals, and contamination) intercorrelating strongly with one another, but only moderately correlating with hoarding symptoms, specifically; (b) OCD patients reporting more frequent and severe OCD, but not hoarding symptoms, relative to either clinical outpatient or nonclinical groups; and (c) OCD, but not hoarding symptoms being consistently associated with negative affect (Wu \u0026amp; Watson, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReported prevalence of hoarding disorder vary substantially with rates ranging from 1.5% to 6% among general adult populations (Postlethwaite et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), to 1.02% among Indian primary care patients (Jaisoorya et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and 16% in a sample of Iranian university students (Barahmand \u0026amp; Hooshmand, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which may be explained by cultural, diagnostic, sample size or methodological differences. Indeed, the methodological design of prevalence research (e.g., modes of data collection and access to clinical samples) can introduce bias, leading to over- or underestimation of a disease or disorder's true prevalence in a population (Postlethwaite et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor some individuals, hoarding symptoms develop in early adulthood and persist as a chronic trait, whereas in others, hoarding starts later in life in response to stress or loss (Grisham et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Woerner et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The ongoing accumulation of possessions can impact the maintenance of a healthy lifestyle and quality of life, and may seriously impact mental health, including increasing the risk of anxiety, depression, and suicidal thoughts (Sekhon \u0026amp; Leontieva, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Woerner et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) as well as physical health difficulties such as cardiovascular or metabolic conditions, chronic pain, and sleep apnoea (Nutley et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the shame and humiliation resulting from hoarding and the familial and societal perceptions thereof often discourage hoarders from seeking help, which perpetuates the cycle of hoarding (Sekhon \u0026amp; Leontieva, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrost and Hristova (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) emphasized that assessing the key features and behaviours associated with hoarding is essential for understanding and formulating treatment plans, and in doing so, identified several self-report, interview, and observational tools for evaluating hoarding and related issues. These tools include the Saving Inventory-Revised (SI-R; Frost et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), Hoarding Rating Scale-Interview (HRS-I; Tolin et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), UCLA Hoarding Severity Scale (UHSS; Saxena et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), Hoarding Assessment Scale (HAS; Schneider et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Clutter Image Rating (CIR; Frost et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Activities of Daily Living in Hoarding Scale (ADL-H; Frost et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Saving Cognitions Inventory (SCI; Steketee et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), Compulsive Acquisition Scale (CAS; Frost et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and Clutter Hoarding Scale (NSGCD, 2003). Such a comprehensive landscape therefore requires measures to be selected based on a subject\u0026rsquo;s openness and insight, with self-report instruments only being feasible for individuals who possess awareness about their condition (Frost \u0026amp; Hristova, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Of note, the 24-item SI-R provides a detailed assessment of hoarding symptoms, but is time-consuming, whereas the 5-item Hoarding Rating Scale-Self-Report (HRS-SR; Tolin et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) provides a means to assess and diagnose hoarding severity across the facets of \u0026lsquo;clutter\u0026rsquo;, \u0026lsquo;difficulty discarding\u0026rsquo;, \u0026lsquo;excessive acquisition\u0026rsquo;, \u0026lsquo;distress\u0026rsquo;, and impairment\u0026rsquo; on 9-point Likert scale, where higher total scores indicate more severe hoarding behavior.\u003c/p\u003e \u003cp\u003eSince the publication of the HRS-SR, it has demonstrated adequate psychometric properties and strong internal consistency with the interview version (Tolin et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and has been widely used and validated in countries such as the United States (Nutley et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and has been translated into Arabic (Hussain et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Chinese (Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Korean (Lee et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Japanese (Tsuchiyagaito et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Spanish (Stamatis et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) as a means of examining cultural differences. Nevertheless, the psychometric properties of the HRS-SR have not to date been systematically examined within an Indian context; important given variation in Eastern beliefs pertaining to emotion and responsibility, which are stronger and more culturally engrained than in the West (Choi, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such understanding has potential to facilitate the effective planning of culturally sensitive treatment approaches in India.\u003c/p\u003e \u003cp\u003eMoreover, most existing studies that have examined the HRS-SR have exclusively used classical test theory (CTT; Hussain et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nutley et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tsuchiyagaito et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), with few using item response theory (IRT; Stamatis et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). IRT, also known as latent trait theory, is a contemporary psychometric framework that offers notable advantages over CTT (Gao \u0026amp; Liu, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), including its capacity to [1] translate item responses into scale-free measures of latent traits, [2] assess item-level parameters that remain consistent across different samples, [3] identify items that function differently across groups, and [4] estimate the standard error of measurement for participants by combining information from items at various levels of the latent trait (Gao \u0026amp; Liu, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, the inclusion of IRT in this study, allows us to systematically assess the HRS-SR psychometric properties within an Indian cohort, whilst producing accurate ability estimates, and ensuring fairness in administration and valid interpretations thereof (Eghan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003ewas obtained by the Office of the Institute Ethics Committee (No./MGMC\u0026amp;H/IEC/JPR/2026/5130). We performed a secondary psychometric analysis to assess the psychometric properties of HRS-SR; data previously collected for an investigation into the mediating role of emotional attachment in the association between loneliness and hoarding disorder among college students (Mahadevaswamy, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The sample comprised data from 357 students aged 18 to 25 years (M\u003csub\u003eage\u003c/sub\u003e = 21.47, SD\u003csub\u003eage\u003c/sub\u003e = 2.24, n\u003csub\u003emale\u003c/sub\u003e = 115, n\u003csub\u003efemale\u003c/sub\u003e = 242) who were enrolled in college at the time and who were fluent in English. Data were collected using an online Google Form distributed across social media platforms between October to December 2025. Informed consent was obtained from all participants, and confidentiality was assured. The sample size is adequate for the present proposed analysis based on Comrey and Lee\u0026rsquo;s (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) guidelines for factor analysis.\u003c/p\u003e \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. Hoarding Rating Scale-Self Report (HRS-SR;\u003c/b\u003e Tolin et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e): The detailed description of HRS-SR is provided in the Introduction.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003e2. The UCLA 3-Item Loneliness Scale (\u003c/b\u003eHughes et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e): The UCLA 3-Item Loneliness Scale is a self-report instrument comprising three items that assess relational connectedness, social connectedness, and self-perceived isolation (e.g., \"How often do you feel that you lack companionship?\"). Response categories are coded as 1 (hardly ever), 2 (some of the time), and 3 (often). Scores for each item are summed to yield a total score ranging from 3 to 9, with higher scores indicating greater loneliness. The scale demonstrates adequate psychometric properties. In the current study, the scale exhibited good internal consistency (α\u0026thinsp;=\u0026thinsp;.81; ω\u0026thinsp;=\u0026thinsp;.81).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003e3. Saving Cognition Inventory (SCI;\u003c/b\u003e Steketee et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2003\u003c/span\u003e): The SCI is a self-report tool comprising 24 items that assesses participants' beliefs about saving or possessions across four subscales: Emotional Attachment (10 items, e.g., \u0026ldquo;Throwing away this possession is like throwing away a part of me\u0026rdquo;), Memory (5 items, e.g., \u0026ldquo;My memory is so bad I must leave this in sight or I\u0026rsquo;ll forget about it.\u0026rdquo;), Control (3 items, e.g., \u0026ldquo;It upsets me when someone throws something of mine away without my permission.\u0026rdquo;), and Responsibility (6 items, e.g., \u0026ldquo;Throwing this away means wasting a valuable opportunity\u0026rdquo;). Each item is rated on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;not at all, 4\u0026thinsp;=\u0026thinsp;sometimes, 7\u0026thinsp;=\u0026thinsp;very much) to indicate the extent to which they experienced each thought when deciding whether to throw something away during the past week. Scores for each individual's subscale are calculated by summing the item scores within each domain. The SCI demonstrated good to excellent internal consistency in the original study: Emotional Attachment (.95), Memory (.89), Control (.86), Responsibility (.90), and the 24-item total score (.96) (Steketee et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). For the present study, we considered only the Emotional Attachment domain to provide evidence of the HRS-SR's convergent validity, which exhibited excellent internal consistency (α\u0026thinsp;=\u0026thinsp;.912; ω\u0026thinsp;=\u0026thinsp;.913).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData were analysed using IBM SPSS (v. 22), IBM AMOS (v. 22), and JAMOVI (v. 2.7.15). Initially, descriptive statistics were calculated, before conducting a confirmatory factor analysis (CFA) to test the unidimensional factor structure of the HRS-SR, and applying a polytomous Rasch Rating Scale Model (RSM) to investigate Likert scale functioning in the context of hoarding behaviour. Parameters for these approaches are outlined below.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClassical Test Theory\u003c/h3\u003e\n\u003cp\u003eTo address the issues of multivariate non-normality, we employed bootstrap analysis with 5000 resamples and a 95% confidence interval. Whilst applying the Bollen\u0026ndash;Stine bootstrap to provide further evidence of model fit with the bootstrap samples. We expected the Bollen-Stine bootstrap to be non-significant (\u003cem\u003ep\u003c/em\u003e \u0026gt; .05), indicating an adequate model fit across bootstrap samples (Coolier, 2020). Model fit was assessed using the following indices: goodness of fit index (GFI \u0026gt; .95), comparative fit index (CFI \u0026gt; .95), Tucker-Lewis Index (TLI \u0026gt; .95), root mean square error of approximation (RMSEA \u0026lt; .08), and standardized root mean square residual (SRMR \u0026lt; .06), where these values indicate excellent fit (Bentler, 1992; Byrne, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Collier, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hu \u0026amp; Bentler, 1995; \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; MacCallum et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Given the sensitivity of the chi-square value to sample size, we used relative chi-square values of 3\u0026ndash;5 to assess fit (Collier, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Standardized factor loadings were considered adequate if they met the .50 cutoff (Hair et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Squared multiple correlations were required to be above .20 (Hooper et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Measurement invariance across gender was also tested. Configural, metric, scalar, and strict invariance were established if changes between models fit indices were within the acceptable limits (ΔRMSEA \u0026lt; .015, ΔCFI \u0026lt; .010, ΔTLI \u0026lt; .010; Chen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cheung \u0026amp; Rensvold, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The internal consistency was assessed using Cronbach's alpha, McDonald's omega, and composite reliability. For Cronbach\u0026rsquo;s alpha, values above 0.7, 0.8, and 0.9 were considered acceptable, good, and excellent, respectively (George \u0026amp; Mallery, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For McDonald's omega, a value above 0.80 was considered acceptable (Cervin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Composite reliability above 0.70, based on factor loadings, indicated good internal consistency (Collier, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Inter-item correlation values ranging from 0.15 to 0.85, and average inter-item correlation between 0.15 and 0.50, were deemed acceptable (Paulsen \u0026amp; BrckaLorenz, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The item discriminant index was considered acceptable if it exceeded 0.50 (Paulsen \u0026amp; BrckaLorenz, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We used Spearman correlation analysis to assess the convergent and divergent validity of the HRS-SR by examining its strength of the relationship with loneliness and emotional attachment, following Cohen\u0026rsquo;s (1998) guidelines (small = .10, medium = .30, large = .50). To further evaluate discriminant validity, we calculated HTMT using a CFA on a correlated three-factor model that included HRS-SF, Emotional Attachment, and Loneliness. HTMT values below .85 (Henseler et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kline, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) indicate adequate discriminant validity.\u003c/p\u003e\n\u003ch3\u003eItem Response Theory: Rasch Rating Model\u003c/h3\u003e\n\u003cp\u003eAfter confirming the assumption of Unidimensionality through CFA, the assumption of Local independence was evaluated using Q3 residual correlations, with a cut off of 0.50 (Ten Klooster et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The polytomous RSM was conducted using the JAMOVI (v. 2.7.15) package. The \u003cem\u003eeRm\u003c/em\u003e R package (Mair et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) within the snowIRT program (Seol, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) was used to estimate item difficulty parameters and to assess item fit using Infit and Outfit mean-square (MnSq) statistics. Acceptable Infit and Outfit MnSq values ranged from 0.70 to 1.30 (Wright \u0026amp; Linacre, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), facilitating the identification of underfitting or overfitting items. Reliability was determined using person reliability, with a cut-off of 0.70 indicating acceptable reliability (Abdulmajid \u0026amp; Khalid, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Both uniform and non-uniform Differential Item Functioning (DIF) across gender was evaluated using the \u003cem\u003edifNLR::difORD\u003c/em\u003e package of R via the snowIRT program (Hladka et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Adjusted \u003cem\u003ep\u003c/em\u003e-values \u0026gt;\u0026thinsp;.05 indicate no significant difference in item functioning between genders, showing that items are interpreted similarly by males and females.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Distribution\u003c/h2\u003e \u003cp\u003eSkewness values ranged from 0.70 to 1.04, and kurtosis values ranged from \u0026minus;\u0026thinsp;0.04 to 0.63; well within the recommended cut-offs to determine univariate normality (skewness\u0026thinsp;\u0026lt;\u0026thinsp;\u0026plusmn;\u0026thinsp;2, kurtosis\u0026thinsp;\u0026lt;\u0026thinsp;\u0026plusmn;\u0026thinsp;4; Collier, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the critical ratio was 25.680, which exceeds the recommended cut-off of 5.0 (Byrne, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), indicating multivariate non-normality. When all items (UCLA 3-item loneliness scale, HRS-SR, emotional attachment domain of SCI) were modelled, the principal axis factoring method using an unrotated factor solution extracted three factors, and the first factor explained 39.583% of the total variance, which is less than 50%, indicating the absence of common method bias (Collier, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Howard et al., 2024). No significant floor or ceiling effects were observed; only 10% had the lowest score, and none had the highest, both below the recommended 15% threshold (Gulledge et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePsychometric Properties using Classical Test Theory\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eConfirmatory Factor Analysis\u003c/h2\u003e \u003cp\u003eThe CFA was conducted to examine the structural validity of the HRS-SR. The initial unidimensional model (Model 1) showed a poor fit (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;36.494/5, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; GFI = .960; TLI = .896; CFI = .948; RMSEA = .133, 95% CI [.095, .175]; SRMR = .045). Modification indices indicated that allowing a covariance between error terms for Items 2 and 3 would improve the model. This covariance was freely estimated in Model 2, which in turn showed a very good fit (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;9.812/4, \u003cem\u003ep\u003c/em\u003e = .044; GFI = .989; TLI = .976; CFI = .990; RMSEA = .064, 95% CI [.010, .116]; SRMR = .022) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); supporting the unidimensional structure (See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) of the HRS-SR as proposed by Tolin et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Standardised factor loadings exceeded .50 (Hair et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), ranging from .602 (Item 2) to .827 (Item 4). All squared multiple correlations were above .20 (Hooper et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), ranging from .362 (Item 2) to .685 (Item 4), showing that all items were strongly associated with the latent construct. All factor loadings and squared multiple correlations were statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05) with 95% confidence intervals not including zero, supporting parameter estimates based on bootstrapping (Collier, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Finally, the Bollen\u0026ndash;Stine bootstrap was non-significant (\u003cem\u003ep\u003c/em\u003e = .205), further indicating adequate fit across bootstrap samples (Collier, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eGoodness-of-fit indexes of the unidimensional model of the HRS-SR (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;357)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\"\u0026minus;\" 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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u0026sup2;/df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCMIN/DF\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\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSEA (90% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSRMR\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\u003eModel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.494/5\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e.133 [.095-.175]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.812/4\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e.064 [.010-.116]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: GFI \u0026ndash; Goodness-of-Fit Index, TLI \u0026ndash; Tucker\u0026ndash;Lewis Index, CFI \u0026ndash; Comparative Fit Index, RMSEA (90% CI) \u0026ndash; Root Mean Square Error of Approximation, SRMR \u0026ndash; Standardized Root Mean Square 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 Loading and Squared Multiple Correlations\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=\"char\" char=\".\" 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=\"left\" 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=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eλ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eULCI\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHRS1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHRS2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHRS3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHRS4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHRS5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: HRS\u0026thinsp;=\u0026thinsp;Hoarding Rating Scale, LLCI \u0026ndash; Lower Limit Confidence Interval, ULCI \u0026ndash; Upper Limit Confidence Interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement Invariance\u003c/h3\u003e\n\u003cp\u003eMeasurement invariance across gender was tested using constrained models. Results showed configural invariance, as indicated by acceptable model fit indices (TLI = .905, CFI = .952, SRMR = .042, RMSEA = .090, 95% CI [.061, .121]). Although the RMSEA value indicated only a reasonable fit, it should be interpreted with caution given RMSEA can overestimate lack of fit in models with few degrees of freedom (e.g., df\u0026thinsp;\u0026lt;\u0026thinsp;10; Kenny et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, changes in fit indices across models met recommended thresholds (ΔRMSEA \u0026lt; .015, ΔCFI \u0026lt; .010, ΔTLI \u0026lt; .010) (Chen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cheung \u0026amp; Rensvold, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), supporting metric, scale, and strict invariance across gender (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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 across gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eΔRMSEA\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\u003eConfigural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.090 [.061-.121]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMatric\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.083 [.058-.109]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScaler\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.079 [.055-.105]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStrict\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.076 [.055-.098]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: TLI \u0026ndash; Tucker\u0026ndash;Lewis Index, CFI \u0026ndash; Comparative Fit Index, RMSEA \u0026ndash; Root Mean Square Error of Approximation, SRMR \u0026ndash; Standardized Root Mean Square 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\u003c/h2\u003e \u003cp\u003eThe internal consistency of the HRS-SR was good (α\u0026thinsp;=\u0026thinsp;.825, ω\u0026thinsp;=\u0026thinsp;.828). Composite reliability was also acceptable (CR = .817). Inter-item correlations ranged from .363 to .617 (M = .485), within the accepted correlation range of .15 to .85 and mean range of.15 to .50 (Paulsen \u0026amp; BrckaLorenz, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). All five HRS-SR items showed high discrimination indices (.571 to .703); exceeding the .50 cut-off (Paulsen \u0026amp; BrckaLorenz, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Item 4 (\u0026ldquo;To what extent do you experience emotional distress because of clutter, difficulty discarding, or problems with buying or acquiring things?\u0026rdquo;) had the highest index, and Item 1 (\u0026ldquo;Because of the clutter or number of possessions, how difficult is it for you to use the rooms in your home?\u0026rdquo;) had the lowest. Deleting any item did not improve Cronbach\u0026rsquo;s alpha (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eDescriptive Statistics, Inter-Item and Item\u0026ndash;Total Correlations, and Internal Consistency of the HRS-SR Items (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;357)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHRS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHRS2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHRS3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHRS4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHRS5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCorrected Item-Total Correlation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCronbach's Alpha if Item Deleted\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\u003eHRS1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.630\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHRS2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.435\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHRS3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.559\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHRS4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.526\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHRS5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: Average Inter-Item Correlation = .485, HRS\u0026thinsp;=\u0026thinsp;Hoarding Rating Scale, SD\u0026thinsp;=\u0026thinsp;Standard Deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eValidity\u003c/h2\u003e \u003cp\u003eSpearman correlation analyses were performed to assess the convergent and divergent validity of the HRS-SF, wherein we expected a stronger association between HRS-SF and the Emotional Attachment domain of the SCI-R than between HRS-SF and Loneliness, thus providing evidence for convergent and divergent validity, respectively. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the HRS-SF demonstrated a strong, significant positive association with the Emotional Attachment domain (\u003cem\u003er\u003c/em\u003e = .636, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), supporting convergent validity. Meanwhile, its significant but moderate positive association with Loneliness provides support for divergent validity. To further assess discriminant validity, we calculated HTMT using a CFA on a correlated three-factor model including HRS-SF, Emotional Attachment, and Loneliness (3-item UCLA). Notably, the CFA findings indicated that this three-factor model demonstrated adequate fit indices (x2/df\u0026thinsp;=\u0026thinsp;371.011/132, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, TLI = .911, CFI = .924, RMSEA = .071 [.063-.080], SRMR = .058). Additionally, HTMT values supported discriminant validity, with scores of .71 for HRS-SR\u0026ndash;Emotional Attachment and .46 for HRS-SR\u0026ndash;Loneliness, both below the recommended threshold of .85 (Henseler et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kline, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\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\u003eDescriptive Statistics, Spearman Correlations Coefficient and Internal Consistency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRS-SR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoneliness\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\u003eHRS-SR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.636\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLoneliness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.410\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.349\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSkewness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKurtosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.431\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.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.810\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.828\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.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: HRS-SR\u0026thinsp;=\u0026thinsp;Hoarding Rating Scale \u0026ndash; Self Report, EA\u0026thinsp;=\u0026thinsp;Emotional Attachment, SD\u0026thinsp;=\u0026thinsp;Standard Deviation, α\u0026thinsp;=\u0026thinsp;Cronbach Alpha, ω\u0026thinsp;=\u0026thinsp;McDonald Omega. **Significant at the level of \u0026lt; .01\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\u003ePsychometric Properties using Item Response Theory\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eAssumption Analysis: Unidimensionality and Local Independence\u003c/h2\u003e \u003cp\u003eAfter confirming the unidimensionality assumption through CFA, local independence was assessed using Yen\u0026rsquo;s Q3 correlation matrix with a cut-off value of .36 (Flens et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The results indicated that the residual correlations ranged from \u0026minus;\u0026thinsp;.389 to .002. Only two item pairs exceeded the threshold: Item 2 and Item 5 (-.389), and Item 1 and Item 3 (-.378). Since few studies use a cut-off as high as .50 (Ten Klooster et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), these values suggest no item redundancies and support local independence. The person reliability index was .727, indicating adequate reliability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eItem statistics of the rating scale model\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: HRS\u0026thinsp;=\u0026thinsp;Hoarding Rating Scale\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eItem Statistics of the Rating Scale Model\u003c/h2\u003e \u003cp\u003eThe difficulty levels of the items varied from 1.01 (item 3) to 1.22 (item 1). Furthermore, the Infit values ranged from 0.935 (item 4) to 1.132 (item 5), while the Outfit values spanned from 0.851 (item 4) to 1.058 (item 3). All values fell within the recommended fit range of 0.7 to 1.3 (Wright et al., 1994), suggesting no signs of overfitting or underfitting items aligning with the expectations of the RSM (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Item Functioning\u003c/h2\u003e \u003cp\u003eDIF analysis examined whether individuals with the same hoarding severity, but different genders, responded differently to HRS-SR items. Both uniform and non-uniform DIF showed no statistically significant differences after adjustment (\u003cem\u003ep\u003c/em\u003e \u0026gt; .05), suggesting HRS-SR items function equally across genders (see Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential Item Functioning across gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\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\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eUniform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eNon-Uniform\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAdj.p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\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 \u003cp\u003eHRS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e5.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.11003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote. Adj.\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;The adjusted \u003cem\u003ep\u003c/em\u003e-values by likelihood ratio test using multiple comparison; HRS\u0026thinsp;=\u0026thinsp;Hoarding Rating Scale\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\u003eThis study assessed the psychometric properties of the HRS-SR among a sample of Indian college students using robust classical test theory (CTT) in addition to more robust item response theory (IRT). Findings verified that the HRS-SR is a valid and reliable tool to assess the severity of hoarding disorder among college students in India.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eClassical Test Theory\u003c/h2\u003e \u003cp\u003eConsistent with prior studies, the CFA supported the unidimensional factor structure of HRS-SR originally proposed by Tolin et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), indicating that the HRS-SR adequately measures the core features of hoarding disorder, including clutter, difficulty discarding, excessive acquisition, distress, and impairment. Such unidimensionality has also been found to be the case across countries and cultures (Hussain et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tsuchiyagaito et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Stamatis et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, all standardized factor loadings exceeded .602, demonstrating sufficient correspondence (see Hair et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) between the distinct items and the composite hoarding disorder factor. Additionally, all squared multiple correlations exceeded .362; indicating adequate variance explained by the latent factor in accordance with Hooper et al.\u0026rsquo;s (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) proposed cut-off of .20. Findings also demonstrated measurement invariance across gender at the configural, metric, scalar, and strict levels (Chen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cheung \u0026amp; Rensvold, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), indicating that the HRS-SR assesses hoarding symptoms equivalently in both male and female participants; further boasting its utility.\u003c/p\u003e \u003cp\u003eThe HRS-SR showed good reliability, with both Cronbach's alpha and McDonald's omega exceeding .80 (Cervin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; George \u0026amp; Mallery, 2003). These findings are consistent with the scale\u0026rsquo;s original conceptualisation (Tolin et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; α\u0026thinsp;=\u0026thinsp;.83) and more recent validations, which report acceptable to excellent internal consistency (Cronbach's α\u0026thinsp;=\u0026thinsp;.78 \u0026minus;\u0026thinsp;.93; Lee et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Stamatis et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tsuchiyagaito et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to Hair et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), although Cronbach\u0026rsquo;s alpha is widely used to assess reliability, it does not weigh individual indicators, which are instead addressed by composite reliability, making it the preferred method. The composite reliability based on standardized factor loadings was .817; further validating its internal consistency. Additionally, the HRS-SR showed adequate inter-item correlation and acceptable average inter-item correlation, indicating that scores on each item positively related to scores on other scale items. In practice, this means that the items measure a common construct (homogeneity) but still have sufficient unique characteristics to ensure they are not redundant or identical to one another (i.e., not isomorphic) (Piedmont, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, all items had a corrected item-total correlation greater than .50 (Paulsen \u0026amp; BrckaLorenz, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), providing strong evidence of each item's consistency with the total HRS-SR score and demonstrating high discriminant validity.\u003c/p\u003e \u003cp\u003eThe HRS-SR was strongly and positively associated with emotional attachment, meaning that students with greater hoarding severity within our sample were more likely to hold maladaptive beliefs about their possessions. This supports the scale's convergent validity. The cognitive behavioural model of hoarding disorder conceptualizes hoarding as a multifaceted problem arising from information processing deficits, difficulties in forming emotional attachments, behavioural avoidance, and erroneous beliefs regarding the nature of possessions (Frost \u0026amp; Hartl, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). In contrast, the HRS-SR showed a positive correlation with loneliness, which itself is related, but not identical to, hoarding. This association supports the scale's divergent validity by indicating that the HRS-SR measures a construct distinct from social/emotional relatedness. Furthermore, the HRS-SR demonstrated adequate discriminant validity, as evidenced by HTMT values below .85 (Henseler et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kline, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) with emotional attachment and loneliness. Together, this data evidences the utility of the HRS-SR within Indian cohorts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eItem Response Theory: Rasch Rating Scale Model\u003c/h2\u003e \u003cp\u003eIn the present study, despite demonstrating the strong psychometric properties of the HRS-SR using CTT, we also applied IRT principles to further establish its psychometric properties. Specifically, we used the Rasch Rating Scale Model, a form of IRT, which is suitable for modelling dichotomous responses and calculates the probability that an individual will provide a correct answer on a dichotomous item (Magno, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe CFA demonstrated unidimensionality, indicating that the HRS-SR measures a single latent construct with local independence, which, in practice, means that the participants\u0026rsquo; responses across items were not statistically related once the latent trait is accounted for (Yang \u0026amp; Kao, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These findings help us to understand that only one underlying characteristic is measured and that responses to one item are not contingent on responses to another (Yang \u0026amp; Kao, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Person-specific reliability was .727, reflecting a high level of consistency across items (Abdulmajid \u0026amp; Khalid, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with all five items indicating a high degree of difficulty; suggesting that students generally need higher ability levels to have at least a 50% chance of answering correctly. This suggests that only students with stronger trait ability are likely to answer these items correctly (Yang \u0026amp; Kao, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Surprisingly, all five items demonstrated high discriminant indices, with each corrected total-item correlation exceeding .50, indicating strong distinction among the items based on CTT-based parameters (Paulsen \u0026amp; BrckaLorenz, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, regarding item fit, all observed infit and outfit values fell within the cut-offs of 0.7, which may indicate item redundancy, and 1.3, which may indicate a given item does not measure hoarding disorder akin to others within the scale (Wright et al., 1994). Thus, the results show no overfitting or underfitting, supporting the scale's unidimensionality and construct validity. Finally, the findings indicated that students having the same underlying ability have the same probabilities of getting an item correct regardless of gender (Narayanon, \u0026amp; Swaminathan, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) as both uniform and non-uniform DIF were non- statistically significant after adjustment (\u003cem\u003ep\u003c/em\u003e \u0026gt; .05). In the present study, however, all five HRS-SR items showed equivalence across male and female responders. This equivalence was evident in both DIF analyses and CTT-based parameters, both of which demonstrated adequate measurement invariance across gender. To our knowledge, in only one study, an IRT approach was used to assess DIF between the English and Spanish versions of HRS-SR (Stamatis et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which that the HRS item pertaining to clutter was the only item to exhibit DIF, indicating that Spanish-speaking participants with similar hoarding symptoms were slightly less likely to endorse cluttering behaviour. However, the impact of this DIF was minimal, even at the item level, and had little effect on the overall test characteristic curves (Stamatis et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStrength and Limitations\u003c/h2\u003e \u003cp\u003eResults should be discussed in light of six limitations and considerations. First, data were derived from a single sample of Indian college students. As such, findings cannot be generalized to other populations and clinical samples, and so would benefit from further replication to assess consistency. Second, owing to this study utilising secondary data, we were unable to assess the test-retest reliability of the HRS-SR, which is essential for establishing temporal reliability over time. Third, despite establishing convergent and divergent validity, future studies may be wise to include other hoarding disorder scales, such as the SI-R (Frost et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), to provide further evidence of convergent validity. Fourth, we did not account for the presence of comorbid psychological disorders and so could not include such information within our models, which may have influenced the findings as studies indicated a high comorbidity rate for major depressive disorder and acquisition-related impulse control disorders, including compulsive buying, kleptomania, and acquiring free items (Frost et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Additionally, Abouzed et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported a significant positive association between hoarding tendencies and autism traits. Similarly, Woerner et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that hoarding symptoms correlated positively with anxiety, depression, and ADHD symptoms. Fifth, it would be advantageous for future studies to include a more robust Graded Response Model. Zein and Akhtar (2024) note that the RSM accounts only for varying item difficulty and assumes equal item discrimination. In contrast, the Graded Response Model both estimates discrimination and threshold parameters for each item and is suited for modelling ordinal data with more than two response categories (i.e., Likert-style). Finally, future studies need to conduct focus groups with practitioners and researchers to assess the utility of such a measure in order for us to better understand its utility moving forward.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present study represents the first validation of the HRS-SR with an Indian sample using both CTT and more contemporary IRT. The CTT-based parameters indicated adequate reliability and a valid unidimensional construct measuring hoarding disorder components, including clutter, difficulty discarding, excessive acquisition, distress, and impairment. The findings also support measurement invariance across gender. Similarly, IRT-based parameters demonstrated high consistency across items, with all five items fitting the unidimensional construct supporting both unidimensionality and construct validity without evidence of misfit. Additionally, all five HRS-SR items function equivalently across male and female responders. Together, this data evidences utility of the HRS-SR within Indian samples, and we call on practitioners and researchers within this area to further explore the utility of it.\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/2026/5130). Informed consent was not required as the study used secondary data.\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, MM, SN, and DF; Methodology, MM, SN, and DF; Data Analysis, MM; Writing-Original Draft Preparation, MM; Writing-Review \u0026amp; Editing, SN and DF. All authors have read and approved the manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNo external funding was received for this study.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eData are available upon reasonable request from the corresponding author, subject to the institute's ethical approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdulmajid AA, Khalid KA (2024) Using Rasch Analysis for Validation of Knowledge and Perception of Orthopedic Workplace-Based Assessment among Postgraduate Orthopedic Trainees\u0026rsquo; Questionnaire. 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Int J Psychol 60(1):e13265. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ijop.13265\u003c/span\u003e\u003cspan address=\"10.1002/ijop.13265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Hoarding, Classical Test Theory, Item Response Theory, India","lastPublishedDoi":"10.21203/rs.3.rs-8924690/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8924690/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Hoarding Rating Scale-Self Report (HRS-SR; Tolin et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) is a well-established and internationally-recognised tool used to assess hoarding severity, which has been validated and translated across countries and cultures. However, to date, its psychometric properties have not been systematically examined within an Indian context where hoarding behaviour is a known issue. This study assessed the psychometric properties of the HRS-SR among 357 Indian college students (M\u003csub\u003eage\u003c/sub\u003e = 21.47, SD\u003csub\u003eage\u003c/sub\u003e = 2.24; 67.8% female) using classical test theory (CTT) and more robust item response theory (IRT). The HRS-SR was found to have a unidimensional factor structure and showed acceptable factor loadings, with good internal consistency (α\u0026thinsp;=\u0026thinsp;.825, ω\u0026thinsp;=\u0026thinsp;.828) and composite reliability (.817). Findings also indicated similar response patterns across male and female responders. Taken together, this study validates and supports the utility of the HRS-SR within Indian samples, with discussions focused around how academic researchers and clinicians can and should capitalise on this tool and build upon our findings.\u003c/p\u003e","manuscriptTitle":"Psychometric Properties of the Hoarding Rating Scale-Self-Report (HRS-SR): Evidence from Classical Test Theory and Item Response Theory Based on Secondary Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 00:44:40","doi":"10.21203/rs.3.rs-8924690/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":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63254560,"name":"Psychology"},{"id":63254561,"name":"Psychiatry"}],"tags":[],"updatedAt":"2026-02-23T00:44:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 00:44:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8924690","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8924690","identity":"rs-8924690","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}