Latent Structure and Clinical Profiles of Negative Symptoms in Schizophrenia: A Factor Analytic and Cluster Analytic Study

Latent Structure and Clinical Profiles of Negative Symptoms in Schizophrenia: A Factor Analytic and Cluster Analytic Study | 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 Latent Structure and Clinical Profiles of Negative Symptoms in Schizophrenia: A Factor Analytic and Cluster Analytic Study Maitreya Milind Palamwar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9448568/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 Background: Negative symptoms in schizophrenia are commonly conceptualized as comprising avolition/apathy (AA) and diminished expression (DE). However, the validity of this distinction when operationalized using the Positive and Negative Syndrome Scale (PANSS) remains unclear, and person-centered approaches to characterizing symptom heterogeneity are limited. Methods: This study analyzed PANSS data from 3,006 individuals with schizophrenia. Confirmatory factor analysis (CFA) was used to evaluate established PANSS models and the AA–DE framework. Exploratory factor analysis (EFA) was conducted to identify data-driven structures, followed by CFA validation. Cluster analysis (Ward’s method and K-means) was applied to factor scores to identify patient profiles. Cluster validity was assessed using silhouette coefficients. Differences in age at onset and sex were examined. Results: The AA-DE model and the Marder five-factor model were non-admissible in CFA. EFA identified a two-factor structure comprising interpersonal-affective impairment and behavioral-volitional impairment, which demonstrated good fit in CFA. Cluster analysis revealed three profiles: behavioral-volitional impairment, interpersonal-affective impairment, and low negative symptoms. The overall silhouette coefficient indicated moderate cluster separation. Clusters differed significantly in age at onset and sex, with the interpersonal-affective profile associated with later onset. Conclusion: Findings suggest that PANSS may not adequately capture the AA-DE distinction, instead yielding alternative dimensional structures. Person-centered analyses identified clinically meaningful subgroups, highlighting heterogeneity in negative symptom presentation and the need for improved measurement approaches. Psychology Psychiatry schizophrenia negative symptoms PANSS factor analysis cluster analysis 1. Introduction Negative symptoms in schizophrenia refer to reductions or deficits in normal behaviors and functions related to motivation, interest, and emotional and verbal expression (Möller, 2007 ). Negative symptoms comprise five key constructs: blunted affect, alogia, avolition, asociality, and anhedonia (Correll and Schooler, 2020 ). Negative symptoms are associated with poor functional outcome and place a substantial burden on patients, families, and healthcare systems (Galderisi et al., 2016 ). Critically, negative symptoms assessed early in illness predict poor role functioning years later (Pogue-Geile and Harrow, 1985 ). However, primary negative symptoms – intrinsic to schizophrenia’s pathophysiology – generally do not respond well to current antipsychotic treatments, representing an unmet medical need (Correll and Schooler, 2020 ). It is also important to keep in mind that these are highly prevalent: up to 60% of individuals with schizophrenia will exhibit clinically significant negative symptoms requiring treatment (Correll and Schooler, 2020 ), and 50–90% of individuals with first-episode psychosis experience them (Mäkinen et al., 2008 ). Multiple instruments to measure symptoms of schizophrenia have been developed over the years. These include the Scale for the Assessment of Negative Symptoms (SANS; Andreasen, 1989 ) and the Brief Negative Symptom Scale (BNSS; Weigel et al., 2023 ) – both reliable and valid measures. The Positive and Negative Syndrome Scale (PANSS; Kay et al., 1987 ) is perhaps the most widely-used measure of clinical symptoms in schizophrenia. It consists of seven positive symptom items, seven negative symptom items, and sixteen general psychopathology items. Multiple studies have proposed alternative factor structures for the PANSS – ranging from the original three-factor solution, to contemporary and popular five-factor models (Lehoux et al., 2009 ), and even seven-factor models (Emsley et al., 2003 ). Currently, a five-factor model proposed by Marder et al. ( 1997 ) enjoys support in literature (Emsley et al., 2003 ; Lançon et al., 2000 ; Wu et al., 2015 ). However, the Marder model, while showing good construct validity, showed “insufficient” structural validity (Roithmeier et al., 2025 ). Certain studies show that the Marder model does not prove to be an adequate fit (White et al., 2010 ). Collectively, these findings highlight ongoing inconsistencies in the latent structure of PANSS, raising concerns regarding its structural validity across samples. Negative symptoms are heterogeneous across both experiential and expressive domains, leading to the identification of two major subdomains: Avolition/Apathy (AA) and Diminished Expression (DE; Kaiser et al., 2017 ; Strauss et al., 2013 ). Correll and Schooler ( 2020 ) mention that of the five major negative symptom domains, avolition, asociality, and anhedonia make up AA, while blunted affect and alogia comprise DE. Liemburg et al. ( 2013 ) concluded that the AA and DE factors were made up from specific PANSS negative and general psychopathology subscales. This factor structure has also been validated across literature (Capatina and Miclutia, 2016 ; Lim et al., 2016 ). Despite growing support for this two-factor conceptualization, its operationalization using PANSS items remains debated, particularly given variability in item loadings across samples. While factor analytic approaches have been central to understanding the latent structure of negative symptoms, they represent variable-centered methods that assume homogeneity across individuals. Such approaches may obscure meaningful heterogeneity in symptom presentation at the patient level. Person-centered methods, such as cluster analysis, provide a complementary framework by identifying subgroups of individuals with distinct symptom profiles. Despite substantial progress in conceptualizing negative symptoms as comprising AA and DE, important gaps remain in their empirical validation, particularly when operationalized using the Positive and Negative Syndrome Scale (PANSS), and relatively few studies have integrated empirically derived factor structures with person-centered approaches to identify clinically meaningful patient profiles. While instruments such as the SANS and BNSS were specifically developed to assess negative symptoms, the PANSS remains the most widely used clinical tool in research and practice, resulting in a heavy reliance on PANSS-derived models despite concerns regarding its structural validity and limitations in capturing the full scope of negative symptoms (Aboraya and Nasrallah, 2016 ; Serafim et al., 2026 ). Prior research has demonstrated considerable variability in both the number and composition of PANSS factors, with studies supporting three-, five-, and seven-factor models, and even among five-factor solutions, differences in item loadings and model fit have been reported, suggesting instability in the latent structure across samples (van der Gaag et al., 2006 ). This variability is particularly relevant for negative symptoms, where attempts to map PANSS items onto the AA-DE framework have yielded inconsistent results, raising questions about the adequacy of PANSS for capturing this distinction despite theoretical and empirical support for the two-factor model. Moreover, many studies examining the AA-DE structure have relied on predefined item assignments rather than systematically evaluating the latent structure of relevant PANSS items within large and diverse clinical samples, limiting the ability to distinguish true latent constructs from model-imposed structures (Liemburg et al., 2013 ). A combined exploratory and confirmatory approach is therefore recommended to both test existing theoretical models and identify data-driven structures where appropriate (Anderson and Gerbing, 1988 ; Ockey, 2013 ). Accordingly, the present study addresses these gaps by examining the latent structure of negative symptoms in a large clinical sample of individuals with schizophrenia using both confirmatory and exploratory factor analytic methods. By evaluating established PANSS models, testing the AA-DE framework, and deriving alternative structures where necessary, this study aims to clarify the organization of negative symptoms and assess the extent to which PANSS can validly capture their underlying dimensionality. In addition, to better characterize heterogeneity at the patient level, the study further applies a person-centered approach to identify distinct symptom profiles based on the derived dimensions and examine their clinical correlates. The present study aimed to examine the latent structure of negative symptoms in a large clinical sample of individuals with schizophrenia and to identify clinically meaningful patient profiles based on these dimensions. Specifically, we sought to: evaluate the fit of established PANSS factor structures using CFA, test the AA-DE model using CFA, explore the underlying structure of PANSS items using EFA, validate the resulting factor structure using CFA, and identify distinct patient profiles using cluster analysis based on the derived factor scores and examine their clinical correlates. Based on prior literature, it was expected that the AA-DE model would demonstrate acceptable fit in CFA. However, given the documented variability in PANSS factor structures across studies and concerns regarding the use of PANSS items to operationalize AA and DE, it was also anticipated that the AA-DE structure might not fully replicate in the present dataset. Accordingly, exploratory analyses were expected to provide additional insight into the latent organization of negative symptoms and to identify alternative factor structures where appropriate. It was further hypothesized that cluster analysis would identify distinct symptom profiles, and that these profiles would differ on clinically relevant variables, including age at onset and sex. 2. Methods 2.1. Study Design and Participants This study involved a secondary analysis of a large clinical dataset of individuals with schizophrenia collected at the Mental Health Research Center (Russia) between 2007 and 2020 (Lezheiko et al., 2022). The sample comprised 3,006 patients (898 men, 2,108 women), with a mean age of 38.4 years (SD = 13.6) and a mean age at illness onset of 26.4 years (SD = 11.1). All participants had provided written informed consent in the original study, which was approved by a local ethics committee. Diagnoses were established according to ICD-10 criteria and confirmed using the Mini International Neuropsychiatric Interview (MINI). 2.2. Measure Clinical symptoms were assessed using the PANSS (Kay et al., 1987 ), a widely used instrument measuring positive, negative, and general psychopathology in schizophrenia. The PANSS comprises 30 items: 7 positive symptom items, 7 negative symptom items, and 16 general psychopathology items (see Table 1 ). Assessments were conducted by trained psychiatrists during the week prior to patient discharge. Table 1 PANSS items in the original three-factor model by Kay et al. ( 1987 ). Positive Scale Negative Scale General Psychopathology P1 (Delusions) N1 (Blunted affect) G1 (Somatic concern) P2 (Conceptual disorganization) N2 (Emotional withdrawal) G2 (Anxiety) P3 (Hallucinatory behavior) N3 (Poor rapport) G3 (Guilt feelings) P4 (Excitement) N4 (Passive/apathetic social withdrawal) G4 (Tension) P5 (Grandiosity) N5 (Difficulty in abstract thinking) G5 (Mannerisms and posturing) P6 (Suspiciousness and persecution) N6 (Lack of spontaneity) G6 (Depression) P7 (Hostility) N7 (Stereotyped thinking) G7 (Motor retardation) – – G8 (Uncooperativeness) – – G9 (Unusual thought content) – – G10 (Disorientation) – – G11 (Poor attention) – – G12 (Lack of judgment and insight) – – G13 (Disturbance of volition) – – G14 (Poor impulse control) – – G15 (Preoccupation) – – G16 (Active social avoidance) Based on established factor structures (Liemburg et al., 2013 ), negative symptom domains were operationalized as AA, comprising items N2 (Emotional Withdrawal), N4 (Apathetic Social Withdrawal), and G16 (Active Social Avoidance), and DE, comprising items N1 (Blunted Affect), N3 (Poor Rapport), N6 (Lack of Spontaneity), G5 (Mannerisms and Posturing), G7 (Motor Retardation), and G13 (Disturbance of Volition). 2.3. Procedure Prior to analysis, items of the PANSS, age at onset, and sex of the participants were extracted from the original dataset. Cases with missing data on variables included in the factor analyses were excluded using listwise deletion. 2.4. Statistical Analyses 2.4.1. Factor Analyses All analyses were conducted in JASP (Version 0.19.1.0) using the lavaan package for CFA. A stepwise approach was used to examine the latent structure of PANSS negative symptoms. CFA models were first specified for the full PANSS, including a unidimensional model and a five-factor model (Marder et al., 1997 ; see Table 2 ). EFA was then conducted on all PANSS items using principal axis factoring with oblimin rotation, with the number of factors determined by parallel analysis. A four-factor solution was also examined, followed by CFA of the EFA-derived structure. Table 2 The Marder five-factor model of the PANSS. Positive Factor Negative Factor Cognitive/ Disorganization Factor Depression/ Anxiety Factor Excitability/ Hostility Factor P1 N1 P2 G1 P4 P3 N2 N5 G2 P7 P5 N3 G5 G3 G8 P6 N4 G10 G4 G14 N7 N6 G11 G6 – G9 G7 G13 – – G12 G16 – – – G15 – – – – Note. Full item names can be found in Table 1 . CFA models were subsequently specified for AA (N2, N4, G16) and DE (N1, N3, N6, G5, G7, G13), along with a unidimensional model including all items. EFA was then conducted on these items using the same parameters, followed by CFA of the resulting structure. Model fit was evaluated using CFI, TLI, RMSEA (90% CI), and SRMR, with thresholds of ≥ .90 (acceptable) and ≥ .95 (good) for CFI/TLI, and ≤ .08 for RMSEA and SRMR. The chi-square statistic was also reported. Internal consistency was assessed using Cronbach’s alpha and McDonald’s omega. 2.4.2. Cluster Analysis Analyses were conducted through IBM SPSS 27, using factor scores derived from the final CFA model. Variables were standardized prior to analysis. Hierarchical cluster analysis using Ward’s method with squared Euclidean distance was first performed to determine the optimal number of clusters based on inspection of agglomeration coefficients and dendrograms. Subsequently, k-means clustering was applied to derive the final cluster solution. A two-step clustering approach was adopted to improve solution stability, with hierarchical clustering used to determine the number of clusters and K-means clustering used to refine cluster membership. Differences between clusters in age at onset were examined using one-way analysis of variance (ANOVA). Given violation of homogeneity of variance, Games-Howell post hoc tests were used. Associations between cluster membership and sex were examined using chi-square tests. Effect sizes were calculated to assess the magnitude of group differences. 3. Results 3.1. Evaluation of Existing PANSS Models CFA was first conducted on the full 30-item PANSS. The unidimensional model demonstrated poor fit to the data. A five-factor model based on previously proposed PANSS structure (Marder et al., 1997 ) was also specified; however, the model was non-admissible, with the covariance matrix of latent variables not positive definite, and therefore fit indices could not be computed (Table 3 ). Table 3 Fit indices for all confirmatory factor analysis models. Model Items χ² (df) CFI TLI RMSEA (90% CI) SRMR Notes PANSS 1-Factor 30 25515.93(405) 0.52 0.49 0.144 (0.142–0.145) 0.14 Poor fit PANSS Marder 5-Factor 30 – – – – – Model failed (non-admissible) PANSS EFA-derived 4-Factor 28 8458.79(344) 0.83 0.81 0.089 (0.087–0.090) 0.08 Poor fit AA-DE 2-Factor 9 – – – – – Model failed (non-admissible) AA-DE 1-Factor 9 1559.05(27) 0.90 0.87 0.137 (0.132–0.143) 0.06 Poor fit EFA-derived 2-Factor 9 608.86(26) 0.96 0.95 0.086 (0.080–0.092) 0.03 Good fit Note. “–“ indicates model could not be computed (non-admissible). All χ² values were significant at p < .01. CFI: comparative fit index , TLI: Tucker-Lewis index , RMSEA: root mean square error of approximation , SRMR: standardized root mean square residual. 3.2. Exploratory Factor Analysis of PANSS Items EFA of the full PANSS item set was conducted using principal axis factoring with oblimin rotation. Parallel analysis suggested a multi-factor solution; however, the extracted factors were not clearly interpretable. In a manually specified four-factor solution, all items demonstrated loadings ≥ 0.30; however, G13 and G16, exhibited substantial cross-loadings across two factors. The two cross-loading items were dropped, and a reduced, 28-item, four-factor model derived from the previous EFA was tested using CFA. However, it demonstrated poor fit (Table 3 ). 3.3. Factor Structure of AA and DE CFA was conducted on negative symptom items based on the proposed AA and DE structure. The two-factor AA-DE model was non-admissible, with a non-positive definite covariance matrix of latent variables. The correlation between AA and DE composite scores was high, r (3004) = 0.84, p < .001, suggesting substantial overlap between the constructs. A unidimensional CFA model including all AA and DE items demonstrated poor fit (Table 3 ). EFA was conducted on the subset of PANSS items corresponding to AA and DE domains. Parallel analysis initially suggested a three-factor solution; however, no items loaded meaningfully on the third factor, and it was therefore not retained. A two-factor solution was extracted and was interpretable. The first factor comprised N1, N2, N3, and N4, representing an interpersonal-affective impairment dimension. The second factor comprised G5, G7, G13, G16, and N6, reflecting a behavioral-volitional dimension. Most items demonstrated clear primary loadings on a single factor, with minimal cross-loadings. Item N6 showed modest cross-loading (0.32 on Factor 1 and 0.41 on Factor 2) but was retained in the second factor based on its higher loading. Factor loadings are presented in Table 4 . Table 4 Factor loadings for the exploratory factor analysis of the items included in th e Liemburg et al. ( 2013 ) model of Avolition/Apathy and Diminished Expression. Item Factor 1 Factor 2 Communality N1 .92 .74 N2 .91 .82 N3 .76 .76 N4 .73 .85 N6 .32 .41 .43 G5 .67 .34 G7 .48 .26 G13 .70 .59 G16 .75 .70 Note. Number of factors was based on parallel analysis. Applied rotation method was oblimin. Full item names can be found in Table 1 . CFA was conducted to evaluate the fit of the two-factor model derived from the EFA of AA-DE items. The model demonstrated good overall fit (Table 3 ). Standardized factor loadings were all statistically significant and in the expected direction. Standardized and unstandardized factor loadings of each item are given in Table 5 . Internal consistency was high for both factors. The first factor demonstrated excellent reliability, α = 0.92, ω = 0.93, while the second factor demonstrated acceptable reliability, α = 0.78, ω = 0.79. Table 5 Factor loadings of the EFA-based 2-factor model of Avolition/Apathy-Diminished Expression items after confirmatory factor analysis. Factor Items Unstandardized Standardized p Factor 1 N1 1.11 .83 < .001 N2 1.22 .89 < .001 N3 1.27 .88 < .001 N4 1.37 .88 < .001 Factor 2 N6 0.90 .63 < .001 G5 0.63 .48 < .001 G7 0.63 .50 < .001 G13 1.02 .78 < .001 G16 1.25 .81 < .001 Note. Full item names can be found in Table 1 . 3.4. Cluster Analysis of Negative Symptom Dimensions Standardized factor scores derived from the final CFA model were used for the cluster analyses. Hierarchical cluster analysis using Ward’s method with squared Euclidean distance was first performed to determine the optimal number of clusters. Inspection of the agglomeration coefficients and dendrogram suggested a three-cluster solution. This solution was subsequently refined using K-means clustering. The three clusters were of unequal size: Cluster 1 ( n = 976), Cluster 2 ( n = 614), and Cluster 3 ( n = 1416). Cluster profiles were interpreted based on mean standardized scores on the two factors. Cluster means and standard deviations are given in Table 6 . Cluster 1 reflected a profile marked by prominent behavioral-volitional impairment; cluster 2 represented a profile characterized primarily by interpersonal-affective impairment; and cluster 3 indicated a low negative symptom profile. Table 6 Cluster descriptives. Cluster n Factor 1 M ± SD Factor 2 M ± SD 1 976 0.04 ± 0.66 1.07 ± 0.66 2 614 1.40 ± 0.69 –0.21 ± 0.77 3 1416 –0.63 ± 0.60 –0.65 ± 0.59 Cluster validity was evaluated using silhouette coefficients based on Euclidean distance. The overall mean silhouette score was 0.39 (min = − 0.05, max = 0.64), which indicated moderate cluster separation. Cluster 1 had a mean silhouette score of 0.37 (min = − 0.01, max = 0.60), cluster 2 had a mean silhouette score of 0.33 (min = − 0.05, max = 0.58), and cluster 3 had a mean silhouette score of 0.43 (min = − 0.03, max = 0.64), demonstrating acceptable cohesion and separation, with the low symptom cluster showing relatively stronger definition. 3.5. Clinical Validation of Clusters Differences between clusters in age at onset were examined using one-way analysis of variance. The overall effect was statistically significant, F (2, 2908) = 1895.08, p < .001, η p 2 = .57. Post hoc comparisons using Games-Howell tests indicated that all clusters differed significantly from one another ( p < .001). The interpersonal-affective impairment cluster was characterized by a markedly later age at onset ( M = 42.47, SD = 9.39) compared to both the behavioral-volitional impairment ( M = 22.84, SD = 7.47) and low symptom clusters ( M = 21.64, SD = 5.91). Although the behavioral-volitional impairment and low negative symptom clusters also differed significantly, this difference was comparatively smaller. The association between cluster membership and sex was examined using a chi-square test and was found to be statistically significant, χ²(2) = 112.72, p < .001, Cramér’s V = .19. The interpersonal-affective impairment cluster showed a higher proportion of females, whereas the behavioral-volitional impairment and low symptom clusters showed relatively higher proportions of males. 4. Discussion The present study examined the latent structure of negative symptoms in schizophrenia using PANSS items in a large clinical sample. Contrary to expectations, the hypothesized AA-DE model did not demonstrate an admissible solution in CFA. Similarly, the widely used Marder five-factor PANSS model was not supported, further highlighting concerns regarding the structural validity of PANSS. In contrast, exploratory analyses identified a two-factor structure comprising an interpersonal-affective impairment dimension and a behavioral-volitional dimension, which demonstrated good fit in subsequent CFA. Additionally, the high correlation between AA and DE composite scores suggests substantial overlap between these constructs when operationalized using PANSS items. These findings suggest that the PANSS may not adequately operationalize the AA-DE distinction, despite its theoretical validity. Furthermore, the application of cluster analysis based on the derived dimensions identified distinct patient profiles, highlighting clinically meaningful heterogeneity in negative symptom presentation beyond what is captured by dimensional models alone. The failure of the AA-DE model to yield an admissible solution is notable, particularly given its strong theoretical and empirical support in prior literature. One possible explanation is that, within the PANSS framework, AA and DE do not represent sufficiently distinct latent constructs. The observed high correlation between AA and DE scores further supports this interpretation, indicating poor discriminant validity and potential construct overlap between these domains. One explanation is that PANSS items do not map cleanly onto the theoretically distinct constructs of motivational (AA) and expressive (DE) deficits. Several PANSS items reflect broad or behaviorally anchored phenomena that may simultaneously capture multiple aspects of negative symptomatology. For example, items such as disturbance of volition (G13) and motor retardation (G7) may index both reduced motivation and diminished expressive output, thereby blurring the boundary between domains. Additionally, the inclusion of general psychopathology items in the operationalization of negative symptoms introduces construct heterogeneity, further reducing specificity. The PANSS was also not designed to assess negative symptoms at a granular level, in contrast to instruments such as the BNSS and SANS, limiting its ability to distinguish between internal experiential deficits and observable expressive impairments. It is therefore possible that the AA-DE distinction, while conceptually robust, is not optimally captured using PANSS-derived item configurations. The present findings also align with broader concerns regarding the structural instability of PANSS. The inability to obtain an admissible solution for the five-factor model, along with the lack of interpretability in the full-scale EFA, underscores the variability in PANSS factor structures reported across studies. Previous research has demonstrated considerable heterogeneity in PANSS models, with differing numbers of factors and inconsistent item loadings across samples (van der Gaag et al., 2006 ). Exploratory analyses of negative symptom items yielded a two-factor solution that differed from the conventional AA-DE framework. The first factor, comprising items N1, N2, N3, and N4, appears to reflect an interpersonal-affective impairment dimension encompassing affective flattening, social withdrawal, and reduced interpersonal engagement. The second factor, consisting of G5, G7, G13, G16, and N6, may be interpreted as a behavioral-volitional dimension, capturing observable motor, volitional, and expressive disturbances. This factor appears to reflect observable or behaviorally anchored manifestations of impairment, in contrast to the more interpersonal and affective features captured by the first factor. Notably, this second factor includes several general psychopathology items, suggesting that PANSS-derived representations of negative symptoms may incorporate elements beyond strictly defined negative symptom domains (Liemburg et al., 2013 ). This interpretation is consistent with prior critiques that PANSS general psychopathology items may not map cleanly onto negative symptom constructs (Wolpe et al., 2025 ). The divergence between the observed factor structure and the AA-DE model may be attributable to several factors. First, the inclusion of general psychopathology items in the operationalization of AA and DE may introduce construct heterogeneity, thereby obscuring the underlying latent structure. Second, PANSS was not specifically designed to assess negative symptoms in a fine-grained manner, in contrast to instruments such as the BNSS and SANS, which were developed to capture these domains more precisely (Gonzalez, 2016 ). Differences in sample characteristics and analytic approaches across studies may contribute to variability in factor solutions. In particular, the use of a combined EFA and CFA approach in the present study allowed for the identification of data-driven structures that may not emerge in strictly confirmatory frameworks. The AA-DE distinction remains well-supported in the literature and continues to provide a useful framework for conceptualizing negative symptoms. However, the present results suggest that PANSS-based operationalizations of these domains may conflate distinct constructs or fail to adequately capture their boundaries. This underscores the importance of considering measurement tools when evaluating the validity of theoretical models. In addition to examining latent structure, the present study employed a person-centered approach to identify distinct patient profiles based on the derived negative symptom dimensions. Three clusters were identified, reflecting (1) predominant behavioral-volitional impairment, (2) predominant interpersonal-affective impairment, and (3) low levels of negative symptoms. Previous person-centered studies have identified both severity-based and domain-specific profiles of negative symptoms in schizophrenia. For instance, Paul et al. ( 2022 ) reported four clusters reflecting low and severe negative symptoms, as well as moderate profiles characterized by elevated blunted affect and avolition, suggesting a combination of severity and domain differentiation. Similarly, Strauss et al. ( 2013 ) identified two clusters corresponding to AA and DE, supporting a domain-based distinction. The present findings partially align with this literature in identifying both a low symptom group and differentiated symptom profiles; however, the observed clusters did not map cleanly onto the AA-DE framework. Instead, the identified profiles reflected interpersonal-affective and behavioral-volitional dimensions, suggesting that PANSS-derived representations of negative symptoms may yield alternative configurations when examined using person-centered approaches. The identified profiles demonstrated meaningful differences in clinical and demographic characteristics. Notably, the interpersonal-affective impairment cluster was associated with a markedly later age at onset, with a large effect size, suggesting substantial differentiation in illness trajectory. In contrast, differences between the behavioral-volitional and low symptom clusters, although statistically significant, were comparatively small. Additionally, cluster membership was associated with sex, although the effect size of this association was modest, indicating that sex differences, while present, were not a primary driver of cluster differentiation. These findings suggest that the expression of negative symptoms may reflect heterogeneous pathways, with certain profiles – particularly those characterized by interpersonal-affective impairment – potentially representing distinct developmental or clinical trajectories. The presence of a later-onset profile is notable and may warrant further investigation in relation to illness course, prognosis, and underlying mechanisms. 4.1. Clinical Implications Clinically, these findings have implications for the assessment and interpretation of negative symptoms using PANSS. The substantial overlap observed between AA and DE domains suggests that PANSS-derived subscale scores may not provide sufficiently distinct information for clinical decision-making or treatment targeting. This is particularly relevant given that different negative symptom domains may respond differently to interventions (Kaiser et al., 2017 ). As such, reliance on PANSS alone may limit the precision of negative symptom assessment in both research and clinical contexts. Furthermore, the identification of distinct symptom profiles suggests that person-centered approaches may provide clinically meaningful information beyond traditional dimensional scores. Such profiles may help inform more targeted assessment strategies and, potentially, differential intervention approaches based on predominant symptom configurations. 4.2. Limitations Several limitations should be noted. Firstly, the study relied exclusively on PANSS items, which may not provide an optimal assessment of negative symptoms. Secondly, the cross-sectional design precludes examination of the stability of the identified factor structure over time. Thirdly, the use of listwise deletion may have introduced bias due to the exclusion of cases with missing data. Fourthly, the cluster solution was derived from cross-sectional data and requires replication to establish its stability and generalizability. Finally, the use of a secondary dataset limits control over data collection procedures and variable selection. 4.3. Future Directions Future research should seek to replicate these findings using instruments specifically designed to assess negative symptoms, such as the BNSS and SANS. Longitudinal studies examining the stability of negative symptom structures over time would also be valuable. Additionally, cross-cultural validation of negative symptom models is warranted, particularly given the use of a Russian clinical sample in the present study. Further work integrating multiple measurement approaches may help clarify the relationship between theoretical models and their empirical representations. Future studies should also examine the longitudinal stability and predictive validity of the identified symptom profiles, particularly in relation to functional outcomes and treatment response. In conclusion, the present study highlights important challenges in modeling the latent structure of negative symptoms using PANSS. While the AA-DE framework remains theoretically meaningful, it may not be adequately captured by PANSS-derived item configurations. Data-driven analyses suggest alternative organizational structures, underscoring the need for more precise measurement approaches in the study of negative symptoms. Finally, the identification of distinct symptom profiles based on these dimensions highlights clinically meaningful heterogeneity in negative symptom presentation, suggesting that person-centered approaches may complement dimensional models in improving characterization and assessment. Declarations Role of the Funding Source This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Acknowledgment The author would like to thank Dr. Vasudeo Paralikar for his guidance and support. Appreciation is also extended to Trisha T. Tendulkar, Shriraj S. Shingre, and Shambhavi Upendra Kulkarni for their assistance and helpful input during the writing process. References Aboraya A, Nasrallah HA (2016) Perspectives on the Positive and Negative Syndrome Scale (Panss): Use, Misuse, Drawbacks, and A New Alternative for Schizophrenia Research. 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Schizophr Res 186:39–45. https://doi.org/10.1016/j.schres.2016.07.013 Kay SR, Fiszbein A, Opler LA (1987) The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia. Schizophr Bull 13:261–276. https://doi.org/10.1093/schbul/13.2.261 Lançon C, Auquier P, Nayt G, Reine G (2000) Stability of the five-factor structure of the Positive and Negative Syndrome Scale (PANSS). Schizophr Res 42:231–239. https://doi.org/10.1016/S0920-9964(99)00129-2 Lehoux C, Gobeil M-H, Lefèbvre A-A, Maziade M, Roy M-A (2009) The Five-Factor Structure of the PANSS: A Critical Review of its Consistency Across Studies. Clin Schizophr Relat Psychos 3:103–110 Liemburg E, Castelein S, Stewart R, Van Der Gaag M, Aleman A, Knegtering H (2013) Two subdomains of negative symptoms in psychotic disorders: Established and confirmed in two large cohorts. J Psychiatr Res 47:718–725. https://doi.org/10.1016/j.jpsychires.2013.01.024 Lim J, Lee S-A, Lam M, Rapisarda A, Kraus M, Keefe RSE, Lee J (2016) The relationship between negative symptom subdomains and cognition. Psychol Med 46:2169–2177. https://doi.org/10.1017/S0033291716000726 Mäkinen J, Miettunen J, Isohanni M, Koponen H (2008) Negative symptoms in schizophrenia—A review. Nord J Psychiatry 62:334–341. https://doi.org/10.1080/08039480801959307 Marder SR, Davis JM, Chouinard G (1997) The effects of risperidone on the five dimensions of schizophrenia derived by factor analysis: combined results of the North American trials. J Clin Psychiatry 58:538–546. https://doi.org/10.4088/jcp.v58n1205 Möller H-J (2007) Clinical evaluation of negative symptoms in schizophrenia. Eur Psychiatry 22:380–386. https://doi.org/10.1016/j.eurpsy.2007.03.010 Ockey GJ (2013) Exploratory Factor Analysis and Structural Equation Modeling. In: Kunnan AJ (ed) The Companion to Language Assessment. Wiley, pp 1224–1244. https://doi.org/10.1002/9781118411360.wbcla114 Paul NB, Strauss GP, Woodyatt JJ, Paul MG, Keene JR, Allen DN (2022) Cluster analysis of negative symptoms identifies distinct negative symptom subgroups. Schizophr Res 246:207–215. https://doi.org/10.1016/j.schres.2022.06.021 Pogue-Geile MF, Harrow M (1985) Negative Symptoms in Schizophrenia: Their Longitudinal Course and Prognostic Importance. Schizophr Bull 11:427–439. https://doi.org/10.1093/schbul/11.3.427 Roithmeier M, Geck S, Bühner M, Wehr S, Weigel L, Priller J, Davis JM, Leucht S (2025) COSMIN review of the PANSS Marder factor solution and other factor models in people with schizophrenia. Schizophr 11:51. https://doi.org/10.1038/s41537-025-00600-6 Serafim PHM, Gadelha A, Massuda R, Louzã M, Gama CS, Roguski L, de Alvim CO, Czepielewski PHP, L.S (2026) The challenges in measuring symptoms of schizophrenia: An exploratory graph analysis of the Positive and Negative Syndrome Scale (PANSS). Schizophr Res 287:46–53. https://doi.org/10.1016/j.schres.2025.11.010 Strauss GP, Horan WP, Kirkpatrick B, Fischer BA, Keller WR, Miski P, Buchanan RW, Green MF, Carpenter WT (2013) Deconstructing negative symptoms of schizophrenia: Avolition–apathy and diminished expression clusters predict clinical presentation and functional outcome. J Psychiatr Res 47:783–790. https://doi.org/10.1016/j.jpsychires.2013.01.015 van der Gaag M, Cuijpers A, Hoffman T, Remijsen M, Hijman R, de Haan L, van Meijel B, van Harten PN, Valmaggia L, de Hert M, Wiersma D (2006) The five-factor model of the Positive and Negative Syndrome Scale I: Confirmatory factor analysis fails to confirm 25 published five-factor solutions. Schizophr Res 85:273–279. https://doi.org/10.1016/j.schres.2006.04.001 Weigel L, Wehr S, Galderisi S, Mucci A, Davis J, Giordano GM, Leucht S (2023) The Brief negative Symptom Scale (BNSS): a systematic review of measurement properties. Schizophr 9:45. https://doi.org/10.1038/s41537-023-00380-x White L, Harvey PD, Opler L, Lindenmayer JP (2010) Empirical Assessment of the Factorial Structure of Clinical Symptoms in Schizophrenia: A Multisite, Multimodel Evaluation of the Factorial Structure of the Positive and Negative Syndrome Scale. Psychopathology 30:263–274. https://doi.org/10.1159/000285058 Wolpe N, Perrottelli A, Giuliani L, Yang Z, Rekhi G, Jones PB, Bernardo M, Garcia-Portilla MP, Kaiser S, Robert G, Robert P, Mane A, Galderisi S, Lee J, Mucci A, Fernandez-Egea E (2025) Measuring the clinical dimensions of negative symptoms through the Positive and Negative Syndrome Scale. Eur Neuropsychopharmacol 93:68–76. https://doi.org/10.1016/j.euroneuro.2024.12.016 Wu B-J, Lan T-H, Hu T-M, Lee S-M, Liou J-Y (2015) Validation of a five-factor model of a Chinese Mandarin version of the Positive and Negative Syndrome Scale (CMV-PANSS) in a sample of 813 schizophrenia patients. Schizophr Res 169:489–490. https://doi.org/10.1016/j.schres.2015.09.011 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9448568","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624938630,"identity":"0e1e8367-fa4a-405d-917a-0da08adc3ce5","order_by":0,"name":"Maitreya Milind Palamwar","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0004-7811-6457","institution":"Fergusson College","correspondingAuthor":true,"prefix":"","firstName":"Maitreya","middleName":"Milind","lastName":"Palamwar","suffix":""}],"badges":[],"createdAt":"2026-04-17 11:36:24","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9448568/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9448568/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107306776,"identity":"05075c7f-0042-45f2-8677-0b29193d0f06","added_by":"auto","created_at":"2026-04-20 08:28:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":510306,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9448568/v1/c44394d9-3c4f-4a92-af00-3ac8b29d69cd.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eLatent Structure and Clinical Profiles of Negative Symptoms in Schizophrenia: A Factor Analytic and Cluster Analytic Study\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNegative symptoms in schizophrenia refer to reductions or deficits in normal behaviors and functions related to motivation, interest, and emotional and verbal expression (M\u0026ouml;ller, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Negative symptoms comprise five key constructs: blunted affect, alogia, avolition, asociality, and anhedonia (Correll and Schooler, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Negative symptoms are associated with poor functional outcome and place a substantial burden on patients, families, and healthcare systems (Galderisi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Critically, negative symptoms assessed early in illness predict poor role functioning years later (Pogue-Geile and Harrow, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). However, primary negative symptoms \u0026ndash; intrinsic to schizophrenia\u0026rsquo;s pathophysiology \u0026ndash; generally do not respond well to current antipsychotic treatments, representing an unmet medical need (Correll and Schooler, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is also important to keep in mind that these are highly prevalent: up to 60% of individuals with schizophrenia will exhibit clinically significant negative symptoms requiring treatment (Correll and Schooler, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and 50\u0026ndash;90% of individuals with first-episode psychosis experience them (M\u0026auml;kinen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultiple instruments to measure symptoms of schizophrenia have been developed over the years. These include the Scale for the Assessment of Negative Symptoms (SANS; Andreasen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) and the Brief Negative Symptom Scale (BNSS; Weigel et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) \u0026ndash; both reliable and valid measures. The Positive and Negative Syndrome Scale (PANSS; Kay et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) is perhaps the most widely-used measure of clinical symptoms in schizophrenia. It consists of seven positive symptom items, seven negative symptom items, and sixteen general psychopathology items. Multiple studies have proposed alternative factor structures for the PANSS \u0026ndash; ranging from the original three-factor solution, to contemporary and popular five-factor models (Lehoux et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and even seven-factor models (Emsley et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Currently, a five-factor model proposed by Marder et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) enjoys support in literature (Emsley et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Lan\u0026ccedil;on et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, the Marder model, while showing good construct validity, showed \u0026ldquo;insufficient\u0026rdquo; structural validity (Roithmeier et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Certain studies show that the Marder model does not prove to be an adequate fit (White et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Collectively, these findings highlight ongoing inconsistencies in the latent structure of PANSS, raising concerns regarding its structural validity across samples.\u003c/p\u003e \u003cp\u003eNegative symptoms are heterogeneous across both experiential and expressive domains, leading to the identification of two major subdomains: Avolition/Apathy (AA) and Diminished Expression (DE; Kaiser et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Strauss et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Correll and Schooler (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) mention that of the five major negative symptom domains, avolition, asociality, and anhedonia make up AA, while blunted affect and alogia comprise DE. Liemburg et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) concluded that the AA and DE factors were made up from specific PANSS negative and general psychopathology subscales. This factor structure has also been validated across literature (Capatina and Miclutia, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lim et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite growing support for this two-factor conceptualization, its operationalization using PANSS items remains debated, particularly given variability in item loadings across samples.\u003c/p\u003e \u003cp\u003eWhile factor analytic approaches have been central to understanding the latent structure of negative symptoms, they represent variable-centered methods that assume homogeneity across individuals. Such approaches may obscure meaningful heterogeneity in symptom presentation at the patient level. Person-centered methods, such as cluster analysis, provide a complementary framework by identifying subgroups of individuals with distinct symptom profiles.\u003c/p\u003e \u003cp\u003eDespite substantial progress in conceptualizing negative symptoms as comprising AA and DE, important gaps remain in their empirical validation, particularly when operationalized using the Positive and Negative Syndrome Scale (PANSS), and relatively few studies have integrated empirically derived factor structures with person-centered approaches to identify clinically meaningful patient profiles. While instruments such as the SANS and BNSS were specifically developed to assess negative symptoms, the PANSS remains the most widely used clinical tool in research and practice, resulting in a heavy reliance on PANSS-derived models despite concerns regarding its structural validity and limitations in capturing the full scope of negative symptoms (Aboraya and Nasrallah, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Serafim et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Prior research has demonstrated considerable variability in both the number and composition of PANSS factors, with studies supporting three-, five-, and seven-factor models, and even among five-factor solutions, differences in item loadings and model fit have been reported, suggesting instability in the latent structure across samples (van der Gaag et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This variability is particularly relevant for negative symptoms, where attempts to map PANSS items onto the AA-DE framework have yielded inconsistent results, raising questions about the adequacy of PANSS for capturing this distinction despite theoretical and empirical support for the two-factor model. Moreover, many studies examining the AA-DE structure have relied on predefined item assignments rather than systematically evaluating the latent structure of relevant PANSS items within large and diverse clinical samples, limiting the ability to distinguish true latent constructs from model-imposed structures (Liemburg et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A combined exploratory and confirmatory approach is therefore recommended to both test existing theoretical models and identify data-driven structures where appropriate (Anderson and Gerbing, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Ockey, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccordingly, the present study addresses these gaps by examining the latent structure of negative symptoms in a large clinical sample of individuals with schizophrenia using both confirmatory and exploratory factor analytic methods. By evaluating established PANSS models, testing the AA-DE framework, and deriving alternative structures where necessary, this study aims to clarify the organization of negative symptoms and assess the extent to which PANSS can validly capture their underlying dimensionality. In addition, to better characterize heterogeneity at the patient level, the study further applies a person-centered approach to identify distinct symptom profiles based on the derived dimensions and examine their clinical correlates.\u003c/p\u003e \u003cp\u003eThe present study aimed to examine the latent structure of negative symptoms in a large clinical sample of individuals with schizophrenia and to identify clinically meaningful patient profiles based on these dimensions. Specifically, we sought to:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eevaluate the fit of established PANSS factor structures using CFA,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003etest the AA-DE model using CFA,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eexplore the underlying structure of PANSS items using EFA,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003evalidate the resulting factor structure using CFA, and\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eidentify distinct patient profiles using cluster analysis based on the derived factor scores and examine their clinical correlates.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBased on prior literature, it was expected that the AA-DE model would demonstrate acceptable fit in CFA. However, given the documented variability in PANSS factor structures across studies and concerns regarding the use of PANSS items to operationalize AA and DE, it was also anticipated that the AA-DE structure might not fully replicate in the present dataset. Accordingly, exploratory analyses were expected to provide additional insight into the latent organization of negative symptoms and to identify alternative factor structures where appropriate. It was further hypothesized that cluster analysis would identify distinct symptom profiles, and that these profiles would differ on clinically relevant variables, including age at onset and sex.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Participants\u003c/h2\u003e \u003cp\u003eThis study involved a secondary analysis of a large clinical dataset of individuals with schizophrenia collected at the Mental Health Research Center (Russia) between 2007 and 2020 (Lezheiko et al., 2022). The sample comprised 3,006 patients (898 men, 2,108 women), with a mean age of 38.4 years (SD\u0026thinsp;=\u0026thinsp;13.6) and a mean age at illness onset of 26.4 years (SD\u0026thinsp;=\u0026thinsp;11.1). All participants had provided written informed consent in the original study, which was approved by a local ethics committee. Diagnoses were established according to ICD-10 criteria and confirmed using the Mini International Neuropsychiatric Interview (MINI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Measure\u003c/h2\u003e \u003cp\u003eClinical symptoms were assessed using the PANSS (Kay et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), a widely used instrument measuring positive, negative, and general psychopathology in schizophrenia. The PANSS comprises 30 items: 7 positive symptom items, 7 negative symptom items, and 16 general psychopathology items (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Assessments were conducted by trained psychiatrists during the week prior to patient discharge.\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\u003e\u003cem\u003ePANSS items in the original three-factor model by\u003c/em\u003e Kay et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive Scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative Scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeneral Psychopathology\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1 (Delusions)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1 (Blunted affect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG1 (Somatic concern)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2 (Conceptual disorganization)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN2 (Emotional withdrawal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG2 (Anxiety)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3 (Hallucinatory behavior)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN3 (Poor rapport)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG3 (Guilt feelings)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4 (Excitement)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN4 (Passive/apathetic social withdrawal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG4 (Tension)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5 (Grandiosity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN5 (Difficulty in abstract thinking)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG5 (Mannerisms and posturing)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP6 (Suspiciousness and persecution)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN6 (Lack of spontaneity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG6 (Depression)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP7 (Hostility)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN7 (Stereotyped thinking)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG7 (Motor retardation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG8 (Uncooperativeness)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG9 (Unusual thought content)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG10 (Disorientation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG11 (Poor attention)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG12 (Lack of judgment and insight)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG13 (Disturbance of volition)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG14 (Poor impulse control)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG15 (Preoccupation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG16 (Active social avoidance)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on established factor structures (Liemburg et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), negative symptom domains were operationalized as AA, comprising items N2 (Emotional Withdrawal), N4 (Apathetic Social Withdrawal), and G16 (Active Social Avoidance), and DE, comprising items N1 (Blunted Affect), N3 (Poor Rapport), N6 (Lack of Spontaneity), G5 (Mannerisms and Posturing), G7 (Motor Retardation), and G13 (Disturbance of Volition).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Procedure\u003c/h2\u003e \u003cp\u003ePrior to analysis, items of the PANSS, age at onset, and sex of the participants were extracted from the original dataset. Cases with missing data on variables included in the factor analyses were excluded using listwise deletion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical Analyses\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Factor Analyses\u003c/h2\u003e \u003cp\u003eAll analyses were conducted in JASP (Version 0.19.1.0) using the lavaan package for CFA. A stepwise approach was used to examine the latent structure of PANSS negative symptoms. CFA models were first specified for the full PANSS, including a unidimensional model and a five-factor model (Marder et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). EFA was then conducted on all PANSS items using principal axis factoring with oblimin rotation, with the number of factors determined by parallel analysis. A four-factor solution was also examined, followed by CFA of the EFA-derived structure.\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\u003e\u003cem\u003eThe Marder five-factor model of the PANSS.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCognitive/ Disorganization Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression/ Anxiety Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcitability/ Hostility Factor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote. Full item names can be found in\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCFA models were subsequently specified for AA (N2, N4, G16) and DE (N1, N3, N6, G5, G7, G13), along with a unidimensional model including all items. EFA was then conducted on these items using the same parameters, followed by CFA of the resulting structure. Model fit was evaluated using CFI, TLI, RMSEA (90% CI), and SRMR, with thresholds of \u0026ge; .90 (acceptable) and \u0026ge; .95 (good) for CFI/TLI, and \u0026le; .08 for RMSEA and SRMR. The chi-square statistic was also reported. Internal consistency was assessed using Cronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s omega.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Cluster Analysis\u003c/h2\u003e \u003cp\u003eAnalyses were conducted through IBM SPSS 27, using factor scores derived from the final CFA model. Variables were standardized prior to analysis. Hierarchical cluster analysis using Ward\u0026rsquo;s method with squared Euclidean distance was first performed to determine the optimal number of clusters based on inspection of agglomeration coefficients and dendrograms. Subsequently, k-means clustering was applied to derive the final cluster solution. A two-step clustering approach was adopted to improve solution stability, with hierarchical clustering used to determine the number of clusters and K-means clustering used to refine cluster membership.\u003c/p\u003e \u003cp\u003eDifferences between clusters in age at onset were examined using one-way analysis of variance (ANOVA). Given violation of homogeneity of variance, Games-Howell post hoc tests were used. Associations between cluster membership and sex were examined using chi-square tests. Effect sizes were calculated to assess the magnitude of group differences.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Evaluation of Existing PANSS Models\u003c/h2\u003e \u003cp\u003eCFA was first conducted on the full 30-item PANSS. The unidimensional model demonstrated poor fit to the data. A five-factor model based on previously proposed PANSS structure (Marder et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) was also specified; however, the model was non-admissible, with the covariance matrix of latent variables not positive definite, and therefore fit indices could not be computed (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\u003e\u003cem\u003eFit indices for all confirmatory factor analysis models.\u003c/em\u003e\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2; (df)\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\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMSEA (90% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePANSS 1-Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25515.93(405)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.144 (0.142\u0026ndash;0.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePoor fit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePANSS Marder 5-Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel failed (non-admissible)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePANSS EFA-derived 4-Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8458.79(344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.089 (0.087\u0026ndash;0.090)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePoor fit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA-DE 2-Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel failed (non-admissible)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA-DE 1-Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1559.05(27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.137 (0.132\u0026ndash;0.143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePoor fit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEFA-derived 2-Factor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e608.86(26)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.086 (0.080\u0026ndash;0.092)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eGood fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote. \u0026ldquo;\u0026ndash;\u0026ldquo; indicates model could not be computed (non-admissible). All\u003c/em\u003e χ\u0026sup2; \u003cem\u003evalues were significant at\u003c/em\u003e p \u003cem\u003e\u0026lt; .01.\u003c/em\u003e CFI: \u003cem\u003ecomparative fit index\u003c/em\u003e, TLI: \u003cem\u003eTucker-Lewis index\u003c/em\u003e, RMSEA: \u003cem\u003eroot mean square error of approximation\u003c/em\u003e, SRMR: \u003cem\u003estandardized root mean square residual.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Exploratory Factor Analysis of PANSS Items\u003c/h2\u003e \u003cp\u003eEFA of the full PANSS item set was conducted using principal axis factoring with oblimin rotation. Parallel analysis suggested a multi-factor solution; however, the extracted factors were not clearly interpretable. In a manually specified four-factor solution, all items demonstrated loadings\u0026thinsp;\u0026ge;\u0026thinsp;0.30; however, G13 and G16, exhibited substantial cross-loadings across two factors.\u003c/p\u003e \u003cp\u003eThe two cross-loading items were dropped, and a reduced, 28-item, four-factor model derived from the previous EFA was tested using CFA. However, it demonstrated poor fit (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Factor Structure of AA and DE\u003c/h2\u003e \u003cp\u003eCFA was conducted on negative symptom items based on the proposed AA and DE structure. The two-factor AA-DE model was non-admissible, with a non-positive definite covariance matrix of latent variables. The correlation between AA and DE composite scores was high, \u003cem\u003er\u003c/em\u003e(3004)\u0026thinsp;=\u0026thinsp;0.84, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, suggesting substantial overlap between the constructs. A unidimensional CFA model including all AA and DE items demonstrated poor fit (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEFA was conducted on the subset of PANSS items corresponding to AA and DE domains. Parallel analysis initially suggested a three-factor solution; however, no items loaded meaningfully on the third factor, and it was therefore not retained. A two-factor solution was extracted and was interpretable. The first factor comprised N1, N2, N3, and N4, representing an interpersonal-affective impairment dimension. The second factor comprised G5, G7, G13, G16, and N6, reflecting a behavioral-volitional dimension. Most items demonstrated clear primary loadings on a single factor, with minimal cross-loadings. Item N6 showed modest cross-loading (0.32 on Factor 1 and 0.41 on Factor 2) but was retained in the second factor based on its higher loading. Factor loadings are presented in 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\u003e\u003cem\u003eFactor loadings for the exploratory factor analysis of the items included in th\u003c/em\u003ee Liemburg et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) \u003cem\u003emodel of Avolition/Apathy and Diminished Expression.\u003c/em\u003e\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 \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCommunality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote. Number of factors was based on parallel analysis. Applied rotation method was oblimin. Full item names can be found in\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCFA was conducted to evaluate the fit of the two-factor model derived from the EFA of AA-DE items. The model demonstrated good overall fit (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Standardized factor loadings were all statistically significant and in the expected direction. Standardized and unstandardized factor loadings of each item are given in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Internal consistency was high for both factors. The first factor demonstrated excellent reliability, α\u0026thinsp;=\u0026thinsp;0.92, ω\u0026thinsp;=\u0026thinsp;0.93, while the second factor demonstrated acceptable reliability, α\u0026thinsp;=\u0026thinsp;0.78, ω\u0026thinsp;=\u0026thinsp;0.79.\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\u003e\u003cem\u003eFactor loadings of the EFA-based 2-factor model of Avolition/Apathy-Diminished Expression items after confirmatory factor analysis.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnstandardized\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote. Full item names can be found in\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003e3.4. Cluster Analysis of Negative Symptom Dimensions\u003c/h2\u003e \u003cp\u003eStandardized factor scores derived from the final CFA model were used for the cluster analyses. Hierarchical cluster analysis using Ward\u0026rsquo;s method with squared Euclidean distance was first performed to determine the optimal number of clusters. Inspection of the agglomeration coefficients and dendrogram suggested a three-cluster solution. This solution was subsequently refined using K-means clustering.\u003c/p\u003e \u003cp\u003eThe three clusters were of unequal size: Cluster 1 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;976), Cluster 2 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;614), and Cluster 3 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1416). Cluster profiles were interpreted based on mean standardized scores on the two factors. Cluster means and standard deviations are given in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Cluster 1 reflected a profile marked by prominent behavioral-volitional impairment; cluster 2 represented a profile characterized primarily by interpersonal-affective impairment; and cluster 3 indicated a low negative symptom profile.\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\u003e\u003cem\u003eCluster descriptives.\u003c/em\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor 1 \u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor 2 \u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCluster validity was evaluated using silhouette coefficients based on Euclidean distance. The overall mean silhouette score was 0.39 (min = \u0026minus;\u0026thinsp;0.05, max\u0026thinsp;=\u0026thinsp;0.64), which indicated moderate cluster separation. Cluster 1 had a mean silhouette score of 0.37 (min = \u0026minus;\u0026thinsp;0.01, max\u0026thinsp;=\u0026thinsp;0.60), cluster 2 had a mean silhouette score of 0.33 (min = \u0026minus;\u0026thinsp;0.05, max\u0026thinsp;=\u0026thinsp;0.58), and cluster 3 had a mean silhouette score of 0.43 (min = \u0026minus;\u0026thinsp;0.03, max\u0026thinsp;=\u0026thinsp;0.64), demonstrating acceptable cohesion and separation, with the low symptom cluster showing relatively stronger definition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Clinical Validation of Clusters\u003c/h2\u003e \u003cp\u003eDifferences between clusters in age at onset were examined using one-way analysis of variance. The overall effect was statistically significant, \u003cem\u003eF\u003c/em\u003e(2, 2908)\u0026thinsp;=\u0026thinsp;1895.08, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.57. Post hoc comparisons using Games-Howell tests indicated that all clusters differed significantly from one another (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001). The interpersonal-affective impairment cluster was characterized by a markedly later age at onset (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;42.47, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.39) compared to both the behavioral-volitional impairment (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22.84, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.47) and low symptom clusters (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21.64, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.91). Although the behavioral-volitional impairment and low negative symptom clusters also differed significantly, this difference was comparatively smaller.\u003c/p\u003e \u003cp\u003eThe association between cluster membership and sex was examined using a chi-square test and was found to be statistically significant, χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;112.72, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, Cram\u0026eacute;r\u0026rsquo;s \u003cem\u003eV\u003c/em\u003e = .19. The interpersonal-affective impairment cluster showed a higher proportion of females, whereas the behavioral-volitional impairment and low symptom clusters showed relatively higher proportions of males.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study examined the latent structure of negative symptoms in schizophrenia using PANSS items in a large clinical sample. Contrary to expectations, the hypothesized AA-DE model did not demonstrate an admissible solution in CFA. Similarly, the widely used Marder five-factor PANSS model was not supported, further highlighting concerns regarding the structural validity of PANSS. In contrast, exploratory analyses identified a two-factor structure comprising an interpersonal-affective impairment dimension and a behavioral-volitional dimension, which demonstrated good fit in subsequent CFA. Additionally, the high correlation between AA and DE composite scores suggests substantial overlap between these constructs when operationalized using PANSS items. These findings suggest that the PANSS may not adequately operationalize the AA-DE distinction, despite its theoretical validity. Furthermore, the application of cluster analysis based on the derived dimensions identified distinct patient profiles, highlighting clinically meaningful heterogeneity in negative symptom presentation beyond what is captured by dimensional models alone.\u003c/p\u003e \u003cp\u003eThe failure of the AA-DE model to yield an admissible solution is notable, particularly given its strong theoretical and empirical support in prior literature. One possible explanation is that, within the PANSS framework, AA and DE do not represent sufficiently distinct latent constructs. The observed high correlation between AA and DE scores further supports this interpretation, indicating poor discriminant validity and potential construct overlap between these domains. One explanation is that PANSS items do not map cleanly onto the theoretically distinct constructs of motivational (AA) and expressive (DE) deficits. Several PANSS items reflect broad or behaviorally anchored phenomena that may simultaneously capture multiple aspects of negative symptomatology. For example, items such as disturbance of volition (G13) and motor retardation (G7) may index both reduced motivation and diminished expressive output, thereby blurring the boundary between domains. Additionally, the inclusion of general psychopathology items in the operationalization of negative symptoms introduces construct heterogeneity, further reducing specificity. The PANSS was also not designed to assess negative symptoms at a granular level, in contrast to instruments such as the BNSS and SANS, limiting its ability to distinguish between internal experiential deficits and observable expressive impairments. It is therefore possible that the AA-DE distinction, while conceptually robust, is not optimally captured using PANSS-derived item configurations.\u003c/p\u003e \u003cp\u003eThe present findings also align with broader concerns regarding the structural instability of PANSS. The inability to obtain an admissible solution for the five-factor model, along with the lack of interpretability in the full-scale EFA, underscores the variability in PANSS factor structures reported across studies. Previous research has demonstrated considerable heterogeneity in PANSS models, with differing numbers of factors and inconsistent item loadings across samples (van der Gaag et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExploratory analyses of negative symptom items yielded a two-factor solution that differed from the conventional AA-DE framework. The first factor, comprising items N1, N2, N3, and N4, appears to reflect an interpersonal-affective impairment dimension encompassing affective flattening, social withdrawal, and reduced interpersonal engagement. The second factor, consisting of G5, G7, G13, G16, and N6, may be interpreted as a behavioral-volitional dimension, capturing observable motor, volitional, and expressive disturbances. This factor appears to reflect observable or behaviorally anchored manifestations of impairment, in contrast to the more interpersonal and affective features captured by the first factor. Notably, this second factor includes several general psychopathology items, suggesting that PANSS-derived representations of negative symptoms may incorporate elements beyond strictly defined negative symptom domains (Liemburg et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This interpretation is consistent with prior critiques that PANSS general psychopathology items may not map cleanly onto negative symptom constructs (Wolpe et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe divergence between the observed factor structure and the AA-DE model may be attributable to several factors. First, the inclusion of general psychopathology items in the operationalization of AA and DE may introduce construct heterogeneity, thereby obscuring the underlying latent structure. Second, PANSS was not specifically designed to assess negative symptoms in a fine-grained manner, in contrast to instruments such as the BNSS and SANS, which were developed to capture these domains more precisely (Gonzalez, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Differences in sample characteristics and analytic approaches across studies may contribute to variability in factor solutions. In particular, the use of a combined EFA and CFA approach in the present study allowed for the identification of data-driven structures that may not emerge in strictly confirmatory frameworks.\u003c/p\u003e \u003cp\u003eThe AA-DE distinction remains well-supported in the literature and continues to provide a useful framework for conceptualizing negative symptoms. However, the present results suggest that PANSS-based operationalizations of these domains may conflate distinct constructs or fail to adequately capture their boundaries. This underscores the importance of considering measurement tools when evaluating the validity of theoretical models.\u003c/p\u003e \u003cp\u003eIn addition to examining latent structure, the present study employed a person-centered approach to identify distinct patient profiles based on the derived negative symptom dimensions. Three clusters were identified, reflecting (1) predominant behavioral-volitional impairment, (2) predominant interpersonal-affective impairment, and (3) low levels of negative symptoms.\u003c/p\u003e \u003cp\u003ePrevious person-centered studies have identified both severity-based and domain-specific profiles of negative symptoms in schizophrenia. For instance, Paul et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported four clusters reflecting low and severe negative symptoms, as well as moderate profiles characterized by elevated blunted affect and avolition, suggesting a combination of severity and domain differentiation. Similarly, Strauss et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) identified two clusters corresponding to AA and DE, supporting a domain-based distinction. The present findings partially align with this literature in identifying both a low symptom group and differentiated symptom profiles; however, the observed clusters did not map cleanly onto the AA-DE framework. Instead, the identified profiles reflected interpersonal-affective and behavioral-volitional dimensions, suggesting that PANSS-derived representations of negative symptoms may yield alternative configurations when examined using person-centered approaches.\u003c/p\u003e \u003cp\u003eThe identified profiles demonstrated meaningful differences in clinical and demographic characteristics. Notably, the interpersonal-affective impairment cluster was associated with a markedly later age at onset, with a large effect size, suggesting substantial differentiation in illness trajectory. In contrast, differences between the behavioral-volitional and low symptom clusters, although statistically significant, were comparatively small. Additionally, cluster membership was associated with sex, although the effect size of this association was modest, indicating that sex differences, while present, were not a primary driver of cluster differentiation.\u003c/p\u003e \u003cp\u003eThese findings suggest that the expression of negative symptoms may reflect heterogeneous pathways, with certain profiles \u0026ndash; particularly those characterized by interpersonal-affective impairment \u0026ndash; potentially representing distinct developmental or clinical trajectories. The presence of a later-onset profile is notable and may warrant further investigation in relation to illness course, prognosis, and underlying mechanisms.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Clinical Implications\u003c/h2\u003e \u003cp\u003eClinically, these findings have implications for the assessment and interpretation of negative symptoms using PANSS. The substantial overlap observed between AA and DE domains suggests that PANSS-derived subscale scores may not provide sufficiently distinct information for clinical decision-making or treatment targeting. This is particularly relevant given that different negative symptom domains may respond differently to interventions (Kaiser et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As such, reliance on PANSS alone may limit the precision of negative symptom assessment in both research and clinical contexts. Furthermore, the identification of distinct symptom profiles suggests that person-centered approaches may provide clinically meaningful information beyond traditional dimensional scores. Such profiles may help inform more targeted assessment strategies and, potentially, differential intervention approaches based on predominant symptom configurations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations should be noted. Firstly, the study relied exclusively on PANSS items, which may not provide an optimal assessment of negative symptoms. Secondly, the cross-sectional design precludes examination of the stability of the identified factor structure over time. Thirdly, the use of listwise deletion may have introduced bias due to the exclusion of cases with missing data. Fourthly, the cluster solution was derived from cross-sectional data and requires replication to establish its stability and generalizability. Finally, the use of a secondary dataset limits control over data collection procedures and variable selection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Future Directions\u003c/h2\u003e \u003cp\u003eFuture research should seek to replicate these findings using instruments specifically designed to assess negative symptoms, such as the BNSS and SANS. Longitudinal studies examining the stability of negative symptom structures over time would also be valuable. Additionally, cross-cultural validation of negative symptom models is warranted, particularly given the use of a Russian clinical sample in the present study. Further work integrating multiple measurement approaches may help clarify the relationship between theoretical models and their empirical representations. Future studies should also examine the longitudinal stability and predictive validity of the identified symptom profiles, particularly in relation to functional outcomes and treatment response.\u003c/p\u003e \u003cp\u003eIn conclusion, the present study highlights important challenges in modeling the latent structure of negative symptoms using PANSS. While the AA-DE framework remains theoretically meaningful, it may not be adequately captured by PANSS-derived item configurations. Data-driven analyses suggest alternative organizational structures, underscoring the need for more precise measurement approaches in the study of negative symptoms. Finally, the identification of distinct symptom profiles based on these dimensions highlights clinically meaningful heterogeneity in negative symptom presentation, suggesting that person-centered approaches may complement dimensional models in improving characterization and assessment.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch1\u003eRole of the Funding Source\u003c/h1\u003e\n\u003cp\u003eThis study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch1\u003eAcknowledgment\u003c/h1\u003e\n\u003cp\u003eThe author would like to thank Dr. Vasudeo Paralikar for his guidance and support. Appreciation is also extended to Trisha T. Tendulkar, Shriraj S. Shingre, and Shambhavi Upendra Kulkarni for their assistance and helpful input during the writing process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAboraya A, Nasrallah HA (2016) Perspectives on the Positive and Negative Syndrome Scale (Panss): Use, Misuse, Drawbacks, and A New Alternative for Schizophrenia Research. Ann Clin Psychiatry 28:125\u0026ndash;131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/104012371602800206\u003c/span\u003e\u003cspan address=\"10.1177/104012371602800206\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson JC, Gerbing DW (1988) Structural equation modeling in practice: A review and recommended two-step approach. 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Eur Neuropsychopharmacol 93:68\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.euroneuro.2024.12.016\u003c/span\u003e\u003cspan address=\"10.1016/j.euroneuro.2024.12.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu B-J, Lan T-H, Hu T-M, Lee S-M, Liou J-Y (2015) Validation of a five-factor model of a Chinese Mandarin version of the Positive and Negative Syndrome Scale (CMV-PANSS) in a sample of 813 schizophrenia patients. Schizophr Res 169:489\u0026ndash;490. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.schres.2015.09.011\u003c/span\u003e\u003cspan address=\"10.1016/j.schres.2015.09.011\" 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":"Fergusson College","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":"schizophrenia, negative symptoms, PANSS, factor analysis, cluster analysis","lastPublishedDoi":"10.21203/rs.3.rs-9448568/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9448568/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eNegative symptoms in schizophrenia are commonly conceptualized as comprising avolition/apathy (AA) and diminished expression (DE). However, the validity of this distinction when operationalized using the Positive and Negative Syndrome Scale (PANSS) remains unclear, and person-centered approaches to characterizing symptom heterogeneity are limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study analyzed PANSS data from 3,006 individuals with schizophrenia. Confirmatory factor analysis (CFA) was used to evaluate established PANSS models and the AA–DE framework. Exploratory factor analysis (EFA) was conducted to identify data-driven structures, followed by CFA validation. Cluster analysis (Ward’s method and K-means) was applied to factor scores to identify patient profiles. Cluster validity was assessed using silhouette coefficients. Differences in age at onset and sex were examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe AA-DE model and the Marder five-factor model were non-admissible in CFA. EFA identified a two-factor structure comprising interpersonal-affective impairment and behavioral-volitional impairment, which demonstrated good fit in CFA. Cluster analysis revealed three profiles: behavioral-volitional impairment, interpersonal-affective impairment, and low negative symptoms. The overall silhouette coefficient indicated moderate cluster separation. Clusters differed significantly in age at onset and sex, with the interpersonal-affective profile associated with later onset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eFindings suggest that PANSS may not adequately capture the AA-DE distinction, instead yielding alternative dimensional structures. Person-centered analyses identified clinically meaningful subgroups, highlighting heterogeneity in negative symptom presentation and the need for improved measurement approaches.\u003c/p\u003e","manuscriptTitle":"Latent Structure and Clinical Profiles of Negative Symptoms in Schizophrenia: A Factor Analytic and Cluster Analytic Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 08:28:12","doi":"10.21203/rs.3.rs-9448568/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":"5e4e63f8-5c58-4eb7-a87d-52e64ac224c1","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66518866,"name":"Psychology"},{"id":66518867,"name":"Psychiatry"}],"tags":[],"updatedAt":"2026-04-20T08:28:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 08:28:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9448568","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9448568","identity":"rs-9448568","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}