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Behavioral Sciences
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Innovations and Integrations in Quantitative Methods for Behavioral Science Research
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Innovations and Integrations in Quantitative Methods for Behavioral Science Research

A special issue of Behavioral Sciences (ISSN 2076-328X).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 8164

Editors

Prof. Dr. Gengfeng Niu

E-Mail Website
Guest Editor
School of Psychology, Central China Normal University, Wuhan 430079, China
Interests: cyber-psychology and behaviors; social development; mental health; social network site; body image and eating behaviors; problematic Internet use; positive psychology; Information technology and family system; perception and interactionwith AI
Special Issues, Collections and Topics in MDPI journals
Dr. Feng Kong

E-Mail Website
Guest Editor
School of Psychology, Shaanxi Normal University, Xi'an 710062, China
Interests: positive psychology; health psychology; well-being; positive mental health; childhood experiences; character strengths (e.g., gratitude); emotional intelligence
Dr. Tour Liu

E-Mail Website
Guest Editor
Academy of Psychology and Behavior, Tianjin Normal University, No. 393 Binshuixi Road, Xiqing District, Tianjin, 300387, China
Interests: psychological assessment; educational evaluation; quantitative psychology; multimodal data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Shu Zhang

E-Mail Website
Guest Editor
Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, 311121, China
Interests: internet use behaviors (cyberloafing, cyber lei-sure); occupational mental health; interpersonal rela-tionships; academic cheating

Special Issue Information

Dear Colleagues,

Quantitative research methods provide the foundation for scientific inquiry in psychology, education, and other behavioral sciences. They enable researchers to test theories, examine data, and generate evidence-based findings. As statistical techniques and computational tools continue to develop rapidly, there is an increasing need for platforms that facilitate methodological exchange and promote quality research practices across disciplines.

This Special Issue, organized in collaboration with the Scholar Forum on Quantitative Research Methods in Social Sciences, seeks to meet this need by presenting innovative quantitative methods and their applications. The collaboration aims to promote methodological exchange and academic communication, and the Special Issue operates independently of the Forum while welcoming submissions from researchers worldwide. It offers a venue for sharing advanced approaches, promoting methodological discussion, and enhancing the quality and impact of quantitative research in behavioral sciences.

We welcome three types of contributions: (1) studies presenting novel statistical methods or improvements to existing analytical techniques, (2) empirical research that demonstrates meaningful methodological integration or theoretical advancement rather than merely combining multiple techniques, and (3) development of new research tools, including software packages, experimental paradigms, and quantitative research procedures.

Topics of interest include, but are not limited to:

  • Development of novel statistical methods with accompanying software packages
  • Introduction and application of innovative analytical techniques
  • Quantitative research with multiple analytic methods
  • Development of new assessment tools based on emerging technologies
  • Tutorials for the application of novel quantitative methods or novel data analytic procedures

Prof. Dr. Gengfeng Niu
Dr. Feng Kong
Dr. Tour Liu
Dr. Shu Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Behavioral Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantitative research methods in social sciences
  • statistical and computational techniques
  • methodological innovation and integration
  • measurement development
  • software tools and statistical packages
  • quantitative research in psychology and education

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (12 papers)

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20 pages, 1213 KB  
Article
Diagnosing Ceiling Effects and Unstable Nonlinearity in Short Ordinal Scales: A TIMSS 2023 Application
by Georgios Sideridis and Mohammed Alghamdi
Behav. Sci. 2026, 16(9), 1485; https://doi.org/10.3390/bs16091485 - 25 Aug 2026
Abstract
Short ordinal self-report scales are widely used to study children’s digital lives, yet their measurement properties can distort conclusions about nonlinear relationships. We introduced an integrated diagnostic workflow for such scales—covering range, structure, reliability, method variance and functional form—and applied it to the [...] Read more.
Short ordinal self-report scales are widely used to study children’s digital lives, yet their measurement properties can distort conclusions about nonlinear relationships. We introduced an integrated diagnostic workflow for such scales—covering range, structure, reliability, method variance and functional form—and applied it to the TIMSS 2023 Digital Self-Efficacy scale across all 63 Grade 4 and 47 Grade 8 education-system and benchmarking samples (source database N = 719,881 children; 630,461 with complete seven-item measurement data). The scale was endpoint-concentrated, markedly at Grade 8, losing 83% and 95% of its test information between the mean and two standard deviations above it; an exact marginal calculation from a testlet model gave 80% and 89%. The apparent multidimensionality was better represented as localized covariance among three similarly worded items than as a separable second dimension, and omega hierarchical of 0.79 and 0.83 supported using the total score. In a factorial simulation evaluating the population projection coefficient on the analysis scale, endpoint concentration raised rejection of no curvature from 5.4% to 16.7% with raw summed scores, while latent scoring returned it to 5.5% and raised power from 71% to 88%, whether the item parameters were known or, in a smaller supporting condition, estimated in the analysis sample. Applied to cybervictimization, the quadratic association was attenuated but did not reverse sign once covariates were matched, and its prediction interval included zero. The range and reliability diagnostics behaved similarly on a second scale from the same assessment; broader applicability of the full workflow is proposed on theoretical grounds rather than established here. Full article
(This article belongs to the Special Issue Innovations and Integrations in Quantitative Methods for Behavioral Science Research)
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21 pages, 2173 KB  
Article
Associations and Potential Pathways Linking Excessive Short-Video Use to Mental Health Among Vocational High School Students in China: An Integrated Analysis Based on Machine Learning and Path Analysis
by Qihan Zhang, Baidong Huang and Hongwen Song
Behav. Sci. 2026, 16(8), 1429; https://doi.org/10.3390/bs16081429 - 19 Aug 2026
Viewed by 127
Abstract
Excessive short-video consumption is a global concern, yet its association with mental health among vocational high school students in China remains under-researched. Among many candidate individual, psychological, and behavioral factors, it remains unclear which are the most robust predictors of mental health. This [...] Read more.
Excessive short-video consumption is a global concern, yet its association with mental health among vocational high school students in China remains under-researched. Among many candidate individual, psychological, and behavioral factors, it remains unclear which are the most robust predictors of mental health. This study develops a comprehensive framework to identify core predictors and examine potential pathways linking short-video use to mental health. A survey was administered to 8346 vocational high school students recruited from vocational high schools in Tianjin, China, yielding 6096 valid responses for multi-stage analysis. First, Random Forest (RF) and linear Support Vector Regression (SVR) screened 13 individual, psychological, and behavioral features to identify core predictors. Subsequently, a Path Analysis (PA) with the Bootstrap method was used to examine statistical mediating pathways involving these core features. Across RF and SVR, social support, excessive short-video use, positive coping, and negative coping emerged as the four most important predictors of mental health. PA showed generally good fit (χ2/df = 5.86, CFI = 0.998, TLI = 0.99, RMSEA = 0.03, SRMR = 0.01). Excessive short-video use was significantly and directly associated with mental health problems (effect = 0.221, 95% CI = [0.189, 0.253], 76.7% of the total association) and showed small indirect associations through three statistical pathways: (1) lower social support (effect = 0.023, 8.1%); (2) higher negative coping (effect = 0.035, 12.0%); and (3) a small serial indirect association from reduced social support to decreased positive coping (effect = 0.007, 2.6%). Findings suggest that potential intervention efforts may consider regulating short-video use while simultaneously enhancing social support and adaptive coping mechanisms as part of strategies to reduce psychological risks. Full article
(This article belongs to the Special Issue Innovations and Integrations in Quantitative Methods for Behavioral Science Research)
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28 pages, 2485 KB  
Article
Multi-Agent LLMs for Occupational Profiling: Psychometric Validation on 1636 Chinese Occupations
by Yuting Han, Xiaoyang Luo, Feng Ji and Xiang Kong
Behav. Sci. 2026, 16(7), 1064; https://doi.org/10.3390/bs16071064 - 26 Jun 2026
Viewed by 468
Abstract
Occupation-level psychological profiles, such as RIASEC interests and Big Five personality, underpin career counseling, person–job matching, and workforce research, but building them at scale has been expensive and limited to a few national taxonomies. The O*NET Interest Profiler, the largest operationalization of RIASEC, [...] Read more.
Occupation-level psychological profiles, such as RIASEC interests and Big Five personality, underpin career counseling, person–job matching, and workforce research, but building them at scale has been expensive and limited to a few national taxonomies. The O*NET Interest Profiler, the largest operationalization of RIASEC, took more than two decades of worker surveys, expert ratings, and iterative empirical calibration to construct, and the 2022 Chinese Occupational Classification has no comparable psychological database. Large language models (LLMs) offer a scalable alternative, but using them as raters raises issues that single-model designs do not resolve: inter-rater reliability, calibration to external benchmarks, and systematic psychometric validation. We propose a multi-agent LLM framework in which three LLMs serve as separate expert raters, in-context anchors align the rating scale, and a separate arbitrator resolves rater disagreements. We applied the framework to all 1636 occupations in the 2022 Chinese Occupational Classification, producing six RIASEC and five Big Five scores per occupation. RIASEC dimensions showed uniformly excellent reliability (intraclass correlation coefficient, ICC [2,1] = 0.87 to 0.98) and high convergent correlations with O*NET (r = 0.84 to 0.96); structural validity received weak support (Tracey’s C = 0.653, ns), though the dimensions differentiated occupational categories as theory predicts, and the profile space recovered the administrative taxonomy (adjusted Rand index, ARI = 0.418). Big Five absolute agreement was uniformly high, although ICC(2,1) values for Conscientiousness and Neuroticism were attenuated by variance compression and model-level calibration offsets rather than rater disagreement. The Big Five scores are, therefore, suited to broad occupational differentiation, particularly on Openness and Extraversion, rather than to fine-grained rank ordering on Conscientiousness or Neuroticism. The framework also yields the first occupation-level RIASEC and Big Five database for the 2022 Chinese Occupational Classification, openly available for applied use. Full article
(This article belongs to the Special Issue Innovations and Integrations in Quantitative Methods for Behavioral Science Research)
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27 pages, 517 KB  
Article
Developing and Validating a Context-Sensitive Scale of Excellence-Driven Behavior in Public Universities: A Mixed-Methods Psychometric Study
by Zhe Cui, Chenxi Sun, Xinan Zhao and Ningning Chen
Behav. Sci. 2026, 16(6), 950; https://doi.org/10.3390/bs16060950 - 9 Jun 2026
Viewed by 281
Abstract
Quantitative behavioral research depends on clear construct specification and psychometrically sound measurement tools, especially when emerging constructs are examined in context-sensitive organizational settings. This study developed and validated a scale of excellence-driven behavior among faculty and staff in Chinese public universities. A mixed-methods [...] Read more.
Quantitative behavioral research depends on clear construct specification and psychometrically sound measurement tools, especially when emerging constructs are examined in context-sensitive organizational settings. This study developed and validated a scale of excellence-driven behavior among faculty and staff in Chinese public universities. A mixed-methods measurement-development design was used. First, the construct domain was derived from Excellence-Driven theory and contextualized within public universities. Second, qualitative evidence from semi-structured interviews and open-ended questionnaires using the Critical Incident Technique was used to generate and refine behavioral indicators. Third, the resulting instrument was examined through item analysis, exploratory factor analysis, confirmatory factor analysis, and reliability and validity assessment. The findings supported a two-dimensional structure consisting of Excellence-Driven Cognition and Learning and Excellence-Driven Display. The scale showed acceptable evidence of internal structure, internal consistency, convergent validity, discriminant validity between the two dimensions, and preliminary criterion-related validity. These results provide initial psychometric support for the use of the scale in future research on excellence-driven behavior among faculty and staff in Chinese public universities, while further evidence regarding temporal stability, measurement invariance, and broader empirical distinction from adjacent constructs is still needed. Full article
(This article belongs to the Special Issue Innovations and Integrations in Quantitative Methods for Behavioral Science Research)
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18 pages, 2040 KB  
Article
BERT-Enhanced HyperGAT with Siamese Networks and Reference Answer Set for Automated Short-Answer Scoring
by Chen Liu, Xiaofen Wan, Zhihao Ni, Sheng Su and Chunhua Kang
Behav. Sci. 2026, 16(6), 946; https://doi.org/10.3390/bs16060946 - 9 Jun 2026
Viewed by 253
Abstract
This paper proposes a novel framework, HyperGAT-BERT-RAS, that integrates (1) a HyperGraph Attention Network (HyperGAT) with BERT for enhanced semantic representation; (2) a Reference Answer Set (RAS) constructed via clustering of full-score answers; and (3) Siamese Neural Networks (SNNs) for similarity-based scoring. Experiments [...] Read more.
This paper proposes a novel framework, HyperGAT-BERT-RAS, that integrates (1) a HyperGraph Attention Network (HyperGAT) with BERT for enhanced semantic representation; (2) a Reference Answer Set (RAS) constructed via clustering of full-score answers; and (3) Siamese Neural Networks (SNNs) for similarity-based scoring. Experiments on the Ohsumed and ASAP-5 datasets demonstrate that (i) HyperGAT-BERT achieves 72.95% accuracy on Ohsumed text classification, outperforming baseline HyperGAT by 3.28%, and (ii) the full HyperGAT-BERT-RAS achieves 78.66% accuracy and 0.7806 F1-score, with RAS contributing the most to performance gains. These improvements suggest the potential for more reliable scoring of diverse student answers, reduced teacher grading burden, and enhanced feasibility of AI-assisted formative assessment in real classrooms, although empirical validation with teachers and students is needed. Full article
(This article belongs to the Special Issue Innovations and Integrations in Quantitative Methods for Behavioral Science Research)
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14 pages, 1203 KB  
Article
Longitudinal Relationships Between Positive Psychological Capacities and Emotional Well-Being Among 950 College Students: A Cross-Lagged Panel Network Analysis
by Ji Dai, Xuan Xia and Dini Xue
Behav. Sci. 2026, 16(6), 894; https://doi.org/10.3390/bs16060894 - 2 Jun 2026
Cited by 1 | Viewed by 463
Abstract
Emotional problems have become increasingly prevalent among university students, underscoring the importance of identifying protective psychological capacities that are linked to lower vulnerability to emotional problems. However, prior research has largely relied on cross-sectional designs and conventional statistical approaches, which limit the ability [...] Read more.
Emotional problems have become increasingly prevalent among university students, underscoring the importance of identifying protective psychological capacities that are linked to lower vulnerability to emotional problems. However, prior research has largely relied on cross-sectional designs and conventional statistical approaches, which limit the ability to clarify the temporal associations among multiple variables. To address this gap, we recruited 950 undergraduate students (61.6% female; Mage = 19.26, SD = 1.18) from 20 universities and conducted a two-wave longitudinal study. Cross-lagged panel network analysis was applied to examine the prospective associations linking positive psychological capacities (e.g., resilience, mindfulness) with emotional outcomes (e.g., negative affect, depression). Results revealed that positive affect and the acceptance dimension of mindfulness were among the most influential nodes within the network and exhibited stronger prospective associations with other positive psychological capacities. Based on the pathways identified in the network analysis, a half-longitudinal mediation model was further estimated to examine whether acceptance and awareness were prospectively associated with lower depressive symptoms through optimism. Together, these findings further clarified the temporal associations among positive psychological capacities and identified a prospective association linking mindfulness and depressive symptoms. These findings suggest that future mental health interventions for university students may benefit from incorporating strategies that promote positive affect and optimism within mindfulness practices. Full article
(This article belongs to the Special Issue Innovations and Integrations in Quantitative Methods for Behavioral Science Research)
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16 pages, 1213 KB  
Article
A Single-Indicator Factor Approach for Correcting Measurement Error in Time-Varying Predictors in Developmental Research
by Kejin Lee
Behav. Sci. 2026, 16(6), 855; https://doi.org/10.3390/bs16060855 - 27 May 2026
Viewed by 547
Abstract
Composite scores (e.g., mean or sum of survey items) are widely used as outcomes or predictors in psychological and social science research despite methodological concerns regarding measurement error. Despite extensive study of measurement error in path models, relatively little attention has been paid [...] Read more.
Composite scores (e.g., mean or sum of survey items) are widely used as outcomes or predictors in psychological and social science research despite methodological concerns regarding measurement error. Despite extensive study of measurement error in path models, relatively little attention has been paid to this methodological issue in latent growth modeling (LGM), particularly when predictors vary over time. Time-varying predictors allow for modeling occasion-specific influences beyond underlying developmental trajectories, yet they are frequently operationalized using composite scores that implicitly assume perfect reliability. To address this gap, the present study investigates the consequences of ignoring measurement error in composite time-varying predictors within the LGM framework. Notably, this is the first study to evaluate the single-indicator (SI) modeling approach as a method for correcting measurement error in time-varying predictors. We compared the traditional LGM incorporating composite predictors with the LGM that incorporates the SI factor approach to account for measurement error in time-varying predictors using the Early Childhood Longitudinal Study, Kindergarten Class of 1998–1999 (ECLS-K) dataset and a Monte Carlo simulation. The Monte Carlo simulation results revealed that ignoring measurement error in time-varying predictors attenuated occasion-specific effects by up to 30%. These findings underscore the necessity of correcting for measurement error using SI factor modeling to ensure the validity of developmental inferences in LGM. Full article
(This article belongs to the Special Issue Innovations and Integrations in Quantitative Methods for Behavioral Science Research)
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16 pages, 498 KB  
Article
Not All Awe Is Equal: Divergent and Unstable Effects of Positive and Negative Awe on Aggressive Behavior
by Fen Ren and Wei Liu
Behav. Sci. 2026, 16(5), 625; https://doi.org/10.3390/bs16050625 - 22 Apr 2026
Viewed by 826
Abstract
Emotions play an important role in shaping aggressive behavior, and understanding their underlying psychological mechanisms is particularly relevant among college students. However, existing research has predominantly focused on reactive aggression, while comparatively less attention has been paid to proactive aggression, which is more [...] Read more.
Emotions play an important role in shaping aggressive behavior, and understanding their underlying psychological mechanisms is particularly relevant among college students. However, existing research has predominantly focused on reactive aggression, while comparatively less attention has been paid to proactive aggression, which is more instrumental in nature and associated with more severe social consequences. In addition, empirical evidence regarding the valence-specific effects of awe remains limited. The present study aimed to examine the differential effects of positive and negative awe on proactive aggression and to explore the role of empathy as a potential mediating mechanism. A total of 110 college students were randomly assigned to one of three conditions: positive awe, negative awe, or neutral emotion. Awe was induced through video clips depicting natural landscapes. Proactive aggression was assessed using a modified bug-killing paradigm, including two behavioral indicators: force intensity and proportion of bugs killed. Empathy was measured using the Interpersonal Reactivity Index. The results revealed a clear differentiation based on the valence of awe. Participants in the positive awe condition exhibited significantly lower levels of proactive aggression than those in the neutral condition across both force intensity (M = 2.86, SD = 0.81 vs. M = 4.17, SD = 0.81) and proportion of bugs killed (M = 0.68, SD = 0.25 vs. M = 0.93, SD = 0.11). In contrast, the inhibitory effects of negative awe were weaker and less consistent. Compared with the neutral condition, negative awe was associated with a lower proportion of bugs killed, although this effect only reached marginal significance (p = 0.06, η2 = 0.04), and no significant difference was observed for force intensity. Mediation analyses indicated that empathy partially mediated the association between positive awe and proactive aggression. Empathy accounted for 31% of the total effect in the force intensity pathway (B = −0.02, t = −4.25, p < 0.001, 95% CI [−0.04, −0.01]) and 18% in the proportion-of-bugs-killed pathway (B = −0.003, t = −2.37, p = 0.02, 95% CI [−0.006, −0.001]). Notably, no significant mediating effect of empathy was observed in the negative awe condition, suggesting that the psychological processes linking awe to proactive aggression may differ as a function of emotional valence. Taken together, the present findings suggest that positive awe is reliably associated with lower levels of proactive aggression among college students, and that this association is partially explained by increased empathy. By contrast, the effects of negative awe appear to be fragile and context-dependent, as reflected in their failure to reach statistical significance, indicator-specific manifestation, and the absence of a consistent mediating pathway. These results highlight the importance of distinguishing between positive and negative awe when examining the behavioral consequences of self-transcendent emotions and underscore the need for further research to clarify the conditions under which negative awe may influence aggressive behavior. Full article
(This article belongs to the Special Issue Innovations and Integrations in Quantitative Methods for Behavioral Science Research)
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27 pages, 1388 KB  
Article
The Best of Two Worlds: IRT-Enhanced Automated Essay Interpretable Scoring
by Wei Xia, Jin Wu, Jiarui Yu and Chanjin Zheng
Behav. Sci. 2026, 16(4), 542; https://doi.org/10.3390/bs16040542 - 6 Apr 2026
Viewed by 1494
Abstract
The Automated Essay Scoring (AES) systems confront two fundamental challenges: opaque “black-box” decision-making that limits educator trust, and insufficient validation across linguistically diverse educational contexts. This study proposes IRT-AESF, an innovative framework that bridges educational measurement theory and artificial intelligence by integrating item [...] Read more.
The Automated Essay Scoring (AES) systems confront two fundamental challenges: opaque “black-box” decision-making that limits educator trust, and insufficient validation across linguistically diverse educational contexts. This study proposes IRT-AESF, an innovative framework that bridges educational measurement theory and artificial intelligence by integrating item response theory (IRT) with deep learning. The framework generates three theoretically grounded psychometric parameters: student ability, item difficulty, and item discrimination, which provide transparent and interpretable explanations for scoring decisions. We rigorously evaluated IRT-AESF through 5-fold cross-validation on three large-scale datasets comprising 41,328 authentic essays from English and Chinese educational settings, including both classroom assessments and high-stakes examinations. Results demonstrate statistically significant improvements over competitive baseline models, achieving an 8.4% relative increase in quadratic weighted kappa while maintaining robust cross-lingual performance. This research advances the development of transparent, trustworthy automated assessment systems that deliver not only scores but meaningful diagnostic insights for educational practice. Full article
(This article belongs to the Special Issue Innovations and Integrations in Quantitative Methods for Behavioral Science Research)
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34 pages, 5476 KB  
Article
A Sequential Generalized Nonparametric Classification Method for Small-Scale Cognitive Diagnostic Assessment
by Junjie Li, Huijing Zheng, Chunhua Kang, Yan Cai and Dongbo Tu
Behav. Sci. 2026, 16(4), 528; https://doi.org/10.3390/bs16040528 - 1 Apr 2026
Viewed by 500
Abstract
Small-scale (e.g., classroom) assessment represents the most common and needed scenario for cognitive diagnostic testing. In such settings, polytomously scored items (e.g., constructed-response tasks) are widely used, as they provide more fine-grained measurement of students’ skills and cognitive processes. However, a significant gap [...] Read more.
Small-scale (e.g., classroom) assessment represents the most common and needed scenario for cognitive diagnostic testing. In such settings, polytomously scored items (e.g., constructed-response tasks) are widely used, as they provide more fine-grained measurement of students’ skills and cognitive processes. However, a significant gap remains between the current methods and pressing practical needs. On one hand, parametric cognitive diagnosis models capable of handling polytomous response data require large samples for stable estimation, making them unsuitable for small-scale classroom use. On the other hand, existing nonparametric classification methods, while robust in small samples, are largely confined to dichotomous (0/1) response data. There is a lack of dedicated nonparametric methods for polytomous responses, creating a disconnect between practical testing and diagnostic tools. To address this real-world necessity, this study proposes the seq-GNPED method. It extends the generalized nonparametric classification framework to polytomous data by introducing weighted ideal category response and a collapsed class iterative algorithm. Simulations and empirical applications confirm that seq-GNPED achieves robust and accurate diagnosis under small sample conditions where parametric models falter, effectively leveraging the informational richness of polytomous items. This work bridges a critical gap by providing a practical, nonparametric tool tailored for fine-grained, classroom-ready cognitive diagnosis. Full article
(This article belongs to the Special Issue Innovations and Integrations in Quantitative Methods for Behavioral Science Research)
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20 pages, 1692 KB  
Article
Leveraging Distance-Based Effectiveness Indicators for Enhanced Behavioral Pattern Discovery in Complex Problem-Solving Assessment
by Pujue Wang, Jiayi Cheng and Hongyun Liu
Behav. Sci. 2026, 16(3), 383; https://doi.org/10.3390/bs16030383 - 6 Mar 2026
Viewed by 712
Abstract
Data-driven approaches have emerged as powerful tools for analyzing process data. This study focuses on two data-driven methods: n-gram chi-square feature selection for extracting key action segments and K-medoids clustering combined with Dynamic Time Warping (DTW) distance for identifying behavioral patterns. To address [...] Read more.
Data-driven approaches have emerged as powerful tools for analyzing process data. This study focuses on two data-driven methods: n-gram chi-square feature selection for extracting key action segments and K-medoids clustering combined with Dynamic Time Warping (DTW) distance for identifying behavioral patterns. To address the limitations that arise when applying these methods to complex tasks where ambiguous raw actions often hinder interpretation, this study introduces distance-based effectiveness indicators to enhance both data-driven methods for analyzing actions in the context of complex problem-solving. The research examines how representing action sequences through state effectiveness (ds) and transition effectiveness (Δds→s′) indicators outperforms the use of raw actions alone within the complex collaborative problem-solving Balance Beam task. Results consistently demonstrated that effectiveness indicators significantly improved the sensitivity of n-gram feature selection, the performance of clustering, and the interpretability of both n-grams and resulting clusters. Specifically, state effectiveness representations (ds→ds′) yielded the best outcomes. These findings advocate for the integration of effectiveness indicators into data-driven process analytics to more effectively capture and explain behavioral patterns of problem-solving. Full article
(This article belongs to the Special Issue Innovations and Integrations in Quantitative Methods for Behavioral Science Research)
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21 pages, 1761 KB  
Article
Developmental Change in Associations Between Mental Health and Academic Ability Across Grades in Adolescence: Evidence from IRT-Based Vertical Scaling
by Yuanqiu Ma, Youyou Duan, Yunxiao Qi, Ying Hu and Tour Liu
Behav. Sci. 2026, 16(1), 78; https://doi.org/10.3390/bs16010078 - 6 Jan 2026
Viewed by 866
Abstract
Adolescence is a critical period when rapid cognitive maturation coincides with heightened emotional vulnerability. This study examined the dynamic association between academic ability and mental health across early adolescence, focusing on vocabulary ability as a core indicator of academic ability. Using large-scale data [...] Read more.
Adolescence is a critical period when rapid cognitive maturation coincides with heightened emotional vulnerability. This study examined the dynamic association between academic ability and mental health across early adolescence, focusing on vocabulary ability as a core indicator of academic ability. Using large-scale data from Grades 1–12 (N = 13,412), a vertically scaled vocabulary ability scale was constructed based on Item Response Theory (IRT) and the Non-Equivalent Anchor Test (NEAT) design to achieve cross-grade comparability. Fixed-parameter calibration was then applied to an independent cross-sectional sample of middle school students (Grades 7–9, N = 401) in Tianjin, combined with the DASS-21 to assess internalizing symptoms (depression, anxiety, stress). Hierarchical multiple regression analyses revealed that higher vocabulary ability was significantly associated with lower levels of depression, anxiety, and stress, with the negative association strongest in Grade 8. The present study provides new empirical evidence for understanding the interactive mechanisms between academic and psychological development during adolescence. Methodologically, the study demonstrates the value of IRT-based vertical scaling in establishing developmentally interpretable metrics for educational and psychological assessment. Full article
(This article belongs to the Special Issue Innovations and Integrations in Quantitative Methods for Behavioral Science Research)
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