AI-assisted discovery of quantitative and formal models in social science by Julia Balla, Sihao Huang, Owen Dugan, Rumen Dangovski, Marin Soljačić

“…By extending neuro-symbolic methods to find compact functions and differential equations in noisy and longitudinal data, we show that our system can be used to discover interpretable models from real-world data in economics and sociology. …”
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Evaluation of the Effectiveness of a Positive Youth Development Program for Secondary Students in Macau by Andrew L. Luk, K. M. Leong, Annah M. L. Au

“…The subjective outcome evaluations including participants' perceptions of the program, program instructors, benefits from the program, and overall satisfaction were positive. Although the longitudinal data from the objective outcome evaluation did not show any notable improvement, the overall effect of the program was found to be positive to the new comers in the junior secondary years. …”
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Effectiveness of the Tier 1 Program of Project P.A.T.H.S.: Findings Based on Three Years of Program Implementation by Daniel T. L. Shek, Rachel C. F. Sun

“…Utilizing the six-wave longitudinal data, ANCOVA, and linear mixed models controlling for differences between the two groups in terms of Wave 1 pretest scores, personal variables, and random effects of schools, it was revealed that participants in the experimental schools showed significantly better development than did participants in the control schools at post-test (Wave 6) based on different indicators of positive youth development derived from the Chinese Positive Youth Development Scale and other measures. …”
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THE TRANSITION “SCHOOL – UNIVERSITY”: PREDICTORS OF ACADEMIC PERFORMANCE OF FIRST-YEAR STUDENTS by T. A. SAPRYKINA

“…On the other hand, in Russia there are almost no studies which have examined this association on national representative samples and using longitudinal data. The presupposition of this study is the notion that educational outcomes of students, among other things, in a meaningful manner are associated with characteristics of learning in higher school. …”
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Simple Sources for Complex Problems. Where Did Californians Come From in 1940? by Myron P. Gutmann

“…This essay is about one approach that derives from a source that does not offer all that is possible with linked longitudinal data, but that nonetheless has significant value. …”
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Teachers’ and Parents’ Assessments of Primary School Children’s Intellectual Investment as Predictors of Change in Need for Cognition by Anke Hufer-Thamm, Rolf Jürgens, Sebastian Bergold, Ricarda Steinmayr

“…The parents (<i>N</i> = 452) provided the data in 2021. Longitudinal data were analyzed by means of latent change score models (LCSMs). …”
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Can intelligent virtual assistants improve cognitive function in older adults? A two-wave mediation study by Tianxin Cai, Shilong Ma, Renyao Zhong

“…Methods This study utilized nationally representative longitudinal data from 2018 to 2020, including a sample of 8343 older adults aged 60–80. …”
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Subjective social status and trajectories of frailty: findings from the English Longitudinal Study of Ageing by Patrick Präg, Lindsay Richards, Asri Maharani

“…Objectives Subjective social status is a known antecedent for many health outcomes, but little research has examined the association between subjective status and frailty among older people. Using longitudinal data, the goal of this study was, first, to identify latent trajectories of frailty over time, and second, to investigate the relationship between subjective social status and frailty trajectory.Methods Data were drawn from the 2002–2019 surveys of the English Longitudinal Study of Ageing, involving 9484 individuals aged 50+ years at baseline. …”
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Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study by Philip Hempel, Antônio H. Ribeiro, Marcus Vollmer, Theresa Bender, Marcus Dörr, Dagmar Krefting, Nicolai Spicher

“…AI-ECG models estimate aging effects as a novel biomarker but have only been evaluated on single ECGs—without utilizing longitudinal data. We validated an AI-ECG model, originally trained on Brazilian data, using a German cohort with over 20 years of follow-up, demonstrating similar performance (r 2 = 0.70) to the original study (0.71). …”
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Examining the mediating role of motivation in the relationships between teacher-created motivational climates and quality of engagement in secondary school physical education. by Daniel Milton, Paul R Appleton, Eleanor Quested, Anna Bryant, Joan L Duda

“…Cross sectional data collected from 832 students (439 males and 386 females) while longitudinal data stemmed 299 students (166 males and 163 females). …”
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Relationship between movement behaviours and life satisfaction in Chinese children: A cross-lagged panel analysis. by Xingyi Yang, Danqing Zhang, Yang Liu

“…This study examined the relationship between movement behaviours and life satisfaction (LS) using longitudinal data from a sample of primary school students. …”
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The Development of Microhistorical Databases in Norway. A Historiography by Gunnar Thorvaldsen, Lars Holden

“…Cross-sectional and vital records are being interlinked with automatic and manual record linkage software. Longitudinal data is available for searching as timelines and in Intermediate Data Structure format from UiT The Arctic University and for searching at Histreg.no, which also caters for manual editing. …”
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Advancing the development of real-world data for healthcare research in China: challenges and opportunities by Jun Zhang, Eric Wu, Jia Zhong, Honghao Fang, Larry Liu, Jipan Xie

“…Across all sectors, examples of barriers identified included inconsistencies in terminology and non-standardised coding practices; the absence of longitudinal data; the absence of transparent data processing and validation practices; and the inability to access and share RWD. …”
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Creating National Weights for a Patient-level Longitudinal Database by Onur Baser, Li Wang, Jon Maguire

“…**Objective:** To create a nationally-representative estimate from longitudinal data by controlling for sociodemographic factors and health status. …”
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Artificial intelligence in education: advancing educational digital inclusion for adults older with diverse neuromuscular conditions by Paula Andrea Valencia-Londoño, Hilderman Cardona-Rodas, Jovani A. Jiménez-Builes

“…While the study demonstrates short-term benefits, it acknowledges limitations, including a small sample size (n = 30) and the absence of longitudinal data. Future research should explore scalable implementations and long-term impacts, particularly for broader demographic groups and other disability types. …”
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Neural correlates of vicarious reward processing and peer victimization experiences in late childhood by Simone Dobbelaar, Sanne Kellij, René Veenstra, Berna Güroğlu

“…This study used a sample of children with prospective longitudinal data on peer victimization over the past two years (nbehavioral = 83, 49.4 % girls, Mage = 10.6 years, nfmri = 62). …”
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Novel Application of Statistical Methods to Identify New Urinary Incontinence Risk Factors by Theophilus O. Ogunyemi, Mohammad-Reza Siadat, Suzan Arslanturk, Kim A. Killinger, Ananias C. Diokno

“…Longitudinal data for studying urinary incontinence (UI) risk factors are rare. …”
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Suicide Research and Adolescent Suicide Trends in New Zealand by Said Shahtahmasebi

“…In recent years, there have been a number of claims and counterclaims from suicide research using time series and longitudinal data; in particular, the linkage of increased antidepressant prescriptions to a decrease in suicide rates. …”
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Does Internet use help to achieve sustainable food consumption? Evidence from rural China by Jian Liu, Yanjun Ren, Yu Hong, Thomas Glauben, Qiang Li

“…Internet use is widely studied as an important socio-economic factor influencing agricultural productivity, income, the adoption of sustainable agricultural practices, and farmer welfare, but scant attention is given to its influence on sustainable food consumption. Using longitudinal data from the China Health and Nutrition Survey (CHNS), this study seeks to better understand the causal effect of Internet use on sustainable food consumption measured by food carbon and food water footprints and shed light on its underlying channels. …”
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Prediction of Multidimensional Poverty Status With Machine Learning Classification at Household Level: Empirical Evidence From Tanzania by Ngong'Ho Bujiku Sende, Snehanshu Saha, Leon Ruganzu, Saibal Kar

“…To achieve this objective, longitudinal data from the 2014/15 and 2020/21 surveys, sourced from the Tanzania National Bureau of Statistics (NBS) were analyzed. …”
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