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Direct linkKim, Yongnam; Steiner, Peter M. – Sociological Methods & Research, 2021
For misguided reasons, social scientists have long been reluctant to use gain scores for estimating causal effects. This article develops graphical models and graph-based arguments to show that gain score methods are a viable strategy for identifying causal treatment effects in observational studies. The proposed graphical models reveal that gain…
Descriptors: Scores, Graphs, Causal Models, Statistical Bias
Irene Campos-Sánchez; Eva María Navarrete-Muñoz; Dries S. Martens; Isolina Riaño-Galán; Aitana Lertxundi; Sabrina Llop; Mónica Guxens; Cristina Rodríguez-Dehli; Nerea Lertxundi; Raquel Soler-Blasco; Martine Vrijheid; Tim S. Nawrot; John Wright; Tiffany C. Yang; Rosie McEachan; Kristine Bjerve Gützkow; Vaia Lida Chatzi; Marina Vafeiadi; Mariza Kampouri; Regina Grazuleviciene; Sandra Andrusaityte; Johanna Lepeule; Desirée Valera-Gran – Journal of Attention Disorders, 2025
Objective: To explore the association between telomere length (TL) and attention deficit hyperactivity disorder (ADHD) symptoms in children at 6-12 years. Method: Data from 1,759 children belonging to the HELIX project cohorts and the Asturias, Gipuzkoa and Valencia cohorts of INMA project were included. TL was determined by blood sample using a…
Descriptors: Foreign Countries, Genetic Disorders, Attention Deficit Hyperactivity Disorder, Mothers
Olivia D. Perrin; Jinhyo Cho; Edward T. Cokely; Jinan N. Allan; Adam Feltz; Rocio Garcia-Retamero – Cognitive Research: Principles and Implications, 2025
Numerate people tend to make more informed judgments and decisions because they are more risk literate (i.e., better able to evaluate and understand risk). Do numeracy skills also help people understand regular science reporting from mainstream news sources? To address this question, we investigated responses to regular science reports (e.g.,…
Descriptors: Numeracy, Critical Thinking, Evaluative Thinking, Bias
Yuan Liang; Ting Ji; Shuying Zhou; Xiaolin Liu; Hao Yan – British Educational Research Journal, 2025
Constructing personalised and effective online language learning models based on individual personality differences is crucial in the field of education. However, there is little research on how to apply these models to students in science and engineering who have varying personality profiles. This study aimed to assess the validity of the Online…
Descriptors: Personality Traits, Language Acquisition, Second Language Learning, College Students
Jogy George; N. R. Suresh Babu – European Journal of Education, 2025
Imitated misbehaviours as a prevalent form of interaction between children often disrupt their classroom experiences. Teachers, being the authority figure in the classroom, are expected to manage imitated misbehaviours and create a classroom climate that can positively influence the children. In line with this, this study analyses the conceptions…
Descriptors: Imitation, Child Behavior, Behavior Problems, Elementary School Students
Kearney, Christopher A.; Childs, Joshua – Improving Schools, 2023
School attendance and absenteeism are critical targets of educational policies and practices that often depend heavily on aggregated attendance/absenteeism data. School attendance/absenteeism data in aggregated form, in addition to having suspect quality and utility, minimizes individual student variation, distorts detailed and multilevel…
Descriptors: Data Analysis, Attendance, Educational Policy, Causal Models
Kitto, Kirsty; Hicks, Ben; Shum, Simon Buckingham – British Journal of Educational Technology, 2023
An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results…
Descriptors: Causal Models, Learning Analytics, Educational Theories, Artificial Intelligence
Rüttenauer, Tobias; Ludwig, Volker – Sociological Methods & Research, 2023
Fixed effects (FE) panel models have been used extensively in the past, as those models control for all stable heterogeneity between units. Still, the conventional FE estimator relies on the assumption of parallel trends between treated and untreated groups. It returns biased results in the presence of heterogeneous slopes or growth curves that…
Descriptors: Hierarchical Linear Modeling, Monte Carlo Methods, Statistical Bias, Computation
Thomas Cook; Mansi Wadhwa; Jingwen Zheng – Society for Research on Educational Effectiveness, 2023
Context: A perennial problem in applied statistics is the inability to justify strong claims about cause-and-effect relationships without full knowledge of the mechanism determining selection into treatment. Few research designs other than the well-implemented random assignment study meet this requirement. Researchers have proposed partial…
Descriptors: Observation, Research Design, Causal Models, Computation
Shimonovich, Michal; Pearce, Anna; Thomson, Hilary; Katikireddi, Srinivasa Vittal – Research Synthesis Methods, 2022
In fields (such as population health) where randomised trials are often lacking, systematic reviews (SRs) can harness diversity in study design, settings and populations to assess the evidence for a putative causal relationship. SRs may incorporate causal assessment approaches (CAAs), sometimes called 'causal reviews', but there is currently no…
Descriptors: Evidence, Synthesis, Causal Models, Public Health
Jensen, Ruth – Journal of Educational Change, 2022
Causal relationships are traditionally examined in quantitative research. However, this article informs the discussion surrounding the potential use of qualitative data to explore causal relationships qualitatively through an empirical illustration of a school leadership development team. As school leadership development is supposed to offer…
Descriptors: Causal Models, Qualitative Research, Educational Improvement, Principals
Luke W. Miratrix – Grantee Submission, 2022
We are sometimes forced to use the Interrupted Time Series (ITS) design as an identification strategy for potential policy change, such as when we only have a single treated unit and cannot obtain comparable controls. For example, with recent county- and state-wide criminal justice reform efforts, where judicial bodies have changed bail setting…
Descriptors: Causal Models, Case Studies, Quasiexperimental Design, Monte Carlo Methods
Matthew Truwit – Society for Research on Educational Effectiveness, 2022
With a growing consensus that students require more than purely academic support, schools across the country have increasingly adopted the community school model, a comprehensive approach to education focused on holistic student development. In these schools, centrally located site coordinators leverage partnerships with local organizations to…
Descriptors: Community Schools, Integrated Services, Job Satisfaction, Teacher Persistence
Minjung Kim; Christa Winkler; James Uanhoro; Joshua Peri; John Lochman – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Cluster memberships associated with the mediation effect are often changed due to the temporal distance between the cause-and-effect variables in longitudinal data. Nevertheless, current practices in multilevel mediation analysis mostly assume a purely hierarchical data structure. A Monte Carlo simulation study is conducted to examine the…
Descriptors: Hierarchical Linear Modeling, Mediation Theory, Multivariate Analysis, Causal Models
Jamie Amemiya; Gail D. Heyman; Caren M. Walker – Cognitive Science, 2024
How do people come to opposite causal judgments about societal problems, such as whether a public health policy reduced COVID-19 cases? The current research tests an understudied cognitive mechanism in which people may agree about what "actually" happened (e.g., that a public health policy was implemented and COVID-19 cases declined),…
Descriptors: Causal Models, Evaluative Thinking, Logical Thinking, Social Problems
