The Structure of Academic Achievement: Searching for Proximal Mechanisms Using Causal Discovery Algorithms

Autor/in	Quintana, Rafael
Titel	The Structure of Academic Achievement: Searching for Proximal Mechanisms Using Causal Discovery Algorithms
Quelle	In: Sociological Methods & Research, 52 (2023) 1, S.85-134 (50 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Quintana, Rafael)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124120926208
Schlagwörter	Academic Achievement; Causal Models; Algorithms; Social Problems; Behavior Problems; Psychological Studies; Executive Function; Student Motivation; Context Effect; Barriers; Longitudinal Studies; Surveys; Markov Processes; Kindergarten; Elementary School Students; Reading Achievement; Mathematics Achievement; Achievement Tests; Family Characteristics; Family Income; Educational Attainment; Teacher Student Relationship; Peer Relationship; Institutional Characteristics; Neighborhoods; Safety; Socioeconomic Status; Early Childhood Longitudinal Survey + Suchen Sie Ihr Suchwort? Schulleistung; Kausalanalyse; Algorithm; Algorithmus; Social problem; Soziales Problem; Schulische Motivation; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Survey; Umfrage; Befragung; Markowscher Prozess; Leseleistung; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Familieneinkommen; Bildungsabschluss; Bildungsgut; Teacher student relationships; Lehrer-Schüler-Beziehung; Peer-Beziehungen; Neighbourhoods; Nachbarschaft; Sicherheit; Socio-economic status; Sozioökonomischer Status
Abstract	Causal search algorithms have been effectively applied in different fields including biology, genetics, climate science, medicine, and neuroscience. However, there have been scant applications of these methods in social and behavioral sciences. This article provides an illustrative example of how causal search algorithms can shed light on important social and behavioral problems by using these algorithms to find the proximal mechanisms of academic achievement. Using a nationally representative data set with a wide range of relevant contextual and psychological factors, I implement four causal search procedures that varied important dimensions in the algorithms. Consistent with previous research, the algorithms identified prior achievement, executive functions (in particular, working memory, cognitive flexibility, and attentional focusing), and motivation as direct causes of academic achievement. I discuss the advantages and limitations of graphical models in general and causal search algorithms in particular for understanding social and behavioral problems. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01