Investigation of Affective Traits Affecting Mathematics Achievement by SEM and MARS Methods

Autor/inn/en	Kuddar, Cagla; Cetin, Sevda
Titel	Investigation of Affective Traits Affecting Mathematics Achievement by SEM and MARS Methods
Quelle	In: International Journal of Assessment Tools in Education, 9 (2022) 2, S.337-356 (20 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Kuddar, Cagla) ORCID (Cetin, Sevda)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Mathematics Achievement; Structural Equation Models; Regression (Statistics); Achievement Tests; Foreign Countries; Mathematics Tests; International Assessment; Grade 4; Elementary School Students; Grade 8; Secondary School Students; Data Analysis; Predictor Variables; Student Interests; Student Attitudes; Bullying; Turkey; Trends in International Mathematics and Science Study + Suchen Sie Ihr Suchwort? Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Regression; Regressionsanalyse; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Ausland; School year 04; 4. Schuljahr; Schuljahr 04; School year 08; 8. Schuljahr; Schuljahr 08; Sekundarschüler; Auswertung; Prädiktor; Studieninteresse; Schülerverhalten; Mobbing; Türkei
Abstract	The purpose of the study is to analyze the affective traits that affect mathematics achievement through Structural Equation Modeling (SEM) as a traditional regression model and Multivariate Adaptive Regression Splines (MARS), as one of the data mining methods. Structural Equation Modeling, one of the regression-based methods, is quite popular for social sciences due to the various advantages it offers; however, it requires very intensive assumptions. MARS method, on the other hand, is a multivariate and adaptive nonparametric statistical regression method used for data classification and modeling. MARS does not need any assumptions such as normality, linearity, homogeneity. It allows variables that do not provide linearity to be included in the analysis. The present study examines whether it is possible to use the MARS method, which is a more flexible method compared to SEM, taking both methods into account. Regarding this goal, the SEM model was created with the program R using the affective data and the achievement variable picked from TIMMS 2019 data. Then, the MARS method was created using the SPM (Salford Predictive Modeler) program. The results of the study showed that at certain points the MARS model gave similar results to the SEM model and MARS model is more compatible with the literature. (As Provided).
Anmerkungen	International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://ijate.net/index.php/ijate
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01