Methodological and Epistemological Issues on Linear Regression Applied to Psychometric Variables in Problem Solving: Rethinking Variance

Autor/in	Stamovlasis, Dimitrios
Titel	Methodological and Epistemological Issues on Linear Regression Applied to Psychometric Variables in Problem Solving: Rethinking Variance
Quelle	In: Chemistry Education Research and Practice, 11 (2010) 1, S.59-68 (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1756-1108
DOI	10.1039/C001048K
Schlagwörter	Chemistry; Problem Solving; Robustness (Statistics); Multiple Regression Analysis; Logical Thinking; Correlation; Psychometrics; Science Instruction; Secondary School Science; Science Achievement; Teaching Methods; Predictor Variables; Cognitive Style + Suchen Sie Ihr Suchwort? Chemie; Problemlösen; Widerstandsfähigkeit; Korrelation; Psychometry; Psychometrie; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Teaching method; Lehrmethode; Unterrichtsmethode; Prädiktor; Cognitive styles; Kognitiver Stil
Abstract	The aim of the present paper is two-fold. First, it attempts to support previous findings on the role of some psychometric variables, such as, M-capacity, the degree of field dependence-independence, logical thinking and the mobility-fixity dimension, on students' achievement in chemistry problem solving. Second, the paper aims to raise some methodological and epistemological issues concerning the implementation of the general linear model (GLM) in this type of research. Multiple regression analysis was used to analyze the data, which were taken from students (N = 86) in tenth grade of high school taking a compulsory course in chemistry. Three different techniques were implemented in order to support a linear model: The Added Variable Plots, the Stepwise Regression and the Best Subsets Regression. Residual analysis and collinearity diagnosis were also performed in order to test the robustness of inferential statistics. The GLM explained 39% of the variance and suggested that only M-capacity and logical thinking were the significant predictors, even though all the correlation coefficients with achievement were statistically significant. The extensive analysis of the linear regression procedures revealed their advantages and also their limitations in terms of statistical robustness. Moreover, a discussion is initiated concerning the explanatory power of linear models and suggests rethinking variance explained under a different philosophical perspective. It is argued that the weakness of the GLM in studying complex dynamical processes, such as problem solving, is rooted not merely in the statistical assumptions that do not hold, or in the variables that are ignored, but substantially it is deeply epistemological. (Contains 7 notes, 6 tables, and 6 figures.) (As Provided).
Anmerkungen	Royal Society of Chemistry. Thomas Graham House, Science Park, Milton Road, Cambridge, CB4 0WF, UK. Tel: +44-1223 420066; Fax: +44-1223 423623; e-mail: cerp@rsc.org; Web site: http://www.rsc.org/cerp
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10