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ERIC Number: ED573050
Record Type: Non-Journal
Publication Date: 2014-Oct
Pages: 12
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-2161-623X
EISSN: N/A
A Regression Model with a New Tool: IDB Analyzer for Identifying Factors Predicting Mathematics Performance Using PISA 2012 Indices
Arikan, Serkan
Online Submission, US-China Education Review A v4 n10 p716-727 Oct 2014
There are many studies that focus on factors affecting achievement. However, there is limited research that used student characteristics indices reported by the Programme for International Student Assessment (PISA). Therefore, this study investigated the predictive effects of student characteristics on mathematics performance of Turkish students. In PISA studies, sampling design, sampling weights, and plausible values have to be taken into consideration in order not to have biased multiple regression results. In order to conduct multiple regression analyses in PISA, software called the International Association for the Evaluation of Educational Achievement (IEA) International Database (IDB) Analyzer is required to be used because the dependent variable consists of several plausible values. This study aims to identify student characteristics that are significant in predicting mathematics performance in Turkey. Results showed that being successful in mathematics is a combination of several factors in which students' beliefs, motivation, and other factors must be organized to achieve mathematics. Among these beliefs and motivation, strong self-efficacy, positive self-concept, and minimum level of anxiety seem to be the key for success.
Publication Type: Journal Articles; Reports - Research
Education Level: Secondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Turkey
Identifiers - Assessments and Surveys: Program for International Student Assessment
Grant or Contract Numbers: N/A