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An empirical model for prediction of...

Wang, Jianjun.

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An empirical model for prediction of students' science achievement in the United States and the People's Republic of China.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An empirical model for prediction of students' science achievement in the United States and the People's Republic of China./
作者:	Wang, Jianjun.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 1993,
面頁冊數:	156 p.
附註:	Source: Dissertation Abstracts International, Volume: 54-08, Section: A, page: 2973.
Contained By:	Dissertation Abstracts International54-08A.
標題:	Science education. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9402723

An empirical model for prediction of students' science achievement in the United States and the People's Republic of China.
Wang, Jianjun.

An empirical model for prediction of students' science achievement in the United States and the People's Republic of China. - Ann Arbor : ProQuest Dissertations & Theses, 1993 - 156 p.

Source: Dissertation Abstracts International, Volume: 54-08, Section: A, page: 2973.

Thesis (Ph.D.)--Kansas State University, 1993.

Appropriate assessment of students' science achievement is a fundamental question in science education. One statistical approach to assessment suggests the establishment of a prediction model. Yet, no prediction model is uniformly supported by theories. The research presented in this dissertation explores a possible empirical model for prediction of students' science achievement in China and the United States. Construction of the model is based on the ninth grade data sets from the Phase II of the Second IEA Science Study (SISS) in the United States and the SISS Extension Study (SES) in Hubei province of China.Subjects--Topical Terms:

521340
Science education.

An empirical model for prediction of students' science achievement in the United States and the People's Republic of China.
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245 1 3 $a An empirical model for prediction of students' science achievement in the United States and the People's Republic of China.
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500 $a Source: Dissertation Abstracts International, Volume: 54-08, Section: A, page: 2973.
500 $a Major Professor: John R. Staver.
502 $a Thesis (Ph.D.)--Kansas State University, 1993.
520 $a Appropriate assessment of students' science achievement is a fundamental question in science education. One statistical approach to assessment suggests the establishment of a prediction model. Yet, no prediction model is uniformly supported by theories. The research presented in this dissertation explores a possible empirical model for prediction of students' science achievement in China and the United States. Construction of the model is based on the ninth grade data sets from the Phase II of the Second IEA Science Study (SISS) in the United States and the SISS Extension Study (SES) in Hubei province of China.
520 $a Previous research divides prediction models into linear vs. non-linear categories. However, as an empirical exploration, neither linear nor non-linear relations should be imposed as a pre-condition of the model construction. In this research, both linear and non-linear functions are treated as special cases of a Taylor polynomial series. The shrinkage method favored by Copas (1983) and Hebel, et. al. (1993) is employed to construct the polynomial coefficients in the truncated Taylor model. The common variables observed in the SES and Phase II SISS projects are classified into five categories, students' gender, attitudes, home background, classroom experience, and personal effort, based on the distinction of visible and latent characteristics and the scree plots from principal component analyses. The latent categories, students' attitudes, home background, classroom experience, and personal effort, are represented by their first principal components. The factors of prediction are constructed by polynomials of the visible variable (gender), the latent principal components, and their interactions. Significant factors are selected through the backward elimination procedure in SAS.
520 $a Factor structures are expressed by factor loadings in each category. The differences in the factor structure and the model complexity between the United States and China are interpreted in terms of the differing educational, political, social and cultural contexts in each country. The empirical results are: (1) Gender has a significant linear effect on students' science achievement; (2) The effects of attitude, home background, classroom experience, and personal effort, are curvilinear. Curvature functions are derived for each factor to elaborate the curvilinearities; (3) In both countries, most significant interactions are at the third polynomial level.
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