자연주의적 탐구조사(Naturalistic Inquiry)

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Using Multivariate Statistics

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Principles and Practice of Structural Equation Modeling (2nd ed.)

The shift in Singaporean pre-service teachers’ epistemological beliefs and in their beliefs about teaching and learning over the course of teacher preparation program

With the onset of television, development of FM broadcasting, changes in recording technologies, and a rise of youth affluence since the 1950s,popular music has become accessible to most parts of the world…

The preference for popular music: Reasons and problems

Previous studies had identified the potential link between achievement goal and students’ technology use. However, the literature on this topic is extremely scarce. The purpose of this study was to investigate the antecedents to technology use among undergraduates via an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model with achievement goal as an external variable. Data were collected from 599 undergraduates using a 38-item self-report questionnaire and analyzed using structural equation modeling. Results of this study showed that the extended UTAUT model had a good fit to the data of this study, with 35% of the variance in behavioral intention and 32% of the variance in use behavior explained by their antecedents. This study found that performance expectancy, effort expectancy, and achievement goal had significant influences on undergraduates’ technology behavioral intention to use technology, while social influence and facilitating conditions from the original UTAUT model did not. It was also found that achievement goal and behavioral intention were significant antecedents that influenced undergraduates’ technology use behavior. The findings of this study contribute to both theory, practice, and policy that enhance our understanding of students’ learning and technology use. 

Influence of achievement goal on technology use among undergraduates in Malaysia

Musical characteristics and cultural attitudes as correlates of musical preferences among polytechnic students

This article aims to propose the Learning Vector Quantization (LVQ) approach to impute missing group membership and sampling weights in inferring the accuracy of population parameters of confirmatory factor analysis (CFA) models with categorical questionnaires. Survey data with missing group memberships, for example, gender, age, or ethnicity, are very familiar. However, the group memberships of examinees are critical for calculating the stratum sampling weights. Asparouhov (2005), Tsai and Yang (2008), and Yang and Tsai (2008) have described that appropriate imputation can further improve the precision of CFA model estimations. Questionnaires with categorical responses are not well established yet. In this study, a Monte Carlo simulation was conducted to compare the LVQ method with the other three existing methods (e.g., listwise-deletion, weighting-class adjustment, non-weighted). Four experimental factors, such as missing data rates, sampling sizes, disproportionate sampling, and different populations, were used to examine the performance of these four methods. The results showed that the LVQ method outperformed the other three methods in terms of accuracy of parameters of CFA model with binary or 5-category responses. The conclusion and discussion sections of this article provide for some practical guidelines.

Timothy Teo

Papers

The shift in Singaporean pre-service teachers’ epistemological beliefs and in their beliefs about teaching and learning over the course of teacher preparation program

The preference for popular music: Reasons and problems

Influence of achievement goal on technology use among undergraduates in Malaysia

Musical characteristics and cultural attitudes as correlates of musical preferences among polytechnic students

Weighting imputation for categorical data