자연주의적 탐구조사(Naturalistic Inquiry)

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Using Multivariate Statistics

Attachment--and loss.

The power of feedback.

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

Modeling Educational Research: The way forward

Introduction The use of Structural Equation Modeling (SEM) in research has increased in popularity since it was first conceived by Wright (1918), a biometrician who was credited with the development of path analysis to analyze genetic theory in biology (Teo & Khine, 2009). In the 1970s, SEM enjoyed a renaissance, particularly in sociology and econometrics (Goldberger & Duncan, 1973). It later spread to other disciplines, such as psychology, political science, and education (Kenny, 1979). The growth and popularity of SEM was generally attributed to the advancement of software development that have increased the accessibility of SEM to substantive researchers who have found this method to be appropriate in addressing a variety of research questions (MacCallum & Austin, 2000). Some examples of these software include LISREL (LInear Structural RELations) by Joreskog and Sorbom (2003), EQS (Equations) (Bentler, 2003), and AMOS (Analysis of Moment Structures) by Arbuckle (2006). Over the years, the combination of methodological advances and improved interfaces in various SEM software have contributed to the diverse usage of SEM. Hershberger (2003) examined major journals in psychology from 1994 to 2001 and found that over 60% of these journals contained articles using SEM, more than doubled the number of articles published from 1985 to 1994. Although SEM continues to undergo refinement and extension, it is popular among applied researchers. This purpose of this paper is provide a non-technical introduction to the various facets of structural equation modeling to researchers in education. What is structural Equation Modeling? Structural Equation Modeling (SEM) is a statistical approach to testing hypotheses about the relationships among observed and latent variables (Hoyle, 1995). It is also a methodology that takes a confirmatory (i.e. hypothesis-testing) approach to the analysis of a theory relating to some phenomenon. Byrne (2006) compared SEM against other multivariate techniques and listed four unique features of SEM: (1) SEM takes a confirmatory approach to data analysis by specifying the relationships among variables a priori. By comparison, other multivariate techniques are descriptive by nature (e.g. exploratory factor analysis) so that hypothesis testing is rather difficult to do. (2) SEM provides explicit estimates of error variance parameters. Other multivariate techniques are not capable of either assessing or correcting for measurement error. For example, a regression analysis ignores the potential error in all the independent (explanatory) variables included in a model and this raises the possibility of incorrect conclusions due to misleading regression estimates. (3) SEM procedures incorporate both unobserved (i.e. latent) and observed variables. Other multivariate techniques are based on observed measurements only. (4) SEM is capable of modeling multivariate relations, and estimating direct and indirect effects of variables under study. Types of Models in SEM Various types of structural equation models are used in research. Raykov and Marcoulides (2006) listed four that are commonly found in the literature. 1) Path analytic models (PA) 2) Confirmatory factor analysis models (CFA) 3) Structural regression models (SR) 4) Latent change model (LC) Path analytic (PA) models are conceived in terms of observed variables. Although they focus only on observed variables, they form an important part of the historical development of SEM and employed the same underlying process of model testing and fitting as other SEM models. Confirmatory factor analysis (CFA) models are commonly used to examine patterns of interrelationships among various constructs. Each construct in a model is measured by a set of observed indicators. A key feature of CFA models is that no specific directional relationships are assumed between the constructs as they are correlated with each other only. …

Using structural equation modeling (SEM) in educational research: Practices and Issues

Previous research has revealed that teacher feedback encourages students’ learning of a second language and improves task performance. While former quasi-experimental studies emphasized the positive effects of formative assessment and feedback by teachers on developing oral presentation competence, it remains unclear about the processes during which teachers provide feedback in the form of verbal comments on oral presentations. Therefore, this paper describes an exploratory case study that examines the nature and processes of three experienced EFL teachers’ commentary on students’ classroom oral presentations in a Chinese university. Data for this study were obtained from the teachers’ self-reported accounts of commentary experiences during semi-structured interviews. Findings showed that the three teachers’ comments on oral presentations were feedback-focused in nature; however, they diversified their comments in terms of delivery mode, function and focus. The study has implications for future teacher feedback research and EFL classroom practice with regard to the ways in which teachers provide feedback on oral presentation tasks.

https://files.eric.ed.gov/fulltext/EJ1195414.pdf

An Exploratory Case Study into Chinese EFL Teachers' Commentary Practices in Oral Presentations.

To respond to an increasing emphasis placed on research capability and productivity, universities in China are reforming their policies regarding faculty’s performance evaluation and promotion criteria. Faculties are motivated to invest in research, and those without a PhD degree are strongly encouraged to enroll in doctoral programs. From the perspective of the theory of planned behavior, we conducted a qualitative study to explore the factors underpinning the intention to pursue a PhD degree among female teachers in a Chinese university. From data collected via semi-structured interviews, we found that participants were evaluating their pursuit of a doctoral degree in the clashes between the entrenched social-cultural beliefs of women’s role and the institutional expectation of a fully devoted scholar. Accordingly, theoretical and practical implications of our findings are discussed.

Understanding Chinese female university teachers’ intention to pursue a PhD degree: some insights from a Chinese university

Measuring attitudes and perceptions is an integral part of empirical studies in educational technology research. Some examples include measurement of students' attitude towards computers (Teo & Lee, 2008) or perceptions of educational robots among adolescents (Liu, 2010), and attitude as a construct in a theoretical model (Teo, 2009). In virtually all studies that involve the measurement of attitude and perception in the field of educational technology, the self‐report is used. Two mistakes that researchers often make in using the self‐report are (1) assuming that items are always on an interval scale (ie, equidistant measurement) and (2) that response options are always on an equivalent scale (ie, participants' responses indicate similar levels). For example, on a 5‐point Likert scale, researchers often treat the difference between a response of “1” or “2” as being equivalent to the difference between “4” and “5.” Another mistake is the assumption that item endorsements are always on an equivalent scale, where a response of “5” means the same thing for all respondents. For example, when asking respondents to answer a question “I like to use the computer,” respondents may be indicating different levels of liking, despite choosing the highest possible category of “5.”

Timothy Teo

Papers

Modeling Educational Research: The way forward

Using structural equation modeling (SEM) in educational research: Practices and Issues

An Exploratory Case Study into Chinese EFL Teachers' Commentary Practices in Oral Presentations.

Understanding Chinese female university teachers’ intention to pursue a PhD degree: some insights from a Chinese university

Towards greater precision in latent construct measurement: What's the Rasch?