A Systematic Evaluation and Comparison Between Exploratory Structural Equation Modeling and Bayesian Structural Equation Modeling

In this study, we contrast two competing approaches, not previously compared, that balance the rigor of CFA/SEM with the flexibility to fit realistically complex data. Exploratory SEM (ESEM) is claimed to provide an optimal compromise between EFA and CFA/SEM. Alternatively, a family of three Bayesian SEMs (BSEMs) replace fixed-zero estimates with informative, small-variance priors for different subsets of parameters: cross-loadings (CL), residual covariances (RC), or CLs and RCs (CLRC). In Study 1, using three simulation studies, results showed that (1) BSEM-CL performed more closely to ESEM; (2) BSEM-CLRC did not provide more accurate model estimation compared with BSEM-CL; (3) BSEM-RC provided unstable estimation; and (4) different specifications of targeted values in ESEM and informative priors in BSEM have significant impacts on model estimation. The real data analysis (Study 2) showed that the differences in estimation between different models were largely consistent with those in Study1 but somewhat smaller.     

Lord's Wald Test for Detecting DIF in Multidimensional IRT Models: A Comparison of Two Estimation Approaches

Lord's Wald test for differential item functioning (DIF) has not been studied extensively in the context of the multidimensional item response theory (MIRT) framework. In this article, Lord's Wald test was implemented using two estimation approaches, marginal maximum likelihood estimation and Bayesian Markov chain Monte Carlo estimation, to detect uniform and nonuniform DIF under MIRT models. The Type I error and power rates for Lord's Wald test were investigated under various simulation conditions, including different DIF types and magnitudes, different means and correlations of two ability parameters, and different sample sizes. Furthermore, English usage data were analyzed to illustrate the use of Lord's Wald test with the two estimation approaches.     

Handling Missing Data in the Modeling of Intensive Longitudinal Data

Myriad approaches for handling missing data exist in the literature. However, few studies have investigated the tenability and utility of these approaches when used with intensive longitudinal data. In this study, we compare and illustrate two multiple imputation (MI) approaches for coping with missingness in fitting multivariate time-series models under different missing data mechanisms. They include a full MI approach, in which all dependent variables and covariates are imputed simultaneously, and a partial MI approach, in which missing covariates are imputed with MI, whereas missingness in the dependent variables is handled via full information maximum likelihood estimation. We found that under correctly specified models, partial MI produces the best overall estimation results. We discuss the strengths and limitations of the two MI approaches, and demonstrate their use with an empirical data set in which children’s influences on parental conflicts are modeled as covariates over the course of 15 days (Schermerhorn, Chow, & Cummings, 2010).     

A Bayesian Approach for Estimating Multilevel Latent Contextual Models

In many applications of multilevel modeling, group-level (L2) variables for assessing group-level effects are generated by aggregating variables from a lower level (L1). However, the observed group mean might not be a reliable measure of the unobserved true group mean. In this article, we propose a Bayesian approach for estimating a multilevel latent contextual model that corrects for measurement error and sampling error (i.e., sampling only a small number of L1 units from a L2 unit) when estimating group-level effects of aggregated L1 variables. Two simulation studies were conducted to compare the Bayesian approach with the maximum likelihood approach implemented in Mplus. The Bayesian approach showed fewer estimation problems (e.g., inadmissible solutions) and more accurate estimates of the group-level effect than the maximum likelihood approach under problematic conditions (i.e., small number of groups, predictor variable with a small intraclass correlation). An application from educational psychology is used to illustrate the different estimation approaches.     

On Sample-Size Calculations for Precise Contrast Analysis in ANCOVA

The analysis of covariance (ANCOVA) is a useful statistical procedure that incorporates covariate features into the adjustment of treatment effects. The consequences of omitted prognostic covariates on the statistical inferences of ANCOVA are well documented in the literature. However, the corresponding influence on sample-size calculations for precise interval estimation has not been fully evaluated. This article aims to explicate the deficiency of approximate methods for ignoring the stochastic nature of covariate variables and to present exact approaches for precise interval estimation of treatment contrasts under the assumption that the covariate variables have a joint multinormal distribution. The desired precision of a confidence interval is assessed with respect to the control of expected half-width and to the assurance probability of interval half-width within a designated value. Numerical appraisals show that the suggested approaches outperform the approximate formulas for the two precision considerations.     

PISA and scientific literacy: similarities and differences between the nordic countries

In this paper we have set out to search for similarities and differences between the Nordic countries concerning patterns of competencies defined as scientific literacy in the Programme for International Student Assessment (PISA) study. The first part focuses on gender differences concerning the two types of competencies, understanding of scientific concepts versus skills in scientific reasoning, based on analyses of sum scores of groups of items. The second part focuses on differences and similarities between countries based on item‐by‐item analyses. Correlations between each Nordic country (as well as the Nordic group as a whole) and every other country have been used to look for a Nordic pattern. In the last part cluster analysis has been used to see how countries establish clusters and whether these clusters represent meaningful groups in a geographical, cultural or political context.     

A critical review of some recent developments in quantitative research on gender and achievement in the United Kingdom

Over recent years the findings of a number of quantitative research studies have been published in the UK on gender and achievement. Much of this work has emanated from Stephen Gorard and his colleagues and has not only been highly critical of existing approaches to handling quantitative data but has also suggested a number of alternative and, what they claim to be, more valid ways of measuring differential patterns of achievement and underachievement between groups. This article shows how much of this work has been based upon rather under‐developed measures of achievement and underachievement that tend, in turn, to generate a number of misleading findings that have questionable implications for practice. It will be argued that this body of work provides a useful case study in the problems of quantitative research that fails to engage adequately with the substantive theoretical and empirical literature and considers some of the implications of this for future research in this area.     

过程体裁教学法与大学英语读写教学

本文陈述了过程体裁教学法的思想模式及其在大学英语读写教学中的实际运用与操作。通过剖析目前大学英语读写教学中存在的主要问题及阐述过程教学法与体裁教学法的优缺点,提出过程体裁教学法能在一定程度上解决现存问题,并对该教学法具体运用中的特点及可能存在的问题进行了探讨。     

Secondary analysis of large-scale assessment data: an alternative to variable-centred analysis

International large-scale assessments are now part of the educational landscape in many countries and often feed into major policy decisions. Yet, such assessments also provide data sets for secondary analysis that can address key issues of concern to educators and policymakers alike. Traditionally, such secondary analyses have been based on a variable-centred approach that gives rise to league tables. In the study reported here, a person-centred analysis is used as an alternative to the traditional approach. Data from the International Civic and Citizenship Education Study (ICCS) were analysed to investigate Asian students' attitudes to their future civic participation. Cluster analysis with validity measures showed that 4 distinct groups of students were identified within the societies studied thus highlighting the diversity within the samples. These results cannot be achieved with a conventional variable approach to analysis, and they suggest the usefulness of exploring alternative approaches to secondary data analysis.     

Learning to Solve Challenging Percentage-Change Problems: A Cross-Cultural Study From a Cognitive Load Perspective

In an experiment, secondary students from Australia and Malaysia (n = 130) were randomly assigned to one of three approaches (equation, unitary, unitary-pictorial) to learn how to solve challenging percentage-change problems. In line with the differential types of cognitive load associated with the three approaches, the unitary-approach group outperformed both the unitary group and the equation group across Australia and Malaysia. In support of cross-cultural findings, the Malaysian students outperformed the Australian students for the equation approach but not the unitary approach nor the unitary-pictorial approach. The Australian students, in contrast, learned better with the unitary-pictorial approach. This study, overall, reveals the “gap” between the Asian and Western countries in the use of problem-solving approaches across different cultural settings.     

Developing Diversity Through Specialisation in Secondary Education: Comparing approaches in New Zealand and England

The paper compares approaches to curriculum specialisation in secondary education in New Zealand and England. In both countries there have been movements towards increased specialisation, though these have been quite different in form and scope. In both countries specialisation cannot be divorced from broader education policies designed to increase devolution and choice and the paper discusses these contexts before analysing the different approaches to specialisation and attempting an explanation. The authors draw on findings from research undertaken in New Zealand schools. The paper identifies three dimensions that have played a part in influencing curriculum specialisation in both countries. These are opportunity, source of impetus and support. It is argued that while local initiative is possible in New Zealand, central planning and guidance is inadequate. In England while central planning is strong and support is available, it is far from clear that real specialisation is encouraged by existing curriculum and assessment frameworks. In these circumstances in both countries it seems likely that vertical, rather than horizontal, diversity will continue to hold sway.     

Parents and school communities in Japan and Scotland: contrasts in policy and practice in primary schools

In this paper Japanese and Scottish cultural and ideological expectations about the role of parents and communities in schools are examined. Findings from three case studies of a Japanese school, a Scottish school and a group of Japanese parents sending their children to a Scottish school show that there are clear policy differences between the two countries. These differences reflect each country's problems and the purposes of the educational reforms that have been introduced and the different strengths and weaknesses of the two systems. The policy differences in the two systems and how these are translated into practice are examined from the perspective of parents and the wider school community. It is argued that what is missing from the policy and practice context in both countries are the resources to enable teachers, parents and other members of the community to work as equal partners.     

Effects of applying a VR-based two-tier test strategy to promote elementary students’ learning performance in a Geology class

Science and technology are driving people’s life changes, including education and the environment. Many scholars have attempted to import technology into the classroom to help students learn in different subjects. However, students often need assistance with unfamiliar learning approaches and learning environments. This study proposed a non-immersive virtual reality (VR) guidance system combined with a two-tier strategy to help students learn geology knowledge. Two groups of students used different learning approaches: the experimental group students were guided by the two-tier test VR guidance system, and the control group students learned with the conventional VR guidance system. According to the experimental result, the two-tier test VR guidance system not only improved the students’ learning achievement in natural science, but also enhanced their learning motivation. In addition, according to the sequential results, we found that VR learning materials or environmental resources can help students answer questions and solve problems more effectively.     

Adapting Science and Technology Education to a Changing Society and to the Diversity of Needs of Arab States

Summaries

English

Science is being increasingly considered as a social activity that must be harnessed to serve human needs. In many developing countries, science and technology education tend to replicate practices in advanced countries. The challenges of responding adequately to the solution of the problems of society and of meeting the different needs of a country or a region have not been realized. The accelerated social and technological changes that are taking place in many Arab states intensify the variety of problems that have been cited. The guidelines suggested towards adapting education to the state's needs and towards overcoming the problems include: emphasizing the development of intellectual and occupational skills which are significant, retainable, and transferable to everyday life; developing respect for manual skills; revising curricula to introduce topics relevant to society; and making efforts towards developing the home environment and the community.     

Fit of Different Models for Multitrait-Multimethod Experiments

In the past, several models have been developed for the estimation of the reliability and validity of measurement instruments from multitrait-multimethod (MTMM) experiments. Suggestions have been made for additive, multiplicative and correlated uniqueness models, whereas recently Coenders and Saris (2000) suggested a procedure to test these models against one another. In this article, the different models suggested for the analysis of MTMM matrixes have been compared for their fit to 87 data sets collected in the United States (Andrews, 1984; Rodgers, Andrews, & Herzog, 1992), Austria (Koltringer, 1995), and the Netherlands (Scherpenzeel & Saris, 1997). As most variables are categorical, the analysis has been carried out on the basis of polychoric-polyserial correlation coefficients and of Pearson correlations. The fit of the models based on polychoric correlations is much worse than the fit of models based on product moment correlations, but in both cases a model that assumes additive method effects fits most data sets better than the other models, including the so-called multiplicative models.     

TOWARD AN INTEGRATION OF THEORY AND METHOD FOR CRITERION-REFERENCED TESTS1,2

In this paper, an attempt has been made to synthesize some of the current thinking in the area of criterion-referenced testing as well as to provide the beginning of an integration of theory and method for such testing. Since criterion-referenced testing is viewed from a decision-theoretic point of view, approaches to reliability and validity estimation consistent with this philosophy are suggested. Also, to improve the decision-making accuracy of criterion-referenced tests, a Bayesian procedure for estimating true mastery scores has been proposed. This Bayesian procedure uses information about other members of a student's group (collateral information), but the resulting estimation is still criterion referenced rather than norm referenced in that the student is compared to a standard rather than to other students. In theory, the Bayesian procedure increases the “effective length” of the test by improving the reliability, the validity, and more importantly, the decision-making accuracy of the criterion-referenced test scores.     

A comparative study of effectiveness of peer assessment of individuals’ contributions to group projects in undergraduate construction management core units

In recent years, various forms of group work have been introduced in university courses across various subject domains, including construction management courses. Although the use of group work in higher education has sound pedagogical reasons and advantages, group work has its own drawbacks. Therefore, the acceptance by students and the success of group work critically depend on a fair and credible assessment of the group process. In this paper, the implementation of different approaches to peer assessment (PA) of individuals’ contributions to group projects in two core units in an undergraduate construction management course in an Australian university is reported. The effectiveness of the adopted PA approaches have been evaluated and validated by students. It has been found that contrary to doubts of the sufficiency of a simplistic approach to PAs, the fairness of a PA approach does not necessarily depend on its complexity. Besides, voluntary group discussions, learning and collaboration are found to aid in improving each of the group’s camaraderie. Hence, it is recommended that academics should develop both a structured methodology to progressively encourage group members to work cohesively in teams and effective PA approaches that measure individual member’s contribution.     

Comparisons of two statistical approaches to study growth curves: The multilevel model and the latent curve analysis

This article compares two statistical approaches for modeling growth across time. The two statistical approaches are the multilevel model (MLM) and latent curve analysis (LCA), which have been proposed to depict change or growth adequately. These two approaches were compared in terms of the estimation of growth profiles represented by the parameters of initial status and the rate of growth. A longitudinal data set obtained from a school‐based substance‐use prevention trial for adolescents was used to illustrate the similarities and differences between the two approaches. The results indicated that the two approaches yielded very compatible results. The parameter estimates associated with regression weights are the same, whereas those associated with variances and covariances are similar. The MLM approach is easier for model specification and is more efficient computationally in yielding results. The LCA approach, however, has the advantage of providing model evaluation, that is, an overall test of goodness of fit, and is more flexible in modeling and hypothesis testing as demonstrated in this study.     

The Impact of Omitted Responses on the Accuracy of Ability Estimation in Item Response Theory

Practitioners typically face situations in which examinees have not responded to all test items. This study investigated the effect on an examinee's ability estimate when an examinee is presented an item, has ample time to answer, but decides not to respond to the item. Three approaches to ability estimation (biweight estimation, expected a posteriori, and maximum likelihood estimation) were examined. A Monte Carlo study was performed and the effect of different levels of omissions on the simulee's ability estimates was determined. Results showed that the worst estimation occurred when omits were treated as incorrect. In contrast, substitution of 0.5 for omitted responses resulted in ability estimates that were almost as accurate as those using complete data. Implications for practitioners are discussed.     

The Impact of Vertical Scaling Decisions on Growth Interpretations

Most growth models implicitly assume that test scores have been vertically scaled. What may not be widely appreciated are the different choices that must be made when creating a vertical score scale. In this paper empirical patterns of growth in student achievement are compared as a function of different approaches to creating a vertical scale. Longitudinal item‐level data from a standardized reading test are analyzed for two cohorts of students between Grades 3 and 6 and Grades 4 and 7 for the entire state of Colorado from 2003 to 2006. Eight different vertical scales were established on the basis of choices made for three key variables: Item Response Theory modeling approach, linking approach, and ability estimation approach. It is shown that interpretations of empirical growth patterns appear to depend upon the extent to which a vertical scale has been effectively “stretched” or “compressed” by the psychometric decisions made to establish it. While all of the vertical scales considered show patterns of decelerating growth across grade levels, there is little evidence of scale shrinkage.