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.     

The Split-Ballot Multitrait-Multimethod Approach: Implementation and Problems

Saris, Satorra, and Coenders (2004) proposed a new approach to estimate the quality of survey questions, combining the advantages of 2 existing approaches: the multitrait–multimethod (MTMM) and the split-ballot (SB) ones. Implemented in practice, this new approach led to frequent problems of nonconvergence and improper solutions. This article uses Monte Carlo simulations to understand why the SB-MTMM is working well in some cases but not in others. The number of SB groups is a crucial element: The 3-group design is performing better. However, the 2-group design can also perform well: The analyses suggest that the interaction between the absolute values of the correlations between the traits and the relative values of the different correlations between traits plays an important role.     

参数已知比例可加模型的有效估计

讨论了比例可加模型的非参数估计.利用局部线性方法和平均方法给出函数的平均估计,结合回切方法,定义了函数的有效估计,并研究了有效估计的相合性与渐近正态性.     

Testing Extension Services through AKAP Models

Abstract

Purpose: The aim of the paper is to analyse the attitude of Italian farms in gaining access to agricultural extension services (AES).

Design/methodology/approach: The ways Italian farms use AES are described through the AKAP (Awareness, Knowledge, Adoption, Product) sequence. This article investigated the AKAP sequence by submitting a questionnaire to a sample of Italian farms, providing questions on each step of the sequence.

Findings: The results confirm the validity of the model and the necessity to evaluate AES in each phase of the sequence, through an in-depth analysis of the possible motivation for not adopting them.

Practical implications: The functional repositioning of agriculture redefines the role of the farm, by introducing new possibilities of production and by fostering multifunctional activities. In this context, new tasks for AES emerge, aiming at sustaining farm development along either sectorial or territorial paths. Difficulties in adopting AES call for both fostering higher levels of access to services on behalf of farms and the adequate supply of services to farms' new needs.

Originality/value: AKAP models have been prevailingly used in developing countries to evaluate the efficacy of extension in increasing agricultural productivity. This article demonstrates how this model could be of help in developed agriculture too in performing new lines of development rooted in the new models of multifunctional agriculture.     

高等学校考试新模式的研究与探索

文章客观地分析了传统考试模式的作用和局限性,并围绕考试的功能,从考试目的、内容、方式、机会等方面深入、全面地提出了构建高等学校考试新模式的思路和举措.     

回归模型中异方差的检验方法

异方差的存在并不破坏普通最小二乘法估计量的无偏性,但是估计量的方差变大了,由于估计量方差的变大,就使通常假设检验的值不可靠,因此选取适当的异方差的检验方法是极其重要的.     

Applicable Adaptive Testing Models for School Teachers

Small‐ and medium‐sized enterprises (SMEs) play an important role in creating a dynamic and successful European economy. Time‐poor managers in these organisations generally have fewer opportunities for training and development than their counterparts in larger organisations. As a result, different requirements are placed on training. The aim of this study was to test the principles of action learning in a virtual environment. The action‐learning programme was based on virtual working but did also involve face‐to‐face workshops, thus providing a blended approach. The project was designed to be “evaluation‐led”, with evaluation progressing alongside the project from design to finalisation. The focus of this paper is on how the evaluation‐led approach unfolded. To this end, we start by explaining our research approach, we then move on to an analysis of the project to conclude with a discussion of the findings and of the lessons learnt. We conclude by highlighting some further research needs.     

Testing Inequality Constrained Hypotheses in SEM Models

Researchers often have expectations that can be expressed in the form of inequality constraints among the parameters of a structural equation model. It is currently not possible to test these so-called informative hypotheses in structural equation modeling software. We offer a solution to this problem using Mplus. The hypotheses are evaluated using plug-in p values with a calibrated alpha level. The method is introduced and its utility is illustrated by means of an example.     

回归模型中异方差的检验方法

异方差的存在并不破坏普通最小二乘法估计量的无偏性，但是估计量的方差变大了，由于估计量方差的变大，就使通常假设检验的值不可靠，因此选取适当的异方差的检验方法是极其重要的．     

考试,考试,还是考试!

Should students have to pass an exam to graduatefrom high school?Picture yourself having the perfect senior year inhigh school.You work hard and earn great grades.Yourock the SAT.You sail through midterms and ace finals.Now all you have to do is be measur…     

Testing Complex Correlational Hypotheses With Structural Equation Models

It is often of interest to estimate partial or semipartial correlation coefficients as indexes of the linear association between 2 variables after partialing one or both for the influence of covariates. Squaring these coefficients expresses the proportion of variance in 1 variable explained by the other variable after controlling for covariates. Methods exist for testing hypotheses about the equality of these coefficients across 2 or more groups, but they are difficult to conduct by hand, prone to error, and limited to simple cases. A unified framework is provided for estimating bivariate, partial, and semipartial correlation coefficients using structural equation modeling (SEM). Within the SEM framework, it is straightforward to test hypotheses of the equality of various correlation coefficients with any number of covariates across multiple groups. LISREL syntax is provided, along with 4 examples.     

A Method for Testing a Priori Regression Models

A method is developed for testing a priori multiple regression models. The method allows one to specify in advance as many unstandardized or standardized coefficients as one wants to and allows the remaining slopes to be free to vary. The comparative strength and predictive power of competing models is accessed by the absolute and proportional difference in R2 and by an accompanying F test. Computer techniques for the procedures are given in the Appendix.     

一元线性结构关系EV模型的假设检验

给出了一元线性结构关系EV模型y=a+bx,Y=y+ε,X=x+u中关于线性关系y=a+bx的假设检验法则.     

专业与通识互动发展之心智模式探索

分析专业教育与通识教育之间的关联。根据传统的专业与通识的四种基本心智模式,提出通识与专业互动的心智新模式。新模式如同太极图中的阴与阳,两者处于恒转的动态平衡。厘清专业与通识之关系后,进而讨论通识与人文素质教育之评鉴。     

Secondary Students' Dynamic Modeling Processes: Analyzing,Reasoning About,Synthesizing, and Testing Models of Stream Ecosystems

In this paper, we explore dynamic modeling as an opportunity for students to think about the science content they are learning. We examined the Cognitive Strategies for Modeling (CSMs) in which students engaged as they created dynamic models. We audio- and videotape-recorded eight pairs of ninth grade science students and analyzed their conversations and actions. In analyzing appropriate objects and factors for their model, some students merely enumerated potential factors whereas others engaged in rich, substantial, mindful analysis. In reasoning about their models, students discussed relationships in depth, concentrated only on the most important key relationships, or encountered difficulty distinguishing between causal and correlational relationships. In synthesizing working models, students mapped their model to aid visualization, focused on their goal, or talked about their model's appearance or form. Students attempted to articulate explanations for their relationships, but sometimes their explanations were shallow. In testing their models, some students tested thoroughly but only a few persisted in debugging their model's behavior so that it matched their expectations. In our conclusion we suggest that creating dynamic models has great potential for use in classrooms to engage students in thought about science content, particularly in those thinking strategies best fostered by dynamic modeling: analysis, relational reasoning, synthesis, testing and debugging, and making explanations.     

Modeling Nonlinear Structural Equation Models: A Comparison of the Two-Stage Generalized Additive Models and the Finite Mixture Structural Equation Model

Researchers have devoted some time and effort to developing methods for fitting nonlinear relationships among latent variables. In particular, most of these have focused on correctly modeling interactions between 2 exogenous latent variables, and quadratic relationships between exogenous and endogenous variables. All of these approaches require prespecification of the nonlinearity by the researcher, and are limited to fairly simple nonlinear relationships. Other work has been done using mixture structural equation models (SEMM) in an attempt to fit more complex nonlinear relationships. This study expands on this earlier work by introducing the 2-stage generalized additive model (2SGAM) approach for fitting regression splines in the context of structural equation models. The model is first described and then investigated through the use of simulated data, in which it was compared with the SEMM approach. Results demonstrate that the 2SGAM is an effective tool for fitting a variety of nonlinear relationships between latent variables, and can be easily and accurately extended to models including multiple latent variables. Implications of these results are discussed.     

CAT模拟结果的分析模式与评价指标

计算机化自适应测验(CAT)模拟是CAT研究的主要方法之一。CAT模拟结果的评价分析内容主要包括三个方面:被试能力估计与被试能力分类分析、题库试题使用情况分析和CAT测验作答过程分析。CAT模拟结果的分析模式主要分为整体分析和细化分析两种模式。本研究从测验模拟返真性能、测验准确性、题库安全性、题库使用率、测验分类效率与准确性、多测验目标约束控制的实现程度等角度概述CAT模拟结果的各类评价指标。CAT模拟结果的评价角度和评价指标需要根据CAT研究目标和测验情境要求加以确定。