An SAS Macro for Implementing the Modified Bollen-Stine Bootstrap for Missing Data: Implementing the Bootstrap Using Existing Structural Equation Modeling Software

The Bollen-Stine bootstrap can be used to correct for standard error and fit statistic bias that occurs in structural equation modeling (SEM) applications due to nonnormal data. The purpose of this article is to demonstrate the use of a custom SAS macro program that can be used to implement the Bollen-Stine bootstrap with existing SEM software. Although this article focuses on missing data, the macro can be used with complete data sets as well. A series of heuristic analyses are presented, along with detailed programming instructions for each of the commercial SEM software packages.     

Variance Estimation Using Replication Methods in Structural Equation Modeling With Complex Sample Data

This article discusses replication sampling variance estimation techniques that are often applied in analyses using data from complex sampling designs: jackknife repeated replication, balanced repeated replication, and bootstrapping. These techniques are used with traditional analyses such as regression, but are currently not used with structural equation modeling (SEM) analyses. This article provides an extension of these methods to SEM analyses, including a proposed adjustment to the likelihood ratio test, and presents the results from a simulation study suggesting replication estimates are robust. Finally, a demonstration of the application of these methods using data from the Early Childhood Longitudinal Study is included. Secondary analysts can undertake these more robust methods of sampling variance estimation if they have access to certain SEM software packages and data management packages such as SAS, as shown in the article.     

Modeling Latent Growth Curves With Incomplete Data Using Different Types of Structural Equation Modeling and Multilevel Software

This article offers different examples of how to fit latent growth curve (LGC) models to longitudinal data using a variety of different software programs (i.e., LISREL, Mx, Mplus, AMOS, SAS). The article shows how the same model can be fitted using both structural equation modeling and multilevel software, with nearly identical results, even in the case of models of latent growth fitted to incomplete data. The general purpose of this article is to provide a demonstration that integrates programming features from different software. The most immediate goal is to help researchers implement these LGC models as a useful way to test hypotheses of growth.     

Structural equation modeling: Back to basics

Major technological advances incorporated into structural equation modeling (SEM) computer programs now make it possible for practitioners who are basically unfamiliar with the purposes and limitations of SEM to use this tool within their research contexts. The current move by program developers to market more user friendly software packages is a welcomed trend in the social and behavioral science research community. The quest to simplify the data analysis step in the research process has—at least with regard to SEM—created a situation that allows practitioners to apply SEM but forgetting, knowingly ignoring, or most dangerously, being ignorant of some basic philosophical and statistical issues that must be addressed before sound SEM analyses should be conducted. This article focuses on some of the almost forgotten topics taken here from each step in the SEM process: model conceptualization, identification and parameter estimation, and data‐model fit assessment and model modification. The main objective is to raise awareness among researchers new to SEM of a few basic but key philosophical and statistical issues. These should be addressed before launching into any one of the new generation of SEM software packages and being led astray by the seemingly irresistible temptation to prematurely start “playing” with the data.     

Software Packages for Bayesian Multilevel Modeling

Multilevel modeling is a statistical approach to analyze hierarchical data that consist of individual observations nested within clusters. Bayesian method is a well-known, sometimes better, alternative of Maximum likelihood method for fitting multilevel models. Lack of user friendly and computationally efficient software packages or programs was a main obstacle in applying Bayesian multilevel modeling. In recent years, the development of software packages for multilevel modeling with improved Bayesian algorithms and faster speed has been growing. This article aims to update the knowledge of software packages for Bayesian multilevel modeling and therefore to promote the use of these packages. Three categories of software packages capable of Bayesian multilevel modeling including brms, MCMCglmm, glmmBUGS, Bambi, R2BayesX, BayesReg, R2MLwiN and others are introduced and compared in terms of computational efficiency, modeling capability and flexibility, as well as user-friendliness. Recommendations to practical users and suggestions for future development are also discussed.     

The Use of Sound Recording Equipment in the Study and Improvement of Teaching

Users assume statistical software packages produce accurate results. In this article, the authors systematically examined Statistical Package for the Social Sciences (SPSS) and Statistical Analysis System (SAS) for 3 analysis of variance (ANOVA) designs, mixed-effects ANOVA, fixed-effects analysis of covariance (ANCOVA), and nested ANOVA. For each model, the authors examined 3 different data sets. With the mixed-effects design, results were always correct for SPSS syntax and SAS syntax. For SPSS point-and-click, the F and p values for the random-effect were always incorrect as the wrong error term is used. With the ANCOVA design, results varied both by software package and by type of sums of squares. With the nested design, the p values for the F and multiple comparison procedure did not agree for the nonnested factor in SPSS point-and-click. Recommendations were made regarding which package to use for each design.     

Alternatives to Multilevel Modeling for the Analysis of Clustered Data

Multilevel modeling has grown in use over the years as a way to deal with the nonindependent nature of observations found in clustered data. However, other alternatives to multilevel modeling are available that can account for observations nested within clusters, including the use of Taylor series linearization for variance estimation, the design effect adjusted standard errors approach, and fixed effects modeling. Using 1,000 replications of 12 conditions with varied Level 1 and Level 2 sample sizes, the author compared parameter estimates, standard errors, and statistical significance using various alternative procedures. Results indicate that several acceptable procedures can be used in lieu of or together with multilevel modeling, depending on the type of research question asked and the number of clusters under investigation. Guidelines for applied researchers are discussed.     

简单格子设计试验的SAS分析方法

大批量处理的精确田间试验，可以采用简单格子设计。本文简要介绍简单格子设计的方法和分析原理，以2重复和4重复简单格子设计的例题，介绍了相应的SAS的分析程序和ODS系统，为格子设计数据的分析提供一套完整的解决方案。     

Analyzing Mixed-Dyadic Data Using Structural Equation Models

Mixed-dyadic data, collected from distinguishable (nonexchangeable) or indistinguishable (exchangeable) dyads, require statistical analysis techniques that model the variation within dyads and between dyads appropriately. The purpose of this article is to provide a tutorial for performing structural equation modeling analyses of cross-sectional and longitudinal models for mixed independent variable dyadic data, and to clarify questions regarding various dyadic data analysis specifications that have not been addressed elsewhere. Artificially generated data similar to the Newlywed Project and the Swedish Adoption Twin Study on Aging were used to illustrate analysis models for distinguishable and indistinguishable dyads, respectively. Due to their widespread use among applied researchers, the AMOS and Mplus statistical analysis software packages were used to analyze the dyadic data structural equation models illustrated here. These analysis models are presented in sufficient detail to allow researchers to perform these analyses using their preferred statistical analysis software package.     

Obtaining Diagnostic Classification Model Estimates Using Mplus

Diagnostic classification models (aka cognitive or skills diagnosis models) have shown great promise for evaluating mastery on a multidimensional profile of skills as assessed through examinee responses, but continued development and application of these models has been hindered by a lack of readily available software. In this article we demonstrate how diagnostic classification models may be estimated as confirmatory latent class models using Mplus, thus bridging the gap between the technical presentation of these models and their practical use for assessment in research and applied settings. Using a sample English test of three grammatical skills, we describe how diagnostic classification models can be phrased as latent class models within Mplus and how to obtain the syntax and output needed for estimation and interpretation of the model parameters. We also have written a freely available SAS program that can be used to automatically generate the Mplus syntax. We hope this work will ultimately result in greater access to diagnostic classification models throughout the testing community, from researchers to practitioners.     

Computer programming for management students

The case for teaching a programming language in the context of a management degree rests upon the perceived value of using mathematical models to structure managerial problems, and upon the relative merits of available languages and packages as modelling tools for students who may not be strongly numerate. An introductory computing course can help students to learn quantitative elements of Business Finance. It encompasses both standard software packages, such as a spreadsheet, together with the elements of BASIC. Students taking subsequent Quantitative Methods courses likewise benefit from the use of packages, but also from using a carefully prepared suite of BASIC programs. This approach is illustrated by materials prepared for automated drawing of project network diagrams on a monitor. Students benefit from reinforcement of the underlying mathematical concepts of network analysis and from extending the programs themselves. This latter process also provides some insight into the nature of commercial software packages and systems.     

Multiple Imputation Strategies for Multiple Group Structural Equation Models

Although structural equation modeling software packages use maximum likelihood estimation by default, there are situations where one might prefer to use multiple imputation to handle missing data rather than maximum likelihood estimation (e.g., when incorporating auxiliary variables). The selection of variables is one of the nuances associated with implementing multiple imputation, because the imputer must take special care to preserve any associations or special features of the data that will be modeled in the subsequent analysis. For example, this article deals with multiple group models that are commonly used to examine moderation effects in psychology and the behavioral sciences. Special care must be exercised when using multiple imputation with multiple group models, as failing to preserve the interactive effects during the imputation phase can produce biased parameter estimates in the subsequent analysis phase, even when the data are missing completely at random or missing at random. This study investigates two imputation strategies that have been proposed in the literature, product term imputation and separate group imputation. A series of simulation studies shows that separate group imputation adequately preserves the multiple group data structure and produces accurate parameter estimates.     

基于SAS软件的生物统计实验自主学习模式探究

在生物统计实验中,通过引入SAS软件进行数据处理,学生可以快速地进行统计计算,掌握分析试验数据的方法和技巧,进而提高教学效果.针对该课程特点,教师要引导学生充分利用网络、图书馆等课外资源进行自主学习,并且针对不同层次的学生制订相应的学习计划,培养学生的自学能力,提高学生的综合素养,为以后的学习、科研工作打好基础.     

基于.NET框架的实验仪器监控系统设计

为提高实验室工作效率,降低人为误差、减少人工成本,使实验操作过程更方便、直观,设计了基于.NET框架的实验仪器监控系统。在Visual Studio 2019平台上运用.NET框架和C#语言实现系统软件开发,通过Modbus-RTU通讯协议采集设备数据,采用轻量化Access数据库结构对数据进行存储。该监控系统具有友好的用户交互界面,能够实时监控多台设备。采用图形化界面显示数据,根据不同用户需求自动生成数据报表供研究人员分析参考。测试结果表明,与传统人工方式相比,时间效率提高8.3%,数据准确率达到100%,证明该系统能提高实验人员工作效率与实验质量。     

Performance of Bootstrapping Approaches to Model Test Statistics and Parameter Standard Error Estimation in Structural Equation Modeling

Though the common default maximum likelihood estimator used in structural equation modeling is predicated on the assumption of multivariate normality, applied researchers often find themselves with data clearly violating this assumption and without sufficient sample size to utilize distribution-free estimation methods. Fortunately, promising alternatives are being integrated into popular software packages. Bootstrap resampling, which is offered in AMOS (Arbuckle, 1997), is one potential solution for estimating model test statistic p values and parameter standard errors under nonnormal data conditions. This study is an evaluation of the bootstrap method under varied conditions of nonnormality, sample size, model specification, and number of bootstrap samples drawn from the resampling space. Accuracy of the test statistic p values is evaluated in terms of model rejection rates, whereas accuracy of bootstrap standard error estimates takes the form of bias and variability of the standard error estimates themselves.     

Popular Structural Equation Modeling Programs for Behavior Genetics

Behavior genetic modeling is a prominent application of multi-group structural equation modeling (SEM). It decomposes phenotypic variance into genetic and environmental sources by leveraging the covariation within and between kin pairs. Although any SEM program with multi-group capabilities can be employed, the software program, Mx, has dominated behavior genetics research. Indeed, even though Mx has not been maintained since 2011, it remains the most popular SEM program in Behavior Genetics articles published in 2016 and 2017. Given the persistence of Mx, the aim of this article is to understand Mx’s performance relative to other popular behavior genetic programs. Through this process, programs employed in behavior genetics research are identified, and their relevant technical features and accessibility are compared. Finally, the relative strengths and limitations of the programs are discussed, and recommendations are provided for behavior genetics researchers.     

Does computer confidence relate to levels of achievement in ICT-enriched learning models?

Employer expectations have changed: university students are expected to graduate with computer competencies appropriate for their field. Educators are also harnessing technology as a medium for learning in the belief that information and communication technologies (ICT’s) can enliven and motivate learning across a wide range of disciplines. Alongside developing students’ computer skills and introducing them to the use of professional software, educators are also harnessing professional and scientific packages for learning in some disciplines. As the educational use of information and communication technologies increases dramatically, questions arise about the effects on learners. While the use of computers for delivery, support, and communication, is generally easy and unthreatening, higher-level use may pose a barrier to learning for those who lack confidence or experience. Computer confidence may mediate in how well students perform in learning environments that require interaction with computers. This paper examines the role played by computer confidence (or computer self-efficacy) in a technology-enriched science and engineering mathematics course in an Australian university. Findings revealed that careful and appropriate use of professional software did indeed enliven learning for the majority of students. However, computer confidence occupied a very different dimension to mathematics confidence: and was not a predictor of achievement in the mathematics tasks, not even those requiring use of technology. Moreover, despite careful and nurturing support for use of the software, students with low computer confidence levels felt threatened and disadvantaged by computer laboratory tasks. The educational implications of these findings are discussed with regard to teaching and assessment, in particular. The TCAT scales used to measure technology attitudes, computer confidence/self-efficacy and mathematics confidence are included in an Appendix. Well-established, reliable, internally consistent, they may be useful to other researchers. The development of the computer confidence scale is outlined, and guidelines are offered for the design of other discipline-specific confidence/self-efficacy scales appropriate for use alongside the computer confidence scale.     

Simulation research and design: a dual-level framework for multi-project research programs

Collaborations between researchers and practitioners have recently become increasingly popular in education, and educational design research (EDR) may benefit greatly from investigating such partnerships. One important domain in which EDR on collaborations between researchers and practitioners can be applied is research on simulation-based learning. However, frameworks describing both research and design processes in research programs on simulation-based learning are currently lacking. The framework proposed in this paper addresses this research gap. It is derived from theory and delineates levels, phases, activities, roles, and products of research programs to develop simulations as complex scientific artifacts for research purposes. This dual-level framework applies to research programs with a research committee and multiple subordinate research projects. The proposed framework is illustrated by examples from the actual research and design process of an interdisciplinary research program investigating the facilitation of diagnostic competences through instructional support in simulations. On a theoretical level, the framework contributes primarily to the literature of EDR by offering a unique dual-level perspective. Moreover, on a practical level, the framework may help by providing recommendations to guide the research and design process in research programs.

     

基于SAS过程的乙基纤维素微粒制备原理与实现方法

乙基纤维素是广泛的水不溶性纤维素衍生物,可制成缓释药丸及具有缓、控释要求的新剂型的辅料.本文研究了基于超临界反溶剂(SAS)过程的乙基纤维素微粒制备原理及其实现方法,以乙醇为有机溶剂、超临界CO2为反溶剂制备出了平均直径在20-40nm范围内的乙基纤维素超细微粒.通过傅立叶红外光谱分析了乙基纤维素超细微粒的结构,从特征基判断其结构没有发生变化.     

Large-Sample Variance of Fleiss Generalized Kappa

Cohen’s kappa coefficient was originally proposed for two raters only, and it later extended to an arbitrarily large number of raters to become what is known as Fleiss’ generalized kappa. Fleiss’ generalized kappa and its large-sample variance are still widely used by researchers and were implemented in several software packages, including, among others, SPSS and the R package “rel.” The purpose of this article is to show that the large-sample variance of Fleiss’ generalized kappa is systematically being misused, is invalid as a precision measure for kappa, and cannot be used for constructing confidence intervals. A general-purpose variance expression is proposed, which can be used in any statistical inference procedure. A Monte-Carlo experiment is presented, showing the validity of the new variance estimation procedure.