SPSS软件在定性数据分析中的技术处理

本文主要讨论了利用SPSS软件做定性资料统计分析时如何恰当进行组织数据,就单变量、多变量的列联表及多选项三种形式的定性数据的输入方式和统计分析进行了详细的介绍.     

Principles and Practice of Scaled Difference Chi-Square Testing

We highlight critical conceptual and statistical issues and how to resolve them in conducting Satorra–Bentler (SB) scaled difference chi-square tests. Concerning the original (Satorra & Bentler, 2001 Satorra, A. and Bentler, P. M. 2001. A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66: 507–514. [Crossref], [Web of Science ®] , [Google Scholar]) and new (Satorra & Bentler, 2010 Satorra, A. and Bentler, P. M. 2010. Ensuring positiveness of the scaled chi-square test statistic. Psychometrika, 75: 243–248. [Crossref], [Web of Science ®] , [Google Scholar]) scaled difference tests, a fundamental difference exists in how to compute properly a model's scaling correction factor (c), depending on the particular structural equation modeling software used. Because of how LISREL 8 defines the SB scaled chi-square, LISREL users should compute c for each model by dividing the model's normal theory weighted least-squares (NTWLS) chi-square by its SB chi-square, to recover c accurately with both tests. EQS and Mplus users, in contrast, should divide the model's maximum likelihood (ML) chi-square by its SB chi-square to recover c. Because ML estimation does not minimize the NTWLS chi-square, however, it can produce a negative difference in nested NTWLS chi-square values. Thus, we recommend the standard practice of testing the scaled difference in ML chi-square values for models M ₁ and M ₀ (after properly recovering c for each model), to avoid an inadmissible test numerator. We illustrate the difference in computations across software programs for the original and new scaled tests and provide LISREL, EQS, and Mplus syntax in both single- and multiple-group form for specifying the model M ₁₀ that is involved in the new test.     

Longitudinal Models for Ordinal Data With Many Zeros and Varying Numbers of Response Categories

Ordinal response scales are often used to survey behaviors, including data collected in longitudinal studies. Advanced analytic methods are now widely available for longitudinal data. This study evaluates the performance of 4 methods as applied to ordinal measures that differ by the number of response categories and that include many zeros. The methods considered are hierarchical linear models (HLMs), growth mixture mixed models (GMMMs), latent class growth analysis (LCGA), and 2-part latent growth models (2PLGMs). The methods are evaluated by applying each to empirical response data in which the number of response categories is varied. The methods are applied to each outcome variable, first treating the outcome as continuous and then as ordinal, to compare the performance of the methods given both a different number of response categories and treatment of the variables as continuous versus ordinal. We conclude that although the 2PLGM might be preferred, no method might be ideal.     

Evaluation of Test Statistics for Robust Structural Equation Modeling With Nonnormal Missing Data

A 2-stage robust procedure as well as an R package, rsem, were recently developed for structural equation modeling with nonnormal missing data by Yuan and Zhang (2012). Several test statistics that have been used for complete data analysis are employed to evaluate model fit in the 2-stage robust method. However, properties of these statistics under robust procedures for incomplete nonnormal data analysis have never been studied. This study aims to systematically evaluate and compare 5 test statistics, including a test statistic derived from normal-distribution-based maximum likelihood, a rescaled chi-square statistic, an adjusted chi-square statistic, a corrected residual-based asymptotical distribution-free chi-square statistic, and a residual-based F statistic. These statistics are evaluated under a linear growth curve model by varying 8 factors: population distribution, missing data mechanism, missing data rate, sample size, number of measurement occasions, covariance between the latent intercept and slope, variance of measurement errors, and downweighting rate of the 2-stage robust method. The performance of the test statistics varies and the one derived from the 2-stage normal-distribution-based maximum likelihood performs much worse than the other four. Application of the 2-stage robust method and of the test statistics is illustrated through growth curve analysis of mathematical ability development, using data on the Peabody Individual Achievement Test mathematics assessment from the National Longitudinal Survey of Youth 1997 Cohort.     

Item Parameter Estimation Under Conditions of Test Speededness: Application of a Mixture Rasch Model With Ordinal Constraints

When tests are administered under fixed time constraints, test performances can be affected by speededness. Among other consequences, speededness can result in inaccurate parameter estimates in item response theory (IRT) models, especially for items located near the end of tests (Oshima, 1994). This article presents an IRT strategy for reducing contamination in item difficulty estimates due to speededness. Ordinal constraints are applied to a mixture Rasch model (Rost, 1990) so as to distinguish two latent classes of examinees: (a) a "speeded" class, comprised of examinees that had insufficient time to adequately answer end-of-test items, and (b) a "nonspeeded" class, comprised of examinees that had sufficient time to answer all items. The parameter estimates obtained for end-of-test items in the nonspeeded class are shown to more accurately approximate their difficulties when the items are administered at earlier locations on a different form of the test. A mixture model can also be used to estimate the class memberships of individual examinees. In this way, it can be determined whether membership in the speeded class is associated with other student characteristics. Results are reported for gender and ethnicity.     

数理统计在数据分析中的应用

在当今各行各业中,数据分析已得到广泛应用,在数据分析之中,数理统计才是关键,如果对数理统计加以有效应用,就可以让各行各业对数据实现高效的处理。因此,数理统计不仅仅在投资风险之中以及保险学之中可以发挥自身的优势,而且在每一个企业的管理与决策之中都发挥着重要的作用。文章对数据分析之中,数理统计的应用进行研究,希望对当今企业的进一步发展有所帮助。     

用EXCEL编制考试阅卷统计系统

用EXCEL软件编制一个方便、实用的考试系统,它能对“单项选择题“和“多项选择题“进行“考试“、“阅卷“和“分数统计“工作进行自动处理,使教师从简单重复的脑力劳动中解脱出来,将更多的精力投入到教学与科研.     

我国大规模考试实施模式之分析

基于公平性、效率和可行性,本文比较和评价了我国当前四种大规模考试实施模式：省级政府、中央政府、企业和政府一企业模式,并根据不同的时限和情景状态,给出了相应的结论和建议。     

大数据背景下数据挖掘在高校固定资产统计中的应用研究

目前我国大部分高校对固定资产实行账物分管的归口管理模式,易造成固定资产实物流动中的失联或者丢失。而数据库管理只是对数据库中已有的数据进行查询等简单统计,通过这些数据获得的信息参考价值不高。隐藏在数据背后的对领导决策有价值的东西,需要分析统计出来。数据挖掘技术可以智能分析数据,从中挖掘统计出有价值的信息,帮助决策者做出正确的决策。     

一种基于Matlab的大文本试飞数据绘图策略研究

为满足大文本试飞数据图形可视化分析需求,基于Matlab语言对大文本试飞数据的可视化绘图策略进行探讨。结合试飞数据绘图分析特点,重点研究大文本试飞数据绘图中的关键策略：①数据加载方式选择;②多参数对比曲线图绘制方法;③绘图操作的继承。通过关键绘图策略程序,减少了试飞数据载入内存时间,完成了3种多参数对比图绘制,达成重复绘图操作的有效继承。该关键策略可在大文本试飞数据可视化绘图分析中发挥关键作用,提高试飞数据绘图分析效率。     

The Model Size Effect in SEM: Inflated Goodness-of-Fit Statistics Are Due to the Size of the Covariance Matrix

The size of a model has been shown to critically affect the goodness of approximation of the model fit statistic T to the asymptotic chi-square distribution in finite samples. It is not clear, however, whether this “model size effect” is a function of the number of manifest variables, the number of free parameters, or both. It is demonstrated by means of 2 Monte Carlo computer simulation studies that neither the number of free parameters to be estimated nor the model degrees of freedom systematically affect the T statistic when the number of manifest variables is held constant. Increasing the number of manifest variables, however, is associated with a severe bias. These results imply that model fit drastically depends on the size of the covariance matrix and that future studies involving goodness-of-fit statistics should always consider the number of manifest variables, but can safely neglect the influence of particular model specifications.     

心理统计学考试成绩的影响因素及教学启示

采用自编问卷考察《心理统计学》考试成绩影响因素的结构、特点及影响途径。结果发现,影响考试成绩的个体内部因素是学习态度、学习信心、学习方法,外部因素是课堂教学质量、教材与课时安排、复习资料;学习态度、学习信心的性别差异显著;学习态度、学习信心、复习资料对考试成绩有直接正向预测性,课堂教学质量、教材与课时安排通过学习态度或学习信心起间接正向预测作用,据此提出了相应的教学措施。     

柴油机实验台测控系统的数据管理

以柴油机性能实验台为物理模型,完成了柴油机常见参数的测量,通过LabVIEW虚拟仪器环境完成参数测量并生成数据文件;利用MATLAB软件的可视化等优点,完成了复杂的矩阵计算和图形处理,而在MATLAB环境中设计了GUI图形用户界面,实现了对在LabVIEW测试系统中生成的数据文件的访问,并进行处理,得到图形,直观的反映了测试参数在特定的时刻的轨迹.     

海洋工程水动力学模型试验数据处理程序开发

为了提高海洋工程模型试验数据处理和分析的精度和效率,基于Visual Studio 2005平台,采用混合编程技术,以C++为主要设计语言,调用Fortran语言编译生成的动态链接库函数,开发了一套数据处理与分析程序。程序集成图形化数据显示、数据处理和数据分析功能,可显示多个通道的统计值和时历曲线,同步播放试验录像,对试验数据进行滤波、多项式拟合、平移、坏点修复和通道组合计算等处理,进行多通道的时域统计分析和频域谱分析,自动生成规范的Excel图表。实践证明,该程序结构合理,界面美观、处理流程简洁、计算结果准确、易学易用,可极大提高实验室的工作效率。     

教育统计学的思维方式及其基本思想

每一门相对独立的学科都拥有其自身的思维方式与相应的基本思想.教育统计学的思维方式是,理性主义指导下的经验主义、演绎主义关照下的归纳主义、以及整体(本质)主义预设下"从部分到整体"的思维方式.其基本思想则是,在上述思维方式的运作下,定量地揭示教育世界中"寓于偶然性中的必然性",即教育统计规律,以为教育研究者提供概括性的"经验命题"之一,或者为教育管理者提供科学性的"决策依据"之一,或者为教育实践者提供一般性的"理论指导"之一.     

Performance of Modified Test Statistics in Covariance and Correlation Structure Analysis Under Conditions of Multivariate Nonnormality

Questions of whether hypothesized structure models are appropriate representations of the pattern of association among a group of variables can be addressed using a wide variety of statistical procedures. These procedures include covariance structure analysis techniques and correlation structure analysis techniques, in which covariance structure procedures are based on distribution theory for covariances, and correlation structure procedures are based on distribution theory for correlations. The present article provides an overview of standard and modified normal theory and asymptotically distribution-free covariance and correlation structure analysis techniques and also details Monte Carlo simulation results on the Type I and Type II error control as a function of structure model type, number of variables in the model, sample size, and distributional nonnormality. The present Monte Carlo simulation demonstrates clearly that the robustness and nonrobustness of structure analysis techniques vary as a function of the structure of the model and the data conditions. Implications of these results for users of structure analysis techniques are considered in the context of current software availability.     

Four New Corrected Statistics for SEM With Small Samples and Nonnormally Distributed Data

When the assumption of multivariate normality is violated and the sample sizes are relatively small, existing test statistics such as the likelihood ratio statistic and Satorra–Bentler’s rescaled and adjusted statistics often fail to provide reliable assessment of overall model fit. This article proposes four new corrected statistics, aiming for better model evaluation with nonnormally distributed data at small sample sizes. A Monte Carlo study is conducted to compare the performances of the four corrected statistics against those of existing statistics regarding Type I error rate. Results show that the performances of the four new statistics are relatively stable compared with those of existing statistics. In particular, Type I error rates of a new statistic are close to the nominal level across all sample sizes under a condition of asymptotic robustness. Other new statistics also exhibit improved Type I error control, especially with nonnormally distributed data at small sample sizes.     

浅谈大数据时代统计学的挑战与机遇

文章主要讨论大数据时代统计学的建设发展问题。根据大数据对统计学的影响,分析我国统计学的发展现状,提出健全和完善统计学科的新思路,积极应对大数据时代对统计学科建设面临的新挑战与新机遇。     

统计的作用谈

为适应市场经济的需要 ,统计应积极从开展统计调查、统计分析和预测加以改进 ,充分发挥其应有的作用 ,更好地为社会主义建设服务。