牛顿环实验的误差估算

该文根据干涉原理，用实验数据对牛顿环实验的误差进行了估算，结果表明；选取环数差大的牛顿环和环序数初始值小的环进行测量，对减少牛顿环实验的误差有利。     

关于牛顿环实验数据的处理

用牛顿环实验中的实测数据,比较牛顿环实验的两种数据处理方法,通过对这两种方法的理论分析,论证了加权平均法是该实验数据处理的最佳方法,提出了借助计算机用matlab软件处理牛顿环实验的数据将简化加权平均法。     

运用EXCEL软件处理牛顿环实验的测量数据

为了更准确更有效地对实验数据进行处理，利用Excel软件并结合牛顿环实验探讨了实验数据处理一般过程和方法。     

牛顿环干涉实验数据处理方法的对比研究

用三种数据处理方法处理牛顿环干涉实验的结果并进行对比研究。     

牛顿环实验的扩展研究

通过观察、测量牛顿环实验过程中出现的变形牛顿环,分析得出变形牛顿环的形成是由于牛顿环装置中玻璃面附着有微小尘埃或玻璃发生形变后产生;同时,探完了变形牛顿环的物理原理及变形环的特点与规律．     

牛顿环实验教学的讨论

牛顿环实验是利用分振幅法获得可相干光束的典型实验,在光学测量和光学加工技术上有着非常重要的应用。文章阐述了牛顿环实验的干涉原理,对牛顿环实验教学中需注意的问题进行了讨论。     

牛顿环实验的环心探讨

分析了在指导牛顿环实验过程中遇到的三种不同环心的观测结果的来源,给出了合理的解释;并通过让学生探讨三种不同的环心的来源,使学生加强了对牛顿环理论的理解。     

牛顿环干涉条纹的位置分析

通过对干涉问题的分析，指出实验中为什么认为牛顿环干涉条纹在空气膜上表面．     

激光牛顿环现象分析及应用

为避免应力变形对钠光牛顿环仪测量的影响,提出了激光牛顿环的测量方法,理论分析了用激光光源替代传统的钠光光源测量平凸透镜曲率半径的原理和可行性,推导了对应的测量公式,并根据实验现象分析了激光牛顿环图像产生的原因,指出了激光牛顿环的缺陷以及解决问题的方向。分别用氦氖激光和钠光两种方法测量了同一个平凸透镜样品的曲率半径。实验结果表明,采用激光测量平凸透镜的曲率半径是可行的,避免了应力变形对测量的影响,且用激光光源所测结果与标准值的相对误差小于0.4%。相对于传统钠光光源的牛顿环仪,激光牛顿环的测量精度更高,更能体现光学非接触测量的优势。     

牛顿环实验的研究

利用公式推理和实践测量对牛顿环实验中环中心出现亮纹、环内凹、环中心出现较大暗斑的变形牛顿环进行了分析，结果表明，环中心出现亮纹、环内凹变形牛顿环对透镜曲率半径测量结果无影响，而环中心出现较大暗斑的变形牛顿环会使透镜曲率半径的测量结果偏大于真值。     

论序数及连续统的可数性与正则公理

在前期一系列论文及著作中([2][4][5])对实数集(连续统)的可数性、康托对角线法等问题充分讨论的基础上,对序数的可数性问题进行分析,并由此引出对ZFC公理系统中的正则公理(基础公理,限制公理)的讨论。对与斯梅尔第18问题密切相关的哥德尔定理进行了分析,得到全新结论。提出实数的一进制表示法并在此基础上讨论康托对角线法的局限性问题。     

序数单词填一填

同学们，你们会用英文从第一数到第十吗？根据下面的提示，你们能将它们拼出来吗？赶紧来挑战一下吧！     

Ordinal Factor Analysis of Graded-Preference Questionnaire Data

We introduce a new comparative response format, suitable for assessing personality and similar constructs. In this “graded-block” format, items measuring different constructs are first organized in blocks of 2 or more; then, pairs are formed from items within blocks. The pairs are presented 1 at a time to enable respondents expressing the extent of preference for 1 item or the other using several graded categories. We model such data using confirmatory factor analysis (CFA) for ordinal outcomes. We derive Fisher information matrices for the graded pairs, and supply R code to enable computation of standard errors of trait scores. An empirical example illustrates the approach in low-stakes personality assessments and shows that similar results are obtained when using graded blocks of size 3 and a standard Likert format. However, graded-block designs might be superior when insufficient differentiation between items is expected (due to acquiescence, halo, or social desirability).     

Residual Structures in Growth Models With Ordinal Outcomes

Growth models allow for the study of within-person change and between-person differences in within-person change. Typically, these models are applied to continuous variables where the residuals are assumed to be normally distributed. With normally distributed residuals there are a variety of residual structures that can be imposed and tested, which have been shown to affect model fit and parameter estimation. This article concerns residual structures in growth models with binary and ordered categorical outcomes using the probit link function. Different residual structures and their appropriateness for growth data are discussed and their use is illustrated with longitudinal data collected as part of Head Start’s Family and Child Experiences Survey 1997 Cohort. We close with recommendations for the specification and parameterization of growth models that use the probit link.     

Ordinal position in the serial learning of rats

Rats were runway trained on each of two, three-trial series consisting of different varieties of reward (X, Y, and Z) and nonreward (N) serving as trial outcomes. The two series are represented as XNY and ZNN. Distinguishing the two series were different brightness and texture cues on the runway floor. Transfer tests, conducted after the rats had developed faster running for rewarded trials than for nonrewarded trials and slower running on Trial 2 of ZNN than on Trial 2 of XNY, provided evidence that trial position, rather than item memories, was controlling the discriminations. In Experiment 1, reversing the floor cues completely reversed the discriminations. In Experiment 2, transfer to NNN did not change the routine patterns of approach that had been established.     

H-Likelihood Approach to Factor Analysis for Ordinal Data

Marginal likelihood-based methods are commonly used in factor analysis for ordinal data. To obtain the maximum marginal likelihood estimator, the full information maximum likelihood (FIML) estimator uses the (adaptive) Gauss–Hermite quadrature or stochastic approximation. However, the computational burden increases rapidly as the number of factors increases, which renders FIML impractical for large factor models. Another limitation of the marginal likelihood-based approach is that it does not allow inference on the factors. In this study, we propose a hierarchical likelihood approach using the Laplace approximation that remains computationally efficient in large models. We also proposed confidence intervals for factors, which maintains the level of confidence as the sample size increases. The simulation study shows that the proposed approach generally works well.     

Confirmatory Factor Analysis of Ordinal Variables With Misspecified Models

Ordinal variables are common in many empirical investigations in the social and behavioral sciences. Researchers often apply the maximum likelihood method to fit structural equation models to ordinal data. This assumes that the observed measures have normal distributions, which is not the case when the variables are ordinal. A better approach is to use polychoric correlations and fit the models using methods such as unweighted least squares (ULS), maximum likelihood (ML), weighted least squares (WLS), or diagonally weighted least squares (DWLS). In this simulation evaluation we study the behavior of these methods in combination with polychoric correlations when the models are misspecified. We also study the effect of model size and number of categories on the parameter estimates, their standard errors, and the common chi-square measures of fit when the models are both correct and misspecified. When used routinely, these methods give consistent parameter estimates but ULS, ML, and DWLS give incorrect standard errors. Correct standard errors can be obtained for these methods by robustification using an estimate of the asymptotic covariance matrix W of the polychoric correlations. When used in this way the methods are here called RULS, RML, and RDWLS.     

Latent Differential Equation Models for Binary and Ordinal Data

This study evaluates latent differential equation models on binary and ordinal data. Binary and ordinal data are widely used in psychology research and many statistical models have been developed, such as the probit model and the logit model. We combine the latent differential equation model with the probit model through a threshold approach, and then compare the threshold model with a naive model, which blindly treats binary and ordinal data as continuous. Simulation results suggest that the naive model leads to bias on binary data and on ordinal data with fewer than 5 levels, whereas the threshold model is unbiased and efficient for binary and ordinal data. Two example analyses on empirical binary data and ordinal data show that the threshold model also has better external validity. The R code for the threshold model is provided.     

序数语法表达式的必有语义特征分析

序数语法表达式是利用语法手段来表达序数的语言形式,具有数量性、对比性、序列性、表序手段的语法性等必有语义特征。必有语义特征内部存在细微差异,数量性包括数量实指、数量虚指、数量无指,对比性包括单比性与通比性、单体性与多体性等,序列性存在强弱差异。     

Familial Ordinal Position in Relation to Guidance and Counseling

Abstract

The study investigated the effects of a 2-phase, spring and summer graduate training program on fifty-three novice teachers preparing to teach disadvantaged youth.

During the first phase of the program students were exposed to several process and didactic experiences intended to shape attitudes as well as to promote formal learning. The summer phase of the program saw teachers engage in paid internships in three elementary schools serving migrant children. Several measures of attitudes and personal values were taken with semantic differentials and the Personal Orientation Inventory. A number of significant changes were noted on most measures. Teachers showed a tendency to evaluate disadvantaged children more favorably and tended to become more like self-actualized adults in personal orientations.