Modeling Ability Differentiation in the Second-Order Factor Model

In this article we present factor models to test for ability differentiation. Ability differentiation predicts that the size of IQ subtest correlations decreases as a function of the general intelligence factor. In the Schmid–Leiman decomposition of the second-order factor model, we model differentiation by introducing heteroscedastic residuals, nonlinear factor loadings, and a skew-normal second-order factor distribution. Using marginal maximum likelihood, we fit this model to Spanish standardization data of the Wechsler Adult Intelligence Scale (3rd ed.) to test the differentiation hypothesis.     

Estimation of MIMIC Model Parameters with Multilevel Data

The purpose of this simulation study was to assess the performance of latent variable models that take into account the complex sampling mechanism that often underlies data used in educational, psychological, and other social science research. Analyses were conducted using the multiple indicator multiple cause (MIMIC) model, which is a flexible and effective tool for relating observed and latent variables. The data were simulated in a hierarchical framework (e.g., individuals nested in schools) so that a multilevel modeling approach would be appropriate. Analyses were conducted accounting for and not accounting for the nested data to determine the impact of ignoring such multilevel data structures in full structural equation models. Results highlight the differences in modeling results when the analytic strategy is congruent with the data structure and what occurs when this congruency is absent. Type I error rates and power for the standard and multilevel methods were similar for within-cluster variables and for the multilevel model with between-cluster variables. However, Type I error rates were inflated for the standard approach when modeling between-cluster variables.     

Testing Measurement Invariance Across Groups in Longitudinal Data: Multigroup Second-Order Latent Growth Model

In latent growth modeling, measurement invariance across groups has received little attention. Considering that a group difference is commonly of interest in social science, a Monte Carlo study explored the performance of multigroup second-order latent growth modeling (MSLGM) in testing measurement invariance. True positive and false positive rates in detecting noninvariance across groups in addition to bias estimates of major MSLGM parameters were investigated. Simulation results support the suitability of MSLGM for measurement invariance testing when either forward or iterative likelihood ratio procedure is applied.     

线性混合效应模型中方差分量两种估计的比较

本文首先给出了由王松桂等提出的广义谱分解估计(GSDE)的定义,以及由Herderson方法得到的方差分量的方差分析估计(ANOVAE),在此基础上提出了谱和线性混合效应模型的概念,证明了在这类模型中,方差分析估计是广义谱分解估计的一种,并且考察了在一定条件下广义谱分解估计优于方差分析估计的充分条件.     

图解法分析放大电路三种基本组态的最大输出幅值及失真

用图解法分析了放大电路三种基本组态的最大输出电压幅值Uomax及其失真现象.     

基于满意解策略的秘书问题研究

秘书问题是一类序贯观察与选择问题,描述了一种动态的信息搜索与决策过程。已有解决秘书问题的方法,主要特征是以取样选项中的最大值作为标杆,其优点是能保证赢的概率最大,其不足是很少考虑决策者的有限理性与启发式偏见。提出了基于次大值标杆策略的设想,通过理论求解以及仿真实验的方法研究了该策略的特征与规律。结果发现:赢的概率随着标杆由最大值向次大值、第三大值等的变化而逐渐降低,且最优截止阀值也不断后移。     

二元函数最值问题解法研究

以例解的形式探究一类二元函数最值问题的解法,给出若干思路及方法,为解一般的二元函数值问题奠定基础,服务于解题教学研究.     

两m相依样本均值差异的经验似然比置信区间

该文讨论了两m相依样本均值差异的经验似然的大样本性质,证明了经验似然比统计量依分布收敛于χ2随机变量,由此给出均值差异的经验似然置信区间。     

与《算术平均法的数学解释》一文作者商榷

文中指出了《重庆职业技术学院学报》2003年第1期中所发表的《算术平均法的教学解释》一文作者的失误,并对算术平均法有关理论依据进行介绍。     

基于可调子块迭代的加速SAGE算法在PET图像重建中的应用

提出了一种可调子块迭代(RBI)方法加速空间交替广义期望最大(SAGE)算法的收敛性.新的可调子块迭代的空间交替广义期望最大算法(RBI-SAGE)组合了RBI算法和SAGE算法的优点用于加速正电子发射断层(PET)图像重建.RBI-SAGE将投影数据分成不连续的子块,每一次迭代仅包含一个这样的子块.在每一个子块中用SAGE算法序列更新参数.实验中,运用RBI-SAGE算法与SAGE算法对PET图像进行重建.结果表明,RBI-SAGE收敛性能比SAGE算法优越,且重建图像质量较高.