Incorporating Uncertainty Into Parallel Analysis for Choosing the Number of Factors via Bayesian Methods

A number of psychometricians have suggested that parallel analysis (PA) tends to yield more accurate results in determining the number of factors in comparison with other statistical methods. Nevertheless, all too often PA can suggest an incorrect number of factors, particularly in statistically unfavorable conditions (e.g., small sample sizes and low factor loadings). Because of this, researchers have recommended using multiple methods to make judgments about the number of factors to extract. Implicit in this recommendation is that, when the number of factors is chosen based on PA, uncertainty nevertheless exists. We propose a Bayesian parallel analysis (B-PA) method to incorporate the uncertainty with decisions about the number of factors. B-PA yields a probability distribution for the various possible numbers of factors. We implement and compare B-PA with a frequentist approach, revised parallel analysis (R-PA), in the contexts of real and simulated data. Results show that B-PA provides relevant information regarding the uncertainty in determining the number of factors, particularly under conditions with small sample sizes, low factor loadings, and less distinguishable factors. Even if the indicated number of factors with the highest probability is incorrect, B-PA can show a sizable probability of retaining the correct number of factors. Interestingly, when the mode of the distribution of the probabilities associated with different numbers of factors was treated as the number of factors to retain, B-PA was somewhat more accurate than R-PA in a majority of the conditions.     

Measuring children’s perceived cost of school science: Instrument development and psychometric evaluation

This investigation addresses the need for valid and reliable instruments that contribute to understanding the factors that lead to the rejection of science-related studies. We discuss the theoretical and methodological limitations of published attitudes toward science questionnaires and describe the development and validation of a short instrument rooted in the cost construct of the expectancy-value model of achievement motivation. We collected data from a sample of six hundred thirty-two 5^th and 6^th (M_age = 10.87; SD = .76) elementary students in Spain. Exploratory and confirmatory factor analysis revealed a parsimonious structure measuring loss of valued alternatives and task effort ​cost. Further psychometric evaluation displayed evidence for convergent, discriminant, and concurrent validity. Likewise, the reliability was acceptable for both three-item scales. These findings support the proposed instrument to measure barriers experienced by Spanish children when studying school science.     

关于我国企业成长影响因素的实证分析

2006年夏我们对近200家企业进行了问卷调查。调查发现,在影响我国企业成长的诸多因素中,组织与管理水平、政策与环境、人力资源与企业文化建设、技术创新四者的影响在不同所有制、不同行业、不同成长阶段、不同规模的企业中,既皆是不可轻视的,但又是极不相同的。     

我国企业竞争情报系统运作的影响因素分析

本文在实践调查的基础上，按照其发展中不同的典型表现和特点，从系统的完备程度和受关注程度两个方面划分了我国企业竞争情报系统运作的三个阶段，进而探讨了每个阶段中影响系统运作效果的因素。     

基于熵权的大型项目决策影响因素模糊分析

在分析大型社会公共项目决策影响因素的基础上，我们建立了基于熵权的模糊决策分析模型。结合模糊数学和熵的思想，运用多属性决策分析中的双基点法，该模型对大型社会公共项目决策的影响因素进行模糊分析，可为项目开发商分析关键影响因素提供决策参考，有一定的实际应用指导意义。     

提高学报影响因子的重要意义及其途径

基于影响因子的定义，探讨了提高学报影响因子的意义，认为影响因子是评价学报质量优劣的重要依据和客观指标之一。通过分析影响期刊影响因子的因素，初步给出了提高影响因子的对策。     

我国商业银行效率分析

加入WTO之后，中外银行业之间的竞争将集中表现为效率的竞争，本文运用因子分析法对我国十家商业银行及两家外资银行的效率状况进行了定量考察，探讨了我国国有商业银行效率低下的原因，提出了提高我国银行业效率的途径。     

影响机械行业供应链多阶响应周期的关键因素分析

响应时间已成为企业发展和市场竞争的主导因素。为了弄清楚供应链多阶响应周期的组成特征,我们进行了大量的调查和研究。在对影响供应链多阶响应周期的因素进行分析之后,得到一个重要的结论:在交通和通信等科学技术如此发达的今天,伙伴合作关系是影响供应链多阶响应周期的根本所在。     

我国电力需求的影响因素分析

通过对我国近20年来电力需求状况的分析，探讨了电力需求同GDP、电价、人口、产业结构调整等诸因素的相互关系，研究诸因素对我国电力消费的影响程度，从而对我国制定电力中长期发展战略具有一定的指导意义。     

引文环境下对《科学学与科学技术管理》的科学计量分析

利用CSSCI（1998-2004）排除著者自引后的数据,从期刊被引的规模——频次、学术影响力、被引半衰期等角度对《科学学与科学技术管理》进行引文计量分析。得出,期刊影响力日益扩大,被引半衰期约3.5年,有一批高频被引著者和高频被引论文。