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Actual costs frequently deviate from the estimated costs in either favorable or adverse direction in construction projects. Conventional cost evaluation methods do not take the uncertainty and correlation effects into account. In this regard, a simulation-based cost risk analysis model, the Correlated Cost Risk Analysis Model, previously has been proposed to evaluate the uncertainty effect on construction costs in case of correlated costs and correlated risk-factors. The purpose of this paper is to introduce the detailed evaluation of the Cost Risk Analysis Model through scenario and sensitivity analyses.

The evaluation process consists of three scenarios with three sensitivity analyses in each and 28 simulations in total. During applications, the model’s important parameter called the mean proportion coefficient is modified and the user-dependent variables like the risk-factor influence degrees are changed to observe the response of the model to these modifications and to examine the indirect, two-sided and qualitative correlation capturing algorithm of the model. Monte Carlo Simulation is also applied on the same data to compare the results.

The findings have shown that the Correlated Cost Risk Analysis Model is capable of capturing the correlation between the costs and between the risk-factors, and operates in accordance with the theoretical expectancies.

Correlated Cost Risk Analysis Model can be preferred as a reliable and practical method by the professionals of the construction sector thanks to its detailed evaluation introduced in this paper.

This article aims to discuss the binary matrix of spatial association which is suggested by Moran, and proposes a new method of the definition of the w matrix to obtain a new space‐time correlation coefficient considering the correlation of both time and space.

From the perspective of the multi‐dimension of space and time, this article proposes a new computational method of a correlation coefficient considering both temporal and spatial factors, based on the analysis of the characteristics of Moran's Global Index and Moran's Local Index. The number of patents granted in mainland China's provinces and municipalities is taken as an example of multi‐dimensional analysis.

The results of quantitative analysis using this space‐time correlation coefficient show that the outcomes calculated by this new correlation coefficient are not only highly correlated with Moran's Index, but also have advantages in analyzing the trends of both spatial and temporal indicators simultaneously, which is verified by the illustration of the algorithm.

Due to a scarcity of data in China, the algorithm is based on data for the last 20 years, which may not be long enough for this research. Although this does not reduce the value of the conclusions of this article, a closer look should be taken at the effectiveness of the new space‐time correlation coefficient in the future.

The results of space‐time correlation coefficient are highly correlated with Moran's Index. In addition, it can not only analyze the “flow” indicators in a certain period but also analyze the “stock” indicators to reflect both space and time changes. These may reflect superiority of space‐time correlation coefficient to Moran's Index.

This new correlation coefficient that considers both temporal and spatial factors and will provide a more scientific and effective tool for spatial econometric analysis in time and space changes of management on society and the economy.

Addresses the standardization of the measurements and the labels for concepts commonly used in the study of work organizations. As a reference handbook and research tool, seeks to improve measurement in the study of work organizations and to facilitate the teaching of introductory courses in this subject. Focuses solely on work organizations, that is, social systems in which members work for money. Defines measurement and distinguishes four levels: nominal, ordinal, interval and ratio. Selects specific measures on the basis of quality, diversity, simplicity and availability and evaluates each measure for its validity and reliability. Employs a set of 38 concepts ‐ ranging from “absenteeism” to “turnover” as the handbook’s frame of reference. Concludes by reviewing organizational measurement over the past 30 years and recommending future measurement reseach.

Normal transformation is often required in structural reliability analysis to convert the non-normal random variables into independent standard normal variables. The existing normal transformation techniques, for example, Rosenblatt transformation and Nataf transformation, usually require the joint probability density function (PDF) and/or marginal PDFs of non-normal random variables. In practical problems, however, the joint PDF and marginal PDFs are often unknown due to the lack of data while the statistical information is much easier to be expressed in terms of statistical moments and correlation coefficients. This study aims to address this issue, by presenting an alternative normal transformation method that does not require PDFs of the input random variables.

The new approach, namely, the Hermite polynomial normal transformation, expresses the normal transformation function in terms of Hermite polynomials and it works with both uncorrelated and correlated random variables. Its application in structural reliability analysis using different methods is thoroughly investigated via a number of carefully designed comparison studies.

Comprehensive comparisons are conducted to examine the performance of the proposed Hermite polynomial normal transformation scheme. The results show that the presented approach has comparable accuracy to previous methods and can be obtained in closed-form. Moreover, the new scheme only requires the first four statistical moments and/or the correlation coefficients between random variables, which greatly widen the applicability of normal transformations in practical problems.

This study interprets the classical polynomial normal transformation method in terms of Hermite polynomials, namely, Hermite polynomial normal transformation, to convert uncorrelated/correlated random variables into standard normal random variables. The new scheme only requires the first four statistical moments to operate, making it particularly suitable for problems that are constraint by limited data. Besides, the extension to correlated cases can easily be achieved with the introducing of the Hermite polynomials. Compared to existing methods, the new scheme is cheap to compute and delivers comparable accuracy.

The aim of this paper is to determine whether or how academic libraries affect student achievement.

The paper uses computation of Pearson's r coefficients and predictor values for correlations of academic library statistics with first‐year retention and six‐year graduation rates reported to the Integrated Postsecondary Education Data System by Southern Regional Education Board four‐year colleges and universities in 2010.

Book collection size in doctoral university libraries has the strongest correlation with retention and graduation rates, in the sample, and predicts for every 10 percent increase a 0.5 percent improvement in retention and a 0.7 percent higher graduation rate.

The paper documents the first impact study to correlate library statistics with both retention and graduation rates from a large sample of doctoral, Masters' and bachelors' degree‐conferring institutions. It calculates 21 predictor values of interest to academic administrators.

The purpose of this paper is to investigate the correlation between university libraries and academic research achievement and analyze if university library resources correlate with academic research achievement.

The paper seeks to verify the correlation between university libraries and academic research achievement and to examine which university library resources relate to research achievement. A variety of research questions were posed concerning the relationship between a university's library resources and academic research achievement. Structural equation models (SEMs) were developed to answer the research questions. Most research questions posed were affirmatively answered using the SEM process.

This study confirmed that labor and budget, investment in e‐resources and an investment in university libraries enhances academic research achievement.

An SEM for verifying the correlation between university libraries and academic research achievement was developed in the study.

This study is the first including an investment factor in e‐resources for verifying the correlation between university libraries and academic research achievement.

The usefulness of ex‐post data as a proxy for ex‐ante returns in the portfolio problem rests on the stability of the co‐movement between returns. Yet despite its importance, this issue has not received sufficient examination in the financial literature, particularly in the direct real estate market. This study aims to address this issue.

To examine the temporal stability of covariance and correlation matrices and individual correlation coefficients this paper uses the Box M tests and the methodology of Shaked using monthly real estate data in the UK over the period 1987 to 2002 and four investment horizons.

The Box M tests reveal that the covariance and correlation matrices both display temporal instability. This suggests that the returns between real estate returns are unstable over time and so provide poor estimates in the ex‐ante modelling process. The analysis also indicates that the covariance matrices are less stable than the corresponding correlation matrices. Nonetheless, when we tested the stability of individual correlation coefficients using the methodology of Shaked we find that stability increases consistently and substantially with the lengthening of the investment horizon and holding period.

Thus, for all practical purposes the pair‐wise correlation between real estate returns can be considered nearly stationary in the long run. This implies that investors can use ex‐post data as a proxy for ex‐ante data in portfolio models especially if longer investment horizons are used to estimate the parameters.

This study is the first to examine temporal co‐movements between UK real estate returns in a portfolio context over different investment horizons.

The purpose of this paper is to examine the degree of dependence and extreme correlation (i.e. tail dependence) among US industry sectors.

This paper makes use of both conventional measures of dependence (the Pearson’s correlation coefficient, Spearman’s rho and Kendall’s tau) and copula measures of extreme correlations (including the same-direction and cross-tail dependence coefficients) to explore sector diversification opportunities. The paper splits the full sample in three periods, namely, 1995 to 2000, 2001 to 2006 and 2007 to 2012, to access the extent to which the dependence results change through time.

This research provides three important findings. First, the degree of dependence and same-direction extreme correlations are high, whereas the cross-extreme correlations are considerably low. Second, the sector pairs offering the best and worst tail diversification change across sample periods. Third, the traditional dependence measures suggest that benefits for sector diversification have decreased over all sample periods, while the potential for sector diversification during extreme events has just started to disappear in the most recent period.

An investor should consider both the normal co-movements and extreme co-movements among sector indices to maximize diversification benefits.

Given the limited empirical investigations of the degree of dependence and extreme correlation at a sector level, the results from this research should provide additional and valuable information for both investors and empirical researchers about portfolio diversification and risk management.

The purpose of this study is to introduce a matching function approach to analyze matching in financial reporting.

The matching function is first analyzed analytically. It is specified as a multiplicative Cobb-Douglas-type function of three categories of expenses (labor expense, material expense and depreciation). The specified matching function is solved by the generalized reduced gradient method (GRG) for 10-year time series from 8,226 Finnish firms. The coefficient of determination of the logarithmic model (CODL) is compared with the linear revenue-expense correlation coefficient (REC) that is generally used in previous studies.

Empirical evidence showed that REC is outperformed by CODL. CODL was found independent of or weakly negatively dependent on the matching elasticity of labor expense, positively dependent on the material expense elasticity and negatively dependent on depreciation elasticity. Therefore, the differences in matching accuracy between industries emphasizing different expense categories are significant.

The matching function is a general approach to assess the matching accuracy but it is in this study specified multiplicatively for three categories of expenses. Moreover, only one algorithm is tested in the empirical estimation of the function. The analysis is concentrated on ten-year time-series of a limited sample of Finnish firms.

The matching function approach provides a large set of important information for considering the matching process in practice. It can prove a useful method also to accounting standard-setters and other specialists such as managers, consultants and auditors.

This study is the first study to apply the new matching function approach.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.