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The statistical method plays an extremely important role in quantitative research studies in library and information science (LIS). The purpose of this paper is to investigate the status of statistical methods used in the field, their application areas and the temporal change patterns during a recent 15-year period.

The research papers in six major scholarly journals from 1999 to 2013 in LIS were examined. Factors including statistical methods, application areas and time period were analyzed using quantitative research methods including content analysis and temporal analysis methods.

The research studies using statistical methods in LIS have increased steadily. Statistical methods were more frequently used to solve problems in the information retrieval area than in other areas, and inferential statistical methods were used more often than predictive statistical methods and other statistical methods. Anomaly analysis on statistical method uses was conducted and four types of anomaly were specified.

The findings of this study can help educators, graduates and researchers in the field of LIS better understand the patterns and trends of the applications of statistical methods in this field, depict an overall picture of quantitative research studies in LIS from the perspective of statistical methods and discover the change patterns of statistical method applications in LIS between 1999 and 2013.

Quantitative methods, especially statistical methods, play an increasingly important role in research of library and information science (LIS). For different journals, the uses of statistical methods vary substantially due to different journal scopes and aims. The purpose of this paper is to explore the characteristics of statistical methodology uses in six major scholarly journals in LIS.

Research papers that used statistical methods from the six major journals were selected and investigated. Content analysis method, descriptive statistical analysis method, and temporal analysis method were used to compare and analyze statistical method uses in research papers of the investigated journals.

The findings of this study show that there was a clear growth trend of statistical method uses in five of the investigated journals; statistical methods were used most in The Journal of the Association for Information Science and Technology and Information Processing & Management; and the top three most frequently used statistical methods were t-test, ANOVA test, and χ2-test.

The findings can be used to better understand the application areas, patterns, and trends of statistical methods among the investigated journals and their statistical methodology orientations in research studies of LIS.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

MIS researchers have consistently adopted survey‐based research method while investigating MIS and related phenomenon, making survey‐based research method one of the widely used research method in MIS research. This study seeks to revisit some of the inherent characteristics of survey‐based research method with the aim of improving the quality, replication, and validation of results in MIS survey‐based studies. Additionally, this study provides information on the most prevalent analytical and statistical tools used in MIS survey research studies.

In this research, the authors adopt the content analysis technique. The choice of content analysis is premised on the desire to investigate the sources of survey data, units of analysis, research methods, and statistical tools used in MIS research with the aim of improving empirical research in the MIS discipline.

The results show the prevalent sources of data, the dominant units of analysis, the most commonly used analytical research methods, and the statistical tools adopted by many MIS researchers. The results indicate that many MIS researchers get their data from US sources, although researchers are increasingly acquiring data from other countries. Also, the results reveal that most MIS survey researchers are using SEM, LISREL, and PLS statistical methods and tools.

The paper concludes with recommendations and implications on how to inform and retool upcoming and existing researchers on the current and future MIS research tools and methods. Editors should ensure that MIS researchers provide as much information as possible about the sources of data, the dominant units of analysis, the analytical research methods used, and the statistical tools adopted; these will demonstrate the rigor of the research process and enable replication, validation, and extension of the research works.

The paper presents the results of a content analysis of 749 survey‐based research articles published between 1990 and 2010 in nine mainstream MIS Journals. Prior studies have broadly addressed aspects of MIS research methodologies like investigating MIS research methods, ranking them, and generated a taxonomy of MIS research methodology. The results of this study make a case for the reporting of, both, the analytical method(s) and statistical tools used by MIS researchers to aid in replicating, validating, and extending the resultant findings of their survey‐based research.

Statistical methods are important for meaningful analysis, critique and interpretation of results. The current study aims to investigate the use of statistical methods used in LIS research articles produced by Pakistani authors during 2001–2016.

Content analysis method with both the qualitative and quantitative components was used. LIS articles published by Pakistani authors in national and international journals from 2001 to 2016 were selected. The descriptive and inferential statistics were used to analyze the usage of statistical techniques.

The findings show that use of descriptive statistics remained higher as compared to inferential statistics in the LIS research produced by Pakistani authors. However, a visible growth trend in the use of inferential statistical techniques is found. Males are two times more likely to use inferential statistics as compared to female authors. Articles published in foreign journals and impact factor journals used more inferential statistics as compared to local and nonimpact factor journals. Parametric inferential statistics is more popular among Pakistani authors as compared to nonparametric. Faculty was more inclined toward using parametric statistic. The percentage of collaboration was higher in the papers using parametric statistics. Few articles reported the tests to fulfill the assumptions of parametric and nonparametric statistics.

This study can be used to better understand the trends of statistical techniques used in LIS research and authors' orientation in this regard. It will be helpful for future researchers in the selection of appropriate statistical techniques to be used.

The intent of this paper is to discuss the use of statistical mathematics in property valuation and the wider question concerning the role of mathematics in the field of economics.

This paper reviews the evolution of the application of mathematics, including statistics in economics and drawing conclusions about applicability and effectiveness of quantitative modelling in property valuation.

This paper discusses the future use of statistical models in valuation and the need to recognise the relationships between market participants and the increasingly complex environment, and their impact on value. This would suggest adopting modelling techniques from behavioural economics.

This paper highlights the difference between quantitative and qualitative models and discusses the role that each can play in property valuation.

This paper provides insights on the development of statistical modelling and discusses the application of the same in property valuation.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

For numerical treatment of resin‐containing systems and forecasting of their properties, certain models of branching are needed. In this review, existing theoretical models of systems containing branched structures (polymers, aggregates, etc.) are analyzed and compared. The criteria of selection of the optimal theoretical model comprise chemical and physical problems available for solution, simplicity of such solution, connection between theoretically forecasted and experimental results, and the time needed for computing. It is concluded that, according to these criteria, the optimal (between existing models) is the statistical polymer method.