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Article

Jin Zhang, Yanyan Wang and Yuehua Zhao

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

The Electronic Library, vol. 35 no. 6
Type: Research Article
ISSN: 0264-0473

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Article

Jin Zhang, Yuehua Zhao and Yanyan Wang

Quantitative methods, especially statistical methods, play an increasingly important role in research of library and information science (LIS). For different journals, the…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Online Information Review, vol. 40 no. 3
Type: Research Article
ISSN: 1468-4527

Keywords

Content available
Article

Mariam AlKandari and Imtiaz Ahmad

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on…

Abstract

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.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

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Article

Erastus Karanja and Jigish Zaveri

MIS researchers have consistently adopted survey‐based research method while investigating MIS and related phenomenon, making survey‐based research method one of the…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

Journal of Systems and Information Technology, vol. 15 no. 2
Type: Research Article
ISSN: 1328-7265

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Book part

Vivian M. Evangelista and Rommel G. Regis

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector…

Abstract

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.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78754-290-7

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Article

Yinhua Liu, Rui Sun and Sun Jin

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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

Details

Assembly Automation, vol. 39 no. 4
Type: Research Article
ISSN: 0144-5154

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Article

Freddy Romm

For numerical treatment of resin‐containing systems and forecasting of their properties, certain models of branching are needed. In this review, existing theoretical…

Abstract

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.

Details

Pigment & Resin Technology, vol. 30 no. 5
Type: Research Article
ISSN: 0369-9420

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Article

Brady Lund

The purpose of this study is to identify typical sample sizes and response rates in questionnaire research studies within the discipline of information systems, as well as…

Abstract

Purpose

The purpose of this study is to identify typical sample sizes and response rates in questionnaire research studies within the discipline of information systems, as well as the top statistical analyses utilized for questionnaire data in these studies.

Design/methodology/approach

A total of 842 articles published between the years of 2000 and 2019 were identified that met the criteria of using a questionnaire as the research method. These articles were analyzed based on the sample size, response rate (if applicable) and statistical analysis methods used.

Findings

The typical questionnaire study received between 136 (first quartile) and 374 (third quartile) respondents, with a median number of 217. Typical response rate ranged between 16.5% and 50.0%, with a median of 27.8%. it was found that articles published in journals included in the Social Science Citation Index had significantly larger numbers of respondents than those not included in the index, though no difference was found for response rate. Studies that utilized more advanced statistical methods (regression analysis, structural equation modeling) were found to have significantly larger sample sizes than those that utilized only descriptive statistics or t-tests. Structural equation modeling, including the partial least squares approach, was used in the largest number of studies.

Originality/value

This study is the first to broadly examine the typical sample size, response rates and methods of statistical analysis used in information systems questionnaire studies. The findings of this study may be useful for systems researchers in developing appropriate procedures for questionnaire-based research.

Details

VINE Journal of Information and Knowledge Management Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2059-5891

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Article

Nada R. Sanders

The usage of formal statistical forecasting procedures has beenshown in numerous studies to improve forecast accuracy and,consequently, organizational performance…

Abstract

The usage of formal statistical forecasting procedures has been shown in numerous studies to improve forecast accuracy and, consequently, organizational performance. However, the process of implementing and managing this technology can run into many stumbling blocks. Identifies six major organizational problems when implementing and developing formal statistical forecasting procedures. Provides solution strategies to these problems and discusses specific managerial implications. This information is important to managers in order to gain the greatest benefit from the forecasting function.

Details

Industrial Management & Data Systems, vol. 95 no. 4
Type: Research Article
ISSN: 0263-5577

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Article

Yicha Zhang, Ramy Harik, Georges Fadel and Alain Bernard

For part models with complex shape features or freeform shapes, the existing build orientation determination methods may have issues, such as difficulty in defining…

Abstract

Purpose

For part models with complex shape features or freeform shapes, the existing build orientation determination methods may have issues, such as difficulty in defining features and costly computation. To deal with these issues, this paper aims to introduce a new statistical method to develop fast automatic decision support tools for additive manufacturing build orientation determination.

Design/methodology/approach

The proposed method applies a non-supervised machine learning method, K-Means Clustering with Davies–Bouldin Criterion cluster measuring, to rapidly decompose a surface model into facet clusters and efficiently generate a set of meaningful alternative build orientations. To evaluate alternative build orientations at a generic level, a statistical approach is defined.

Findings

A group of illustrative examples and comparative case studies are presented in the paper for method validation. The proposed method can help production engineers solve decision problems related to identifying an optimal build orientation for complex and freeform CAD models, especially models from the medical and aerospace application domains with much efficiency.

Originality/value

The proposed method avoids the limitations of traditional feature-based methods and pure computation-based methods. It provides engineers a new efficient decision-making tool to rapidly determine the optimal build orientation for complex and freeform CAD models.

Details

Rapid Prototyping Journal, vol. 25 no. 1
Type: Research Article
ISSN: 1355-2546

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