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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.

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.

Undertakes a comparative study of the statistical capability of three spreadsheets which are commonly used in the business sector. The spreadsheets considered are Lotus 1‐2‐3, Microsoft Excel and Quattro Pro. Considers five areas of statistical analysis regularly used by business decision makers (rather than specialist personnel). In order to obtain an objective measure of the statistical provision of each spreadsheet, comparison has also been made with dedicated statistical software regularly used by business decision makers, namely MINITAB. By making this comparison, argues that the spreadsheet is not only a tool for analysis, but also for presentation. Moreover, considers that two spreadsheets in particular, namely Excel and Quattro Pro, offer a user‐friendly statistical provision which should be sufficient for most business decision makers.

This paper aims to introduce matching in propensity score analysis (PSA) as an alternative statistical approach for researchers looking to make causal inferences using intact groups.

An illustrative example demonstrated the varying results of analysis of variance, analysis of covariance and PSA on a heuristic data set. The three approaches were compared by results and violations of statistical assumptions.

Through the illustrative example, it is demonstrated how different statistical approaches can produce varied results. Only PSA mitigated pre-existing group differences without violating the assumption of independence.

This paper attempts to answer calls in the literature for more robust statistical methodologies to better inform human resource development practice and theory.

Attaining high response rates in survey‐based supply chain management (SCM) research is becoming increasingly difficult, but small samples can limit the reliability and validity of empirical research findings. The purpose of this article is to analyze the status quo and provide a discussion of methodological issues related to the use of small samples in SCM research.

An in‐depth review of 75 small sample survey studies published between 1998 and 2007 in three journals in the field that frequently publish survey‐based research papers (TJ, IJPDLM, and JBL) was conducted, and key characteristics of these studies were compared with the characteristics from 44 small sample survey studies published in leading operations management (JOM) and management (AMJ) journals.

The review of papers published in TJ, IJPDLM, and JBL shows that small samples are frequently used in SCM research. This study provides an overview of current practices, opportunities for improvement, and a number of specific recommendations that may help increase the analytical rigor of (future) survey‐based studies that rely on small samples.

The recommendations provided in this article can greatly benefit researchers in the field of SCM. By following these proposals, the reliability and validity of research findings will be increased, researchers will be better equipped to investigate interesting questions where small samples are the norm rather than the exception (e.g., the study of dyadic supply chain relationships), and important and valid contributions to the theory and practice of SCM will be generated.

This paper aims to identify scale purification criteria for both uni- and multidimensional reflective scales and apply these criteria to an evaluation of the methodological status quo of the hospitality literature.

Based on a literature review, the authors develop a taxonomy of statistical and judgmental criteria across scale levels, from which best practices are derived. Recent publications in leading hospitality journals are then evaluated based on these scale purification steps.

The authors uncover a lack of transparency when reporting scale purification practices. Moreover, methodological steps are often entirely omitted or insufficiently followed, especially when it comes to judgmental scale purification practices.

The authors focus on reflective scales in the hospitality discipline. Methodological traditions in other fields might lead to different results if the chosen approach was to be repeated there.

The authors provide a set of suggestions that will help researchers in hospitality and adjacent disciplines to greater consensus and consistency of application regarding the methodological steps when carrying out scale purification in reflective scales.

Application of scale purification in hospitality research has been scarce. The authors extend existing research and provide the most comprehensive study so far of present and best scale purification practices, using both statistical and judgmental criteria.

The purpose of this paper is to investigate trends in the use of research methodologies and publications in manufacturing strategy (MS) literature across geographical regions and suggests possible future research opportunities.

This literature review is based on a sample of 512 subject-relevant journal articles and uses content analysis as the primary method for data analysis. The paper investigates developments in the use of research methodologies – in terms of research design, data collection methods, country of data collection, sample size, respondent type, statistical techniques used and time horizon of studies; and publication trends in terms of authorship type, authorship collaboration, most prolific authors, top journals, most prolific universities, and citation analysis.

Research in MS has substantially changed from conceptual quantitative to empirical quantitative designs. NA and Europe show a declining research interest. However, other regions of the world are consistently showing higher interest. Significant opportunities and synergies exist for collaborative research among regions.

Though the literature review is limited in its selection of articles and journals it sketches a picture that may surrogate the whole research community in MS.

Trends in publications and use of research methodologies provide directions for designing research projects relevant to various geographical regions. This will help develop a holistic understanding of MS that is meaningful for managers of today’s organizations.

This paper provides broader and deeper review of the MS literature. Complex patterns in data are revealed using cross-tabulations and advanced cross-tabulations that have not been performed in previous content-analysis–based literature reviews in MS. These patterns will help position future research studies.

Describes the ability of modern computer‐driven multivariate statistical analysis to deal with complex data and the development of statistical models for predicting financial distress. Applies multivariate techniques to 1986‐1991 financial ratio data for Australian failed (29) and nonfailed (42) companies; and explains the techniques used (principal components analysis, factor analysis, discriminant analysis and cluster analysis) and the different types of information they can provide to help identify the distress levels of companies. Predicts that multivariate methods will change the way researchers think about problems and design their research. An unusually clear exposition of the application of multivariate methods.

The purpose of this paper is to propose a new analytical approach in human thought computation with an application to the child language acquisition research.

Certain fuzzy statistical concepts, such as fuzzy samples, fuzzy mode, fuzzy category test, and their relevant properties, were presented. Empirical data sets on children's language and cognitive development were discussed. Finally, the paper makes a comparison result between the traditional statistical analysis and fuzzy statistical analysis.

The results show that the new method is better able to capture the intricacies and complexities of the nature and processes in acquiring languages than the traditional methods.

The paper presents fuzzy statistical analysis as a new analytical approach in child language research.