本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

Rasch Models for Measurement

A bibliometric based survey on AHP and TOPSIS methods has been conducted.Scopus database was employed to retrieve the required data for this analysis.Assessment of quantitative and qualitative bibliometric indicators was obtainable.Efficacy of these methods promotes the development of related research.More scientific research interests will be devoted to these methods in the future. In recent years, the employment of multiple criteria decision analysis (MCDA) techniques in solving complex real-world problems has increased exponentially. The willingness to build advanced decision models, with higher capabilities to support decision making in a wide range of applications, promotes the integration of MCDA techniques with efficient systems such as intelligence and expert systems, geographic information systems, etc. Amongst the most applied MCDA techniques are Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The development of a comprehensive perspective on research activities associated with the applications of these methods provides insights into the contributions of countries, institutes, authors and journals towards the advancements of these methods. Furthermore, it helps in identifying the status and trends of research. This in turn will help researchers in shaping up and improving future research activities and investments. To meet these aims, a bibliometric analysis based on data harvested from Scopus database was carried out to identify a set of bibliometric performance indicators (i.e. quantitative indicators such as productivity, and qualitative indicators such as citations and Hirsch index (h-index)). Additionally, bibliometric visualization maps were employed to identify the hot spots of research. The total research output was 10,188 documents for AHP and 2412 documents for TOPSIS. China took a leading position in AHP research (3513 documents; 34.5%). It was also the leading country in TOPSIS research (846 documents; 35.1%). The most collaborated country in AHP research was the United States, while in case of TOPSIS it was China. The United States had gained the highest h-index (78) in AHP research, while in TOPSIS it was Taiwan with h-index of 46. Expert Systems with Applications journal was the most productive journal in AHP (204; 2.0%) and TOPSIS research (125; 5.2%), simultaneously. University of Tehran, Iran and Islamic Azad University, Iran were the most productive institutions in AHP (173; 1.7%) and TOPSIS (115; 4.8%) research, simultaneously. The major hot topics that utilized AHP and will continue to be active include different applications of geographic information systems, risk modeling and supply chain management. While for TOPSIS, they are supply chain management and sustainability research. Overall, this analysis has shown increasing recognition of powerful of MCDA techniques to support strategic decisions. The efficacy of these methods in the previous context promotes their progress and advancements.

https://scholar.ptuk.edu.ps/bitstream/123456789/349/1/TOPSIS.pdf

A bibliometric-based survey on AHP and TOPSIS techniques

Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods

Research on the diffusion of evidence-based treatments within substance abuse treatment: a systematic review.

Organizational ethical climates and employee's trust in colleagues, the supervisor, and the organization

This work stems from the idea of describing the scientific productivity of Italian statisticians. There are several problems that must be addressed in achieving this goal: What data should be used? Have the data been cleaned? What techniques can be used? We propose the use of multiple sources and multiple metrics to get a complete information base. We check the correctness of the data using multivariate outlier identification techniques. We appropriately transform the data. We apply robust clustering to verify the existence of homogeneous groups. We suggest the use of forward search to establish a ranking among scholars. The proposed methodology, which, in this case, allowed us to group scholars into four homogeneous groups and sort them according to multidimensional data, can be applied to other similar applications in bibliometrics.

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Robust analysis of bibliometric data

It is known that the service quality is considered a latent variable that is derived from the combination of some other independent latent variables (dimensions). The known variables (attributes), generally expressed by an ordinal scale, are observed by handing out questionnaires to the users of the service, in order to measure those dimensions. Therefore questionnaires, like every measuring instrument, have to be calibrated. Statistical calibration is a procedure that achieves the best approximation of the real measure as it eliminates measurement errors. The classical calibration models are based on the possibility of comparing the measure obtained by the instrument with the true measure. The aim in classical multivariate calibration problem is to obtain the best approximation of the true measure by an indirect measure (Salini et al., 2002). In psychometric field this is not possible because the true measure is latent, so it is not observable. Therefore the aim is to measure the effective quality of the service, which differs from the perceived quality. In the survey about service quality the "calibration of questionnaire" would make clear what influences the opinion of subjects about the satisfaction with each attribute. Two factors randomly influence the propensity of a subject to one category rather than another: a specific attribute factor and a specific subject factor (Bertoli Barsotti, Franzoni, 2001). The latter factor justifies the differences among subjects and, in this particular case, it constitutes exactly the measurement error that has to be eliminated. In this paper the Rasch model will be considered, a statistical tool arising from psychometric field, which allows the examination of the service quality through theknown variables: for each of those an objective measure is obtained. In particular, the application of Rasch model will concern the quality of university teaching. Moreover a method will be proposed to obtain a segmentation of student population only based on satisfaction index.

/pdf/the-rasch-model-to-measure-service-quality-2pfr79h4ow.pdf

The Rasch Model to Measure Service Quality

Topic models are a well known clustering approach for textual data, which provides promising applications in the bibliometric context for the purpose of discovering scientific topics and trends in a corpus of scientific publications. However, topic models per se provide poorly descriptive metadata featuring the discovered clusters of publications and they are not related to the other important metadata usually available with publications, such as authors affiliation, publication venue, and publication year. In this paper, we propose a methodological approach to topic modeling and post-processing of topic models results to the end of describing in depth a field of research over time. In particular, we work on a selection of publications from the international statistical literature, we propose an approach that allows us to identify sophisticated topic descriptors, and we analyze the links between topics and their temporal evolution.

A decade of research in statistics: a topic model approach

This paper deals with the measurement of a service or product quality using Customer Satisfaction Survey results. Many different methods are used to analyse customer satisfaction data. Some use statistical models which estimate the relationship between the latent and manifest variables (LISREL, PLS, etc.), whilst others use dimensionality reduction methods (FA, PCA, etc.). All of these methods require a numerical quantification of the categories and consequently the distance between the numerical labels is fixed and the linear relationship between the variables implicitly assumed. Moreover these methods produce a customer satisfaction measure for each subject and an evaluation of its importance on the satisfaction level for each item. When analyzing quality and satisfaction levels together, the Rasch model (RM) appears to be particularly appropriate. A Likert scale is not required and non-linear relationships are involved. Moreover, a Rasch analysis can also act as a useful diagnostic tool for cal...

/pdf/the-rasch-model-in-customer-satisfaction-survey-data-2och4hai1v.pdf

Francesca De Battisti

Papers

Organizational ethical climates and employee's trust in colleagues, the supervisor, and the organization

Robust analysis of bibliometric data

The Rasch Model to Measure Service Quality

A decade of research in statistics: a topic model approach

The Rasch Model in Customer Satisfaction Survey Data