Archives

  • Electronic Journal of Applied Statistical Analysis
    Vol. 18 No. 2 (2025)

  • Electronic Journal of Applied Statistical Analysis
    Vol. 18 No. 1 (2025)

  • Electronic Journal of Applied Statistical Analysis
    Vol. 17 No. 3 (2024)

  • Electronic Journal of Applied Statistical Analysis (Special Issue: Advances in Distribution Theory for Social Statistic)
    Vol. 17 No. 2 (2024)

    Advances in Distribution Theory for Social Statistics

    Mohammad Al-Kadri, Ahmad Hanandeh, Ayat Al-Momani(a) and Amer Al-Omari(b)

    (a)Department of Statistics, Science Faculty, Yarmouk University, Jordan
    (b)Department of Mathematics, Al Al-Bayet University, Jordan

    This special issue on Advances in Distribution Theory for Social Statistics seeks to explore innovative solutions to practical challenges in social statistics and related distribution methodologies. It aims to foster the development of novel weighted distributions and methodological enhancements within the realm of statistics. By gathering articles on cutting-edge statistical methods associated with distribution theory, statistical modeling, and other related advancements, the issue aims to bridge the gap between social science and distribution theory. We welcome submissions that address open questions in social statistics and offer creative applications of statistical methods to pertinent issues in social studies.

    Download Full Editorial

  • Electronic Journal of Applied Statistical Analysis (Special Issue: Big Data in Economics' Applications)
    Vol. 17 No. 1 (2024)

    Big Data in Economics' Applications

    Tran Thi Le Hien(a) and Ho Thanh Tri(b)

    (a)Faculty of Finance and Accounting, Ho Chi Minh City University of Industry and Trade, Viet Nam. No. 140 Le Trong Tan Street, Tay Thanh Ward, Tan Phu District, Ho Chi Minh City, Vietnam
    (b)Hung Vuong University of Ho Chi Minh City, Vietnam. No. 736 Nguyen Trai Street, Ward 11, District 5, Ho Chi Minh City, Vietnam

    The present special issue deals with some interesting topics related to technology, accounting, finance, tourism, and it is inspired by the ideas of technology and big data in different business aspects of the economy today, the main theme of the 1st international conference on economics (ICE), held in Ho Chi Minh in 2023, called ”Big data in economics”. Big data has gradually been attached to importance in the economic field, especially the traditional economic management has not met the needs of society. The Special Issue aims to collect the new ideas in the applications of technology and big data in the economy today.

    Download Full Editorial

     

     

  • Electronic Journal of Applied Statistical Analysis
    Vol. 16 No. 3 (2023)

  • Electronic Journal of Applied Statistical Analysis
    Vol. 16 No. 2 (2023)

  • Electronic Journal of Applied Statistical Analysis (Special Issue: Applied Statistical Analysis using PLS Path Modeling)
    Vol. 16 No. 1 (2023)

    Applied Statistical Analysis using PLS Path Modeling

    Jun-Hwa (Jacky) Cheah(a), Mumtaz Ali Memon(b), Man-Lai Cheung(c), Hiram Ting(d)

    (a)University of East Anglia, United Kingdom
    (b)NUST Business School, National University of Sciences and Technology, Islamabad, Pakistan
    (c)University of Newcastle, Australia
    (d)UCSI University and Sarawak Research Society, Malaysia

    The main theme of this special issue is “Applied Statistical Analysis using PLS Path Modelling” with the contribution of eight papers. Most of the contributions are inspired by the International Symposium on Applied Structural Equation Modelling and Methodological Matter 2019 (SASEM 2019) which took place in Malaysia. During the symposium, scholars from social sciences had the opportunity to share their idea on the Partial Least Squares Path Modeling (PLSPM) method, which addressing to methodological issues and real applications in various social science fields, such as business, agricultural science, engineering, and medicine. The application of the method was beneficial to any field because it can deal with small sample sizes and non-normal distribution (Hair et al., 2022). In addition, if the aim of the research is to (i) predict key target constructs and identify key driver constructs, and (ii) explore (and extend) an existing structural theory, the reasons would fit PLSPM like “hand in glove” (Chin et al., 2020; Hair et al., 2022). Therefore, we strongly believe the method will continue to grow exponentially, with more powerful graphical user programs that are user-friendly (i.e., SmartPLS4, ADANCO, WarpPLS, etc.), new development of PLSPM techniques (i.e., PLSpredict, cross-validated predictive ability test, conditional mediation model, etc.), and PLS guideline research materials (Becker et al., 2023; Cheah et al., 2021; 2023; Guenther et al., 2023; Hair et al., 2022; Sarstedt and Cheah, 2019).

    Download Full Editorial

  • Electronic Journal of Applied Statistical Analysis
    Vol. 15 No. 3 (2022)

  • Electronic Journal of Applied Statistical Analysis (Special Issue: Inference and Sampling)
    Vol. 15 No. 2 (2022)

    Inference and Sampling

    Amer I. Al-Omari(a), Ahmad A. Hanandeh(b)

    (a)Al al-Bayt University, Jordan
    (b)Yarmouk University, Jordan

    Recently, the need to solve real-world problems has increased the need for mathematics skills. Moreover, real-world problems are usually not determinate but are affected by random phenomenons. Therefore, the statistical modeling of environments often plays an important role in solving real-world applications mathematically. Due to the complexities of models, closed forms of solutions cannot usually be established. Therefore, computation and simulation technologies are needed. In this Special Issue, articles concerning mathematical or statistical modeling that require computation and simulation skills are presented.

    Download Full Editorial

  • Electronic Journal of Applied Statistical Analysis
    Vol. 15 No. 1 (2022)

  • Electronic Journal of Applied Statistical Analysis
    Vol. 14 No. 2 (2021)

  • Electronic Journal of Applied Statistical Analysis
    Vol. 14 No. 1 (2021)

  • Electronic Journal of Applied Statistical Analysis (Special Issue: Work and Organizational Psychology)
    Vol. 13 No. 3 (2020)

    Work and Organizational Psychology

    Emanuela Ingusci(a), Margherita Brondino(b), Evangelia Demerouti(c) and Monica Molino(d)

    (a)University of Salento, Lecce, Italy
    (b)University of of Verona, Italy
    (c)Eindhoven Technology University, Netherlands
    (d)University of Turin, Italy

    The present special issue deals with some interesting topics related to Work and Organizational Psychology and it is inspired by the healthy workplaces issue, main theme of the 17th Congress of Italian Association of Psychologist, held in Lecce in 2019, called “The future of work, the work of future: the psychology to innovate, transform and develop in the organizations”. Healthy workplaces encourage and support physical and psychological health and well-being of employees. Moreover, a healthy organization focuses not only on a successful business but also on a positive connection between organizational profitability and workers’ well-being. This perspective incorporates four different levels of analysis: individual, group, organizational and inter- organizational, thus defining a systemic vision of work. Theoretical frame- works such as the Job Demands-Resources Model integrate these perspectives and can be used to strengthen the systemic vision of work. The Special Issue aims at deepening the knowledge about those factors related to work and well-being under a positive psychology perspective.

    E. Ingusci, M. Brondino, E. Demerouti, M. Molino (Guest Editors of EJASA)

    Download the Editorial

  • Electronic Journal of Applied Statistical Analysis
    Vol. 13 No. 2 (2020)

  • Electronic Journal of Applied Statistical Analysis
    Vol. 13 No. 1 (2020)

  • Electronic Journal of Applied Statistical Analysis (Special Issue: Student mobility)
    Vol. 12 No. 4 (2019)

    Student mobility, university and post-university choices

    Isabella Sulis(a), Mariano Porcu(a), Francesca Giambona(b)

    (a)University of Cagliari, Department of Social and Political Sciences, Italy
    (b)Department of Statistics, Informatics, Applications “G. Parenti”, Italy

    This Special Issue has been promoted within the intensive activities of a network of scholars (composed mainly of statisticians and demographers, but also economists, sociologists and psychologists) that, since 2014, estab- lished a lively collaboration on themes related to students’ mobility choices and university effectiveness in Italy with the aim to share findings, create contacts and promote joint research initiatives. Most of the scholars from the network had access to micro cohort data of the Italian National Student Archive (ANS), thanks to a protocol of understanding that their universities (namely, the University of Cagliari, Florence, Naples Federico II, Palermo, Sassari, Turin) signed with the MIUR (Italian Ministry of Education and University Research). This opportunity has allowed the research team to create an ad hoc databases addressed to investigate upon detailed informa- tion on students’ individual choices at national and local level. As a result of this unique collaboration several local and national projects on this topic have been funded, among them, the most relevant is the grant PRIN 2017 From high school to job placement: micro-data life course analysis of uni- versity student mobility and its impact on the Italian North-South divide1, and several initiatives (e.g. workshops, books, seminars, specialized sections in conferences) have been devoted to share data, research questions, meth- ods, main findings and to disseminate results within the scientific community and stakeholder. In this framework this special issue aimed, with an open call, to gather qualified research papers that propose developments of new statistical methods for the analysis of students’ mobility and university ef- fectiveness or give new insights into these topics by using original data and soundly statistical techniques.

    Isabella Sulis, Mariano Porcu, Francesca Giambona (Guest Editors of EJASA)

    Download the Editorial


     

     

  • Electronic Journal of Applied Statistical Analysis (Special Issue Data Analytics and Statistical Inference)
    Vol. 12 No. 3 (2019)

    Data Analytics and Statistical Inference

    M. Gallo(a), M.R. Srinivasan(b), and M. Subbiah(b) 

    (a)University of Napoli – L’Orientale, Naples, Italy
    (b)University of Madras, Chennai, India

    The volume, variety and velocity of data available to researchers have reached an unprecedented level. The nature of the data available from various sources and for a variety of applications have provided a fertile ground for the statisticians to develop new tools or revisit and refine the existing methods. The inferential approaches of both classical and Bayesian methods with its modifications are serving as boon to the applied statisticians as it helps in providing natural solutions to many problems which data scientists are looking for in their empirical studies of larger dimension. The special issue has opened doors into new frontiers of statistics and its applications which will provide direction and scope to the fast growing domain of analytics. The issue covers some of the fundamental issues of importance and upcoming applications.

    M. Gallo, M.R. Srinivasanb, and M. Subbiahb (Guest Editors of EJASA)

    Download the Editorial

  • Electronic Journal of Applied Statistical Analysis
    Vol. 12 No. 2 (2019)

  • Electronic Journal of Applied Statistical Analysis
    Vol. 12 No. 1 (2019)

  • Electronic Journal of Applied Statistical Analysis
    Vol. 11 No. 2 (2018)

  • Electronic Journal of Applied Statistical Analysis
    Vol. 11 No. 1 (2018)

  • Electronic Journal of Applied Statistical Analysis (Special Issue STATISTICS IN SPORTS)
    Vol. 10 No. 3 (2017)

    STATISTICS IN SPORTS

    (Project BDSports (Big Data Analytics in Sports) bodai.unibs.it/BDSports)

    Paola Zuccolotto(1), Marica Manisera(1), and Ron Kenett(2)
    (1)Big & Open Data Innovation Laboratory (BODaI-Lab), University of Brescia (Italy)
    (2)KPA Ltd and Neaman Institute, Technion (Israel) - University of Turin (Italy)

    This Special Issue has been promoted by BDSports (Big Data Analytics in Sports), a project developed as part of the activities of the Big & Open Data Innovation Laboratory of the University of Brescia, born in 2016 thanks to the financial support of Fondazione Cariplo and Regione Lombardia.

    BDSports is designed to set up a unique collaboration of experts interested in sport analytics both from a scientific and a practical point of view. The goal is to create a network able to facilitate contacts and joint research initiatives. Specifically, the project aims to organise events, carry out Special Issues in scientific journals, share ideas and data in order to publish scientific and non-scientific papers, collaborate with teams in various sports by supplying them analytics and apply for research grants. The data scientists expertise covers a wide range of quantitative tools in the fields of statistical modelling, multivariate data analysis, data mining, algorithmic modelling and machine learning.

    Paola Zuccolotto, Marica Manisera, Ron Kenett (Guest Editors of EJASA)

    Download the Editorial

     


  • Electronic Journal of Applied Statistical Analysis
    Vol. 10 No. 2 (2017)

  • Electronic Journal of Applied Statistical Analysis
    Vol. 10 No. 1 (2017)

1-25 of 45