PurposeThe purpose of this paper is to create an automatic interpretation of the results of the method of multiple correspondence analysis (MCA) for categorical variables, so that the nonexpert user can immediately and safely interpret the results, which concern, as the authors know, the categories of variables that strongly interact and determine the trends of the subject under investigation.Design/methodology/approachThis study is a novel theoretical approach to interpreting the results of the MCA method. The classical interpretation of MCA results is based on three indicators: the projection (F) of the category points of the variables in factorial axes, the point contribution to axis creation (CTR) and the correlation (COR) of a point with an axis. The synthetic use of the aforementioned indicators is arduous, particularly for nonexpert users, and frequently results in misinterpretations. The current study has achieved a synthesis of the aforementioned indicators, so that the interpretation of the results is based on a new indicator, as correspondingly on an index, the well-known method principal component analysis (PCA) for continuous variables is based.FindingsTwo (2) concepts were proposed in the new theoretical approach. The interpretative axis corresponding to the classical factorial axis and the interpretative plane corresponding to the factorial plane that as it will be seen offer clear and safe interpretative results in MCA.Research limitations/implicationsIt is obvious that in the development of the proposed automatic interpretation of the MCA results, the authors do not have in the interpretative axes the actual projections of the points as is the case in the original factorial axes, but this is not of interest to the simple user who is only interested in being able to distinguish the categories of variables that determine the interpretation of the most pronounced trends of the phenomenon being examined.Practical implicationsThe results of this research can have positive implications for the dissemination of MCA as a method and its use as an integrated exploratory data analysis approach.Originality/valueInterpreting the MCA results presents difficulties for the nonexpert user and sometimes lead to misinterpretations. The interpretative difficulty persists in the MCA's other interpretative proposals. The proposed method of interpreting the MCA results clearly and accurately allows for the interpretation of its results and thus contributes to the dissemination of the MCA as an integrated method of categorical data analysis and exploration.

Official statistics place particular emphasis on communication and dissemination of surveys’ results to citizens and stakeholders. This is typically done through the publication of press releases and presentation of aggregated data of statistical surveys. The use of web services and software that allow users to interact with the results of official statistics comes to further enhance communication, dissemination, literacy and overall quality of official statistics. This paper is related to the objectives and context of reaching a wider audience through engaging users and explains how an NSO (National Statistical Office) member without specialized knowledge of frontend-backend programming techniques can create such web services in R programming environment through “Shiny” library. The paper also reviews the issue of hosting “Shiny” apps and presents existing approaches. For demonstration purposes, an experimental version of such an application was constructed that presents in an interactive way the quarterly results of the new statistical product of the Hellenic Statistical Authority (ELSTAT) on Greek business demography.

The purpose of this paper is to study the impact of the COVID-19 pandemic on the agribusiness sector and specifically on the use of informatics and communication technologies (ICTs). The study uses data from the ICT survey of the Hellenic Statistical Authority (ELSTAT) for the years 2019 and 2021 and compares the answers for the same variables of the exact same agribusiness enterprises concerning the use of the internet and e-commerce and analyses them with methods of exploratory statistical analysis and unsupervised learning. The analysis of the data highlights the changes, the opportunities, and limitations in the use of ICT by companies in this sector, contributing to the further study of the phenomenon of ICT adoption in enterprises while offering an informative snapshot of Greek agribusiness to people who formulate policies or determine decision-making at domestic and European level, researchers and stakeholders.

The Hellenic Statistical Authority (ELSTAT), the National Statistical Institute of Greece, as the guarantor of the quality of official statistics in Greece, has been pursuing, since 2016, an ambitious strategy aiming to foster Statistical Literacy, focusing on strengthening ties with citizens in their dual capacity both as providers of data and ultimately as users of statistics, and thus, operate as crucial enablers of a smoothly functioning virtuous circle of official statistics. Objectives include the development of an understanding of basic methodologies and tools used in official statistics, along with the awareness of its institutional foundations and core principles. This critically contributes to the value of official statistics being spread and effectively communicated, making, at the same time, a convincing case for fact-based decision making in the daily lives of the main stakeholders. This article motivates the approach followed in developing a specific strategy on statistical literacy, outlines its philosophy and main objectives and browses through the array of initiatives and actions undertaken over the last five years. In addition, it explores the responsiveness of citizens to these initiatives and the extent to which these initiatives lead to an increased engagement of key targeted stakeholders.

E-commerce worldwide is transforming the economy at the macro level while also affecting the consumption habits of households. This paper aims to map the effects of these changes through exploratory data analysis. For this purpose, data from Official Statistics were analyzed, as they were collected via sample surveys of Greek Statistical Authority (ELSTAT) from 2009 to 2018. This work is of multiple interest, as not only the phenomenon under study is an area of general scientific interest, but also the period of data collection includes the time horizon of the beginning of the economic crisis in Greece.

Technology development has made a decisive contribution to the digitisation of businesses, which makes it easier for them to work more efficiently. However, in recent years, data leakages have shown an increasing trend. To investigate the level of awareness among Cypriot accountancy firms about cyber-related risks, we use the data from a recent survey of Cypriot professional accountants' members of Institute of Certified Public Accountants of Cyprus (ICPAC). The categorical nature of the data and the purpose of our research led us to use methods of multidimensional statistical analysis. The emergence of intense differences between accounting companies in relation to the issue as we will present is particularly interesting.

The purpose of this paper is to present a technical path in order to formulate the creation of small customised web applications that handle big data. Such web tools could be easily integrated in knowledge-based or data driven decision support systems and provide added value. The paper studies the various ways in which relevant application can be developed and the findings resulted in a specific technical proposal. This is a combination of techniques and technologies that can be pursued to achieve more efficient and costless data analysis. To this direction, a novel demo web app was developed with open source code in which a user can login, upload data and analyse them in real-time with the multivariate exploratory method of multiple correspondence analysis (MCA). The application was created under R programming language and underlines the use of shiny library. The paper contributes to the scientific facilitation of researchers and professionals with a minimum technical background in order to perform online complex data analytics tasks. Eventually, this architecture enables extensibility and customisation that suggests how decision support modules should be delivered in intelligent data systems.