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New tools for the construction, analysis and interpretation of social indicators based on ordinal variables
Full text linkDealing with ordinal variables in social measurement raises many methodological issues, particularly when consistent and meaningful indicators are to be defined about subjective data. Quite often, ordinal data are transformed into cardinal variables, through scaling procedures, so as to apply quantitative multivariate tools. These procedures are sometimes reasonable, but are often quite questionable, since they alter the intrinsic nature of the data.
In this paper, we introduce new tools for addressing the construction of social indicators, based on ordinal variables. The approach draws upon partial order theory, a branch of discrete mathematics providing concepts and tools that fit very naturally the needs of ordinal data analysis. Partial order tools allows for the extraction of information directly out of the relational structure of the data and provide robust results, not requiring binding assumptions.
In order to exploit the potentialities of partial order theory in social measurement, we present an exemplificative application pertaining European data (European Social Survey). We show how indicators construction can be dealt with through this alternative approach, getting new insights into the data and allowing for better understanding of social phenomena and better communication to policy-makers
Evaluation is not aggregation: assessing material deprivation through partial order theory
No full textIn this presentation, we outline a new methodology for assessing multidimensional material deprivation, based on partial order theory. The methodology is designed to deal with binary variables; it avoids any form of aggregation between non numerical variables and allows for taking into account, in purely ordinal terms, exogenous judgments about the relative relevance of deprivation dimensions. The methodology, thus, overcomes many of the epistemological, conceptual and technical drawbacks affecting “classical” assessment procedures that, implicitly or not, rely on aggregative and compensative approaches
Hierarchical latent variable models for dimensionality reduction: an application on composite indicators
Full text linkThis thesis is devoted to the development of new hierarchical latent variable models for Dimensionality Reduction with a specific focus on the construction of Composite Indicators (CIs). Since our society is producing a huge quantity of data, the construction of model-based CIs represents an interesting and still open methodological challenge. This dissertation is motivated by the necessity to provide model-based CIs which are built according to a statistical approach avoiding subjective choices (e.g., normative weights), therefore, the new insights and proposals hope to represent a contribution to the current literature. This thesis provides an introduction to CIs, a brief review of the most used methods in Multidimensional Data Analysis framework, a discussion about measurement models and methodological proposals to model latent concepts. Factor Analysis and its hierarchical extensions have been introduced in order to set the starting point of the analysis. A first proposal represents a new latent factor model that could be used for building CIs, it aims to investigate the hierarchical structure of the data in order to define two levels of CIs. The model, named Hierarchical Disjoint Non-Negative Factor Analysis is composed of two novelties: a model which is the two level hierarchical extension of FA and its disjoint extension with non-negative loadings. The latter model is enriched by considerations about the CIs used for tracking coherent policy conclusions. A set of features, properties and rules useful to build "good" CIs have been presented and explained. The last proposal in the thesis represents a new model for positive data correlation matrices which aims to detect reliable concepts and to build the hierarchy from them to the most general one. The proposed models are illustrated both via simulation studies and real data applications, to analyze their performances and abilities. In particular, the main application in this thesis regards the construction of a hierarchically aggregated index for the multidimensional phenomenon Waste Management in European Union. Waste Management is becoming even more important for its impact on human-being's lives, and many data have been produced about it, therefore the construction of a CI able to reduce its dimensionality and to highlight the main dimensions of it has a extraordinary usefulness in order to provide support to EU countries' action and policies
New tools for the construction of ranking and evaluation indicators in multidimensional systems of ordinal variables
Full text linkThe problem of ranking and evaluation in multidimensional ordinal datasets is one of the most important issue in applied statistics, particularly in the socio-economic field.
Unfortunately, dealing with ordinal variables raises many conceptual and methodological issues, particularly when consistent and meaningful indicators are to be defined out of qualitative data.
Methodological difficulties rise particularly when single ordinal indicators are to be aggregated into a composite indicator, to get unidimensional scores for comparing and ranking statistical units.
Basically, it can be asserted that the issue of ranking and evaluation in an ordinal setting is still an open problem, since the statistical methodologies, applied in the common practice or proposed at theoretical level, are unsatisfactory in many respects.
Motivated by these issues and by the relevance of the topic, in this paper, we introduce new tools for addressing the construction of indicators for ranking and evaluation purposes in an ordinal context, with the aim to overcome the main problems of the classical composite indicator approach. The proposed methodology draws upon Partial Order SET (POSET) theory, a branch of discrete mathematics providing concepts and tools, fitting very naturally the needs of ordinal data analysis. POSET theory provides useful technical tools for addressing the evaluation problem. But more important than this, it helps reformulating the ranking and evaluation problem in such a way that satisfactory solution to the issues outlined above can indeed be worked out, fully respecting the qualitative features of data
Socio-economic evaluation with ordinal variables: integrating counting and POSET approaches
Full text linkThe evaluation of material deprivation, quality of life and well-being very often requires to deal with multidimensional systems of ordinal variables, rather than with classical numerical datasets. This poses new statistical and methodological challenges, since classical evaluation tools are not designed
to deal with this kind of data. The mainstream evaluation methodologies generally follow a counting approach, as in a recent proposal by Alkire and Foster pertaining to the evaluation of multidimensional poverty. Counting procedures are inspired by the composite indicator approach and share similar drawbacks with it, computing aggregated indicators that may be poorly reliable.
A recent and alternative proposal is to address the ordinal evaluation problem through partial order theory which provides tools that prove more consistent with the discrete nature of the data. The goal of the present paper is thus to introduce the two proposals, showing how the evaluation methodology based on partial order theory can be integrated in the counting approach of Alkire and Foster
Analysing the structure of poverty by Fuzzy partial order
No full textWe propose to employ Fuzzy Multi-Criteria Analysis FMCA, which originated in the decision theory context, to describe the structure of poverty. In the last years, poverty has been more and more recognized as
a multidimensional, fuzzy and complex phenomenon, which cannot be duly
represented by mono-dimensional monetary indicators. Our purpose is not to meausure poverty but to provide its structural representation in terms of the pattern of implications existing among different poverty descriptors in the context of a specific scenario, that is a selected geographical region. FMCA is adapted to address the case of the most southern Italian region, Sicily. The case study of Sicilian households is based on the European Statistics about Income and Living Conditions (EU-SILC) referring to the year 2004. The analysis shows deep-rooted poverty conditions with many relational implications across poverty descriptors, some of them describing high levels of deprivation and social exclusio
Assessing the quality of communication in statistics: the application of a model
Full text linkIn a previous work, we dealt with the fundamental components involved in an effective statistics communication process, namely ethics, rhetoric and aesthetics. The present paper aims at building a conceptual model for evaluation purposes and at developping a practical approach to assess the effectiveness of statistical communication, particularly when official data are of concern.
The first part of the paper presents the conceptual model, by identifying
- the dimensions to evaluate (the “codes”, i.e. Invention, Layout, Expression and Execution),
- the evaluation criteria (Appropriateness, Correctness and Clarity) and
- the components of the communication process (Audience, Channel, Context, Topic and Data).
In order to assess the conceptual model, a compliance procedure was drawn and applied on six official publications, five produced by some National Statistical Bureaus and international organizations. The procedure allowing the quality criteria to be assessed (at total and component level) requires subjective evaluations to be collected from a group of selected judges through an assessing table. To reduce the complexity of the evaluation process, the judgements were expressed through a simple binary scale (presence / absence).
Subsequently, data collected through the assessing table for each publication have been reduced (through the modal criterion) by condensing the scores expressed on the dimensions for each criterion (Appropriateness, Correctenss and Clarity).
The successive data analysis aimed at getting insight into the quality weaknesses and strengths of the publications to be assessed. The analysis is devoted primarily to comparing publications both in terms of general quality and along each single quality criterion. Further focuses are performed, stratifying data by communication components.
Traditional statistical data analysis procedures based upon linear mathematical instruments, hardly applicable on data discrete in their nature.In particular, dealing with binary data rises some methodological difficulties, especially in the aggregating process aimed at getting unidimensional scores allowing for a direct comparison and ranking of statistical units (publications, in our case). Admittedly, the traditional approach can be criticized in many respects. Aggregating variables of different nature is not always as meaningful as it may appear. Moreover, scaling tools tend to impose a quantitative latent model to the data, which is often forcing and does not respect their true qualitative nature.
Recently, new methodolgies have been proposed aimed at dealing with discrete ordinal data, when evaluation, comparisons and rankings are of concern. Such methodologies are based on Partially Ordered SEt Theory (POSET theory), part of Discrete Mathematics that offers many tools and results to explore and analyze the structure of discrete datasets, like that of interest in the present study. Posets of finite cardinality can be conveniently depicted by means of certain directed acyclic graphs, called Hasse diagrams.
Based on its score on each evaluation criterion, each publication is given a particular position in the diagram. The structural information contained in the resulting pattern was then extracted by means of poset tools and was used to perform comparisons among the publications, ranking them in quality terms.
The application aims just at showing how the procedure (assessing table, data collection and POSET data analysis) can be used to reach meaningfull and interpretable results allowing different pubblications to be compared and ranked with reference to the evaluation criteria
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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