Search

Agent-based models (ABMs) often rely on psychometric constructs such as 'opinions', 'stubbornness', 'happiness', etc. The measurement process for these constructs is quite different from the one used in physics as there is no standardized unit of measurement for opinion or happiness. Consequently, measurements are usually affected by 'psychometric distortions,' which can substantially impact models' predictions. Even if distortions are well known in psychometrics, their existence and nature is obscure to many researchers outside this field. In this paper, we introduce distortions to the ABM community. Initially, we show where distortions come from and how to observe them in real-world data. We then show how they can strongly impact predictions, qualitative comparison with data and the problem they pose for validation of models. We conclude our analysis by discussing how researchers may mitigate this problem and highlight possible future modelling trends that will address this problem. [ABSTRACT FROM AUTHOR]

Social research is rich in methods for analysing societal differences. Yet, although qualitative characteristics are a key component to understanding such differences, the analysis of qualitative data remains a major methodological challenge in most social research, particularly when aiming to compare more than a few cases. The article proposes an innovative method for systematic comparison of qualitative data (SCQual), applying Weber's original idea of ideal types as a tool to measure and compare societal differences. This method allows for the quantification of qualitative data, thereby enabling systematic analysis and repeatable comparison of the qualitative characteristics of societal differences for a relatively large number of cases. Developed for the comparative analysis of essential characteristics of social orders, particularly comparative welfare state analysis, SCQual may also be applicable to other areas of social research. [ABSTRACT FROM AUTHOR]

Recently, the data analysts pay more attention to the assessment of rater agreement, especially in areas of medical sciences. In this context, the statistical indices such as kappa and weighted kappa are the most common choices. These indices are simple to calculate and interpret, although, they fail to describe the structure of agreement, particularly when the available outcome has an ordinal nature. In the previous decades, statisticians suggested more efficient statistical tools such as diagonal parameter, linear by linear association and agreement plus linear by linear association models for describing the structure of rater agreement. In these models, the equal interval scores are the common choice for the levels of the ordinal scales. In this manuscript, we show that choosing the common equal interval scores does not necessarily lead to the best fit and propose a modification using a power transformation for the ordinal scores. We also use two different data sets (IOTN and ovarian masses data) to illustrate our suggestion more clearly. In addition, we utilize the category distinguishability concept for interpreting the model parameter estimates. [ABSTRACT FROM AUTHOR]

Many practical problems of quality control involve the use of ordinal scales. Questionnaires planned to collect judgments on qualitative or linguistic scales, whose levels are terms such as “good,” “bad,” “medium,” etc., are extensively used both in evaluating service quality and in visual controls for manufacturing industry. In an ordinal environment, the concept of distance between two generic levels of the same scale is not defined. Therefore, a population (universe) of judgments cannot be described using “traditional” statistical distributions since they are based on the notion of distance. The concept of “distribution shape” cannot be defined as well. In this article, we introduce a new statistical entity, the so-called ordinal distribution, to describe a population of judgments expressed on an ordinal scale. We also discuss which of the traditional location and dispersion measures can be used in this context and we briefly analyze some of their properties. A new dispersion measure, the ordinal range, as an extension of the cardinal range to ordinal scales, is then proposed. A practical application in the field of quality is developed throughout the article. [ABSTRACT FROM AUTHOR]