Causality as validity: some implications for the social sciences

We analyse the concept of causality in the social sciences, whose development is insufficient and lesser than the methodology developed for its study. The nature of the causal process as the production of effects remains unclear and the relationships considered to be manifestations of that process cannot be taken for proof of its existence. Given these difficulties, we suggest that, aside from the inherited interpretations, the practice of the concept of causality makes reference to correctly specified relationships not confounded by others; characteristics identical to those which define validity. In that way, causality is equivalent to the validity of a relationship. Beyond merely re-understanding causality, this proposal permits the deduction that the temporal precedence of the cause is a necessary condition only for one type of causality, making it possible to consider other types, not admitted by the traditional notion, in which the cause is consequent or simultaneous to the variable to be explained. Examples and characteristics of these types of causality are presented and considered to be useful for the social sciences.     

Introducing dimensional analysis to the social sciences

This study serves to introduce dimensional analysis to the social sciences. Dimensional analysis, a method fundamental to research in the physical sciences, is presented first in a conceptual framework. Questions such as: what makes the method work, what use is it, and what are its limitations … are dealt with summarily. The method is then applied to the modeling of interurban migration. The selection of independent variables, the synthesis of dimensionless groups, and the analysis of data are considered in some detail.While a blanket endorsement of the method must be withheld pending further research, results of the present study are encouraging. Dimensional analysis may eventually find wide application in the social sciences.     

Modeling the act of measurement in the social sciences

Motivated by time-series experimental designs, we develop a model of the act of measurement in the social sciences. Meaningful measurements are represented by operators that obey a non-commutative algebra. Thus, the order in which information is extracted matters. In addition, responses to questions about an attribute depend on whether information about another attribute has previously been extracted. Measurement “forces” the subject to obtain one value of the attribute, the one measured by the observer. An uncertainty principle imposes a fundamental limit on the ability to extract detailed information about two distinct attributes within a short period of time.     

Computer assisted text analysis in the social sciences

We use the term “Computer Assisted Text Analysis” in a broad sense to refer to a range of current techniques from quantitative social science and content analysis to ‘data mining’ and ‘text classification’, including the analysis of open-ended survey questions, transcribed interviews and speeches, wherever, in fact, the researcher is confronted with data in the form of natural language texts of social scientific interest. These methods are often used in exploratory data analysis, but can also be applied systematically with moderate statistical rigour in the development and testing of hypotheses at various theoretical levels, ranging from the statistics of word usage to changes within or between discourses over time. The general approach is in the tradition of content analysis, by which words which occur together in relatively close proximity in the same context are interpreted as relating to a common theme or concept in the discourse studied. We review a comprehensive set of tools to identify and visualize structures of co-occurrence of words and concepts both within, and in comparing, a number of texts. These produce results not essentially different from those reached by representing word co-occurrences in terms of network analysis or neural network programming using schematic linguistic templates of various kinds. A comparison of the relational data analysis vs. a dictionary-based MDS approach shows that these provide very close if not identical results, despite the fact that the underlying assumptions are frequently represented as different theoretical approaches.     

Fifty years of measurement and scaling in the Dutch social sciences

It is evident from recent issues of journals like Psychometrika and Applied Psychological Measurement that the Dutch contribution to the development of the area of measurement and scaling is considerable. In the first part of this paper, we try to answer the question how this could happen. In the early development De Groot in Amsterdam and Van de Geer in Leiden created an academic climate that made it possible for many researchers to develop their skills. Some other possible reasons are sketched for the flourishing of this area in the Netherlands, but it is difficult to assess which reasons were most important. In the second part of the paper, we analyze the international impact of the Dutch contribution by a citation analysis in four fields of measurement and scaling: factor analysis, test theory including item response theory, latent class analysis, and optimal scaling and multidimensional scaling. It appears that Dutch researchers not only publish a lot, but also that the impact of their publications is at the same level as publications produced world-wide.     

Causal inference,time and observation plans in the social sciences

This paper first discusses the role of time in causal inferences in the social sciences. It then compares in detail how panel and event history observation designs affect causal analysis. It shows that the collection of event history data is an extremely useful approach for uncovering causal relationships or mapping out systems of causal relations. It concludes that event history data provide an optimal basis for a causal understanding of social processes because they allow the social researcher to relate the change in future outcomes to conditions in the past at each point in time.     

The use of quasi-experiments in the social sciences: a content analysis

This article examines the use of various research designs in the social sciences as well as the choices that are made when a quasi-experimental design is used. A content analysis was carried out on articles published in 18 social science journals with various impact factors. The presence of quasi-experimental studies was investigated as well as choices in the design and analysis stage. It was found that quasi-experimental designs are not very often used in the inspected journals, and when they are applied they are not very well designed and analyzed. These findings suggest that the literature on how to deal with selection bias has not yet found its way to the practice of the applied researcher.     

Chaos theory and social science: A methodological analysis

This article investigates the relevance of chaos theory for social science. The application of chaos models in the analysis of social phenomena is accompanied by some important scientific problems. First, whether observations of social phenomena are generated by nonlinear dynamics cannot be ascertained beyond considerable doubt, especially when these observations contain measurement errors; i.e., there is a problem of external validity. Secondly, and more important, as a theory of irregular cyclical social behaviour is lacking, inductive-statistical theoryformation about such behaviour, which is based on fitting a mathematical model of chaos to observations of social phenomena, is impossible unless additional information is used concerning the context and circumstances wherein the social phenomena occur; i.e., the internal validity of any theoretical explanation that is derived from only a fitted mathematical model (of chaos) cannot be assessed. So, research into the suggestion derived from mathematical chaos theory that irregular cycles may be present in the development of social phenomena over time requires theory-formation about irregular cyclical social behaviour on the basis of established theoretical insights and empirical evidence instead of fitting sophisticated mathematical models of chaos to observations of social phenomena.