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.     

Use of dimensional analysis in social science research

Dimensional analysis is a branch of applied mathematics which is only now beginning to emerge as an important methodology in social and behavioral science research. Unlike regression analysis, dimensional analysis “discovers” the functional relationship in the data from the denned dimensions; hence its ability to extend regression analysis in developing predictive models makes it particularly useful in social science research. In addition, many of the limitations with the use of regression analysis in social science which limit its usefulness in applications, such as multicollinearity, normality assumptions, homoscedasticity assumptions, etc. are not limitations in dimensional analysis. In this study, the application of dimensional analysis to the study of population migrations, specifically white flight in a large urban school district, is presented. Finally, a review of relevant literature and the use of dimensional analysis in other social science areas are presented.     

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.     

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.     

A regularity theory of causality for the social sciences

Quality & Quantity - This article discusses a regularity theory of causality (RTC) for the social sciences. With RTC, causality is a relationship between X and Y characterized by three...     

Event history and survival analysis in the social sciences II. Advanced applications and recent developments

A previous paper (Hutchison, 1988) in this journal has provided an introduction to the basic concepts of survival and event history analysis, originally developed in medical research, econometrics and engineering, and argued the case for their wider application in the social sciences. This paper introduces some further complications that the researcher is likely to meet, and offers some guidelines for handling problems that arise in applying such methods to the highly complex social situations involved.     

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.     

Publication bias in the German social sciences: an application of the caliper test to three top-tier German social science journals

Systematic research reviews have become essential in all empirical sciences. However, the validity of research syntheses is threatened if the preparation, submission or publication of research findings depends on the statistical significance of these findings. The present study investigates publication bias in three top-tier journals in the German social sciences, utilizing the caliper test. For the period between 2001 and 2010, we have collected 156 articles that appeared in the Kölner Zeitschrift für Soziologie und Sozialpsychologie (KZfSS), the Zeitschrift für Soziologie (ZfS) and the Politische Vierteljahresschrift (PVS). In all three journals, we found empirical evidence for the existence of publication bias at the 10 % significance level. We also investigated possible causes linked to this bias, including single versus multiple authorship as well as academic degree. We found only weak support for the relationships between individual author characteristics and publication bias.     

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.     

High-frequency words have higher frequencies in Turkish social sciences article

Quality & Quantity - Words, sentences, and paragraphs are the basis of texts. When we consider texts as data and want to establish a relationship between qualitative and quantitative...