Data analysis and statistical modelling

Abstract

Abstract An empirical test of research hypotheses is one of the main aims of scientific research. This chapter focuses on quantitative data and their exploration by analytic statistics. Basic notions are briefly introduced to readers in three parts. Section 5.1 summarizes key features of regression analysis. Here, the notions of confounding, mediation, effect modification, and interaction are discussed. This section is extended by brief descriptions of structural equation modelling and multilevel analysis. As a main methodological challenge, the problem of causal inference is highlighted, with a short description of counterfactual approaches (Section 5.2). As the text is restricted to introductory information, a few examples derived from published research are included to illustrate some of the arguments. In the final sections, two crucial aspects of analytic scientific research are addressed. Firstly, we describe the main features of systematic reviews and meta-analyses as tools of updating and summarizing current evidence on specific research topics. Given an excessive and abundant flow of scientific information these ways of structuring and examining available knowledge are indispensable for researchers as well as for practitioners interested in science (Section 5.3). Finally, we bring readers’ attention of to some core ethical aspects of scientific research by referring to the dual use of scientific knowledge and to principles of scientific integrity. We also discuss the critical topic of scientific misconduct, and we emphasize the role of personal responsibility of individual researchers (Section 5.4).