Empirical research often lacks theory. This book progressively works out a method of constructing models which can bridge the gap between empirical and theoretical research in the social sciences. This might improve the explanatory power of models. The issue is quite novel, and it benefited from a thorough examination of statistical and mathematical models, conceptual models, diagrams and maps, machines, computer simulations, and artificial neural networks. These modelling practices have been approached through different disciplines.

The proposed method is partly inspired by reverse engineering. The standard covering law approach is abandoned, and classical induction restored to its rightful place. It helps to solve several difficulties which impact upon the social sciences today, for example how to extend an explanatory model to new phenomena, how to establish laws, and how to guide the choice of a conceptual structure.

The book can be used for advanced courses in research methods in the social sciences and in philosophy of science.

Includes supplementary material: sn.pub/extras

Inhalt
General Introduction.- 1 The determinants of infant mortality: how far are conceptual frameworks really modelled ?.- 2 The role of statistical and formal techniques in experimental psychology.- 3 Explanatory models in suicide research: explaining relationships.- 4 Attitudes towards ethnic minorities and and support for ethnic discrimination, A test of complementary models.- 5 Computer simulation methods to model macroeconomics.- 6 The explanatory power of Artificial Neural Networks.- 7 On modelling in human geography.- 8 The explanatory power of migration models.- 9 The role of models in comparative politics.- 10 Elementary mathematical modelization of games and sports.- 11 Computer modelling of theory, explanation for the 21st century.- 12 The logicist analysis of explanatory theories in archaeology.- General Conclusion.- Name index.