Research Designs

superficial one- dimensional divisions, building on different data and methods usage, like e.g., between analysing units or variables, analysing words or numbers, drawing on big or small N etc. are challenged to introduce more constructive and flexible multidimensional distinctions, drawing on different assumptions and approaches, like e.g. between heuristic and reductionist approach, between inductive and deductive reasoning etc. that can be more productively applied as guidelines in research practice and in designing a research process. In so doing the popular qualitative - quantitative continuum is defined as reduced to essentials but useful concept, subject to understanding its multiple dimensions fundament.

To enable informed decision making regarding research design, the main world-view families, like prevailing positivism with its modernized derivations, interpretative approach and critical approach, both with variants, as well as pragmatism as a potential alternative are introduced and discussed together with purpose(s) of the research, being exploratory, descriptive or causal and with the scope of the research, being applied or basic, not neglecting research range and time aspect as well as the researcher him/herself.

Positions on above key issues have to be determined before analytical methods and techniques can be composed in a sound and coherent research design. But soundness and coherence still cannot be achieved without awareness of basic features of methods and techniques available to researchers. Therefore, research- design-relevant characteristics of methodologies, methods and techniques that can typically be applied in various steps of a research process are presented, illustrated with examples from wide-ranging field of social sciences and discussed, e.g. 1) data (evidence) collection methods and techniques, based on communication or observation, further characterised by degree of structure, method of administration, degree of disguise and the setting, using logic of sampling or casing (including field work, interviews, focus groups, projections, as well as surveys, web surveys and selection of secondary data, taking into account conceptualization and operationalization of variables) and 2) data analysis methods, including univariate (frequencies and distributions), bivariate (correlation, t-test and contingency) and multivariate methods (clustering, regression and factor analysis) for analysis of variables, as well as production of thick descriptions, analytical reading through set lens and coding (using pre-set codes or going through open, axial and selective coding phase). The course concludes with highly abstract and idealised concept of various predetermined types of research designs, without discriminating any of them in favour of the other.


Designed at intermediate level, the course certainly cannot provide expertise in elements of a research design (e.g. expertise in methods, not even in selected ones) but can swiftly review most of them to create a holistic picture of the architecture of research designs. At the end students are expected to better understand the variety of opportunities and existing designs and be able to take advantage of distinctions between them in their own research projects by making informed decisions. These basic competences are expected to grow with further study of various methods and techniques and with experiences gained in continuous research work.

Some experiences with commonly referenced research designs and their building blocks are appreciated. The more students know about applicability of various methods and techniques the more they can gain from the course.