I have recently been studying QCA, a research methodology that in contrast to quantitative analysis, facilitate the examination of small N data sets containing equifinal and/or asymmetric outcome solutions.
My first use of the method will be to re-examine my 2009 dissertation study data set (N=71) that resulted in non-significant findings based on structural equation modelling and t-Tests.
QCA is a case-oriented set theory-based method that generally follows four procedural steps;
My first use of the method will be to re-examine my 2009 dissertation study data set (N=71) that resulted in non-significant findings based on structural equation modelling and t-Tests.
QCA is a case-oriented set theory-based method that generally follows four procedural steps;
- The preparation of causal data and outcome variables into dichotomous (crisp-set), multi-value (MvQCA), and/or fuzzy-value (FsQCA) values.
- The production of a truth table using showing causal variable values as column data and cases as row data.
- The Boolean reduction of the truth table matrix into solutions supporting the selected outcome variable. and
- The determination of necessary and/or sufficient conditions and the calculation of consistency and coverage parameters.