QCA (version 3.22)

modelFit: Theory evaluation

Description

Function to enable theory evaluation, as introduced by Ragin (1987, p.118) and extended Schneider & Wageman (2012, p.295), by producing parameters of fit for all possible intersections between a given theoretical statement (a SOP expresison) and the solutions found by function minimize().

Usage

modelFit(model, theory = "", select = NULL, ...)

Value

A list containing objects of class "QCA_pof" with the parameters of fit. For a single theoretical expression and a single model, the object is a simple "QCA_pof" object.

Arguments

model

A minimization object of class "QCA_min".

theory

Character, a SOP expression.

select

Character or numerical vector to select one or more models.

...

Other arguments, mainly for internal use.

Author

Adrian Dusa

Details

Following Ragin's (1987) original work, theory evaluation amounts to intersecting a theoretical expectation with a model resulting from a minimization process.

There are in fact four intersections: presence - presence, presence - absence, absence - presence and absence - absence, where by “absence” is actually meant a negation of an expression using the function negate().

When multiple models exist, all of them are automatically detected, negated and intersection with the theory. Intersections and parameters of fit are going to be produced using a single theoretical expression.

In case of high model diversity, it is sometimes useful to select only a subset to fit against theory. In such situations, the argument select can be provided with either the name(s) of the model (for instance "C1P5" for intermediate solutions) or simply the number of the model(s) of interest (for conservative and parsimonious solutions).

References

Ragin, C.C. (1987) The Comparative Method: Moving beyond Qualitative and Quantitative Strategies. Berkeley: University of California Press.

Schneider, C.Q.; Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis (QCA). Cambridge: Cambridge University Press.

See Also

intersection, negate, pof

Examples

# Lipset fuzzy version data

ttLF <- truthTable(LF, outcome = SURV, incl.cut = 0.8)


# parsimonious solution
pLF <- minimize(ttLF, include = "?")

# modelFit(model = pLF, theory = "DEV*STB")


# hypothetical exploration of intermediate solutions
iLF <- minimize(ttLF, include = "?", dir.exp = "1,0,0,0,0")

# modelFit(iLF, "DEV*STB")