Suchergebnisse

This article argues that concept misformation and conceptual stretching undermine efforts to combine qualitative and quantitative methods in multimethod research (MMR). Two related problems result from the mismatch of qualitatively and quantitatively construed concepts. Mechanism muddling occurs when differences in the connotation of qualitatively and quantitatively construed concepts embed different causal properties into conceptual definitions. Conceptual slippage occurs when qualitatively and quantitatively construed concepts use incompatible nominal, ordinal, or radial scales. Instead of gaining leverage from the synthesis of large- and small- N analysis, these problems can push MMR in two diametrically opposed directions, emphasizing one methodological facet at the cost of the other.

This article argues that concept misformation and conceptual stretching undermine efforts to combine qualitative and quantitative methods in multimethod research (MMR). Two related problems result from the mismatch of qualitatively and quantitatively construed concepts. Mechanism muddling occurs when differences in the connotation of qualitatively and quantitatively construed concepts embed different causal properties into conceptual definitions. Conceptual slippage occurs when qualitatively and quantitatively construed concepts use incompatible nominal, ordinal, or radial scales. Instead of gaining leverage from the synthesis of large- and small-N analysis, these problems can push MMR in two diametrically opposed directions, emphasizing one methodological facet at the cost of the other. Adapted from the source document.

"Compositional data, also called multiplicative ipsative data, are common in survey research instruments in areas such as time use, budget expenditure and social networks. Compositional data are usually expressed as proportions of a total, whose sum can only be 1. Owing to their constrained nature, statistical analysis in general, and estimation of measurement quality with a confirmatory factor analysis model for multitrait-multimethod (MTMM) designs in particular are challenging tasks. Compositional data are highly non-normal, as they range within the 0-1 interval. One component can only increase if some other(s) decrease, which results in spurious negative correlations among components which cannot be accounted for by the MTMM model parameters. In this article the authors show how researchers can use the correlated uniqueness model for MTMM designs in order to evaluate measurement quality of compositional indicators. They suggest using the additive log ratio transformation of the data, discuss several approaches to deal with zero components and explain how the interpretation of MTMM designs differs from the application to standard unconstrained data. The authors show an illustration of the method on data of social network composition expressed in percentages of partner, family, friends and other members in which they conclude that the face-to-face collection mode is generally superior to the telephone mode, although primacy effects are higher in the face-to-face mode. Compositions of strong ties (such as partner) are measured with higher quality than those of weaker ties (such as other network members)." (author's abstract)

Historically, developmental investigators have tended to assess phenomena of interest in one way at one age in one culture. In this essay, I explore some rationales and advantages for a multiculture, multiage, multimethod science.