Composing Group-Level Constructs From Individual-Level Survey Data

Heleen van Mierlo

doi:10.1177/1094428107309322

Composing Group-Level Constructs From Individual-Level Survey Data

2008-04-08

215

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AI

DOI：10.1177/1094428107309322

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Group-level constructs are often derived from individual-level data. This procedure requires a composition model, specifying how the lower level data can be combined to compose the higher level construct. Two common composition methods are direct consensus composition, where items refer to the individual, and referent-shift consensus composition, where items refer to the group. The use and selection of composition methods is subject to a number of problems, calling for more systematic work on the empirical properties of and distinction between constructs composed by different methods. To facilitate and encourage such work, the authors present a methodological framework for addressing the distinction between and the baseline psychometric quality of composed group constructs, illustrated by an empirical example in the group job-design domain. The framework primarily represents a developmental tool with applications in multilevel theory building and scale construction, but also in meta-analysis or secondary analysis, and more general, the validation of group constructs.

Keyword:

aggregation

composition models

measuring group constructs

Composing Group-Level Constructs From Individual-Level Survey Data

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