Individual Performance: From Common Source Bias to Institutionalized Assessment

Lotte Bøgh Andersen, Eskil Heinesen, Lene Holm Pedersen

    Research output: Contribution to journalJournal articlepeer-review

    Abstract

    Performance is perhaps the most central concept in public administration research, and this article discusses theoretically and investigates empirically how we can obtain more consistent performance measures. Theoretically, we combine existing arguments in public administration with institutional theory and the sociology of professions. Empirically, we ask whether different measures of individual performance produce different results. The investigated performance measures vary with regard to risk of common data source bias, standardization of assessment criteria, and external verification of the assessment. Our investigated explanatory variables are intrinsic motivation, public service motivation, and job satisfaction. Combining survey and administrative data for 747 lower secondary school teachers (teaching 5,679 students in 85 schools), we analyze 4 different measures of the same performance dimension for the same teachers: the teachers’ self-reported contributions to students’ academic skills, the students’ marks for the year’s work given by the teacher, marks in oral exams with one external examiner and the teacher, and marks in written exams with at least one external examiner. The associations are systematically stronger when the performance measure comes from the same data source as the explanatory variables, but when separate data sources are used and the measurement scale is institutionalized, the level of external verification does not matter much. Based on institutional theory and the sociology of professions, we develop a theoretical argument that can explain this.
    Original languageEnglish
    JournalJournal of Public Administration Research and Theory
    Volume26
    Issue number1
    Pages (from-to)63-78
    Number of pages16
    ISSN1053-1858
    DOIs
    Publication statusPublished - Jan 2016

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