2012Palgrave Macmillan UK eBooksRequires access

Data

Björn Jindra

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Abstract

The correct application of statistical methodology is crucial for the collection and analysis of firm-level innovation data. In this chapter we discuss and provide information on central elements of the collection and analysis of the data used. We provide the rationale as to why the research exploits the IWH FDI micro-database instead of other existing datasets. We also describe the genesis of the total population, survey method, sampling criteria and representativeness and non-respondent bias in the 2007 survey. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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What this paper is about

The correct application of statistical methodology is crucial for the collection and analysis of firm-level innovation data. In this chapter we discuss and provide information on central elements of the collection and analysis of the data used. We provide the rationale as to why the research exploits the IWH FDI micro-database instead of other existing datasets. We also describe the genesis of the total population, survey method, sampling criteria and representativeness and non-respondent bias in the 2007 survey. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Available abstract

The correct application of statistical methodology is crucial for the collection and analysis of firm-level innovation data. In this chapter we discuss and provide information on central elements of the collection and analysis of the data used. We provide the rationale as to why the research exploits the IWH FDI micro-database instead of other existing datasets. We also describe the genesis of the total population, survey method, sampling criteria and representativeness and non-respondent bias in the 2007 survey. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Key concepts: Representativeness heuristic, Respondent, Sampling bias, Computer science, Data collection, Sampling (signal processing), Exploit, Survey methodology

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