Method in Data Collection
March 30, 31 and April 3 2020
9:30 - 16:30

This 3-day FLAMES course provides general guidelines for the operationalization of abstract concepts through the construction of indicators and the design of data collection instruments (e.g., questionnaires).

This course offers an overview of the basic aspects of metrology as a science, including a general theory of measurements, a theory of measurement error, and the development of methods and means of measurement, with an emphasis of those tools applied to gather data about people.

Measurement is the attaching of numbers to objects in a meaningful way (Stevens, 1951). Empirical science is invariably based on measurements. These measurements can have a highly structured form (think of CERN's grid computing to detect the Higgs boson or God particle in 2012), and they can have a highly unstructured form (think of talk-as-you go qualitative interviews). In both cases, the instruments produce the data that serves as evidence in descriptive, inductive and deductive studies. Any measure of any attribute X depends on a number of conditions of measurement, which include (Tobi, 2015): the observer, the instrument (which sometimes coincides with the observer), the time of measurement, and a (great) number of ceteris paribus (all-things equal) conditions. The quality of measurement is a decisive factor in the validity of any research project. Research results can only be trusted in so far as the measurements on which it relies can be trusted. Whatever level of sophistication your analyses of data reach, the conclusions can never be stronger than your measurements allow (the GIGO principle: garbage in, garbage out). When designing your research, a considerable amount of effort must therefore be invested in the construction of your instruments.

At the end of this course, participants are able to:

1. Know about basic aspects of measurement (validity, reliability, precision, discriminating power, measurement scale)
2. Understand the design and limitations of commonly used data collections methods (secondary data; primary data including observation, interviewing, and content analysis)
3. Deal with problems that might occur in the data collection phase (non-response, reflexivity)
4. Anticipate on problems that might occur in the data analysis phase (analysis design, power considerations, missing data, confounders, extraneous variables).

University of Antwerp
City Campus (CST) - Prinsstraat 13 - 2000 Antwerp
30/3, 31/3 and 3/4 9-16:30

Lecturer: prof. dr. Jarl Kampen (StatUa/FLAMES UAntwerp)

* PhD's and postdocs of a Flemish university: free of charge
* Other academics: € 180
* Non-profit/Social sector: € 300
* Private sector: € 600

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