FSD Bulletin

Issue 16 (1/2005)

ISSN 1795-5262

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FSD Bulletin is the electronic newsletter of the Finnish Social Science Data Archive. The Bulletin provides information and news related to the data archive and social science research.


Finnish Social Science Data Archive
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New Interpretations of Existing Data

Interview with Juha Kääriäinen

Hannele Keckman-Koivuniemi | Mari Kleemola 20.04.2005

Juha Kääriäinen, a professor in the department of Social Policy and Social Work at Tampere University, uses FSD data both for teaching and research. He teaches courses on quantitative research methods, and courses focusing on his personal research interests, which are criminality and deviance, regional variation in welfare, and social capital.

In the quantitative methodology courses held by the department, SPSS package is used. Students also familiarize themselves with the data held by FSD, and with the archive's Research Methods Web Resource (MOTV in Finnish). This has led to many students using FSD data as exercise material during the methodology course, and later on using archived data in their theses.

Interpretation skills needed

Students often feel that quantitative research methods are difficult and challenging. According to Kääriäinen, methodology courses should focus on removing the barriers to the use of quantitative methods and analysis.

- After all, we are talking about a research tool. One makes observations based on the data and then interprets what these observations mean. Modern statistical packages are easy to use. In our courses, we teach students to understand tables and figures, find the most essential information, and to take into account restrictions imposed by quantitative methods. It is important to understand what kind of conclusions one can draw from a dataset using any particular method.

Students rarely collect large-scale data

Students usually want to collect the data for their theses themselves. However, this often results in small and local samples.

Kääriäinen says he recommends the use of FSD datasets, since their use reduces the cost and effort required, and enables students to carry out a larger-scale quantitative analysis. He points out, however, that when students use existing datasets they must demonstrate a sound theoretical understanding.
- If a student uses an existing dataset, his/her thesis should display a more extensive theoretical knowledge than the theses of students who collect the data themselves. In this way, students can demonstrate their theoretical competence and, if need be, ability to detach themselves from the dataset(s) used.

Secondary analysis and research question

If an undergraduate student orders data from FSD, also the supervisor is required to sign the access application. Kääriäinen supports this practice. He says it forces the supervisor and the student to assess together whether the dataset(s) in question are suitable for the intended purpose.
- When using existing datasets, the main challenge is to match the research question to the data. Students may fear that the data will influence their research question too much. Thesis supervisors must make clear that the research process is different when existing datasets are used for secondary analysis.

Students need to familiarize themselves with the data already at the planning stage. The research question must be formulated keeping the characteristics of the datasets in mind.
- The data have an influence on the research and - sometimes fortunately - compel people to define their research questions more clearly. It is the supervisor's responsibility to clarify to students that, despite the restrictions imposed by the data, there are still plenty of choices and new interpretations to make. It is the researcher who decides how to address, analyse and interpret the data. After all, the data do not impose any restrictions on the theoretical framework.

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