Data analysis

The idea behind data analysis is to make the data more comprehensible and thus create new information on the subject. The aim of the analysis is to condense the data without losing the information and to increase the information value by making a scattered data clear and sensible (Eskola & Suoranta 1998, 138). Hirsjärvi et al. (1997, 207–208) have defined three steps to take before making conclusions: 1. Checking, if there are mistakes or missing information in the data, 2. Complementing the data, if necessary and 3.

Organizing the data. The most common instruction is that the analysis of the data should be started as soon as possible after the collection of the data. In this study, most of the recorded interviews were transcribed soon after the interviews. However, the analysis of the data in two cases took some time. The data gained from the statistical review was analyzed with the help of SPSS-program and Excel.

Kovalainen (lecture 21.9.2004) mentions eight means to analyze a data:

1. Qualitative analysis with the help of quantitative measures 2. Word analysis

3. Dividing the data into different themes 4. Dividing the data into different codes 5. Specification of the contents

6. Discussion analysis

7. Discursive ways to analyze 8. Narratives and stories

Means number 3 and 4 were used in this study, so these two means are studied a bit more closely. Dividing the data into different themes means organizing the data into groups that have a same theme. These themes should help answer the research question.

When the interview is a theme interview, it helps to start the analyzing by dividing the data according to the question themes. It is desirable that during the analysis also new themes come up. The themes are based on the interviewer’s interpretation on what the interviewee said (Kovalainen, lecture 21.9.2004; Sandberg, lecture 28.10.2004).

Dividing the data into different codes usually requires first dividing it to themes.

After that one can look for even more usual types from the data. In coding, the researcher goes through the data systematically, reads the text many times and tries to find different subjects from the text. Each theme is given a certain code (Kovalainen, lecture 21.9.2004; Sandberg, lecture 28.10.2004).

In this study, the data was at first organized into different themes, such as “General information on the company”, “International operations” and “Connections to Russia”.

These themes mainly stemmed from the theory and the interview questions. After that, these themes were marked with different colours to the written interview (e.g. green colour meant that the part dealt with Connections to Russia). Afterwards, the themes were organized to their own groups, so that it is easy to find the relevant information concerning a certain theme.

This study required an organization classification in order to describe the reader the nature of the investigated companies and to justify the selection of the case companies.

The classification helps the reader to get a good general view on the field of active companies this study is concerned with. The organization classification in this study is implemented with the help of typology and taxonomy and thus these two tools are described shortly in the following.

The terms “typology” and “taxonomy” can be defined in several ways and there is no common opinion about their interpretation. The definitions used in this study are the most commonly used in literature. Typology involves choosing and naming the groups to the classification before the empirical data is gathered and the specific organizations are placed to the groups. The groups and variables are decided with the help of prior theory. Taxonomy is quite the opposite; it involves organizational classes emerging from the empirical data. The groups are defined through sorting organizational features on the basis of similarity or contrast. In typology, the grouping happens a priori and in taxonomy, a posteriori (Rich 1982, 760; McKelvey 1975, 509).

The major role of typologies and taxonomies is to both make order and make sense of the data they contain. They are communication systems and aim at combining the greatest information content with greatest ease of information retrieval. They are also called shorthand devices by which organizations can be compared and hypotheses tested. At their best, they are means for ordering and comparing companies and clus-tering them into categorical types without losing sight of the underlying richness and diversity that exist within the group. (Rich 1992, 758–759). The organization classification process is presented in Figure 4.

Figure 4 An overview on the classification process

TYPOLOGIES

Figure 4 presents the different ways to put together an organizational classification.

The classification used in this study is a taxonomy that is empirical and numerical.

Thus, the characters come from the empirical data; the researcher selected the characters of interest and placed the organizations into types based on data analysis. The result was polythetic groups. Polythetic typologies contain groups in which a big proportion of variables are shared by the members of each group, but in which members are not necessarily alike on any individual variable. The individuals that have the greatest num-ber of shared features are placed together, so that no single character is either necessary or sufficient for membership in a certain group (Rich 1992, 765).