Identifying Categories

According to Elo and Kyngäs (2007, 111), a researcher formulates a general description of the research topic by generating categories. These categories are named by using a content-characteristic word. With the help of abstraction, those subcategories that have similar events and incidents are put together as categories whereas the categories are grouped as main categories. This abstraction process continues as long as possible and the researcher should have three different categories left in the end: sub-categories, generic-categories and main-categories.

I started the analysis process by reading the data through several different times and making the notes while reading. The aim was to describe the different aspects of the

content so I went through the data year by year, writing down all the key words and aspects that occurred during the particular year. This was the most time consuming part of the analysis process and I ended up having dozens different aspects describing each different year. In terms of the content analysis, this is completely normal since as many headings as necessary should be written down in order to describe all the aspects of the content (Elo and Kyngäs 2008, 111).

After the coding, I started generating categories. I started by searching the similarities and differences and then grouping the similar headings under the same generic-category. I was able to identify nine different generic-categories which included all the found headings.

The identified generic-categories were environmental protection, research and technology,

nuclear power, renewable energy, energy conservation and energy efficiency, policy issues, mechanisms and instruments, markets and European energy cooperation.

It turned out that the several headings were overlapping in a sense that they could have been easily placed under two or more different generic-categories. In the content analysis this is not necessarily a problem since the categories are not supposed to be exclusive but one heading may be placed under two or more generic-categories. However, in those cases where the particular heading clearly described more the other category, it was my decision to select the category which described the heading the most.

For example a renewable energy was mentioned several times as a tool to prevent the climate change but it was also, quite obviously, seen as an alternative technology to

diminish the usage of the fossil fuels in the future. In this case, the renewable energy could have been put under the environmental protection or under the more specific renewable energy category (which in the next step was grouped under the alternative technologies instead of environmental concern).

After careful interpretation, I came to a conclusion that in these cases the renewable energy was seen as a tool, not as a mean, to prevent the climate change so I decided to group it under the alternative technologies category. The energy policies will regulate the alternative technologies (which renewable energy is) which will most likely benefit the environment in the future. In this sense, the renewable energy belongs first under the renewable energy generic-category although it has a positive effect on the environment in a sense that it will most likely reduce the usage of the fossil fuels.

After the creation of these categories, I started grouping the similar generic categories under the main categories. I formed four main categories which were environmental concern, modernization of technologies, reconstruction of governance and international and regional cooperation. Each of the main categories contained two or more generic-categories whereas they included several different headings. As it is normal in the inductive content analysis, the research questions were determining the categorization process and the categories were named by using a content-characteristic word. The table 3 on the next page demonstrates the found categories

Table 2 Data Analysis – Finnish Energy Policy Trends 1995 – 2013

MAIN CATEGORY SUB CATEGORY EXPLANATION

Environmental Concern Environmental Protection All the environment related energy policy discussion, research and technology Research and Technology which requires some political action to benefit the environment

Nuclear Power Includes different strategies to modernize

Modernization of Energy Technology Renewable Energy the Finnish energy technology

EC & EE

Reconstruction of Governance Policy issues Political measures and means to reform the Finnish

Mechanisms and Instruments energy policy regime

Cooperation Markets Energy and environment related cooperation across the national borders

European Energy Cooperation

These identified categories are providing the information which will be needed in order to respond to the research questions which will be done in the next chapter. The main

categories are representing the four most important Finnish energy policy trends during the years 1995-2013 and since the aim of this research is to identify the Finnish energy policy trends in the selected time period, it was the first step to take to detect these trends.

The sub categories however are providing more detailed information of the identified trends. The identified sub categories will be crucial in terms of finding out whether the Finnish energy policy trends are manifesting the policy orientation towards the low carbon future and whether these trends are in tune with the international climate change regime.

Each of the sub-categories are consisting several different, partly over-lapping headings which will be presented in details in the following chapter.

I selected the inductive content analysis for this research because it allows the concepts to derive from the data itself (Krippendorff 2004a, 36.) In terms of categorizing the data and finding the energy policy trends, it was important to allow the categories to emerge

naturally from the text which inductive content analysis made possible. According to Horn and Korsunova (2011, 48), allowing categories to emerge from the text naturally, offers a richer and more detailed understanding of them.

However, Tuomi and Sarajärvi (2008, 96) have pointed out, that a purely inductive content analysis is difficult to conduct. This is because of the widely accepted presumption that the observations are normally, at least to some point, theory bounded. Based on this

conclusion, there are no objective observations because the concepts and methods are selected by the researcher which will inevitably affect to the results. Because of this lack of objectiveness it is important that researcher ensures the reliability and validity of the research by describing the background of the work in detail as well as the choices that have been made during the research process.

The main purpose of the inductive content analysis is to provide an answer to the research question or questions by grouping the information and concepts (Tuomi and Sarajärvi 2009, 112). Zhang and Wildemuth (2009, 309) are pointing out that the qualitative content analysis gives a tool to condense raw data into categories and themes based on a valid inference and interpretation. The process often uses the inductive reasoning where themes and categories emerge from the data through careful examination and comparison.