Questionnaires

To analyze the data collected through the questionnaires I used SPSS, which helps me analyze large sets of data quickly and through different methods (Weinberg and Abramowitz, 2015, p. 2). First I had to add the questions into the variable view, choosing the appropriate values and ways they would be measured.

Next I could add the data from the questionnaires into the categories that I had created and then they were ready to be analyzed.

Most of the questions that I used were the closed-ended ones, as it is hard to translate open-ended answers into numbers. However, for some of the

questions, for example those relating to nationality and reason for travelling to Alta, I was able to split them into variables as the answers could be categorized.

When it came to the actual tools I used to analyze the data, I focused mainly on finding out frequencies, as well as some cross-tabulation. The way I was using the questionnaires, it was more useful to find out the percentage of people choosing specific answers, as it would fit better with the data gathered from the interviews.

Using frequency distribution, which is part of descriptive statistics, I can find out how frequent certain answers are within each category of questions. This is useful, for example in figuring out how many of the respondents answered

“Spending time in nature” when asked what motivates them to travel or how many put “Spending time together” as their number one choice for what

motivated them to choose a particular experience. (Weinberg and Abramowitz, 2015, p.21)

When looking at the frequency distribution, we are not just looking at how many answered this or that, but we are looking at how many answered this out of the total respondents. This is called relative frequency and can be translated into percentages. In SPSS you can choose to see both presented, which I did in most cases, as I find that looking at a percentage can give you a clearer impression of the frequency than just looking at the numbers. (Weinberg and Abramowitz, 2015, p.21)

Sometimes the respondents do not answer a question, which means that we could end up with missing data. However, SPSS also shows this and displays both the percent – which also includes the missing data – and the valid percent.

This way we can get an accurate percentage based on the people who have actually answered the question. (Weinberg and Abramowitz, 2015, p.21)

Frequencies and percentages tend to be presented with some kind of visual aid, for example a bar graph or a pie graph. Both have their own uses. I prefer pie graphs when I look at questions regarding motivation, as it does a better job at showing one category in relation to the others. Meanwhile, for questions

regarding the importance of text versus pictures, a bar graph looks cleaner than a pie and provides just as good an illustration. (Weinberg and Abramowitz, 2015, pp. 21-24)

In addition to frequencies distribution, I also wanted to explore some relationships between different data. For example, I wanted to compare what people said was the reason they travelled to Alta to what they said motivates them to travel and/or to pick a particular experience. This is also part of the frequency category, but the tools we are using is a contingency table, or cross-tabulation, as it is named in SPSS. With a contingency table we can look at two categories and quantify the relationship between them. An example of this could be to see how many of those who picked northern lights as a reason for coming to Alta also

picked “seeing a famous attraction” as their motivation for travelling. (Field, 2015, p. 721)

In addition to looking at the statistics from the questionnaires, I also looked at the answers I got from the open-ended questions. Some of these, like nationality and reason for coming to Alta, I was able to put into SPSS, but others could not be translated into statistics as easily. These I ended up working with in a similar way as I did when analyzing the interviews. As there was less text to work with, it was easier to categorize and put into fitting categories.

Looking at some of the answers I got from the Holmen Questionnaires, I also took some extra steps in the analysis-process. Some of the answers I received came from cruise passengers and many of them wrote their reason for coming to Alta as “part of a cruise”. Now, I could just have left it at that and left that category alone. However, considering some of the results that I had found across the line, the cruise answers stood out like a sore thumb and I had an inkling that they shared more similarities with the rest.

Some of the cruise passengers had written the name of the ship and I also knew where they had come from and what kind of cruise they were on through talking to them. These were the passengers who had not been allowed to visit Tromsø due to Covid-19 (Henriksen and Mehren, 27.02.20). I knew the name of the boat and their travel route. With this information in hand, I did some detective work to see exactly what kind of cruise they were on. And, lo and behold, I found not only a blog detailing their journey, but also the name of the tour and the similar packages that the cruise ship offered. I will reveal more about the

information I discovered in the discussion-section below, but I will say this much;

it fits in with a similar pattern to the rest.