Research Method

Dörnyei (2007) distinguishes between two main research approaches: Qualitative and Quantitative Research. The main difference resides in the divergent methods of data collection and data production. Qualitative data mainly focuses on spoken data, which is recorded and set into written form through transcription. Therefore,

“Qualitative research involves data collection procedures that result primarily in open-ended, non-numerical data, which is then analysed primarily by non-statistical methods.

[…]” (Dörnyei, 2007, p. 24)

Quantitative data, on the other hand, is mainly based on numbers. In contrast to qualitative research,

“Quantitative research involves data collection procedures that result primarily in numerical data which is then analysed primarily by statistical methods. Typical example: survey using a questionnaire, […]” (Dörnyei, 2007, p. 24)

Figure 3.2: Class distribution

Dörnyei (2007) also suggests an additional option which is mixed methods research, which combines both methods “either at the data collection or at the analysis levels” (Dörnyei, 2007, p. 24).

3.3.1 Quantitative Research

In the present study, I chose to use the quantitative research approach, as it appeared to be the most suitable method for my research according to Dörnyei’s (2007) definition of quantitative data. He lists various characteristics of quantitative research. I will list and briefly discuss the characteristics I consider most relevant for pointing out why I opted for a quantitative research approach:

 The usage of numbers, as already mentioned in his definition. He explains that numbers can be very powerful. Particularly in research though, they are powerless when the contextual background of the numbers is missing. In other words, exact descriptions are necessary and the category or the content to which the numbers are connected or associated with always needs to be specified (Dörnyei, 2007). As this study is based on descriptive statistics, the main focus lays on the analysis and presentation of numerical and percentage data that outlines results that show what is the case. This includes the calculation of the sum of responses for each statement or question as well as the subsequent statistical comparison of the results. Hence, numbers play an important role regarding the process of data analysis in the present study.

 Quantitative research focuses on common features of groups of people, rather than on the individual itself. It is not interested in single, individual answers but in the shared opinions of numerous people. Dörnyei defines quantitative research as “the study of variables” (2007, p. 33). These variables are counted and hence capture the common features (Dörnyei, 2007). As already mentioned, the present study’s statistics and findings rely on the sum of responses for each single statement or each question. Thus, it aims at collecting data about the shared opinion and attitudes of the study’s participants and hence describe common features.

 Quantitative research bases itself on statistics, which means that the data in quantitative research is analyzed statistically, for example, by calculating the average of several figures. This is only one of many examples though, as the range of methods for statistical analysis is very wide (Dörnyei, 2007). The present study uses descriptive statistics in order to show and outline what is the case, which is why it is considered a

descriptive study. This is done by calculating the sum of responses for each statement and presenting the findings in form of percentage and numerical data.

 Quantitative research lays great importance on generalizability. The conclusions that are drawn from the results, meaning the numbers, variables and statistics, are facts that can be generalized and ideally add up to universal laws (Dörnyei, 2007).

Nevertheless, Dörnyei (2007) stresses the importance of evaluating if and how much of the findings can be generalized. He explains that overgeneralization occurs when the results of a study are generalized to a population that has not been involved in the study. Therefore, if, for example, a research’s population are secondary school students, the findings should not be generalized and adopted to primary school students or university students. Generalization is a rather delicate topic in quantitative study and thus, “researchers need to exercise great caution when pitching the level of generalization in their research reports” (Dörnyei, 2007, p. 213).

Discussing the topic of (over)generalization, Dörnyei sheds light on two main struggles. On the one hand, he claims that limiting the findings and discussions to specific subgroups can reduce the audience due to a lack of broad relevance, which is why many researchers wish and tend to generalize. On the other hand, he points out that the basis of big claims are big and broad studies.

These studies would have to include numerous different representatives who come from different age groups, ethnicities, school types and subject matters etc. According to Dörnyei (2007), one should find a balance between these two considerations, which can prove to be quite challenging. In fact, the present study’s findings cannot be generalized, as they only reflect the opinions and attitudes of the survey population. Therefore, the study’s results cannot be adopted to students in general.

McKinley and Rose (2017) stress that low response rates can also be quite challenging in quantitative research. The response rate often depends on the form of sampling one uses for the research, as well as on the respondent self-selection (Dörnyei, 2007). Dörnyei (2003) lists numerous sampling procedures and distinguishes between probability sampling and non-probability sampling. For my study, I used the non-non-probability snowball sampling, which

“involves a ‘chain reaction’ whereby the researcher identifies a few people who meet the criteria of the particular study and then asks the participants to identify further appropriate members of the population.” (Dörnyei, 2007, p. 98)

Using this sampling method, I had to face the already mentioned problem of respondent self-selection. This means that participation on the part of research population was not compulsory.

Instead, their personal contribution to my study depended solely on their willingness to

participate (Dörnyei, 2007). As the snowball sampling somehow implies voluntary participation though, I had to hope for as many students as possible to be motivated to support my research.

McKinley and Rose (2017) point out another challenge that researchers have to face in regards to response rates, arguing that they do not necessarily meet one’s expectations. When compiling a quantitative research, be it a questionnaire or any other form of test, the researchers have certain expectations in mind. Often, those expectations diverge from the results, which can be quite challenging and raise some problems. McKinley and Rose (2017) state the example of a study, which was aimed at students who were in their first year of university. Therefore, the expected age of those students was around 18-19 years. However, the first person to participate in the study was a 56-year-old man, who was a first-year student. Therefore, they “did not appear to be getting responses from a whole range in the target sample […]” (McKinley &

Rose, 2017, p. 85). This can on the one hand be of advantage when it comes to generalization, as mentioned by Dörnyei, because some key factors (in this case the key factor age) of the research population turn out to be broader than expected and therefore the relevance of the study extends itself to a wider age range. On the other hand, it can be challenging for researchers to get results that they have not considered at all in their preparation phase. Concerning the present study, the findings did not entirely meet my personal expectations as a researcher. On the one hand, results regarding the general functions of meme and what can be learned through them reflect my own expectations and attitudes. On the other hand, the results of the survey’s section concerning the use of a selection of illustrated memes as a teaching tool did not always equal my personal thoughts, ideas and opinions. Therefore, in the case of the present study, some results are rather unexpected as well. This does not cause any severe complications or challenges though because other studies offer a great chance of comparison and encourage hypothesizing. Moreover, even though the findings themselves cannot be generalized very much, the further comparison to existing literature allows a certain level of generalization.