Language proficiency: foreign languages

Section 4.3.2 analyzes participants’ L2 skills in more detail, nevertheless, a short overview is essential to complete the description of the sample. Similarly to participants’ L1 profiles, their foreign language skills prove to be a highly multilingual set. Indeed, Table 4.29 shows that only 8% do not speak any foreign languages in addition to their L1. Taking into account the 6.4% rate of complete monolingualism, in fact six of these students are at least bilingual.

Nevertheless, in contrast with mother tongues, participants’ L2 profiles are arranged more proportionately. Respondents most often (37.1%) indicated knowledge of two foreign languages, while proficiency in one (26.7%) and three (24.3%) was equally common. It is important to note that these languages were marked in addition to participants’ mother tongues, therefore the figures do not limit but rather enrich their language portfolio.

Table 3.29 Number of participants per number of foreign languages spoken

Number of L2s N % Cumulative %

0 30 8.0 8.0

1 100 26.7 34.7

2 139 37.1 71.7

3 91 24.3 96.0

4 15 4.0 100.0

Total 375 100.0

In a related article (Csillagh, 2015), I argued that despite the decisive role of the Swiss primary and secondary education in fostering skills in multiple foreign languages, the university students that participated in the study showed remarkable initiative when it came to language learning. One sign of this can be seen in Table 3.30, which reveals that a good number of participants (42.1%) continue to hone their L2 skills during their university studies. The majority of them studied one language at the time the survey took place, but some were learning as many as four.

Table 3.30 Number of participants per number of languages studied at the moment

Number of Ls studied N % Cumulative %

0 217 57.9 57.9

1 112 29.9 87.7

2 40 10.7 98.4

3 4 1.1 99.5

4 2 .5 100.0

Total 375 100.0

In sum, among all the languages participants spoke or studied, L2s showed the greatest diversity. The comparison in Figure 3.17 demonstrates that while participants tended to select one language as their mother tongue, they continued to study and speak a number of languages.

Interestingly, the difference between perceptions of the two activities are marked. One intriguing aspect of the language data to be explored more thoroughly in the next chapter is the discrepancy between respondents’ answers regarding their mother tongue and L2 learning activities on the one hand and their views on speaking and learning foreign languages on the other. Mother tongues were often also listed among students’ L2s learned at the moment, and, reversely, some L2s were considered as being learned but not spoken. German was among the most frequent of the latter kind, after the languages categorized as other.

Note: categories represented by less than 1% were omitted from the chart.

3.7.9 Representativeness

As was shown in the course of this chapter, in general the sample represents a good enough fit when compared to the overall population of the four faculties included. On the one hand, this allows for certain generalizations over the population investigated. On the other hand, since students took part in the study on a voluntary basis, by deciding to fill out the online questionnaire, it can be argued that sampling was driven by the availability of participants.

Thus, it is questionable to what degree these students, motivated to participate in such a study, represent the general population, many of whom elected not to respond to the call.

Dörnyei (2007) observes that, in the case of such non-probability sampling, researchers “have to be particularly careful about the claims [they] make about the more general relevance of [their] findings.” He also concedes that, “in most applied linguistic research it is unrealistic or simply not feasible to aim for perfect representativeness in the psychometric sense” (ibid.), confirmed by the fact that the majority of research in social sciences employs some kind of convenience sampling. One solution to this wide-spread phenomenon lies in the meticulous description of the sample, providing as many socio-demographic and psychometric details as possible. In addition, thorough comparisons with the target population can help identify the

8.0%

Figure 3.17 Proportions of participants per number of languages in the different categories

extent to which the conclusions can be generalized. Secondly, it is sometimes prudent to apply research findings to the particular sample at hand and to refrain from overgeneralizations.

The stance that I take in the present study is one of caution, however, I point out general trends that might affect the entire population wherever applicable. This approach is based, in part, on the detailed presentation of the study participants, as offered above. Moreover, this dissertation aims to explore the role of contextual influences, thus shedding light on the possible role of the Geneva environment on students’ attitudes toward English. As a result, its conclusions often hint at more generalizable trends that point beyond the limits of the present study.

At the same time, the direct generalizations that the research allows remain conservative and serve rather as indications of the broader implications of the project. Therefore, they are discussed as such In addition, much attention was paid, thanks to the statistical procedures described below, to the potential effects of the type of sampling used, which will be discussed at the end of this chapter (section 3.9.3).

3.8 Analysis

Data was collected through the university’s LimeSurvey platform, where the original data file was also created. The data was then exported to Microsoft Excel, where cleaning and decoding took place. In the next phase, reliability measures were controlled and the scales described above were computed using SPSS. The descriptive statistics discussed in these chapters were also obtained through SPSS, where the rest of the analysis was conducted. The next chapter examines the results of the tests run using the software in more detail, whereas in this section I focus on the preliminary steps of creating the data pool and explain some of the main statistical concepts.

Once initiated, data collection in LimeSurvey is especially seamless since the software produces an output file in Excel format for direct use in most statistical programs. On the other hand, as SPSS computes advanced statistics based on numerical data, some of the answers had to be decoded to enhance the potential of the dataset. The decoding, along with a number of crucial steps that were taken to prepare the data for further analysis, was covered in the phase of data cleaning.