Fieldwork and data collection

The fieldwork was undertaken in the following border towns: Ouessa in Burkina Faso and Hamile in Ghana, (see figure 2, chap 1). Participants were more than ready to talk to the researcher after they learned that the researcher is a native Dagara studying in America, and is interested in the development and promotion of the Dagara language, culture, and the values of the Dagara language in general. Some participants, however, were a bit skeptical when they realized that they had to sign a consent form before their voices were tape recorded. Even after reading through the motivations for the research in the consent form, they were still a bit hesitant to sign. Fortunately, however, two explanations made the data collection very successful.

First, my acquaintances were able to convince or assure them that the data obtained from them will be treated with absolute confidentiality. I also assured them that the random code numbers (e.g. P001, P002, etc.) they see on the biographic data form will be assigned to each of them in place of their names in order to conceal their actual identity. I further promised to them that no information will be released that could reveal anyone’s identity beyond his/her voice on the audio recording, if permission is granted to allow audio clips to be played back during educational presentation or classroom teaching and learning activities. However, no information

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that they gave would be held against any of them in the future.

Second, I took the time to explain the benefits of the research. I made them understand that the research will help Dagara native speakers to evaluate the importance of their own language and how it can be developed and used to explain some of the new and foreign scientific terms that they meet in their daily lives. It will also help them to improve on their cultural relationship as well as enhancing the economic activities between the two communities (Hamile and Ouessa). But the real purpose of the research (eliciting lexical borrowings) was was not disclosed to participants. Notwithstanding a few recruits, specifically females, refused to participate, either out of skepticism or the mere fear of not being able to answer some of my questions satisfactorily. This is despite the fact that I assured each of them that the questions were not based on facts or a good knowledge of Dagara culture or the language structure.

No public announcement or advertisement was made for the data collection procedure. The main way of recruiting was through acquaintances of the investigator who introduced him to local friends and families (participants) whose level of education and age met the criteria of selection to participate in the data collection. This method of recruitment has been chosen because it offers the possibility to access participants through people who already know them or know a bit about their linguistic and social backgrounds. This method is especially helpful in communities with fairly closed social networks. Through acquaintances that live on the research sites, participants were reassured of their protection. Data solicitation then took place through face-to-face interviews between the investigator and the prospective participants. The interview was conducted entirely in Dagara by the investigator; this allowed participants to produce spontaneous speeches in their own vernacular.

98 4.8 Statistical tools

In language contact research, quantitative analysis of data is an important approach to explain the linguistic phenomena characterizing a speech community. This is usually the case where the study performs a random sampling of a population. In this study, the following statistical test application will be used: cross tabulations (M. Triola 2010:598), chi-square (Chi² or χ²) test (Hatch and Lazaraton 1991), involving the use of probability value, and finally a descriptive statistical analysis of the data. Two important concepts are used in Chi square test statistics calculation. They are, the degree of freedom (df) and probability value (p-value). The degree of freedom is usually the sample size, that is, the number of quantities that can vary (dependent or independent variables). Degree of freedom is found by subtracting 1 from the total number of variables. The degree of freedom for 5 dependent variables (English, French, Twi, Moré and Dioula) for example will be equal to 4. As such, the probability value is found by finding the df value from the vertical figures and read across from the horizontal value under 0.05 significance value. For the significance level, it is set at α = 0.05 (cf. Hatch and Lazaraton 1991: 603), so where the probability value is less than α = 0.05, it means we are 95% confident that the outcome is not by chance, but is deemed significant. For detailed Chi square calculations see appendices 6 and 7.

Cross tabulation is a statistical process that summarizes categorical data to create a contingency table. The contingency table shows frequencies corresponding to two or more variables where some of the variables are used to categorize rows and others categorize columns. These tables provide a basic picture of the interrelation between two variables and can help find interactions between them. Following Guy’s (1993:235) approach to variables interpretation, I present here only one of the variables as an example to explain further how the contingency table

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works. In my study, we have the following independent variables: gender (male and female), age, and level of education, and the dependent variables are the amount of English, French, Twi, Moré and Dioula borrowings. A contingency table can be created to display for example, the number of individuals who are males and females and the amount of borrowings from each of the donor languages that each individual by gender has used.

The table allows us to see at a glance the proportions of males and females borrowings we intend to interpret. The significance of the difference between the two proportions can be assessed with a statistical test such as Pearson's chi-squared test and the critical/probaility value method, provided that the entries in the table represent individuals randomly sampled from the population about which we want to draw a conclusion. If the borrowing proportions of males and females in the different columns vary significantly between rows, we say that there is a contingency between the two variables, i.e., the two variables are not independent. If there is no contingency, we say that the two variables are independent.

Guy (1993) further states that the “null set hypothesis always states that there is no relationship between the independent and the dependent variables, and the observed distribution of the data is due merely to random fluctuation and sampling error”. In this wise significance, values of the contingency table are derived from a chi-squared test. The chi-square test for two- way designs, according to Hatch and Lazaraton (1991), is a test that compares the relation of frequencies for two variables both of which have two levels. In my study, since the data samples are from individuals of different social groups (age, gender, level of education), the question is whether their different social backgrounds might have any relation regarding the amount of borrowings they use in speech. Furthermore, the chi-square test will also help finding out if there is any relation between the registers, age, sex and level of education and the major languages

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from Burkina and Ghana that tend to provide loans to Dagara language. In other words, I will analyze how speakers’ age, sex, and level of education vary with respect to the amount of borrowing from French, English, Twi, Dioula, and Moré.

4.9 Summary

In order to study and understand language use patterns and style in any given community, whether monolingual or bilingual, the interview method should be carefully considered. In my study, I adopt the Labovian sociolinguistic interview approach to elicit data on single loanword adaptations by Dagara-French and Dagara-English bilingual speakers. The Labovian interview method allows the researcher to begin with general questions, leading to specific questions on four different semantic fields and, finally, targeting language issues. I also took into consideration the social groups involved. In this regard, I ensured that data were collected from individuals that were sampled randomly based on age, gender, and level of education. Other social factors included Dagara social network integration and migration which are all factors influencing language contact and loan words adaptations. The participants were therefore carefully selected through acquaintances who helped with the recruitment.

Data were transcribed and coded following Winford’s (2003) borrowing typology, including ‘pure’ loanwords and loanblends, loanshifts, and native creations. This enabled me to propose hypotheses about borrowing. The interview was guided by a questionnaire that included six modules or topics of conversation: 1) health, 2) religion/culture, 3) economic, 4) education, and 5) language.

The data collection was not devoid of problems, as I explained the difficulties of getting participants to talk to me during the farming season, and in reaching females participants. Nonetheless, in general terms, the fieldwork was very successful as I was able to collect data

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from the target number of 100 participants within two and a half months thanks to the help of acquaintances of mine, as I acknowledged earlier.

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