my project investigates the effectiveness of mainstream teaching of topic specific vocabulary through contextualisation. It examines how this facilitates the bilingual child with Down Syndrome to develop and retain the topic specific new vocabulary items. In the first phase of this study, the child with DS’s parents received the target topic specific vocabulary list before the topic being introduced. Therefore, the child received L1 support to understand the L2 target vocabulary items by her parents.
The second phase of the research focused on the nature of understanding transferred to L1 and also it assesses how this vocabulary transfers happens between two languages. At this phase, the child with DS’s parents did not receive the target topic specific vocabulary list before hand and the child did not receive any extra support from her L1 to the development of L2 vocabulary items. This study analyses how the typical mainstream teaching of topic specific L2 vocabulary enables the bilingual child with DS to develop and retain new topic specific vocabulary items. The analysis in this chapter assesses whether there any significant differences of L2 to L1 vocabulary transfer between the child with DS and other TD bilingual children.
Excel works, but you will get more reliable analysis if you use SPSS. There could be software programmes for vocabulary analysis. I suggest you have a look at methodologies of similar researches. You will find a clear picture of how to do it.
Because this is a case study, you have to treat each vocabulary item as a data point. For example, if you wanted to test if there is a difference between items retained with and without additional contextualization, you could use excel function =ttest(array1, array2, tails, type), where your array1 is a selection of binary scores for each vocabulary item recalled under condition 1 and array2 is a selection of scores (whether or not the item was recalled) for these items under condition 2. To get significance you can ask excel to return a critical t-value for your chosen p-value and your degrees of freedom (in this case based on the n of vocabulary items), you can use =tinv(probability, degrees of freedom).
If your data is binary, it is not normally distributed and therefore not suitable for analysis with a t-test. More appropriate tests would a Fisher's Exact test, or a Chi-squared test (if you have a lot of data). [http://www.real-statistics.com/chi-square-and-f-distributions/fishers-exact-test/]
In you analysis, you will also need to keep in mind that learning certain words in the first set may have had a facilitating or inhibiting effect on learning other words in the second set (e.g., words in each set might be semantically related in some way or other, collocationally related or formally similar). In other words, there is possibility that the two word sets are not truly independent.
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