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What measurements should linguists use when comparing texts written by different writers? We report aspects of a systematic evaluation of 381 different language measures derived from 200 analytic tools, carried out during the pilot for a study exploring genetic contributions to language variation. The measures covered lexis, structure, meaning, and discourse features, and were evaluated with a focus on capturing numerically the qualitative features that linguists consider central to differentiating one text from another. We review principles for selecting analytic tools, and the choices faced by the researcher in processing and analysing data. We then identify and demonstrate five of the measures, which between them provide a useful profile of different linguistic features, and note correlations with psychometric measures taken for each writer. We conclude with some caveats regarding general issues of validity and some indications about potential links between our work and research into authorship attribution for forensic purposes

Word associations have traditionally been used in linguistic research as a means of accessing information about the organisation of the mental lexicon. A number of important studies have revealed differences in word association behaviour in the L1 and the L2, but have failed to find consistent behaviour patterns. The study reported here suggests that this failure might be due to two factors: the choice of stimulus words and the categorisation of responses, which impose artificial constraints on both association behaviour and the exploration of response types. In order to move the investigation of L2 word associations forward, this study compares native speaker responses to a specific set of stimuli with those of advanced non-native speakers. The types of association response made by each group of subjects are investigated by means of a retrospective interview, and patterns in response types are mapped. This results in a new method of categorising word association responses, and provides more precise insight into the differences between L1 and L2 association patterns.