This presentation outlines the findings of a small scale research project charged with exploring the role of the simultaneous translator or interpreter in the international research process, highlighting important differences in how the role is perceived and explores the implications of this on the research process. It involves a detailed analysis of the distinctions between linguistic, cultural and other forms of interpretation. It concludes with recommendations for how interpreters can be more effectively utilised within the process.
This paper describes how Neuro Linguistic Progamming as a technique can be integrated in qualitative research with children to enhance the effectiveness. We propose that this integration leads to more valuable results than generally is expected with regard to pretesting advertising with children from six up to eight years of age. Herewith we take the view that these children could give relevant information, which as a result of development related factors, is hardly accessible with normal qualitative techniques. At the time of writing this paper, research is on its way to demonstrate the above. At the seminar we will present the results.
Neuro-Linguistic Programming is a phrase that can be very off-putting, yet it sums up three very simple ideas that form the core of the insights and discoveries Grinder and Handler made. The 'Neuro' part of NLP recognises the fact that all human behaviour is neurologically based. We experience the world through sight, hearing, smell, taste and touch - the five senses - and then make 'sense' of the information. This neurological process is both invisible and visible, occuring as electro-chemical transmissions in our nervous systems as well as constantly changing physiological responses. The 'Linguistic' component of NLP refers to the fact that we use language to order, classify and communicate these sensory experiences, whilst 'Programming' encapsulates the idea that we we all develop patterns of behaviour that we use (and re-use again and again) to achieve particular results. Thus NLP provides a model of understanding about subjective experience; how we organise it so we can make sense of the scramble of stimuli we receive; how we use language to share our unique experience with others; and how we act in response, either intentionally or unintentionally. The relevence of NLP to marketing, advertising and research lies in the fact that the early founders of this model spent a great deal of time trying to understand how excellent communicators achieve success whilst others do not. They began by studying three famous people - Virginia Satir, a famous family therapist; Gregory Bateson, a high profile anthropologist; and Dr Milton Erikson, a hynotherapist. Through processes of minute observations and careful listening, they found for example that Virginia Satir paid close attention to both the body posture and the linguistic patterns of her clients. Handler and Grinder found that poor communication and misunderstanding occurred when two people (or a person talking to a group) 'mismatched' i.e. diplayed different patterns of behaviour from another person, so breaking rapport. By copying their example and analysing what these excellent communicators were doing instinctively and unconsciously, Bandler and Grinder found that they could improve their communication skills without years of trial and error or intensive personal tuition. They went on to model all sorts of people such as athletes, business people, dancers, teachers and politicians, all the time becoming consciously aware of the processes involved in excellence. This paper will discuss two NLP models which have direct direct application to an understanding of brands and brand communication. These are: Representational systems and Hierarchical levels of communication.
The applications of computer language analysis in studies originated when micro-computing, linguistics and marketing came together. Since the principal element available for studies is language itself, analysts have concentrated upon creating methods and tools which enable them to analyse it. A word's meaning depends upon the context of the other words which surround it; lexicologists have created tools which make the best use of this environment and have applied this to market research. In this way, it has been possible to create new computer programmes which bear little resemblance to software available on the market (word-processing, data bases, translating machines). It was only when users began to have easy access to computers that they concentrated upon the full development of language analysis. It finds its legitimacy not by trying to replace other methods of analysis, but by working alongside them with the aim of obtaining clearer results and responding to the increasing demands of advertisers who want to know, to the exact word, the positioning of their product, the image of their brand or the specificity of their target. The development of computer language analysis is highly dynamic, and one is often surprised to discover how many people are actually working on it.
The applications of computer language analysis in studies originated when micro-computing, linguistics and marketing came together. Since the principal element available for studies is language itself, analysts have concentrated upon creating methods and tools which enable them to analyse it. A word's meaning depends upon the context of the other words which surround it; lexicologists have created tools which make the best use of this environment and have applied this to market research. In this way, it has been possible to create new computer programmes which bear little resemblance to software available on the market (word-processing, data bases, translating machines). It was only when users began to have easy access to computers that they concentrated upon the full development of language analysis. It finds its legitimacy not by trying to replace other methods of analysis, but by working alongside them with the aim of obtaining clearer results and responding to the increasing demands of advertisers who want to know, to the exact word, the positioning of their product, the image of their brand or the specificity of their target. The development of computer language analysis is highly dynamic, and one is often surprised to discover how many people are actually working on it.
This paper sets out to illustrate the application of N.L.P. to qualitative research and in particular to communication. It was born a year ago from a number of factors: first and foremost the growing difficulty in learning, even by means of the strict application of traditional methodologies, consumer attitudes toward a communicated element, be it a name, packaging or advertising communication. NLP is a new discipline that permits profound readings of the response behavior to a stimulus that has been received. It was created in California during the '70s as a result of studies on the structure of language and elaboration of in formation applied to communication, conducted by a linguist, John Grinder, and a mathematician, Richard Handler, interested in communication problems. It may be considered point of arrival of the currents of humanistic psychology (Maslow, Rogers, Peris and Berne) enriched by the studies of Bateson and Watzlawikz on communication and by the contributions of Milton Erickson and the currents of communication pragmatics.
In 'broadening the use of research' is essential to comprehend each other in direct communication and also in depth of meaning. This latter understanding- of the use of language linked with culture makes a vast difference to the collection and interpretation of research data. Case histories from a consultant researcher's note book are described to demonstrate the problems that can happen at every stage of research through from concept to presentation. There is continuous need for understanding in the development of research.
It is necessary in a multilingual survey to ensure the comparability of the data, no matter in which language it was collected. For purely closed-ended (multiple-choice) questions this is moderately straightforward, and depends largly on an accurate translation of the questionnaire. For open-ended questions, however, in which a free discursive or verbatim response is invited, answers given in different languages may be compared only after some form of translation. This translation is normally achieved by referring the verbatim responses back to a multi-lingual code-frame. The translation of this code-frame may well lie on the critical path of the data analysis: it is certainly a translation which cannot be completed until all the data is in. Advancing technology, particularly in the field of Computational Linguistics, allows us to consider another approach. This is to translate automatically the verbatim responses themselves before coding. Machine Translation (MT) has had some spectacular failures in the past, and is only just beginning to give useful results in particular restricted contexts.