This is a comparative study of the multiple ways of measuring dissimilarities between state sequences. The third section shows the limitations of the standard approaches, and the available variations and variants, including alternative calculations of dissimilarities, multichannel SA and prototypical sequences. The second section relies on a range of concrete applications to present the main usual steps in SA studies: conceptualisation, data collection and preparation, exploration, calculation of dissimilarities and post-dissimilarity treatments. A brief historical account of the method’s development highlights the original intuition, its standardisation, and the dialectic between unity and diversity of the field. It shows how SA differs from other social scientific methods, especially longitudinal, statistical methods. The entry firstly defines sequences, provides examples of them, and presents SA’s underlying sociological concepts and objectives. Numerous fields in the social and political sciences deal with sequences,including life course analysis, the sociology of professional careers, political sociology, the study of political regimes, and various geographical or ethnographic practices. Its core tool, the optimal matching algorithm, screens and discriminates longitudinal processes according to the nature of events, their duration and their order. SA is a unique method for representing, comparing and clustering sequences, for extracting prototypical sequences and for mining sequence populations. Sequence analysis (SA) is the systematic descriptive and causal study of sequences, that is, successions of standard categorical states or events. In Encyclopedia of Research Methods, edited by P. (2008) Sequential analysis test.BLANCHARD P. To test the function with these data: x= seqanalysis(x)Ĭreated by Giuseppe cite this file, this would be an appropriate format: Cardillo G. If it goes out the twilight zone, the sperimental plan can be stopped and non more couple will be required. The algorithm will discard all non informative couple then, it will start to move along the chart. Result are resumed in this table:Ĭouple A B Note 1 1 1 Non informative 2 1 0 All for A 3 0 0 Non informative 4 1 0 All for A 5 1 0 All for A 6 1 1 Non informative 7 0 1 All for B 8 1 1 Non informative 9 1 0 All for A 10 1 0 All for A 11 1 0 All for A 12 1 1 Non informative 13 1 0 All for A 14 0 1 All for B 15 0 0 Non informative 16 1 0 All for A 17 1 0 All for A 18 1 0 All for A 19 1 1 Non informative 20 1 0 All for A A positive result in a patient by the drug is indicated by 1 a negative result by 0. 1952 8:186Įxample: During a sperimentation between two terapies, two drugs A and B were administered to patients couples (one received A, the other B). Thus a conclusion may sometimes be reached at a much earlier stage than would be possible with more classical hypothesis testing or estimation, at consequently lower financial and/or human cost. Instead data is evaluated as it is collected, and further sampling is stopped in accordance with a pre-defined stopping rule as soon as significant results are observed. In statistics, sequential analysis or sequential hypothesis testing is statistical analysis where the sample size is not fixed in advance.
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