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# Introduction to Sequence Analysis using Stata

The workshop offers participants an introduction to sequence analysis using the user written sequence analysis package of commands developed for Stata. Sequence analysis, which has its roots in biomedical research, has of recent become increasing used in social sciences to study a wide variety of ordered social phenomena, such as; life course development, career development and labour market or employment histories. Researchers studying social networks, implement sequence analysis to model whole networks evolve over time. Social network epidemiologists use sequence analysis to model social contact in order to increase their understanding of how a disease spreads.

Sequence analysis aims to find similarities between sequences, or to detect typical sequences. Similarities between sequences may arise from common causes (common ancestors), or due to causal relationships between the sequences. It does not however, focus on the relationships of the elements within the sequences, rather it is a description of the characteristics of the entire sequences. Although sequences usually refer to phenomena that are ordered temporally (events or states that unfold over a period of time), a sequence may also reflect spatial order, preference order, hierarchical order, logical order, or other types of order.

A variety of techniques have been designed to describe, quantify, and predict sequences, this course focuses on both the graphical analysis of sequences and the calculation of both sequence statistics (descriptive measures of various characteristics of sequences) and sequence similarity statistics (measures of similarity or dissimilarity between sequences.  Sequence statistics and similarity statistics  can then  be incorporated into subsequent analyses – such as regression models, cluster analysis or multidimensional scaling.

In common with TStat’s Workshop philosophy, each individual session, is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained), and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the Workshop, theoretical sessions are reinforced by case study examples, in which the course tutor discusses current research issues, highlighting potential pitfalls and the advantages of individual techniques. The intuition behind the choice and implementation of a specific technique is of the utmost importance. In this manner, course leaders are able to bridge the “often difficult” gap between abstract theoretical methodologies, and the practical issues one encounters when dealing with real data.

Participants should have a computer or laptop with Stata 13 or higher.

The workshop provide a hands on introductory course to techniques to analyze sequences and to Stata’s SQ-Ados, focusing on the following aspects of sequence analysis:

Types of sequences
Data Management for sequence data
Descriptive Statistics
Visualization
Sequence Similarity
Grouping

Throughout the course of the workshop, a broader concept of the term sequences, which includes  both “words” and sequences of job positions, will be analyzed.

The 2016 edition of this course has now taken place. As a result of the general level of interest shown in the program, the workshop will however, be offered in 2017.

L’iscrizione al corso dovrà avvenire tramite lo specifico modulo di registrazione e pervenire a TStat S.r.l. almeno 15 giorni prima dell’inizio del corso stesso. E’ possibile richiedere il modulo di registrazione compilando il seguente form oppure inviando una mail a formazione@tstat.it

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The workshop offers participants an introduction to sequence analysis using the user written sequence analysis package of commands developed for Stata. Sequence analysis, which has its roots in biomedical research, has of recent become increasing used in social sciences to study a wide variety of ordered social phenomena, such as; life course development, career development and labour market or employment histories. Researchers studying social networks, implement sequence analysis to model whole networks evolve over time. Social network epidemiologists use sequence analysis to model social contact in order to increase their understanding of how a disease spreads.