#SOCIAL SEQUENCE ANALYSIS FULL#

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: Unrestricted and uncontrolled access to the complete career history data (in terms of position, institution, contract type, location etc. Received: OctoAccepted: JPublished: July 29, 2020Ĭopyright: © 2020 Vinkenburg et al.

PLoS ONE 15(7):Įditor: Ting Ren, Peking University, CHINA Our findings on career patterns in research inform further studies and policy making on career development, research funding, and gender equality.Ĭitation: Vinkenburg CJ, Connolly S, Fuchs S, Herschberg C, Schels B (2020) Mapping career patterns in research: A sequence analysis of career histories of ERC applicants. We find grantees among all patterns, and limited evidence of gender differences. With multinomial logit analyses, we estimate the relative likelihood of researchers with certain characteristics in each pattern. We distinguish five career patterns for early and established men and women researchers.

We use sequence analysis to identify career patterns of researchers across positions and institutions, based on full career histories of applicants to the European Research Council frontier research grant schemes. We also do not know whether career patterns of researchers can be considered conventional in terms of steady progress or international mobility, nor do we know if career patterns differ between men and women in research as is commonly assumed.

All analytical steps are demonstrated with hands-on examples using Stata (for data preparation and basic sequence analysis using commands from the SQ and SADI ados) and R (TraMineR and ancillary packages).Despite the need to map research careers, the empirical evidence on career patterns of researchers is limited. Recent approaches combining sequence analysis with event history analysis and propensity score matching will be also discussed. More recently, sequences are scrutinized in other fields of social sciences such as geography, archaeology, and economics This workshop covers longitudinal data management (only briefly) and basic techniques of sequence analysis to (1) describe and (2) visualize sequences, (3) to identify typical patterns among a set of sequences using cluster analysis, and (4) to examine the antecedents and consequences of these patterns. Sequence analysis has attracted increasing attention in the social sciences for the analysis of longitudinal life-course data, as labor market careers, transitions to adulthood, or family formation. These tools were first introduced to social sciences in the form of optimal matching analysis by Andrew Abbott who imported this approach from biology and computer science. Sequence analysis is a set of tools to analyze sequences of categorical states that represent processes that unfold over time. This workshop introduces sequence analysis for social science research. With Marcel Raab (University of Mannheim).

#SOCIAL SEQUENCE ANALYSIS FULL#

Social sequence analysis