Life-Course Data and the Longitudinal Classification of Education
In this chapter, we present a longitudinal approach to the classification of education as applied to data from Starting Cohort 6 of the NEPS. Arguing that educational achievement is a time-dependent process involving the timing and sequence of transitions
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Abstract
In this chapter, we present a longitudinal approach to the classification of education as applied to data from Starting Cohort 6 of the NEPS. Arguing that educational achievement is a time-dependent process involving the timing and sequence of transitions in an educational state, we examine the following two questions: 1) How can inter- and intra-individual variations of educational achievement be analytically described and compared ? and 2) How can longitudinal data on educational careers be adequately measured and coded in analytically meaningful ways ? We present CASMIN and ISCED-97 as helpful coding frames to capture educational achievement. Referring to life-course data from NEPS Starting Cohort 6, we present a longitudinal assignment scheme of educational attainment that we implemented in a generated transition data file called Education, which accompanies the Scientific Use File. Education provides upward transitions in ISCED and CASMIN for respondents in an easy-to-manage event-time format. Using the file, researchers can easily reconstruct the educational level measured in standard classifications for each respondent at each point in the recorded lifetime. Finally, we demonstrate the power of Education through two simple exemplary analyses.
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Introduction
This chapter presents a longitudinal approach for classifying educational achievement that we have applied to recently published data from the NEPS Starting Cohort 6. Education is without a doubt one of the major resource structuring social chances and forms of participation of individuals in modern societies (Blossfeld, 1985). Since educational attainment is a predominant mechanism of status attainment and social mobility (Blau & Duncan, 1967; Müller & Mayer, 1976), it is the subject of a wide ar© Springer Fachmedien Wiesbaden 2016 Hans-Peter Blossfeld et al. (eds.), Methodological Issues of Longitudinal Surveys, DOI 10.1007/978-3-658-11994-2_37
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Jan Skopek and Manuel Munz
ray of research on social inequality and stratification (e. g., Shavit & Blossfeld, 1993; Breen & Jonsson, 2005; Breen et al., 2009). As a result, educational level is one of the most considered variables in empirical studies of social-science research. Hence, providing data on individuals’ educational attainment is of crucial importance for survey-data providers. Moreover, educational level is also a subject of change over time and is thus substantially intertwined with a broad range of individuals’ life-course events. However, commonly used datasets provide information on education only at a certain point in time (for instance, the highest educational level of an individual at the time of interview), thereby limiting a methodologically adequate consideration of education as a time-dependent variable. In terms of interview time, this cross-sectional approach might be quite efficient for many survey contexts. However, this practice has serious shortcomings if one considers education from a more substantial perspective. First, educational attainment should be conceiv
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