Appendix: Political Discourse in Adolescents with Their Mothers

  • Gerald Young
Part of the The Springer Series in Adult Development and Aging book series (SSAD)


The synthetic theory of development was used as a basis to develop discourse coding systems (i.e., a text complexity measure which examines the cognitive complexity of narrative texts and a conversational support scale designed to investigate the quality of social discourse). The scales were applied to an empirical study of 48 mother—adolescent dyads who participated in individual interviews and a discussion on two sociopolitical topics. This study had been analyzed previously (Santolupo & Pratt, 1994) with other scales, and the general result was that older adolescents showed more complexity of political reasoning on a pretest than did younger adolescents. However, there was no evidence in support of several of the predictions, and inspection of the coding systems used suggested that their limitations may have played in this result. The new coding schemes were applied to the transcripts of the adolescent—mother discourse with the goal of supporting the predictions that were not substantiated previously.

Both the text complexity measure and the conversation support scale gave significant results where none were found in the original analysis. For example, text complexity was found to correlate positively with increasing age and with increasing education level of the adolescents. Also, a positive correlation between the conversational support measure and an authoritative parenting style was found. In addition, the two scales were correlated positively between them for mothers. The two new coding systems appear to be useful measures because they are based on an internally consistent developmental framework and produce hypothesis-consistent data.


Parenting Style Cognitive Level Cognitive Complexity Text Complexity Synthetic Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Gerald Young
    • 1
  1. 1.Glendon CollegeYork UniversityTorontoCanada

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