Macrostructural in control Cinderella narratives (Greenslade et al., 2020)
2020-07-29T23:31:44Z (GMT) by
Purpose: Macrostructural narrative analyses are important clinical measures, revealing age-related declines and disorder-related impairments in the accuracy, completeness, logical sequencing, and organization of content. The current study aims to provide preliminary data on typical aging and psychometric evidence supporting multilevel Main Concept, Sequencing, and Story Grammar (MSSG) analyses that capture these aspects of narratives.
Method: Transcripts of Cinderella narratives for 92 healthy control participants stratified across four age brackets from the online database AphasiaBank were coded by Richardson and Dalton (2016) for main concept (MC) analysis. In the current study, MSSG analyses were completed for (a) logical sequencing, independently and in combination with MC accuracy and completeness (MC + sequencing), and (b) story grammar organization (i.e., inclusion of episodic components and complexity of episodes). Interrater agreement (99%–100%) revealed highly reliable scoring.
Results: Descriptive statistics for the typically aging sample are presented for sequencing, MC + sequencing, total episodic components, and episodic complexity. Scores for participants over 60 years of age were lower (poorer) than scores for those 20–59 years of age, supporting the construct validity of score use for identifying age-related declines in performance.
Conclusions: This study’s novel MSSG analyses of narrative production efficiently assess the logical sequencing and story grammar organization of content in healthy controls. Preliminary reliability and validity evidence support the use of all scores to measure age-related changes in narrative macrostructure. Data from this typically aging sample provide a foundation for future research and clinical assessment aimed at quantifying narrative deficits in adults with communication disorders.
Supplemental Material S1. Coding sheet, general instructions, materials for conducting reliability analyses.
Greenslade, K. J., Stuart, J. E. B., Richardson, J. D., Dalton, S. G., & Ramage, A. E. (2020). Macrostructural analyses of Cinderella narratives in a large nonclinical sample. American Journal of Speech-Language Pathology. Advance online publication. https://doi.org/10.1044/2020_AJSLP-19-00151