Statistical analysis and interpretation in a follow-up study of prelingually deaf children implanted before 5 years...

One important challenge in the follow-up study was the need to develop composite measures that were highly reliable and equally representative of communication skills measured at two distinct developmental periods. A second challenge was the need to partition variance in the outcome variables among conceptually important sets of variables in an attempt to identify unique and significant predictors and to identify likely pathways of influence. A final challenge was the need to preserve sample size in the presence of attrition from the initial data collection period and missing data.

Primary analyses were based on principal components analysis to form composite measures of highly correlated variables followed by hierarchical multiple regression to determine the contribution of predictor sets ordered to reflect important causal assumptions and conceptual questions. The principal components analyses resulted in unidimensional, highly reliable composite measures of communication skill, educational environment, and information processing. The hierarchical multiple regression analyses allowed partitioning variance to isolate the unique contributions made by particular variables or sets of variables. Importantly, these analyses also allowed inferences about pathways of influence from early predictors to later outcomes. Multiple imputation was used to construct complete data sets, preserving power and consistency across the numerous analyses.

Collectively, the chosen statistical analyses provide a pragmatic and parsimonious solution to the challenges posed by the data collected in this study. The analyses allowed clear conclusions about the major predictors of early and late communication skill in this sample and identified likely pathways through which early child, family, and educational environment variables have their influence.