Analysis of change: modeling individual growth.

Area of Science:

• Developmental Psychology
• Neuroscience
• Biostatistics

Background:

• Traditional research on change often uses increment/decrement models, posing measurement and analysis challenges.
• Individual growth curves offer an alternative perspective, viewing change as a continuous underlying process.
• Difference scores, commonly used to study change, present inherent limitations.

Purpose of the Study:

• To review and contrast traditional methods of studying change with individual growth curve analysis.
• To clarify problems associated with using difference scores in change research.
• To illustrate the application of growth curve analysis in pediatric neuroscience.

Main Methods:

• Review of conceptualizations of change in psychological and developmental research.
• Comparative analysis of difference scores versus growth curve modeling for measuring change.
• Application of growth curve analysis to longitudinal data on cognitive function recovery.

Main Results:

• Conceptualizing change as increments/decrements complicates measurement and analysis.
• Individual growth curves provide a more accurate representation of continuous change.
• Growth curve analysis offers advantages over traditional methods for studying change and its correlates.

Conclusions:

• Individual growth curves are a more robust approach for studying developmental change.
• Growth curve analysis enhances the measurement and understanding of change, particularly in clinical populations.
• This methodology is valuable for research on recovery of function after pediatric brain injury.