VI. PERSON-SPECIFIC INDIVIDUAL DIFFERENCE APPROACHES IN DEVELOPMENTAL RESEARCH.

Area of Science:

• Psychometrics
• Quantitative Psychology
• Statistical Modeling

Background:

• Common analytic approaches like polynomial curve modeling and repeated measures ANOVA derive individual differences from a single, shared model.
• These methods rely on aggregate data (means and covariance matrices) to estimate parameters.
• A limitation is the assumption of a common underlying structure across all individuals.

Purpose of the Study:

• To demonstrate how common analytic approaches create individual difference measures.
• To introduce time-series related approaches as an alternative for modeling individual differences.
• To highlight the flexibility of time-series methods in capturing person-specific parameters.

Main Methods:

• Recasting common analytic models (e.g., factor models) into time-series frameworks.
• Utilizing single-subject time series approaches, such as vector autoregressive (VAR) and P-technique factor models.
• Comparing parameter estimation between common models and time-series models.

Main Results:

• Demonstration that conventional methods generate individual differences based on a common model.
• Illustration of how time-series approaches, like P-technique factor models, yield parameters that can vary between individuals.
• Empirical examples showcasing the differences in results between factor models and time-series factor models.

Conclusions:

• Time-series approaches offer a more nuanced way to model individual differences compared to traditional methods.
• The ability of methods like P-technique factor analysis to model individual-specific parameters is a significant advantage.
• These findings have implications for understanding heterogeneity in psychological and behavioral data.