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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Individual influence on model selection.

Sonya K Sterba1, Jolynn Pek

  • 1Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA. Sonya.Sterba@Vanderbilt.edu

Psychological Methods
|August 1, 2012
PubMed
Summary
This summary is machine-generated.

Psychology researchers often overlook how individual data points can skew model selection results. This study introduces three diagnostics to assess case influence on model ranking, improving the reliability of statistical modeling.

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Area of Science:

  • Psychology
  • Statistics
  • Quantitative Research Methods

Background:

  • Psychology increasingly employs model selection over isolated model fit evaluation.
  • The impact of individual cases on model selection outcomes is underappreciated.
  • Existing research often focuses on case influence on single model fit, not selection.

Purpose of the Study:

  • To introduce the issue of case influence on model selection in psychology.
  • To propose three novel influence diagnostics for common model selection indices.
  • To provide practical guidance and software for applied researchers.

Main Methods:

  • Development of three influence diagnostics for chi-square difference test, Bayesian Information Criterion (BIC), and Akaike's Information Criterion (AIC).
  • Diagnostics derived from full information maximum likelihood estimation byproducts.
  • Validation through simulated and empirical examples across various research designs (cross-sectional, longitudinal, multilevel) and outcome distributions.

Main Results:

  • Proposed diagnostics are computationally efficient, requiring no heavy burden.
  • Demonstrated generality of diagnostics across diverse research scenarios.
  • Illustrates how understanding case influence enhances interpretation of sample representativeness.

Conclusions:

  • Awareness of case influence on model selection is crucial for robust psychological research.
  • The proposed diagnostics offer a practical tool for applied researchers to assess model stability.
  • Improved understanding of case-level impact leads to more reliable and representative findings.