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Summary

A new limited information estimator offers unbiased and efficient results for structural equation models in small addiction studies, outperforming traditional full information methods. This approach enhances accuracy for researchers working with smaller sample sizes.

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Area of Science:

Psychology
Statistics
Addiction Research

Background:

Structural equation modeling (SEM) with full information maximum likelihood (FIML) is standard for addiction research but suffers from bias in small samples.
Limited information estimators, like bias-corrected factor score path analysis, offer unbiased estimates in small to moderate sample sizes.
Underutilization of limited information methods stems from unfamiliarity, lack of practical guidance, and insufficient comparative studies.

Purpose of the Study:

To delineate a bias-corrected limited information estimator for SEM in addiction research.
To provide practical guidance and R code for applying this method.
To compare the performance of limited versus full information estimators in small to moderate samples.

Main Methods:

Step-by-step analysis of a sequential mediation case study on internet addiction.
Implementation using R code with the lavaan package.
A simulation study to assess estimator divergence and performance.

Main Results:

The limited information estimator demonstrated superior performance over FIML in small to moderate sample sizes.
Key advantages included reduced bias, increased efficiency, and enhanced statistical power.
Differences between estimators were consistent, highlighting the practical implications for addiction research.

Conclusions:

The bias-corrected limited information estimator is a valuable alternative to FIML for addiction researchers with small to moderate sample sizes.
This method provides more accurate and reliable results, addressing a critical limitation in current practice.
Accessible guidance and software facilitate the adoption of this improved analytical approach.