Resilient seedling, representing robust analysis in addiction research.

Small Sample, Big Insights: A Better Way to Analyze Addiction Research

Rhea Morgan in Mind & Education December 2025 • 4 min read.

"Tired of limitations with small sample sizes in addiction studies? Discover a robust alternative to traditional SEM that delivers reliable results."

Addiction research often relies on understanding complex relationships between different factors, using latent variables to represent these concepts. Structural equation modeling (SEM) is a popular method for examining these relationships, but it can struggle with smaller sample sizes, leading to biased results.

Traditional SEM methods, which depend on large samples for accurate estimations, often fall short when applied to studies with fewer participants. This limitation can hinder the progress of research, especially when recruiting a large sample group is difficult, costly, or time consuming. Recent studies suggest a need for alternative approaches that can handle smaller datasets without sacrificing reliability.

This article introduces a robust alternative: bias-corrected factor score path analysis (BCFSPA). This method offers a way to achieve more reliable and unbiased results when working with small to moderate sample sizes in addiction research. We'll explore how BCFSPA works, why it's effective, and how it can be implemented using readily available software.

BCFSPA: Unlocking Insights from Limited Data

Resilient seedling, representing robust analysis in addiction research.

Bias-corrected factor score path analysis (BCFSPA) presents a step-by-step approach to overcome the limitations of traditional SEM. BCFSPA breaks down the analysis into manageable parts, separately estimating measurement models for each latent variable before examining the relationships between these variables. This piecewise approach reduces model complexity, thus improving the stability and accuracy of the results, especially when sample sizes are not large.

Unlike full information methods that can be sensitive to model misspecification (where a part of the model is incorrectly defined), BCFSPA limits the impact of such errors. This is particularly valuable when some aspects of the research model may not be perfectly understood or accurately measured. The process involves:

Estimating Measurement Models: BCFSPA first estimates separate measurement models for each latent variable. This involves specifying how the observed variables relate to the underlying constructs.
Calculating Factor Scores: The next step computes factor scores, which represent the estimated values of each latent variable for each participant.
Correcting Covariance: Bias correction is applied to the covariance matrix of the factor scores. This accounts for measurement error and indeterminacy inherent in factor scores.
Path Analysis: Path analysis is then conducted using the corrected covariance matrix to estimate the relationships between the latent variables.

Recent research shows that BCFSPA consistently outperforms conventional full information maximum likelihood estimators in small to moderate sample scenarios. Studies show it can improve the accuracy and efficiency of addiction research, especially when participant numbers are a constraint. The method delivers results with reduced bias, even when the data doesn't follow a perfect normal distribution.

A Path Forward for Addiction Research

BCFSPA offers a promising alternative for addiction researchers grappling with the challenges of small to moderate sample sizes. By employing this method, researchers can enhance the reliability and accuracy of their findings, leading to more informed conclusions and a deeper understanding of complex phenomena.

While BCFSPA presents a significant advancement, it’s essential to acknowledge its limitations. It requires individual factor models, and implementation can become complex with multidimensional indicators. As research evolves, addressing these constraints will pave the way for even more robust and versatile analytical techniques.

The future of addiction research lies in embracing innovative methods like BCFSPA, which empower researchers to extract meaningful insights from limited data. By expanding our analytical toolkit, we can accelerate the pace of discovery and improve outcomes for individuals and communities affected by addiction.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.addbeh.2018.10.032, Alternate LINK

Title: A Robust Alternative Estimator For Small To Moderate Sample Sem: Bias-Corrected Factor Score Path Analysis

Subject: Psychiatry and Mental health

Journal: Addictive Behaviors

Publisher: Elsevier BV

Authors: Ben Kelcey

Published: 2019-07-01

Everything You Need To Know

What is bias-corrected factor score path analysis (BCFSPA), and why is it useful in addiction research?

Bias-corrected factor score path analysis, or BCFSPA, is a statistical method designed to analyze relationships between different factors in research, particularly when you have a limited number of participants. It's an alternative to traditional structural equation modeling (SEM), offering a way to get more reliable results from smaller datasets. BCFSPA is useful when large sample groups are difficult or costly to recruit, or when data is not perfectly normally distributed.

Why is structural equation modeling (SEM) sometimes inadequate for addiction research, and how does BCFSPA address these limitations?

Structural equation modeling (SEM) is a statistical technique used to examine relationships between observed variables and latent variables. However, SEM often requires large sample sizes to produce stable and accurate results. In addiction research, where obtaining large participant groups can be challenging, SEM may lead to biased or unreliable conclusions due to the constraints of smaller datasets. BCFSPA is designed to overcome this problem.

Can you explain the step-by-step process of how bias-corrected factor score path analysis (BCFSPA) works?

BCFSPA works in a step-by-step manner. First, it estimates separate measurement models for each latent variable to see how observed variables relate to underlying concepts. Next, it calculates factor scores, estimating the value of each latent variable for each participant. It then corrects the covariance matrix of the factor scores to account for measurement errors. Finally, it uses path analysis with the corrected covariance matrix to assess the relationships between latent variables. This approach reduces model complexity and the impact of model misspecification.

In what specific ways does BCFSPA improve the accuracy and reliability of addiction research findings?

BCFSPA improves the accuracy of addiction research because it reduces bias and increases reliability, particularly when dealing with small to moderate sample sizes. By breaking down the analysis into manageable steps, correcting for measurement error, and being less sensitive to model misspecification, BCFSPA delivers more robust results compared to traditional SEM. This is crucial when participant numbers are limited, and researchers need to draw valid conclusions from their data.

What are the broader implications of using BCFSPA for advancing addiction research and developing effective interventions?

BCFSPA's ability to handle small to moderate sample sizes has significant implications for addiction research. It allows researchers to investigate complex relationships with limited data, which is common in addiction studies due to challenges in recruiting large participant groups. This can lead to a deeper understanding of addiction-related phenomena and the development of more effective interventions. Additionally, the use of BCFSPA enhances the reliability of findings, contributing to evidence-based practices in the field.