# Developing and Testing a Multidimensional Measure of Recovery Capital: Toward Standardized Measurement to Understand andReduce Socioeconomic Disparities in Alcohol Recovery Outcomes

> **NIH NIH R21** · STATE UNIVERSITY OF NEW YORK AT BUFFALO · 2020 · $183,861

## Abstract

Project Summary/Abstract
The purpose of this study is to advance the science of recovery from alcohol problems through development of
a standardized survey measure of recovery capital. Recovery capital is a theoretical construct that
encompasses the ways in which physical, social, human, and cultural resources shape a person's likelihood of
sustaining recovery from addiction. Recent studies have highlighted the importance of recovery indicators
beyond alcohol consumption/abstinence, such as gains in health and overall quality of life. Although research
indicates that recovery capital plays an important role in holistic recovery outcomes and that deficits in
recovery capital may partially drive socioeconomic disparities in such outcomes, the field is limited by the lack
of a psychometrically sound quantitative measure. Currently, the predominant measure of recovery capital is
the 50-item Assessment of Recovery Capital (ARC), recently made available as a brief 10-item version (BARC-
10). The ARC appears misaligned with the theoretical literature; for example, its sobriety subscale implies an
abstinence-based definition of recovery, which is inconsistent with recovery capital theory. Building from a
preliminary study indicating psychometric weaknesses with the ARC when used with a racially and ethnically
diverse low-income sample (N=273), in the proposed study we will develop a new measure of recovery capital,
the Multidimensional Inventory of Recovery Capital. We will employ best practices in measurement
development to generate an item pool that is informed by theoretical conceptualizations of recovery capital, as
well as the perspectives of diverse individuals in recovery. Initial items will be refined through focus groups and
cognitive interviews with people in recovery from alcohol problems, stratified by income and race/ethnicity, and
will include individuals recovering without assistance from formal treatment systems. The emergent measure
will be rigorously pilot-tested in a national sample using Amazon MTurk (n=500) and will use graded response
models as an extension of item response theory to assess and calibrate item performance. The study will also
establish the psychometric properties of the final measure in a nationally representative sample (n=500). Using
a sequential mixed methods approach, our study will address the following specific aims: (1) Develop a new
theory-aligned measure of recovery capital, the Multidimensional Inventory of Recovery Capital, using
qualitative analysis of focus group and cognitive interviewing data with an economically and racially inclusive
sample of people in recovery from alcohol problems; (2) Pilot test the Multidimensional Inventory of Recovery
Capital in an economically and racially diverse sample of people in recovery from alcohol problems and assess
its feasibility, reliability, and item performance; and (3) Establish a final version of the Multidimensional
Inventory of Recovery Capital through asses...

## Key facts

- **NIH application ID:** 9874491
- **Project number:** 1R21AA028099-01
- **Recipient organization:** STATE UNIVERSITY OF NEW YORK AT BUFFALO
- **Principal Investigator:** Elizabeth A Bowen
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $183,861
- **Award type:** 1
- **Project period:** 2020-06-10 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9874491

## Citation

> US National Institutes of Health, RePORTER application 9874491, Developing and Testing a Multidimensional Measure of Recovery Capital: Toward Standardized Measurement to Understand andReduce Socioeconomic Disparities in Alcohol Recovery Outcomes (1R21AA028099-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9874491. Licensed CC0.

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