Single case experimental designs in agricultural advisor training: A novel method for evaluating capacity building in farmer mental health interventions

Overview

This study evaluated the feasibility and suitability of using single-case experimental designs to assess training outcomes for agricultural advisors in digital mental health interventions for farmers. Recognizing that inter-individual data from nomothetic research may not apply at the intra-individual level, the research applied an idiographic approach through a quasi-randomized multiple-baseline single-case experimental design. The study focused on agricultural advisors being trained in a digital acceptance and commitment therapy intervention, examining whether this methodological approach could be implemented successfully in the context of capacity building for farmer mental health support. This work addresses the need to diversify the evidence base for digital mental health interventions and lay-delivered psychological interventions, which has relied predominantly on aggregate, group-level analyses.

Methods and approach

The study enrolled 18 agricultural advisors in a quasi-randomized multiple-baseline single-case experimental design structured similarly to a pilot randomized-controlled trial. Participants were required to complete a three-item measure daily over a 55-day period, attend two 2.5-hour training sessions delivered via Zoom, and complete three longer surveys at preintervention (Time 1), immediately postintervention (Time 2), and three months postintervention (Time 3). The multiple-baseline design allowed for individual-level analysis of training experiences and outcomes. The study protocol incorporated intensive repeated measurement alongside standard pre-post-follow-up assessment points to capture both idiographic patterns and nomothetic comparisons across the training intervention period.

Results

Participant retention, data missingness, and errors observed in the study were deemed appropriate, supporting the conclusion that the single-case experimental design method is feasible and suitable for evaluating agricultural advisor training in digital mental health interventions. Outcomes at the nomothetic level at Times 2 and 3 were generally consistent with expectations, though the abstract does not specify the nature of these outcomes in detail. The successful implementation of the design across 18 participants, despite the demands of daily measurement over 55 days, demonstrated that this methodological approach can be applied in agricultural advisor capacity-building contexts. The combination of intensive idiographic data collection and standard assessment timepoints proved viable for examining training effects at both individual and aggregate levels.

Implications

The findings establish single-case experimental designs as a feasible alternative methodology for evaluating capacity building in agricultural mental health contexts, complementing the predominantly nomothetic evidence base. The successful application of this idiographic approach suggests it can provide intra-individual evidence that addresses limitations of conclusions drawn solely from inter-individual data. Future research should employ single-case experimental designs in this domain and extend investigation to target multiple levels of analysis, including psychological, sociocultural, and biophysiological dimensions. This methodological diversification is positioned as imperative for building a more comprehensive evidence base for both digital mental health interventions for farmers and lay-delivered psychological interventions more broadly.

Disclosure

• Research title: Single case experimental designs in agricultural advisor training: A novel method for evaluating capacity building in farmer mental health interventions
• Authors: Alison Stapleton, Barbara Moore, Greg Stynes, Noel D. Richardson, Tomás Russell, Louise McHugh
• Publication date: 2026-01-26
• DOI: https://doi.org/10.37433/aad.v7i2.657
• OpenAlex record: View
• Image credit: Photo by Lirset on Freepik (Source • License)
• Disclosure: This post was generated by artificial intelligence. The original authors did not write or review this post.