> ## Documentation Index
> Fetch the complete documentation index at: https://strella-7d79fa26-mintlify-8c581790.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Choosing Incentives and Study Size for Your Research

> Set the right incentive amount and participant count for your Strella study based on research type, audience seniority, and time commitment.

Getting your incentive and participant count right before you launch saves time, budget, and frustration. An incentive that's too low slows recruitment; a sample that's too large burns budget before you've learned anything actionable. This guide walks you through both decisions so you can configure your study with confidence.

## Choosing the Right Incentive

The amount you offer depends on three factors: the **type of research study**, the **target audience**, and the **time commitment** you're asking of participants. Higher-effort sessions and harder-to-reach audiences warrant higher pay.

<Tip>
  Use these signals to adjust your incentive mid-study if recruitment stalls:

  * **Increase pay for senior-level professionals** or participants with high-demand, specialized skills — their time is scarcer and they have a higher opportunity cost.
  * **Complex or sensitive topics** (healthcare decisions, financial stress, workplace conflict) require higher incentives to compensate participants for the extra cognitive or emotional effort.
  * **If recruitment is running slow**, raising the per-minute rate is one of the fastest levers you can pull to speed things up without changing your screener criteria.
</Tip>

<CardGroup cols={2}>
  <Card title="Consumer audiences" icon="users">
    Standard rates apply for general-population consumers. Adjust upward for longer sessions or topics that require personal disclosure.
  </Card>

  <Card title="Professional & B2B audiences" icon="briefcase">
    Senior managers, specialists, and executives command higher rates. Budget accordingly when targeting decision-makers.
  </Card>

  <Card title="Sensitive research topics" icon="shield">
    Studies covering health, finances, or personal hardship should offer premium incentives to reflect the emotional investment participants make.
  </Card>

  <Card title="Slow-recruiting studies" icon="gauge">
    Raise the per-minute rate rather than lowering your screener bar. You'll attract more qualified participants without compromising data quality.
  </Card>
</CardGroup>

## Choosing How Many Participants to Invite

The right sample size depends on your research goals and study type. Generative discovery studies need fewer participants to surface repeating themes, while evaluative or quantitative studies typically require more to produce statistically meaningful patterns.

<Tip>
  **Start small, analyze results, and scale up if needed.** It's faster and cheaper to add participants after reviewing early findings than to over-recruit upfront. Use the [Audience Extension](/recruiting/extend-a-panel-audience) feature to top up your order once you know how many more sessions you need.
</Tip>

<Accordion title="Qualitative discovery & exploration">
  For open-ended discovery — understanding motivations, pain points, or mental models — **5–8 participants** per distinct segment is usually enough to reach thematic saturation. Themes stop being novel faster than most researchers expect.
</Accordion>

<Accordion title="Usability testing">
  Classic usability research suggests **5 participants** uncovers \~85% of major usability issues. Run a first wave of 5, fix the biggest issues, then test again.
</Accordion>

<Accordion title="Concept or message testing">
  For evaluating reactions to specific concepts, messaging, or prototypes, **10–15 participants** gives you enough range to see divergent responses across segments.
</Accordion>

<Accordion title="Quantitative or survey-style studies">
  When you need statistical confidence — for example, measuring feature preference across a population — plan for **50+ participants** and consult a sample size calculator based on your margin of error requirements.
</Accordion>

<Note>
  If your study covers multiple distinct audience segments, apply your target count **per segment**, not as a total across all groups.
</Note>