Experiment Design

The guides in this section bridge the gap between knowing a feature and knowing how to use it in a research design. Where the feature guides cover what the tools do, this section covers how to apply them to the decisions researchers actually face: how to present stimuli, how to counterbalance conditions, how to structure a branching study, how to record the right data at the right moment.

Most researchers arrive at Labvanced with a design already in mind. They know they need a stimulus list, or a between-subjects manipulation, or a response-contingent branching path. What they need is a direct answer to the question: how do I build this specific design in Labvanced? That is what this section is for.

What this section covers

Experiment design in Labvanced involves four broad concerns, each of which maps to a set of platform features:

Stimulus management. How stimuli are defined, organized, and delivered to participants. This includes text, image, and audio stimuli presented from structured lists, as well as dynamic stimuli driven by participant responses or external variables.

Trial structure and ordering. How trials are sequenced, randomized, and counterbalanced. Labvanced supports fixed ordering, within-session randomization, and between-participant condition assignment through the group and session system.

Response collection and accuracy. How participant responses are captured, coded for correctness, and fed back into the experiment logic. This connects response objects, variables, and the event system.

Branching and adaptive logic. How the experiment adapts at runtime: skipping blocks based on performance, routing participants to different task versions, or adjusting difficulty dynamically. This is handled through the event system in combination with variables.

The pages in this section address each of these areas with concrete, design-first guidance. They are intended as a companion to the feature documentation, not a replacement for it.

In this section

Data Frame Patterns

How to apply data frames to common research designs: sequential stimulus presentation, counterbalancing, and file-based stimuli.