A well-designed experiment provides strong evidence of cause and effect. We help you design, execute, and analyze rigorous experiments that stand up to academic scrutiny.
Our PhD methodologists have extensive experience designing experiments across psychology, medicine, economics, education, and engineering.
We help you translate your research problem into clear, testable hypotheses with defined independent, dependent, and control variables.
We guide you through the gold standard of experimental design, including random assignment, placebo controls, and double-blinding protocols.
We help you design experiments with multiple independent variables to test interactions and control for nuisance variables.
We implement strategies to minimize selection, performance, detection, and attrition bias, ensuring your results are credible.
We calculate the minimum sample size needed to detect your hypothesized effect size with adequate statistical power.
We help you prepare pre-registration documents for platforms like OSF or AsPredicted, increasing transparency and reducing bias.
A structured, replicable workflow ensures your experiment runs smoothly and your results are trustworthy.
We start by clarifying your research question and translating it into a testable hypothesis with clearly defined variables.
We select the optimal design (between-subjects, within-subjects, factorial, etc.) and define how each variable will be measured or manipulated.
We develop standard operating procedures, case report forms, and data management plans to ensure consistent, high-quality data collection.
We analyze your data using the appropriate statistical tests and prepare a complete, APA-formatted results section.
Choose the right experimental design for your research question and receive expert guidance on implementation.
Each participant experiences only one condition. Independent groups, random assignment, and control conditions are key.
The same participants experience all conditions. We help with counterbalancing and order effect management.
Multiple independent variables are manipulated simultaneously to test main effects and interactions.
Non-randomized designs for natural settings, including pre-post tests and nonequivalent control groups.
Small-scale trials to test procedures, refine protocols, and estimate effect sizes for larger studies.
Flexible designs that allow modifications based on interim data analysis without compromising validity.