Developing your research protocol

Summary

This training course covers the main stages of designing and developing an experimental approach as part of their doctoral research project. It enables doctoral students from different disciplines (health, social sciences, sciences) to create or rework their experimental process using a variety of experimental methods and tools. The training course enables participants to progress through the various stages of the experimental approach, from a complementary cross-disciplinary perspective:

– Formulation of the problem and formulation of one or more hypotheses Designing an experimental project 
– Definition of the experimental protocol (design of experiments) 
– Data acquisition 
– Data reduction 
– Identification of trends (and modeling if possible) 
– Validation and/or invalidation of hypotheses 
– Drafting of experiment reports 
– Possible planning of a new experiment

It allows trainees to discuss their practices and progress collectively, supported by relevant tools and methods

Program

• Collective and individual assessment of practices 
  -Implementation of the main stages of the experimental process (in cycles) 
  Formulation of the problem and formulation of one or more hypotheses 
  • Understanding the state of the art in order to formulate a precise and relevant problem in one’s field of research o 
  • Identifying variables to be predicted under the influence of other experimental variables in order to formulate one or more hypotheses 
  Designing an experimental project 
  • Establish an overall project to test the hypotheses 
  • Question the resources needed to carry out the project in order to choose a feasible project 
  Define the experimental protocol (design of experiments) 
  • Define how the protocol will work (schedule, equipment, resources, etc.) 
  • Define your criteria for significance
  • Selecting project stakeholders 
  Data acquisition 
  • Sampling and measurements
  • Monitoring of collected data 
  Data reduction 
  • Based on statistical analyses: variance analyses, medians, averages, standard deviations, etc. 
  • Readjustment of data analysis based on significant data extracted 
  Identification of trends (and modeling if possible) 
  • Testing and responding to hypotheses 
  • Quantification of observed influences: modeling (if possible) 
  Validation and/or invalidation of hypotheses 
  • Developing an argumentative process by exploiting the data 
  Writing experiment reports 
  • Effective writing methods 
  Possible plans for further experimentation 
  • Plan new experiments specifying the effect of experimental variables

  At each stage, methodological elements are proposed, tested, and discussed. Each doctoral student works on their own experimental project and puts the proposed methods into practice, applying their own critical perspective. 
  Each student leaves with elements that are ready to use, depending on the progress of their experimental project.

Trainer

• Marine Pansu (ALM)

Informations

• Email : contact@almformation.com
• Courses dates : February 25th – 26th -27 of 2026 (from 9:30 am to 5 pm)
• Place : Université Paris Cité – Bâtiment Condorcet – Salle 054A – 4 Rue Elsa Morante 75013 Paris