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Overview of the Research Process

The research process involves multiple phases for every project, and there are dozens of ongoing projects in the lab at different phases at any given time. If you imagine multiplying many ongoing projects X several phases within each project, you will get a picture of the overall complexity of all of the research going on in the lab. Because of this complexity, it is essential that everyone in the lab stay coordinated and that our research processes be standardized.

Every research project involves the following phases:

  • Brainstorming. The first step in any project is the initial idea. Ideas for projects come from many sources, including reading other research papers, reflecting on everyday experiences, and observing how learning happens in classrooms.
  • Designing. We then need a suitable design to test the idea. We invest a lot of time and energy thinking about the most effective design and procedures for each project.
  • Creating Materials. All of our research requires that we have people learn about something and that we assess learning in some way. These items constitute the materials in our experiments. Sometimes we use materials we already have in the lab, but in other cases we need to develop entirely new sets of materials for our experiments.
  • Programming. Almost all of our research involves writing computer programs to run the experimental procedure and record data.
  • Piloting. A pilot experiment is a small-scale preliminary study conducted in order to evaluate feasibility, time, cost, and adverse events, as well as improve upon the study design prior to implementation of a full-scale research project.
  • Collecting Data. Data collection is a critical phase of the lab's operation. Data collection involves running experiment sessions, which follow specific steps outlined in this handbook.
  • Scoring and Entering Data. Once data have been collected, those data often need to be scored (graded) by research assistants and entered into spreadsheets or databases.
  • Cleaning Data. Data sets typically need to be cleaned prior to data analysis. Data cleaning involves things like removing subjects who admitted to cheating or did not complete the entire experiment, and formatting the data file according to lab conventions in preparation for data analysis.
  • Analyzing Data. The next step involves carrying out statistical analyses on the datasets.
  • Writing. Everything we do needs to be documented. Writing can and should occur throughout all phases of the research (e.g., method sections should be written when preparing new experiments; results should be written as data analyses are being completed; notes toward an introduction can be written during initial brainstorming). Ultimately, written reports are submitted to journals for publication and to funding agencies as part of annual reporting requirements.