72  Lab report preparations

Here’s a diagramme of the lab report workflow:

graph TD
    A(Read the instructions) --> B(Create a project plan)
    B --> C(Search for and read<br>relevant literature)
    C --> D(Analyse the data)
    D --> E(Write the lab report<br>#40;see next chapter#41;)

72.1 Read the instructions

Thoroughly review the assignment brief, rubric, and the lab report template, so you know exactly what is expected.

72.2 Create a project plan

Note: Please keep in mind that this schedule is just a template. There are substantial interindividual differences in how people work, and you will need to decide how much time you personally need for each of these steps.

Take a day to break the assignment down into manageable chunks and put these specific mini-deadlines into your calendar. It’s often a good idea to work backwards from the deadline:

  • Deadline: The deadline is on 11/05.
  • Final Review: Imagine you’d like to have two days to go over everything again, with a particular focus on details you might have missed previously. E.g., check spellings, verify if all references are in APA style, ensure sentences are complete, confirm everything flows well, and check if tables and figures are numbered correctly. → Do this on 09/05 and 10/05.
  • Write the lab report: Writing takes a significant amount of time, and it is highly recommended to reserve approximately two weeks to draft the Introduction, Methods, Results, and Discussion sections. → Do this between 24/04 and 08/05.
  • Analyse the data: Before you can write about the results, you need to know what they are. Set aside plenty of time to clean the dataset, run any necessary calculations or statistical tests, and generate your graphs and tables. → Do this between 11/04 and 23/04.
  • Search for and read relevant literature: To write a strong introduction and discussion, you need thorough context. Give yourself at least a week to search academic databases, read through abstracts, select the most relevant papers, and take detailed notes. → Do this between 30/03 and 10/04.

72.3 Search for and read relevant literature

If there are key readings associated with the instructions, read these first. Make sure you understand the key points. Get an AI to explain things to you that you don’t understand, but seem relevant.

Conduct your own literature search. Use Web of Science.1 Take some time to familiarise yourself with the search interface. There is a self-guided Web of Science course you can complete in your own time.

The literature search should be a process with multiple iterations over which you refine your search terms. Sort your search hits by relevance and publication date (newest to oldest) and identify the most relevant recent publications.

Add the relevant papers to your reference management software (e.g., Zotero or EndNote). Both Zotero and EndNote have browser plugins. That is, once you’re on an article page, you only need to click on the browser plugin to add it to your reference management software.

Once you’ve added relevant papers to your reference management software, consider tagging them (e.g., ‘summativeY1’). This will make it easier to find them again in the future.

Then read the relevant papers. Properly. Reading abstracts is not enough. Ask an AI if you can’t make sense of something you read in a paper. Add annotations to the papers, make notes on a piece of paper or use a note-taking app.

Next, identify relevant citations in these articles (i.e., perform a backward search) and read those. Also do a forward search on Web of Science (i.e., find out which newer papers have cited the papers you’re reading) and read the relevant papers identified using this method.

72.4 Analyse the data

  • Formulate Your Questions & Choose Tests: Based on the instructions, pinpoint the exact research questions you need to answer. Choose the appropriate statistical tests for your specific data types and design.
  • Familiarise Yourself with the Software: Find out how to run your chosen tests with the statistical software of your choice (e.g., SPSS, R, Jamovi).
  • Data Preparation & Cleaning: Before you start the actual analysis, have a close look at the data:
    • Are there any variables you still need to compute or recode to run your analyses?
    • Generate descriptive statistics and basic plots (like histograms or boxplots) to look for outliers that should potentially be removed.
    • Look out for anything else that seems weird about the data set as a whole or about individual participants (e.g., impossible values or missing data).
  • Assumption Checks: Before running your main tests, perform the necessary assumption checks. Based on these results, decide if you need to run parametric or non-parametric analyses.
  • Run Your Analyses:
    • Execute your main analyses first to answer your primary research questions.
    • Then, decide if there are any control or explorative analyses you want to perform to provide further context to your findings.

  1. We would not recommend Google Scholar for this purpose. While Google Scholar provides expansive coverage of academic literature, its utility for rigorous scientific research is limited. Key limitations include a lack of transparency regarding its indexing algorithms and corpus size, lack of reproducibility, inconsistent metadata quality, and the inclusion of non-peer-reviewed sources, making it less reliable than curated databases like Web of Science.↩︎