DAPR3 Group Report 2024-25

Your task is to describe and analyse some data in order to provide answers to a set of research questions. Analyses will draw on the methodologies we have discussed in lectures, readings and lab exercises.

Stop! Before you do anything else, make sure that every member of the group has a record of everyone else’s exam numbers, which can be found on their matriculation cards (see here for more information). Whoever submits the coursework on behalf of the group will need to provide these numbers at submission time.

Group work policy

Please note that each group should submit a single report (two documents: see what-you-need-to-submit). All group members are expected to contribute to the report, but you should not work with other groups.

Similarity checks will be performed, and further investigations will be carried out if assignments from different groups are similar.

Use of AI

Academic integrity is an underlying principle of research and academic practice. All submitted work is expected to be your own. AI tools (e.g., ELM) should not be used for assessments on DAPR3. Using AI would constitute academic misconduct

What you need to submit

Each group is required to submit 2 documents.

  1. A final compiled report (.pdf format), detailing your analyses, results, interpretation and conclusions.
    • No longer than 4 pages, excluding optional 4 page appendix (see below)
    • Doesn’t contain visible R code - like the “analysis and results” section of a published paper, which describes, reports, presents and interprets (more detail in how-to-approach-the-questions).
  2. The .Rmd or .R document which reproduces the results you give in the report.

Page limit

Your report should be no longer than 4 pages.

If you are knitting to html, please open the html in a browser and print to pdf in order to check how many pages your submission takes up).

An OPTIONAL Appendix of maximum four pages1 can be used to present additional tables and figures, if you wish (but there is no requirement to do this).

The appendix is a good place for supplementary materials. By this we mean figures and tables that are not strictly necessary for the reader to understand and replicate your results, but provide additional context to your report.

  • good use of the appendix:
    “The model met the assumptions of linear regression with residuals showing a constant mean of approximately zero across the fitted values (see appendix Fig X), and …”
  • not so good use of the appendix:
    “The model met assumptions (see appendix Fig X).”

Report Formatting

Given what we’ve learned in DAPR1 and 2, we expect most groups to do their analyses using RMarkdown. Hopefully you are aware of the benefits this has for reproducibility.
However, you are welcome to write the report in a separate word-processor in order to format the report easily without having to remember the nuances of formatting in RMarkdown. This has the additional benefit of being able to use a collaborative word-processor such as Google Docs.

Unfortunately there are no straightforward ways to live edit .Rmd/.R documents collaboratively; we suggest that each group ensures it knows who holds the “master copy” of the script that will be submitted.

You should then export your word-processor document to .pdf format for submission. The code should remain as a file with a .Rmd or .R suffix.

We don’t mind which of these approaches each group takes: The important thing to remember is that the data analysis (cleaning, models, and plots) that are presented in the report should match those produced by your code.

If you do wish to do some or all of your formatting in RMarkdown, then we suggest the following readings for help:

Feel free also to post formatting questions on the Piazza discussion forum.

A note on knitting .Rmd directly to pdf

Getting RMarkdown to knit directly to pdf can be a pain, and formatting is difficult.

We recommend:

  • knit to .html, then Ctrl+P to print to .pdf
  • knit to .docx, then export to .pdf

Submitting your files

Pre-submission checks

Before submitting, we strongly advise you to check that your group’s code runs. The easiest way to check this is to:

  • Clear your environment, restart your R session (top menu, Session > Restart R), and run your code line by line to see if any errors arise.

Filenames

For both files which you submit, the filename should be your group name with the appropriate extension, and nothing else.
For example, the group named Canary would submit two files:

  • one of Canary.R / Canary.Rmd
  • Canary.pdf

For anyone who has obtained permission to complete the task individually, please name each file with your exam number (the letter “B” followed by a six digit number - which can be found on your student card: See here for more information). For example, if your exam number was B123456 you would submit:

  • one of B123456.R / B123456.Rmd
  • B123456.pdf

You should also write your exam number in the “Submission Title” box prior to submission.

Where to submit

ONLY ONE PERSON FROM EACH GROUP NEEDS TO SUBMIT

We suggest that you do this together/on a call, so that all group members are able to confirm that they are happy to submit.

Go to the Assessments page on Learn, and look for “Assessment Submission”. There you will find two submission boxes where you can submit.

For each file you should complete the “Submit File” popup by entering the exam numbers of all of your group members in the “Submission Title” box (see below).

Late penalties

Submissions are considered late until both files are submitted on Turnitin (see the PPLS policy on late penalties on the UG Hub).

Grading

We are primarily marking each group’s report, and not your code
Grades and feedback are provided for the finished reports, with marks awarded for providing evidence of the ability to:

  • understand and execute appropriate statistical methods to answer each of the questions
  • provide clear explanation of the methods undertaken
  • provide clear and accurate presentation and interpretation of results and conclusions.

Why we still want your code
We still require your code so that we can assess the reproducibility of your work. We also use it as a way to give you extra marks based on the elegance of your coding and/or use of RMarkdown.

Rubric

Grading is based on:

  • Strategy (40%)
    • Clear and useful description of the sample used in the analyses
    • appropriate analyses conducted to address the research aims, building on methods taught in the class
    • Clear explanation of the analyses undertaken.
    • Transparent and justified explanations of any decisions made about the data prior to and during analyses
  • Results (40%)
    • Appropriate presentation of the relevant results, with sufficient information of the inferential conclusions (for instance, a test statistic, standard error, and p-value, not just one of these). Remember to cite degrees of freedom where needed.
    • Clear and accurate interpretation (in the form of a written paragraph(s) referencing relevant parts of your results and statistics) leading to a conclusion regarding the question.
    • Use of clearly interpretable plots and tables where appropriate to provide additional presentation of findings.
  • Writing & Formatting (10%)
    • Clarity of writing and structure, use of appropriate language avoiding unnecessary jargon or overly complex explanations.
  • Code Check (10%)
    • Ability of the code to precisely reproduce the analyses, results, and plots presented in the report.

Important (Helpful) Tips:

  • The .pdf report should not contain visible R code, meaning that a large part of the challenge comes in clearly describing all aspects of the analysis procedure.

  • A reader of your report should be able to more or less replicate your analyses without referring to your R code (or using R).

  • Write as if the reader has a basic understanding of statistics and does not necessarily use R.

  • You do not need to include information about the study background or the collection of the data in your report.

Peer-adjusted marking

Once the group project has been submitted, every member of the group will complete the peer-assessment, in which you will be asked to award “mark adjustments” to yourself and to each of the other members of your group. This will be done through Learn; details will be made available over the next couple of weeks. Each submitted mark should be understood as follows: Relative to the group as a whole, how much did each member contribute? If someone made an average contribution, you should award that person the middle score. If they contributed substantially more, you may choose to give a higher mark; if they genuinely contributed less, you might choose a lower mark. Marks for each group member are scaled then averaged together, and then used as “weights” to adjust the overall project mark. You can see an example of how this logic works by visiting https://uoe-psy.shinyapps.io/peer_adj/ where there is a “live demo” of a peer-adjustment system.

If you don’t contribute any peer-adjustment marks, other members’ marks will hold more weight in the adjustments made.

Any Questions?

This document contains a basic overview of the task and of how to submit it. If you have any questions concerning the coursework report, we ask that you post them on the designated section of the Piazza discussion forum on Learn. If you have a question, it is likely your classmates may have the same question. Before posting a question, please check the on-line board in case it has already been answered.

TASK

Conduct and report on an analysis that addresses the research aims of the study detailed below.
Data: https://uoepsy.github.io/data/dapr3_2425_report_data.csv, and a data dictionary can be found in Table 1

Study Background & Aims
Efficient visual search ability is critical for numerous professions, including aviation security, radiology, lifeguarding, and military roles. The present study is interested in investigating the effects of object familiarity and colour on visual search time, via a set up based on the popular board game known as “Dobble” or “Spot it!” (see https://www.dobblegame.com/en/games/), in which the aim is to find the symbol that matches across two cards that each display 8 symbols of varying sizes, orientations and colours (e.g. Figure 1).

Figure 1: Example card pair from the Dobble game (taken from http://measureology.uk/seeing-dobble/)

50 participants took part in the study, with each participant completing 48 trials. For each trial, participants were presented with one of 48 pairs of cards, and were tasked with (as quickly as possible) identifying the one (and only one) symbol that was present on both cards. Across the 48 trials, each participant saw 8 trials in which the matching symbol was red, 8 in which it was yellow, 8 blue, 8 black, 8 green and 8 purple. For each participant the order in which they were presented with the set of 48 pairs of cards was entirely random.

The research aims are to investigate if certain colours of symbols are associated with faster/slower search time than others at the outset of the study, and whether any improvement over the duration of the experiment (due to participants becoming familiar with objects and/or task) is different for different coloured symbols.

Table 1: Data Dictionary: dapr3_2425_report_data.csv
variable description
PID Unique Participant Identifier
age Age of Participant (years)
card.pair Card-Pair Identifier (out of a set of 48 different card-pairs)
sym.col Colour of the matching symbol on the card-pair
trial.n Trial Number (1-48)
search.time.ms Time taken to correctly identify matching symbol, in milliseconds

Footnotes

  1. meaning that your final .pdf file should be max 8 pages.↩︎