Skills Lab 06: Sample Take-Away Paper

Author

JM

Skills Lab 06

Important Points

  • Use what you know: The TAP is about what we have taught you about working with, presenting, and analysing data (like it says on the tin!). Use previous Skills Labs, tutorials, and worksheets to solve the tasks.

  • Read the academic misconduct info carefully: You must work independently, without help/input from others. Use of ChatGPT or other programmes to generate or check code or answers is NOT permitted.

  • Don’t panic: If you hit a technical problem, come to a regular or tech help drop-in (see the Help! page on Canvas)

  • KNOW YOUR DEADLINE: You must submit before the deadline or you will receive a 0. Make sure you know YOUR deadline and get it done well before!

FAQs

The following questions were contributed during the session on Poll Everywhere.

What can we do to firmly secure a mark in the 70-80 band?

  1. You should demonstrate the techniques, functions, skills, and concepts that we have taught you on this module.
  2. You should ensure that your answers demonstrate a clear connection what you say you are going to do, and what you actually produce with your code.
  3. Your answers, both written and code, should be clear, accurate, relevant, and without errors.

You are strongly recommended to read the rubric for more information.

Is there a rubric for the TAP?

Yes! The Assignment page on Canvas for this assessment has a rubric attached. You’re strongly recommended to read it.

What does “descriptive information” mean?

This refers to information about your variables that you might calculate in order to present a clear picture of your data. For more about this, see the tip in Task 2.1.

How do you group by two things for the summary table?

This is covered in Skills Lab 4.

How do you interpret the results of the t-test?

This is covered in Lecture 5.

What is the rendering and submission process?

This is covered on Canvas, on the Take-Away Paper Information page under Submission Guidance. In particular, there is a video guide walking you through all of the steps.

How much is the TAP worth?

The TAP is worth 20% of your overall module mark on Analysing Data.

Task 1: Cleaning and Preparation

1.1

Inspect your dataset, and compare it to the Study Brief and Codebook. Identify any discrepancies between the two, using the Study Brief and Codebook as a guide for what your dataset should look like, how it needs to be cleaned, and what variables it should contain.

Then, make a list below of the steps you must take to clean or change your dataset so that it matches the Codebook.

Tip

For this task especially, it is imperative that you read the Study Brief and Codebook carefully. Your answer here should be a short list of the steps that you need to take.

1.2

Complete the steps you have listed in task 1.1 to prepare your dataset for analysis, using the coding and analysis techniques we have covered on the module thus far.

syn_data <- syn_data |> 
  dplyr::mutate(
    syn_type = dplyr::case_when(
      syn_graph_col == "Yes" & syn_seq_space == "Yes" & gc_score <= 1.43 ~ "Both",
      syn_graph_col == "Yes" & gc_score <= 1.43 ~ "Grapheme-colour",
      syn_seq_space == "Yes" ~ "Sequence-space",
      .default = "Non-syn"
    )
  )

syn_data |> 
  dplyr::count(syn_type)

Task 2: Summary Table

2.1

Referring to the Study Brief and what you have learned on the module thus far, what descriptive information would be useful in order to investigate the research question?

Make a list below and explain why you included each element (150 words or less).

Tip

Your answer for this section should include a list of the descriptives that you will calculate, and for each one, a brief (approx one sentence) explanation in your own words of why this piece of information would be useful. For a revision of this in depth, see Tasks 1 and 2 of Tutorial 4 and the recording of Skills Lab 4.

2.2

Produce a summary table of the descriptive information that you listed in task 2. Then, format that summary table as an HTML table and print it out below.

You must make use of the coding and analysis techniques we have covered on the module thus far, but you may customise your table further if you wish.

Task 3: Visualisation

3.1

Referring to the Codebook and what you have learned on the module thus far, what data visualisation would be would be useful to understand the relationship between synaesthesia type and the outcome variable?

State which visualisation you chose, and why you chose it, in a few sentences below (150 words or less). You should only chose ONE visualisation.

Tip

To get a good mark here, you should:

  • Choose an appropriate visualisation of the data
  • Clearly explain why this visualisation is appropriate and what you would learn from it

3.2

Produce the data visualisation you have chosen and print it out below.

You must make use of the coding and analysis techniques we have covered on the module thus far, but you may customise your visualisation further if you wish.

Task 4: Analysis

4.1

Referring to the Codebook and what you have learned on the module thus far, what statistical analysis will test the key relationship of interest? State the following in a few sentences below (150 words or less):

  • Which analysis you chose, and why you chose it.
  • The null hypothesis for this analysis.
  • The alternative hypothesis for this analysis.
Tip

Make sure that you choose an analysis that will test the hypothesis laid out in the Study Brief. You may want to refer to the relevant lecture and/or tutorial for help with the null and alternative hypotheses for your chosen test.

4.2

Conduct the analysis you have chosen and print out the output below.

Tip

Make sure that you are using:

  • The correct dataset - you must use the whole dataset (rather than a summary)
  • The correct variables - you must carefully check the Study Brief to make sure you are producing a test that corresponds to the hypothesis
## Per the study brief, narrow dataset down to only relevant groups
syn_data_t <- syn_data |> 
  dplyr::filter(syn_type %in% c("Grapheme-colour", "Sequence-space"))

## Perform the relevant analysis
syn_data_t |> 
  t.test(scsq_organise ~ syn_type, data = _)

4.3

Briefly interpret the result of this test in a few sentences (50 words or less). Optionally, report the results of your test in APA style to support your interpretation. Is the lead researcher’s prediction supported by the evidence or not?