R markdown file

You will need to write an Rmarkdown file

The lab report is the methods, results, and discussion section of an experimental study, presenting and discussing the results of a statistical analysis. You will submit your report as RMarkdown file through Canvas. This page details everything you need to know in order to prepare this report successfully. If you have questions about the report writeup that you feel aren’t covered here, post on the Discussion board for more help.

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The design for this experiment will come from the two research questions that you wrote about in Psychology as a Science last term. You should choose one of the two studies – Red or Green, with information about both at the end of this page – to write your report. You can choose whichever you like, regardless of which you chose last term.here is the full description of the. assignment:

to write this report, you will be provided with simulated data using the same design as either the Red or Green studies. So, the results you will analyse and report will be different from the original study. You should treat this as a replication attempt – in other words, you should write your report as if you had run the same study as the original, using the same materials and design, but with a different group of participants. See Details on Report Sections, below, for more information on how to write each section.

For advice on how to write a particularly good report, see How to Get a First. For help on a specific statistical test or operation in R, see the index of functions and the relevant practical slides.

Key Details
Length: 2000 words
This is the maximum length of the text of your Markdown document when you knit it (excluding tables, figures, captions, and references). However, this limit has been set to be extremely generous; we expect most reports to be about 1000 – 1500 words.
Submission format: RMarkdown
Deadline: Week 9 of the semester (but see Sussex Direct for your specific deadline)

Timeline of the Report
The following are some important steps for your report and when they will fall in the term. The only actual deadline is the report submission, which will fall in Week 9 for most students (see Sussex Direct for your exact deadline). More information about each of these steps will be covered in the practicals as the term progresses.

Week 2: Report introduced and RMarkdown revised in practicals
Weeks 5/6: Lecture and practical on the analysis for Green study
Weeks 6/7: Lecture and practical on the analysis for Red study
Weeks 7/8: Lecture on report writing and report creation workshops
Week 9: Report DUE!

Requirements for the Lab Report
The outline below details the information that your lab report must include. For more details on each, see Prof Field’s guide to lab report writing (Links to an external site.). If you want to write an outstanding lab report, see the How to Get a First page as well.

Creating the Report

Getting the Data and Markdown File
The RMarkdown file below is the file that you should write your report in and submit. It has code chunks with brief directions and a basic outline of the sections you should complete, which is where you should write both your analysis code in R and the text of the report itself. It also has a function to import a dataset customised for each student. You must follow the instructions in the RMarkdown exactly or you may not be able to access your data properly.

You are welcome to delete the comments (ie #comments in code chunks or <!–comments–> in the text) in this Markdown document if you do not need them, and add/remove code chunks as you like, except you MUST NOT remove the cand_no and data code chunks!

Download and use this Markdown file: Lab Report Markdown (including data)


Save this Markdown file to the r_docs folder in your analysing_data project folder and open it in RStudio.
Rename the file so that it replacing “your_candidate_number” in the file name with your actual candidate number.
In the cand_no code chuck, put in your candidate number and run this entire code chunk
You can do this by clicking the green “play” button in the upper right hand corner of the code chunk
Check your Environment to make sure that there is now an object called candidate_number, and that it contains your correct candidate number
Then, in the data code chunk, choose one of the two lines to run
Run the line containing red_data() if you are writing about the red study
Run the line containing green_data() if you are writing about the green study
Delete the line that you are not using!
You should now have a dataset saved in the object data, which you can view by typing its name in the console and pressing Enter. You should use this data to perform your analysis and write your lab report. This data should be unique to you; please get in touch as soon as possible if you have any questions about this data.

Reporting Statistics
You should report all statistics in APA style. Refer to resources like the the Purdue OWL (Links to an external site.) to ensure your reporting and graphs are formatted, labeled, etc. correctly.

You must report your results using inline code. This means that instead of copying the numbers themselves from a table, you add a small bit of code in the middle of the text of your report that inserts the right number for you. This means that if you e.g. make a change to your dataset, you can knit your document again and all of the numbers in your report will update themselves accordingly.

Pulling out values using in-line code is covered in the tutorials for each analysis. You can also find more help with in-line code using the following resources:

Guidance on RStudio page (Links to an external site.)
R for Data Science, section 27.4.6 (Links to an external site.)
You must include at least one figure of your data in your report. Graphing is covered in detail in the tutorials on Summarising Data (Links to an external site.)and Visualising Data from PAAS (Links to an external site.), and Week 4 on this module. You should also refer to the relevant practical session for the study you chose (see the Timeline above) for examples of good graphs. Finally, the Week 7 lecture on lab report preparation will give you some ideas of what figures to include.

All citations must be given both in the text and in the reference list at the end in APA style. You are strongly recommended to use a reference manager to collate and insert your references. See How to Get a First for more information.

Importing pre-formatted citations into Markdown documents is difficult, so you are recommended to create the reference list in a different programme and simply copy and paste the final into your Markdown file. You should ensure that the resulting reference list is formatted correctly, using Markdown formatting (Links to an external site.).

Word count
First, the stated 2000-word limit is a hard limit – markers are instructed to stop reading at 2000 words. However, you do not have to write 2000 words, and you should not pad out your report if you have completed the required elements in fewer.

The word count applies to the final, knitted version of your document – not to the RMarkdown file itself, nor to any R code that does not show in the knitted document. It also does not include:

Figure or table captions
Text within figures or tables
You must edit your code chunks so that no raw code appears in your knitted document, even though you will only submit the Markdown file (see below). In other words, your final Markdown document that you submit must, when knitted, produce a clean, professional report, with no error messages, raw code, or R script in the output.

You are welcome to personalise the appearance of your report as you like, within reason. This means that you can change the formatting to suit your preferences – e.g. font, section numbering, colours of figures, etc. – but you should try to create a formal and professional-looking report. So, you must use the standard section headers (already provided for you in the lab report Markdown), and avoid comic sans or silly fonts, particularly garish figure colours, etc. We will not require exact APA style for this report, except for reporting statistics (see tutorials for examples) and references. It is most important that you are consistent throughout your document and submit a file that is complete and correct (ie knits without errors).

You will not be marked down for outlandish style choices, as long as they do not interfere with the accuracy or readability of your report. However, your marker may comment to let you know that you should avoid those choices in the future!

What to Submit
You should submit the completed Markdown file only, not the knitted output! You must download the Markdown document posted above, do your analysis and write your report in that document, and then submit it for this assessment. Your final knitted document should produce all tables and figures successfully, be formatted correctly (ie with no missing or extra symbols or other issues), and should overall look clean, professional, and polished. Then, you should submit the Markdown document that will produce that report when knitted.

IMPORTANT: We will attempt to knit your submitted Markdown file as part of the marking process, so you must ensure that your document knits successfully and correctly! We will also not accept submissions in any other format (e.g., Word, HTML).

Complete/format as desired using the YAML header
Participants – description of participants in the study, including gender and age
Design – clear statement of the research question and the variables measured
Descriptive statistics – general pattern of results and summaries of data
Inferential statistics – results of all tests performed
A graph of your results
Summary of the experimental design and results
Implications – In light of your reading and the wider research field, what have the results told you about your research question that you didn’t know before?
Limitations – What have the results not been able to tell you about your research question, and why?
Applications – What are the next steps to continue investigating this research question?
Conclusion – What is the takeaway message from this study?
For overall advice on writing a good report, see How to Get a First and guidance from the Week 7 lecture.

For more information on how your report will be marked, see Marking and Feedback Information.

Details on Report Sections
The idea behind this report is simulating a replication attempt. This means you should treat your data and results as if it were real data from a study you had conducted in an attempt to replicate the results of whichever original study you chose (Red or Green). Both of these studies have been the subjects of other replication attempts, so this is a completely realistic scenario. This means that you can assume the design, variables, stimuli, etc. were the same as the original paper describes, but that the people who participated were a different sample than the original study. This means that while you will be using the same analysis, the actual numbers (the data itself) and the specific results of the tests you run will be different.

As the outline above makes clear, this lab report is essentially the second half of a typical lab report. So, you should not write an introduction, and you only need to write about half of the methods section. The following sections give more detail on what you should include. You will want to refer to the tutorials and practicals from this module for help on all of the following tasks, and the original paper for a real example of how such a design might be presented in a published paper.

You only need to include two of the typical four subsections of the Methods section: Participants and Design.

Complete this section by including:

The number of people who participated in the experiment initially
The mean, standard deviation, and range of the participants’ ages
How many people you removed, and for what reason(s)
The final number of people whose data was included in your results
A table of participant descriptives by experimental condition and gender (if you have it)
You do not need to write about any information that you have not been given (e.g. gender, recruitment, participant ethnicity, etc.). You should only briefly describe your participants based on what you can learn from the data you have, and describe any exclusions from the dataset.

Complete this section by including:

A statement of the conceptual hypothesis/research question
A description of the operationalisation of each variable that you will use in your analysis, including:
Whether each variable was a factor, a rating, etc.
How each variable was created or measured, such as the levels (for factors) or range of possible scores (for ratings)
How to interpret each variable, such as what a high/low score means
A clear and simple statement of the operational hypothesis, and what statistical analysis you will do to test it

Complete this section by including:

A written description of the general pattern of results
Include a summary of the data (such as means or counts in each condition), either in your text or in a table
The results of the same statistical test you said you would conduct in your Design section
Report your results as demonstrated in the relevant tutorial(s)
Use inline code for reporting numbers, as demonstrated in the relevant tutorial(s)
An interpretation of the results of this test in simple language
A figure of the data
For more on reporting the results of analyses clearly and well, see How to Get a First. You can also refer to the relevant tutorials and practicals (particularly in weeks 5, 6, and 7) for example write-up text. Remember that you must write your results in your own words.

Complete this section by including:

A summary of your results, stated in non-statistical terms
A detailed explanation of what your findings contribute to the research area, namely:
How do your results compare to the original study?
Did you get the same pattern of results?
What do you think this means for the effect you are investigating?
How do your results compare to other studies and/or replication attempts on the same or similar topic?
Did you get the same pattern of results?
What do you think this means for the effect you are investigating?
Based on your own analysis and the strengths and limitations of your study, do you think that this effect is real? (That is, does red really make men more attractive, or does high status really motivate people to buy green products?) Why or why not?
Do you think this research question is worth investigating further? If so, what do you think the next studies should investigate, and how? Make some concrete and actionable suggestions for future studies.
Write a short, clear conclusion summarising your main finding(s) and the main takeaway point(s) from your discussion.
For more detail about writing discussions, be sure to review the Week 7 lecture. If you would like to write a good discussion, see further guidance under How to Get a First.

The Studies

Green: Status and Green Purchases (Griskevicius et al., 2010)
For this lab report, you should base your analysis on Experiment 1 in the original paper. You should make sure to read Experiment 1 itself for context and more information about materials and procedure as well as the introduction and discussion of the paper.

Original Paper
PDFPreview the document

ProQuest (Links to an external site.)

All materials to help you better understand the experiment, including the texts of the stories for the status manipulations, can be found on the CREP page for the green study (Links to an external site.).

Related Reading
Sundie et al. (2011) (Links to an external site.) Peacocks, Porsches, and Thorstein Veblen: Conspicuous Consumption as a Sexual Signaling System
Hartmann & Apaolaza-Ibanez (2012) (Links to an external site.) Consumer attitude and purchase intention toward green energy brands: The roles of psychological benefits and environmental concern
Steg et al. (2014) (Links to an external site.) An Integrated Framework for Encouraging Pro-environmental Behaviour: The role of values, situational factors and goals
Anderson, Hildreth, & Howland (2015) (Links to an external site.) Is the Desire for Status a Fundamental Human Motive? A Review of the Empirical Literature

Red: Colour and Attraction (Elliot et al., 2010)
For this lab report, you should base your analysis on Experiment 1 in the original paper. You should make sure to read Experiment 1 itself for context and more information about materials and procedure as well as the introduction and discussion of the paper.

Original Paper

ProQuest (Links to an external site.)

All materials to help you better understand the experiment, including photos with coloured frames, can be found on the CREP page for the red study (Links to an external site.).

Related Reading
Francis (2013) (Links to an external site.) Publication bias in β€œRed, rank, and romance in women viewing men,” by Elliot et al. (2010)
Elliot & Maier (2013) (Links to an external site.) The red-attractiveness effect, applying the Ioannidis and Trikalinos (2007b) test, and the broader scientific context: A reply to Francis (2013)
Hesslinger, Goldbach, & Carbon (2015) (Links to an external site.) Men in red: A reexamination of the red-attractiveness effect
Peperkoorn, Roberts, & Pollet (2016) (Links to an external site.) Revisiting the Red Effect on Attractiveness and Sexual Receptivity: No Effect of the Color Red on Human Mate Preferences

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