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# System prepended metadata

title: Group 1

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# Group 1

# Assessment criteria: 
1. My teammate pulled their weight. I.e. attended Q&A, actively contributed during group meetings, helped to google/troubleshoot and supported others in their learning
2. My teammate interacted with the group in a way that created an inclusive environment for us to learn from each other
 
3. My teammate communicated well and within a reasonable response time (and if not, have provided an explanation)

4. My teammate was organised for our meeting(s) 
    * Attended (but because things happen and life gets in the way, communicate reasonably)
    * Discussed agenda
    * Came to the meeting prepared and was accountable, meaning they did what they say they would do
    
5. My teammate posted a picture of something that made me smile/interested/amused me this week - Week 7 mental breakdown below:

![](https://i.imgur.com/gKFJJ7h.jpg)
*Courtesy of Samantha*


# Reproducibility Plan

There are 2 studies reported in this paper. For each study, the goal is to reproduce the …

- demographic descriptives (reported in Participants)
- figures (column graphs reporting total calories as a function of SES and energy need/condition/glucose)
- tables of descriptive statistics (M, SD, Range) - NOT APPLICABLE (only anova table in paper)

## Demographics: The goal is to reproduce the demographic descriptives in the Participants section here:
![](https://i.imgur.com/fGI0Rt6.png)

### Experiment 1:
- No. of participants: 294 (126 males, 168 females)
- Age (Mean: 34.29, SD: 12.97, Range: 18-69)
### Experiment 2:
- No. of participants in FINAL sample: 400 (150 males, 248 females, 2 neither)
- Age (Mean: 33.5, SD: 12, Range: 18-73)

## Figures > Experiment 1:
### The goal is to reproduce these violin graphs:
![](https://i.imgur.com/fKo6V8q.png)
Perceived Contradiction: Participants exposed to conflicting headlines perceived greater contradiction between the six headline pairs (M = 25.3) than those exposed to non- conflicting headlines (M = 13.4). This manipulation check indicates that our conflict manipulation was perceived as intended. There was no effect of Headline Format on per- ceived contradiction and no interaction between Headline Conflict and Format.

Confusion: Participants exposed to conflicting headlines indicated greater agreement that ‘the headlines create confusion about how to be healthy’ than those exposed to non-conflicting headlines (4.52 vs 3.65 on a 5-point scale).There was no effect of Headline Format on confusion and no interaction between Headline Conflict and Format. The manipulation check indicates that our conflict manipulation was perceived as intended. There was no effect of Headline Format on perceived contradiction and no interaction between Headline Conflict and Format.

Scientific Advancement: Advancement of knowledge was measured on a 3 point scale in which zero corresponded to ‘We know the same amount (as before)’, 1 corresponded to ‘We know more’ (than before)’ and -1 corresponded to ‘We know less’ (than before)’. The mean response of those exposed to non-conflicting headlines (0.007) was greater than the mean response of those exposed to conflicting headlines (-0.25) -- AKA Those exposed to conflicting headlines gave a significantly lower mean rating, which was also significantly lower than the midpoint of the scale; indicating that the conflicting headlines result in us knowing slightly less than we did before -- There was no significant effect of Headline Format on perceived Scientific Advancement and no significant interaction between Headline Conflict and Format.

### And this column graph:

![](https://i.imgur.com/cPKGTZA.png)
Figure 2 shows that participants exposed to conflicting headlines more frequently selected ‘we know less’, relative to those exposed to non-conflicting headlines. Mirroring this, participants exposed to conflicting headlines less fre- quently selected ‘we know more’. In all conditions the modal response was ‘we know the same’.

## Figures > Experiment 2:
### The goal is to reproduce these violin graphs:

![](https://i.imgur.com/WTphfu1.png)


## OSF
https://osf.io/eqnfg/
https://osf.io/afrb3/

> By start of next week, share plan of how we will tackle this. (Example: https://internship3361.netlify.app/posts/2021-03-05-example-reproducibility-plan/)

## Next Steps (for Week 4)
### To get started we need to…
1. download the csv for each study from OSF
2. Wrap your head around the data
3. locate a codebook (if there is one) to work out which variables contain demographic information
4. Figure out what's a reasonable timeline to get each section done so everyone is on the same page each week/can properly help each other figure out the code 
CODING STEP 1: work out how to count participants by gender/condition and calculate the mean, SD, and range of ages in each sample

## Next Steps (Week 5)
1. Remember to do your peer review! 
2. Edited S&R by end Wk.5
3. Try to get Mean, SD and descriptive plots for Exp.2 by Tuesday/ Thursday next week 
4. Have fuuuuun! 🤗

## Coding Q&A (29/6)
> ### Next Steps (to do in week 5)
> 1. Create github account and send your username (by Thursday)
> 2. Add means, confidence intervals and colors to ggplot fir Exp 1
> 3. Start reproducing histogram for experiment 1
> 4. Leave each other feedback for S&R 


> ### Coding notes 
> 1. Use select() function before glimpse() to not have all your data appear at the same time - looks less overwhelming 
> 2. Don't have to be aesthetically exactly the same as authors' plot. Jenny gave green light to Sam's plots so far  
> 3. Option 1: add geom_point(), error bars, and boxplots with ggplot. Option 2: try googling pirateplot() function. Jenny said we could look at the authors' codes and see how they did it.  
> 4. Use package patchwork and use function patchwork() to put two graphs in one row. (still need to figure out how to put one graph at the bottom/next row)
> 5. Jenny S recommends for us to have the same codes to make it easier for Amy to check through our reports at the end 


# Coding Q&A (13/7)
1. Ask Jenny aboout KISS and DRY principles for exploratory analysis 
2. Making plot codes more concise 
3. ![](https://i.imgur.com/0t580D3.png) can we shorten that using apply() function? 


# Meeting agenda (13/7)
1. Group presentation 
> a. Do we want to make pres slides with g.slides or R? 
> b. Discuss Q1-2? Should everyone come up with an answer for Q3-4 and then compile? 
2. How are we going to tidy codes on git? 

# Next Steps (Week 7)
1. Tidy codes
2. Put codes onto Git and have one Rmd file 
3. Group presentation   