# data[].attributes.body
to make a post click the pink plus in the bottom right corner and write... the posts are anonymous and you can upvote the posts you really want to be answered. leave your posts white so that it's easy for Andy to differentiate them from this one :D

# data[].attributes.body
Could you explain a little bit more about the difference between random vs PEST? and is RiskyTask2 the same as PEST?

# data[].attributes.body
As we can use the EEG labs we did, I have a few more questions regarding this, as it wasn’t an actual research study:

In terms of things like researcher name, experiment ID, the name of the experiment and online information URL, should we just omit these from the entry or just write N/A?

For Part B of the entry, should we answer these questions to the best of our knowledge and answer questions like 'What do you believe the hypothesis would be?' with our own opinion based on what we've learnt about these tasks throughout the term?

# data[].attributes.body
What does the 30 for number of trials for each safe option mean for risky task 1?

# data[].attributes.body
Do we need appendices for our code that we do for our own analysis?

# data[].attributes.body
Can we get more info on why we’re doing the pest vs random comparison? What are we supposed to expect? Like I get they both use two different methods in extracting CEs but why do we need two methods?

# data[].attributes.headline
What data analysis tool (R, MATLAB etc) was used to calculate the CE's for each participant?

# data[].attributes.subject
What data analysis tool (R, MATLAB etc) was used to calculate the CE's for each participant?

# data[].attributes.body
I feel like it is MATLAB, but want to double check

# data[].attributes.body
Do we have to provide the graph that shows the logistic function to work out CEs? (like what we saw at the end of the task when we completed each condition) Or can we just produce a figure that shows the distribution of CEs for each condition in each task, e.g., a boxplot

# data[].attributes.body
Hi, i was wondering

1)  is it ok that we combine KCL1 with KCL2 and Oxford 1 as a “preliminary study” and put this info in our methods section rather than results, seeing as pennies/pounds and KCL/Oxford isn’t the main focus of our research question? I.e. in our results we will use the combined big dataset from the beginning

2) if one set of data doesn’t meet the assumptions of a t test (eg we are doing two independent two tailed t tests and 4 one sample t tests one of which doesn’t meet assumptions), do we have to automatically use a non parametric test for all the six tests seeing as we will correct for multiple comparisons for those six together.. or do we just use a non parametric for the one that doesn’t meet the assumptions and t tests for the rest?

Thank youuu

# data[].attributes.body
What should be do if we can’t think of “non basic” limitations for the study.

# data[].attributes.body
in the methods section of lab reports the demographics of the sample data are usually given, e.g we used KCL and Oxford undergrads (n = (i know we can get this by coding the dataset), mean age = x, sd = y, males = x, femlaes = y etc...)

do we have access to this data?

# data[].attributes.body
How much knowledge of decision making should we assume the reader has? Do we need to define terms such as risk-seeking and risk-averse?

# data[].attributes.body
would log(p/1-p) be the formula used when fitting a logistic regression when calculating the CE's?

# data[].attributes.body
when pooling together data e.g HIGH conditions and LOW conditions (initial t.test showed no difference so we can pool data, this would answer hypothesis two), in subsequent one sample t.tests there comes a problem of violations to assumptions as the sample is no longer from independent observations.

# data[].attributes.body
Is it possible to get more information on the Oxford sample and how that data was collected?

# data[].attributes.body
If we only choose to focus on the 2 mandatory research questions, do we have to mention the other IV's in our study design, e.g pennies vs pounds and KCL vs other unis?

# data[].attributes.headline
Do figure legends also count into the word count? 

# data[].attributes.subject
Do figure legends also count into the word count? 

# data[].attributes.body
What is the rational for the PEST condition in addition to the Random condtion?

# data[].attributes.body
Do we need to include the logistic regression plot of the probability of choosing the safe value changing over the trials, with the CE calculation?

# data[].attributes.body
Do we have to use the present tense in discussion even when we are talking about our results?

# data[].attributes.body
Most of the results are not significant... can you say what sort of direction we can take in our discussion because saying something like 'virtual money' wasn't a good enough reward is a little redundant with all the research we did.

# data[].attributes.body
If we wanted, are we allowed to include the code we used when we did the tasks in an appendix? it’s quite difficult to understand the number of trials for the random task as we did not write the code ourselves

# data[].attributes.body
Should we explain prospect theory in the introduction? it is relevant but you previously stated you do not want us to repeat what was in our choices lab report.

# data[].attributes.body
What are these for? I can't seem to open these datasets properly in MATLAB

# data[].attributes.body
Are we meant to create a figure showing the values of the CEs for all participants for the lab report?

# data[].attributes.body
How can i manually determine a new alpha value to compare my p-values to? Is it necessary to correct for multiple comparisons? (i did 1 independent ttest, 2 dependent, and 3 single samples)