Measurement framework - move to wiki suggestions

[kbjarkefur]

dime-data-handbook/chapters/sampling-randomization-power.tex

Lines 628 to 636 in 7e2e8c1

	This is especially beneficial if you are implementing a more complex randomization,
	for example, sample 10\% of the patients, show a video for 50\% of the sample,
	and ask a longer version of the questionnaire to 20\% of both
	the group of patients that watch the video and those that did not.
	The real time randomization is much more likely to be implemented correctly,
	if your field staff simply can follow a list with the randomized categories
	where you are in control fo the pre-determined proportions and the random order.
	This way, you can also control with precision,
	how these categories are evenly distributed across all health facilities.

@mariaruth
the below examples are super but i wonder if should move to wiki? feels like a finer level of detail than other sections.

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dime-data-handbook/chapters/sampling-randomization-power.tex

Lines 125 to 141 in 7e2e8c1

	The data map should also include metadata about the handling of all information.
	These characteristics may be updated as the project progresses.
	For example, you will need to note the original source of each dataset,
	as well as the project folder where
	the raw original data and codebooks are stored
	and where the back-ups for the each raw dataset are stored.
	Some of the characteristics in your master datasets and your data map
	should be filled in during the planning stage,
	but both of them should be active resources
	that are updated as your project evolves.
	Finally, your master data should not include any unlabeled missing values.
	If the information is missing for one unit,
	then the reason should always be indicated with a code.
	An example for such reason could be that a unit was not included in the treatment assignment
	as it was not sampled in the first place,
	or was not located in the data collection at a given round.

@luizaandrade
Move this detailed discussion to the wiki?