@lakens You may be interested in the change over time in unemployment rates (previous quarter, last year etc.), but ultimately you are estimating unemployment rates at time t. E.g. with https://t.co/MmEdx9ubKj you can do all kinds of tests, but I see this as an estimation problem
@lakens There is no test here. The idea is to estimate the time-series (or a CI of) unemployment rates. Or, e.g. to estimate at regional level poverty rates. You can of course do tests on all these estimands too (e.g. is estimate 1 the same as estimate 2).
@lakens See e.g. Lohr, S. L. (2021). Sampling: design and analysis. Chapman and Hall/CRC. (available online if you search).
A policy-making example: if you want to estimate unemployment rate quarterly, you can define that a change of say .1% is 'relevant', implying s.e should be ~.05
@lakens I think we had this discussion before, but in some fields - such as official stats - its is common to think about the desired size of the s.e., e.g. as expressed in the coëfficiënt of variation. Precision is important there.
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@EikoFried Asking sad ---- happy as two items seems weird, unless you want to do latent variable modeling.
That still leaves the uni vs bipolar issue. Lots of literature on this, but inconclusive imo. I think depends on topic and population. Qualitative test before fielding survey?
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@EikoFried And about the composite effect: normalising just transforms the score, but doesnt take away the experiment varies multiple things at once. (the no. of points + the answer type). Would be nice to replicate this with 1-7 VAS.
@EikoFried One place to start is https://t.co/MQlMvzAHAD. This is a 'meta-analytic' tool of 100s of question-and-answer experiments, including things like the number of answer options, answer scale, end-point labels etc.