# Reference Collection to push back against "Common Statistical Myths"

**URL:** <https://discourse.datamethods.org/t/reference-collection-to-push-back-against-common-statistical-myths/1787>\
**Category:** data analysis\
**Tags:** teaching, journal\
**Created:** [June 27, 2019, 12:51pm UTC](https://discourse.datamethods.org/t/reference-collection-to-push-back-against-common-statistical-myths/1787 "2019-06-27T12:51:23Z")\
**Posts on this page:** 1\
**Showing post:** 6

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**Author:** ![R\_cubed](https://discourse.datamethods.org/user_avatar/discourse.datamethods.org/r_cubed/32/1518_2.png) [@R\_cubed](https://discourse.datamethods.org/u/R_cubed)\
**Post date:** [June 28, 2019, 3:13pm UTC](https://discourse.datamethods.org/t/reference-collection-to-push-back-against-common-statistical-myths/1787/6 "2019-06-28T15:13:44Z")

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Would this be the appropriate thread to add references on the issues related to using parametric assumptions on ordinal data? This has always bothered my mathematical conscience.

Prof. Harrell had posted a great link to a recent paper in another thread:

> [@Responder Analysis: Loser x 4](https://discourse.datamethods.org/t/responder-analysis-loser-x-4/1262/3):
>
> [Much](https://www.sciencedirect.com/science/article/pii/S0022103117307746?via%3Dihub) has been written on that. It might go all the way from a minor to a major breach. Why take the chance when we have wonderful ordinal regression models that make no assumptions about inter-category spacings?

A draft copy an be found here (I assume it is OK to post a link to the draft):

Analyzing Ordinal Data with Metric Models: What Could Possibly Go Wrong?

> **[Analyzing Ordinal Data with Metric Models: What Could Possibly Go Wrong?](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2692323)**
>
> We surveyed all articles in the Journal of Personality and Social Psychology (JPSP), Psychological Science (PS), and the Journal of Experimental Psychology: Gen

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