Timeline for Dealing with missing values where the question was not asked
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
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| Apr 7, 2019 at 19:08 | comment | added | Confounded | Thank you for your reply @Tim. Unfortunately, I only have information on a few target moments, so can't use equi-percentile approach. | |
| Apr 7, 2019 at 17:20 | comment | added | Tim | @Confounded equipercentile equating (mentioned above) seems to be what you're asking. | |
| Apr 7, 2019 at 16:29 | comment | added | Confounded | The expression you use for linear equating can result in the shift in the range of support of the distribution of Y compared to X. How can we match the mean without changing the range of support. Say, X is defined on an interval [a, b] and we want to transform its sample (or empirical distribution) to have the same support but different mean - how can we achieve this? And what about variance? And how do we transform only one of them, ie transform mean keeping variance the same, or transforming variance keeping mean the same. Thanks | |
| Jan 29, 2018 at 15:25 | comment | added | AdamO | The challenge with evaluating consistency in this way is two-fold: basically the type I and type II error cannot be characterized. As we know in survey design, the period effect can be enormous: those response to the first wave of a survey are different from those who response to later waves/invitations. A significant finding may indicate a shift in the type of responding sample rather than a questionnaire effect. Similarly, a null finding may coincide with a questionnaire effect balanced with the sample characteristics. | |
| Feb 9, 2015 at 19:43 | history | edited | Tim | CC BY-SA 3.0 |
error correction
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| Dec 3, 2014 at 9:40 | history | edited | Tim | CC BY-SA 3.0 |
added 49 characters in body
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| Dec 3, 2014 at 9:20 | history | answered | Tim | CC BY-SA 3.0 |