Return to Answer

I will go out on a limb here (but I feel that limb is pretty strong), and say that your predictor $X$ is a Likert score. It looks llike a Likert (ordinal), it walks like a Likert (5 levels), it quacks like a Likert (poor quality of the measuring instrument).
If that is the case, then I have actually to commend your dataset owner for wanting to treat $X$ as binary, exactly for the 2 reasons you gave. I would add a 3rd one to that: there are not too many tests adapted for a strictly ordinal variable (e.g. Mann-Whitney U, or brms as suggested in another answer).
With all due respect to @Frank Harrell, in the case of a Likert score, such binning is very clearly defined, and very easily interpretable; 1-3 correspond to no expression of "agreement" (or however the 5 Likert choices were worded), while 4-5 correspond to an expression of "agreement". There is a very clear threshold; on one side disagreement or indifference, on the other "agreement".
To be honest, when dealing with Likert scores (answers to a single Likert question), I almost always deprecate them to a binomial variable. Note that is is why 4-point Likert scale are often used; to force the respondent to pick a side (and eliminate the interpretation of "neutral"). Think of this binning as similar to how Net Promoter Scores (NPS) are computed (going from an 11 points ordinal scale to a 3-points, or even to a binary variable).
So I see nothing wrong in deprecating the Likert score to a binomial (yes/no) variable. You wrote "one should not bin ordinal or continuous data". This is certainly true for continuous variables; you lose a lot -too much?- information. Note though that all dichotomous diagnostic tests (e.g. Covid, TB, pregnancy) do exactly that; take a continuous outcome, use a "properly selected" threshold, and give a binary answer. When it comes to ordinal variables, specifically Likert scores, it is not even clear anymore that you would lose anything. Given the fact that a Likert score is indeed ill-defined, ambiguous, has very low resolution (5 choices), and that there are few tests which respect this ordinality, you are not losing much (if any) information by deprecating it to a binomial variable.

PS: if indeed $x$ is a Likert score, you may want to add this "detail" to your original question. It will definitively clarify your context.

I will go out on a limb here (but I feel that limb is pretty strong), and say that your predictor $X$ is a Likert score. It looks llike a Likert (ordinal), it walks like a Likert (5 levels), it quacks like a Likert (poor quality of the measuring instrument).
If that is the case, then I have actually to commend your dataset owner for wanting to treat $X$ as binary, exactly for the 2 reasons you gave. I would add a 3rd one to that: there are not too many tests adapted for a strictly ordinal variable (e.g. Mann-Whitney U, or brms as suggested in another answer).
With all due respect to @Frank Harrell, in the case of a Likert score, such binning is very clearly defined, and very easily interpretable; 1-3 correspond to no expression of "agreement" (or however the 5 Likert choices were worded), while 4-5 correspond to an expression of "agreement". There is a very clear threshold; on one side disagreement or indifference, on the other "agreement".
To be honest, when dealing with Likert scores (answers to a single Likert question), I almost always deprecate them to a binomial variable. Note that this is why 4-point Likert scales are often used; to force the respondent to pick a side (and eliminate the interpretation of "neutral"). Think of this binning as similar to how Net Promoter Scores (NPS) are computed (going from an 11 points ordinal scale to a 3-points, or even to a binary scale).
So I see nothing wrong in deprecating the Likert score to a binomial (yes/no) variable. You wrote "one should not bin ordinal or continuous data". This is certainly true for continuous variables; you lose a lot -too much?- information. Note though that all dichotomous diagnostic tests (e.g. Covid, TB, pregnancy) do exactly that; take a continuous outcome, use a "properly selected" threshold, and give a binary answer. So it has its uses. When it comes to ordinal variables, specifically Likert scores, it is not even clear anymore that you would lose anything. Given the fact that a Likert score is indeed ill-defined, ambiguous, has very low resolution (5 choices), and that there are few tests which respect this ordinality, you are not losing much (if any) information by deprecating it to a binomial variable.

PS: if indeed $X$ is a Likert score, you may want to add this "detail" to your original question. It will definitively clarify your context.

I will go out on a limb here (but I feel that limb is pretty strong), and say that your predictor $X$ is a Likert score. It looks llike a Likert (ordinal), it walks like a Likert (5 levels), it quacks like a Likert (poor quality of the measuring instrument).
If that is the case, then I have actually to commend your dataset owner for wanting to treat $X$ as binary, exactly for the 2 reasons you gave. I would add a 3rd one to that: there are not too many tests adapted for a strictly ordinal variable (e.g. Mann-Whitney U, or brms as suggested in another answer).
With all due respect to @Frank Harrell, in the case of a Likert score, such binning is very clearly defined, and very easily interpretable; 1-3 correspond to no expression of "agreement" (or however the 5 Likert choices were worded), while 4-5 correspond to an expression of "agreement". There is a very clear threshold; on one side disagreement or indifference, on the other "agreement".
To be honest, when dealing with Likert scores (answers to a single Likert question), I almost always deprecate them to a binomial variable. Note that is is why 4-point Likert scale are often used; to force the respondent to pick a side (and eliminate the interpretation of "neutral").
So I see nothing wrong in deprecating the Likert score to a binomial (yes/no) variable. You wrote "one should not bin ordinal or continuous data". This is certainly true for continuous variables; you lose a lot -too much?- information. Note though that all dichotomous diagnostic tests (e.g. Covid, TB, pregnancy) do exactly that; take a continuous outcome, use a "properly selected" threshold, and give a binary answer. When it comes to ordinal variables, specifically Likert scores, it is not even clear anymore that you would lose anything. Given the fact that a Likert score is indeed ill-defined, ambiguous, has very low resolution (5 choices), and that there are few tests which respect this ordinality, you are not losing much (if any) information by deprecating it to a binomial variable.

PS: if indeed $x$ is a Likert score, you may want to add this "detail" to your original question. It will definitively clarify your context.

I will go out on a limb here (but I feel that limb is pretty strong), and say that your predictor $X$ is a Likert score. It looks llike a Likert (ordinal), it walks like a Likert (5 levels), it quacks like a Likert (poor quality of the measuring instrument).
If that is the case, then I have actually to commend your dataset owner for wanting to treat $X$ as binary, exactly for the 2 reasons you gave. I would add a 3rd one to that: there are not too many tests adapted for a strictly ordinal variable (e.g. Mann-Whitney U, or brms as suggested in another answer).
With all due respect to @Frank Harrell, in the case of a Likert score, such binning is very clearly defined, and very easily interpretable; 1-3 correspond to no expression of "agreement" (or however the 5 Likert choices were worded), while 4-5 correspond to an expression of "agreement". There is a very clear threshold; on one side disagreement or indifference, on the other "agreement".
To be honest, when dealing with Likert scores (answers to a single Likert question), I almost always deprecate them to a binomial variable. Note that is is why 4-point Likert scale are often used; to force the respondent to pick a side (and eliminate the interpretation of "neutral"). Think of this binning as similar to how Net Promoter Scores (NPS) are computed (going from an 11 points ordinal scale to a 3-points, or even to a binary variable).
So I see nothing wrong in deprecating the Likert score to a binomial (yes/no) variable. You wrote "one should not bin ordinal or continuous data". This is certainly true for continuous variables; you lose a lot -too much?- information. Note though that all dichotomous diagnostic tests (e.g. Covid, TB, pregnancy) do exactly that; take a continuous outcome, use a "properly selected" threshold, and give a binary answer. When it comes to ordinal variables, specifically Likert scores, it is not even clear anymore that you would lose anything. Given the fact that a Likert score is indeed ill-defined, ambiguous, has very low resolution (5 choices), and that there are few tests which respect this ordinality, you are not losing much (if any) information by deprecating it to a binomial variable.

PS: if indeed $x$ is a Likert score, you may want to add this "detail" to your original question. It will definitively clarify your context.

I will go out on a limb here (but I feel that limb is pretty strong), and say that your predictor $X$ is a Likert score. It looks llike a Likert (ordinal), it walks like a Likert (5 levels), it quacks like a Likert (poor quality of the measuring instrument).
If that is the case, then I have actually to commend your dataset owner for wanting to treat $X$ as binary, exactly for the 2 reasons you gave. I would add a 3rd one to that: there are not too many tests adapted for a strictly ordinal variable (e.g. Mann-Whitney U, or brms as suggested in another answer).
With all due respect to @Frank Harrell, in the case of a Likert score, such binning is very clearly defined, and very easily interpretable; 1-3 correspond to no expression of "agreement" (or however the 5 Likert choices were worded), while 4-5 correspond to an expression of "agreement". There is a very clear threshold; on one side disagreement or indifference, on the other "agreement".
To be honest, when dealing with Likert scores (answers to a single Likert question, I almost always deprecate them to a binomial variable. Note that is is why 4-point Likert scale are often used; to force the respondent to pick a side (and eliminate the interpretation of "neutral").
So I see nothing wrong in deprecating the Likert score to a binomial (yes/no) variable. You wrote "one should not bin ordinal or continuous data". This is certainly true for continuous variables, but only in as much as you will lose a great deal of power (i.e. require a much larger sample size), but not because you will get "wrong" answers. In fact, all dichotomous diagnostic tests (e.g. Covid, TB, pregnancy) do exactly that; take a continuous outcome, use a "properly selected" threshold, and give a binary answer. When it comes to ordinal variables, specifically Likert scores, it is not even clear anymore that you would lose any power. Given the fact that a Likert score is indeed ill-defined, ambiguous, has very low resolution (5 choices), and that there are few tests which respect this ordinality, you are not losing much (if any) information by deprecating it to a binomial variable.

PS: if indeed $x$ is a Likert score, you may want to add this "detail" to your original question. It will definitively clarify your context.

I will go out on a limb here (but I feel that limb is pretty strong), and say that your predictor $X$ is a Likert score. It looks llike a Likert (ordinal), it walks like a Likert (5 levels), it quacks like a Likert (poor quality of the measuring instrument).
If that is the case, then I have actually to commend your dataset owner for wanting to treat $X$ as binary, exactly for the 2 reasons you gave. I would add a 3rd one to that: there are not too many tests adapted for a strictly ordinal variable (e.g. Mann-Whitney U, or brms as suggested in another answer).
With all due respect to @Frank Harrell, in the case of a Likert score, such binning is very clearly defined, and very easily interpretable; 1-3 correspond to no expression of "agreement" (or however the 5 Likert choices were worded), while 4-5 correspond to an expression of "agreement". There is a very clear threshold; on one side disagreement or indifference, on the other "agreement".
To be honest, when dealing with Likert scores (answers to a single Likert question), I almost always deprecate them to a binomial variable. Note that is is why 4-point Likert scale are often used; to force the respondent to pick a side (and eliminate the interpretation of "neutral").
So I see nothing wrong in deprecating the Likert score to a binomial (yes/no) variable. You wrote "one should not bin ordinal or continuous data". This is certainly true for continuous variables; you lose a lot -too much?- information. Note though that all dichotomous diagnostic tests (e.g. Covid, TB, pregnancy) do exactly that; take a continuous outcome, use a "properly selected" threshold, and give a binary answer. When it comes to ordinal variables, specifically Likert scores, it is not even clear anymore that you would lose anything. Given the fact that a Likert score is indeed ill-defined, ambiguous, has very low resolution (5 choices), and that there are few tests which respect this ordinality, you are not losing much (if any) information by deprecating it to a binomial variable.

PS: if indeed $x$ is a Likert score, you may want to add this "detail" to your original question. It will definitively clarify your context.