Is going from continuous data to categorical always wrong?
$begingroup$
When I read about how to setup your data, one thing I have often come across is that transforming some continuous data into categorical data is not a good idea, since you may very well make the wrong conclusion if the thresholds are poorly determined.
However, I currently have some data (PSA values for prostate cancer patients), where I think the common consensus is that if you are below 4 you probably don't have it, if you are above you are at risk, and then something like above 10 and 20, you probably have it. Something like that.
In that case, would it still be incorrect to categorize my continuous PSA values into groups of let's say 0-4, 4-10, and >10 ? Or is it actually okay since the thresholds are "well determined" so to speak.
categorical-data continuous-data
asked 6 hours ago
Denver DangDenver Dang
1748
$endgroup$
add a comment |
$begingroup$
When I read about how to setup your data, one thing I have often come across is that transforming some continuous data into categorical data is not a good idea, since you may very well make the wrong conclusion if the thresholds are poorly determined.
However, I currently have some data (PSA values for prostate cancer patients), where I think the common consensus is that if you are below 4 you probably don't have it, if you are above you are at risk, and then something like above 10 and 20, you probably have it. Something like that.
In that case, would it still be incorrect to categorize my continuous PSA values into groups of let's say 0-4, 4-10, and >10 ? Or is it actually okay since the thresholds are "well determined" so to speak.
categorical-data continuous-data
asked 6 hours ago
Denver DangDenver Dang
1748
$endgroup$
$begingroup$
It depends (as usual). For instance, if you are studying how physicians will make decisions, and they make decisions based on these categories, then it behooves you to use the same categories. If you are instead studying the biological consequences associated with elevated PSA, then most likely you do not want to categorize PSA at all. Thus, there is no definite answer to your broad question "is it okay."
$endgroup$
– whuber♦
2 hours ago
$begingroup$
What are you trying to do with the data? Aren't boundaries like that usually related to what you want to figure out, so that putting them in by hand is begging the question?
$endgroup$
– RemcoGerlich
1 hour ago
$begingroup$
I am setting the data up for a logistic regression model. So the main question is actually whether to just use the continuous data, or have discrete data instead.
$endgroup$
– Denver Dang
1 hour ago
$begingroup$
It's not clear to me what 'continuous' data is. It's not something that exists in reality. There's no such thing as a measurement/statistic with infinite precision.
$endgroup$
– JimmyJames
18 mins ago
add a comment |
$begingroup$
When I read about how to setup your data, one thing I have often come across is that transforming some continuous data into categorical data is not a good idea, since you may very well make the wrong conclusion if the thresholds are poorly determined.
However, I currently have some data (PSA values for prostate cancer patients), where I think the common consensus is that if you are below 4 you probably don't have it, if you are above you are at risk, and then something like above 10 and 20, you probably have it. Something like that.
In that case, would it still be incorrect to categorize my continuous PSA values into groups of let's say 0-4, 4-10, and >10 ? Or is it actually okay since the thresholds are "well determined" so to speak.
categorical-data continuous-data
asked 6 hours ago
Denver DangDenver Dang
1748
$endgroup$
When I read about how to setup your data, one thing I have often come across is that transforming some continuous data into categorical data is not a good idea, since you may very well make the wrong conclusion if the thresholds are poorly determined.
However, I currently have some data (PSA values for prostate cancer patients), where I think the common consensus is that if you are below 4 you probably don't have it, if you are above you are at risk, and then something like above 10 and 20, you probably have it. Something like that.
In that case, would it still be incorrect to categorize my continuous PSA values into groups of let's say 0-4, 4-10, and >10 ? Or is it actually okay since the thresholds are "well determined" so to speak.
categorical-data continuous-data
categorical-data continuous-data
asked 6 hours ago
Denver DangDenver Dang
1748
asked 6 hours ago
Denver DangDenver Dang
1748
asked 6 hours ago
Denver DangDenver Dang
1748
asked 6 hours ago
Denver DangDenver Dang
1748
asked 6 hours ago
Denver DangDenver Dang
1748
1748
$begingroup$
It depends (as usual). For instance, if you are studying how physicians will make decisions, and they make decisions based on these categories, then it behooves you to use the same categories. If you are instead studying the biological consequences associated with elevated PSA, then most likely you do not want to categorize PSA at all. Thus, there is no definite answer to your broad question "is it okay."
$endgroup$
– whuber♦
2 hours ago
$begingroup$
What are you trying to do with the data? Aren't boundaries like that usually related to what you want to figure out, so that putting them in by hand is begging the question?
$endgroup$
– RemcoGerlich
1 hour ago
$begingroup$
I am setting the data up for a logistic regression model. So the main question is actually whether to just use the continuous data, or have discrete data instead.
$endgroup$
– Denver Dang
1 hour ago
$begingroup$
It's not clear to me what 'continuous' data is. It's not something that exists in reality. There's no such thing as a measurement/statistic with infinite precision.
$endgroup$
– JimmyJames
18 mins ago
add a comment |
$begingroup$
It depends (as usual). For instance, if you are studying how physicians will make decisions, and they make decisions based on these categories, then it behooves you to use the same categories. If you are instead studying the biological consequences associated with elevated PSA, then most likely you do not want to categorize PSA at all. Thus, there is no definite answer to your broad question "is it okay."
$endgroup$
– whuber♦
2 hours ago
$begingroup$
What are you trying to do with the data? Aren't boundaries like that usually related to what you want to figure out, so that putting them in by hand is begging the question?
$endgroup$
– RemcoGerlich
1 hour ago
$begingroup$
I am setting the data up for a logistic regression model. So the main question is actually whether to just use the continuous data, or have discrete data instead.
$endgroup$
– Denver Dang
1 hour ago
$begingroup$
It's not clear to me what 'continuous' data is. It's not something that exists in reality. There's no such thing as a measurement/statistic with infinite precision.
$endgroup$
– JimmyJames
18 mins ago
$begingroup$
It depends (as usual). For instance, if you are studying how physicians will make decisions, and they make decisions based on these categories, then it behooves you to use the same categories. If you are instead studying the biological consequences associated with elevated PSA, then most likely you do not want to categorize PSA at all. Thus, there is no definite answer to your broad question "is it okay."
$endgroup$
– whuber♦
2 hours ago
$begingroup$
It depends (as usual). For instance, if you are studying how physicians will make decisions, and they make decisions based on these categories, then it behooves you to use the same categories. If you are instead studying the biological consequences associated with elevated PSA, then most likely you do not want to categorize PSA at all. Thus, there is no definite answer to your broad question "is it okay."
$endgroup$
– whuber♦
2 hours ago
$begingroup$
What are you trying to do with the data? Aren't boundaries like that usually related to what you want to figure out, so that putting them in by hand is begging the question?
$endgroup$
– RemcoGerlich
1 hour ago
$begingroup$
What are you trying to do with the data? Aren't boundaries like that usually related to what you want to figure out, so that putting them in by hand is begging the question?
$endgroup$
– RemcoGerlich
1 hour ago
$begingroup$
I am setting the data up for a logistic regression model. So the main question is actually whether to just use the continuous data, or have discrete data instead.
$endgroup$
– Denver Dang
1 hour ago
$begingroup$
I am setting the data up for a logistic regression model. So the main question is actually whether to just use the continuous data, or have discrete data instead.
$endgroup$
– Denver Dang
1 hour ago
$begingroup$
It's not clear to me what 'continuous' data is. It's not something that exists in reality. There's no such thing as a measurement/statistic with infinite precision.
$endgroup$
– JimmyJames
18 mins ago
$begingroup$
It's not clear to me what 'continuous' data is. It's not something that exists in reality. There's no such thing as a measurement/statistic with infinite precision.
$endgroup$
– JimmyJames
18 mins ago
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Is there a sharp discontinuity at your thresholds?
For instance, suppose you have two patients A and B with values 3.9 and 4.1, and another two patients C and D with values 6.7 and 6.9. Is the difference in the likelihood for cancer between A and B much larger than the corresponding difference between C and D?
If yes, then discretizing makes sense.
If not, then your thresholds may make sense in understanding your data, but they are not "well determined" in a statistically meaningful sense. Don't discretize. Instead, use your test scores "as-is", and if you suspect some kind of nonlinearity, use splines.
This is very much recommended.
answered 6 hours ago
Stephan KolassaStephan Kolassa
46.4k796171
$endgroup$
1
$begingroup$
That link at the bottom is full of great points. Future readers of this answer should check it out.
$endgroup$
– eric_kernfeld
3 hours ago
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "65"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f397258%2fis-going-from-continuous-data-to-categorical-always-wrong%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Is there a sharp discontinuity at your thresholds?
For instance, suppose you have two patients A and B with values 3.9 and 4.1, and another two patients C and D with values 6.7 and 6.9. Is the difference in the likelihood for cancer between A and B much larger than the corresponding difference between C and D?
If yes, then discretizing makes sense.
If not, then your thresholds may make sense in understanding your data, but they are not "well determined" in a statistically meaningful sense. Don't discretize. Instead, use your test scores "as-is", and if you suspect some kind of nonlinearity, use splines.
This is very much recommended.
answered 6 hours ago
Stephan KolassaStephan Kolassa
46.4k796171
$endgroup$
1
$begingroup$
That link at the bottom is full of great points. Future readers of this answer should check it out.
$endgroup$
– eric_kernfeld
3 hours ago
add a comment |
$begingroup$
Is there a sharp discontinuity at your thresholds?
For instance, suppose you have two patients A and B with values 3.9 and 4.1, and another two patients C and D with values 6.7 and 6.9. Is the difference in the likelihood for cancer between A and B much larger than the corresponding difference between C and D?
If yes, then discretizing makes sense.
If not, then your thresholds may make sense in understanding your data, but they are not "well determined" in a statistically meaningful sense. Don't discretize. Instead, use your test scores "as-is", and if you suspect some kind of nonlinearity, use splines.
This is very much recommended.
answered 6 hours ago
Stephan KolassaStephan Kolassa
46.4k796171
$endgroup$
1
$begingroup$
That link at the bottom is full of great points. Future readers of this answer should check it out.
$endgroup$
– eric_kernfeld
3 hours ago
add a comment |
$begingroup$
Is there a sharp discontinuity at your thresholds?
For instance, suppose you have two patients A and B with values 3.9 and 4.1, and another two patients C and D with values 6.7 and 6.9. Is the difference in the likelihood for cancer between A and B much larger than the corresponding difference between C and D?
If yes, then discretizing makes sense.
If not, then your thresholds may make sense in understanding your data, but they are not "well determined" in a statistically meaningful sense. Don't discretize. Instead, use your test scores "as-is", and if you suspect some kind of nonlinearity, use splines.
This is very much recommended.
answered 6 hours ago
Stephan KolassaStephan Kolassa
46.4k796171
$endgroup$
Is there a sharp discontinuity at your thresholds?
For instance, suppose you have two patients A and B with values 3.9 and 4.1, and another two patients C and D with values 6.7 and 6.9. Is the difference in the likelihood for cancer between A and B much larger than the corresponding difference between C and D?
If yes, then discretizing makes sense.
If not, then your thresholds may make sense in understanding your data, but they are not "well determined" in a statistically meaningful sense. Don't discretize. Instead, use your test scores "as-is", and if you suspect some kind of nonlinearity, use splines.
This is very much recommended.
answered 6 hours ago
Stephan KolassaStephan Kolassa
46.4k796171
answered 6 hours ago
Stephan KolassaStephan Kolassa
46.4k796171
answered 6 hours ago
Stephan KolassaStephan Kolassa
46.4k796171
answered 6 hours ago
Stephan KolassaStephan Kolassa
46.4k796171
46.4k796171
1
$begingroup$
That link at the bottom is full of great points. Future readers of this answer should check it out.
$endgroup$
– eric_kernfeld
3 hours ago
add a comment |
1
$begingroup$
That link at the bottom is full of great points. Future readers of this answer should check it out.
$endgroup$
– eric_kernfeld
3 hours ago
1
1
$begingroup$
That link at the bottom is full of great points. Future readers of this answer should check it out.
$endgroup$
– eric_kernfeld
3 hours ago
$begingroup$
That link at the bottom is full of great points. Future readers of this answer should check it out.
$endgroup$
– eric_kernfeld
3 hours ago
add a comment |
Thanks for contributing an answer to Cross Validated!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f397258%2fis-going-from-continuous-data-to-categorical-always-wrong%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
$begingroup$
It depends (as usual). For instance, if you are studying how physicians will make decisions, and they make decisions based on these categories, then it behooves you to use the same categories. If you are instead studying the biological consequences associated with elevated PSA, then most likely you do not want to categorize PSA at all. Thus, there is no definite answer to your broad question "is it okay."
$endgroup$
– whuber♦
2 hours ago
$begingroup$
What are you trying to do with the data? Aren't boundaries like that usually related to what you want to figure out, so that putting them in by hand is begging the question?
$endgroup$
– RemcoGerlich
1 hour ago
$begingroup$
I am setting the data up for a logistic regression model. So the main question is actually whether to just use the continuous data, or have discrete data instead.
$endgroup$
– Denver Dang
1 hour ago
$begingroup$
It's not clear to me what 'continuous' data is. It's not something that exists in reality. There's no such thing as a measurement/statistic with infinite precision.
$endgroup$
– JimmyJames
18 mins ago