Logistic regression help
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Dear list
I am trying to carry out a logistic regression analysis and have a quick question with regards to the best way to input my independent variables. I have three input variables: ethnicity (5 groups), age and deprivation score. Although age and deprivation score are continuous variables, I have also been asked to split them into groups (4 for age and 5 for deprivation) which are pre-determined by previous work on this subject matter. The dependent variable is simply whether or not a person took a particular test. I have tried generating models both with the age and deprivation variables as they are and also with the new categorical age and deprivation variables. However, when looking at interaction terms, I find that the interaction between age and deprivation is significant when they are input as the continuous variables but not significant when I used the categorical versions. Why would this happen? Furthermore, which is the best way to go? I have read information on logistic regression until my head hurts, but still dont feel completely satisfied as to how I should determine the best model possible. Any advice would be appreciated please! Thanks Lou |
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Keith Starborn
www.statisticsdoc.com Dear Lou, Categorizing continuous variables into categorical variables can result is a considerable loss of statistical power because the test for the categorized version of the variable uses more degrees of freedom that the test for the continuous variable. In addition, categorizing a continuous variable can result in a loss of predictive information. HTH, KS ---- Lou <[hidden email]> wrote: > Dear list > > I am trying to carry out a logistic regression analysis and have a quick > question with regards to the best way to input my independent variables. > I have three input variables: ethnicity (5 groups), age and deprivation > score. Although age and deprivation score are continuous variables, I > have also been asked to split them into groups (4 for age and 5 for > deprivation) which are pre-determined by previous work on this subject > matter. The dependent variable is simply whether or not a person took a > particular test. > > I have tried generating models both with the age and deprivation variables > as they are and also with the new categorical age and deprivation > variables. However, when looking at interaction terms, I find that the > interaction between age and deprivation is significant when they are input > as the continuous variables but not significant when I used the > categorical versions. Why would this happen? Furthermore, which is the > best way to go? I have read information on logistic regression until my > head hurts, but still dont feel completely satisfied as to how I should > determine the best model possible. > > Any advice would be appreciated please! > > Thanks > > Lou -- For personalized and experienced consulting in statistics and research design, visit www.statisticsdoc.com |
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In reply to this post by Charlotte-9
Dear Keith,
Thanks for your advice which was very helpful. I feel a bit stuck as to know what to do about this really. My boss (who knows rougly zero about statistics) is insisting that I categorise these variables since I am comparing results with a previous report which did the same. Does it take meaning away from the analysis if I discuss results obtained using the original continuous variables and then discuss results separately using the categorised versions (i.e. generate two separate models)? Not sure if this really defies logic too much and how I would justify this in the final report. Although I have a lot to learn in this field, the report that this work is being based on has a lot of dubious findings with regards to the stats, so I'm very keen to ensure that the one I produce is accurate!! Many thanks, Lou On Thu, 15 Jun 2006 11:36:45 -0400, Statisticsdoc <[hidden email]> wrote: >Keith Starborn >www.statisticsdoc.com > >Dear Lou, > >Categorizing continuous variables into categorical variables can result is a considerable loss of statistical power because the test for the categorized version of the variable uses more degrees of freedom that the test for the continuous variable. In addition, categorizing a continuous variable can result in a loss of predictive information. > >HTH, > >KS > >---- Lou <[hidden email]> wrote: >> Dear list >> >> I am trying to carry out a logistic regression analysis and have a quick >> question with regards to the best way to input my independent variables. >> I have three input variables: ethnicity (5 groups), age and deprivation >> score. Although age and deprivation score are continuous variables, I >> have also been asked to split them into groups (4 for age and 5 for >> deprivation) which are pre-determined by previous work on this subject >> matter. The dependent variable is simply whether or not a person took a >> particular test. >> >> I have tried generating models both with the age and deprivation >> as they are and also with the new categorical age and deprivation >> variables. However, when looking at interaction terms, I find that the >> interaction between age and deprivation is significant when they are input >> as the continuous variables but not significant when I used the >> categorical versions. Why would this happen? Furthermore, which is the >> best way to go? I have read information on logistic regression until my >> head hurts, but still donÂt feel completely satisfied as to how I should >> determine the best model possible. >> >> Any advice would be appreciated please! >> >> Thanks >> >> Lou > >-- >For personalized and experienced consulting in statistics and research |
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In reply to this post by Charlotte-9
Keith Starborn
www.statisticsdoc.com Lou, I bet most of the people on this listerserv have faced a similar dilemma at some time in their careers. Which one is best from the point of view of using the data to answer your questions and generate information that you can act on? Probably, keeping the variables continuous is better from that point of view. As to the politics of the situation, in your position, I would run the analyses both ways (continuous and categorized) in order to: a.) show that I did the analysis the way I was told to; and b.) found something else that works better. You know the situation best of all. HTH, KS ---- Lou <[hidden email]> wrote: > Dear Keith, > > Thanks for your advice which was very helpful. I feel a bit stuck as to > know what to do about this really. My boss (who knows rougly zero about > statistics) is insisting that I categorise these variables since I am > comparing results with a previous report which did the same. Does it take > meaning away from the analysis if I discuss results obtained using the > original continuous variables and then discuss results separately using > the categorised versions (i.e. generate two separate models)? Not sure if > this really defies logic too much and how I would justify this in the > final report. Although I have a lot to learn in this field, the report > that this work is being based on has a lot of dubious findings with > regards to the stats, so I'm very keen to ensure that the one I produce is > accurate!! > > Many thanks, > > Lou > > On Thu, 15 Jun 2006 11:36:45 -0400, Statisticsdoc <[hidden email]> > wrote: > > >Keith Starborn > >www.statisticsdoc.com > > > >Dear Lou, > > > >Categorizing continuous variables into categorical variables can result > is a considerable loss of statistical power because the test for the > categorized version of the variable uses more degrees of freedom that the > test for the continuous variable. In addition, categorizing a continuous > variable can result in a loss of predictive information. > > > >HTH, > > > >KS > > > >---- Lou <[hidden email]> wrote: > >> Dear list > >> > >> I am trying to carry out a logistic regression analysis and have a quick > >> question with regards to the best way to input my independent variables. > >> I have three input variables: ethnicity (5 groups), age and deprivation > >> score. Although age and deprivation score are continuous variables, I > >> have also been asked to split them into groups (4 for age and 5 for > >> deprivation) which are pre-determined by previous work on this subject > >> matter. The dependent variable is simply whether or not a person took a > >> particular test. > >> > >> I have tried generating models both with the age and deprivation > variables > >> as they are and also with the new categorical age and deprivation > >> variables. However, when looking at interaction terms, I find that the > >> interaction between age and deprivation is significant when they are > input > >> as the continuous variables but not significant when I used the > >> categorical versions. Why would this happen? Furthermore, which is the > >> best way to go? I have read information on logistic regression until my > >> head hurts, but still donÂt feel completely satisfied as to how I > should > >> determine the best model possible. > >> > >> Any advice would be appreciated please! > >> > >> Thanks > >> > >> Lou > > > >-- > >For personalized and experienced consulting in statistics and research > design, visit www.statisticsdoc.com -- For personalized and experienced consulting in statistics and research design, visit www.statisticsdoc.com |
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Hi, Lou. I agree with all that Keith has said. I might add that the non
significant interaction using categorical variables could be due either to the fact that by lumping together the Y responses over a range of X inputs you created a categorical variable whose variance is large enough that it is not possible to detect any interaction and/or that the endpoints of the bin categories you are using occur at points in the data that obscure the interaction you found using the continuous data. In some cases, it may actually serve you to have a model without an interaction effect - it is possible, however, that the confidence intervals around such a model will be larger than they would be from a model with an interaction. On the other hand, using the continuous data apparently allowed you to detect some underlying "process" (the interaction you found). If you are trying to understand what creates the patterns you see in your data, both models give you information about the resolution at which certain processes are revealed or obscured. Apparently lumping the way you have obscures the interaction. You might want to try binning your x variables differently than the previous report did, just to see if there is a way to categorize the x variables such that an interaction is detected. You could probably use plots from your continuous model to give you an idea about where appropriate bins thresholds might lie. I tend to be one who likes to use models as a way of revealing the "scale" at which the data should be approached in order to answer the question at hand. A different question about the same data could require a different type of model. Models also help you find out if the scale you are looking at is missing information about some underlying effects that could effect the application of the results. Just some thoughts. Lucinda ----- Original Message ----- From: "Statisticsdoc" <[hidden email]> Newsgroups: bit.listserv.spssx-l To: <[hidden email]> Sent: Thursday, June 15, 2006 12:36 PM Subject: Re: Logistic regression help > Keith Starborn > www.statisticsdoc.com > > Lou, > > I bet most of the people on this listerserv have faced a similar dilemma > at some time in their careers. Which one is best from the point of view > of using the data to answer your questions and generate information that > you can act on? Probably, keeping the variables continuous is better from > that point of view. > > As to the politics of the situation, in your position, I would run the > analyses both ways (continuous and categorized) in order to: a.) show that > I did the analysis the way I was told to; and b.) found something else > that works better. You know the situation best of all. > > HTH, > > KS > > ---- Lou <[hidden email]> wrote: > > Dear Keith, > > > > Thanks for your advice which was very helpful. I feel a bit stuck as to > > know what to do about this really. My boss (who knows rougly zero about > > statistics) is insisting that I categorise these variables since I am > > comparing results with a previous report which did the same. Does it > > take > > meaning away from the analysis if I discuss results obtained using the > > original continuous variables and then discuss results separately using > > the categorised versions (i.e. generate two separate models)? Not sure > > if > > this really defies logic too much and how I would justify this in the > > final report. Although I have a lot to learn in this field, the report > > that this work is being based on has a lot of dubious findings with > > regards to the stats, so I'm very keen to ensure that the one I produce > > is > > accurate!! > > > > Many thanks, > > > > Lou > > > > On Thu, 15 Jun 2006 11:36:45 -0400, Statisticsdoc > > <[hidden email]> > > wrote: > > > > >Keith Starborn > > >www.statisticsdoc.com > > > > > >Dear Lou, > > > > > >Categorizing continuous variables into categorical variables can result > > is a considerable loss of statistical power because the test for the > > categorized version of the variable uses more degrees of freedom that > > the > > test for the continuous variable. In addition, categorizing a > > continuous > > variable can result in a loss of predictive information. > > > > > >HTH, > > > > > >KS > > > > > >---- Lou <[hidden email]> wrote: > > >> Dear list > > >> > > >> I am trying to carry out a logistic regression analysis and have a > > >> quick > > >> question with regards to the best way to input my independent > > >> variables. > > >> I have three input variables: ethnicity (5 groups), age and > > >> deprivation > > >> score. Although age and deprivation score are continuous variables, > > >> I > > >> have also been asked to split them into groups (4 for age and 5 for > > >> deprivation) which are pre-determined by previous work on this > > >> subject > > >> matter. The dependent variable is simply whether or not a person > > >> took a > > >> particular test. > > >> > > >> I have tried generating models both with the age and deprivation > > variables > > >> as they are and also with the new categorical age and deprivation > > >> variables. However, when looking at interaction terms, I find that > > >> the > > >> interaction between age and deprivation is significant when they are > > input > > >> as the continuous variables but not significant when I used the > > >> categorical versions. Why would this happen? Furthermore, which is > > >> the > > >> best way to go? I have read information on logistic regression until > > >> my > > >> head hurts, but still donÂt feel completely satisfied as to how I > > should > > >> determine the best model possible. > > >> > > >> Any advice would be appreciated please! > > >> > > >> Thanks > > >> > > >> Lou > > > > > >-- > > >For personalized and experienced consulting in statistics and research > > design, visit www.statisticsdoc.com > > -- > For personalized and experienced consulting in statistics and research > design, visit www.statisticsdoc.com > |
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In reply to this post by Charlotte-9
Hi Lucinda,
Thanks very much for your response. You have certainly helped me to think more clearly about the issues surrounding this problem and I'll be re- reading your reply in order to help me fathom out what's going on with this data. Thanks again, Lou On Thu, 15 Jun 2006 13:06:37 -0700, LUCINDA M TEAR <[hidden email]> wrote: >Hi, Lou. I agree with all that Keith has said. I might add that the non >significant interaction using categorical variables could be due either to >the fact that by lumping together the Y responses over a range of X inputs >you created a categorical variable whose variance is large enough that it is >not possible to detect any interaction and/or that the endpoints of the bin >categories you are using occur at points in the data that obscure the >interaction you found using the continuous data. > >In some cases, it may actually serve you to have a model without an >interaction effect - it is possible, however, that the confidence intervals >around such a model will be larger than they would be from a model with an >interaction. On the other hand, using the continuous data apparently >allowed you to detect some underlying "process" (the interaction you found). >If you are trying to understand what creates the patterns you see in your >data, both models give you information about the resolution at which certain >processes are revealed or obscured. Apparently lumping the way you have >obscures the interaction. You might want to try binning your x variables >differently than the previous report did, just to see if there is a way to >categorize the x variables such that an interaction is detected. You could >probably use plots from your continuous model to give you an idea about >where appropriate bins thresholds might lie. I tend to be one who likes to >use models as a way of revealing the "scale" at which the data should be >approached in order to answer the question at hand. A different question >about the same data could require a different type of model. Models also >help you find out if the scale you are looking at is missing information >about some underlying effects that could effect the application of the >results. > >Just some thoughts. > >Lucinda > > > >----- Original Message ----- >From: "Statisticsdoc" <[hidden email]> >Newsgroups: bit.listserv.spssx-l >To: <[hidden email]> >Sent: Thursday, June 15, 2006 12:36 PM >Subject: Re: Logistic regression help > > >> Keith Starborn >> www.statisticsdoc.com >> >> Lou, >> >> I bet most of the people on this listerserv have faced a similar dilemma >> at some time in their careers. Which one is best from the point of view >> of using the data to answer your questions and generate information that >> you can act on? Probably, keeping the variables continuous is better >> that point of view. >> >> As to the politics of the situation, in your position, I would run the >> analyses both ways (continuous and categorized) in order to: a.) show that >> I did the analysis the way I was told to; and b.) found something else >> that works better. You know the situation best of all. >> >> HTH, >> >> KS >> >> ---- Lou <[hidden email]> wrote: >> > Dear Keith, >> > >> > Thanks for your advice which was very helpful. I feel a bit stuck as >> > know what to do about this really. My boss (who knows rougly zero about >> > statistics) is insisting that I categorise these variables since I am >> > comparing results with a previous report which did the same. Does it >> > take >> > meaning away from the analysis if I discuss results obtained using the >> > original continuous variables and then discuss results separately using >> > the categorised versions (i.e. generate two separate models)? Not sure >> > if >> > this really defies logic too much and how I would justify this in the >> > final report. Although I have a lot to learn in this field, the report >> > that this work is being based on has a lot of dubious findings with >> > regards to the stats, so I'm very keen to ensure that the one I produce >> > is >> > accurate!! >> > >> > Many thanks, >> > >> > Lou >> > >> > On Thu, 15 Jun 2006 11:36:45 -0400, Statisticsdoc >> > <[hidden email]> >> > wrote: >> > >> > >Keith Starborn >> > >www.statisticsdoc.com >> > > >> > >Dear Lou, >> > > >> > >Categorizing continuous variables into categorical variables can >> > is a considerable loss of statistical power because the test for the >> > categorized version of the variable uses more degrees of freedom that >> > the >> > test for the continuous variable. In addition, categorizing a >> > continuous >> > variable can result in a loss of predictive information. >> > > >> > >HTH, >> > > >> > >KS >> > > >> > >---- Lou <[hidden email]> wrote: >> > >> Dear list >> > >> >> > >> I am trying to carry out a logistic regression analysis and have a >> > >> quick >> > >> question with regards to the best way to input my independent >> > >> variables. >> > >> I have three input variables: ethnicity (5 groups), age and >> > >> deprivation >> > >> score. Although age and deprivation score are continuous >> > >> I >> > >> have also been asked to split them into groups (4 for age and 5 for >> > >> deprivation) which are pre-determined by previous work on this >> > >> subject >> > >> matter. The dependent variable is simply whether or not a person >> > >> took a >> > >> particular test. >> > >> >> > >> I have tried generating models both with the age and deprivation >> > variables >> > >> as they are and also with the new categorical age and deprivation >> > >> variables. However, when looking at interaction terms, I find that >> > >> the >> > >> interaction between age and deprivation is significant when they >> > input >> > >> as the continuous variables but not significant when I used the >> > >> categorical versions. Why would this happen? Furthermore, which is >> > >> the >> > >> best way to go? I have read information on logistic regression until >> > >> my >> > >> head hurts, but still donÃÂt feel completely satisfied as to how I >> > should >> > >> determine the best model possible. >> > >> >> > >> Any advice would be appreciated please! >> > >> >> > >> Thanks >> > >> >> > >> Lou >> > > >> > >-- >> > >For personalized and experienced consulting in statistics and >> > design, visit www.statisticsdoc.com >> >> -- >> For personalized and experienced consulting in statistics and research >> design, visit www.statisticsdoc.com >> |
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In reply to this post by Charlotte-9
Hi Keith,
I completely agree with what you have said. I will run the analyses both ways and, if nothing else, will hopefully learn some new things for my own benefit and knowledge even if I'm stuck in a situation of having to do things a certain way for my current job. It's just a shame and worrying, to be honest, that my bosses (and lots of people in general) misuse statistics in such a fashion, but I don't think that this is a new problem! Thanks for your help, Lou On Thu, 15 Jun 2006 15:36:51 -0400, Statisticsdoc <[hidden email]> wrote: >Keith Starborn >www.statisticsdoc.com > >Lou, > >I bet most of the people on this listerserv have faced a similar dilemma at some time in their careers. Which one is best from the point of view of using the data to answer your questions and generate information that you can act on? Probably, keeping the variables continuous is better from that point of view. > >As to the politics of the situation, in your position, I would run the analyses both ways (continuous and categorized) in order to: a.) show that I did the analysis the way I was told to; and b.) found something else that works better. You know the situation best of all. > >HTH, > >KS > >---- Lou <[hidden email]> wrote: >> Dear Keith, >> >> Thanks for your advice which was very helpful. I feel a bit stuck as to >> know what to do about this really. My boss (who knows rougly zero about >> statistics) is insisting that I categorise these variables since I am >> comparing results with a previous report which did the same. Does it >> meaning away from the analysis if I discuss results obtained using the >> original continuous variables and then discuss results separately using >> the categorised versions (i.e. generate two separate models)? Not sure if >> this really defies logic too much and how I would justify this in the >> final report. Although I have a lot to learn in this field, the report >> that this work is being based on has a lot of dubious findings with >> regards to the stats, so I'm very keen to ensure that the one I produce is >> accurate!! >> >> Many thanks, >> >> Lou >> >> On Thu, 15 Jun 2006 11:36:45 -0400, Statisticsdoc <[hidden email]> >> wrote: >> >> >Keith Starborn >> >www.statisticsdoc.com >> > >> >Dear Lou, >> > >> >Categorizing continuous variables into categorical variables can result >> is a considerable loss of statistical power because the test for the >> categorized version of the variable uses more degrees of freedom that >> test for the continuous variable. In addition, categorizing a continuous >> variable can result in a loss of predictive information. >> > >> >HTH, >> > >> >KS >> > >> >---- Lou <[hidden email]> wrote: >> >> Dear list >> >> >> >> I am trying to carry out a logistic regression analysis and have a >> >> question with regards to the best way to input my independent variables. >> >> I have three input variables: ethnicity (5 groups), age and deprivation >> >> score. Although age and deprivation score are continuous variables, I >> >> have also been asked to split them into groups (4 for age and 5 for >> >> deprivation) which are pre-determined by previous work on this subject >> >> matter. The dependent variable is simply whether or not a person took a >> >> particular test. >> >> >> >> I have tried generating models both with the age and deprivation >> variables >> >> as they are and also with the new categorical age and deprivation >> >> variables. However, when looking at interaction terms, I find that the >> >> interaction between age and deprivation is significant when they are >> input >> >> as the continuous variables but not significant when I used the >> >> categorical versions. Why would this happen? Furthermore, which is the >> >> best way to go? I have read information on logistic regression until my >> >> head hurts, but still donÃÂt feel completely satisfied as to how I >> should >> >> determine the best model possible. >> >> >> >> Any advice would be appreciated please! >> >> >> >> Thanks >> >> >> >> Lou >> > >> >-- >> >For personalized and experienced consulting in statistics and research >> design, visit www.statisticsdoc.com > >-- >For personalized and experienced consulting in statistics and research |
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In reply to this post by Charlotte-9
Hi Lucinda,
Having just re-read your response below, I wondered if you could just explain a little more what you mean by 'In some cases, it may actually serve you to have a model without interaction effect'. Sorry to be a pain, I'm just not exactly clear what you mean. Are you saying that even if an interaction seems to exist, it may sometimes be better to look at the model omitting the interaction term? Thanks for your help Lou On Thu, 15 Jun 2006 13:06:37 -0700, LUCINDA M TEAR <[hidden email]> wrote: >Hi, Lou. I agree with all that Keith has said. I might add that the non >significant interaction using categorical variables could be due either to >the fact that by lumping together the Y responses over a range of X inputs >you created a categorical variable whose variance is large enough that it is >not possible to detect any interaction and/or that the endpoints of the bin >categories you are using occur at points in the data that obscure the >interaction you found using the continuous data. > >In some cases, it may actually serve you to have a model without an >interaction effect - it is possible, however, that the confidence intervals >around such a model will be larger than they would be from a model with an >interaction. On the other hand, using the continuous data apparently >allowed you to detect some underlying "process" (the interaction you found). >If you are trying to understand what creates the patterns you see in your >data, both models give you information about the resolution at which certain >processes are revealed or obscured. Apparently lumping the way you have >obscures the interaction. You might want to try binning your x variables >differently than the previous report did, just to see if there is a way to >categorize the x variables such that an interaction is detected. You could >probably use plots from your continuous model to give you an idea about >where appropriate bins thresholds might lie. I tend to be one who likes to >use models as a way of revealing the "scale" at which the data should be >approached in order to answer the question at hand. A different question >about the same data could require a different type of model. Models also >help you find out if the scale you are looking at is missing information >about some underlying effects that could effect the application of the >results. > >Just some thoughts. > >Lucinda > > > >----- Original Message ----- >From: "Statisticsdoc" <[hidden email]> >Newsgroups: bit.listserv.spssx-l >To: <[hidden email]> >Sent: Thursday, June 15, 2006 12:36 PM >Subject: Re: Logistic regression help > > >> Keith Starborn >> www.statisticsdoc.com >> >> Lou, >> >> I bet most of the people on this listerserv have faced a similar dilemma >> at some time in their careers. Which one is best from the point of view >> of using the data to answer your questions and generate information that >> you can act on? Probably, keeping the variables continuous is better >> that point of view. >> >> As to the politics of the situation, in your position, I would run the >> analyses both ways (continuous and categorized) in order to: a.) show that >> I did the analysis the way I was told to; and b.) found something else >> that works better. You know the situation best of all. >> >> HTH, >> >> KS >> >> ---- Lou <[hidden email]> wrote: >> > Dear Keith, >> > >> > Thanks for your advice which was very helpful. I feel a bit stuck as >> > know what to do about this really. My boss (who knows rougly zero about >> > statistics) is insisting that I categorise these variables since I am >> > comparing results with a previous report which did the same. Does it >> > take >> > meaning away from the analysis if I discuss results obtained using the >> > original continuous variables and then discuss results separately using >> > the categorised versions (i.e. generate two separate models)? Not sure >> > if >> > this really defies logic too much and how I would justify this in the >> > final report. Although I have a lot to learn in this field, the report >> > that this work is being based on has a lot of dubious findings with >> > regards to the stats, so I'm very keen to ensure that the one I produce >> > is >> > accurate!! >> > >> > Many thanks, >> > >> > Lou >> > >> > On Thu, 15 Jun 2006 11:36:45 -0400, Statisticsdoc >> > <[hidden email]> >> > wrote: >> > >> > >Keith Starborn >> > >www.statisticsdoc.com >> > > >> > >Dear Lou, >> > > >> > >Categorizing continuous variables into categorical variables can >> > is a considerable loss of statistical power because the test for the >> > categorized version of the variable uses more degrees of freedom that >> > the >> > test for the continuous variable. In addition, categorizing a >> > continuous >> > variable can result in a loss of predictive information. >> > > >> > >HTH, >> > > >> > >KS >> > > >> > >---- Lou <[hidden email]> wrote: >> > >> Dear list >> > >> >> > >> I am trying to carry out a logistic regression analysis and have a >> > >> quick >> > >> question with regards to the best way to input my independent >> > >> variables. >> > >> I have three input variables: ethnicity (5 groups), age and >> > >> deprivation >> > >> score. Although age and deprivation score are continuous >> > >> I >> > >> have also been asked to split them into groups (4 for age and 5 for >> > >> deprivation) which are pre-determined by previous work on this >> > >> subject >> > >> matter. The dependent variable is simply whether or not a person >> > >> took a >> > >> particular test. >> > >> >> > >> I have tried generating models both with the age and deprivation >> > variables >> > >> as they are and also with the new categorical age and deprivation >> > >> variables. However, when looking at interaction terms, I find that >> > >> the >> > >> interaction between age and deprivation is significant when they >> > input >> > >> as the continuous variables but not significant when I used the >> > >> categorical versions. Why would this happen? Furthermore, which is >> > >> the >> > >> best way to go? I have read information on logistic regression until >> > >> my >> > >> head hurts, but still donÃÂt feel completely satisfied as to how I >> > should >> > >> determine the best model possible. >> > >> >> > >> Any advice would be appreciated please! >> > >> >> > >> Thanks >> > >> >> > >> Lou >> > > >> > >-- >> > >For personalized and experienced consulting in statistics and >> > design, visit www.statisticsdoc.com >> >> -- >> For personalized and experienced consulting in statistics and research >> design, visit www.statisticsdoc.com >> |
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Suppose you have data on 100 cases in a continuous variable. These are 100 distinct pieces of information (let's assume there are no ties for the sake of example). Now you want to shrink these 100 distinct data items into 4 or 5 distinct peices of information. You are definitely depriving of a lot of statistical power from the LR Model. This is how I explain to my clients when they insist to categorise a continous variable, which does not make any logical sense. Hope this will be helpfule, though few years late.
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You can always present the variable values grouped into a few brackets, but
for the sake of analysis (e.g. as a predictor) it is better, as you claim, to retain the full information. Having only 100 cases, however, one or two outliers may distort the results. Thus perhaps you should look for that possibility, and then perhaps group the highest values into one average value, or drop the outliers. But I wouldn't go farther than that. You do not ask anything about logistic regression (the subject of your message). If the variable involved is intended as a predictor for Log Reg, I'd just let it as it is. If it is intended to be a dependent variable, I'd use ordinary least square regression, not logistic regression. Hector -----Mensaje original----- De: SPSSX(r) Discussion [mailto:[hidden email]] En nombre de khaaver Enviado el: Sunday, June 12, 2011 06:42 Para: [hidden email] Asunto: Re: Logistic regression help Suppose you have data on 100 cases in a continuous variable. These are 100 distinct pieces of information (let's assume there are no ties for the sake of example). Now you want to shrink these 100 distinct data items into 4 or 5 distinct peices of information. You are definitely depriving of a lot of statistical power from the LR Model. This is how I explain to my clients when they insist to categorise a continous variable, which does not make any logical sense. Hope this will be helpfule, though few years late. -- View this message in context: http://spssx-discussion.1045642.n5.nabble.com/Logistic-regression-help-tp106 9096p4481329.html Sent from the SPSSX Discussion mailing list archive at Nabble.com. ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD ----- No virus found in this message. Checked by AVG - www.avg.com Version: 10.0.1382 / Virus Database: 1513/3696 - Release Date: 06/11/11 ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD |
Categorisation of continuous data not always a bad thing
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