Checking Correlation of Categorical variables in SPSS The 2019 Stack Overflow Developer Survey Results Are InHow to get correlation between two categorical variable and a categorical variable and continuous variable?Can we predict correlation between independent variables based on dependent variablesDissmissing features based on correlation with target variableAssociation Categorical VariablesTransform Categorical Variables into NumericalWhen creating a classification model, should predictors with little correlation to the response variable be included in the model?Correlation between nominal categorical variablesHow to select variables based on the mean correlation in a correlation matrix?Correlation / regression / association between one categorical variable and two non-independent othersMethods for Finding correlation between various types of columns?

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Checking Correlation of Categorical variables in SPSS



The 2019 Stack Overflow Developer Survey Results Are InHow to get correlation between two categorical variable and a categorical variable and continuous variable?Can we predict correlation between independent variables based on dependent variablesDissmissing features based on correlation with target variableAssociation Categorical VariablesTransform Categorical Variables into NumericalWhen creating a classification model, should predictors with little correlation to the response variable be included in the model?Correlation between nominal categorical variablesHow to select variables based on the mean correlation in a correlation matrix?Correlation / regression / association between one categorical variable and two non-independent othersMethods for Finding correlation between various types of columns?










2












$begingroup$


I am building a predictive model for a classification problem using SPSS. Of the Independent variables, I have both Continuous and Categorical variables. SPSS gives only correlation between continuous variables.



Is there a way in SPSS to find the correlation



a)between two categorical variables

b)between categorical and continuous variables?










share|improve this question











$endgroup$




bumped to the homepage by Community 1 hour ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.



















    2












    $begingroup$


    I am building a predictive model for a classification problem using SPSS. Of the Independent variables, I have both Continuous and Categorical variables. SPSS gives only correlation between continuous variables.



    Is there a way in SPSS to find the correlation



    a)between two categorical variables

    b)between categorical and continuous variables?










    share|improve this question











    $endgroup$




    bumped to the homepage by Community 1 hour ago


    This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.

















      2












      2








      2


      0



      $begingroup$


      I am building a predictive model for a classification problem using SPSS. Of the Independent variables, I have both Continuous and Categorical variables. SPSS gives only correlation between continuous variables.



      Is there a way in SPSS to find the correlation



      a)between two categorical variables

      b)between categorical and continuous variables?










      share|improve this question











      $endgroup$




      I am building a predictive model for a classification problem using SPSS. Of the Independent variables, I have both Continuous and Categorical variables. SPSS gives only correlation between continuous variables.



      Is there a way in SPSS to find the correlation



      a)between two categorical variables

      b)between categorical and continuous variables?







      correlation categorical-data spss






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Jun 24 '16 at 1:01









      Dawny33

      5,55183389




      5,55183389










      asked Jun 23 '16 at 22:11









      srisri

      1113




      1113





      bumped to the homepage by Community 1 hour ago


      This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.







      bumped to the homepage by Community 1 hour ago


      This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.






















          2 Answers
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          active

          oldest

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          0












          $begingroup$

          For testing the correlation between categorical variables, you can use:



          1. binomial test: A one sample binomial test allows us to test whether the proportion of successes on a two-level categorical dependent variable significantly differs from a hypothesized value. For example, using the hsb2 data file, say we wish to test whether the proportion of females (female) differs significantly from 50%, i.e., from .5. We can do this as shown below.


          2. chi-square test: A chi-square goodness of fit test allows us to test whether the observed proportions for a categorical variable differ from hypothesized proportions. For example, let's suppose that we believe that the general population consists of 10% Hispanic, 10% Asian, 10% African American and 70% White folks. We want to test whether the observed proportions from our sample differ significantly from these hypothesized proportions.


          You can find the code for doing above analysis from the link below:



          Source: http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm



          I'm not sure about comparing numerical and categorical variables but one thing you can do is to transform the numerical variable into categories. For example, if it is age, then, you can transform it to: [0,10), [10,20), ... , [90,100), so that you can compare the correlation of age with other categorical variables.



          Hope it helps.






          share|improve this answer









          $endgroup$




















            0












            $begingroup$

            Another approach is to use the SPSSINC HETCOR extension command. It calculates set of Pearson, polychoric, or polyserial correlations according the measurement levels of the variables. This extension requires both the Python and R Essentials.






            share|improve this answer









            $endgroup$













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              2 Answers
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              2 Answers
              2






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              active

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              0












              $begingroup$

              For testing the correlation between categorical variables, you can use:



              1. binomial test: A one sample binomial test allows us to test whether the proportion of successes on a two-level categorical dependent variable significantly differs from a hypothesized value. For example, using the hsb2 data file, say we wish to test whether the proportion of females (female) differs significantly from 50%, i.e., from .5. We can do this as shown below.


              2. chi-square test: A chi-square goodness of fit test allows us to test whether the observed proportions for a categorical variable differ from hypothesized proportions. For example, let's suppose that we believe that the general population consists of 10% Hispanic, 10% Asian, 10% African American and 70% White folks. We want to test whether the observed proportions from our sample differ significantly from these hypothesized proportions.


              You can find the code for doing above analysis from the link below:



              Source: http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm



              I'm not sure about comparing numerical and categorical variables but one thing you can do is to transform the numerical variable into categories. For example, if it is age, then, you can transform it to: [0,10), [10,20), ... , [90,100), so that you can compare the correlation of age with other categorical variables.



              Hope it helps.






              share|improve this answer









              $endgroup$

















                0












                $begingroup$

                For testing the correlation between categorical variables, you can use:



                1. binomial test: A one sample binomial test allows us to test whether the proportion of successes on a two-level categorical dependent variable significantly differs from a hypothesized value. For example, using the hsb2 data file, say we wish to test whether the proportion of females (female) differs significantly from 50%, i.e., from .5. We can do this as shown below.


                2. chi-square test: A chi-square goodness of fit test allows us to test whether the observed proportions for a categorical variable differ from hypothesized proportions. For example, let's suppose that we believe that the general population consists of 10% Hispanic, 10% Asian, 10% African American and 70% White folks. We want to test whether the observed proportions from our sample differ significantly from these hypothesized proportions.


                You can find the code for doing above analysis from the link below:



                Source: http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm



                I'm not sure about comparing numerical and categorical variables but one thing you can do is to transform the numerical variable into categories. For example, if it is age, then, you can transform it to: [0,10), [10,20), ... , [90,100), so that you can compare the correlation of age with other categorical variables.



                Hope it helps.






                share|improve this answer









                $endgroup$















                  0












                  0








                  0





                  $begingroup$

                  For testing the correlation between categorical variables, you can use:



                  1. binomial test: A one sample binomial test allows us to test whether the proportion of successes on a two-level categorical dependent variable significantly differs from a hypothesized value. For example, using the hsb2 data file, say we wish to test whether the proportion of females (female) differs significantly from 50%, i.e., from .5. We can do this as shown below.


                  2. chi-square test: A chi-square goodness of fit test allows us to test whether the observed proportions for a categorical variable differ from hypothesized proportions. For example, let's suppose that we believe that the general population consists of 10% Hispanic, 10% Asian, 10% African American and 70% White folks. We want to test whether the observed proportions from our sample differ significantly from these hypothesized proportions.


                  You can find the code for doing above analysis from the link below:



                  Source: http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm



                  I'm not sure about comparing numerical and categorical variables but one thing you can do is to transform the numerical variable into categories. For example, if it is age, then, you can transform it to: [0,10), [10,20), ... , [90,100), so that you can compare the correlation of age with other categorical variables.



                  Hope it helps.






                  share|improve this answer









                  $endgroup$



                  For testing the correlation between categorical variables, you can use:



                  1. binomial test: A one sample binomial test allows us to test whether the proportion of successes on a two-level categorical dependent variable significantly differs from a hypothesized value. For example, using the hsb2 data file, say we wish to test whether the proportion of females (female) differs significantly from 50%, i.e., from .5. We can do this as shown below.


                  2. chi-square test: A chi-square goodness of fit test allows us to test whether the observed proportions for a categorical variable differ from hypothesized proportions. For example, let's suppose that we believe that the general population consists of 10% Hispanic, 10% Asian, 10% African American and 70% White folks. We want to test whether the observed proportions from our sample differ significantly from these hypothesized proportions.


                  You can find the code for doing above analysis from the link below:



                  Source: http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm



                  I'm not sure about comparing numerical and categorical variables but one thing you can do is to transform the numerical variable into categories. For example, if it is age, then, you can transform it to: [0,10), [10,20), ... , [90,100), so that you can compare the correlation of age with other categorical variables.



                  Hope it helps.







                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Jun 24 '16 at 1:58









                  Yilun ZhangYilun Zhang

                  992




                  992





















                      0












                      $begingroup$

                      Another approach is to use the SPSSINC HETCOR extension command. It calculates set of Pearson, polychoric, or polyserial correlations according the measurement levels of the variables. This extension requires both the Python and R Essentials.






                      share|improve this answer









                      $endgroup$

















                        0












                        $begingroup$

                        Another approach is to use the SPSSINC HETCOR extension command. It calculates set of Pearson, polychoric, or polyserial correlations according the measurement levels of the variables. This extension requires both the Python and R Essentials.






                        share|improve this answer









                        $endgroup$















                          0












                          0








                          0





                          $begingroup$

                          Another approach is to use the SPSSINC HETCOR extension command. It calculates set of Pearson, polychoric, or polyserial correlations according the measurement levels of the variables. This extension requires both the Python and R Essentials.






                          share|improve this answer









                          $endgroup$



                          Another approach is to use the SPSSINC HETCOR extension command. It calculates set of Pearson, polychoric, or polyserial correlations according the measurement levels of the variables. This extension requires both the Python and R Essentials.







                          share|improve this answer












                          share|improve this answer



                          share|improve this answer










                          answered Jun 24 '16 at 12:56









                          JKPJKP

                          1011




                          1011



























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                              ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result