multicollinearity

  1. R

    Other remedial measures for multicollinearity?

    I have log transform my model already, choose per capita variables but multicollinearity is still a problem. I plan to drop the variable which has the highest vif (over 200)... however, I read somewhere that I will be subjected to specification error/bias. I need to address this one since...
  2. D

    Interaction effects regression wiith dummy variables

    Hello everyone, I am doing an analysis about the export diversification effect during the financial crisis. I have my dependent variable as log (GDP per capita) and some independent variables. To measure diversification I calculated an Index which is called HI, this Index takes values from...
  3. K

    Multicollinearity problem in binary logistic regression

    I'd like to ask for some help with a binary logistic regression. In SPSS I am trying to build a binary logistic regression with 4 independent continuous variables (Sample size - 85). I have a dichotomous dependent variable (a clinical form of multiple sclerosis) and quite a few independent...
  4. S

    Dealing with multicollinearity

    I have a following model: y=a+b1x1+b2x2+b3x1x2+b4x3+other terms $ Let's say, I am only interested in coefficient b3. The magnitude of coefficient b3 is 0.54 and its t value is 4.50 and the magnitude of the coefficient b2 is 2.00 and it's t value is 3.00. Now after including the interaction...
  5. A

    1)Test multicollinearity b4/after Stepwise Log Regress? 2)Final Equatn 3)Model Select

    Hi all, I need to perform logistic regression for my study. Pretty new to it but have read up on it and have tried it on SPSS. I would like to seek the advice from all of you regarding it. A brief description on my study: I want to see which factors (categorical and continuous) are...
  6. C

    Interactions in logistic regression - Please help

    Hi everyone, Currently I'm writing my thesis on the effects of divorce on children. It's a complex design so I guess I'll be posting more questions here :) My dependent variable is whether or not the respondent has ever been in a relationship. My independents are 2 dichotomous variables ...
  7. E

    Automating VIF calculation on mutliple linear models

    I came up with this code to calculate VIF for variables for each model I am evaluating displayvif<-function(x){ for (i in 1:length(x)) vif(x[[i]]) } where x is models<-lapply(d1,function(data){lm(reformulate(termlabels=".",response=names(data)[1]),data)}) and where d1...
  8. S

    Is there a need to include fixed effects when assessing multicollinearity VIFs?

    I am currently in the progress of performing multicollinearity diagnostics for a logistic regression model using tolerance and VIF calculations based on recommendations in Allison (2012) (Logistic Regression Using SAS: Theory and Application, Second Edition). In my model I include three sets of...
  9. C

    Does SPSS VIF take into account multicollinerity involving intercept?

    I am trying to find out whether multicollinearity involving the intercept is taken into account in SPSS' calculation of the VI in the REGRESSION procedure. I was unable to find an explanation in the official SPSS resources as to how SPSS calculates the VIF. The Command Syntax reference only...
  10. P

    Bizarre multicollinearity (inferred from VIF) in regression

    I'm really confused about why I have such high VIFs in a multiple regression I've run. My predictors are country (2 different countries, so -1 and 1), religiosity, and political engagement, and I'm looking at how my three predictors and their various interactions predict an outcome variable...
  11. D

    URGENT: Omitted Dummy Variable /// Multicollinearity

    Hi guys, i'm a complete newbie when it comes to statistically evaluating data for my thesis and would therefore very much appreciate your help. I have two questions: 1) My regression equation is including a vector of industrial dummy variables as independent variable (meaning three columns...
  12. N

    multicollinearity

    Hello, I understand that multicollinearity is different from variable interactions. What I don't understand is whether an interaction of two variables can account for some of the common effects (multicollinearity) that contribute to the high R^2. I did not include variable interactions in...
  13. C

    Dealing with linear dependent variables

    I have a large dataset with many subject each with responses from a consecutive year going back 10 years (ie 100,000 persons per year (not necessarily 10 data points per person as they may not have been part of the study in prior years) dating back 10 years). I have data on each specific...
  14. C

    Can I compare regression models with same # predictors and same sample?

    Hi there, As a part of my dissertation I want to compare a focal, new, variable against other well-known predictors of my outcome, so I plan to run a hierarchical regression in SPSS with the focal variable entered on the last step. The problem is that my focal variable is highly correlated...
  15. M

    Length between parameters in Multiple regression

    In multiple regression we know that as an estimate of β and this gives the minimum sum of squares of the residuals: And we know that The question is how to demonstrate that Thanx :)
  16. S

    Fixing multicollinearity in panel data regression

    Hey guys. It's a big question in short format; I have data from 42 countries over 12 years and one of my independent variables is GDP (transformed by ln). I have a few other macro level variables and I would like to run some pooled models but I run into trouble - I have a lot of...
  17. D

    Using Orthogonal Polynomials to remove multicollinearity

    Hi I have a homework about using orthogonal polynomials to remove multicollinearity in polynomial models. I've found the concept of polynomial orthogonality and how to produce them in a math book (apostol), But I can't find anything about how to use them in a regression model to reduce...
  18. M

    Clustered Standard Errors vs. Multilevel Logit Model--Which one to use

    I'm working on a project with 233 observations from school districts in 10 states. Observations vary from between 3 and 108 observations per state. I am not interested in the state effects. I am only interested in the district level effects (the district level is Level 1-- there is no data on...
  19. P

    Hierarchical / variance partitioning - criticisms of the method

    I'm about to use variance partitioning to interpret my results of a given linear regression model and across models and have come across various criticisms of it most notably by Pedhazur*. Also, the criticisms are of both the approaches to VP - commonality analysis and incremental partitioning...
  20. K

    How to test for Multi-collinearity among dummy explanatory variables

    Hello, Almost all the explanatory variables in my data are dummy variables and I would like to test for multi-collinearity among them. Does it make sense to still use the traditional Pearson correlation coefficients or the Variance Inflation Factor tests? If not, can you suggest what other...