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I wanted to know can I take education as an ordinal variable in my analysis. For example Olevels as 1, A levels as 2, Bachelors as 3, Masters as 4, PHD as 5 and then run a simple linear regression? … To clarify education is an independent variable …

I have 10 independent ordinal variables, each having 5 levels, all intended to measure the same latent construct, and one ordinal dependent variable named rank with 5 levels. … Is there any way I can use them as is in regression? …

I am not very familiar with SEMs, so I'm not sure if their results would also apply to regression problems. Does anyone know about similar studies/literature in the context of regression? … Edit: Just to answer the question below: I'm mainly interested in the case where the outcome variable is ordinal (with possibly an ordinal covariate). …

We want to perform ordinal logistic regression with the following properties: Dependent variable: Three ordinal variables (low, medium, and high) Independent variables: Age : Continuous variable Region … Do I need to make any transformation for nominal categorical variable "Region" before performing ordinal logistic regression? For instance, dummy variable conversion? …

This whole concept forms my dependent variable. So basically I have six dependent variables measured on ordinal scale (five point Likert scale). … From here Logistic regression with ordinal variables I got that I can use ordinal regression method by converting my ordinal independent variables to categorical (because this is my main problem now, I …

I applied linear regression and calculated the Pearson R-coefficient, but is this a good method considering the ordinal variable? … Or should I apply a transformation to the homework, or use a different kind of regression altogether? Thanks in advance. …

There is a significant correlation if I just type logistic risk size creating a logistic regression for the variable, but if I run it as logistic risk i.size there is no change, but I can see the … I'm quite suspicious this change is significant but I think my problem is I can't get it to recognize it as an ordinal variable. …

I have ordinal encoded the ordinal variables (values range from 0 to 9). I have standard scaled my numerical variables (values with mean = 0, standard deviation = 1). … However, ref 2 says that the linear and logistic regression models do not require feature scaling unless there is regularization, so I expect similar is true for CoxPH regression. …

My colleague recommended I perform logistic regression. I have only performed frequency, correlation, and linear regression in SAS so far. I am new to statistics and coding. … This is my code: proc logistic data = inc.data_scores desc; class general_health1; model physician (event='1') = general_health1; run; general_health1 is the ordinal variable. …

It's common for many datasets to have ordinal versions of numerical variables, such as age groups (e.g. "Under 20", "20-30", "30-40", etc.) or time groups (e.g. … Is this still okay to do with ordinal variables? Using the time group variable above, it would look like this: Original Var. Label | Original Var. Coding | Squared Var. …

I've got some ordinal variables b and a and a categorized variable c. I would like to fit a multinomial logit regression from the library car. I tried to ignore the ordinal scale. … scale and treated them as factors to fit the multinomial logit regression require(car) a<-as.factor(a) b<-as.factor(b) c<-as.factor(c) multinom(formula = a ~ b + c) Call: multinom(formula = a ~ …

All of them are ordinal (take values 0,1,2,3). Sample consist of 200 observations. Is it possible construct multinominal logistic regression in this case. …

To test the assumption, you can compare a model in which you use the ordinal variable as a single predictor to one in which you discretize the responses and treat them as multiple predictors (as you would … I am working with data and I would like to use ordinal independent variables in logistic regression. …

I have a set of data where I would like to do logistic regression modeling the odds of a binary outcome variable (Therapy), with Stage as an ordinal explanatory variable (0,1,2,3,4). I am using SAS. … PROC LOGISTIC data=new; class EyeID Therapy (ref ="0") Stage (param = ordinal) Gender (ref="M") Ethnicity (ref="C")/ param = ref; model Therapy = Stage Gender age A1c Ethnicity; oddsratio Stage; run; …

I want to create a dependent ordinal variable from independent ordinal variables with 6 levels {'Very satisfied': 5, 'Satisfied': 4,'Neither satisfied nor dissatisfied': 3,'Dissatisfied': 2,'Very Dissatisfied … ': 1,'Not Applicable' : 0} by applying kmodes clustering to create a dependent variable ("Overall Satisfaction"), and fit an ordinal logistic regression model. is this appropriate? …