# Tag Info

Accepted

### Is there Factor analysis or PCA for ordinal or binary data?

Traditional (linear) PCA and Factor analysis require scale-level (interval or ratio) data. Often likert-type rating data are assumed to be scale-level, because such data are easier to analyze. And the ...
• 58.3k
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### Why are the numbers on a ball in a lotto draw categorical nominal instead of categorical ordinal?

You could color-code the balls without fundamentally changing the game. Instead of 6-12-11, we get red-blue-pink. You could go with letters without fundamentally changing the game. Instead of 6-12-11,...
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### How to set up neural network to output ordinal data?

Update: Meanwhile, I delved more into the topic and even wrote some of the methods from the literature (see file losses.py). It includes the loss I mention here (ordinal encoding) but many others as ...
• 1,611

### Is the difference between two ordinal variables ordinal?

Clearly not, in general. Take a 4-level pain scale for example (none, mild, moderate, severe). Going from moderate to severe pain may be far worse than going from mild to moderate pain. Yet they ...

### Relevance of assumption of normality, ways to check and reading recommendations for non-statisticians

You are right to be confused, as this is a confusing issue indeed. I'm afraid it will be hard to find everything good to know in a single reference, but some may give that to you - I will not look ...
• 26.2k
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### Suppose I have 100 integers and I sample 10 without repetition. What is the expected rank of the lowest out of 10 samples?

To obtain an answer we must know how many ties there are among these $100$ integers and where they occur: that's too complicated and likely is not the intent of the question. (Nevertheless, the ...
• 329k
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### How to set up neural network to output ordinal data?

I think the approach to only encode the ordinal labels as class 1 is represented as [0 0 0 0 ...] class 2 is represented as [1 0 0 0 ...] class 3 is represented as [1 1 0 0 ...] and use binary ...
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### Guidance on when to use cumulative vs. stopping ratio vs. continuation ratio vs. adjacent category ordinal regression models

The key thing to consider is the interpretation of each model (more so, in fact, than the proportional odds assumption, which is essentially just a constraint one puts on the parameters in the model). ...
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### If a variable is coded as yes/no/unknown, is it categorical or ordinal?

This cannot be answered without knowing exactly what 'unknown' means in your dataset. If 'unknown' refers to missing data, then it's likely best to replace them with 'NA' and analyse the variables as ...
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### Is the difference between two ordinal variables ordinal?

If you are taking the difference of two ordinal responses, you are not treating the responses as ordinal, but instead treating them as if they were interval. This isn't always appreciated. One example ...
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### Brant test in R

I implemented the brant test in R. The package and function is called brant and it's now available on CRAN. The brant test was defined by Rollin Brant to test the parallel regression assumption (...

### Including ordinal independent variables in a linear mixed effects model (using the lme4 package in R)

(This answer applies to [generalized] linear models generally, not just mixed models.) This answer on SO discusses the interpretation of linear models with ordinal independent (predictor) variables. ...
• 44.7k
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### Ordinal regression: logit, probit, complementary log-log or negative log-log?

There is no general guidance on this question, except that if you had to pick one model without knowing anything about the fit of any of the models, you might pick the logistic link (proportional odds ...

### How can I predict a continuous variable using only ordinal covariates?

A problem with treating ordinal variables as numeric/continuous is that it assumes, often incorrectly, that predictor categories are equidistant with respect to their effect on the response variable. ...
• 1,182

### Which glm family to use for ordinal DV?

Given you have an ordinal response using the function MASS::polr should be more appropriate; it implements a proportional odds logistic regression routine. A very ...
• 45.1k
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### Is the month ordinal or nominal variable?

I find it hard to believe that there are grounds for regarding year or month as qualitative. You don't give a precise reference and you don't report the argument, so further comment on that view is ...
• 58.4k
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### Why ordinal target in classification problems need special attention?

Dave's comments are on the right track. I'll try to expand on them. Ordinal regression is half-way between classification and real-valued regression. When you perform multiclass classification of your ...
• 9,418
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### Most appropriate model for 0-10 scale integer data

With this sort of dependent variable, there are two basic alternatives: Treat the variable as continuous, or ordinal. If you decide on continuous, then a starting place is "regular" OLS ...
• 125k

### Questions about Wilcoxon signed rank test

An excellent question. As @Glen_b implied, the signed rank test, unlike the Wilcoxon unpaired 2-sample test, is metric-dependent. A better test is the Kornbrot rank difference test discussed here. ...

### What to do with categorical data when calculating standardized z-scores?

Are ordinal data treated the same as continuous data when calculating standardized z-scores? No, they are not: When dealing with data on different measurement scales it is important that your ...
• 129k
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### If my sample size is large, is it ok to conduct a linear regression with a 7-point Likert item as a dependent variable?

Regardless of the sample size, nobody prohibits you from using linear regression. People sometimes use linear regression in situations where the dependent variable is binary, counts, or ordinal. In ...
• 140k

### Questions about Wilcoxon signed rank test

A signed rank test relies on taking pair differences. Consequently you're asserting that the difference between a 1 and a 3 on a corresponding pair of Likert item is the same as the difference ...
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### What are the disadvantages of dichotomizing an ordinal DV?

Everything. The meaning of the variable is lost, information is lost, and statistical power is diminished. Why even consider this? Why is it so difficult to deal with ordinal regression models? If ...
Accepted

### Is there a table to interpret "how good" was the Kendall's coefficient of concordance (W)

Your cited paper itself calls those divisions "clearly arbitrary." Therefore they're no more scientific than any other partitioning you might like to make and are used merely for discussion. If you ...
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### Relevance of assumption of normality, ways to check and reading recommendations for non-statisticians

I will write a second more practically oriented answer. The first one is rather on a more abstract level about understanding the relevance (or limited relevance) of the model assumptions. So here are ...
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### Performance Metrics for Classification Models with an Ordinal Response Variable

I would definitely not use confusion matrices, misclassification rates, precision, recall or similar metrics for all the reasons explained in relevant threads, which all apply equally to ordinal ...
• 128k

### Is the difference between two ordinal variables ordinal?

The only mathematical relations that exists between ordinal data is "greater than", "less than", and "equal". Any other mathematical relation, such as addition, ...
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### How to choose between ordered logit and ordered probit regression?

It is not the outcome distribution that determines the best ordered choice model. What matters is the error term distribution in the underlying random utility or latent regression model (given the ...
• 37.6k