Categorical data can take on a "limited" (usually fixed) number of possible values. Not to be confused with `factor-analysis`.
24
votes
4answers
5k views
Does it ever make sense to treat categorical data as continuous?
In answering this question on discrete and continuous data I glibly asserted that it rarely makes sense to treat categorical data as continuous.
On the face of it that seems self-evident, but ...
2
votes
1answer
1k views
How to test the statistical significance for categorical variable in linear regression?
If in a linear regression I have categorical variable... how do I know the stastical signifance of the categorical variable?
Let's say the factor $X_1$ has 10 levels... there will be 10 different ...
6
votes
3answers
589 views
Testing paired frequencies for independence
I hope this isn't either far too basic or redundant. I have been looking around for guidance but so far I am still uncertain of how to proceed.
My data consists of counts of a particular structure ...
4
votes
2answers
2k views
Dummy variable trap issues
I am running a large OLS regression where all the independent variables (around 400) are dummy variables. If all are included, there is perfect multicollinearity (the dummy variable trap), so I have ...
25
votes
3answers
11k views
Can principal component analysis be applied to datasets containing a mix of continuous and categorical variables?
I have a dataset that has both continuous and categorical data. I am analyzing by using PCA and am wondering if it is fine to include the categorical variables as a part of the analysis. My ...
3
votes
1answer
163 views
Interpreting interaction terms in logit regression with categorical variables
I have data from a survey experiment in which respondents were randomly assigned to one of four groups:
...
5
votes
3answers
3k views
Multiple Chi-Squared Tests
I have cross classified data in a 2 x 2 x 6 table. Let's call the dimensions response, A and ...
4
votes
1answer
3k views
How to perform principal components analysis on binary (Yes/No) data using SPSS?
I have a dataset with a large number of Yes/No responses. Can I use PCA or any other data reduction analyses for this type of data? Please advise how I go about doing this using SPSS.
5
votes
1answer
363 views
If a factor variable is to be dropped in model selection, should all levels be dropped simultaneously? If so, why?
In answer to a previous question factor pooling in model selection was discussed.
If a factor or categorical variable is to be dropped in model selection, should all levels be dropped simultaneously? ...
6
votes
2answers
2k views
Quickly evaluate (visually) correlations between ordered categorical data in R?
I'm looking for correlations between the answers to different questions in a survey ("umm, let's see if answers to question 11 correlate with those of question 78"). All answers are categorical (most ...
7
votes
1answer
631 views
Is it possible to create “parallel sets” plot using R?
Thanks to Tormod question (posted here) I came across the Parallel Sets plot. Here is an example for how it looks:
(It is a visualization of the Titanic dataset. Showing, for example, how most of ...
6
votes
1answer
378 views
Should I run separate regressions for every community, or can community simply be a controlling variable in an aggregated model?
I am running an OLS model with a continuous asset index variable as the DV. My data is aggregated from three similar communities in close geographic proximity to one another. Despite this, I thought ...
5
votes
2answers
3k views
Continuous and Categorical variable data analysis
I have three variables:
distance (continuous, variable range negative infinity to positive infinity)
isLand (discrete categorical/ Boolean, variable range 1 or 0)
occupants (discrete categorical, ...
3
votes
1answer
548 views
How to fit Bradley–Terry–Luce model in R, without complicated formula?
The Bradley–Terry–Luce(BTL) model states that $p_{ji} = logit^{-1}(\delta_j - \delta_i)$, where $p_{ij}$ is the probability that object $j$ is judged to be "better", heavier, etc, than object $i$, ...
3
votes
4answers
1k views
Measure of association for 2x3 contingency table
I have a 2x3 contingency table - the row variable is a factor, the column variable is an ordered factor (ordinal level). I'd like to apply either symmetrical or asymmetrical association technique. ...
5
votes
2answers
3k views
How to transform ordinal data from questionnaire into proper interval data?
Are there any straightforward methods of transforming ordinal level data into interval level (just as there are for doing it the other way round)? And performable in Excel or SPSS?
Having the data, ...
3
votes
1answer
1k views
Factor analysis for ordinal variables that have different categories
I have a data set that contains about 40 categorical variables that are taken as independent variables (and believed to be related to some unobservable human resource factors) and 4 categorical ...
1
vote
1answer
773 views
How can I perform a chi-square test for independence on signal samples?
Let's say I have two signals $x$ and $y$, sampled $N$ times, i.e.
$$ x = [ x_{1}, x_{2}, ..., x_{N} ] $$
$$ y = [ y_{1}, y_{2}, ..., y_{N} ] $$
I would like to check whether $x$ and $y$ are ...
1
vote
1answer
619 views
More than one outcome (dependent) variables in ordinal logistic regression
I want to run ordinal logistic regression (OLR) in SPSS. My data include 6 predictor variable (two continuous and 4 categorical ) but my outcome variables are also 6 (categorical-likert scale).
e.g my ...
-3
votes
1answer
524 views
How to call glm when response variable is categorical in R?
I have following data stored in a file. I am applying 'glm' in R to find linear regression equation to best predict the 'output'.
...
3
votes
4answers
1k views
Cross tabulation of two categorical variables: recommended techniques
I'm aware that this one is far from yes or no question, but I'd like to know which techniques do you prefer in categorical data analysis - i.e. cross tabulation with two categorical variables.
I've ...
3
votes
1answer
125 views
Does it make sense to cut a continuous variable to intervals?
I'm trying to fit a two-class logistic model, using many many features. When inspecting one of the features, I binned it so I could inspect its behavior. In each bin I count the number of 'good class' ...
5
votes
1answer
118 views
Analyzing changes in a repeated categorical measure
I am trying to analyze why some individuals change their response to a categorical survey item Y between time T1 and T2, given some explanatory variables X1 and X2
Specifically, the data looks like ...
3
votes
1answer
292 views
Characterizing datasets and estimating statistically significant differences?
I have four datasets: D1, D2, D3 and D4. Each dataset contains elements (different sample size in each dataset) that can be described by 100 categories (1 represents that the element belongs to that ...
3
votes
1answer
1k views
Data transformation for Principal Components Analysis from different Likert scales
I have data from a survey comprised of several measures that used different Likert-type scaling (4-, 5-, and 6-point scales). I would like to run a principal components analysis using the data from ...
1
vote
1answer
557 views
Regression technique for data comprised of categorical explanatory variables & a continuous response variable
i suppose one way to characterize data is by a combination of the variable types that comprises it:
...
6
votes
4answers
2k views
How to implement dummy variable using n-1 variables?
If I have a variable with 4 levels, in theory I need to use 3 dummy variables. In practice, how is this actually carried out? Do I use 0-3, do I use 1-3 and leave the 4's blank? Any suggestions?
...
5
votes
1answer
482 views
What is the advantage of transforming variables from nominal to ordinal/numerical when it reduces variance explained in CatPCA?
Context
I have a dataset of 8 categorical variables. And I want to apply Categorical Principal Component Analysis (CatPCA).
Before doing that, I have been advised to look at the transformation plots ...
3
votes
1answer
51 views
Coding of semi-numerical variables
I've asked this question before, in the context of logistic-regression, but I'd like to pose it in a broader context here.
There are a lot of variables with interval or ratio-type values for some ...
3
votes
2answers
167 views
How can factor-levels be automatically chosen in R to maximize the number of positive coefficients in a regression model?
I am performing simple (first-order terms) linear regression on data having several categorical variables (i.e. factors), and it is often desired that for each factor, one of the levels should add ...
3
votes
2answers
2k views
Factor analysis on mixed (continuous/ordinal/nominal) data?
What approaches are there to perform FA on data that is clearly ordinal (or nominal for that matter) by nature? Should the data be transformed our are there readily available ...
3
votes
2answers
369 views
Plotting changes in a three-valued ordinal variable across two time points using R
I have a dataset with 4025 participants across two time points. I have scored them on a three-point categorical variable (Unlikely, Possible, Probable) at each time ...
2
votes
2answers
260 views
Multinomial-Dirichlet model with hyperprior distribution on the concentration parameters
I will try to describe the problem at hand as general as possible. I am modeling observations as a categorical distribution with a parameter probability vector theta.
Then, I assume the parameter ...
2
votes
2answers
364 views
Why don't dummy variables have the continuous adjacent category problem in cluster analysis?
I know that if we use categorical variables in cluster analysis we would assume that the scale is continuous and we don't have this concept of distance between two adjacent categories.
But what is the ...
2
votes
2answers
297 views
Quantifying effect of a categorical variable in time series analysis
I have a dependent variable ($y$ axis in the pictured graph) trending over time ($x$ axis). I also have a categorical variable, which was constant until one point in time, then changed to another ...
1
vote
2answers
364 views
Multinomial logistic regression vs binary logistic regression
Lets say we have a dependent variable $Y$ with few categories and set of independent variables.
What are the advantages of Multinomial logistic regression over set of binary logistic Regressions? ...
1
vote
1answer
333 views
Use of further analysis on factors formed by principal component analysis in regression
I want to find out the relationship between 6 independent variable (4 categorical, 2 continuous) and 6 dependent variables (5 likert scale).
As my data is categorical (likert scale) I thought of using ...
1
vote
0answers
321 views
How to model categorical (discrete-valued) time series?
Just want to make a little survey,
What are, according to you, the best approach to model categorical time series?
I'm building a model able to generate time series reproduicing the characteristics ...
11
votes
2answers
542 views
Alternative to sieve / mosaic plots for contingency tables
I once stumbled across a type of plot for categorical data (i.e., contingency tables) on the internet, which I really liked, but I've never found it again, and I don't even know what it's called. It ...
6
votes
2answers
806 views
How to choose number of dummy variables when encoding several categorical variables?
I'm building a logistic regression, and two of my variables are categorical with three levels each. (Say one variable is male, female, or unknown, and the other is single, married, or unknown.)
How ...
5
votes
1answer
375 views
How can I use optimal scaling to scale an ordinal categorical variable?
In an answer to this question about treating categorical data as continuous, optimal scaling was mentioned. How does this method work and how is it applied?
4
votes
1answer
263 views
Dealing with a categorical variable that can take multiple levels simultaneously
I recently posted a question with many parts and I'd like to focus in on just one issue that I didn't emphasize in the original post.
My data is a list of records, each one representing an ...
4
votes
3answers
6k views
Can I use multiple regression when I have mixed categorical and continuous predictors?
It looks like you can use coding for one categorical variable, but I have two categorical and one continuous predictor variable. Can i use multiple regression for this in SPSS and if so how?
thanks!
4
votes
2answers
9k views
How to deal with not-binary categorical variables in logistic regression (SPSS)
I have to do a binary logistic regression with a lot of independent variables. Most of them are binary. Few ones are categorical with more than two possible values.
Which is the best way to deal with ...
2
votes
0answers
69 views
Using minimum description length for a categorical distribution
My data is as follows: in a routing node (check figure ), I can see the entry and exit gate of each packet, so I have pairs like this:
(1,5) (2,5) (1,6)...
...
2
votes
2answers
1k views
Categorical variables in multinomial logistic regression end up converted into binary variables
When I run multinomial logistic regression with some of the explanatory variables as categorical, my algo (glm) turns them in binary variables, automatically. For examples if one categorical variable ...
2
votes
1answer
426 views
Omitted dummy variable coefficient in OLS
I am running an OLS regression using dummy variables built from categorical variables. Say, race became race1, race2 and race3. I omit race1 in order to escape the dummy variable trap and run OLS and ...
2
votes
1answer
705 views
Residualize a binary variable to remedy multicollinearity?
Imagine a regression model where there is a continuous-valued response variable and three continuous-valued explanatory variables. For concreteness, imagine that we are interested in the effects of ...
2
votes
2answers
194 views
Collapsing regression of nominal data to single significance value
I'm working with a dataset of combined continuous and categorical data, and I'm constructing a regression model based on both types of data simultaneously, using dummy variables. The model will look ...
2
votes
2answers
244 views
Plugging in mean values/proportions to a logistic regression with continuous-discrete interaction
I have a logistic regression (in SAS, for reference) with continuous and categorical predictors (with reference coding), and an interaction term between one of each type (assume for now that the ...
