Tagged Questions

Categorical data can take on a limited (usually fixed) number of possible values called categories. Categorical values "label", they do not "measure". Nominal and dichotomous/binary scale types are categorical. Some people consider ordinal scale categorical too.

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In R, using glm to model call counts in treated/not treated forests [on hold]

Ok, so I'm new to using R and trying to learn this on my own because my uni doesn't have a prof who uses R. I've tried googling for answers, and I feel like I'm sort of getting answers, but I'm still ...
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3answers
35 views

Testing and reporting interactions in multiple regression

I have a model with two between-participants predictors -- one continuous (a), and one categorical with two levels (b) -- and ...
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0answers
5 views

Linear mixed model construction validation

I have 6 groups of fish made up of 8 individuals. Each group is tested three times under different treatments. These group level treatments are hungry , ...
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0answers
9 views

dependent and independent categorical variable across eight trials

Study: significantly selected color for a particular letter. My sample size is 30 and the study is within subject. Independent variable -> letters (total 8)... Dependent variable -> colors ( total ...
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0answers
15 views

Medical Malpractice Datasets [on hold]

I hope this is the right place to ask this question. I'm looking for medical malpractice and error datasets from hospitals. To be very precise, for a given hospital I'd like a total count of deaths ...
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0answers
19 views

Appropriate classification model for combination of continuous, binary and categorical inputs

I have a binary classification problem for classify my samples to two classes (class_1 and class_2). I have different kinds of ...
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0answers
15 views

creating an indexed dummy variable as a predictor in OLS

I am performing on OLS with two predictors and a response variable. The data is a time series of 450 days approximately. There is an irregular pattern in my response variable - it sometimes ...
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0answers
24 views

A measure of correspondence between ranked ordinal data

I would like to find an appropriate way to measure the similarity between two sets of data with the following characteristics: Both sets contain 10 categorical observations. The categories ...
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0answers
12 views

Treating categorical variables as continuous in CFA

On reading a number of prominent CFA studies on the structure of PTSD symptoms, I notice how every study appears to treat the variables as continuous. This puzzles me, as their data come from ...
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0answers
16 views

Loading data with missing values as numeric data [migrated]

I am trying to impute missing values using the mi package in r and ran into a problem. When I load the data into r, it recognizes the column with missing values as a factor variable. If I convert it ...
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2answers
26 views

Kernel methods on Categorical Data

I have a basic understanding of kernel methods and the kernel-trick and the advantages of it, why it is preferred over conventional machine learning algorithms etc. However, I have some trouble using ...
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0answers
47 views

How to transform continuous data with extreme bimodal distribution

Is there a way to transform a continuous predictor variable (grant) that has a bimodal distribution into a normal distribution (see density plot below)? I have tried log(x+c), z-score and inverse ...
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0answers
29 views

Comparing two population of non ordinal data (categorical data)

I have 2 dataset $D_1$ and $D_2$ whose elements can assume values between 0 and 1. The cardinality of $D_1$ and $D_2$ is the same. Think for example at the measurement of the temperature in a set of ...
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0answers
52 views

How to fit OLS with many categorical levels, on more than one category

This question is not meant to be a software question, but I will illustrate the issue using R a bit. My Understanding of the Simple Case If I have a simple linear model with a categorical variable ...
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0answers
19 views

Can I run a multidimensional scaling analysis with purely categorical data?

I have data where participants categorized facial expressions using one of seven emotion labels (Angry, Disgusted, Fearful, Happy, Neutral, Sad, and Surprised). Can I take the resulting confusion ...
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1answer
17 views

Log Linear Models: Interpretation when None Fit

This is question 9.6 from Categorical Data Analysis by Alan Agresti (Wiley, 2013). The question asks us to find a Log Linear with the best fit for a 2x2x2 contingency table. The following are the ...
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0answers
34 views

variable error on logistic regression/ proc catmod- Building predictive model

I am using logistic regression to fit a model with categorical/multinomial varaibles. data-description: There are over 300 variables as independent variables, sample size is 5000 which is divided into ...
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0answers
14 views

How to determine significance across categories of binary data?

I have subjects that fit into one of three, mutually exclusive groups, "favorable," "intermediate," and "unfavorable" based on their genetics. They can then be classified as either a "responder" or a ...
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1answer
78 views

Is there any correlation or causation here?

I have the following data, where 2 properties (P1 and P2) can be either True or False ...
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0answers
26 views

Sample size for logistic regression with categorical independent variables

Trying to find a sample size for logistic regression I found a rule of thumb in http://www.medcalc.org/manual/logistic_regression.php I cite: Sample size considerations. Sample size calculation for ...
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0answers
16 views

How can I evaluate binary response models that have weighted observations?

I'm working with a binary response data set, but the importance of each observation varies over a factor of 100. Models to fit the data can accept a weight for each observation. But when it comes time ...
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0answers
17 views

the approach of performing correlation analysis between categorical and numerical

What are the approaches to perform correlation analysis for the following pairs: ...
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1answer
12 views

How valid is it to run one-way ANOVA on other means?

For background, I have a set of data where I asked two sets of 50 people to repeat a task 12 times, which generates a single number. Each 1x12 vector represents a single vector of data. I then group ...
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0answers
16 views

How can I group different levels of classes of a categorical variable in logistic regression?

Suppose I have a categorical variable neighborhood, which can take the classes Neighborhood1, Neighborhood2, Neighborhood3. I would like to know which neighborhoods can be grouped and what ...
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3answers
35 views

What to do when some categories have too few observations

I have an ordinal, categorical variable with five levels, of which the last two have only one observation for each. Should I leave them alone, omit them, incorporate them in another category, or do ...
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1answer
44 views

Multiple Regression with Categorical Predictor Variables of More than Two Levels

I'm planning on running a hierarchical multiple regression. In the first step, I would like to enter demographic characteristics, second step continuous predictor variables of interest, and third step ...
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0answers
14 views

Autoregressive distributed lag (ADL) models and Dummy variables

Is it okay to use an Autoregressive Distributed Lag (ADL) model with a dummy variable as the dependent variable? Or should I use a combination of logit/probit with an ADL model? I realize it might ...
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0answers
9 views

Testing for differences within a categorical variable: question about contrasts

I have a three-group categorical variable coded as: 0, 1, and 2. I ran an OLS regression with the above variable as an independent variable. I found that groups 1 and 2 are each significantly ...
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0answers
45 views

Significance testing of categorical variables - is chi squared as good as it gets?

I am testing categorical variables for significant relationships, 5 age groups and 3 answer options (yes/no/don't know). Is there a test which will specifically tell me if e.g. age group 1 is ...
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0answers
35 views

How to create dummy variables for interaction term between categorical variables?

I'm building a logistic regression model estimating the probability of hospitalization based on several predictor variables, including indicator variables for various diagnosis categories (coded as ...
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2answers
106 views

Linear regression including categorical variables with hundreds of levels

I am trying to teach myself data science by solving some of the problems available on the internet. Currently I am trying to predict a fraud event with the aid of 4 categorical variables. Each of the ...
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1answer
35 views

Too many significant dummy variables in fixed-effect panel model

I am doing panel analysis of state drug policies. My data set includes 50 states and ten time points. I am using one-way state fixed-effect models, controlling for heteroskedasticity and ...
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0answers
12 views

Dealing with categorical variables in regression [duplicate]

I have this categorical variable that represents the country for a person. How can I use this in regression model. If there had been less levels for a category I could have used dummy binary variables ...
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0answers
19 views

zinb estimates change when using factor variables

I use Stata SE 13 and I have a problem with the command zinb in Stata. I have binary variable female which is 1 if respondent is female, 0 otherwise (no other ...
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1answer
32 views

Interaction Dummy

I have a model: $$ \ln({\rm earnings}) = 2.618656-0.0899657{\rm female}+0.382019{\rm white}-0.2754126{\rm female}\times{\rm white} $$ ${\rm female}$ and ${\rm white}$ are dummy variables. t= ...
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1answer
38 views

Coding of categorical random effects in R: int vs factor

I have a problem with coding of a 2-level categorical predictor variable in R, and subsequently using it as a random slope in lmer(). I can keep the factor as numeric, coded using the treatment ...
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0answers
29 views

Dummy variable interpretation [duplicate]

I have a model: $$ \ln({\rm earnings}) = a+b_1{\rm female}+b_2{\rm white}+b_3{\rm female}\times{\rm white} $$ ${\rm female}$ and ${\rm white}$ are dummy variables. t= female=-1.65 white=8.86 ...
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1answer
70 views

Dummy variable interaction regression [duplicate]

I have a model: $$ \ln({\rm earnings}) = a+b_1{\rm female}+b_2{\rm white}+b_3{\rm female}\times{\rm white} $$ ${\rm female}$ and ${\rm white}$ are dummy variables. I have interpreted $b_1$ and $b_2$: ...
3
votes
1answer
85 views

Interpretation of interaction term

I have a model: $$ \ln({\rm earnings}) = a+b_1{\rm female}+b_2{\rm white}+b_3{\rm female}\times{\rm white} $$ ${\rm female}$ and ${\rm white}$ are dummy variables. I have interpreted $b_1$ and $b_2$: ...
0
votes
1answer
28 views

Factor analysis with categorical reponses and missing data

I factor analyzing a measure with 55 categorical items (3 categories each). I am use CFA to test a 7 factor model. I have a very large sample (>10,000), but approximately 20% of the sample is missing ...
1
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0answers
17 views

Categorical Variables - Factor Reduction - Can I use the dependent variable?

I am working on a basic fraud detection model. I have about 10 independent features and I am trying to predict if a given transaction is genuine or fraud. Most of the features are categorical and each ...
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3answers
115 views

Why are mixed data a problem for euclidean-based clustering algorithms?

Most classical clustering and dimensionality reduction algorithms (hierarchical clustering, principal component analysis, k-means, self-organizing maps...) are designed specifically for numeric data, ...
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2answers
41 views

Interaction between a dummy variable and a variable with a quadratic form

I am finishing up an econometrics assignment and this problem has me stuck. I have estimated a regression equation for ln hourly wages on a gender dummy variable, several race dummy variables, a ...
1
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1answer
32 views

Convert categorical percentage data into an overall mean

I have survey data in which the answer choices were "categorical" (0, <15%, 15-30%, 30-45%, 45-60%, 60-75%, 75-90%, >90%). In retrospect, this should have been a free response question, but I'm ...
1
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1answer
34 views

How to correlate categorical personality and music genre preference scores?

I'm currently a third year Biology student and I've annoyingly screwed myself over by not following the golden rule of stats, always know how to analyze your data prior to conducting the experiment. ...
0
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0answers
31 views

Scoring function for categorical data

I would appreciate guidance on the following problem. There are three sets of urns, Set 1, 2, and 3. Each set contains the same number of urns, Urn 1, 2, 3. Each urn contains some number of Red ...
2
votes
0answers
40 views

Split Factor Levels Or Not In Variable Selection

This question is related to previous ones but I believe distinct. I am primarily interested in prediction and I have access to LASSO variable selection (but without factor level grouping) using the ...
0
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0answers
14 views

Team level analysis: how to aggregate control variables such as tenure, gender and working status?

I'm currently conducting research on a team level analysis (59 teams) in which i estimate the effect of voice climate -> team voice behavior, team voice behavior -> team learning behavior, and, team ...
4
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0answers
35 views

glmnet: How to make sense of multinomial parameterization?

Following problem: I want to predict a categorical response variable with one (or more) categorical variables using glmnet(). However, I cannot make sense of the output glmnet gives me. Ok, first ...
0
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0answers
33 views

Multivariate regression with categorical response variables

Explanation of Data: I started with a data set where each user belong a specific group and their contribution to different domains. After multiple pivots and pre-processing attempts, I got my data in ...