Questions tagged [stratification]

A sampling technique in which the population of interest is partitioned into subsets ("strata") based on characteristics known at all units before sampling.

167 questions
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Interaction variables work differently when population is split

Let's say I have two predictors to predict financial risk: Gender and shopping habits. Gender has levels of "Male" and "Female", while shopping habits has "quick shopper" and "slow shopper". I am ...
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Stratified Log Rank Test with Unequally Sized Strata

If I perform a stratified log rank test in a situation with many strata (currently I have 8), in 6 of the 8 there are very small numbers (i.e., 1 or 2%), but the other strata are fairly balanced, how ...
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How to partition data with multiple categorical features?

I have a data set with size of 30k+, and it has several categorical features and also several numerical features. When I try to split the data into training/testing data set, I need to answer a ...
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Stratification or minimisation…Necessary with sample sizes exceeding 250?

I was having a discussion with a statistician who developed a simple software package to do participant group allocation using the minimisation technique. In this discussion the person mentioned that ...
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Interpreting hazard ratios in stratified models

I am unsure how to interpret hazard ratios from cox-proportional hazard models that include 1 or more stratified terms. For example, say I run a cox regression with treatment as a covariate and ...
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what's the difference between stratified sampling and matching?

I am trying to find a good technique to balance data in which the minority class is about 1% of the data. As I understood, the most common practice is matching. What is the difference, though, ...
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compute AUC and statistical testing of stratified data

I have a dataset where samples are stratified in groups. That is, there are N samples per examination, and M examinations per subject. I would like to account for this when estimating the ...
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Calculating a sample size based on the target width of a confidence interval with stratification

I am reviewing a sampling design devised by a colleague and completely fail to understand it, although I am not a novice in statistics (but not a huge expert either). The said colleague is no longer ...
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Odds ratio not between the two odds ratio of a stratified analysis

This is not the first time I have performed stratified analyses based on logistic regressions, but I have never before been confronted with this situation. When I test my relationship in the overall ...
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Does fitting Cox-model with strata and strata-covariate interaction differ from fitting two Cox models?

In Regression Modeling Strategies by Harrell (second edition) there is a section (S. 20.1.7) discussing Cox models including an interaction between a covariate whose main effect on survival we want to ...
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Reweighting sample to reflect census?

I'm working on a revision of a manuscript. I have a longitudinal cohort of adolescents that were followed throughout junior high school. One of the reviewers considers the attrition to be considerable,...
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random vs. stratified Cox model

What is the difference in interpretation of the effect estimates in a Cox model stratified by sex vs. with a random statement for sex? Thanks.
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Difference between Multistage Sampling and Stratified Random Sampling?

I know the question is a very elementary one, but I simply cannot understand the difference other than the fact that an SRS is a form of Multi-Stage Sampling. Can anyone provide a simple example(s) to ...
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Difference between Confounding and Effect Modifier

I am searching everywhere about the differences between Confounding and Effect modifier, but not able to find it. Kindly help me in this. Can you please answer using examples rather than definitions.
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Should stratified cross-validation be used in a regression context?

After a number of Google searches and looking at various stack posts, I cannot find much information or discussion about using stratified cross-validation in regression context. I am modeling forest ...
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Nested CV with Stratified Kfold

I am using nested CV for model evaluation and my target variable has imbalanced classes. With Sklearn, I am using GridSearchCV and cross_val_score to perform the nested cross validation. Each takes ...
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Calculating sample size for a stratified random sample

I have a total population of 4,000 farmers, divided into three unique regions. Approximately 50% of the farmers live in Region A (N=2,000), 27% in Region B (N=1,080), and 23% in Region C (N=920). I am ...
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Least square estimate for post-stratification sampling

I figured out via the normal linear regression method that Beta0 hat = ybar - Beta1 hat xbar. But I am not sure how to find out the least square estimate for Chat. Is anyone able to help me? Thanks!! ...
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Stratified Sampling: Given required bound, calculated $n_h$ is bigger than $N_h$?

I'm dealing with a question that has given me a peculiar result and I would like someone's opinion on how to deal this: Say you have a population of $N=550$ objects: $N_1=75$ red and $N_2=475$ blue. ...
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Stratifying a multivariate dataset

I woul like to stratify a multivariate dataset in different stratas in such a way that the elements of each strata are similar. My idea is to have different groups whith the element within a group ...
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Why class stratified sampling is not compatible for naïve bayesian modeling, if sampling is used?

I read a book recently and it mentioned related to prior probability of naïve bayesian: "Since the probability of an outcome is calculated from the data set, it is important that the data set used ...
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Fold size and stratified k-fold cross-validation dilemma

So, as far as I understand, all folds in k-fold cross-validation need to be the same size, or as close as possible. However, if one uses stratified cross-validation, each class should be represented ...
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how to use boot package to do stratified bootstrapping?

Here's a toy data set that replicates my problem. I am interested in knowing the confidence intervals of an empirical distribution that is composed of the scores of each school at the proportion that ...
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Optimal multivariate binning where the cut-points must be the same for all observations

I have a large data set with many discrete and continuous variables. All the variables are present in every observation. I want to explain (the log of) one continuous variable using all the other ...
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What methods exist for multi-output stratification (multi-label with different mutually exclusive label sets)?

To elaborate a bit: Methods do exist for single-output multi-label stratification (e.g., see scikit-multilearn). And there's a perfectly serviceable naive method for multi-class problems. The ...
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Stratified cross validation with groups

I have a model for a binary classification problem that I want to cross validate. The data is divided into groups. Some groups contain samples in both classes, others only contain samples from one ...
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How to control for severe medical cases using survival analysis and Cox regression?

I have a longitudinal medical record dataset. My cohort is made up of patients with a particular disease. There are no members of this cohort without this disease. Disease indications are denoted by a ...
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Model to Impute strata data according to higher level data

I've got regional level data and I want to impute said data on a county level strata (smaller strata with respect to the regional strata). I know I can do that if I have a series of variables on the ...
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Regression model when stratified randomization is used

I have been going through articles on stratified randomization but I haven't seen one that elaborate regression function generated and also elaboration of strata effect on the outcome. I will ...
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Sampling methods - Stratified Vs. Probability Proportionate to Size Cluster

India has 29 States, each of which is further sub-divided into smaller administrative Districts of varying size and population. I need to download micro (firm-level) data from each state, but I do not ...
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an alternative allocation method for small RCTs?

stratification and minimization are two randomization options to achieve balance in terms of covariates or baselines in small RCTs. in recent years, researchers seem to have favoured minimization, ...
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Stratified Kfold splits by inputs instead of just being based on outputs

It's known that a stratified split can easily be done if there is a known imbalance in the output distribution (or output classes). Is it possible to do a stratified k-fold using the input ...
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Stratification vs. interaction

I'm doing a secondary analysis on a large sample of children from 4- to 18-years-old using logistic regression. In addition to analyzing effects of predictors (age, sex, IQ, autism severity, medical ...
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What are the differences between Domain Estimation and Poststratification

Both of these two methods will firstly select a simple random sample without replacement and decide which groups these observations belong to and use ratio estimation to get quantities of interest. It ...
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How does the boot package in R handle collecting bootstrap samples if strata are not specified but the function separates the dataset by strata?

My current understanding is 1) if strata are not specified, then boot randomly selects rows with replacement from the entire dataset. If dataset is actually stratified then boot would often return ...
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How can stratified kfolds perform worse than regular kfolds?

I am working with unbalanced classes to solve a classification problem (whether individuals pay their fees or not). My class imbalance is 75% positive (paid) and 25% negative (unpaid). I have read ...
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what type of sample is this?

I have the folowing table: according to this example, there are 40 observations distributed over 10 stores and 4 weeks of the month. Objective: to make a sample of 90%, 80%, 75% and 50% of the 40 ...
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Benefit of scaling survey design weights

In the european social survey the design weights are scaled to the sample size (see quote and source): As with the design weights also the post-stratification weights are scaled to the sample size, ...
249 views

Correlation with stratified data structure

I have a dataset with two stratas, the first level is called block, which represents the census block. the second level is two predetermined subgroups in each blocks, each subgroup contains several ...
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Sampling - optimal allocation for quantiles?

Say we have variable with a typical heavy tailed distribution following the Pareto principle. We divide the population into two strata (following the 80 / 20 rule) and use a stratified sampling design ...
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Remove duplicates from training set for classification

Let us say I have a bunch of rows for a classification problem: $$X_1, ... X_N, Y$$ Where $X_1, ..., X_N$ are the features/predictors and $Y$ is the class the row’s feature combination belongs to. ...
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Should the same train/test set be used for all models if training multiple binary models for multi-class classification?

I have 100 classes in my input space. I plan to build 100 random forest classifiers that will determine if a given input matches a class (1) or not (0), such that a True/False model exists for each ...
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Cox model with a predictor variable within only one stratum

I'm investigating survival in a cancer patients cohort. The Cox model I'm using is stratified by stage and adjusted for several variables. I would like to add one variable RT (Radiotherapy) which is ...
627 views

Bootstrapping dataset with imbalanced classes

I am trying to build an ensemble model to classify dataset with imbalanced data, where some of classes have just a few samples. And, because of this dataset property, when I am doing re-sampling with ...
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Prove significant effect of third variable on a correlation

So I was measuring correlations in Boxscores of basketball players in the NBA. 3PA DRB -0.205499 I was trying to find some interesting correlations. ...
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svydesign fpc term in 2stage stratification [closed]

I am having some problems with svydesign() in R. My sample design is a 2 stage stratification. ...
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How to combine two sets of samples from an unnormalised distribution?

Imagine that I have an unnormalised distribution $P$ with density function $p(x)$ for $x \in \mathbb{R}^d$. $P$ has two well-separated modes and there are two sets of i.i.d. samples with the size $n$ ...
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What is the error of the global mean given known equal errors for each stratum?

This was asked to me by a colleague. It seems to be an easy one but I wasn't able to find the derivation online. I've added my own attempt below.