Comparing Inference Methods for Non‐probability Samples

Buelens, Bart; Burger, Joep; Brakel, Jan A. van den

doi:10.1111/insr.12253

Cited by 48 publications

(39 citation statements)

References 65 publications

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“…In general, our findings support the view given in [65] that ML methods can usefully be used to remove selection bias when dealing with non-probability samples. Prior research has shown that PSA successfully removes bias in some situations but at the cost of increasing the variance of the estimates ( [10]; [11]).…”

Section: Resultssupporting

confidence: 89%

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Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys

Ferri‐García

Rueda

2020

PLoS ONE

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Modern survey methods may be subject to non-observable bias, from various sources. Among online surveys, for example, selection bias is prevalent, due to the sampling mechanism commonly used, whereby participants self-select from a subgroup whose characteristics differ from those of the target population. Several techniques have been proposed to tackle this issue. One such is Propensity Score Adjustment (PSA), which is widely used and has been analysed in various studies. The usual method of estimating the propensity score is logistic regression, which requires a reference probability sample in addition to the online nonprobability sample. The predicted propensities can be used for reweighting using various estimators. However, in the online survey context, there are alternatives that might outperform logistic regression regarding propensity estimation. The aim of the present study is to determine the efficiency of some of these alternatives, involving Machine Learning (ML) classification algorithms. PSA is applied in two simulation scenarios, representing situations commonly found in online surveys, using logistic regression and ML models for propensity estimation. The results obtained show that ML algorithms remove selection bias more effectively than logistic regression when used for PSA, but that their efficacy depends largely on the selection mechanism employed and the dimensionality of the data.

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Section: Resultssupporting

confidence: 89%

“…In this respect, [65] reviewed existing inference methods to correct for selection bias in nonprobability samples. These authors considered a situation where only a nonprobability sample is available and compared a range of predictive inference methods (pseudo-designbased and model-based) in a general framework.…”

Section: Discussionmentioning

confidence: 99%

Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys

Ferri‐García

Rueda

2020

PLoS ONE

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“…Other applications of machine learning algorithms in PSA involve their use in nonresponse adjustments; more precisely, they have been studied using Random Forests as propensity predictors [21]. Regarding the nonprobability sampling context covered in this study, [12] presented a simulation study using decision trees, k-Nearest Neighbors, Naive Bayes, Random Forests, and a Gradient Boosting Machine that support the view given in [6] about machine learning methods being used for removing selection bias in nonprobability samples. All of those algorithms, along with Discriminant Analysis and Model Averaged Neural Networks, will be used for propensity estimation in this study.…”

Section: Propensity Score Adjustmentmentioning

confidence: 74%

“…In order to correct this selection bias produced by non-random selection mechanisms, some inference procedures are proposed in the literature. A first class of methods includes statistical models aiming to predict the non-sampled units of the population [4][5][6]. Specifying an appropriate super-population model capable of learning the variation of the target variables is important for this model-based method.…”

Section: Introductionmentioning

confidence: 99%

Inference from Non-Probability Surveys with Statistical Matching and Propensity Score Adjustment Using Modern Prediction Techniques

2020

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Online surveys are increasingly common in social and health studies, as they provide fast and inexpensive results in comparison to traditional ones. However, these surveys often work with biased samples, as the data collection is often non-probabilistic because of the lack of internet coverage in certain population groups and the self-selection procedure that many online surveys rely on. Some procedures have been proposed to mitigate the bias, such as propensity score adjustment (PSA) and statistical matching. In PSA, propensity to participate in a nonprobability survey is estimated using a probability reference survey, and then used to obtain weighted estimates. In statistical matching, the nonprobability sample is used to train models to predict the values of the target variable, and the predictions of the models for the probability sample can be used to estimate population values. In this study, both methods are compared using three datasets to simulate pseudopopulations from which nonprobability and probability samples are drawn and used to estimate population parameters. In addition, the study compares the use of linear models and Machine Learning prediction algorithms in propensity estimation in PSA and predictive modeling in Statistical Matching. The results show that statistical matching outperforms PSA in terms of bias reduction and Root Mean Square Error (RMSE), and that simpler prediction models, such as linear and k-Nearest Neighbors, provide better outcomes than bagging algorithms.

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“…A good overview of the various methods is given in Elliott and Valliant (2017). There are three important approaches: the pseudo-design based inference (or pseudo-randomisation (Buelens et al, 2018)), statistical matching and predictive inference.…”

Section: Introductionmentioning

confidence: 99%

The R package NonProbEst for estimation in non-probability surveys

Rueda¹,

Ferri‐García²,

Castro³

2020

The R Journal

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Different inference procedures are proposed in the literature to correct selection bias that might be introduced with non-random sampling mechanisms. The R package NonProbEst enables the estimation of parameters using some of these techniques to correct selection bias in non-probability surveys. The mean and the total of the target variable are estimated using Propensity Score Adjustment, calibration, statistical matching, model-based, model-assisted and model-calibratated techniques. Confidence intervals can also obtained for each method. Machine learning algorithms can be used for estimating the propensities or for predicting the unknown values of the target variable for the non-sampled units. Variance of a given estimator is performed by two different Leave-One-Out jackknife procedures. The functionality of the package is illustrated with example data sets.

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Comparing Inference Methods for Non‐probability Samples

Cited by 48 publications

References 65 publications

Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys

Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys

Inference from Non-Probability Surveys with Statistical Matching and Propensity Score Adjustment Using Modern Prediction Techniques

The R package NonProbEst for estimation in non-probability surveys

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