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Methods and Applications of Statistics in the Social and Health...
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Methods and Applications of Statistics in the Social and Health Sciences, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 4837

Special Issue Editors

Prof. Dr. María Del Mar Rueda

E-Mail Website
Guest Editor
Department of Statistics and Operational Research, University of Granada, 18071 Granada, Spain
Interests: Survey sampling; web surveys; resampling methods; inference for finite population; machine learning methods; indirect questioning techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Beatriz Cobo Rodríguez

E-Mail Website
Guest Editor
Department of Quantitative Methods for the Economy and Business, University of Granada, 18011 Granada, Spain
Interests: randomized response techniques; robability and non probability sampling

Special Issue Information

Dear Colleagues,

Statistical methods are becoming more and more needed in the field of social, behavior and health sciences, and especially, in the context of the COVID-19 pandemic. New statistical techniques are emerging to solve real problems and difficulties that arise from these areas for data analysis.

The purpose of this Special Issue is to bring together outstanding contributions using new methods from various mathematical and statistical research areas with real-world applications. This issue provides a collection of articles that illustrate the applicability of novel mathematical and statistical tools to a wide range of topics such as non-probabilistic surveys, and research on sensitive behaviors such as addictions, poverty studies, health and the social impact of COVID-19, among others.

Prof. Dr. María Del Mar Rueda
Dr. Beatriz Cobo Rodríguez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • statistics
  • data analysis
  • non-probabilistic surveys
  • inequality and poverty
  • missing data
  • coverage bias
  • web surveys
  • randomized response techniques
  • health surveys

Published Papers (3 papers)

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Research

14 pages, 1048 KiB  
Article
A Conceptual Framework for Quantifying the Robustness of a Regression-Based Causal Inference in Observational Study
by Tenglong Li, Kenneth A. Frank and Mingming Chen
Mathematics 2024, 12(3), 388; https://doi.org/10.3390/math12030388 - 25 Jan 2024
Viewed by 738
Abstract
The internal validity of a causal inference made based on an observational study is often subject to debate. The potential outcomes framework of causal inference stipulates that causal inference is essentially a missing data problem, and we follow this spirit to define the [...] Read more.
The internal validity of a causal inference made based on an observational study is often subject to debate. The potential outcomes framework of causal inference stipulates that causal inference is essentially a missing data problem, and we follow this spirit to define the ideal sample as the combination of the observed data and the missing/counterfactual data for regression models. The robustness of a causal inference can be quantified by the probability of a robust inference for internal validity in regression, i.e., the PIVR, which is the probability of rejecting the null hypothesis again for the ideal sample provided the same null hypothesis has been already rejected for the observed sample. Drawing on the relationship between the PIVR and the mean counterfactual outcomes, we formalize a conceptual framework of quantifying the robustness of a regression-based causal inference based on a joint distribution about the mean counterfactual outcomes, holding the observed sample fixed. Interpretatively, the PIVR is the statistical power of the null hypothesis significance testing that is thought to be built on the ideal sample. We demonstrate the conceptual framework of quantifying the robustness of a regression-based causal inference with an empirical example. Full article
(This article belongs to the Special Issue Methods and Applications of Statistics in the Social and Health Sciences, 2nd Edition)
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15 pages, 462 KiB  
Article
Identifying Bias in Social and Health Research: Measurement Invariance and Latent Mean Differences Using the Alignment Approach
by Ioannis Tsaousis and Fathima M. Jaffari
Mathematics 2023, 11(18), 4007; https://doi.org/10.3390/math11184007 - 21 Sep 2023
Cited by 1 | Viewed by 913
Abstract
When comparison among groups is of major importance, it is necessary to ensure that the measuring tool exhibits measurement invariance. This means that it measures the same construct in the same way for all groups. In the opposite case, the test results in [...] Read more.
When comparison among groups is of major importance, it is necessary to ensure that the measuring tool exhibits measurement invariance. This means that it measures the same construct in the same way for all groups. In the opposite case, the test results in measurement error and bias toward a particular group of respondents. In this study, a new approach to examine measurement invariance was applied, which was appropriately designed when a large number of group comparisons are involved: the alignment approach. We used this approach to examine whether the factor structure of a cognitive ability test exhibited measurement invariance across the 26 universities of the Kingdom of Saudi Arabia. The results indicated that the P-GAT subscales were invariant across the 26 universities. Moreover, the aligned factor mean values were estimated, and factor mean comparisons of every group’s mean with all the other group means were conducted. The findings from this study showed that the alignment procedure is a valuable method to assess measurement invariance and latent mean differences when a large number of groups are involved. This technique provides an unbiased statistical estimation of group means, with significance tests between group pairs that adjust for sampling errors and missing data. Full article
(This article belongs to the Special Issue Methods and Applications of Statistics in the Social and Health Sciences, 2nd Edition)
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15 pages, 1738 KiB  
Article
Sperm Abnormality Detection Using Sequential Deep Neural Network
by Suleman Shahzad, Muhammad Ilyas, M. Ikram Ullah Lali, Hafiz Tayyab Rauf, Seifedine Kadry and Emad Abouel Nasr
Mathematics 2023, 11(3), 515; https://doi.org/10.3390/math11030515 - 18 Jan 2023
Cited by 2 | Viewed by 2720
Abstract
Sperm morphological analysis (SMA) is an essential step in diagnosing male infertility. Using images of human sperm cells, this research proposes a unique sequential deep-learning method to detect abnormalities in semen samples. The proposed technique identifies and examines several components of human sperm. [...] Read more.
Sperm morphological analysis (SMA) is an essential step in diagnosing male infertility. Using images of human sperm cells, this research proposes a unique sequential deep-learning method to detect abnormalities in semen samples. The proposed technique identifies and examines several components of human sperm. In order to conduct this study, we used the online Modified Human Sperm Morphology Analysis (MHSMA) dataset containing 1540 sperm images collected from 235 infertile individuals. For research purposes, this dataset is freely available online. To identify morphological abnormalities in different parts of human sperm, such as the head, vacuole, and acrosome, we proposed sequential deep neural network (SDNN) architecture. This technique is also particularly effective with low-resolution, unstained images. Sequential deep neural networks (SDNNs) demonstrate high accuracy in diagnosing morphological abnormalities based on the given dataset in our tests on the benchmark. Our proposed algorithm successfully detected abnormalities in the acrosome, head, and vacuole with an accuracy of 89%, 90%, and 92%, respectively. It is noteworthy that our system detects abnormalities of the acrosome and head with greater accuracy than current state-of-the-art approaches on the suggested benchmark. On a low-specification computer/laptop, our algorithm also requires less execution time. Additionally, it can classify photos in real time. Based on the results of our study, an embryologist can quickly decide whether to use the given sperm. Full article
(This article belongs to the Special Issue Methods and Applications of Statistics in the Social and Health Sciences, 2nd Edition)
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