LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 144

Search options

  1. Book ; Online: Small Sample Size Solutions

    Miocević, Milica / van de Schoot, Rens

    2020  

    Abstract: Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small ...

    Abstract Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapter illustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. This essential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect. The statistical models in the book range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics
    Keywords Psychology
    Size 1 electronic resource (284 pages)
    Publisher Taylor and Francis
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT020480682
    ISBN 9780367221898 ; 9780367222222 ; 9780429273872 ; 0367221896 ; 0367222221 ; 0429273878
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

    More links

    Kategorien

  2. Article ; Online: The SAFE procedure: a practical stopping heuristic for active learning-based screening in systematic reviews and meta-analyses.

    Boetje, Josien / van de Schoot, Rens

    Systematic reviews

    2024  Volume 13, Issue 1, Page(s) 81

    Abstract: Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of ... ...

    Abstract Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.
    MeSH term(s) Humans ; Heuristics ; Problem-Based Learning ; Systematic Reviews as Topic ; Software
    Language English
    Publishing date 2024-03-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 2662257-9
    ISSN 2046-4053 ; 2046-4053
    ISSN (online) 2046-4053
    ISSN 2046-4053
    DOI 10.1186/s13643-024-02502-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  3. Article ; Online: Addressing the challenges of reconstructing systematic reviews datasets: a case study and a noisy label filter procedure.

    Neeleman, Rutger / Leenaars, Cathalijn H C / Oud, Matthijs / Weijdema, Felix / van de Schoot, Rens

    Systematic reviews

    2024  Volume 13, Issue 1, Page(s) 69

    Abstract: Systematic reviews and meta-analyses typically require significant time and effort. Machine learning models have the potential to enhance screening efficiency in these processes. To effectively evaluate such models, fully labeled datasets-detailing all ... ...

    Abstract Systematic reviews and meta-analyses typically require significant time and effort. Machine learning models have the potential to enhance screening efficiency in these processes. To effectively evaluate such models, fully labeled datasets-detailing all records screened by humans and their labeling decisions-are imperative. This paper presents the creation of a comprehensive dataset for a systematic review of treatments for Borderline Personality Disorder, as reported by Oud et al. (2018) for running a simulation study. The authors adhered to the PRISMA guidelines and published both the search query and the list of included records, but the complete dataset with all labels was not disclosed. We replicated their search and, facing the absence of initial screening data, introduced a Noisy Label Filter (NLF) procedure using active learning to validate noisy labels. Following the NLF application, no further relevant records were found. A simulation study employing the reconstructed dataset demonstrated that active learning could reduce screening time by 82.30% compared to random reading. The paper discusses potential causes for discrepancies, provides recommendations, and introduces a decision tree to assist in reconstructing datasets for the purpose of running simulation studies.
    MeSH term(s) Humans ; Machine Learning ; Computer Simulation ; Problem-Based Learning
    Language English
    Publishing date 2024-02-17
    Publishing country England
    Document type Systematic Review ; Journal Article
    ZDB-ID 2662257-9
    ISSN 2046-4053 ; 2046-4053
    ISSN (online) 2046-4053
    ISSN 2046-4053
    DOI 10.1186/s13643-024-02472-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  4. Book ; Online: Measurement Invariance

    Beuckelaer, Alain De / Schmidt, Peter / Schoot, Rens Van De

    2015  

    Abstract: Multi-item surveys are frequently used to study scores on latent factors, like human values, attitudes and behavior. Such studies often include a comparison, between specific groups of individuals, either at one or multiple points in time. If such latent ...

    Abstract Multi-item surveys are frequently used to study scores on latent factors, like human values, attitudes and behavior. Such studies often include a comparison, between specific groups of individuals, either at one or multiple points in time. If such latent factor means are to be meaningfully compared, the measurement structures including the latent factor and their survey items should be stable across groups and/or over time, that is 'invariant'. Recent developments in statistics have provided new analytical tools for assessing measurement invariance (MI). The aim of this special issue is to provide a forum for a discussion of MI, covering some crucial 'themes': (1) ways to assess and deal with measurement non-invariance; (2) Bayesian and IRT methods employing the concept of approximate measurement invariance; and (3) new or adjusted approaches for testing MI to fit increasingly complex statistical models and specific characteristics of survey data. The special issue started with a kick-off meeting where all potential contributors shared ideas on potential papers. This expert workshop was organized at Utrecht University in The Netherlands and was funded by the Netherlands Organization for Scientific Research (NWO-VENI-451-11-008). After the kick-off meeting the authors submitted their papers, all of which were reviewed by experts in the field. The papers in the eBook are listed in alphabetical order, but in the editorial the papers are introduced thematically. Although it is impossible to cover all areas of relevant research in the field of MI, papers in this eBook provide insight on important aspects of measurement invariance. We hope that the discussions included in this special issue will stimulate further research on MI and facilitate further discussions to support the understanding of the role of MI in multi-item surveys
    Keywords Science (General) ; Psychology
    Size 1 electronic resource (217 p.)
    Publisher Frontiers Media SA
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT020091840
    ISBN 9782889196500 ; 288919650X
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

    More links

    Kategorien

  5. Article: Editorial: Moving Beyond Non-informative Prior Distributions: Achieving the Full Potential of Bayesian Methods for Psychological Research.

    Koenig, Christoph / Depaoli, Sarah / Liu, Haiyan / van de Schoot, Rens

    Frontiers in psychology

    2021  Volume 12, Page(s) 809719

    Language English
    Publishing date 2021-12-09
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2563826-9
    ISSN 1664-1078
    ISSN 1664-1078
    DOI 10.3389/fpsyg.2021.809719
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  6. Article ; Online: Posttraumatic stress symptoms and interpersonal processes in burn survivors and their partners.

    Boersma-van Dam, Elise / van de Schoot, Rens / Engelhard, Iris M / Van Loey, Nancy E E

    European journal of psychotraumatology

    2023  Volume 13, Issue 2, Page(s) 2151097

    Abstract: Background: ...

    Abstract Background:
    MeSH term(s) Humans ; Stress Disorders, Post-Traumatic ; Emotions ; Burns ; Nonoxynol ; Survivors
    Chemical Substances Nonoxynol (26027-38-3)
    Language English
    Publishing date 2023-03-03
    Publishing country United States
    Document type Multicenter Study ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2586642-4
    ISSN 2000-8066 ; 2000-8066
    ISSN (online) 2000-8066
    ISSN 2000-8066
    DOI 10.1080/20008066.2022.2151097
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  7. Article: Prevalence and course of posttraumatic stress disorder symptoms in partners of burn survivors.

    Boersma-van Dam, Elise / van de Schoot, Rens / Geenen, Rinie / Engelhard, Iris M / Van Loey, Nancy E

    European journal of psychotraumatology

    2021  Volume 12, Issue 1, Page(s) 1909282

    Abstract: ... ...

    Abstract Background
    Language English
    Publishing date 2021-05-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2586642-4
    ISSN 2000-8066
    ISSN 2000-8066
    DOI 10.1080/20008198.2021.1909282
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  8. Article: Latent Growth Mixture Models to estimate PTSD trajectories.

    Van de Schoot, Rens

    European journal of psychotraumatology

    2015  Volume 6, Page(s) 27503

    Language English
    Publishing date 2015-03-02
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2586642-4
    ISSN 2000-8066 ; 2000-8066 ; 2000-8198
    ISSN (online) 2000-8066
    ISSN 2000-8066 ; 2000-8198
    DOI 10.3402/ejpt.v6.27503
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  9. Article: Approximate Measurement Invariance of Willingness to Sacrifice for the Environment Across 30 Countries: The Importance of Prior Distributions and Their Visualization.

    Arts, Ingrid / Fang, Qixiang / van de Schoot, Rens / Meitinger, Katharina

    Frontiers in psychology

    2021  Volume 12, Page(s) 624032

    Abstract: Nationwide opinions and international attitudes toward climate and environmental change are receiving increasing attention in both scientific and political communities. An often used way to measure these attitudes is by large-scale social surveys. ... ...

    Abstract Nationwide opinions and international attitudes toward climate and environmental change are receiving increasing attention in both scientific and political communities. An often used way to measure these attitudes is by large-scale social surveys. However, the assumption for a valid country comparison, measurement invariance, is often not met, especially when a large number of countries are being compared. This makes a ranking of countries by the mean of a latent variable potentially unstable, and may lead to untrustworthy conclusions. Recently, more liberal approaches to assessing measurement invariance have been proposed, such as the alignment method in combination with Bayesian approximate measurement invariance. However, the effect of prior variances on the assessment procedure and substantive conclusions is often not well understood. In this article, we tested for measurement invariance of the latent variable "willingness to sacrifice for the environment" using Maximum Likelihood Multigroup Confirmatory Factor Analysis and Bayesian approximate measurement invariance, both with and without alignment optimization. For the Bayesian models, we used multiple priors to assess the impact on the rank order stability of countries. The results are visualized in such a way that the effect of different prior variances and models on group means and rankings becomes clear. We show that even when models appear to be a good fit to the data, there might still be an unwanted impact on the rank ordering of countries. From the results, we can conclude that people in Switzerland and South Korea are most motivated to sacrifice for the environment, while people in Latvia are less motivated to sacrifice for the environment.
    Language English
    Publishing date 2021-07-22
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2563826-9
    ISSN 1664-1078
    ISSN 1664-1078
    DOI 10.3389/fpsyg.2021.624032
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  10. Article ; Online: Editorial

    Christoph Koenig / Sarah Depaoli / Haiyan Liu / Rens van de Schoot

    Frontiers in Psychology, Vol

    Moving Beyond Non-informative Prior Distributions: Achieving the Full Potential of Bayesian Methods for Psychological Research

    2021  Volume 12

    Keywords Bayesian statistics ; Bayesian modeling and inference ; prior distributions ; informative prior distributions ; cumulative science ; psychological research ; Psychology ; BF1-990
    Language English
    Publishing date 2021-12-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

To top