Please note that this special issue is currently in progress. Articles will be added to this collection as they are published. The original call for papers is available below.
The effects of omitted variables and measurement error on cross-sectional network psychometric models
Discovering cyclic causal models in psychological research
Comparing maximum likelihood and maximum pseudolikelihood estimators for the Ising model
Introducing semantic loadings in factor analysis: Bridging network psychometrics and cognitive networks for understanding depression, anxiety and stress
Simplifying Bayesian analysis of graphical models for the social sciences with easybgm: A user-friendly R-package
Modeling and managing behavior change in groups: A Boolean network method
Call for Papers (finished)
advances.in/psychology, a groundbreaking journal redefining academic publishing, invites submissions for a Special Issue on Network Psychometrics. This Special Issue will showcase cutting-edge methods, groundbreaking conceptual issues, and innovative applications of Network Psychometrics, as well as new R packages. Our objective is to publish research that will propel the field forward and provide valuable insights for scholars and practitioners.
Network Psychometrics is a rapidly growing area of psychometrics that explores the application of network methods to address both technical and conceptual challenges in psychology and related fields. This interdisciplinary field draws on psychometrics, network science, statistical mechanics, computer science, and many other disciplines. Network Psychometrics gained significant momentum after the publication of the Mutualism Model of Intelligence by Van der Maas et al. in 2006 and scholarly and popular attention further increased with the release of free and open-source R packages in 2012 (such as the qgraph package by Epskamp et al., 2012).
In recent years, the growth of Network Psychometrics has been driven by the increasing availability of open-source software and the development of new methods, techniques, and metrics for cross-sectional and intensive longitudinal data. Network Psychometrics has been applied in a diverse range of domains, including dimensionality assessment, personality, intelligence, psychopathology, education, disinformation research, attitudes, text analysis, and many others.
In terms of its impact, Network Psychometrics is an increasingly important area within psychometrics, with the potential to inform and enhance our understanding of various psychological and social phenomena. Its methods and techniques can be used to better address complex problems in a broad range of fields. As a result, Network Psychometrics is considered one of the fastest-growing and significant areas of psychometrics, attracting growing interest from researchers and practitioners globally. The broad scholarly attention it has attracted, the range of relevant applications, and the rapidly expanding accessibility of analytic tools supporting work make this Special Issue particularly timely and important.
At advances.in/psychology, we believe it is time to revolutionize academic publishing. We are accomplishing this by using novel processes and compensating our reviewers fairly, ensuring an inclusive, equitable and high- quality review process. Our review will be fast, fair, and prioritize innovation, open science, and out-of-the-box thinking.
Furthermore, we will utilize innovative AI methods to aid in the peer-review process, making it more efficient and accurate while ensuring that high-quality papers receive the recognition they deserve.
Invited Topics
Submissions may present new analytical tools (i.e., new R packages), represent a specific application of Network Psychometrics that advances scholarly understanding or practical uses of the approach, or conceptual and commentary pieces about the state of the area and guidance for future directions. Specific topics may include:
- Innovative new methods in Network Psychometrics
- Important conceptual issues in Network Psychometrics
- New R Packages for Network Psychometrics
- Interesting applications of Network Psychometrics
- Network Psychometrics and non-standard data types (e.g., text data)
- Issues and Future Directions for Network Psychometrics
- Tutorial Papers on Network Psychometrics using new or existing R packages
We welcome original research articles, reviews, and perspectives that explore these and other topics related to Network Psychometrics. All submissions will undergo a rigorous and fair peer-review process.
It is important to note that papers published in this special issue will not be charged any article processing fee.
Authors interested in submitting their work should first send the title, author list, and an abstract of up to 200 words via https://advances.in/psychology/10.56296/network-psychometrics/. The deadline for the abstract submission is April 30, 2023.
We are pleased to announce that selected works will be invited to submit a full paper for publication in advances.in/psychology, before the deadline of September 30, 2023. Accepted papers will be published online on a rolling basis.
Submission Instructions
Please take note of the following instructions for the full manuscript submission:
- The manuscript should be written using RMarkdown and/or LaTeX, following the APA 7 article format. We highly recommend using the papaja package for R (https://github.com/crsh/papaja) as it provides an easy-to-use RMarkdown template and enables seamless integration of text and R-code/analysis.
- The RMarkdown/LaTeX document, as well as all codes and data (if applicable), should be stored in an Open Science Framework Repository. Proper citation of all utilized packages and their versions must be included in the paper.
- Only a PDF version of the manuscript will be submitted to the journal system, but it should contain a link to the Open Science Framework repository where the RMarkdown/LaTeX files, R Code, and Data are stored.
- The writing style should be informative and avoid technical jargon or unnecessary complexities. The use of figures and plots to explain statistical/mathematical concepts is encouraged (see one example of using figures to explain complex statistical concepts here: https://d2r55xnwy6nx47.cloudfront.net/uploads/2018/11/AlgorithmicComplexity_5601.jpg). The target audience includes applied researchers from various fields, so the paper should be written in a pedagogical manner that includes and does not alienate readers.
- If the paper introduces a new method/technique/metric, an applied section demonstrating the method using real-world data is highly encouraged. This will increase the impact and appeal of the work for applied researchers.
- There is not word limit for work submitted to the special issue, but authors are encouraged to be concise in their writing.
To submit your abstract, please fill out the form below.
We look forward to your submissions and collaborating with you to publish groundbreaking research in the field of Network Psychometrics.
Best regards,
Hudson Golino, Ph.D., Editor of the Special Issue on Network Psychometrics
Associate Professor of Quantitative Methods
Department of Psychology, University of Virginia
