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Psych
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Application of Modern Psychometric Techniques in Psychology
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Application of Modern Psychometric Techniques in Psychology

A special issue of Psych (ISSN 2624-8611). This special issue belongs to the section "Psychometrics and Educational Measurement".

Deadline for manuscript submissions: closed (15 September 2020) | Viewed by 3991

Special Issue Editor

Dr. Kuan-Yu Jin

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Guest Editor
Hong Kong Examinations and Assessment Authority
Interests: psychological assessment; psychological testing

Special Issue Information

Dear Colleagues,

Quantitative methods are often used to measure and analyze psychological constructs. In recent decades, psychometrics has developed rapidly with the raise of technology. Modern psychometric methods (such as Rasch measurements, item response theory, and cognitive diagnostic models) can achieve objective measurement and have several advantages over conventional methods. Although modern psychometric methods have been widely used in education and medicine, they are not very common in the psychology field.
The goal of this Special Issue of Psych is to promote and illustrate the applications of modern psychometrics techniques in quantifying, modeling, and predicting psychological constructs. We welcome authors to submit their manuscripts on (but not limited to) the following themes:

  1. Applying modern psychometric techniques to analyze psychological data;
  2. Validating instruments using modern psychometric techniques;
  3. Adopting modern psychometric techniques to (re)examine existing psychological theories;
  4. Comparing the differences between traditional and modern techniques in data analysis;
  5. Providing new models or methods suitable for specific psychological scenarios.

Dr. Kuan-Yu Jin
Guest Editor

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. Psych is an international peer-reviewed open access quarterly 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 1200 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

  • Rasch measurements
  • Item response theory
  • Cognitive diagnostic models
  • Differential item functioning
  • Computerized adaptive testing
  • Mixture models
  • Multilevel models
  • Multidimensional models
  • Structural equation modeling

Published Papers (2 papers)

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Research

16 pages, 1140 KiB  
Article
Interaction Map: A Visualization Tool for Personalized Learning Based on Assessment Data
by Eric Ho and Minjeong Jeon
Psych 2023, 5(4), 1140-1155; https://doi.org/10.3390/psych5040076 - 24 Oct 2023
Cited by 1 | Viewed by 1162
Abstract
Personalized learning is the shaping of instruction to meet students’ needs to support student learning and improve learning outcomes. While it has received increasing attention in education, limited resources are available to help educators properly leverage assessment data to foster personalized learning. Motivated [...] Read more.
Personalized learning is the shaping of instruction to meet students’ needs to support student learning and improve learning outcomes. While it has received increasing attention in education, limited resources are available to help educators properly leverage assessment data to foster personalized learning. Motivated by this need, we introduce a new visualization tool, the interaction map, to foster personalized learning based on assessment data. The interaction map approach is engineered by the latent space item response model, a recent development in assessment data-leveraging social network analysis methodologies. In the interaction map, students and test items are mapped into a two-dimensional geometric space, in which their distances tell us about the student’s strengths and weaknesses with individual or groups of test items given their overall ability levels. Student profiles can be generated based on these distances to display individual student strengths and weaknesses. Finally, we introduce a user-friendly, free web-based software IntMap in which users can upload their own assessment data and view the customizable interaction map and student profiles based on settings that users can adjust. We illustrate the use of the software with an educational assessment example. Full article
(This article belongs to the Special Issue Application of Modern Psychometric Techniques in Psychology)
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16 pages, 1145 KiB  
Article
A Practical Cross-Sectional Framework to Contextual Reactivity in Personality: Response Times as Indicators of Reactivity to Contextual Cues
by Zenab Tamimy, Sandor Rózsa, Natasa Kõ and Dylan Molenaar
Psych 2020, 2(4), 253-268; https://doi.org/10.3390/psych2040019 - 13 Nov 2020
Viewed by 2230
Abstract
Contextual reactivity refers to the degree in which personality states are affected by contextual cues. Research into contextual reactivity has mainly focused on repeated measurement designs. In this paper, we propose a cross-sectional approach to study contextual reactivity. We argue that contextual reactivity [...] Read more.
Contextual reactivity refers to the degree in which personality states are affected by contextual cues. Research into contextual reactivity has mainly focused on repeated measurement designs. In this paper, we propose a cross-sectional approach to study contextual reactivity. We argue that contextual reactivity can be operationalized as different response processes which are characterized by different mean response times and different measurement properties. We propose a within-person mixture modeling approach that adopts this idea and which enables studying contextual reactivity in cross-sectional data. We applied the model to data from the Revised Temperament and Character Inventory. Results indicate that we can distinguish between two response specific latent states. We interpret these states as a high contextual reactive state and a low contextual reactive state. From the results it appears that the low contextual reactive state is generally associated with smaller response times and larger discrimination parameters, as compared to the high contextual reactivity state. The utility of this approach in personality research is discussed. Full article
(This article belongs to the Special Issue Application of Modern Psychometric Techniques in Psychology)
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