The Bergen Social Media Addiction Scale Validity in a Romanian Sample Using Item Response Theory and Network Analysis

Background: Recent studies with the mood rhythm instrument (MRhI) have shown that the presence of recurrent daily peaks in specific mood symptoms are significantly associated with increased risk of psychiatric disorders. Using a large sample collected in Brazil, Spain, and Canada, we aimed to analyze which MRhI items maintained good psychometric properties across cultures. As a secondary aim, we used network analysis to visualize the strength of the association between the MRhI items. Methods: Adults (n = 1275) between 18–60 years old from Spain (n = 458), Brazil (n = 415), and Canada (n = 401) completed the MRhI and the self-reporting questionnaire (SRQ-20). Psychometric analyses followed three steps: Factor analysis, item response theory, and network analysis. Results: The factor analysis indicated the retention of three factors that grouped the MRhI items into cognitive, somatic, and affective domains. The item response theory analysis suggested the exclusion of items that displayed a significant divergence in difficulty measures between countries. Finally, the network analysis revealed a structure where sleepiness plays a central role in connecting the three domains. These psychometric analyses enabled a psychometric-based refinement of the MRhI, where the 11 items with good properties across cultures were kept in a shorter, revised MRhI version (MRhI-r). Limitations: Participants were mainly university students and, as we did not conduct a formal clinical assessment, any potential correlations (beyond the validated SRQ) cannot be ascertained. Conclusions: The MRhI-r is a novel tool to investigate self-perceived rhythmicity of mood-related symptoms and behaviors, with good psychometric properties across multiple cultures.

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Depressive and anxiety symptoms in refugees: Insights from classical test theory, item response theory and network analysis

Clinical Psychology & Psychotherapy ◽

10.1002/cpp.2499 ◽

2020 ◽

Cited By ~ 1

Author(s):

Pascal Schlechter ◽

Paul O. Wilkinson ◽

Judith Knausenberger ◽

Katharina Wanninger ◽

Svea Kamp ◽

...

Keyword(s):

Item Response Theory ◽

Network Analysis ◽

Item Response ◽

Classical Test Theory ◽

Anxiety Symptoms ◽

Test Theory ◽

Response Theory ◽

Classical Test

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Building an Attitude Scale about E-learning among Isra University Students according to the Item Response Theory in Measurement

International Journal of Higher Education ◽

10.5430/ijhe.v10n2p188 ◽

2020 ◽

Vol 10 (2) ◽

pp. 188

Author(s):

Mohammad Al-Dlalah ◽

Hani Alkhaldi ◽

Malek Alkhutaba ◽

Taghreed Al-Momani

Keyword(s):

Social Media ◽

Item Response Theory ◽

University Students ◽

Item Response ◽

Learning Process ◽

Attitude Scale ◽

Learning Approach ◽

Response Theory ◽

E Learning ◽

Education Process

Nowadays, E-learning is one of the most inevitable technique and learning approach, because it offers indispensable advancement of the education process. Furthermore, it supports and enhances many facets of the learning process. This study aimed to build an attitude scale about e-learning among Isra University students according to the item response theory (IRT) in measurement. To achieve the study’s objectives, the main dimensions of the scale were identified, and 44 items were unevenly formatted. The tool was applied to 550 students who were social media users at Isra University. After analyzing the participants’ responses according to IRT, the final form of the scale consisted of 29 items that were divided into three main dimensions. The reliability of the scale was 0.94%, while the scale had multiple indications of validation.

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A method for exploring attitude systems by combining Belief Network Analysis and Item Response Theory (ResIN)

10.31234/osf.io/uzdcg ◽

2021 ◽

Author(s):

Dino Carpentras ◽

Adrian Lueders ◽

Michael Quayle

Keyword(s):

Item Response Theory ◽

Network Analysis ◽

Item Response ◽

Real Data ◽

Response Theory ◽

Belief Network ◽

Partisan Polarization ◽

New Class ◽

Identification Measurements ◽

Socially Relevant

Belief network analysis (BNA) is a new class of methods with strong potential to research the organization and development of abstract meaning systems. By mapping the attitude system, this method provides a more profound understanding of often “fuzzy” concepts such as ideologies, worldviews, and norm systems. BNA therefore holds potential implications for a plethora of socially relevant issues. For example, by informing the architecture of extreme belief sets or lines of conflict underlying partisan polarization. Despite the huge potential of this approach, it has some major limitations. Indeed, BNA methods start from the simplistic assumption that opposing groups should be perfectly symmetric in their attitudes (e.g. the more democrats are positive, the more republicans should be negative about each topic). Another important aspect of BNA methods is that they are often grounded on new, instead of well-established theories. This sometimes results in problems of interpretation and reliability of the results.In this article, we introduce a new method by combining BNA with item response theory (IRT). We refer to it as the Response-Item Network (or ResIN) method. This method has the advantage of being grounded in the well-developed psychometrics literature. Furthermore, it allows us to analyze attitudes from different groups without assuming symmetric behavior. This allows us to explore more deeply relationships and differences in the attitude system.Besides validating ResIN using IRT, we also test this method on real data, showing that it produces new insights compared to both classical BNA and IRT. Indeed, we are able to easily distinguish attitudes which belong to the republican and to the democrat side, even in counter-intuitive situations. We furthermore validated the reliability of these results by relying on additional data, such as self-identification measurements.

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