Proactive Personality Measurement Using Item Response Theory and Social Media Text Mining

This prospective study was designed to propose a novel method of assessing proactive personality by combining text mining technology and Item Response Theory (IRT) to measure proactive personality more efficiently. We got freely expressed texts (essay question text dataset and social media text dataset) and item response data on the topic of proactive personality from 901 college students. To enhance validity and reliability, three different approaches were employed in the study. In Method 1, we used item response data to develop a proactive personality evaluation model based on IRT. In Method 2, we used freely expressed texts to develop a proactive personality evaluation model based on text mining. In Method 3, we utilized the text mining results as the prior information for the IRT estimation and built a proactive personality evaluation model combining text mining and IRT. Finally, we evaluated those three approaches via the confusion matrix indicators. The major result revealed that (1) the combined method based on essay question text, micro-blog text with pre-estimated IRT parameters performed the highest accuracy of 0.849; (2) the combined method using essay question text and pre-estimated IRT parameters performed the highest sensitivity of 0.821; (3) the text classification method based on essay question text had the best performance on the specificity of 0.959; and (4) if the models were considered comprehensively, the combined method using essay question text, micro-blog text, and pre-estimated IRT parameters achieved the best performance. Thus, we concluded that the novel combined method was significantly better than the other two traditional methods based on IRT and text mining.

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Evaluation Model of Acute Mountain Sickness Based on item Response Theory

2019 10th International Conference on Information Technology in Medicine and Education (ITME) ◽

10.1109/itme.2019.00039 ◽

2019 ◽

Author(s):

Zeng Fang Liu ◽

Xing Jia Wei ◽

Fang Fang Liu ◽

Dian Jun Lu

Keyword(s):

Item Response Theory ◽

Item Response ◽

Evaluation Model ◽

Acute Mountain Sickness ◽

Response Theory ◽

Mountain Sickness

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Robustness of Projective IRT to Misspecification of the Underlying Multidimensional Model

Applied Psychological Measurement ◽

10.1177/0146621620909894 ◽

2020 ◽

Vol 44 (5) ◽

pp. 362-375

Author(s):

Tyler Strachan ◽

Edward Ip ◽

Yanyan Fu ◽

Terry Ackerman ◽

Shyh-Huei Chen ◽

...

Keyword(s):

Item Response Theory ◽

Item Response ◽

Real Data ◽

Model Parameters ◽

Simulation Studies ◽

Response Theory ◽

Computational Stability ◽

Data Set ◽

Response Data ◽

Higher Dimensional

As a method to derive a “purified” measure along a dimension of interest from response data that are potentially multidimensional in nature, the projective item response theory (PIRT) approach requires first fitting a multidimensional item response theory (MIRT) model to the data before projecting onto a dimension of interest. This study aims to explore how accurate the PIRT results are when the estimated MIRT model is misspecified. Specifically, we focus on using a (potentially misspecified) two-dimensional (2D)-MIRT for projection because of its advantages, including interpretability, identifiability, and computational stability, over higher dimensional models. Two large simulation studies (I and II) were conducted. Both studies examined whether the fitting of a 2D-MIRT is sufficient to recover the PIRT parameters when multiple nuisance dimensions exist in the test items, which were generated, respectively, under compensatory MIRT and bifactor models. Various factors were manipulated, including sample size, test length, latent factor correlation, and number of nuisance dimensions. The results from simulation studies I and II showed that the PIRT was overall robust to a misspecified 2D-MIRT. Smaller third and fourth simulation studies were done to evaluate recovery of the PIRT model parameters when the correctly specified higher dimensional MIRT or bifactor model was fitted with the response data. In addition, a real data set was used to illustrate the robustness of PIRT.

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An ontological artifact for classifying social media: Text mining analysis for financial data

International Journal of Accounting Information Systems ◽

10.1016/j.accinf.2020.100469 ◽

2020 ◽

Vol 38 ◽

pp. 100469

Author(s):

Zamil Alzamil ◽

Deniz Appelbaum ◽

Robert Nehmer

Keyword(s):

Social Media ◽

Text Mining ◽

Financial Data ◽

Social Media Text

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Using Item Response Theory to Obtain Individual Information From Randomized Response Data: An Application Using Cheating Data

Applied Psychological Measurement ◽

10.1177/0146621607312277 ◽

2008 ◽

Vol 32 (8) ◽

pp. 595-610 ◽

Cited By ~ 13

Author(s):

Jean-Paul Fox ◽

Rob R. Meijer

Keyword(s):

Item Response Theory ◽

Item Response ◽

Randomized Response ◽

Response Theory ◽

Response Data ◽

Individual Information

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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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CyberCan: A New Dictionary for Cantonese Social Media Text Segmentation

10.31235/osf.io/tyjr7 ◽

2021 ◽

Author(s):

Fei Shen ◽

Wenting Yu ◽

Chen Min ◽

Qianying Ye ◽

Chuanli Xia ◽

...

Keyword(s):

Social Media ◽

Text Mining ◽

Word Segmentation ◽

Unstructured Data ◽

Text Segmentation ◽

Chinese Word ◽

Chinese Word Segmentation ◽

Text Data ◽

Social Media Text

Text mining has been a dominant approach to extracting useful information from massive unstructured data online. But existing tools for Chinese word segmentation are not ideal for processing social media text data in Cantonese. This project developed CyberCan (https://github.com/shenfei1010/CyberCan), a lexicon of contemporary Cantonese based on more than 100 million pieces of internet texts. We compared the performance of CyberCan with existing Mandarin and Cantonese lexicons in terms of their word segmentation performance. Findings suggest that CyberCan outperforms all existing lexicons by a considerable margin.

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Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

European Journal of Personality ◽

10.1002/per.2290 ◽

2020 ◽

Vol 34 (5) ◽

pp. 826-844 ◽

Cited By ~ 1

Author(s):

Louis Tay ◽

Sang Eun Woo ◽

Louis Hickman ◽

Rachel M. Saef

Keyword(s):

Machine Learning ◽

Social Media ◽

Text Mining ◽

Personality Assessment ◽

Ground Truth ◽

Psychometric Validation ◽

Learning Approaches ◽

Text Data ◽

Personality Psychology ◽

Social Media Text

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology

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The Bayesian Multilevel Trifactor Item Response Theory Model

Educational and Psychological Measurement ◽

10.1177/0013164418806694 ◽

2018 ◽

Vol 79 (3) ◽

pp. 462-494 ◽

Cited By ~ 2

Author(s):

Ken A. Fujimoto

Keyword(s):

Item Response Theory ◽

Item Response ◽

Latent Trait ◽

Theory Model ◽

Response Theory ◽

Irt Model ◽

Response Data ◽

Proposed Model ◽

Method Effects ◽

Bayesian Irt

Advancements in item response theory (IRT) have led to models for dual dependence, which control for cluster and method effects during a psychometric analysis. Currently, however, this class of models does not include one that controls for when the method effects stem from two method sources in which one source functions differently across the aspects of another source (i.e., a nested method–source interaction). For this study, then, a Bayesian IRT model is proposed, one that accounts for such interaction among method sources while controlling for the clustering of individuals within the sample. The proposed model accomplishes these tasks by specifying a multilevel trifactor structure for the latent trait space. Details of simulations are also reported. These simulations demonstrate that this model can identify when item response data represent a multilevel trifactor structure, and it does so in data from samples as small as 250 cases nested within 50 clusters. Additionally, the simulations show that misleading estimates for the item discriminations could arise when the trifactor structure reflected in the data is not correctly accounted for. The utility of the model is also illustrated through the analysis of empirical data.

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