Evidence map›Paper›PMID 34367029›Full record

ArticleFrontiers in psychology2021

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

Gancheng Zhu, Yuci Zhou, Fengfeng Zhou, Min Wu, Xiangping Zhan, Yingdong Si, Peng Wang, Jun Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Gancheng ZhuKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
Yuci ZhouNormal College, Weifang Institute of Technology, Weifang, China.
Fengfeng ZhouKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
Min WuNormal College, Weifang Institute of Technology, Weifang, China.
Xiangping ZhanSchool of Psychology, Shandong Normal University, Jinan, China.
Yingdong SiSchool of Psychology, Shandong Normal University, Jinan, China.
Peng WangSchool of Psychology, Shandong Normal University, Jinan, China.
Jun WangSchool of Psychology, Shandong Normal University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

Indexed as

item response theorymachine learningmeasurementproactive personalitytext mining

Identifiers

PMID34367029
PMCPMC8339287

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.