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 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.
Highlights
In the measurement of proactive personality, most researchers have relied on the traditional self-report questionnaire
Text–Item Response Theory (IRT) Combination Classification Results In Table 7, when prior information is added, the classification accuracy of the three methods + IRT are 0.731, 0.693, and 0.849, respectively, which are higher than that of IRT classification, indicating that the accuracy is improved after prior probability is added
The present study aims to propose a novel method of assessing proactive personality among college students to offer a more accurate measurement of proactive personality by combining text mining and IRT, with subjective and objective data
Summary
In the measurement of proactive personality, most researchers have relied on the traditional self-report questionnaire. Even without motivation to fake, self-report could be distorted by self-presentation (Hogan et al, 1996; Hickman et al, in press). Personality prediction models developed with text mining technology showed great potential in replacing the traditional approach (Azucar et al, 2018). Social media platform user generates abundant text data and the technology of text mining develops more and more rapidly, which brings new opportunities for researchers to measure proactive personality. Previous studies have confirmed that text data can help improve the accuracy of clinical test scores (He, 2013). The target of this study was to combine freely expressed text data (essay question text and micro-blog) with questionnaire data, in order to find out the optimal approach to measure proactive personality
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