Using the association rule mining in psychological research: A case study of trauma data
By reviewing the literature in psychological sciences it can be found there is no considerable research using rule mining algorithm. The core results relied on the classic statistics aimed hypothesis testing. In practice, there are big recorded data in psychology which have been mostly ignored. The purpose of this study is to clarify the importance of association rule mining which can lead to find micro-theories from messy data. Method: The participants in this research were a sample of 325 (85.3% female and 14.7% male) people living in Tehran in 2021 who were selected by convenience sampling through online platforms supported by the internet. All of the participants completed childhood trauma, social-emotional competence, internalized shame, disability/shame, cognitive flexibility, distress tolerance, The Toronto Alexithymia scales. The data are analyzed using Rstudio.4.1 and Apriori package. Results: 39368 rules initially discovered from 7 variables and 20 top rules with support ranged 0.003-0.243, confidence ranged 0.05-1, lift ranged 0.15-3.43 selected. They indicated new relationships between disability/shame schema and the other 6 variables. There was set at least 2 variables in each of the rules. Conclusion: Using Association Rule Mining as a knowledge-driven can be used and of interest to all mind researchers for exploring the hidden pattern among a database. This pattern leads to practical and theoretical knowledge.
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