Diary Study sebagai Metode Pengumpulan Data pada Riset Kuantitatif: Sebuah Literature Review
DOI:
https://doi.org/10.47679/jopp.022.12200007Keywords:
diary study, experience sampling method, metode penelitian, penelitian kuantitatif, research methods, quantitative studiesAbstract
Beberapa tantangan dalam penelitian kuantitatif yang masih belum bisa diatasi selama ini adalah validitas ekologis pada penelitian eksperimen dan recall bias pada penelitian survey. Guna menjawab permasalahan tersebut, penulis tertarik dengan penggunaan Diary Study/ Experience Sampling Method (ESM). ESM merupakan metode pengumpulan data secara berkala yang bersifat real-time di latar alami partisipan. Penelitian ini menggunakan metode systematic literature review untuk menganalisis definisi, prosedur serta kelebihan dan kekurangan ESM. Berdasar hasil telaah pustaka, ESM diketahui dapat menggambarkan proses inter maupun intra-individu, memiliki validitas ekologis yang bagus, meminimalisasi recall bias dan mendapatkan data yang beragam. Sayangnya, penggunaan ESM masih terkendala beberapa hal seperti self-selction bias, atrisi, motivasi serta kompleksnya managemen maupun teknik analisis data yang digunakan. Hal-hal yang dapat mengatasi permasalahan tersebut telah didiskusikan.
Abstract. Some existing challenges in quantitative research are ecological validity in experimental psychology and recall bias in the survey study. In order to answer those questions, the author was interested in the experience sampling method (ESM) or diary study. ESM is a research method that collects data frequently in participants’ real-world settings. This study performed a systematic literature review to analyze the definition, procedures, as well as the benefits and drawbacks of ESM. According to the literature review, ESM is known to describe both intra-individual (within-subject) and extra-individual (between-subject) processes, has better ecological validity compared to experimental research, can minimize recall bias, and was able to obtain various data. Unfortunately, there were some drawbacks such as self-selection bias, attrition, motivation, also the complexity of its data management and analysis. Some procedures to minimize those issues have been discussed.
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