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The increase of qualitative research on web-based platforms has created a methodological concern regarding data depth and validity. Web surveys and online interviews can often contain "shallow response bias," failing to replicate the nuanced follow-up questions (probes) that are more inherent in face-to-face human interaction. There is a critical need to investigate whether probing tactics in qualitative interviews can be redesigned to elicit deeper narratives in a digital environment that provides sufficient scientific context. Phenomenological inquiry, for one example, often relies on “embodied resonance” of a persona reflecting on his/her experience. In online qualitative interviews, the phenomenological inquiry's effectiveness requires a more rigorous approach where an interview participant can effectively process memory, explicitly record experiences, and perform detailed expressions of experiences. This research, therefore, expands the theory of phenomenological studies, where probing tactics for interviews require a remote explication framework to guide interview participants. This paper proposes and tests the “mixed probing tactic” approach as a solution for qualitative interviews where participants are self-administered. To provide evidence to this approach, recent literatures with context-specific probing tactics are critically reviewed. The critical analysis provides a pre-measure of the methodology depth in bridging new online interview environments and digital tools, to effectively journal legitimate data sources critical for qualitative analysis. The study found that the use of “Imagery Interviews” can enhance the participant's ability to internalize knowledge and contexts connected to a subject matter. Meanwhile, a “repetitive reflection exercise” removes the granularity of information or fragmented views by interview participants. By using specific combinations of probing tactics in digital interviews, this approach bypasses the obstacles of research validity in qualitative investigations. This work is useful to researchers who are primarily interested in understanding the effectiveness of integrating asynchronous tools and methods to improve the reliability and validity of data from longitudinal interviews.
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