Reflexivity in quantitative research: A rationale and beginner's guide

Abstract

Reflexivity is the act of examining one's own assumption, belief, and judgement systems, and thinking carefully and critically about how these influence the research process. The practice of reflexivity confronts and questions who we are as researchers and how this guides our work. It is central in debates on objectivity, subjectivity, and the very foundations of social science research and generated knowledge. Incorporating reflexivity in the research process is traditionally recognized as one of the most notable differences between qualitative and quantitative methodologies. Qualitative research centres and celebrates the participants' personal and unique lived experience. Therefore, qualitative researchers are readily encouraged to consider how their own unique positionalities inform the research process and this forms an important part of training within this paradigm. Quantitative methodologies in social and personality psychology, and more generally, on the other hand, have remained seemingly detached from this level of reflexivity and general reflective practice. In this commentary, we, three quantitative researchers who have grappled with the compatibility of reflexivity within our own research, argue that reflexivity has much to offer quantitative methodologists. The act of reflexivity prompts researchers to acknowledge and centre their own positionalities, encourages a more thoughtful engagement with every step of

“I love being in the outdoors”: A qualitative descriptive study of outdoor adventure education program components for adolescent wellbeing

“…Further, an interview schedule with a consistent scripted introduction, a description of the session's structure, and a confidentiality statement aimed to minimize interviewer bias (Ranney et al, 2015). Finally, reflexivity (the act of deliberately examining how one's beliefs or assumptions may influence the research process) was embedded throughout the different stages of this study as a means to address any researcher biases (Jamieson et al, 2023).…”

Uncovering Sampling Biases, Advancing Inclusivity, and Rethinking Theoretical Accounts in Second Language Acquisition: Introduction to the Special Issue SLA for All?

“…Jamieson et al. (2023) discussed this binary between quantitative and qualitative research in terms of reflexivity, which is the practice as a researcher of considering how one's own position is shaping the research. They noted that reflexivity has been embraced much more in qualitative research, whereas quantitative researchers have tried to remain invisible behind their numerical data: a situation that Jamieson et al.…”

Questionable Metascience Practices

“…However, the reduction of this selective reporting will not reduce researcher bias if the researcher's preregistered decision rule is "heads I win, tails you lose!" As Clark et al (2022) put it, "the dice have often been loaded before pre-registration" (p. 13, see also Dellsén, 2020;Jamieson et al, 2023). Consequently, it is a QMP to assume that a preregistered study is less biased than a non-preregistered study, because selective questioning in the preregistered study may be more problematic than selective reporting in the non-preregistered study (for similar concerns, see Devezer et al, 2021, p. 16;Freiling et al, 2021, p. Second, it might be argued that preregistration reduces selective reporting when all other variables are held constant, including variables associated with selective questioning.…”

“…However, contrary to these positions, research is always undertaken from one perspective or another, so it is always "biased" from one perspective or another, and what are seen as decreases in bias from one perspective may be regarded as increases in bias from another. Consequently, a more tenable position is that open science practices help to reveal different perspectives rather than to reduce bias (Field & Derksen, 2021;Grossmann, 2021;Jamieson et al, 2023;Pownall, 2022). For example, a robustness or multiverse analysis allows readers to understand how different analytical approaches produce or "enact" different results (Del Giudice & Gangestad, 2021; Morey, 2019; Rubin, 2020; for a discussion of the "enactment" perspective, see Derksen & Morawski, 2022).…”

“…For example, a robustness or multiverse analysis allows readers to understand how different analytical approaches produce or "enact" different results (Del Giudice & Gangestad, 2021; Morey, 2019; Rubin, 2020; for a discussion of the "enactment" perspective, see Derksen & Morawski, 2022). In addition, researcher positionality statements can reveal researchers' perspectives rather than reduce their biases (Jamieson et al, 2023).…”