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

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 the research process, and thus, as we argue, contributes to the ongoing reappraisal of openness and transparency in psychology. In this paper, we make the case for integrating reflexivity across all research approaches, before providing a ‘beginner's guide’ for quantitative researchers wishing to engage reflexively with their own work, providing concrete recommendations, worked examples, and reflexive prompts.     

The culture of perceptual expertise and the other-race effect

In our commentary, we propose that the ORE can be viewed as a form of perceptual expertise. Like experts, we recognize own-race faces at the subordinate level as individuals and novices when recognize other-race faces at the basic level of race. Applying a perceptual expertise account, we explain the ORE in terms of its cognitive, neural, and motivational factors. We suggest that by creating a culture of “other-race” expertise, improvements in other-race face recognition can be achieved.     

The relationship between numerical mapping abilities,maths achievement and socioeconomic status in 4- and 5-year-old children

Background

Early numeracy skills are associated with academic and life-long outcomes. Children from low-income backgrounds typically have poorer maths outcomes, and their learning can already be disadvantaged before they begin formal schooling. Understanding the relationship between the skills that support the acquisition of early maths skills could scaffold maths learning and improve life chances.

Aims

The present study aimed to examine how the ability of children from different SES backgrounds to map between symbolic (Arabic numerals) and non-symbolic (dot arrays) at two difficulty ratios related to their math performance.

Sample

Participants were 398 children in their first year of formal schooling (Mean age = 60 months), and 75% were from low SES backgrounds.

Method

The children completed symbolic to non-symbolic and non-symbolic to symbolic mapping tasks at two difficulty ratios (1:2; 2:3) plus standardized maths tasks.

Results

The results showed that all the children performed better for symbolic to non-symbolic mapping and when the ratio was 1:2. Mapping task performance was significantly related to maths task achievement, but low-SES children showed significantly lower performance on all tasks.

Conclusion

The results suggest that mapping tasks could be a useful way to identify children at risk of low maths attainment.     

Factor analysis by generalized least squares

Aitken's generalized least squares (GLS) principle, with the inverse of the observed variance-covariance matrix as a weight matrix, is applied to estimate the factor analysis model in the exploratory (unrestricted) case. It is shown that the GLS estimates are seale free and asymptotically efficient. The estimates are computed by a rapidly converging Newton-Raphson procedure. A new technique is used to deal with Heywood cases effectively.The work on this project was done when the first author was Research Statistician at Educational Testing Service, Princeton, N. J. The second author was in part supported by a grant from the Research Committee of the University of Wisconsin Graduate School. The authors wish to thank Michael Browne for many helpful comments and Marielle van Thillo for valuable assistance in the numerical computations.