qualitative data is collected, for instance, to understand patterns of well-being across a large country which would follow the familiar logic of stratified probability sampling (Alexander, 2017).

4) Integrating qualitative and quantitative work can be done in different ways. Following classical inferential logic, small-N qualitative work can be conducted using the case study method to develop hypotheses, which can then be tested for their generalizability, possibly mediated by a theoretical model, with quantitative data collected from a representative sample of respondents (Rao, 1997b). The integrated collection and analysis of qualitative and quantitative information can also be analyzed using Bayesian inference (Humphries and Jacobs 2015).

5) Machine learning and Natural Language Processing are a double-edged sword. They offer tremendous advantages in moving us towards analyzing narrative data at scale. Yet, supervised methods that rely on biased training sets, such as sentiment dictionaries developed for western contexts applied to non-western linguistic cultures, can result in substantial bias. Furthermore, as Woolcock (2021) has argued, when machines are used for analyzing data, narratives have the danger of being analyzed out of context and without nuance, resulting in misinterpretation. In other words, relying on machines without sensitive human intervention has the danger of turning reflexively collected data into non-reflexive analysis.

6) Process, studied carefully with qualitative methods, can be extremely valuable to understand the mechanisms of change and thus complement time-variant quantitative studies, particularly impact evaluations. Understanding process matters not just for research but also for policy, where an exclusive emphasis on policies that can only be assessed using experiments or impact evaluations can sharply limit our capacity to imagine and create a better world (Rao, 2019).

7) The potential for the direct participation of “respondents,” “beneficiaries,” and “subjects” in research is vastly unexplored. If our purpose as researchers is to assist in the process by which people become better-off, then more can be done to design and share research with people, and even co-create tools so that they can conduct research on themselves without the mediation of experts. This has implications for many things, including the measurement of well-being and poverty.

# <u>References</u>

<mark>Ada Lovelace Institute, 2021,</mark> _<mark>Participatory Data Stewardship: A Framework for Involving People in the Use of Data,</mark>_ <mark>September, Creative Commons License</mark> <mark>Alexander JT, R Andersen R, PW Cookson, K Edin, J. Fisher, D.B Grusky, M. Mattingly, C. Varner, 2017, A Qualitative Census of Rural and Urban Poverty.</mark> _The ANNALS of the American Academy of Political and Social Science_ <mark>.;672(1):143-161.</mark> Anderson, Elijah, 1990, _Streetwise: Race, Class and Change in an Urban Community_ , University of Chicago Press, Chicago and London Ang, Yuen Yuen, 2019, _Integrating Big Data and Thick Data to Transform Public Services Delivery_ , IBM Center for the Business of Government, Washington DC Appadurai, Arjun, 1989, “Small-Scale Techniques and Large-Scale Objectives,” Chapter 11 in P. Bardhan (editor), _Conversations Between Economists and Anthropologists_ , Oxford University Press, Oxford and Delhi 19