The founding of Joint Program in Survey Methodology (JPSM) in 1993 resulted from an initiative of the United States Federal Statistical Agency heads, the head of the Office of Management and Budget’s Statistical Policy Office, and the chair of the US President’s Council of Economic Advisors. The founders of JPSM brought together a consortium of organisations, disciplines, and researchers to provide the necessary expertise in survey research, which deals with theories and practices of various data collection and analysis activities for effective decision making. The survey research world has changed since JPSM’s founding. Examples include the rise of nonprobability surveys, the growing reliance on probability panels and the new opportunities that now exist to use unstructured data collected for various purposes. The recent renaming of the degree programs – degree-seeking students now receive either a Master of Science in Survey and Data Science or a PhD in Survey and Data Science – and corresponding changes in the instructional programs reflect the changes that have occurred in the field. In this talk, Dr Lahiri will provide information on JPSM’s Master of Science Degree program in Survey and Data Science.

The demand for various agriculture, socio-economic, and health statistics for small geographical areas is steadily increasing at a time when survey agencies are constantly looking for ways to reduce costs to meet fixed budgetary requirements. In the current survey environment, the application of standard sample survey methods, which require a large sample, is generally not feasible for small domains, as the cost would be prohibitive. One of the key factors that leads to the success of small area estimation (SAE) methodology in many applications is the availability of strong auxiliary variables. The accessibility of Big Data from disparate sources (eg, administrative/register records, social media data, mobile phone data, sensor data, satellite data, etc.) brings new opportunities for statisticians to develop innovative SAE methods. We will begin the lecture by discussing the challenges of producing small area estimates using sample survey data alone. We will then discuss how sample survey data can be effectively integrated with alternative databases in producing reliable small area estimates. As for illustration of SAE methodology, we will consider an application where survey data can be combined with geospatial data in producing improved estimates of crop acreage for small geographical areas.

Dr Partha Lahiri is a Professor of Survey Methodology and Mathematics at the University of Maryland College Park and an adjunct research professor at the Institute of Social Research, University of Michigan, Ann Arbor. He is currently serving as the Director of Joint Program in Survey Methodology (JPSM). His areas of research interest include data linkages, Bayesian statistics, survey sampling and small-area estimation. Dr Lahiri served on the editorial board of several international journals and on many advisory committees, including the US Census Advisory committee and US National Academy panel. He also served as an advisor or consultant for various international organisations such as the United Nations and the World Bank.  He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. Dr Lahiri is the recipient of the 2020 SAE award for his  outstanding contribution to the research, application, and education of small area estimation. He was awarded the Neyman Medal in a joint session of the 3rd Congress of Polish Statistics and 2022 International Association of Official Statistics (IAOS) held in Krakow, Poland, for his outstanding contributions to the development of statistical sciences. Dr Lahiri is now serving as the President-Elect of the International Association of Survey Statisticians.