by Angela Guess

According to a recent press release, “Science progresses when researchers build on prior work to extend, test, and apply theories. Aggregating the quantitative findings from prior research – meta-analysis — plays a significant role in advancing science, however current techniques have limitations. They assume prior studies share similar substantive factors and designs, yet many studies are heterogenous. A new method, co-created by MIT Sloan School of Management Prof. Hazhir Rahmandad, solves this problem by aggregating the results of prior studies with different designs and variables into a single meta-model. Rahmandad’s ‘generalized model aggregation’ (GMA) may have a wide range of applications from identifying an equation for basal metabolic rate to estimating the mechanisms that moderate choice overload in marketing. His paper, ‘A Flexible Method for Aggregation of Prior Statistical Findings’ which was co-authored by MIT Sloan research scientist Mohammad Jalali and Prof. Kamran Paynabar of Georgia Tech, was published last week in PLOS One.”

The release goes on, “For example: In obesity research, over 47 studies have estimated human basal metabolic rate (BMR) as a function of different body measures like fat, lean mass, age, and height. The ability to combine these into a single equation would benefit research and practice. In the energy sector, multiple methods exist to estimate diffuse solar energy in a location using data from distant sensors, yet there is no method for a model that aggregates these methods into a single estimating equation. In occupational health, studies have estimated the effectiveness of return-to-work interventions after injury or illness, but the variables in study design and methods have precluded aggregation of the findings. In marketing, choice overload remains a point of debate. While some studies show fewer choices lead to higher customer satisfaction, others dispute the importance of choice overload. A single model that combines prior studies could resolve this issue.”

Read more here.

Photo credit: MIT