Introducing Mega-analysis

How to find truth in an ocean of correlations – with breakers, still waters, tidal waves, and undercurrents? In the old age of responsible research and publication, we would collect estimates reported in previous research, and compute a correlation across correlations. Those days are long gone.

In the age of rat race research and publication it became increasingly difficult to do a meta-analysis. It is a frustrating experience for anyone who has conducted one: endless searches on the Web of Science and Google Scholar to collect all published research, input the estimates in a database, find that a lot of fields are blank, email authors for zero-order correlations and other statistics they had failed to report in their publications and get very little response.

Meta-analysis is not only a frustrating experience, it is also a bad idea when results that authors do not like do not get published. A host of techniques have been developed to find and correct publication bias, but the problem that we do not know the results that do not get reported is not solved easily.

As we enter the age of open science,  we do not have to rely any longer on the far from perfect cooperation from colleagues who have moved to a different university, left academia, died, or think you’re trying to prove them wrong and destroy your career. We can simply download all the raw data and analyze them.

Enter mega-analysis: include all the data points relevant for a certain hypothesis, cluster them by original publication, date, country, or any potentially relevant property of the research design, and add the substantial predictors you find documented in the literature. The results reveal not only the underlying correlations between substantial variables, but also the differences between studies, periods, countries and design properties that affect these correlations.

The method itself is not new. In epidemiology, and Steinberg et al. (1997) labeled it ‘meta-analysis of individual patient data’. In human genetics, genome wide association studies (GWAS) by large international consortia are common examples of mega-analysis.

Mega-analysis includes the file-drawer of papers that never saw the light of day after they were put in. It also includes the universe of papers that have never been written because the results were unpublishable.

If meta-analysis gives you an estimate for the universe of published research, mega-analysis can be used to detect just how unique that universe is in the milky way. My prediction would be that correlations in published research are mostly further from zero than the same correlation in a mega-analysis.

Mega-analysis bears great promise for the social sciences. Samples for population surveys are large, which enables optimal learning from variations in sampling procedures, data collection mode, and questionnaire design. It is time for a Global Social Science Consortium that pools all of its data. As an illustration, I have started a project on the Open Science Framework that mega-analyzes generalized social trust. It is a public project: anyone can contribute. We have reached mark of 1 million observations.

The idea behind mega-analysis originated from two different projects. In the first project, Erik van Ingen and I analyzed the effects of volunteering on trust, to check if results from an analysis of the Giving in the Netherlands Panel Survey (Van Ingen & Bekkers, 2015) would replicate with data from other panel studies. We found essentially the same results in five panel studies, although subtle differences emerged in the quantative estimates. In the second project, with Arjen de Wit and colleagues from the Center for Philanthropic Studies at VU Amsterdam, we analyzed the effects of volunteering on well-being conducted as part of the EC-FP7 funded ITSSOIN study. We collected 845.733 survey responses from 154.970 different respondents in six panel studies, spanning 30 years (De Wit, Bekkers, Karamat Ali & Verkaik, 2015). We found that volunteering is associated with a 1% increase in well-being.

In these projects, the data from different studies were analyzed separately. I realized that we could learn much more if the data are pooled in one single analysis: a mega-analysis.


De Wit, A., Bekkers, R., Karamat Ali, D., & Verkaik, D. (2015). Welfare impacts of participation. Deliverable 3.3 of the project: “Impact of the Third Sector as Social Innovation” (ITSSOIN), European Commission – 7th Framework Programme, Brussels: European Commission, DG Research.

Van Ingen, E. & Bekkers, R. (2015). Trust Through Civic Engagement? Evidence From Five National Panel StudiesPolitical Psychology, 36 (3): 277-294.

Steinberg, K.K., Smith, S.J., Stroup, D.F., Olkin, I., Lee, N.C., Williamson, G.D. & Thacker, S.B. (1997). Comparison of Effect Estimates from a Meta-Analysis of Summary Data from Published Studies and from a Meta-Analysis Using Individual Patient Data for Ovarian Cancer Studies. American Journal of Epidemiology, 145: 917-925.


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Filed under data, methodology, open science, regression analysis, survey research, trends, trust, volunteering

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