Open science in sociology. What, why and now.

WHAT

By now you’ve heard the term “open science”. Although it has no global definition, its advocates tend toward certain agreements. Most definitions focus on the practical aspects of accessibility.

“…the practice of science in such a way that others can collaborate and contribute, where research data, lab notes and other research processes are freely available, under terms that enable reuse, redistribution and reproduction of the research and its underlying data and methods.”


FORSTER, open science teaching resource

Some definitions enter the realm of ethics, feminism and social justice.

“…to imagine and design inclusive infrastructures, practices, and workflows for scientific practice that intentionally enable meaningful participation and redress (these new) forms of exclusion.


Denisse Albornoz,OCSDNet

Whatever the ontology, open science is inevitably something that challenges the status quo in science. Usage of term indicates there is something undesirable about science, otherwise advocates would simply advocate “science”.

The “open” part of the concept refers to any number of things depending on whom you ask. Commonly it means:

Open access – making the results of scientific techniques, research and theory accessible to everyone; as opposed to only in paywalled journals.

Transparency <open process> – making all methods, code, data and any biases or conflicts of interest known before and after the research is conducted. So long as doing this does not harm human subjects or violate any laws.

Open source – on the technology side of science, all programs, apps, algorithms, tools and scripts should be transparent and usable by others. This means that when a scientist develops a new technology, anyone else’s technologies can interact and interface with it. Moreover, anyone can modify the technology to better suit their own needs.

Open academia <open communication/democracy/feminism> – allowing anyone to participate in academia. That inequalities, prejudice and domination that take place in the social world are eliminated from the academic world, if not that the academic world has a goal of eliminating them in the social world. That everyone has the same place in scientific discussions, and no science is conducted by pressuring others or taking advantage of existing power structures. That no science takes place in secret, except for research that requires obfuscation for its completion.

Again, the definitions can cover a broad range. The above are just a snippet, although they strike me as the most common usages; except for ‘open academia’, this is reserved for certain justice motivated scholars.

WHY

Although I do not proclaim to be the arbiter or knower of right or wrong in academia (and life in general), the following facts seem wrong to me.

Double-work and the co-opting of journals

Scientists provide their work as editors and reviewers, because the peer review and publication process is the centerpiece of all of science. Peer reviewers and editors are the only consistent form of quality control in science. The academic journal was a functional response to previous forms of knowledge transmission that required direct scientist/practitioner to student interactions which were geographically limited and reached a very narrow audience.

The journal made it possible to transmit knowledge across the globe. Moreover, the journal reduced the simultaneous discovery and re-discovery problems of science, because no one could prove they discovered something first, and others worked on problems that were already solved unknowingly. It represents one of the first ‘open science’ movements because it was driven by the idea that science was at an impasse and could only move forward through transparent and open exchange of ideas arbitrated by being part of the public record through publishing.

Ironically, the journal format came full circle and began to undermine science. After over two centuries of journals run by non-profit academic associations, for-profit publishing houses began ‘offering’ their services to meet the growing global demand for journals and their content and the rising costs of editing and distribution. In many cases, these publishing houses were able to purchase the journals by offering the academic societies the exclusive right to determine what went in them. Within just 30 years, five conglomerates owned the titles, content or certain features of over 50% of all journal articles published globally.

The content, as always, is still a product of the scientists and the voluntary work of editors and peer reviewers. The publishing houses make large profits, but pay nothing to these workers. The editors and peer reviewers earn their income from universities mostly. The very universities that pay high fees to purchase the right to provide the journals in their libraries. This is a double tax on the universities – paying the producers of content to produce and then paying the distributors of that content to consume it. The content does not change at any point in between these two forms of payment, in other words, the publishers do not add any scientific value to this content.

Matters got even worse with the publishing houses over the past decades. As creative and deceitful capitalists, the publishing houses realized they could generate even more profit by collaborating with the private sector. For example pharmaceutical companies’ profits were directly determined by the findings of studies published in journals. Pharmaceutical companies, or any companies whose profits were determined by the outcomes of scientific experiments, would be willing to invest in shaping those outcomes if they could. Enter a novel concept pioneered by Elsevier: selling journals or journal space to private companies to boost their profits. Win-win for them.

Publish-or-perish begets questionable research practices

Thanks to the advent of the scientific journal, knowledge could be evaluated, used and further transmitted across space and time. The utility of the journal and other forms of academic publication such as books, proved so effective that they became the primary source for others to evaluate the importance of scientists and their work. This gave rise to the norm we are all familiar with, publish-or-perish.

In a survey of psychologists, John et al. (2012) found that 50% claimed they had selectively reported studies that supported their hypothesis (as in, selectively excluding those that didn’t). Moreover, 35% admitted to reporting unexpected findings as having been predicted from the start. Nearly 2% outright admitted to faking data.

Publish-or-perish and questionable research practices have a causal relationship. Except for occasional sociopathic or psychotic individuals, there is no reason for a scientist to engage in questionable research practices. No reason, except scientists’ very existence on scientists may depend on it. So many studies in reality lead to results that go in all directions, support the null or (most importantly) do not provide groundbreaking new results.

Through the peer review and editorial process, journals select studies that are path-breaking. Studies that will move knowledge forward and be of the greatest interest to readers. When faced with prospects of not getting tenured, not getting grant funding and being forced out of academia, a human’s (scientist’s) rational calculations change. Suddenly, rounding that p-value from 0.054 to < 0.05 or even adding some cases to the data becomes a cognitively defensible decision.

Like any profession, science is competitive. Those who publish more, or get more citations to their publications tend to get ahead. Those who don’t, don’t. Professional athletes use incredible tactics to gain competitive advantage. Of course steroids are well-known, but other tactics are much harder to detect. For example, endurance athletes often use blood transfusions to boost recovery and performance. This is what it means to be human, scientist or not.

One of the most radical events in the social and behavioral sciences is Diederik Stapel’s entire career faking data and results that were published in at least 54 articles that consumed millions of Euro in funding. It took almost two decades for critics and whistleblowers to finally out him. Psychology is not alone. In political science LaCour and Green published a study in Science that attitudes toward gay marriage could be changed if heterosexual people listened to a homosexual person’s story, but it turns out LaCour fabricated results of a follow up survey that never took place as uncovered by Broockman. In economics Reinhart and Rogoff published numerous studies identifying a negative impact of high debt rates on national economic growth, when in fact several points in their dataset had conspicuously missing values. When these values were added there was no longer support for their claim as identified by Herndon, Ash and Pollin.

I suspect that most questionable research practices are not intentional. The sociopathic (~psychotic) Stapel’s of the world are rare. This pressure to find a job after doing doctoral studies and then to get tenured, means a trade off between conducting science in its ideal form – so learning as much as possible about the existing literature on a subject, mastering the necessary methods to perform the research and executing the research, possibly with several iterations, and facing the prospect of null results – with science in a form that will lead to publication as fast as possible.

This ‘fast as possible’ leads to amateur science. For example, in the rush to get my first publication I attempted to use “multiple imputation”, but lacked the time to properly learn this method. Instead I simply generated several datasets and averaged them into one and re-ran the analysis on this one. This was not an intentional misuse of a method. It is a questionable research practice as a result of context. Think about matrix algebra. It is the basis of many advanced statistical techniques regularly used by social scientists. How many of us have a strong grasp of matrix mathematics? I don’t. And yet I’ve published several studies using structural equation modeling.

WHAT & WHY in SOCIOLOGY

I am aware of nothing about sociology that suggests it needs a special adaptation of open science. Most research cannot be strictly delineated as sociology or not sociology anyways. The boundaries of a discipline, especially within the social sciences, exist mostly in the institutional structure of universities. Eliason suggested that sociology is unique because it overemphasizes quantitative techniques, has needlessly long articles, lacks writing for the popular press and emphasizes research at the expense of teaching. In my experience the previous sentence perfectly describes all social and behavioral science disciplines at once. Even article length, something I thought might be peculiar to sociology, is not special. Political science and management research have very long articles. Consider that and ASR and ESR for example, limit words to 9,000 and 8,000 or less – this is relatively average if not short for social science.

Actually, I would argue the most unique thing about sociology at the moment relates to open science. Two points in particular: (A) that sociology has not had the same incredible scandals as other disciplines and (B) that sociology lags behind other social sciences in promoting open science.

A lack of scandals, not scandalousness

Could sociologists be more scientific and ethical in their research behaviors than those in other disciplines? Given identical institutional and career structures that favor productivity and innovation over replicating or checking each other’s work, I doubt it. Sociology journals and their editors, for example, rarely retract articles despite evidence of serious methodological mistakes. Carina Mood once accurately pointed out mistakes in the interpretation of odds-ratios in some American Sociological Review articles, but the editors refused to publish her comments, much less consider retractions. She shared her exchange with ASR in an email to me and discusses some of it in a working paper. An exceptional recent event was the retraction of one of Legewie’s sociological studies, but this required he himself to initiate the retraction after someone pointed out errors in his work. Until 2020, the Retraction Watch database (www.retractiondatabase.org) listed no retractions from the top sociology journals, and only two among the well-known, one in Sociology and another in Social Indicators Research.

This year, something new happened. Five articles published in Social Problems, Criminology, and Law & Society Review were retracted. These articles had the common co-author Eric Stewart. It turns out that the data he provided were faked. There is no other logical conclusion that this after exceptionally rigorous work by Pickett (a co-author of Stewart) provided evidence that the Stewart studies had consistently incorrect means and standard deviations, unverifiable surveys (sources, methods, original materials), magically changing case numbers despite identical statistical results, sometimes half the data had duplicate cases and impossible clustering structures in the data.

As an aside, one of Pickett’s findings was that the data had non-uniform terminal digit distributions. This means that the right-most digits in the reported statistics differs markedly from a uniform distribution. In particular, at the third-digit numbers should be uniformly distributed with 0-9 appearing roughly 10% of the time. In one of the papers, zeros appear less than 2% of the time. If you are considering faking data, keep in mind that it is roughly impossible to do it in a way that cannot be detected by careful investigation. Any algorithm used to generate results (even copying and pasting) leaves is statistical marks.

Perhaps we sociologists should be partly relieved, as this is just confirmation that we are as much a part of social science and its problems, as any other discipline. However, the Stewart retractions which should have been breaking news for sociology, went mostly unnoticed. The results of the investigation leading to the retractions is not published in a flagship sociology journal where it belongs. Instead it appears in Econ Journal Watch – something unlikely to be read by any sociologist. Moreover, the retraction notices from the original journals do not cite outright fraud. Stewart continues to promote his work in print claiming the main findings still hold, and several other of his studies with similar irregularities have not been retracted.

Another, extremely important event was a case of ethnomethodological research conducted by Lindsay, Boghossian, and Pluckrose in the mid 2010s. This is sociological self-examination at its best, although their backgrounds are mostly outside of the discipline of sociology. They wrote a series of 20 papers presenting fake results and making arguably unethical claims. They invented the papers to mimic the style of articles published in journals well-known for sociological research on topics of identity, hegemony and marginalization. Seven of their papers were published or had revise and resubmit recommendations before whistleblowing forced them to cancel the project. Some highlights: one paper contained sections from Hitler’s Mein Kampf. Another suggested men should be trained similar to dogs to prevent rape, and a third that white men should be forced to sit in chains on the floors of university classrooms, instead of normal desks. I am not commenting on the merit contained in these ideas, only that they all contained faked data, non-existent methods or conclusions not supported by the data. That these studies easily flew under the radar of a number of high impact journals points out how easy it is to publish without doing the necessary research work.

Lagging behind closed doors

October 6th, 2020. I entered the search terms “open science” (with quotations to search the exact phrase) and “sociology” (with quotations to only return results that contain the word) into Google Scholar. Six pages of results without a single sociology journal. On page 7, Merton’s “Priorities in scientific discovery: a chapter in the sociology of science” appears. Publication date 1957.

In 1973, Wilson, Smoke and Martin found that 80% of studies published in the top three sociology journals of that time rejected the null hypothesis, in other words they had p-values below a threshold. This suggests publication bias, if not p-hacking. Sahner (Table 5) analyzed all article submissions to the Zeitschrift für Soziologie, 1972-1980. Of those that contained significance tests, 70% were significant at p < 0.05 suggesting that authors prefer to submit significant results. More recently, Gerber and Malhotra (2008) reviewed articles published in American Journal of Sociology, American Sociological Review and The Sociological Quarterly, and specifically looked at the boundary of t = 1,96 (i.e., p<0.05) to find that as many as 4-out-of-5 studies were ‘significant’. This suggests publication bias as well. Sociology has yet to have a systematic review of p-hacking by comparing p-values within ‘significant’ results. Meanwhile psychology and political science for example are teeming with papers on “p-hacking” and “publication bias”.

Sociology is rather intransparent. An estimated 78% of the major sociology journals have long-standing transparency policies. Unfortunately, these policies are mostly artifacts on paper without much enforcement. For example, only 37% of sociology articles published in the mainstream journals between 2012-2014 include shared data and/or materials. In 2015, a small group of sociologists tried to obtain materials from the authors of 53 prominent sociological studies. They obtained these from just 19%, and only 20% of all the authors they contacted bothered to respond despite several requests. This suggests sociologists are free to hide the data and materials that led to their findings without recourse, despite such guidelines.

Other disciplines have embraced the Transparency and Openness Promotion Guidelines (TOP). The TOP guidelines with help of the Center for Open Science support journals to improve science. Journals can become signatories of TOP, and in doing so they either adopt and enforce new transparency guidelines, or certify that they already meet certain transparency standards. Most of the top psychology journals and several political science journals signed on. Other major journals such as the Journal of Applied Econometrics and later the American Economic Review adopted their own enforced transparency guidelines.

Until 2017, the only higher ranking sociology journals that signed TOP were Sociological Methods and Research and American Journal of Cultural Sociology. In 2017, Elsevier dictated that all its journals adopt guidelines and this added Social Science Research to the list. At the time of writing this, the flagship journals American Journal of Sociology and American Sociological Review neither signed TOP nor enforce their own guidelines. Of top German sociology journals, the Kölner Zeitschrift für Soziologie und Sozialpsychologie is the only signatory.

If intransparency is pervasive in sociology, then research cannot be (a) checked for errors, (b) reproduced or (c) simply critiqued. Even when exact reproducibility is not the goal, as often is the case with context-specific interpretive research, most research methods remain shrouded in mystery. This requires readers to take a giant leap to trust what others report. Part of the problem is that sociologists express little interest in reproduction or checking others’ works. There are few replications in the history of sociology, and if anything, they decreased over time until recently. For example, searching the articles in American Journal of Sociology and American Sociological Review reveals 22 replication studies from 1950-1980 and only 8 from 1981-2010.

Something telling about a lack of willingness to open sociology comes from sociology’s most ‘powerful’ society, the American Sociological Association. They collectively petitioned the US government to not make data transparency a requirement attached to grant funding in 2019.

NOW

What to do about it? Here are some simple steps to consider.

Transparency

Make all the materials – research design, methodological steps, data (when legally and ethically possible), analyses, conflict of interest and any software code – available online. The practical reason is that others can follow your work and expand it in the future. Doubly practical is that you don’t need to respond to email requests for your materials. So long as you are not a deceitful sociopath, you want others interested in your work and to replicate your work. Even if a study, seems to ‘prove you wrong’, the fact that it replicated your work is evidence of how important your work is and the topic of study. You are a piece of a much larger community of knowledge construction. Constructive exchange can lead to collaboration with critics to generate better future research without personal conflicts.

The immediate value of transparency is that being transparent forces you to be careful. Knowing everything will be public information increases the value of attention to detail. Put in its converse: not sharing your workflow publicly can indirectly foster lower quality standards, in addition to creating possibilities for misconduct. All this enables rather than hinders knowledge, and increases inter-researcher trust.

Transparency should not be much extra work. During the research process you should take high quality notes for yourself. You will often return to your data and research in the future and thus need those notes. This is a best practice with or without sharing your work. When you engage in this best practice, you have a deep familiarity with your data and can draw meaningful conclusions and easily redact identifying characteristics in your data in the case of qualitative research. In case you cannot share data, you can still reveal the design and expectations; or allow controlled access to the data. Human subjects must be protected at all costs, and yes this often means data sharing is not possible .

The ‘transparency work’ of the qualitative research process can be reduced by software platforms that provide semi-automated annotation and coding. Even if you do not share data, you can build an open workflow from the beginning that allows others to understand every step of the data generating process. However, this work can also be extremely tedious and the incentives not immediately clear. More fruitful discussion if not research assistant funding is needed in this area moving forward.

If you are using quantitative methods, immediately stop hiding your work. If you ran 100 models and 99 did not support your hypothesis, then this is your finding. If a journal does not want to publish this, point the editors and reviewers to the importance of null results and the problems of publication bias. If they still refuse, consider boycotting this journal and sharing your negative experience in public.

Preregistration

Preregistration can drastically reduce bias and hacking prior to collecting data. When you clearly outline your plans including how you will analyze the data, before conducting the research, there is little room for hacking so long as you stick to the plan. Moreover, preregistration can be done directly with a journal although sociology journals are laggards here because they generally do not offer this option. In a preregistration, even if you just put an pre-analysis plan or research design and goals online, you must think much harder about factors such as meaning, causality, inter-subjectivity and ‘how the world probably works’. You cannot hide behind results in this process and therefore you must anticipate counterarguments and explore counterfactual logic. This improves the clarity of theory and research, creating an immense gain in efficiency and effectiveness.

Regardless of the methods you use there are many opportunities to take advantage of preregistration. Some forms of qualitative research, for example those involving grounded theory and interpretivist methods, require decisions during the research process that cannot be foreseen. This uncertainty can be outlined in a preregistration stating explicitly when flexibility is and is not admissible. Moreover, simply putting a qualitative research plan online prior to conducting the research is equivalent to a pre-analysis plan. This research design need not compromise your data collection work because you can register the plan on a platform like the Open Science Framework and then embargo it, so that it is preserved but not made public until after the research concludes. Some scholars using quantitative methods might assume that preregistration is not possible because they work with secondary survey data. But the regularity and release of these survey data are known in advance, and these scholars can preregister their studies before the next round of data are collected with the knowledge of which questions and countries will be available.

Decommodify science

The central functions of the scientific publishing industry are printing and disseminating knowledge, which historically solved a problem of how to share knowledge across universities and countries. The business functions of publishing, however, come with harmful byproducts. Publishing firms extract profits from scientists twice. First, scientists provide free labor in the form of editing and peer reviewing, in addition to producing the results for the articles to be printed. Next, researchers, or their employers, must purchase the product of their own labor; labor not paid for by the publishers. The journal article as a product comes at a high cost, and often only in packages of journals meaning that universities have to pay for extra material their scholars do not use.

Sometimes publishing houses neglect science in favor of profits, but Elsevier has been particularly problematic. They sponsored weapon fairs, created and sold ‘fake’ journals to pharmaceutical companies to publish ‘results’ supporting their drugs, purchased the Social Science Research Network and created paywalls or removed legally shared working versions of articles, charge fees for open access articles, and actively lobbied against open access legislation (For a concise summary with links see Tal Yarkoni’s blog entry). This brought massive counter movements against Elsevier in the scientific community (for example, The Cost of Knowledge). You can take action and refuse to review for or publish with unethical publishers if you feel it is justified. Thus, you should inform yourself about the publishers. Your libraries are a source of information, because they deal with the business side of publishers.

If you are in Europe, check if your institution is a signatory of ProjektDEAL. A consortium of universities are collectively bargaining with publishers via ProjektDEAL demanding that publishers reduce fees and eliminate the double paying of universities. The primary objective is that publishers sign country-wide subscription agreements that enable access for all universities at once. Wiley agreed to such a model and this marks a paradigm change. It indicates how the publishing industry looks in the future, so long as the OS Movement proceeds. If you are not in Europe, consider starting a similar initiative, for example the entire University of California system of 10 universities, 5 medical centers and several research institutions that collectively produce roughly 10% of the world’s academic publications recently followed ProjektDEAL and boycotted Elsevier.

You can work around the publishing business. Prior to submitting an article or after it is published, you have the right to share a preprint – a draft of the paper you share publicly so long as it is not published elsewhere or sold for profit. Posting preprints reduces the power that publishing firms have over science, in addition to giving others immediate access to your work. But simply posting preprints on your academic website is not open enough. Use a preprint service, for example through the Open Science Framework, to ensure that your preprints appear in search engines such as Google Scholar. SocArXiv for example, is the go to location for sociology. This enables scholars to find and directly access research results based on the words they contain, uninhibited by paywalls – a crucial aspect to practicing sociology in the Global South. Preprint services are free and open access.

Meta-constructing social theory

Certain hypotheses are constantly tested in social science. The impact of income inequality on health, racial bias on police brutality and public opinion on elections, just to name a few. At some point more tests of the same hypothesis stop contributing to scientific knowledge, and may even harm it by introducing more ‘noise’ into the scientific discourse.

I study social policy preferences and the impact immigration has on them. In this area there has been sustained efforts to test the hypothesis that immigration has a negative impact on support for policies of the welfare state; things related to protecting against risks of aging, unemployment and health. To justify this hypothesis, scholars construct theoretical variations of group dynamics arguments, often drawing on resource competition, nationalism and social identity. Despite claiming to test the hypothesis, the formal models applied to data suggest any number of data-generating processes. They often have little in common other than some measure of immigration and some measure of policy preferences. The results of their tests go in all directions, i.e., a positive, negative or nil effect of immigration. It would appear that the topic is at a standstill, new analyses of the same handful of cross-national survey data sink in the mire. How to break through such a scientific impasse?

In designing the Crowdsourced Replication Initiative (CRI) with co-PIs, Alexander Wuttke and Eike Mark Rinke, we asked researchers to to do research; and we gave them semi-structured tasks and observed them. Specifically they were supposed to come up with the best possible way to test the immigration hypothesis given the same International Social Survey Data source. Although we are currently meta-analyzing the hypothesis test-results (see our virtual APSA poster) to determine which modelling decisions impact the outcomes, we also have a second goal in mind: to discover what is behind the specification curve.

Each research team had to design a best possible test. This is at once a statistical question and a theoretical question. They needed to think carefully about the data-generating process and attempt to recover it in a model. We asked them to write down their research designs after doing this thought exercise, but before analyzing any data. From their researcher choices we can identify where key consensus and disagreements exist about the data-generating model, thus is not only evident in their designs but also in a structured deliberation and voting procedure. This process offers a major advantage over ‘normal’ theoretical discussion and debate among academics, because we have the results that go along with the different modeling choices; and, let’s be honest, when else do over 150 researchers get together and focus on a single hypothesis? By observing this process we can identify where data-generating theories differ and how important these differences are for the results. This will allow us to map where immigration and social policy scholars should focus their theoretical efforts in the future to reduce the most uncertainty, i.e., the largest gains in knowledge.

We have a sound piece of scientific research from Brady and Finnigan (2014) from which we draw our working hypothesis for the CRI crowdsourced researchers: That immigration undermines support for social policies. Brady and Finnigan found little or no support of this hypothesis, at least not in a generalizable macro-comparative sense. This was the launching point for the research of the 77 teams who by now managed to submit replicable results (yes there are still a few out there we are hoping will submit a final model or fix issues we identified in our replication of their models).

Although we are in the process of analyzing the ocean of data generated by this project; a sneak preview offers exciting evidence of the possibility for meta-construction of theory.

Here are two glimpses of what’s to come. One are the deliberation and voting results summarized (Figure 1). The other are differences in definitions of ‘immigration’ (Table 1). We used Kialo, an online structured deliberation platform, to allow participants to discuss the data-generating model after they proposed their own ideas for how to best test the hypothesis. Readers can observe how this deliberation unfolded as we divided the participants into two groups: here and here. Later (after they had the possibility to update their models based on the deliberation) they were given other teams’ models or our own variations on those models to vote on and rank in terms of their appropriateness for testing the hypothesis without having seen the results of those models. Figure 1 quantifies both the Kialo veracity scoring and survey-based voting into one overall scale and then plots the average score of models by their features. Each different color is a discrete set of model features with the zero (y-axis) set to the average support of models choosing an OLS estimator (among the least preferred).

Figure 1. Researcher Preferences for Recovering the Data-Generating Model
“Model” is the hypothesized general impact of immigration on support for social policy. Data and code still being prepared for online sharing, stay tuned.

In Figure 1, it becomes clear looking at the longest bars in each color category that models that incorporate all 5 waves of the ISSP data, include countries of Eastern Europe, include heterogeneous error variation by country-year and year (like a cross-classified model), and incorporate survey sampling weights are preferred over the others. Some of this runs counter to the state of the art. For example, most research follows a logic that major immigrant destination societies – the “Rich 13” and “Rich 17” advanced democracies – should be where “public opinion is likely most influential for the politics of social policy” (Brady and Finnigan 2014:24).

To summarize the motivation for looking across all possible countries, especially Eastern Europe, one crowdsourced researcher put it like this: “Either there is an effect of ‘immigration stock (increase)’ or not“.

Another followed up on this point stating: “To test the general hypothesis we should use as many countries as available and account for variations in GDP and social welfare expenditures in the models.”

These comments demonstrate the majority voice in the CRI that if immigration has a an impact on social policy preferences we should see it across all countries of the globe, not restricting our analysis to only very rich, strong welfare states.

Although Brady and Finnigan and all other research in this area comes to no consensus on whether there is a negative impact of immigration on support for social policy preferences, we should remain skeptical of results if we do not trust the data-generating model. In other words, if our tests do not match what most researchers see as the appropriate theoretical perspective, results are inconclusive and thus uninformative. The deliberation and voting offer us clues where to focus theoretical effort, namely specifying why more countries of the world should (or should not) show a causal effect of immigration on social policy preferences and whether this should (or should not) appear across several decades or only certain times. I am not aware of extensive theory that attempts to tackle these issues. Now is the time to write it!

Even more productive for the possibility of meta-construction of theory is the correspondence between the actual decisions made by the researchers and the subjective and objective outcomes of those decisions. Again, our results are in progress, but we offer a snapshot in Table 1 of different ways the researchers chose to measure immigration as their main hypothesis test variable (1 out of dozens of model decisions to compare). In the first row, 67 out of 77 teams used a “Stock of Foreign-Born” measure in at least one of their models, and 27% of their models using the “Stock” variable showed support of immigration having a negative and significant statistical impact on support for social policy at p<0.05.

Table 1. Crowdsourced Researcher Decisions, Deliberations and Results.
Five different measurement strategies for the immigration test variable.

In the column ‘Positive Test Result Rate’, we see that the ‘Difference’ between “Stock” models (referenced as [1] in Table 1) and those instead using “Flow” to measure immigration models (referenced as [2]) is 3.6. In other words, “Stock” models arrive at support of the hypothesis 3.6 percentage points more than “Flow” models, all else equal. “Stock” models were not more or less popular than “Flow” models, with the average vote score of 0.43 on a scale of 0 (worst) to 1 (best equipped to test the hypothesis) versus 0.45 for “Flow”.

The values in bold indicate that “Change in Flow” models (those measuring derivatives of “Flow”) were among the most popular in the voting process. So the rate of change of the flow of immigrants is seen as an important component in testing this hypothesis. Interestingly, these models were 4 percentage points more likely than “Stock” and “Flow” models to support the hypothesis. When measuring immigration as specific to certain outgroups (from Muslim-majority countries, non-Western countries or refugees), the “Flow” of these various ‘Outgroups’ was seen as more popular than “Stock” of ‘Outgroups’ by a large margin, but the results were over 10 percentage points less supportive of the hypothesis.

What can we learn from this. We argue that a full analysis of the massive range of modeling decisions will give us a guide to move this entire research area forward. Some other decisions for example were different social policy domains, whether ethnic and fractionalization is the ‘real’ cause of the ‘immigration’ effect, construction of latent social policy preference measures, whether or not GDP and unemployment are part of the data-generating assumptions just to name a few out of hundreds. We are only scratching the surface here, but it seems that observing researchers make research decisions, deliberating them, voting and making final choices, we will gain immense knowledge as to where better theory is necessary. As such we see meta-constructing of social theory as a promising avenue for social science. This would be the concept of theory designed replication writ large.

Help. We can’t know the data-generating model.

Lack of data-generating models. A problem ransacking macro-comparative research efforts. We got strong data-generating theories. Institutions, politics, materials, procedures, conflicts and symbols help explain macro-level events, like the introduction of social security or the onset of war. But we can’t model these theories as causes of the data we observe. There is far more theoretical complexity than there are countries.

There just aren’t enough countries to provide reliable measures of central tendency given theoretical complexities. Even if our theory were as simple as Y will be significantly different from some value (e.g., zero) given X, we should have at least 10 countries. If we were to actually conduct a power analysis it would tell us we need more like 200 countries depending on how big of a difference we expect in Y given X. I’m just gonna leave aside that countries are not a population in the typical sense (what is a country again exactly? exactly.).

What should we do?

  • Option A: stick to qualitative case studies.
  • Option B: continue running various country-level regressions and ignore the problem.
  • Option C: wait until data from 180+ countries covering a period of 50 years becomes available.

Maybe we just ask a better question. Why do we need to do macro-comparative analysis if we already got good theory?

Answer: Reliability and utility.

Without systematic evidence we don’t actually have theories, only ideas or conjectures. Logic and in-depth case study help define data-generating theories for one country in one time period. So we want to test if this might apply to most countries in most time periods. In testing this we get Pomeranian.

Even with the same variables, regressions on country data over time go all over the place given only tiny changes to the estimation procedure. That was a finding of the CRI. The effect of immigration on social policy preferences could be anything.

To the right: Mr. Summerbottom showcases the size and direction of effects from macro-comparative regressions using the same data. Outliers cropped.

Even if we run all possible model configurations, and even if we consider Bayesian posterior distributions. We still don’t know if we have a correct model specification, because we cannot truly test it. We can only make sweeping statements like, ‘in all possible model combinations, variable X was not significant so it probably does not have an effect’. Too bad we might have some kind of suppression from an unobserved variable!

Don’t drink the water. There is no method that can substitute for human logic. Zombie modeling where the researcher lumbers aimlessly through thousands of models looking for blood doesn’t work. The answer is therefore Answer D, none of the above.

We need new school meta. We need to logically analyze researcher’s decisions. We can’t do much with the limited data that are out there for macro-comparative research. Thus, we need to get down with specifications. By comparing specification curves and researcher decision trees, we can identify the difference between critical and benign. Some programmers offer us R packages for this (thanks Joachim Gassen for rdfanalysis!).

Some models come to the same results regardless of whether the researchers apply weighting, use latent variables or correct for autocorrelation; others not. When we identify the decisions a researcher made that carry the potential to influence the results to the greatest degree, we simultaneously identify where to focus our theoretical work. These decisions cannot be improved by running more models, instead they require, as Andrew Abbott once put it, ‘more sitting in our offices and staring at the wall’. We need to dig in and use logic, reflection and mental energy to improve our theories in small-N macro-comparative research.

Better than a Computer. A Crowd of Researchers.

Crowdsourcing researchers. A new use for an old tactic. When Silberzahn and colleagues asked if football referees are skin-color biased in their assignment of red cards, they brought a new meta to social research. Rather than the typical one research team, one project; they got together twenty-nine teams. All got the same research question and same data. What can we learn?

Hold on. A single academic strapped with programming skills can run just about every possible statistical model configuration. Level up this academic and they can program machine learning routines to tell us almost anything we need to know. So why do we need a crowd of researchers to analyze the same data?

The answer: theory.

Computers do not do theory. Computers crunch data. They can’t tell us where the data came from. Running every possible in an effort to ‘test robustness’ means testing for things that do not exist. Even worse, it means taking the results of tests for things that cannot possibly exist and using them to draw conclusions on the robustness of a test for something that could exist. Throwing all possible variables into a model or ordering variables in every possible configuration will maximize statistical predictions, yes. But what good is predicting something that cannot exist?

An example: Policymakers decide they want to increase the number of females in society. A computer determines that bearing children is a good predictor of being biologically female. Now, imagine that in a statistical model, being pregnant or ever having had biological children explains 75% of the variance in the sex of a given population (its probably about 100% at this point in human history, but lets allow room for error). The policymakers thanks to the computer, conclude that if 1,000,000 more people were pregnant, a predicted 750,000 of them should be female and only 250,000 male, plus a margin of error. Thus, they conclude that getting 1,000,000 people pregnant is likely to increase the number of females by 250,000 in their society, assuming the society is currently 50% female.

Epic fail.

If we want to develop causal theories that provide useful knowledge for societies, human logic is necessary. Rather than having one computer report 8.8 million false positives after running 9 billion different regression models, humans can identify correct, or at least ‘better’, model specifications. In fact, we would not even know they are ‘false positives’ without a human to understand that getting someone pregnant does not turn them into a female, for example.

But relying on the logic of one human, or even one team of humans, is risky. There are researcher degrees of freedom that make their research unreliable. Their prior beliefs, knowledge, experience and context lead to variation in results. These priors lead them along different paths through the garden of research. With the same research question and even the same data researchers often come to different results as demonstrated by the Silberzahn study and our Crowdsourced Replication Initiative (CRI).

Sounds like a meta-problem. So what good is crowdsourcing if we cannot rely on the crowd?

Answer: social interaction.

Crowdsourcing when done with careful planning and central organization, allows participants to comment on, if not deliberate, each others research choices. Suddenly meta-uncertainty turns into the power of meta-logic. Not just one team and their narrow ideas, but a communal debate with diverse inputs. Both the Silberzahn et al study and our CRI involved deliberation and voting on research designs. Combined with the growing area of specification curve analysis, crowdsourcing increases credibility for social research at the level of the population under study and the meta-level of the researchers themselves.

Finally, the relevance of crowdsourcing for collaborative theory construction is an untapped but promising avenue for the future. Crowd research departs from the current system that favors individualism by rewarding novelty of individual researchers. Crowdsourcing instead isa system of consensus building and direct responsiveness to theoretical claims. It could resolve the perpetual problem of scholars, areas and disciplines talking ‘past’ each other. If not consensus, it can identify critical unresolved questions to guide future research.

Crowdsourcing can move us toward open science in the Mertonian sense of a communalistic endeavor. To achieve this, all participants should be co-authors on the project, get to discuss each others’ models and theory, and get to update their own results during the process. We need machines to facilitate this kind of large scale research, but they cannot produce communal, logical exchanges. For that we need to stick with the crowd.