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The Primacy of Ideology in Explaining Antiscientific Attitudes /
“There is a cult of ignorance in the United States, and there has always been. The strain of anti-intellectualism has been a constant thread winding its way through our political and cultural life, nurtured by the false notion that democracy means that ‘my ignorance is just as good as your knowledge.’” (Isaac Asimov, 1980)
On the heels of a global pandemic, Americans have lost an estimated 5.5 million years of life, an average of 14 life years lost per death in 2020. Recently, the U.S. hit 600,000 deaths and 33,000,000 confirmed infections, the world’s highest death toll. The costs of COVID-19 to the economy in the U.S. amount to between $10 and $16 trillion when accounting for lost gross domestic product, premature deaths, and long-term health impairment. Yet, more than a year into the pandemic, conservative Republicans remain far less likely than liberal Democrats to view COVID-19 as a significant threat to public health; feel vulnerable to infection; comply with COVID-19 prevention guidelines; and be willing to vaccinate against the coronavirus. A poll conducted in late May 2021 found that 41% of conservative Republicans –as compared to 4% of liberal Democrats– would refuse COVID-19 shots. Perhaps unsurprisingly, the U.S. COVID-19 vaccination map, as provided by the U.S. Center for Disease Control, bears an eerie resemblance to the 2020 election map, suggesting that political factors such as ideology and partisanship play a major role in what is effectively a life and death decision.
Hesitancy toward COVID-19 vaccines, use of masks, and social distancing are merely the most recent manifestations of a larger pattern of antiscientific views propagated by the political right. While Donald J. Trump made science denialism a linchpin of his policy agenda, he is hardly the first. The 1964 Goldwater presidential campaign, for example, was suffused with anti-intellectualism, criticism of leading U.S. universities, and antiscientific sentiment. This trend has continued, more or less unabated, for over 50 years. Conservative hostility toward science encompasses defunding research on stem cells, barring federal funds for women’s reproductive rights, skepticism –if not downright rejection– of climate science, politicizing scientific research, and denying the detrimental effects of tobacco and sugar on public health (Mooney, 2007; Oreskes & Conway, 2011). It is not only that conservative elites are beholden to corporate interests or the religious right –these are only parts of the story– it is also that some conservatives view science as an obstacle to the free market and to unbridled political power. Historian Matthew Dallek wrote, “in some quarters on the far right, liberty and progress are enemies.” Distrust in science and scientists has become a staple of contemporary conservative politics. As these views seep into the public at large, social scientists are obliged to understand the social and psychological processes that foster antiscientific views.
Of course, some people are more prone to antiscientific thinking than others. Educational attainment and religiosity are predictably associated with distrust toward science. But from the perspective of political psychology, other factors such as one’s ideology and psychology also yield valuable insights. Several studies have found conservatives to be more receptive to conspiratorial ways of thinking and more susceptible to scientific misinformation as well as other epistemic vices (e.g., see Azevedo & Jost, 2021; van der Linden et al., 2021). There is also evidence that social dominance orientation (capturing preferences for group-based dominance and anti-egalitarianism), right-wing authoritarianism (obedience to authority, cultural traditionalism, and aggressiveness and punitiveness directed at deviants and dissenters), and system justification (motivation to defend, bolster, and justify the social, economic, and political systems on which we depend) help to explain left-right (or liberal-conservative) differences in views about science.
My curiosity about the role of political ideology in predicting attitudes about science grew during a Fulbright stay in Professor John Jost’s Social Justice Lab at NYU. While there, I became especially interested in the relative importance of various psychological predictors of antiscientific views, independent of demographic factors such as education and religion. To tackle this research question, we leveraged two large survey datasets, (a) an exploratory, quota-based survey of 1,500 adults that was nationally representative of the U.S. population in terms of age, education, income, and sex; and (b) a confirmatory (replication) convenience sample of 2,119 Americans from the same population. We collected the confirmatory sample to minimize the influence of false positives and to maximize the generalizability and robustness of our results. In both data sets we systematically investigated the effects of 15 different predictor variables on distrust of climate science, skepticism about science (vs. faith) in general, and trust in ordinary people (vs. scientific experts). We conducted a series of regularized regressions –a technique used in social science to avoid model overfitting–to identify a subset of predictors that exhibited the most reliable effects. We cross-validated these analyses using several other Machine Learning techniques to ensure that the results were not attributable to potentially idiosyncratic methodological choices. We also conducted multiverse analyses for each dependent variable and across samples so that we could assess how robust the conclusions were in relation to analytical choices and specifications.
Our results, which were recently published in a special issue of Group Processes & Intergroup Relations, show that most of the 15 predictors – age, education, gender, religiosity, income, symbolic ideology, five measures of operational ideology, political partisanship, right-wing authoritarianism, social dominance orientation, and system justification – were significantly associated with attitudes about science when looking at only two variables at a time. However, when all of the variables were entered into a simultaneous regression, we saw that distrust of climate science was predicted consistently by lower levels of education and higher levels of religiosity, as expected – and also by higher levels of social dominance orientation and political conservatism. Interestingly, partisanship, that is, identification with the Republican Party, did not ‘survive’ as a robust predictor of distrust of climate science. The most important predictor was political ideology – whether it was measured symbolically in terms of ideological self-placement or operationally in terms of policy preferences. The higher the conservatism scores across ideological measures, the higher the distrust in climate science.
Looking at the results of the multiverse analyses as illustrated in Figure 1 below, when accounting for all possible combinations of variables in the model (see panel A and B), ideology not only explains more variance, it clearly dominates partisanship in models that explain most variance. Conservatism predicted antiscientific attitudes significantly more than 99.99% of the time across all possible model specifications (see Panel C). Thus, ideology was a superior predictor of distrust in climate science compared to partisan identification because it explained more variance, was highly stable and robust across various operationalizations, and exhibited comparatively large effect-sizes – even in models including competing predictors.
We were also interested in determining whether these patterns would generalize to attitudes toward science in general – e.g., skepticism about science (vs. faith) – and attitudes towards epistemic authorities, comparing trust in ordinary people with trust in scientific and other experts. We obtained similar results as above, even after adjusting for distrust of climate science in particular, except that right-wing authoritarianism (rather than social dominance) was associated with skepticism toward science in general. Trust in ordinary people (vs. scientific experts), our third dependent variable, was robustly predicted – in order of their relative importance – by political conservatism, distrust in climate science, right-wing authoritarianism, lower education attainment, Republican partisanship, and system justification. It may be worth noting that the explanatory power of these models was comparatively large, explaining approximately 60% of the variance when predicting distrust in climate science, 50% for skepticism about science (vs. faith), and 30% for trust in ordinary people (vs. scientific experts).
There are a few big-picture takeaways from this research program. Overall, we found that – contrary to the common assumption that ordinary citizens are ideologically “innocent” or “ignorant” (see Azevedo et al., 2019; Jost, 2006, 2021) – conservatism robustly predicts anti-scientific attitudes and does so much better than affiliation with the Republican Party per se. We also found little support for the mainstream view that the role of conservative ideology in shaping attitudes towards science is largely confined to skepticism about climate change – or support for the claim that science skepticism is a bipartisan issue. Indeed, our studies find conservatives tend to value faith and feelings over science while liberals put a premium on science over and above distrust in climate science.
In an op-ed for The Hill, Mellman writes about our research “by three-to-one conservatives agreed, ‘We believe too often in science, and not enough in faith and feelings.’ Liberals disagreed by nearly the same margin. By two-to-one, liberals would rather put their ‘trust in the opinions of experts and intellectuals,’ while by a slightly lesser margin, conservatives repose their trust in “the wisdom of ordinary people.” Another takeaway is that we see a robust connection between psychological dispositions (such as right-wing authoritarianism, social dominance orientation, and system justification) and social and political behavior, and psychological dispositions explain a great deal of unique variance even after adjusting for demographics, partisanship, and political ideology (see also Azevedo et al., 2017, 2019; Womick et al., 2019). Figure 2 below displays a cross-tabulation of attitudes towards science across ideological groups.
There are a few big-picture takeaways from this research program. Overall, we found that – contrary to the common assumption that ordinary citizens are ideologically “innocent” or “ignorant” (see Azevedo et al., 2019; Jost, 2006, 2021) – conservatism robustly predicts anti-scientific attitudes and does so much better than affiliation with the Republican Party per se. We also found little support for mainstream view that the role of conservative ideology in shaping attitudes towards science is largely confined to skepticism about climate change – or support for the claim that science skepticism is bipartisan issue. Indeed, our studies find conservatives tend to value faith and feelings over science while liberals put a premium on science over and above distrust in climate science. In an op-ed for The Hill, Mellman writes about our research “by three-to-one conservatives agreed, ‘We believe too often in science, and not enough in faith and feelings.’ Liberals disagreed by nearly the same margin. By two-to-one, liberals would rather put their ‘trust in the opinions of experts and intellectuals,’ while by a slightly lesser margin, conservatives repose their trust in “the wisdom of ordinary people.” Another takeaway is that we see a robust connection between psychological dispositions (such as right-wing authoritarianism, social dominance orientation, and system justification) and social and political behavior, and psychological dispositions explain a great deal of unique variance even after adjusting for demographics, partisanship, and political ideology (see also Azevedo et al., 2017, 2019; Womick et al., 2019). Figure 2 below displays a cross-tabulation of attitudes towards science across ideological groups.
Today, perhaps more than ever, we observe an exceptional wave of anti-intellectualism and, sometimes, rejection of science on the basis of ideological commitments. These beliefs, however, are not without adverse consequences. The unjustified denial of science –irrespective of whether it regards social distancing and preventive infection measures during a pandemic, hesitancy of vaccination, or supporting candidates impending climate change legislation – is profoundly detrimental to public health, the environment, and the economy. To conclude, the clarity, consistency, robustness, replicability, and generalizability of our results suggest that there are genuine liberal-conservative differences in attitudes toward the scientific community and, by extension, the scientific process. We also hope that research of this type can shed light on the question of which social and psychological processes lead citizens in a democracy to make reasonable, informed decisions about complex scientific questions such as those pertaining to climate change, childhood vaccination, and the handling of pandemic diseases. As the journalist George Pyle teaches us, “the key to having a free society — one that is both really free and truly a society — is for most of us to be pretty good at knowing when we should do as we are told and when we should not.” In our time, it is no exaggeration to suggest that the fate of our democracy is yoked to a socially shared capacity and willingness to understand and trust scientific evidence and expertise, not because they are infallible but because they are vastly superior to the propagandistic alternatives at hand.
This work was published open access in Group Processes and Intergroup Relations. Please also check out our online repository hosting the reproducible reports of all analyses reported in the article (for both individual datasets and taken together, as well as the reproducible reports of the multiverse analyses).
This is the first draft on my published op-ed at Psychology Today, which unfortunately did not feature all figures or links (in addition to edits), hence I reproduce it in full as intended here.
Primary References
Asimov, I. (1980, January 21). A Cult of Ignorance. Newsweek. https://aphelis.net/cult-ignorance-isaac-asimov-1980
Azevedo, F., Jost, J. T., & Rothmund, T. (2017). “Making America great again”: System justification in the US presidential election of 2016. Translational Issues in Psychological Science, 3(3), 231. https://doi.org/10.1037/tps0000122
Azevedo, F., Jost, J. T., Rothmund, T., & Sterling, J. (2019). Neoliberal ideology and the justification of inequality in capitalist societies: Why social and economic dimensions of ideology are intertwined. Journal of Social Issues, 75(1), 49-88. https://doi.org/10.1111/josi.12310
Azevedo, F., & Jost, J. T. (2021). The ideological basis of antiscientific attitudes: Effects of authoritarianism, conservatism, religiosity, social dominance, and system justification. Group Processes & Intergroup Relations, 24(4), 518-549. https://doi.org/10.1177/1368430221990104
Buckley, W. F. (1986). God and Man at Yale: The Superstitions of Academic Freedom. Regnery Publishing.
Jost, J. T. (2006). The end of the end of ideology. American Psychologist, 61(7), 651.
Jost, J. T. (2021). Left & right: The psychological significance of a political distinction. New York: Oxford University Press. https://global.oup.com/academic/product/left-and-right-9780190858339?cc=us&lang=en&#
Mooney, C. (2007). The Republican war on science. Hachette UK.
Oreskes, N., & Conway, E. M. (2011). Merchants of doubt: How a handful of scientists obscured the truth on issues from tobacco smoke to global warming. Bloomsbury Publishing USA.
Van der Linden, S., Panagopoulos, C., Azevedo, F., & Jost, J. T. (2021). The paranoid style in American politics revisited: An ideological asymmetry in conspiratorial thinking. Political Psychology, 42(1), 23-51. https://doi.org/10.1111/pops.12681
Womick, J., Rothmund, T., Azevedo, F., King, L. A., & Jost, J. T. (2019). Group-based dominance and authoritarian aggression predict support for Donald Trump in the 2016 US presidential election. Social Psychological and Personality Science, 10(5), 643-652. https://doi.org/10.1177/1948550618778290
Measuring populist attitudes – identifying a core /
A central question in contemporary populism research is to what extent people hold so-called populist attitudes. In light of this question, various scales have been proposed to measure populist attitudes (see here or Chapters 6 and 7 here for an overview). While the scales differ in various aspects, what they have in common is that, in one way or another, they draw on trailblazing measurement studies that were conducted in the USA or the Netherlands using a similar battery of survey items assumed to measure populist attitudes (see first six items in Table 1, below). At the moment of writing, the scale devised in the Dutch study remains perhaps the most influential battery of items to measure populist attitudes and examine relationships between populism and broader political behaviour. And while such studies are insightful and enrich the discipline, the integrity of their results depends on having an appropriate measurement of populist attitudes.
Survey items of the populist attitudes scale:
populism_1 | The politicians in Congress need to follow the will of the people.
populism_2 | The people, not the politicians, should make our most important policy decisions.
populism_3 | The political differences between the people and the elite are larger than the differences among the people.
populism_4 | I would rather be represented by an ordinary citizen than an experienced politician.
populism_5 | Politicians (elected officials) talk too much and take too little action.
populism_6 | What people call “compromise” in politics is really just selling out on one’s principles.
populism_7 | The particular interests of the political class negatively affect the welfare of the people
populism_8 | Politicians always end up agreeing when it comes to protecting their privileges.
itemsQuestion wordingpopulism_1The politicians in Congress need to follow the will of the people.populism_2The people, not the politicians, should make our most important policy decisions.populism_3The political differences between the people and the elite are larger than the differences among the people.populism_4I would rather be represented by an ordinary citizen than an experienced politician.populism_5Politicians (elected officials) talk too much and take too little action.populism_6What people call “compromise” in politics is really just selling out on one’s principles.populism_7The particular interests of the political class negatively affect the welfare of the peoplepopulism_8Politicians always end up agreeing when it comes to protecting their privileges.
In a recent article in Politics, we argue that the measurement and empirical evaluation of both the individual items in particular and the scale as a whole have yet to receive sufficient attention despite some efforts being made in other studies. With that crucial caveat in mind, we use a cross-national sample (9 countries, n = 18,368) and apply Item Response Theory (IRT) to the most commonly used populism scale in order to evaluate whether that scale (1) accurately extracts people’s populist attitudes from self-reported answers, and (2) provides a pan-European measurement for the European context. This exercise allows us to make three crucial observations that are relevant for (populism) scholars and beyond.
First, the IRT analysis of the traditional 6-item scale shows that all six items have difficulties in capturing high and extreme values of an aggregate populist attitudes instrument. Some of these items, like the ‘elite-procrastination’ item (item 5 in Table 1), perform notably better than others. While the brilliant study by Castanho Silva et al. (2019) confirms this is not uncommon across a wide variety of populism scales and samples, this observation presents a challenge for the findings and measurement of populist attitudes to the extent that most research in this area focuses precisely on the most -and least- populist. Hence, suggesting further scale development is required for accurate inferences.
With that in mind, we propose two additional items to formulate an 8-item scale (items 7 and 8 in Table 1). While our analysis shows the new items clearly outperform some of the traditional items, the addition of these new items gauging the perceived conflict between ‘the people’ and ‘the elite’ does not expand the extent to which we accurately capture the full range of the latent populist construct. For that reason, in a final step, we set out to fit a more parsimonious measurement through the refinement of the 8-item scale that can serve as the basis for future studies. By combining Occam’s razor and psychometric principles, we deduce a core measure comprised of 3 high-performing items covering the hypothesised theoretical concept and consisting of one item from the traditional 6-item scale (item 1 in Table 1) and the two novel items (items 7 and 8 in Table 1).
Overall, the intention of this exercise is not necessarily to provide a ‘minimal’ populist instrument that comprises no more than three items. Rather, we wish to highlight some of the areas for improvement of existing measures and propose a foundation (at least in a pan-European context) that researchers can build on and use as a baseline for further advancement of this field.
Text originally published at Politics Online. Drafted by Steven M. Van Hauwaert, Christian H. Schimpf and Flavio Azevedo and published on 12th July, 2019. Reproduced here for (self)-archiving purposes.
Gun Control [Replication] /
Gun Control /
Following in the steps of Big Brother? How Brazil’s rightward shift is similar and dissimilar to that of the U.S. in 2016 /
Flávio Azevedo, University of Cologne, Germany,
Daniel Mucciolo, Universidade do Contestado, Brazil,
Da'Quallon D. Smith, Columbia University, USA
Brazil is a country of superlatives. It is the world’s 5th largest and most populous country in the world, extremely rich in natural resources, and Latin America’s most powerful economy. Brazil is also the primary home of the Amazon Forest,the earth's lungs, which absorbs ¼ of the world’s carbon dioxide. In an ever-interconnected global economy and environment, Brazil’s election results matter well beyond its borders. On the last Sunday of October, as the country’s constitution mandates, a run-off election pitted Fernando Haddad, who took over Luiz Inácio Lula da Silva’s Workers Party, against a candidate pundits have been calling the “Trump of the Tropics." But appearances can be deceiving. While parallels can indeed be drawn between Donald Trump and Brazil's President-elect Jair Bolsonaro, differences between the two countries' circumstances and the candidates' ideological characteristics are palpable.
Think of the disparate backgrounds wherein the two contests took place. Contrary to the U.S. in 2016, Brazil's latest election took place while the country was reeling from the worst economic crisis in its history, with sky-high unemployment and record-shattering murder rates. As if things were not bad enough, Brazil is still embroiled in an ongoing 4-year long corruption investigation that has shaken the nation to its core, incriminating politicians across all parties and ideological proclivities. Dubbed operation car wash, it has said to have uncovered the largest corruption scheme in the world, and one that has sent Brazil's favourite son, Lula, to prison.
Against the backdrop of a seemingly never-ending political corruption scandal, it is no surprise that only 13% of Brazilians are satisfied with democracy, only 11% think the country is going in the right direction, and the legislative and executive branches are among the country’s most distrusted institutions. In a country in which voting is mandatory, 42.1 million people chose not to select a candidate in the runoff - that is one in every three Brazilian adults. What these numbers show is a generalized disillusionment – or political alienation, if you will – with the political establishment. But perhaps the most telling contextual factor that differentiates Brazil’s shift to the (far)-right from that in other countries is that the Worker’s Party (PT) had won the last 4 Presidential elections. And while PT oversaw the most prosperous times Brazilians have ever seen, recent political, public security, and economic crises dissolved popular support for PT as well as trust in the entirety of Brazil’s political class. Not only will the lower house have 30 different parties (a record), but almost all of the major political parties had their representations in Congress severely reduced. Indeed, the erosion of traditional parties – particularly on the mainstream right – created a vacuum so large that many Brazilians thought of this election as a referendum on the status quo: a choice between “more of the same” and anything else. Enter Jair Bolsonaro, a retired Army captain and longtime congressman from Rio de Janeiro who – prior to Operation Car Wash and Rousseff’s impeachment – received some notoriety for defending what was once seen as inconceivable rhetoric. For example, during Rousseff’s impeachment proceedings, Bolsonaro drew national condemnation for dedicating his in-favor vote to the memory of Colonel Ustra, a convicted human rights violator and torturer of the military dictatorship – and who personally tortured Rousseff in 1970.
As far as the political campaign goes, there are some striking similarities between Trump and his Brazilian counterpart. Both were widely discredited by political and cultural elites; both uttered racist, sexist, and homophobic slurs without serious consequences (electoral or otherwise); they lauded themselves as incorruptible, promised to drain the swamp, invested heavily in social media, and circulated misinformation on too-big-to-notice-until-it's-too-late platforms such as Facebook, Twitter & WhatsApp; and despite calling the news media “fake” – or perhaps because of it - dominated national media coverage.
In addition, Trump and Bolsonaro share two ideological appendices to their conservative politics: populism and authoritarianism. In 2004, Mudde synthesized the core elements of populist political actors and defined populism as “a thin-centered ideology that considers society to be ultimately separated into two homogeneous and antagonistic camps, ‘the pure people’ versus ‘the corrupt elite’, and which argues that politics should be an expression of the volonté générale (general will) of the people” (Mudde and Kaltwasser, 2017). Similarly, Hawkins (2009) argues that populism is “a Manichean discourse that identifies Good with a unified will of the people and the Evil with a conspiring elite.” Both scholars, and the literature in general, tend to agree that central to populism is (a) framing of political divisions as the clash of two opposed and monolithic forces, (b) which is characterized by the assignment of a moral dimension via the juxtaposition of totalizing qualifiers such as good vs. evil, us vs. them, pure vs. corrupt (van Hauwaert, Schimpf, Azevedo, 2018). It is this moralization and covert in-grouping and out-grouping that predisposes and links populist views to both authoritarian and conservative views. Moreover, it is why, in practice, this partnership take hold almost exclusively on the social and cultural dimension of political orientation. In this sense, populist authoritarianism goes beyond the belief in an ordered society wherein transgressions should be punished severely – also known as law and order, a longstanding staple of authoritarian conservatism – to also include the identification, derogation, and targeting of “deviants.” Indeed, it has been shown that belligerence towards minority groups and the endorsement of the establishing and maintaining group-based hierarchies is a stable and robust predictor differentiating preference for mainstream conservative vs. populist authoritarian candidates (Womick, Rothmund, Azevedo, King, & Jost, 2018). Populist authoritarians seek to perpetuate societal inequalities – if not expand them – which they see as natural and legitimate (Azevedo, Jost, & Rothmund, 2017; Mudde, 2007; p. 23).
When fused, authoritarian populism is seen as a social pathology imbued by a paranoid style of politics which ultimately threatens liberal democratic values. The argument is that democratic rule is built upon the integration of pluralism in the political system, which is institutionalized by fair and free elections, separation of powers, the rule of law, and the equal protection rights and liberties for all people. Indeed, democracies’ checks and balances exist to limit the power of the executive branch and protect citizens from abuse. Populists, however, while claiming to speak for the people, conceive democratic procedure and its institutions as unnecessary obstacles to defending the Nation, as an impediment to their conception of popular will (Müller, 2017). Often through the exploitation of economic grievances, populists advocate for a return to nationalism, encourage prejudice and foment distrust toward globalization, international alliances and trade pacts. When in power, populists tend to reject pluralism and minority rights, clash against the free media, and decrease the extent to which civil liberties and political rights are upheld. Unsurprisingly, for most of the last decade, intellectuals, news media, and politicians have echoed voices of concern against the rise of populism, which is now a major player in politics around the globe.
However, while Bolsonaro and Trump share a populist authoritarian ideology, Bolsonaro’s rhetoric differs from Trump’s in at least two important ways: ambivalence towards democracy and overt, unapologetic generalized prejudice. We focus on the former first. Contrary to mainstream conservatives, who operate within the boundaries of democratic institutions, the members of the far-right display a varying degree of undemocratic proclivities (Golder, 2016). In a nutshell, the far-right is composed of two groups: the populist radical right and the extreme right. While the populist radical right is critical of democratic institutions – particularly those designed to preclude unchecked majority rule and ensure separation of powers – it is still supportive of elections and democratic rule. The discourse of members of the extreme right, on the other hand, not only shows contempt for democracy and its institutions but often encourages the transfer of governing power and legitimacy away from the Nation’s people. In a 1999 televised interview, Bolsonaro affirmed his support for military intervention, closing the Congress, and said these words about democracy: “You will never change anything in this country through voting. Nothing. Absolutely nothing. Unfortunately, things will only change when a civil war kicks off and we do the work the [military] regime did not. Killing some 30.000, killing them! If a couple of innocents die, that’s OK.” Bolsonaro is also a staunch defender of the murderous legacy of Brazil’s dictatorship. In 2016, while being interviewed on radio, Bolsonaro said this about the practice of torturing captured dissidents: “the mistake was not to torture, it was not to kill them.” Even during the Presidential campaign in 2018, Bolsonaro suggested to a crowd of supporters that they would shoot down PT supporters and send them to Venezuela where they would be forced to eat grass. So, in comparing Trump’s with Bolsonaro’s rhetoric, there is a qualitative difference in support for democracy. Even if the American President tries – and sometimes succeeds – to blur the lines separating the three branches of government, Trump never publicly suggested that autocratic forms of government were preferable to democracy.
The second difference relates to the presence of covert vs. overt unapologetic prejudice. In all likelihood, Bolsonaro and Trump share the same prejudices (and levels thereof), but when it comes to public political discourse and decorum, there is a qualitative difference. In 2011, Bolsonaro outright stated that his children would never have relations with a person of color because they had been well educated and doing so would constitute promiscuity. He is also on record saying that he would be incapable of loving a homosexual son, and would prefer his death than have him “show up with some bloke with a mustache.” In 2017, Bolsonaro referred to the gender of his daughter – after four male sons – as “a moment of weakness,” not only implying his masculinity or effort affected the sex of his children, but also passing judgment on which sex is superior. We could go on. The take-home message is Bolsonaro’s rhetoric bears the hallmarks of the extreme right and thus conflating it with Trump’s is a grave solecism.
But why should we care about the electoral consequences of a country thousands of miles away?
First, Bolsonaro is against environmental regulations and plans to merge the ministries of Agriculture and Environment, in support of agroindustry, which effectively means the invasion of Indigenous people’s lands and unrestrained deforestation of the Amazon. Fewer trees will contribute to global warming, which affects us all. Second, Brazil is a regional geopolitical leader integrating all of its South American neighbors physically, economically and politically. Its stability plays a strategic role in ensuring local shocks don’t travel across the region. Recently, Brazil’s democratic institutions have shown uncanny resilience in the face of three concomitant crises. The military never intervened, court decisions were respected (despite popular upheaval) and constitutional processes were followed. However, as Bolsonaro has promised to crack down on dissidents, the media, and even the electoral court Brazil’s democracy could fall and cause a domino effect across the entire region. Third, Bolsonaro’s unapologetic prejudice against women, homosexuals, blacks and natives, promises to bolster fringe and extreme groups, increase domestic violence and hate crimes – just like it has in the U.S. as Trump repeatedly fails to denounce far-right groups – leading to the death of innocent human beings and human rights violations. Fourth, Bolsonaro has promised to embolden police officers and promote shot-to-kill public policies. In a country with already staggering amounts of police violence and extra-judicial killings, the institutional backing will only increase police impunity and violence – particularly against the poor and racial minorities. Innocent people will suffer for no other reason than authoritarian-fueled ideology. And the conditions of the incarcerated – Brazil has the third largest prison population in the world, who already live in subhuman conditions – have been predicted to substantially deteriorate. Fifth, and least important of all, we may be witnessing the death of conservatism as we know it. Despite a few overlaps with populist and authoritarian views, and the ease with which they constructed alliances in the northern and southern hemisphere, these differ considerably in terms of aspirations and modus operandi. Populist Authoritarianism is brash and passionate while conservatism is modest and cautious. Conservatives tend to respect hierarchy, favor continuity and revere traditional values (Freeden, 1996) while authoritarian populists embody anti-elitism, exacerbation of societal differences, and unmitigated prejudice. Yet, that conservatism’s worldwide drifts into populist authoritarianism does not seem to set off alarm bells. Indeed, conservatives seem oblivious to realize their cultured and traditional precepts have been hijacked before their eyes.
Published at the ISPP Blog & Newsletter.
Bibliography
Azevedo, F., Jost, J. T., & Rothmund, T. (2017). “Making America great again”: System justification in the US presidential election of 2016. Translational Issues in Psychological Science, 3(3), 231.
Freeden, M. (1996). Ideologies and political theory: A conceptual approach. Oxford University Press on Demand.
Golder, M. (2016). Far right parties in Europe. Annual Review of Political Science, 19, 477-497.
Hawkins, K. A. (2009). Is Chávez populist? Measuring populist discourse in comparative perspective.Comparative Political Studies, 42(8), 1040-1067.
Levitsky, S., & Ziblatt, D. (2018). How democracies die. Crown.
Mudde, Cas (2007). Populist Radical Right Parties in Europe. Cambridge University Press.
Mudde, C., & Kaltwasser, C. R. (2017). Populism: A very short introduction. Oxford University Press.
Müller, J. W. (2017). What is populism?. Penguin UK.
Van Hauwaert, S., Schimpf, C. H., & Azevedo, F. The individual level measurement of populism in Europe and the Americas: Insights from IRT as a scale development technique. The Ideational Approach to Populism: Theory, Method & Analysis. Routledge.
Womick, J., Rothmund, T., Azevedo, F., King, L. A., & Jost, J. T. (2018). Group-Based Dominance and Authoritarian Aggression Predict Support for Donald Trump in the 2016 US Presidential Election. Social Psychological and Personality Science, 1948550618778290.
Useful Resources for Social Science Research /
The list of resources below is intended for (my) personal use. It contains primers, tutorials, guides I have used/encountered and/or want to learn more about it. These are related to producing better science, open-science, methods, statistics, visualization, R & RStudio.
Open Science
What is open science? [with Vazire, Lakens, Corker, MicNuijten, and Simons by SPSP]
Better science
Meta-science
Reproducibility
Preregistration
Preregistration template of Open Stats Lab and Project Tier
Preregistration template for existing data (secondary data analysis)
Writing
preprints
Reproducibility & Replicability Crisis
All Sciences
The Irreproducibility Crisis of Modern Science: Causes, Consequences and the Road to Reform
Scientists Self-Deception, Bias, Cognitive Fallacies in Research making
Loss of confidence project
Is science really facing a reproducibility crisis, and do we need it to? [Fanelli]
Publication Bias (file drawer problem) & Null Findings
Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling [ReproducibiliTea Podcast]
Questionable research Practices
Replication, Communication, and the Population Dynamics of Scientific Discovery
Emphasis on (Rate of) Published Positive Results - per scientific field
samples
WEIRD (Western, Educated, Industrialized, Rich, and Democratic)
MTurk
Replication Rate in Social Sciences
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Evaluating replicability of laboratory experiments in economics
Variation in Replicability: A “Many Labs” Replication Project
[P-B/F-D] Quantifying Support for the Null Hypothesis in Psychology
Studies
In 72% of these cases, nonsignificant results were misinterpreted (Quantifying Support for the Null Hypothesis in Psychology
Critiques & Replies
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The Reproducibility & Replicability Crisis & Politics
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degrees of freedom
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simr: R package for Power Analysis for Generalised Linear Mixed Models by Simulation
Power analysis at OSF from SIPS with Soderberg, Hennes, and Lane
Andrew Gelman's You need 16 times the sample size to estimate an interaction than to estimate a main effect
Sufficient Sample Sizes for Multilevel Modeling by Cora Maas & Joop Hox
Tom Snijder's summary of available software and guides on Multilevel modeling
Robust methods
Linear & Generalized Linear Models
J. Fox: Applied Regression Analysis and Generalized Linear Models with car and effect packages
Time Series
Course on Modern Methods in Time-series (in R, with code) by Kevin Kotzé
Causal Inference in graphs with Animated (GIF) Plots
Controlling for and Matching on a variable, Instrumental Variables, Fixed Effects, Difference-in-Differences, Regression Discontinuity (github repo, thread) by Nick Huntington-Klein
Survey Methodology PODCASTs
Psychometrics
Reliability
Item Response Theory [IRT]
Multidimensional Item Response Theory [MIRT]
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Estimating psychological networks and their accuracy: A tutorial paper
Bootstrapping edges after regularization & Power and Robustness of Networks [both R tutorials]
R & oPEN sCIENCE
Reproducible Research
Intros, Courses, & Examples:
Tools for Reproducible Research (Karl Broman, Spring, 2016)
r Resources
R-Intros
R for psychologists with piping [Nick Michalak]
R-Series: Complete COurses
Data Science Methods for Psychology - University of Oregon (HardSci)
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Intro
R-packages
ggplot2: is a system for declaratively creating graphics, based on The Grammar of Graphics.
Publication geared
ggally: the ally of ggplot2 (display of multiple regression coefficients and its diagnostics, networks, time-series, distributions, etc.).
ggstatsplot: Plots with Statistical Details (most common types of statistical tests (parametric, non-parametric, and robust versions of t-tets, anova, correlation, and contingency tables)
ggpubr: ggplot2 based publication ready plots [tutorial at sthda]
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Logical fallacies - list thereof (youtube video -> great resource)
Bias - list thereof with examples.
Everything else
Economics (Behavioral)
Gelman already explains why economists get paid more back in 2014: economists come up with ideas that rich people like.
Evaluating replicability of laboratory experiments in economics
Representativeness of women and URM in Economics [link; link2]
Course on Econometrics
Writing
R Journalism - Reproducible Journalism by Washington Post Investigative Data Reporter
Logical fallacies - list thereof (youtube video -> great resource)
Bias - list thereof with examples.
Political Science
P-values in science /
what is it?
P-values is short for probability values. But probability of what? P-value is the probability of obtaining an effect that is at least as extreme as the one you found in your sample - assuming the null hypothesis is true.
No, seriously, what is it?
If the definition was an accessible and intelligible explanation, statistics would not be considered a difficult topic, nor would the meaning/usefulness of p-values be a contentious issue in the scientific community.[1] While there many (many!) attempts to address this issue, what better way to start a blog in methods and statistics than by taking a shot at one of the crux of science-making? In any case, instead of writing about definitions, perhaps it is useful to illustrate the process in which p-values are important so we are better placed to understand what they are, and most importantly, what they are not.
The probability of event x is the relative frequency of x, divided by the sum of the frequencies of all possible outcomes Nt, as the number of trials go to infinity.
P-values are meaningful under the frequentist approach to probability (which is just one perspective under the larger umbrella of probability theory, e.g., Bayesian and Likelihood approaches). Simply put, frequentists view the probability P of an uncertain event x as the frequency of that event based on previous observations. For example, in a set of random experiments wherein the only possible outcomes are x, y, and z, then the frequency of occurrence of x, based on previous observations (frequency of occurrence of x, y, and z) is a measure of the probability of the event x. If you run the experiment ad infinitum, that is. The rationale behind frequentist thinking is that as the number of trials approaches infinity, the relative frequency will converge to exactly the true probability.
Illustration
Let's proceed with an illustration. Say you are a researcher interested in the birth rate of baby girls. And you would like to know whether there are more baby girls being born than baby boys. Here, there are only two possible outcomes, to observe or not to observe the event of interest (i.e., baby girls being born). To investigate that research question, you start by logging the gender of every born baby in the nearest hospital for a full 24 hours. Then, as shown above, you estimate the probability of your event of interest, P(Girls), which is the ratio between the frequency of baby girls born, divided by the total number of observed births (Boys and Girls). You look at your records and see that you observed 88 births in total, 40 baby boys, and 48 baby girls. Then, the estimated probability of a baby girl being born, according to your data, is P(Girls) = 0.5455. These results could be interesting to policy makers, practitioners, and scholars because your data seems to suggest there are more baby girls being born than baby boys.
But before we run to the world and tell it about this puzzling truth, perhaps we should consider the role of different philosophical approaches the scientific method and how these translate into two forms in dealing with uncertainty and its statistical operationalization.
The role of Uncertainty
Now, you have to find a way to show the policy makers and the scientific community that your finding is "true". But in actuality, you can only show that your results are somewhat 'likely' to be true. Indeed, it is on estimating how probable these results reflect the true probability that the scientific method and statistics intermingle. To start, note that you observed only 88 trials (births), not a infinite number of trials, or a large-enough sample. This means that, statistically (from a frequentist perspective) your estimate did not have the necessary number of trials to converge to the true probability. Another way to look at this is to think about the lack of precision of your estimate. For example, you did not survey the whole population of hospitals and babies being born therein, but just a sample of it. In fact, even more restricted, your surveyed only 24 hours worth of data out of 2,080 hours in say a year, in only one hospital. Given that the true probability is unknown, the limitations of your study could influence the accuracy of your estimate and bias it away from the true population parameter. So the best you can do is to estimate it and assess the degree to which it is an accurate estimate.
The role of Probability distributions
So how do you quantify the strength of your findings? Scientists often resort to a specific method known as statistical inference, which is based on the idea that it is possible - with a degree of confidence - to generalize results from a sample to the population. To give you a more exact explanation, to infer statistically is to abide by a process of deducing properties of an underlying probability distribution via analysis of data. Note the term underlying probability distribution. Researchers relying on empirical and quantitative methods rely heavily on the assumption that the studied phenomenon follows the same pattern as a known probability distribution, whose characteristics and properties are known. Hence, these are compared - the theoretical vs. the empirical distributions - as to make inferences. In your case, you are studying the relative frequency of baby girls versus baby boys. You do your googling and find out that since your variable of interest is a dichotomous and largely randomly determined process (i.e., meaning that there are only two possible outcomes at each event/trial and we assume this to be random), then human births could be understood in Statistics as ensuing from a Binomial distribution. Another example of the same stochastic process is a coin toss (e.g., heads vs. tails), which is known as a Bernoulli trial). In any case, random and dichotomous outcomes, in the long run, tend to follow a Binomial distribution. Now that the probability distribution you assume to underlie human births has been identified, you can use it to compare it to your data.
The role of Hypothesis testing
If you subscribe to the scientific method, in order to perform this comparison, you need a hypothesis, a testable and falsifiable hypothesis. In your case, you would like to test whether you have enough evidence to say 'there are more baby girls being born than baby boys'. Note that a proportion of 0.5 indicates that there are as many baby girls being born as baby boys. That being said, a hypothesis could be "the proportion of baby girls being born is larger than 0.5", or equivalently, you could have said "the probability of baby girls being born is larger than 0.5." Either way, you seem to want to compare the estimate you drew from your data P(Girls) = .5454... to the estimated presumed to be of the population: P(Girls) = P(Boys) = 0.5. This is testable and falsifiable because we presume to know the properties of the stochastic process underlying our data.[2]
The role of confidence LEVEL
The last thing you need before you can assess the degree to which your estimate is likely is to set your preferred the level of confidence. One important reason why you need a degree of confidence is due to sample variation which affects your population estimate P(Girls). Suppose you learned the hospital wherein you collected data found your idea very interesting and decided continue logging the gender of every birth for the next 99 days. After this period, you are shown the results of the Hospital's research below. The picture shows the fluctuation of daily estimates of the probability of baby girls being born. On the y-axis, the number of times a given ratio was found is displayed. On the x-axis, the relative frequencies (or ratios or probabilities) are represented. As before, each count per bar represents an individual day's worth of collected data. For example, the first dark blue bar (on the extreme left), means that there was one day in which the proportion of baby girls and baby boys was estimated to be 0.43. In that day, there were more boys than girls born. The last bar (on the extreme right) was a day in which the proportion of babies girls exceed that of baby boys from 1 to 2. What we learn with this graph is that, had you chosen another day to conduct your original survey, you would have likely found a different estimate of population parameter of interest: P(Girls). Due to this fluctuation, it is good scientific practice to provide a confidence interval to your population estimate. This confidence interval, which is calculated from the sample, asserts that you could be X% sure that the true probability of baby girls being born, or the true population parameter P(Girls), would be contained in the estimated intervals.
The red-dashed line depicts the mean ratio, 0.55, which means that it was estimated that, in the course of 100 days, on average, for every 100 babies (irrespective of gender), 55 are baby girls.
Obviously, you want to be as confident as possible, right? Yes! But when it comes to choosing a confidence level for your estimate, there are advantages and drawbacks at every level of confidence. In general, the more confident you want to be, the larger the confidence interval. And vice-versa. Talk about being caught between a rock and a hard place.
The table on the right shows exactly this for your P(Girls) estimate. If you choose a confidence level of 90%, this means that 90% (9 our of 10) of the times you run a random experiment, the true probability of baby girls being born would be contained in the estimated intervals. Instead, if you choose a confidence level of 95%, this means that 19 times out of 20 you run your random experiment (95%), the true estimate of P(Girls) will be contained in the confidence interval. And so on.
Formula for confidence intervals of proportions, in the long run.
But how does one calculate the confidence intervals? Again, some googling or textbooks may be necessary. But since you are in a hurry, having lost so much time reading this post, you decide to go for the fastest option: Wikipedia. There, at the page of the Binomial distribution, you learned that the approximate [3] confidence interval for proportion estimates is a function of the estimated probability (p hat), the number of trials (n), and z. Two aspects are clear from the formula, first is that the higher the n, the smaller the confidence interval (give n is in the denominator). The second is that z appears to be a multiplier, which is to say, the larger it is, the larger the confidence interval.
Z is a value from the standard Normal distribution (mean=0, standard deviation = 1), for the wished confidence level (e.g., 90%, 95% or 99%). In fact, the confidence level is the area of the standard normal delimited by z values. For example, for standard normal distribution, the area comprised between -1.96 and 1.96 is equal to 95% of the total area under the curve of the normal distribution. Analogously, area comprised between -2.576 and 2.576 is equal to 99% of the total area of the distribution. If you are in doubt as to why we use the normal distribution as to calculate a Z score for the construction of a confidence interval for outcomes ensuing from binomial distribution, it is because at large enough N (in the long run), and p hat (estimate of proportion) not too close to zero or one, then the distribution of p hat converges to the normal distribution. These confidence levels are intrinsically related to p-values.[5]
Are we there yet?
Yes! Let put your hypothesis to a test. As mentioned above, your hypothesis is that the ratio of baby girls being born is larger than the 0.5. So, one way to think about out testing your hypothesis is to pitch it against the confidence interval you estimated. The other way is explained below in detail. You are saying the ratio of baby girls is larger than .50, correct? The implication being 0.50 is not one of the values comprising you confidence intervals - in the long run. If the 0.50 is contained in confidence interval, it would mean the true probability of baby girls being born could very well be 0.5 at a given level of confidence (90%, 95%, 99%, etc). Ergo, the data collected would not support your hypothesis that there are more baby girls being born than baby boys. Let's check back at the table above, where we estimated four confidence intervals, one for each confidence level. At every considered confidence level, the confidence intervals include 0.50. That is to say while your estimate - or sample parameter - is 0.5454, we cannot discard the the possibility that the true probability of baby girls being born might be 0.50. Note that we still don't know what the 'truth' is. And if we want to get closer to it, we should continue to repeat the experiment.
However, there is another frequentist interpretation wherein the p-value - and not so much an estimate with a confidence interval - is the star of the show.
The role of philosophical approaches to statistics
So far we have interpreted Frequentist inference as a method with which one can achieve a point estimate (e.g., probability, mean differences, counts, etc.) with an accompanying measure of uncertainty (confidence interval based on the confidence level). And this is a proper way to think of statistical inference within the Frequentist paradigm. However, this approach lacks in terms of practicality to those researchers seeking *an objective answer*, a decision for a given problem. Think of a pharmaceutical company testing whether a drug helps decrease the mortality of a new disease, or, a materials company testing the grade of concrete and steel for constructions in Seismic zones. In these circumstances, null hypothesis significance testing (NHST), is a particularly useful method for statistical inference.
On the philosophical sphere, one important difference between the two frequentist camps (probabilistic vs. hypothesis testing frequentists) ensues from the different answers given to the question where should Statistcs or statistical inference lead to? Should Statistics lead researchers to a conclusion/decision (even if based on probabilities) or should Statistics lead to an estimation/probability statement with an associated confidence interval? Above, we explored the basis of the latter approach. Now we will focus on the former.
The null hypothesis significance testing (NHST)
NHST is the amalgamation of the work of Fischer and Neyman-Pearson. And while these viewpoints differ - by quite a bit - they are unified in providing a framework for hypothesis testing.[4] The underlying reason for 'testing' is that scientists want to generalize the results ensuing from a study with a sample to the population in a way that yields a yes/no decision-making process. As seen above, however, this approach may lead to the observation of biased estimates due to chance alone but also a variety of other factors. NHST is a method designed to quantify the validity of a given generalization. from sample to population. In other words, to infer statistically will always involve probabilities - not surety - and thus, error is inevitable.
Due to this, a framework was developed to assess the extent to which a given decision abides by probabilistic principles and minimizes error. In the standard approach, the relation between two variables are compared (say X and Y). A hypothesis versing about the relation of the variables is proposed (say X > Y) and it is compared to an alternative proposing no relationship between the two variables (implying X = Y). This no-relation hypothesis is called Null Hypothesis, or Ho, and it is based on it being true or false that scientists consider the likelihood their own hypothesis being true. The reasoning behind relying on the null hypothesis is best described by one of the most important 20th century philosopher's of Science, Karl Popper who said all swans are white cannot be proved true by any number of observations of white swans as we may have failed to spot a black swan somewhere. However, it can be shown to be false by a single sighting of a black swan. Scientific theories of this universal form, therefore, can never be conclusively verified, though it may be possible to falsify them. Point being, it is easier to disprove a hypothesis because it is impossible to test every possible outcome. So, instead, Science advances only through disproof. So, given that taking decisions based on probabilities will always give rise to the possibility of an error, when comparing two hypothesis, four outcomes should be considered based on whether the null hypothesis is true or false, and on whether the decision to reject (or fail to) was correct or wrong.
A similar thinking explains the legal principle presumption of innocence, in which you are innocent until proven guilty. Lets think of possible scenarios in a criminal trial. If one is innocent, and the court/jury acquits you, then the decision and the truth match. This is the correct inference and it is called True Negative. "True" refers to the match between decision and the truth, while "negative" has to do with a failed rejection of the null. Similarly, if one is indeed guilty and the court/jury decides for a conviction, the inference is again correct and it is termed True Positive. Where "positive" stands for rejecting the null. Statistics, and NHST, mostly concerns itself with the remaining options. When you are convicted, but didn't commit the crime (False Positive), and when you are acquitted, but did commit a crime (False Negative). These are termed Error Type I and II, respectively, and they are key concepts introduced by Neyman and Person to the Fischerian perspective.
Null Hypothesis Significance Testing outcomes [or Table of error types]
The type I error rate is also known as the significance level, or α level. It is the probability of rejecting the null hypothesis given that it is true. The α value is analogous to the confidence level explained above via the relation CL = 1 - α. Meaning that if the researcher want to adopt a confidence level of 95%, then she or he is automatically adopting an α level of 5%. As for the testing part, the way hypothesis testing is done is relatively simple: you have to assess the probability of the sample data as a realization of the null hypothesis. In other words, one compares probabilistically two sets of data: one ensuing from the null hypothesis and the other from the alternative - both assuming the phenomenon follows a given stochastic process (i.e., data points are observed according to a known distribution). This is done by calculating the probability the sample data has been generated by the null hypothesis. Then, given a significance level, a comparison is deemed 'statistically significant' if the relationship between the sets of data an *unlikely* realization of the null hypothesis. How unlikely depends on the chosen significance level. Makes sense?
Let's take your research example one last time. Since we have a binary variable, it is easy to demonstrate the calculation of the p-value. Your sample data consists of 88 observations where 48 baby girls and 40 baby boys were born. The estimated probability of P(Girls) = 0.5455. You are interested in showing that there are more baby girls being born than baby boys. When thinking about testable hypotheses, the null-hypothesis would be a null difference between the proportions of baby girls and boys. This is mathematically represented by P(Girls) = 0.50. Then, the alternative would be P(Girls) > 0.50. On the philosophical sphere, we learned that you cannot show that P(Girls) > 0.50, but you could show that P(Girls) = 0.50 is so *unlikely*, given your data, that you would consider P(Girls) > 0.50 as being the more likely scenario, until there is further evidence. This is where the key to understanding inferences lies. And this is what p-values do, it informs you about the probability - or how likely or unlikely - your sample data is a realization of the null hypothesis, based on the chosen significance level. So, a null hypothesis is rejected if the relationship between the data sets would be an unlikely realization of the null hypothesis. This is why the definition of p-value contains the "assuming the null-hypothesis is true." It is because the p-value is the probability of the sample data being an outcome of the data generated by the null.
Bare with me through the calculations. What is the probability of observing 48 baby girls in 88 trials, assuming the true probability is 0.5? To calculate this, we need to know two things. First, how many ways one can observe 88 births yielding a total of 48 baby girls? The answer stems from the combination of 88 Bernoulli trials whose sum total 48. There are 1.831258e25 ways as shown below. Second, we need to know what is the probability that we will observe exactly 48 baby girls AND 40 baby boys, in 88 births. P(48 Gilrs) x P(40 Boys) is very low, at 3.231174e-27. We multiply these two probabilities to find the probability of 48 baby girls, in 88 births, assuming the probability is 0.5, which is almost 0.06.
That is to say, if you collect data again, assuming P(Girls) = P(Boys) = 0.50, which is the null hypothesis, there is a 6% chance that I will observe a ratio of 0.5455. By now, I hope we understand the meaning of "assuming the null hypothesis is true" part of the p-value definition. It is because we calculate the likelihood of a given configuration of results while using the parameters set forth by the null hypothesis. That said, we can now break down the meaning of "at least as extreme" part of the definition.
P-values give you the cumulative probability, rather than just a probability. That is to say, p-values assess the probability of observing a given value (in your case, 48 out of 88) and all other possible values that are more extreme. In your case, the p-value gives you the probability to observe 48 or more baby girls being born so we need to calculate the probability of observing 49, 50, 51 ... 86, 87, 88 baby girls, in 88 trials, assuming that P(Girls) = P(Boys) = 0.50. Just as an illustration we also perform the calculations for 49 births.
As you can see from the table below, the probabilities will decrease, as it becomes less and less likely that you would observe an increasingly larger proportion of baby girls being born, if 0.50 is the true probability. So the cumulative probability of observing 48 births, or more, assuming P(Girls) = P(Boys) = 0.50, is 0.223. This is the big moment you were waiting for. 0.223 is the p-value when testing whether he can reject the null hypothesis (P(Girls) = 0.50) in favor of the alternative (P(Girls) > 0.50). It indicates that it is quite likely (almost 1/4 of the times) to observe a set of data showing 48 births or more assuming P(Girls) is indeed 0.50. As for the 'testing part', it require nothing more than comparing the obtained p-value with the criterion for significance. Since most scientists use an α level of 0.05, and the p-value of 0.223 is larger than the α level, we do not have enough evidence to reject Ho. So, pending future studies, we can only consider that the probabilities of baby girls and boys being born are - roughly - the same.
Other researchers, however, could criticize that your alternative hypothesis is tendentious. In the sense that, with one day's worth data, you should perhaps consider a broadened alternative hypothesis. One which didn't provide a direction, one which didn't consider only half of the possible outcomes. Indeed, when setting the alternative as P(Girls) > 0.50, you are not considering P(Girls) < 0.50. And while your estimate indicates a larger proportion of baby girls, you shouldn't assume that your sample's suggestion indicates the truth. So, perhaps more appropriately, you would like to also consider P(Girls) < 0.50. In that case, we can say that your null hypothesis is P(Girls) = 0.50, and your alternative is P(Girls) ≠ 0.50, which covers both directions. In that case, what do we need to do to calculate the p-value for the non-directional hypothesis? We can either repeat the process, for 40, 39, 38... 1 baby girls being born, or multiply the calculated p-value by 2, both yield the same result. Note that you should start with 40, not 48 or 44. This is because you observed a difference of 4 more than your expectations which is 44, if P(Girls) = 0.50. Thus, you calculate the cumulative probability of observing the same difference of 4 from the expectation, but in the other direction: 44 - 4 = 40. The p-value is 0.223*2 = 0.446. The increase in p-values shows that by considering both directions, it is even more common (twice as common) to observe a difference of 4 births from the expected value of 44, assuming the probability P(Girls) = 0.50, in 88 trials.
The role of Power
Now you say "OK, great. But I was confident there were more baby girls than baby boys being born. Before I am completely convinced I was wrong, is there perhaps something I may have missed precluding me from arriving at the correct conclusion?" And despite the fact that the answer to this sort of question in Science is always "yes" (i.e., in the best case scenario, there are always improvements to be made), there is one key aspect of hypothesis testing we have not yet addressed: Type II error. This occurs when there is in fact a true difference (in your case, in the proportion of ratios) but hypothesis testing fails to reject the null hypothesis. This is termed a false negative. It is when one is guilty of a crime, but the court/jury acquits the defendant. In many ways, Type II error is the other side of Type I error, where you false conclude there is a difference, when it is not (i.e., false positive). Both are said to be "False" because it is the wrong decision. Thus, ideally, we want to avoid both, and always find effects when they exist, and fail to find them when they don't. Curiously, Type II error rate is deemed the lesser evil (in comparison to Type I error) by the scientific community. Perhaps because the harm done when incurring in this type of error is the maintenance of the status quo. Personally, I have my doubts about this, and the reproducubility crises in science can better showcase this point.
where n is sample size, p0 is the hypothesized population value, Φ is the standard Normal distribution function, and α is significance level
But more to the point, Type II error, or β, depends on three components, the magnitude of effect, the sample size (or sampling error) and the statistical significance used. The rationale is the following. Assuming a constant significant level, if the magnitude of effects are small, a larger sample size is necessary detect "a signal" (or reject the null). If the magnitude of effects are large, then sample sizes can be smaller and still not incur in Type II error. But if you are somewhat acquainted the scientific method or hypothesis testing, β is hardly ever mentioned. Instead, researchers tend to speak of 1 - β, which is known as power. As in, power to detect signal. Power is the probability that the test correctly rejects the null hypothesis when the alternative hypothesis is true. Importantly, power analysis can be used not only to calculate β (probability of false negative), power (1 - β), but also the minimum sample size n required so that one can be reasonably likely to detect an effect. So, you can use this knowledge to better understand your study's results. By using the formulas to the top-right corner, you would find that the power (1 - β) of your study is 0.214 if you were only interested in showing P(Girls) > P(Boys) - or 0.137 for P(Girls) ≠ P(Boys). This means, at best, you had a 0.214 probability to reject the null when it is true. By contrast, the false negative rate (type II error) is 86%. From these calculations, it becomes clear the sample size (N=88) is too small to identify such a small difference in proportions - from P(Girls) = 0.50 to P(Girls) = 0.5455, whose nominal effect size is 0.091. Indeed, by using the last formula above to calculate the minimal sample size necessary to detect a significant difference with the surveyed effect magnitude. Results show a whooping 948 births as a necessary to be 80% sure that you are able to detect a meaningful difference, when there is one. 80% is usually the power sought after in academia, but different fields use different power ratios (think of medicine, where doctors may prefer a false positive and require additional confirmatory tests, than have a false negative, and send a sick patient home). If you would like to be 90% sure, your study would need 1261 observed births, 1560 for a 95% true positive rate, and 2205 for 99%. In the long run, that is. Also bear in mind that these numbers related to testing non-directional hypothesis, that is, you are considering both P(Girls) > P(Boys) and P(Girls) < P(Boys). The required sample size would be slightly smaller if you only consider P(Girls).
To think about it in another way, imagine 99 other independent researchers were to replicate your study with the same protocol while not polling together their data. And lets assume you were right, and that the true probability of girls being born P(Girls) is 0.55. Then, by cataloging all births for each day, we can plot a summary of these 100 studies as displayed on the right. Each point represents the found ratio/proportion for one specific day while the line represents its confidence interval. Colored in red are the days in which the researcher would find that P(Girls) > P(Boys), and in black that we do not enough evidence to reject P(Girls) = P(Boys). There are 17 instances in which the confidence interval does not include 0.50, and 83 instances that it does. Note that these numbers are slightly different than those reported above by power and Type II error rates. This is because these always refer to "in the long run". That is, if these 100 replications were to be repeated in time, then on average, researchers would identify a significant difference (p-value lower than 0.05 or confidence interval not including the 0.5 in about 14% of the time). And yes, p-values and confidence intervals are equivalent at the same significant level. I personally prefer the former because it has one important advantage: in comparison to p-values, confidence intervals reflect the results at the level of data measurement. Another interesting implication of the above plot is that if you were to rely on one day's worth of data, most days you would take the 'wrong' conclusion. For this reason, power analysis in an integral part of study design. Ideally, these analyses should be done prior to data collection. [6]
To sum up
P-values only make sense under the umbrella of frequentist statistics. This sub-field of statistics has two main camps, one which interpret that statistics should be about probabilistic estimates accompanied by confidence intervals, and another which recognizes the importance and/or necessity to provide objective decisions based on data. NHST is s used to determine whether there is enough evidence in a sample of data to infer that a certain condition is true for the entire population. In this process, p-values play an important role, however, it is also fundamental to conduct a priori power analysis as to insure the study is properly designed for the investigation at hand. Science progresses through disproof. Einstein said ‘A thousand scientists can’t prove me right, but one can prove me wrong’. We can’t prove a hypothesis true, but we can prove its falsehood.[7] [8]
Footnotes
[1] Particularly in Social sciences.
[2] Bayesians would likely disagree this rationale is useful and argue that testing against Ho = 0 yields very little information. Instead, Bayesians use Bayes' theorem to update the probability for a hypothesis as more evidence or information becomes available.
[3] The approximate formula for the confidence interval of proportions should only be used when the population size is 20 times larger than the sample size.
[4] Confidence level: γ = (1 − α). This is the probability of not rejecting the null hypothesis given that it is true. Confidence levels and confidence intervals were introduced by Neyman in 1937.
[5] In practice, this is closer to Fischer's ideas than Neyman-Persons's.
[6] In the future, I will include a meta-analysis of these studies to showcase the importance of cumulative and open-science practices.
[7] In the future, I will include a thought experiment showing how p-values are counter-intuitive and completely backward from what a scientist generally wants.
[8] Soon I will include the R-code and link to a R-markdown document with of all these calculations on GitHub.
[R-Course] 11. Notes on RStudio & Resources /
[R-Course] 10. Asking for Help in R /
[R-Course] 09. Importing and Exporting data in R /
[R-course] 08. Manipulating Datasets /
[R-Course] 07. Generating Random Numbers /
[R-Course] 06. Intro to R technicals /
[R-Course] 05. Practicals n.2 /
[R-Course] 04. Vectorized Operations /
[R-Course] 03. Practicals n.1 /
[R-Course] 02. Evaluating common mathematical expressions /
[R-Course] 01. Introduction to R & Statistics /
Learning Statistics on Youtube /
Youtube.com is the second most accessed website in the world (surpassed only by its parent, google.com). It has a whopping 1 billion unique views a month. [1, 2] It is a force to be reckoned with. In the video sharing platform, there are many brilliant and hard-working content creators producing high-quality and free educational videos that students and academics alike can enjoy. I made a survey on Youtube content that could be useful for those interested in learning Statistics, and I listed and categorized them below.
Truth be told, this post is a glorified Google search in many respects. In any case, I had intended for a long time to gather this information as to facilitate the often laborious task of finding pertinent resources for learning statistical science in a non-static format (i.e., videos) that is easily accessible, high-quality, instructive and free.
Another motivation had to do with my teaching obligations. In this fall, I will teach a graduate course in Stats with R. To this end, I considered becoming a content creator myself, as to allow students to access the course's content from the convenience of their homes. In this process, I found some excellent statistical courses on Youtube. Some were really useful in terms of their organization, others in terms of content, interesting explanations, pedagogical skills, availability of materials, etc. Altogether, searching for resources was a very instructive experience, whose fruits should be shared.
Importantly, in this process, I learned that youtube is not short of 'introductory course on ___.' Not of Statistics, Probability or R, anyways. Which is a good thing. And often, you even see these three together. Also in abundance, are courses on the ABC's of probability theory, classical statistics (i.e., up to ANOVA, ANCOVA), and on basics of applied statistics (e.g., Econometrics, Biostatistics, and Machine Learning). Indeed, Machine Learning (mostly through Data Science) is really well represented on Youtube.
Due to the sheer amount of channels, I organized them into three broad categories: use of R as statistical software, use of other statistical software, and lecture format only. I also listed each channel's content/topic, whether authors provided slides, code, additional materials online (with links), and relevant remarks.
1. Learning Statistics with R
| Youtube channel | Content | Software | Online Materials? | Remarks |
|---|---|---|---|---|
| Mike Marin | [Intro] Basic Stats in R | R | Yes, good materials | University British Columbia |
| Michael Butler | [Intro] to R and Stats, Modern | R | Yes | Good intro to R + Exercises |
| EZLearn | [Intro] Basic Stats in R | R | Exercises w/ solutions | - |
| Renegade Thinking: Courtney Brown | [Intro] Undergraduate Stats | R | Yes | Good Lectures |
| Barton Poulson | [Intro] Classical Stats, Programming & Solved Exercises | R, Python, SPSS | Yes | Gives intro to Python, R, SPSS and launching an OLP |
| Ed Boone | [Intro] Basic R and SAS | R & SAS | Yes | - |
| Bryan Craven | [Intro] Basic Stats in R | R | - | |
| Lynda.com | [Intro] Basics of R and Descriptives | R | Yes | OLP |
| Bryan Craven | [Intro] Basic Stats in R | R | No | - |
| Laura Suttle (Revolution) | [Intro] R tour for Beginners | R | No | - |
| Phil Chan | [Intro] Classical and Bio-stats | R, SPSS, Eviews | No | - |
| Gordon Anthony Davis | [Intro] R Programming Tutorial | R | No | Thorough intro for beginners |
| Nathaniel Phillips | [Intro] R Programming Tutorial | R | Yes | Videos as a pedagogical tool for his R book |
| David Langer | [Intro] Basics of R | R | No | Excellent pedagogical Skills |
| MrClean1796 | [Intro] Math, Physics and Statistics, lecture & R | R | No | - |
| Brian Caffo | Advanced & Bio-Stats, Reproducible Research | R | Yes, Coursera and GitHub | Professor of Bio-statistics, Johns Hopkins Univ. |
| Abbass Al Sharif | In-depth Machine Learning | R | Yes | Excellent lectures and resources |
| James Scott | Advanced Stats | R | Yes, and GitHub | Several Course Materials on GitHub |
| Derek Kane | Machine Learning | R | Yes | Excellent Videos, Fourier Analysis, Time series Forecasting |
| DataCamp | Programming, DataViz, R Markdown [free] | R | Yes, paid. 9$ for students | - |
| Maria Nattestad | DataViz | R | Personal Website | Plotting in R for Biologists |
| Christoph Scherber | Mixed, GLM, GLS, Contrasts | R | Yes | - |
| Librarian Womack | Time Series, DataViz, BigData | R | Yes, Course and .R | Materials online |
| Jarad Niemi | R Workflow, Bayesian, Statistical Inference | R | Yes | - |
| Justin Esarey | Bayesian, Categorical and Longitudinal Data, Machine Learning | R | Yes, lots and lots | Political Scientist |
| Jeromy Anglim | Research Methods | R | Blog:Psych & Stats, GitHub | + Rmeetups and Notes on Gelman, Carlin, Stern, and Rubin |
| Erin Buchanan | Under- & post-graduate Stats, SEM | R, G*Power, Excel | Yes | Excellent pedagogical strategies |
| Richard McElreath | From Basic to Advanced Bayesian Stats | R and Stan | Yes, lots | Book lectures |
| edureka | Data Science | R, Hadoop, Python | Yes, online learning plattaform | R Intro w/ Hadoop [free] |
| Learn R | R programming, stats on webiste | R, Python | Yes, and One R Tip A Day | On website, lots of starter's code |
| Data School | Machine Learning, Data Manipulation (dplyr) | Python, R | Yes, dplyr | Machine Learning with Hastie & Tibshirani |
| Econometrics Academy | Statistics (via Econometrics) | R, STATA, SPSS | Yes | OLP, Excellent Materials and Resources |
| Jalayer Academy | Basic Stats + Machine Learning | R, Excel | No | Also Lectures |
| Michael Levy | Authoring from R, Markdown, Shiny | R | No | - |
| Melvin L. | Machine Learning, R Programming, PCA, DataViz | R, Python, Gephi | No | Interesting Intro for Spark |
| OpenIntroOrg | Intro to Stats/R plus Inference, Linear Models, Bayesian | R | Yes, Coursera and OpenIntro | Coursera Courses, Resources in SAS |
| Mike Lawrence | Tidyverse, Wrangling & DataViz, plus Bayesian Inference | R, Stan, rstan | Yes, w/ Lit too | And relevant repos on GitHub |
2. Learning Statistics with other software
| Youtube channel | Content | Software | Online Materials? | Remarks |
|---|---|---|---|---|
| Jonathan Tuke | Basic Stats | Matlab | No | - |
| Saiful Yusoff | PLS, Intro to MaxQDA | SmartPLS, MaxQDA | Yes | BYU |
| James Gaskin | SEM, PLS, Cluster | SPSS, AMOS, SmartPLS | Yes | BYU |
| Quantitative Specialists | Basic Stats | SPSS | No | Upbeat videos |
| RStatsInstitute | Basic Stats | SPSS | No | Instructor at Udemy |
| how2stats | Basic Stats, lecture and software demonstrations | SPSS | Yes | Complete Classical Stats |
| BrunelASK | Basic Stats | SPSS | - | |
| The Doctoral Journey | Basic Stats | SPSS | Yes | - |
| StatisticsLectures | Basic Stats, lecture format | SPSS | Yes | discontinued, but thorough basic stats |
| Andy Field | Classical Stats, lecture and software demonstrations | SPSS | Yes, registration needed | Used heavely in Social Sciences |
| Quinnipiac University:Biostatistics | Classical Stats | SPSS | No | - |
| The RMUoHP Biostatistics | Basic and Bio-Stats | SPSS, Excel | No | - |
| PUB708 Team | Classical Statistics | SPSS, MiniTab | No | - |
| Professor Ami Gates | Classical Stats | SPSS, Excel, StatCrunch | Yes | - |
| H. Michael Crowson | Intro and Basic Stats in several Softare | SPSS, STATA, AMOS, LISREAL | Yes? | - |
| Math Guy Zero | Classical Stats + SEM | SPSS, Excel, PLS | No | Lots of materials |
| BayesianNetworks | Bayesian Statistics, SEM, Causality | BayesianLab | Yes | - |
| Khan Academy | Programming 101 | Python | Yes | - |
| Mike's SAS | Short intro to SAS, SPSS | SAS, SPSS | No | - |
| Christian A. Wandeler | Basic Stats | PSPP | No | - |
3. Lectures on statistics
| Youtube channel | Content | Software | Online Materials? | Remarks |
|---|---|---|---|---|
| Stomp On Step 1 | [Intro] Bio-Stats, Basic | Lectures | Yes | USMLE |
| Khan Academy | [Intro] Basic Stats, lecture format | Lectures | Yes | - |
| Joseph Nystrom | [Intro] Basic Stats | Lectures | Yes | Active & unorthodox teaching |
| Statistics Learning Centre | [Intro] Basic Stats | Lectures | Yes | Register to access materials |
| Brandon Foltz | [Intro] Basic Stats | Lectures | soon | Excellent visuals |
| David Waldo | [Intro] Probability Theory | Lectures | No | - |
| Andrew Jahn | [Intro] Basic Stats | Lectures | No | FSL, AFNI and SPM [Neuro-immaging] |
| Professor Leonard | [Intro] Stats and Maths | Lectures | No | Excellent pedagogical skills |
| ProfessorSerna | [Intro] Basic Stats | Lectures | No | - |
| Harvard University | [Intro] Thorough Introduction to Probability Theory and Statistics | Lectures | No | In-depth |
| Victor Lavrenko | Machine Learning, Probabilistic, Cluster, PCA, Mixture Models | Lectures | Yes, very complete | Excellent Content, and lots of it |
| Jeremy Balka's Statistics | Graduate-level Classical Stats, Lecture | Lectures | Yes, very thorough | Excellent altogether, p-value vid great! |
| Methods Manchester Uni | Discussion on a wide variety of methods, SEM | Lectures | Yes | Methods Fair |
| Steve Grambow | Series on Inference | Lectures | Yes | Great Lectures on Inference [DUKE] |
| Statistics Corner: Terry Shaneyfelt | Statistical Inference | Lectures | Yes | from a clinical perspective |
| Michel van Biezen | Complete Course of Stats | Lectures | Yes, 1, 2, 3 | Thorough and complete, plus Physics and Maths |
| Oxford Education | Bayesian statistics: a comprehensive course | Lectures | Yes | - |
| Nando de Freitas | Machine Learning | Lectures | Yes, also here and here | - |
| Alex Smola | Machine Learning | Lectures | Yes, slides and code | - |
| Abu (Abulhair) Saparov | Machine Learning | Lectures | Yes | Taught by Tom Mitchell and Maria-Florina Balcan |
| Geoff Gordon | Machine Learning, Optimization | Lectures | Yes | - |
| MIT OpenCourseWare | Probability Theory, Stochastic Processes | Lectures | Yes, here, and here | - |
| Alexander Ihler | Machine Learning | Lectures | Yes, along w/ many others classes | - |
| Royal Statistical Society | Important Statistical issues | Lectures | Yes | Interesting topics |
| Ben Lambert | Graduate and Advanced Stats | Lectures | No | Asymptotic Behaviour of Estimators, SEM, EFA |
| DeepLearning TV | Machine (and Deep) Learning | Lectures | No | Excellent pedagogical skills |
| Mathematical Monk | Machine Learning, and Probability Theory | Lectures | No | - |
| Matthew McIntosh | Probability Theory, Generalized Linear Models, underlying mathematics | Lectures | No | Excellent mathematical derivations |
Final Remarks
These collection of channels listed here are not supposed to be exhaustive. If I have neglected a youtube channel that you think should figure in this list, please let me know via the contact form below and I shall include it. Thank you very much!
postscriptum [21/09/2016]
I am delighted with the reaction this post received on social media, which is mainly due to being published on R-bloggers (Thank you Tal Galili) As of today, it has received 450 shares and 500 likes [from both here and here].