+ 15. (2023) A Practical Guide to Dealing with Attrition in Experiments Journal of Experimental Political Science.

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A Lo, J. Renshon, L. Bassan-Nygate+. “A Practical Guide to Dealing with Attrition in Political Science Experiments.” Journal of Experimental Political Science, September 18, 2023, 1–15. https://doi.org/10.1017/XPS.2023.22.

Paired R package: attritevis.

+ 14. (2023) Mapping Literatures with Networks: an Application to Redistricting Political Analysis.

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A Lo, Judge-Lord, D., Hudson, K.+ and Mayer, K. (2023). Mapping Literature with Networks: An Application to Redistricting. Political Analysis, 1-10.

Understanding the gaps and connections across existing theories and findings is a perennial challenge in scientific research. Systematically reviewing scholarship is especially challenging for researchers who may lack domain expertise, including junior scholars or those exploring new substantive territory. Conversely, senior scholars may rely on longstanding assumptions and social networks that exclude new research. In both cases, ad hoc literature reviews hinder accumulation of knowledge. Scholars are rarely systematic in selecting relevant prior work or then identifying patterns across their sample. To encourage systematic, replicable, and transparent methods for assessing literature, we propose an accessible network-based framework for reviewing scholarship. In our method, we consider a literature as a network of recurring concepts (nodes) and theorized relationships among them (edges). Network statistics and visualization allow researchers to see patterns and offer reproducible characterizations of assertions about the major themes in existing literature. Critically, our approach is systematic and powerful but also low-cost; it requires researchers to enter relationships they observe in prior studies into a simple spreadsheet --- a task accessible to new and experienced researchers alike. Our open-source R package enables researchers to leverage powerful network analysis while minimizing software-specific knowledge. We demonstrate this approach by reviewing redistricting literature.

Paired R package: netlit. R netlit package vignette

+ 13. (2023) Do Equity Diversity and Inclusion (EDI) requirements shape student attitudes toward inclusion? Politics Groups & Identities.

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Adida, C. L., A Lo, Rhee, I., & Williams, S+. (2023). Do equity, diversity and inclusion (EDI) requirements change student political attitudes?. Politics, Groups, and Identities, 1-10.

Over the past few years, equity-diversity-inclusion (EDI) requirements have proliferated in higher learning institutions. What impact have they had on students? This paper leverages a unique opportunity to evaluate the effect of an EDI requirement at a large public university: taught by the same instructor over the course of four quarters in three years, the course administered short anonymous surveys to students at the beginning and at the end of each term. These surveys measured student attitudes toward various policy debates from refugee admissions to affirmative action. Repeated surveys and repeated quarters allow us to evaluate changes in student attitudes during each quarter and averaged over three years. Results indicate an increase in inclusionary attitudes driven largely by students at the lowest baseline levels. Further analyses allow us to rule out social desirability bias, and suggest one possible mechanism: participation in small-group peer discussions.

+ 12. (2022) The Polarization of Politics and Public Opinion and their Effects on Racial Inequality in COVID Mortality. PLOSOne.

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A Lo, Pifarré i Arolas, H., Renshon, J. & Liang, S#. The polarization of politics and public opinion and their effects on racial inequality in COVID mortality. Plos one, 17(9), e0274580.

Evidence from the early months of the COVID-19 pandemic in the U.S. indicated that the virus had vastly different effects across races, with black Americans faring worse on dimensions including illness, hospitalization and death. New data suggests that our understanding of the pandemic's racial inequities must be revised given the closing of the gap between black and white COVID-related mortality. Initial explanations for inequality in COVID-related outcomes concentrated on static factors---e.g., geography, urbanicity, segregation or age-structures---that are insufficient on their own to explain observed time-varying patterns in inequality. Drawing from a literature suggesting the relevance of political factors in explaining pandemic outcomes, we highlight the importance of political polarization---the partisan divide in pandemic-related policies and beliefs---that varies over time and across geographic units. Specifically, we investigate the role of polarization through two political factors, public opinion and state-level public health policies, using fine-grained data on disparities in public concern over COVID and in state containment/health policies to understand the changing pattern of inequality in mortality. We show that (1) apparent decreases in inequality are driven by increasing total deaths---mostly among white Americans---rather than decreasing mortality among black Americans (2) containment policies are associated with decreasing inequality, likely resulting from lower relative mortality among Blacks (3) as the partisan disparity in Americans who were "unconcerned" about COVID increased, racial inequality in COVID mortality decreased, generating the appearance of greater equality consistent with a "race to the bottom" explanation as overall deaths increased and substantively swamping the effects of containment policies.

+ 11. (2022) United they stand: findings from an escalation prediction competition. International Interactions.

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P. Vesco, Hegre, H., Colaresi, M., Bastiaan Jansen, R., Lo, A., Reisch, G., and Weidmann, N. "United they stand: findings from an escalation prediction competition." International Interactions, 1-37.

This article presents results and lessons learned from a prediction competition by ViEWS to improve collective scientific knowledge on forecasting (de-)escalation on the African continent. The competition call asked participants to forecast changes in state-based violence for the true future (October 2020 - March 2021) as well as for a held-out test partition. An external scoring committee, independent from both the organizers and participants, was formed to evaluate the models based on both qualitative and quantitative criteria, including performance, novelty, uniqueness and replicability. All models contributed to advance the research frontier by providing novel methodological or theoretical insight, including new data, or adopting innovative model specifications. While we discuss several facets of the competition that could be improved moving forward, the collection passes an important test. When we build a simple ensemble prediction model -- which draws on the unique insights of each contribution to differing degrees -- we can measure an improvement in the prediction from the group, over and above what any individual model can achieve. This wisdom of the crowd effect suggests that future competitions that build on both the successes and failures of ours, can contribute to scientific knowledge by incentivising diverse contributions as well as focusing a group's attention on a common problem.

+ 10. (2021) Refugees to the Rescue? Motivating pro-refugee public engagement during the COVID-19 pandemic. Journal of Experimental Political Science.

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C. Adida, Lo, A., Prather, L., and Williamson, S. "Refugees to the Rescue? Motivating Pro-Refugee Public Engagement during the COVID-19 Pandemic," Journal of Experimental Political Science. 1–15 (2021). Manuscript Materials

Migrants are often scapegoated during public health crises. Can such crises create opportunities for migrant inclusion instead? As the COVID-19 pandemic unfolds, many refugee organizations have stepped up their outreach with stories of refugees helping out in the crisis. We have partnered with the country’s leading refugee advocate organizations to test whether solidarity narratives increase public engagement with refugee advocates. We employ a Facebook experimental design to evaluate the effectiveness of refugee narratives. We test whether (1) migrant narratives framed in the context of COVID-19, (2) COVID-19 migrant narratives targeted to more or less local communities, and (3) COVID-19 migrant narratives labeled as refugee vs. immigrant efforts, enhance public engagement with refugee organizations. Our results indicate that migrant narratives framed in the context of COVID-19 do not motivate greater engagement than those that make no mention of the pandemic. Our results provide suggestive evidence that locally-targeted efforts motivate greater engagement. Finally, we find no difference between the “refugee” and “immigrant” label, but we show that both labels can motivate greater engagement than ads that include neither. Importantly, this is true even in the context of COVID-19, an uncertain environment where worries of backlash might be warranted. These results suggest promising strategies for migrant policy organizations to promote engagement during and possibly after the pandemic.

+ 9. (2021) Years of life lost to COVID-19 in 81 countries. Scientific Reports.

Materials. Media: CGTN, CNN, Daily Mail, EurekAlert, Independent, Liberation, Live Science, Mirage, Mirror, MSN, NYTimes, Telegraph, US News, Washington Post, Yahoo. Manuscript.

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Pifarré i Arolas, H., Acosta, E., Casasnovas, G.L., Lo, A., Nicodemo, C., Riffe, T., & Myrskylä, M. Years of life lost to COVID-19 in 81 countries. Sci Rep 11, 3504 (2021). https://doi.org/10.1038/s41598-021-83040-3

Understanding the mortality impact of COVID-19 requires not only counting the dead, but analyzing how premature the deaths are. We calculate years of life lost (YLL) across 81 countries due to COVID-19 attributable deaths, and also conduct an analysis based on estimated excess deaths. We find that over 20.5 million years of life have been lost to COVID-19 globally. As of January 6, 2021, YLL in heavily affected countries are 2 to 9 times the average seasonal influenza; three quarters of the YLL result from deaths in ages below 75 and almost a third from deaths below 55; and men have lost 45% more life years than women. The results confirm the large mortality impact of COVID-19 among the elderly. They also call for heightened awareness in devising policies that protect vulnerable demographics losing the largest number of life-years.

+ 8. (2020) Family Matters: How Immigrant Histories Can Promote Inclusion. American Political Science Review.

Manuscript and materials. Article in The Conversation.

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Williamson, S., Adida, C., Lo, A., Platas, M., Prather, L., & Werfel, S. (2020). Family Matters: How Immigrant Histories Can Promote Inclusion. American Political Science Review, 1-8. doi:10.1017/S0003055420001057

Immigration is a highly polarized issue in the United States, and negative attitudes toward immigrants are common. Yet, almost all Americans are descended from people who originated outside the country, a narrative often evoked by the media and taught in school curricula. Can this narrative increase inclusionary attitudes toward migrants? We draw from scholarship showing that perspective-taking decreases prejudice toward out-groups to investigate whether reminding Americans about their own immigration history increases support for immigrants and immigration. We propose that priming family experiences can indirectly stimulate perspective-taking and induce empathy toward the out-group, which we test with three separate survey experiments conducted over two years. Our findings show that priming family history generates small but consistent inclusionary effects. These effects occur even among partisan subgroups and Americans who approve of President Trump. We provide evidence that increased empathy for immigrants constitutes one mechanism driving these effects.

+ 7. (2019) Americans preferred Syrian refugees who are female, English-speaking, and Christian on the eve of Donald Trump’s election. PLoS ONE.

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Adida CL, Lo A, Platas MR (2019) Americans preferred Syrian refugees who are female, English-speaking, and Christian on the eve of Donald Trump’s election. PLoS ONE 14(10): e0222504. 22: https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0222504

What types of refugees do Americans prefer for admission into the United States? Scholars have explored the immigrant characteristics that appeal to Americans and the characteristics that Europeans prioritize in asylum-seekers, but we currently do not know which refugee characteristics Americans prefer. We conduct a conjoint experiment on a representative sample of 1800 US adults, manipulating refugee attributes in pairs of Syrian refugee profiles, and ask respondents to rate each refugee’s appeal. Our focus on Syrian refugees in a 2016 survey experiment allows us to speak to the concurrent refugee crisis on the eve of a polarizing election, while also identifying religious discrimination, holding constant the refugee’s national origin. We find that Americans prefer Syrian refugees who are female, high-skilled, English-speaking, and Christian, suggesting they prioritize refugee integration into the U.S. labor and cultural markets. We find that the preference for female refugees is not driven by the desire to exclude Muslim male refugees, casting doubt that American preferences at the time were motivated by security concerns. Finally, we find that anti-Muslim bias in refugee preferences varies in magnitude across key subgroups, though it prevails across all sample demographics.

+ 6. (2018) Perspective taking can promote short-term inclusionary behavior toward Syrian refugees. PNAS.

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Adida, C., Lo A, and Platas, M., 2018. Perspective taking can promote short-term inclusionary behavior toward Syrian refugees, Proceedings of the National Academy of Sciences, 115 (38) 9521-9526.

We investigate whether American citizens can be persuaded to adopt more inclusionary behavior toward refugees using a minimally invasive online perspective-taking exercise frequently used by refugee advocates in the real world. Through the use of a randomized survey experiment on a representative sample of American citizens we find that this short and interactive perspective-taking exercise can promote, in the short term, Americans’ willingness to act on behalf of Syrian refugees, by writing anonymous letters of support to the White House. This effect, while driven primarily by self-identified Democrats, is also apparent among self-identified Republicans.

*Winner of the APSA Migration & Citizenship Section 2018 Best Paper Award.

Replication files.

+ 5. (2016) Framework for making better prediction by directly estimating variables’ predictivity. PNAS.

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Lo A, Chernoff, H., Zheng, T. and Lo, S.H., 2016. Framework for making better prediction by directly estimating variables’ predictivity, Proceedings of the National Academy of Sciences, 113(50), pp.14277-14282.

Good prediction, especially in the context of big data, is important. Common approaches to prediction include using significance-based criterion for evaluating variables to use in models and evaluating variables and models simultaneously for prediction using cross-validation or independent test data. The first approach can lead to choosing less predictive variables, as significance does not imply predictivity. The second approach can be improved through considering a variable’s predictivity as a parameter to be estimated. The literature currently lacks measures that do this. We suggest a novel measure that evaluates variables’ abilities to predict, the I-score. It is effective in differentiating between noisy and predictive variables in big data and can be related to a lower bound for the correct prediction rate.

+ 4. (2016) Network-guided interaction mining for blood pressure phenotype of unrelated individuals in GAW19. BMC Proceedings.

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A. Lo, M. Agnes, J. Auerbach, R. Fan, S. Lo, P. Wang, & T. Zheng, 2016.”Network-guided interaction mining for blood pressure phenotype of unrelated individuals in GAW19” BMC Proceedings. 10 (Suppl 7):13.

Interactions between genes are an important part of the genetic architecture of complex diseases. In this paper, we use literature-guided individual genes known to be associated with type 2 diabetes (referred to as “seed genes”) to create a larger list of genes that share implied or direct networks with these seed genes. This larger list of genes are known to interact with each other, but whether they interact in ways to influence hypertension in individuals presents an interesting question. Using Genetic Analysis Workshop data on individuals with diabetes, for which only case-control labels of hypertension are known, we offer a foray into identification of diabetes-related gene interactions that are associated with hypertension. We use the approach of Lo et al. (Proc Natl Acad Sci U S A 105: 12387-12392, 2008), which creates a score to identify pairwise significant gene associations. We find that the genes GCK and PAX4, formerly known to be found within similar coexpression and pathway networks but without specific direct interactions, do, in fact, show significant joint interaction effects for hypertension.

+ 3. (2016) Identifying regions of disease-related variants in admixed populations with the summation partition approach. BMC Proceedings.

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Auerbach, J., Agne, M., Fan, R., A. Lo, Lo, S., Zheng, T. & P. Wang. "Identifying regions of disease-related variants in admixed populations with the summation partition approach." BMC Proc 10, 32 (2016).

We propose a new method for identifying disease-related regions of single nucleotide variants in recently admixed populations. We use principal component analysis to derive both global and local ancestry information. We then use the summation partition approach to search for disease-related regions based on both rare variants and the local ancestral information of each region. We demonstrate this method using individuals with high systolic blood pressure from a sample of unrelated Mexican American subjects provided in the 19th Genetic Analysis Workshop.

+ 2. (2016) The Spousal Bump: Do Cross-Ethnic Marriages Increase Political Support in Multiethnic Democracies? Comparative Political Studies.

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Adida, C., Combes, N., Lo, A., & Verink, A. The Spousal Bump: Do Cross-Ethnic Marriages Increase Political Support in Multiethnic Democracies? Comparative Political Studies, April 2016; vol. 49, 5: pp. 635–661.

In democratic Africa, where ethnicity is a key driver of vote choice, politicians must attract voters across ethnic lines. This article explores one way politicians can do this: by appealing to a coethnic bond through their spouse. We propose that cross-ethnic spouses can help candidates send credible signals of coalition building before an election. We test this argument with a survey experiment in Benin, where President Yayi has married across ethnic lines. Our results confirm that priming the first lady’s ethnicity increases support for President Yayi among her coethnics. We generalize these results by combining new data on leader-spouse ethnicity with Afrobarometer survey data. Our results suggest that cross-ethnic marriages are one tool leaders can use to shore up support in multiethnic elections. Replication files.

+ 1. (2015) Why significant variables aren’t automatically good predictors. PNAS.

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Lo, A., Chernoff, H., Zheng, T. and Lo, S.H., 2015. Why significant variables aren’t automatically good predictors. Proceedings of the National Academy of Sciences, 112(45), pp.13892-13897.

A recent puzzle in the big data scientific literature is that an increase in explanatory variables found to be significantly correlated with an outcome variable does not necessarily lead to improvements in prediction. This problem occurs in both simple and complex data. We offer explanations and statistical insights into why higher significance does not automatically imply stronger predictivity and why variables with strong predictivity sometimes fail to be significant. We suggest shifting the research agenda toward searching for a criterion to locate highly predictive variables rather than highly significant variables. We offer an alternative approach, the partition retention method, which was effective in reducing prediction error from 30% to 8% on a long-studied breast cancer data set.