Resumo: Como mensurar fenômenos que não podem ser diretamente observados? O principal objetivo desse artigo é demonstrar por que a análise fatorial é a resposta mais adequada para responder a essa pergunta. Metodologicamente, utilizamos um banco de dados com diferentes medidas de democracia para ilustrar como a técnica de análise fatorial de componentes principais pode ser utilizada para medir as duas dimensões da poliarquia propostas por Robert Dahl (1971): contestação e inclusividade. Em termos substantivos, esperamos facilitar a compreensão dessa técnica nas Ciências Sociais em geral e na Ciência Política em particular.
Abstract: How do we measure phenomena that cannot be directly observed? The principal aim of this paper is to demonstrate why factor analysis technique is the best answer to this question. Methodologically, we use a database with different indicators of democracy to show how principal component analysis can be employed to measure the two polyarchy dimensions proposed by Robert Dahl (1971): contestation and inclusiveness. On substantive grounds, we hope to facilitate the understanding of factor analysis technique in Social Sciences in general and in Political Science in particular

Públicas (UFAL), além disso agradecemos ao CNPQ e a FACEPE pelo apoio financeiro. ResumoQuais são as vantagens da triangulação metodológica? Apesar de ser consensual a importância da combinação de técnicas quantitativas e qualitativas, ainda são raros os trabalhos que efetivamente utilizam uma abordagem multimétodo. Este artigo apresenta uma introdução aos métodos mistos. Nosso público alvo são estudantes de graduação e pós-graduação em estágios iniciais de treinamento. Metodologicamente, sintetizamos as principais recomendações da literatura e utilizamos dois exemplos para ilustrar como a combinação de técnicas pode ser empregada em desenhos de pesquisa empíricos. Com este artigo, esperamos difundir a utilização de métodos mistos nas Ciências Sociais brasileiras 1 . Palavras-chaves: Métodos Mistos. Triangulação Metodológica. Ciência Política.

The article provides a non-technical introduction to the p value statistics. Its main purpose is to help researchers make sense of the appropriate role of the p value statistics in empirical political science research. On methodological grounds, we use replication, simulations and observational data to show when statistical significance is not significant. We argue that: (1) scholars must always graphically analyze their data before interpreting the p value; (2) it is pointless to estimate the p value for non-random samples; (3) the p value is highly affected by the sample size, and (4) it is pointless to estimate the p value when dealing with data on population.

In response to the COVID-19 pandemic, governments worldwide have implemented social distancing policies with different levels of both enforcement and compliance. We conducted an interrupted time series analysis to estimate the impact of lockdowns on reducing the number of cases and deaths due to COVID-19 in Brazil. Official daily data was collected for four city capitals before and after their respective policies interventions based on a 14 days observation window. We estimated a segmented linear regression to evaluate the effectiveness of lockdown measures on COVID-19 incidence and mortality. The initial number of new cases and new deaths had a positive trend prior to policy change. After lockdown, a statistically significant decrease in new confirmed cases was found in all state capitals. We also found evidence that lockdown measures were likely to reverse the trend of new daily deaths due to COVID-19. In São Luís, we observed a reduction of 37.85% while in Fortaleza the decrease was 33.4% on the average difference in daily deaths if the lockdown had not been implemented. Similarly, the intervention diminished mortality in Recife by 21.76% and Belém by 16.77%. Social distancing policies can be useful tools in flattening the epidemic curve.

Which substantive meaning we can draw from the R2 statistic? The coefficient of determination is defined as the sum of squares due to the regression divided by the sum of total squares. Usually, R2 is interpreted as representing the percentage of variation in the dependent variable explained by variation in the independent variables. This definition is found by both econometrics and statistics handbooks and is widely accepted among quantitative scholars. However, this view is challenged by one of the most distinguished political science methodologists. Gary King argues that the R2 is highly misused as a measure of the influence of X on Y. This paper analyzes the R2 statistic using a non-technical approach. Our purpose is to provide an intuitive understanding of the coefficient of determination and its major shortcomings.

O principal objetivo desse trabalho é aprofundar a discussão a respeito do conceito de correlação de Pearson. Para tanto, apresentamos as suas propriedades, aplicações e limites. Metodologicamente, utilizamos simulação básica para demonstrar como a presença de outliers pode tanto ocultar correlações reais quanto forjar associações inexistentes. Além disso, discutimos os perigos analíticos das correlações espúrias. Com esse artigo esperamos facilitar não só a interpretação, mas também a utilização da técnica de correlação na Ciência Política

We employ Newcomb–Benford law (NBL) to evaluate the reliability of COVID-19 figures in Brazil. Using official data from February 25 to September 15, we apply a first digit test for a national aggregate dataset of total cases and cumulative deaths. We find strong evidence that Brazilian reports do not conform to the NBL theoretical expectations. These results are robust to different goodness of fit (chi-square, mean absolute deviation and distortion factor) and data sources (John Hopkins University and Our World in Data). Despite the growing appreciation for evidence-based-policymaking, which requires valid and reliable data, we show that the Brazilian epidemiological surveillance system fails to provide trustful data under the NBL assumption on the COVID-19 epidemic.

Introduction: What if my response variable is binary categorical? This paper provides an intuitive introduction to logistic regression, the most appropriate statistical technique to deal with dichotomous dependent variables. Materials and Methods: we estimate the effect of corruption scandals on the chance of reelection of candidates running for the Brazilian Chamber of Deputies using data from Castro and Nunes (2014). Specifically, we show the computational implementation in R and we explain the substantive interpretation of the results. Results: we share replication materials which quickly enables students and professionals to use the procedures presented here for their studying and research activities. Discussion: we hope to facilitate the use of logistic regression and to spread replication as a data analysis teaching tool.