1,720,987 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Essays on panel data and sample selection methods
Full text linkThe availability of panel data allows researchers to control for unobserved heterogeneity in economic models, but raises important computational and statistical challenges. For instance, fixed effects estimators suffer from the incidental parameter problem and lead to high-dimensional estimation problems. In this dissertation, I aim to address both theoretical and practical issues in the estimation of panel data models.
Sample selection is one of the most common forms of endogeneity in empirical economics. It arises when the main dependent variable is selected into the sample through a nonrandom process. The classical solution to account for sample selection is the Heckman selection model (HSM). In this dissertation, I extend the HSM in two dimensions: (1) I relax the homogeneity restrictions that the HSM imposes; and (2) I develop a panel data version of the model that accounts for unobserved heterogeneity.
In Chapter 1, I develop a distribution regression model with sample selection for panel and network data. The model is a semiparametric generalization of the HSM that accommodates much richer patterns of heterogeneity in the selection process, covariates and unobserved effects. I provide a computationally attractive two-step fixed-effect estimation procedure, a bias correction method and a multiplier bootstrap algorithm to conduct uniform inference on the function-valued model parameters. I apply this model to the gravity equation of international trade network accounting for possibly endogenous zero trade decisions and unobserved country heterogeneity.
Chapter 2 focuses on the distribution regression model with sample selection for cross-sectional data. In this chapter, I study the identification of the model and apply the model to wage decompositions in the UK accounting for possibly endogeneous selection into employment. Here I decompose the difference between the male and female wage distributions into four effects: composition, wage structure, selection structure and selection sorting.
In Chapter 3, I propose a novel estimation algorithm for panel data models with multiple high-dimensional fixed effects and missing data. The algorithm absorbs the fixed effects iteratively until they are eventually eliminated. Applying this algorithm to a large-scale US employer-based health insurance data, I conclude that narrow network plans reduce health care utilization
Essays in econometrics: bias corrections and robust inference
Full text linkUnobserved heterogeneity is common in economic data and has nontrivial impacts on modeling, estimation and statistical inference. This dissertation consists of three chapters that explore and illustrate the implications of unobserved heterogeneity for different types of data. The first two chapters focus on panel data, and the third chapter focuses on cross--sectional data.
A popular way to control for unobserved heterogeneity in panel data models is to include fixed effects, but fixed effect estimators of dynamic and nonlinear panel models are subject to the incidental parameter problem. This problem has two implications for applied research: (1) point estimators are largely biased, and (2) confidence intervals have incorrect coverages. Chapter 1 proposes a new method for bias reduction based on indirect inference. The method simulates data using the model with estimated individual effects, and finds estimators of the model parameters by equating the fixed effect estimates obtained from observed and simulated data. The asymptotic framework provides consistency, bias correction, and asymptotic normality results. An application to female labor force participation and numerical simulations illustrate the finite--sample performance of the method.
Chapter 2 is coauthored with Victor Chernozhukov at MIT, Iván Fernández-Val at BU, and Hiroyuki Kasahara and Paul Schrimpf at The University of British Columbia. It is based on the observation that existing jackknife methods that deal with the incidental parameter problem require stationary variables. However, many applications feature covariates of interests that have trends or structural breaks. This chapter proposes a new jackknife bias correction method that relaxes stationarity. The method is named crossover jackknife because it partitions the panel in two halves, each including half of the time series observations for each cross sectional unit, but where the time periods are crossed over between the two halves of the cross section units. We derive the theoretical properties of this method and illustrate its finite--sample performance via calibrated numerical simulations.
Chapter 3 is coauthored with Hiroaki Kaido at BU. In many important discrete choice models, whether the model makes a unique prediction or not depends on its policy-relevant features and can be examined by testing restrictions on underlying structural parameters. Imposing strong assumptions completes the model and allows to predict unique outcomes, but it masks heterogeneity of the data and affects statistical inference. We provide a new test of model incompleteness using a score-based statistic. Our test statistic remains computationally tractable even with a moderate number of nuisance parameters because they only need to be estimated in the restricted complete model. Two empirical applications illustrate the computational feasibility of the method. A Monte Carlo experiment shows the score test outperforms existing tests in terms of local power
The causal impact of user-centered counseling on women's reproductive health outcomes: evidence from Malawi
No full text2024Nearly half of all pregnancies in the U.S. —and comparable numbers worldwide—are unintended, leading to adverse health outcomes for mothers and children, financial constraints and family instability, diminished educational and developmental outcomes for children, and worse labor market outcomes for women. Effective family planning counseling can mitigate these consequences by correcting misinformation and misconceptions around contraceptive methods, and improve overall contraceptive decision-making for women. User-centered counseling (UCC) refines the standard counseling approach by first eliciting women’s contraceptive preferences, then tailoring the session to discuss a subset of methods aligned with those preferences. We conduct a randomized controlled trial in Malawi involving over 700 women, where we evaluate the effects of UCC alongside partner invitation (PI), where women are encouraged to involve their partners in the counseling process. In the first chapter, we study the impacts of both interventions on several outcomes around contraceptive method use dynamics and fertility. We find that women encouraged to involve their partners tended to move from injectables to implants, suggesting a partner-driven preference. However, UCC appears to neutralize this effect. Contraceptive consistency, proxied by the maximum number of consecutive months women use same method or number of months women use any method, was significantly influenced by UCC, fostering more regular use of contraceptives, although it was somewhat mitigated when combined with PI. In terms of fertility outcomes, we observe that UCC seems to reduce pregnancies and births, but when combined with PI, the effect is the opposite. Finally, neither UCC nor PI significantly alter the likelihood of discontinuation. In the second chapter, we explore how women’s preferences over contraceptive methods evolve and whether method uses are concordant with these preferences or not. We first document that preferences vary significantly in both the short and long term. We then observe that both user-centered counseling and partner invitation interventions have minimal impact on aligning women’s contraceptive preferences with their actual usage, both in the short and long term. In the third chapter, we apply two methods to examine the heterogeneous impacts of UCC on a range of family planning outcomes. Firstly, we utilize the Sorted Effects method, which involves estimating and ordering partial effects based on covariates, graphically representing this variation, and also comparing the mean characteristics of the most with the least affected women. Secondly, we adopt the Generic Machine Learning method, suitable for our data’s high-dimensional nature, which helps estimate features (Best Linear Predictor (BLP) and Group Average Treatment Effects (GATES)) of the conditional average treatment effects without relying on predetermined covariates. Nonetheless, our experiment’s power to uncover meaningful heterogeneities in the impact of UCC is limited
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