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Institution

University of Notre Dame

EducationNotre Dame, Indiana, United States
About: University of Notre Dame is a education organization based out in Notre Dame, Indiana, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 22238 authors who have published 55201 publications receiving 2032925 citations. The organization is also known as: University of Notre Dame du Lac & University of Notre Dame, South Bend.


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Posted Content
01 Jan 2011
TL;DR: This article explains what adjusted predictions and marginal effects are, and how they can contribute to the interpretation of results, and shows how the marginsplot command provides a graphical and often much easier means for presenting and understanding the results from margins.
Abstract: As Long & Freese show, it can often be helpful to compute predicted/expected values for hypothetical or prototypical cases. Stata 11 introduced new tools for making such calculations – factor variables and the margins command. These can do many of the things that were previously done by Stata’s own adjust and mfx commands, as well as Long & Freese’s spost9 commands like prvalue. Unfortunately, the complexity of the margins syntax, the daunting 50 page reference manual entry that describes it, and a lack of understanding about what margins offers over older commands may have dissuaded researchers from using it. This paper therefore shows how margins can easily replicate analyses done by older commands. It demonstrates how margins provides a superior means for dealing with interdependent variables (e.g. X and X^2; X1, X2, and X1 * X2; multiple dummies created from a single categorical variable), and is also superior for data that are svyset. The paper explains how the new asobserved option works and the substantive reasons for preferring it over the atmeans approach used by older commands. The paper primarily focuses on the computation of adjusted predictions but also shows how margins has the same advantages for computing marginal effects.

1,228 citations

Posted Content
TL;DR: In this article, an analysis of the quantitative effects of agency costs in a real business cycle model is presented, showing that these costs can explain why output growth displays positive autocorrelation at short horizons.
Abstract: An analysis of the quantitative effects of agency costs in a real business cycle model, showing that these costs can explain why output growth displays positive autocorrelation at short horizons.

1,221 citations

Journal ArticleDOI
Yoshio Abe1, C. Aberle2, T. Akiri, J. C. dos Anjos  +185 moreInstitutions (31)
TL;DR: The Double Chooz experiment presents an indication of reactor electron antineutrino disappearance consistent with neutrino oscillations, and an observed-to-predicted ratio of events of 0.944±0.016 and a deficit can be interpreted as a nonzero value of the still unmeasured neutrinos mixing parameter sin(2)2θ(13).
Abstract: The Double Chooz experiment presents an indication of reactor electron antineutrino disappearance consistent with neutrino oscillations. An observed-to-predicted ratio of events of 0.944±0.016(stat)±0.040(syst) was obtained in 101 days of running at the Chooz nuclear power plant in France, with two 4.25GWth reactors. The results were obtained from a single 10m3 fiducial volume detector located 1050 m from the two reactor cores. The reactor antineutrino flux prediction used the Bugey4 flux measurement after correction for differences in core composition. The deficit can be interpreted as an indication of a nonzero value of the still unmeasured neutrino mixing parameter sin⁡22θ13. Analyzing both the rate of the prompt positrons and their energy spectrum, we find sin⁡22θ13=0.086±0.041(stat)±0.030(syst), or, at 90% C.L., 0.017

1,214 citations

Journal ArticleDOI
TL;DR: Results of a study that measured consumer satisfaction with the EC channel through constructs prescribed by three established frameworks, namely the Technology Acceptance Model (TAM), Transaction Cost Analysis (TCA), and Service Quality (SERVQUAL) found that TAM components--perceived ease of use and usefulness--are important in forming consumer attitudes and satisfaction withThe EC channel.
Abstract: Although electronic commerce (EC) has created new opportunities for businesses as well as consumers, questions about consumer attitudes toward Business-to-Consumer (B2C) e-commerce vis-a-vis the conventional shopping channels continue to persist. This paper reports results of a study that measured consumer satisfaction with the EC channel through constructs prescribed by three established frameworks, namely the Technology Acceptance Model (TAM), Transaction Cost Analysis (TCA), and Service Quality (SERVQUAL). Subjects purchased similar products through conventional as well as EC channels and reported their experiences in a survey after each transaction. Using constructs from the three frameworks, a model was constructed and tested to examine the determinants of the EC channel satisfaction and preference using the survey data.Structural equation model analyses indicate that metrics tested through each model provide a statistically significant explanation of the variation in the EC consumers' satisfaction and channel preference. The study found that TAM components--perceived ease of use and usefulness--are important in forming consumer attitudes and satisfaction with the EC channel. Ease of use also was found to be a signi.cant determinant of satisfaction in TCA. The study found empirical support for the assurance dimension of SERVQUAL as determinant in EC channel satisfaction. Further, the study also found general support for consumer satisfaction as a determinant of channel preference.

1,210 citations


Authors

Showing all 22586 results

NameH-indexPapersCitations
George Davey Smith2242540248373
David Miller2032573204840
Patrick O. Brown183755200985
Dorret I. Boomsma1761507136353
Chad A. Mirkin1641078134254
Darien Wood1602174136596
Wei Li1581855124748
Timothy C. Beers156934102581
Todd Adams1541866143110
Albert-László Barabási152438200119
T. J. Pearson150895126533
Amartya Sen149689141907
Christopher Hill1441562128098
Tim Adye1431898109010
Teruki Kamon1422034115633
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023115
2022543
20212,777
20202,925
20192,774
20182,624