CLARIFY: Software for Interpreting and Presenting Statistical Results
TLDR
Clarify is a program that uses Monte Carlo simulation to convert the raw output of statistical procedures into results that are of direct interest to researchers, without changing statistical assumptions or requiring new statistical models.Abstract:
Clarify is a program that uses Monte Carlo simulation to convert the raw output of statistical procedures into results that are of direct interest to researchers, without changing statistical assumptions or requiring new statistical models. The program, designed for use with the Stata statistics package, offers a convenient way to implement the techniques described in: Gary King, Michael Tomz, and Jason Wittenberg (2000). "Making the Most of Statistical Analyses: Improving Interpretation and Presentation." American Journal of Political Science 44, no. 2 (April 2000): 347-61.
We recommend that you read this article before using the software.
Clarify simulates quantities of interest for the most commonly used statistical models, including linear regression, binary logit, binary probit, ordered logit, ordered probit, multinomial logit, Poisson regression, negative binomial regression, weibull regression, seemingly unrelated regression equations, and the additive logistic normal model for compositional data. Clarify Version 2.1 is forthcoming (2003) in Journal of Statistical Software.read more
Citations
More filters
Journal ArticleDOI
Ethnicity, Insurgency, and Civil War
James D. Fearon,David D. Laitin +1 more
TL;DR: This article showed that the current prevalence of internal war is mainly the result of a steady accumulation of protracted conflicts since the 1950s and 1960s rather than a sudden change associated with a new, post-Cold War international system.
Journal ArticleDOI
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
TL;DR: A unified approach is proposed that makes it possible for researchers to preprocess data with matching and then to apply the best parametric techniques they would have used anyway and this procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences.
Posted Content
When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks
Tim Loughran,Bill McDonald +1 more
TL;DR: This article developed an alternative negative word list, along with five other word lists, that better reflect tone in financial text and linked the word lists to 10 K filing returns, trading volume, return volatility, fraud, material weakness, and unexpected earnings.
Journal ArticleDOI
When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks
Tim Loughran,Bill McDonald +1 more
TL;DR: In this paper, the authors show that word lists developed for other disciplines misclassify common words in financial text and develop an alternative negative word list, along with five other word lists, that better reflect tone of financial text.
Journal ArticleDOI
Maternal mortality for 181 countries, 1980-2008: a systematic analysis of progress towards Millennium Development Goal 5.
Margaret C. Hogan,Margaret C. Hogan,Kyle J Foreman,Mohsen Naghavi,Stephanie Y. Ahn,Mengru Wang,Susanna M Makela,Alan D. Lopez,Rafael Lozano,Christopher J L Murray +9 more
TL;DR: Although only 23 countries are on track to achieve a 75% decrease in MMR by 2015, countries such as Egypt, China, Ecuador, and Bolivia have been achieving accelerated progress and substantial, albeit varied, progress has been made towards MDG 5.
References
More filters
Journal ArticleDOI
Numerical Recipes in C: The Art of Scientific Computing
Mary C. Seiler,Fritz A. Seiler +1 more
Book
Regression Models for Categorical and Limited Dependent Variables
TL;DR: In this article, the authors propose Continuous Outcomes Binary Outcomes Testing and Fit Ordinal Outcomes Numeric Outcomes and Numeric Numeric Count Outcomes (NOCO).
Book
Analysis of Incomplete Multivariate Data
TL;DR: The Normal Model Methods for Categorical Data Loglinear Models Methods for Mixed Data and Inference by Data Augmentation Methods for Normal Data provide insights into the construction of categorical and mixed data models.
Journal ArticleDOI
Regression Models for Categorical and Limited Dependent Variables
TL;DR: Introduction Continuous Outcomes Binary Outcomes Testing and Fit Ordinal Outcomes Nominal outcomes Limited Outcomes Count Outcomes Conclusions
Journal ArticleDOI
Non-Uniform Random Variate Generation.
B. J. T. Morgan,Luc Devroye +1 more
TL;DR: This chapter reviews the main methods for generating random variables, vectors and processes in non-uniform random variate generation, and provides information on the expected time complexity of various algorithms before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.