The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

This project points to an article in The Stata Journal describing a set of routines to preprocess nominal data (firm names and addresses), perform probabilistic linking of two datasets, and display candidate matches for clerical review.The ado files and supporting pattern files are downloadable within Stata.

List of Top Schools of The Stata Journal sorted by citations.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

Explore the historical Whois records related to stata-journal.com (Domain). Get insights into ownership history and changes over time.

Revised STATA do-file and dataset prepared for journal article resubmission.

This repository contains the data for indicators which were reanalysed in the paper:

Ho L, Mercer SW, Henderson D, Donaghy E, Guthrie B. Did the UK Quality and Outcomes Framework pay-for-performance programme improve quality of primary care? Systematic review with quantitative synthesis.

For any queries, please contact Bruce Guthrie, Professor of General Practice, University of Edinburgh bruce.guthrie@ed.ac.uk

Data is contained in a set of Excel files. Also provided is the STATA code used in analysis which uses the itsa command to fit interrupted time series analysis models, and lincom to estimate absolute impact at 1 and 3 years after intervention. Users will have to import from wherever they save these files (our own import and graph save commands are commented out).

Please refer to these documents for details of how to use itsa and how to use lincom for this purpose.

Linden A. Conducting interrupted time-series analysis for single- and multiple-group comparisons. The Stata Journal. 2015;15(2):480-500 https://journals.sagepub.com/doi/10.1177/1536867X1501500208

Linden A. A comprehensive set of postestimation measures to enrich interrupted time-series analysis. The Stata Journal. 2017;17(1):73-88 https://journals.sagepub.com/doi/epdf/10.1177/1536867X1701700105

Description

Subscriptions to economics journals at US libraries, for the year 2000.

Usage

data("Journals")

Format

A data frame containing 180 observations on 10 variables.

title

Journal title.

publisher

factor with publisher name.

society

factor. Is the journal published by a scholarly society?

price

Library subscription price.

pages

Number of pages.

charpp

Characters per page.

citations

Total number of citations.

foundingyear

Year journal was founded.

subs

Number of library subscriptions.

field

factor with field description.

Details

Data on 180 economic journals, collected in particular for analyzing journal pricing. See also https://econ.ucsb.edu/~tedb/Journals/jpricing.html for general information on this topic as well as a more up-to-date version of the data set. This version is taken from Stock and Watson (2007).

The data as obtained from the online complements for Stock and Watson (2007) contained two journals with title “World Development”. One of these (observation 80) seemed to be an error and was changed to “The World Economy”.

Source

Online complements to Stock and Watson (2007).

References

Bergstrom, T. (2001). Free Labor for Costly Journals? Journal of Economic Perspectives, 15, 183–198.

Stock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.

Examples

data and transformed variables

data("Journals") journals <- Journals[, c("subs", "price")] journals$citeprice <- Journals$price/Journals$citations journals$age <- 2000 - Journals$foundingyear journals$chars <- Journals$charpp*Journals$pages/10^6

Stock and Watson (2007)

Figure 8.9 (a) and (b)

plot(subs ~ citeprice, data = journals, pch = 19) plot(log(subs) ~ log(citeprice), data = journals, pch = 19) fm1 <- lm(log(subs) ~ log(citeprice), data = journals) abline(fm1)

Table 8.2, use HC1 for comparability with Stata

fm2 <- lm(subs ~ citeprice + age + chars, data = log(journals)) fm3 <- lm(subs ~ citeprice + I(citeprice^2) + I(citeprice^3) + age + I(age * citeprice) + chars, data = log(journals)) fm4 <- lm(subs ~ citeprice + age + I(age * citeprice) + chars, data = log(journals)) coeftest(fm1, vcov = vcovHC(fm1, type = "HC1")) coeftest(fm2, vcov = vcovHC(fm2, type = "HC1")) coeftest(fm3, vcov = vcovHC(fm3, type = "HC1")) coeftest(fm4, vcov = vcovHC(fm4, type = "HC1")) waldtest(fm3, fm4, vcov = vcovHC(fm3, type = "HC1"))

changes with respect to age

library("strucchange")

Nyblom-Hansen test

scus <- gefp(subs ~ citeprice, data = log(journals), fit = lm, order.by = ~ age) plot(scus, functional = meanL2BB)

estimate breakpoint(s)

journals <- journals[order(journals$age),] bp <- breakpoints(subs ~ citeprice, data = log(journals), h = 20) plot(bp) bp.age <- journals$age[bp$breakpoints]

visualization

plot(subs ~ citeprice, data = log(journals), pch = 19, col = (age > log(bp.age)) + 1) abline(coef(bp)[1,], col = 1) abline(coef(bp)[2,], col = 2) legend("bottomleft", legend = c("age > 18", "age < 18"), lty = 1, col = 2:1, bty = "n")

Additional file 3: Stata Search Code. Stata code used to conduct the search of the Stata manual, Stata journal and user-written code.

This is a dataset underlying article "Relational Work and Its Pitfalls: Nonprofits’ Participation in Government-Sponsored Voluntary Accreditation" published by Journal of Public Administration Research and Theory

Quality assessment of the included 32 studies in the meta-analysis.

Stata dta and do file of the paper "Stoop, J., Van Soest, D., and Vyrastekova, J. (2018). Rewards and cooperation in social dilemma games. Journal of Environmental Economics and Management, 88, 300-310".The do file shows the statistical analyses of this paper, showed in the order in which they appear in the paper.

The pooled effect size (summary r) after conversion.

Stata code for Diagnosing Expertise: Decision Making and Performance Among Physicians', forthcoming in Journal of Labor Economics, January 2017.

The characteristics of SMA-related 32 studies included in this meta-analysis.

This record contains the Stata do files for "Provider practice style and patient health outcomes: The case of heart attacks", Journal of Health Economics 47 (2016) 64–80 by J. Currie, W. B. MacLeod and J. van Parys. The data is not publicly available.

Network meta-analysis synthesizes direct and indirect evidence in a network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness network meta-analysis is often criticized for its complexity and for being accessible only to researchers with strong statistical and computational skills. The evaluation of the underlying model assumptions, the statistical technicalities and presentation of the results in a concise and understandable way are all challenging aspects in the network meta-analysis methodology. In this paper we aim to make the methodology accessible to non-statisticians by presenting and explaining a series of graphical tools via worked examples. To this end, we provide a set of STATA routines that can be easily employed to present the evidence base, evaluate the assumptions, fit the network meta-analysis model and interpret its results.

This dataset and code archive accompanies the paper "Gambling on Crypto Tokens?" by Chava, Hu, and Paradkar (2024), forthcoming in the Journal of Financial and Quantitative Analysis. It includes:

CSV files containing the raw and processed data used in the analysis

Stata DO files with clearly documented code replicating the regression results from the study

In-code descriptions and comments outlining variable definitions and linking specific code segments to published tables and figures

The study explores speculative trading behaviors and financial patterns in cryptocurrency markets, drawing parallels with gambling activities. This resource enables full replication and supports further research on digital finance and market psychology.

Subjects: Business and Management; Social Sciences License: Creative Commons CC0 1.0 Universal (Public Domain Dedication) DOI: https://doi.org/10.7910/DVN/HC97UC

All replication code for the above paper. This includes general-purpose R and Stata code, all simulation code, all empirical data sets, and R and Stata code to replicate the empirical results

September 1., 2016 REPLICATION FILES FOR «THE IMPACT OF STATE TELEVISION ON VOTER TURNOUT», TO BE PUBLISHED BY THE BRITISH JOURNAL OF POLITICAL SCIENCE The replication files consist of two datasets and corresponding STATA do-files. Please note the following: 1. The data used in the current microanalysis are based on the National Election Surveys of 1965, 1969, and 1973. The Institute of Social Research (ISF) was responsible for the original studies, and data was made available by the NSD (Norwegian Center for Research Data). Neither ISF nor NSD are responsible for the analyses/interpretations of the data presented here. 2. Some of the data used in the municipality-level analyses are taken from NSD’s local government database (“Kommunedatabasen”). The NSD is not responsible for the analysis presented here or the interpretation offered in the BJPS-paper. 3. Note the municipality identification has been anonymized to avoid identification of individual respondents. 4. Most of the analyses generate Word-files that are produced by the outreg2 facility in STATA. These tables can be compared with those presented in the paper. The graphs are directly comparable to those in the paper. In a few cases, the results are only generated in the STATA output window. The paper employs two sets of data: I. Municipal level data in entered in STATA-format (AggregateReplicationTVData.dta), and with a corresponding data with map coordinates (muncoord.dta). The STATA code is in a do-file (ReplicationOfAggregateAnalysis.do). II. The survey data is in a STATA-file (ReplicationofIndividualLevelPanel.dta) and a with a corresponding do-file (ReplicationOfIndividualLevelAnalysis 25.08.2016.do). Please remember to change the file reference (i.e. use-statement) to execute the do-files.