This package contains two files designed to help read individual level DHS data into Stata. The first file addresses the problem that versions of Stata before Version 7/SE will read in only up to 2047 variables and most of the individual files have more variables than that. The file will read in the .do, .dct and .dat file and output new .do and .dct files with only a subset of the variables specified by the user. The second file deals with earlier DHS surveys in which .do and .dct file do not exist and only .sps and .sas files are provided. The file will read in the .sas and .sps files and output a .dct and .do file. If necessary the first file can then be run again to select a subset of variables.

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

Perturbed version of the data used in Van Dijcke, Gunsilius, and Wright (2024).

Contains (perturbed) tenure in days and job title of (anonymized) employees at US tech companies.

There are two files needed to replicate the analyses described and depicted in “Compulsory Voting and Voter Information Seeking,” by Shane P. Singh and Jason Roy, and its associated supplemental material. 1. The data, included in Stata format as “replication data, R&P.dta” 2. The Stata code, in a do-file, included as “replication code, R&P.do” To proceed with the replication, open the data in Stata. Then, open the do-file. The code can be run directly from the do-file. The do-file indicates which analyses correspond to the figures in the manuscript and the appendix. All models were estimated in Stata 13.

General information: The data sets contain information on how often materials of studies available through GESIS: Data Archive for the Social Sciences were downloaded and/or ordered through one of the archive´s plattforms/services between 2004 and 2018.

Sources and plattforms: Study materials are accessible through various GESIS plattforms and services: Data Catalogue (DBK), histat, datorium, data service (and others).

Years available: - Data Catalogue: 2012-2018 - data service: 2006-2018 - datorium: 2014-2018 - histat: 2004-2018

Data sets: Data set ZA6899_Datasets_only_all_sources contains information on how often data files such as those with dta- (Stata) or sav- (SPSS) extension have been downloaded. Identification of data files is handled semi-automatically (depending on the plattform/serice). Multiple downloads of one file by the same user (identified through IP-address or username for registered users) on the same days are only counted as one download.

Data set ZA6899_Doc_and_Data_all_sources contains information on how often study materials have been downloaded. Multiple downloads of any file of the same study by the same user (identified through IP-address or username for registered users) on the same days are only counted as one download.

Both data sets are available in three formats: csv (quoted, semicolon-separated), dta (Stata v13, labeled) and sav (SPSS, labeled). All formats contain identical information.

Variables: Variables/columns in both data sets are identical. za_nr ´Archive study number´ version ´GESIS Archiv Version´ doi ´Digital Object Identifier´ StudyNo ´Study number of respective study´ Title ´English study title´ Title_DE ´German study title´ Access ´Access category (0, A, B, C, D, E)´ PubYear ´Publication year of last version of the study´ inZACAT ´Study is currently also available via ZACAT´ inHISTAT ´Study is currently also available via HISTAT´ inDownloads ´There are currently data files available for download for this study in DBK or datorium´ Total ´All downloads combined´ downloads_2004 ´downloads/orders from all sources combined in 2004´ [up to ...] downloads_2018 ´downloads/orders from all sources combined in 2018´ d_2004_dbk ´downloads from source dbk in 2004´ [up to ...] d_2018_dbk ´downloads from source dbk in 2018´ d_2004_histat ´downloads from source histat in 2004´ [up to ...] d_2018_histat ´downloads from source histat in 2018´ d_2004_dataservice ´downloads/orders from source dataservice in 2004´ [up to ...] d_2018_dataservice ´downloads/orders from source dataservice in 2018´

More information is available within the codebook.

We include Stata syntax (dummy_dataset_create.do) that creates a panel dataset for negative binomial time series regression analyses, as described in our paper "Examining methodology to identify patterns of consulting in primary care for different groups of patients before a diagnosis of cancer: an exemplar applied to oesophagogastric cancer". We also include a sample dataset for clarity (dummy_dataset.dta), and a sample of that data in a spreadsheet (Appendix 2).

The variables contained therein are defined as follows:

case: binary variable for case or control status (takes a value of 0 for controls and 1 for cases).

patid: a unique patient identifier.

time_period: A count variable denoting the time period. In this example, 0 denotes 10 months before diagnosis with cancer, and 9 denotes the month of diagnosis with cancer,

ncons: number of consultations per month.

period0 to period9: 10 unique inflection point variables (one for each month before diagnosis). These are used to test which aggregation period includes the inflection point.

burden: binary variable denoting membership of one of two multimorbidity burden groups.

We also include two Stata do-files for analysing the consultation rate, stratified by burden group, using the Maximum likelihood method (1_menbregpaper.do and 2_menbregpaper_bs.do).

Note: In this example, for demonstration purposes we create a dataset for 10 months leading up to diagnosis. In the paper, we analyse 24 months before diagnosis. Here, we study consultation rates over time, but the method could be used to study any countable event, such as number of prescriptions.

It needs Stata 19 to run. This Do file opens the quarterly Excel file.

This file contains the data set (in stata format) and do file associated with the paper entitled "Risk misperceptions of structured financial products with worst-of payout characteristics revisited"

Project summary: The CLIMBS-UP survey examined experiences of early-career scholars in economics, biology, physics, and psychology. In the paper associated with these data, we examined the differential negative impacts that marginalized early career scholars experienced due to the COVID-19 pandemic compared to more privileged groups. Participants were doctoral students (n = 2,687), postdoctoral scholars (n = 335), and assistant professors (n = 221) who completed an online survey administered in April and May 2021 (note, responses shared in the data file are only from those who completed at least 94% of the survey, there were an additional 323 respondents who did not complete the full survey). Participants were recruited from four STEM fields (biology, economics, physics, and psychology) at 124 different departments in the United States that were randomly selected and stratified by prestige based on the 2011 National Research Council S-rankings. We divided all departments in the four fields into terciles reflecting top, middle, and bottom tier rankings and randomly selected 10 departments per field/tercile. We oversampled Minority Serving Institutions (MSIs) to ensure at least one MSI was represented in each tier. The STATA data file contains information only for the outcome variables (COVID impacts and job outcomes) for the associated paper (Douglas, Settles, Cech, et al., under review) and does not include any identifiable demographic information other than field and career stage (COV19outcomes.dta). This project also includes a copy of the questionnaire only containing survey items used for the associated paper (COV19survey.pdf).Method: We asked participants to rate the amount of change they have experienced in their research progress, workload, concern about career advancement, and support from mentor(s). They were also asked about their disruptions to work due to life challenges including physical health problems, mental health problems, and additional caretaking responsibilities. We compared these impacts across seven socio-demographic statuses (ie., gender, race, caregiving status, disability status, sexual identity, first generation undergraduate status, and career stage). As the analyses use multiple demographic characteristics that can be used to identify participants, the data file here is limited to career stage, field, and all reported outcome variables including COVID-19 impacts, job satisfaction, professional role confidence, turnover intentions, and burnout. Below is a description of each variable in the downloadable Stata data file (COV19outcomes.dta).

STATA files containing primary analysis for Duke and Progress Merger

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

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.

Stata do files for Global Determinants of Coastal Migration under Climate Change. For each country, there is one file of code. The code procudes the regression results in the paper. The excel file contains the source code for making the figures in the paper. In the paper, we collected survey data in Argentina, France, Mozambique and the United States to compare migration behavior under different flood risk scenarios. We show that migration is more likely in higher- than in lower-income contexts, and that flood risk is an important driver of migration. Consistent determinants of migration across contexts include response efficacy, self-efficacy, place attachment and age, with variations between scenarios. Other factors such as climate change perceptions, migration costs, social networks, household income, and rurality are also important but context-specific. Furthermore, important trade-offs exist between migration and in-situ adaptation.

It is a widely accepted fact that evolving software systems change and grow. However, it is less well-understood how change is distributed over time, specifically in object oriented software systems. The patterns and techniques used to measure growth permit developers to identify specific releases where significant change took place as well as to inform them of the longer term trend in the distribution profile. This knowledge assists developers in recording systemic and substantial changes to a release, as well as to provide useful information as input into a potential release retrospective. However, these analysis methods can only be applied after a mature release of the code has been developed. But in order to manage the evolution of complex software systems effectively, it is important to identify change-prone classes as early as possible. Specifically, developers need to know where they can expect change, the likelihood of a change, and the magnitude of these modifications in order to take proactive steps and mitigate any potential risks arising from these changes. Previous research into change-prone classes has identified some common aspects, with different studies suggesting that complex and large classes tend to undergo more changes and classes that changed recently are likely to undergo modifications in the near future. Though the guidance provided is helpful, developers need more specific guidance in order for it to be applicable in practice. Furthermore, the information needs to be available at a level that can help in developing tools that highlight and monitor evolution prone parts of a system as well as support effort estimation activities. The specific research questions that we address in this chapter are: (1) What is the likelihood that a class will change from a given version to the next? (a) Does this probability change over time? (b) Is this likelihood project specific, or general? (2) How is modification frequency distributed for classes that change? (3) What is the distribution of the magnitude of change? Are most modifications minor adjustments, or substantive modifications? (4) Does structural complexity make a class susceptible to change? (5) Does popularity make a class more change-prone? We make recommendations that can help developers to proactively monitor and manage change. These are derived from a statistical analysis of change in approximately 55000 unique classes across all projects under investigation. The analysis methods that we applied took into consideration the highly skewed nature of the metric data distributions. The raw metric data (4 .txt files and 4 .log files in a .zip file measuring ~2MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

Purpose: To determine the prevalence of cataract and its association with sun exposure and other environmental risk factors in three different geographically diverse populations of India.

Design: Population based cross sectional study during 2010-2016

Participants: People aged > 40 years residing in randomly sampled villages were enumerated (12021) and 9735 (81%) underwent ophthalmic evaluation from plains, hilly and coastal regions (3595, 3231, 2909 respectively)

Methods: A detailed questionnaire-based interview about outdoor activity in present, past and remote past, usage of sun protective measures, exposure to smoke, and detailed ophthalmic examination including assessment of uncorrected and best corrected visual acuity, measurement of intraocular pressure, slit lamp examination, lens opacities categorization using LOCS III and posterior segment evaluation was done. Lifetime effective sun exposure was calculated using Melbourne formula and expressed as quantiles. These were su...

The Stata Do-file to perform the 3-Lv. LMM.. Visit https://dataone.org/datasets/sha256%3A34f5b683f38f52f08c537314f866bf03857a616231d1bcffa835d83a2eefb261 for complete metadata about this dataset.

Since the data utilized are protected by copyright, I provide the pseudo-data set, including variable definitions. The commands used are summarized in the Stata do-file.

Supplementary online material (S2 File) containing the dataset (STATA format) used in Justice Nyakura study (VL uptake among PBF on ART in Mazowe, Zimbabwe, 2017)

1. PERCEIVE regional panel datasets - secondary data collected from Eurostat, EU Commission on Strutural Fund Expenditures and quality of government for NUTS 1, 2 and 3 regions from 1990-2015, (STATA files). See codebook for more detail about variables

2. Flash Eurobarometer survey data on "Awarness of EU Regional Policy" and questionaires (STATA files)

3. Standard Eurobaromter survey data, annual, from 2000-2016 and questionaires (STATA files)

4. Expenditure data on EU Structural Funds, latest three budget periods (2000-2020) (Excel file)

5. Orignal PERCEIVE survey data (STATA file) and description of survey questions, descriptive results (word file)

This stata dta file contains the dataset for MarareT et al study on changes in ART uptake, delays and retention before-during Treat All in Harare, Zimbabwe