In 2018, representatives from all the Swedish parliamentary parties (plus the Feminist Initiative) agreed to participate in and conduct an online survey among their party members. The questions are similar to those asked in the Swedish party membership survey in 2015. The survey was administered by the Laboratory of Opinion Research (LORE) at the University of Gothenburg. At the end of the field period at the beginning of May 2019, a total of 20,605 party members had responded. The response rate varies between 9.4 percent (Feminist Initiative) and 29.9 percent (Christian Democrats). In spring 2022, the data file for SPMS 2019 was reworked to enable better possibilities of sharing data upon request. This entailed the recoding of two variables (year of birth and year of entry into the party), as well as the removal of all variables and response alternatives with free text fields and postcodes. Below is a list of recoded or deleted variables. Further information can be found in the enclosed code books. The complete dataset is available with restricted access. Recoded variables: yearmember; yearofbirth Removed variables: Q32_2_text; Q33_2_text; Q112; Q110_6_text; postcode_clean Added variables: county, uncertain_county The data is available as a STATA dataset (.dta) as well as coded comma-separated values (.csv).

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

Stata replication files for "Reading fiction and economic preferences of rural youth in Burkina Faso" to appear in Economic Development and Cultural Change in 2019. The Stata dataset contains observations for the 557 treatment and control in the program, identified by an id variable. Most variables are suffixed by mars13, mai13, aout13, mars14 and mai14 the French abbreviations of month and then year, for the session in which the variable was measured. Many variable names and labels are in French. Consult paper for English equivalents of variable names after running replication do file.

This spreadsheet provides all cleaned and validated data used in the analysis of the GSLWG survey to gather opinions about the governance of taxonomic lists. Data are anonymous. Interpertations of variables are available in a separate codebook file, also available on Dryad and associated with this manuscript. In addition to raw survey data, additional supplemental data are provided: 1. Coding manual providing definitions for each variable included in the survey dataset in .csv format 2. The data analysis code in Stata .do format and PDF format 3. The survey instrument in several languages in PDF format 4. A detailed description of the survey methodology and data analysis approach in PDF format 5. The full results of the survey in tabular form 6. Additional figures presenting survey results All data are also available on the website of the Open Science Framework (OSF), along with survey pre-registration data: https://osf.io/tz7ra/?view_only=4b1bc810ef794f7f9bb57240611989af Methods Data was collected using an online survey of taxonomists, other types of scientists, and users of taxonomic information. It was processed to clean data for analysis according to the standards recorded in the survey codebook, which is also availalbe on Dryad and associated with this manuscript. Data cleaning was performed using Stata. Full information about survey methods are availalbe in the accompanying article and the survey methods supplemental data also availalbe on Dryad. This survey was pre-registered with the Open Science Framework with a full description of survey development, implementation, and analysis methods: https://osf.io/tz7ra/?view_only=4b1bc810ef794f7f9bb57240611989af

File formats:

.xls: Excel file with variable names in 1. row and variable labels in 2. row

.xpt/.xpf: SAS XPORT data file (.xpt) and value labels (formats.xpf).

Note that the following variables were renamed in the output file: sumcadhssb -> SUMCADHS, sumcwursk -> SUMCWURS, adhdnotest -> ADHDNOTE, subs_subnotob -> SUBS_SUB, and that the internally recorded dataset name was shortened to "Liebrenz" .dta: Stata 13 data file

This Stata .do file provides the code used to analyse the data extracted and coded from systematic reviews included in the paper: Cumpston MS, Brennan SE, Ryan R, McKenzie JE. 2023. Statistical synthesis methods other than meta-analysis are commonly used, but are seldom specified: a survey of systematic reviews of interventions Input file: Synthesis methods data file: Cumpston_et_al_2023_other_synthesis_methods.xlsx (https://doi.org/10.26180/20785396) Associated file: Synthesis methods data dictionary (https://doi.org/10.26180/20785948) Study protocol: Cumpston MS, McKenzie JE, Thomas J and Brennan SE. The use of ‘PICO for synthesis’ and methods for synthesis without meta-analysis: protocol for a survey of current practice in systematic reviews of health interventions. F1000Research 2021, 9:678. (https://doi.org/10.12688/f1000research.24469.2)

Note: Naming convention of the variables. The naming convention for the variables links to the data dictionary. The character prefix identifies the section of the data_directory (e.g. variables names with the prefix 'Chars' are from the 'CHARACTERISTICS' section). The number of the variable reflects the item number in the data dictionary, except that the first digit is removed because this is captured by the character prefix. For example, Chars_2 is item number 1.2 under the 'CHARACTERISTICS' section of the data dictionary.

As the UK went into the first lockdown of the COVID-19 pandemic, the team behind the biggest social survey in the UK, Understanding Society (UKHLS), developed a way to capture these experiences. From April 2020, participants from this Study were asked to take part in the Understanding Society COVID-19 survey, henceforth referred to as the COVID-19 survey or the COVID-19 study.

The COVID-19 survey regularly asked people about their situation and experiences. The resulting data gives a unique insight into the impact of the pandemic on individuals, families, and communities. The COVID-19 Teaching Dataset contains data from the main COVID-19 survey in a simplified form. It covers topics such as

• Socio-demographics
• Whether working at home and home-schooling
• COVID symptoms
• Health and well-being
• Social contact and neighbourhood cohesion
• Volunteering

The resource contains two data files:

• Cross-sectional: contains data collected in Wave 4 in July 2020 (with some additional variables from other waves);
• Longitudinal: Contains mainly data from Waves 1, 4 and 9 with key variables measured at three time points.

Key features of the dataset

• Missing values: in the web survey, participants clicking "Next" but not answering a question were given further options such as "Don't know" and "Prefer not to say". Missing observations like these are recorded using negative values such as -1 for "Don't know". In many instances, users of the data will need to set these values as missing. The User Guide includes Stata and SPSS code for setting negative missing values to system missing.

• The Longitudinal file is a balanced panel and is in wide format. A balanced panel means it only includes participants that took part in every wave. In wide format, each participant has one row of information, and each measurement of the same variable is a different variable.

• Weights: both the cross-sectional and longitudinal files include survey weights that adjust the sample to represent the UK adult population. The cross-sectional weight (betaindin_xw) adjusts for unequal selection probabilities in the sample design and for non-response. The longitudinal weight (ci_betaindin_lw) adjusts for the sample design and also for the fact that not all those invited to participate in the survey, do participate in all waves.

• Both the cross-sectional and longitudinal datasets include the survey design variables (psu and strata).

A full list of variables in both files can be found in the User Guide appendix.

Who is in the sample?

All adults (16 years old and over as of April 2020), in households who had participated in at least one of the last two waves of the main study Understanding Society, were invited to participate in this survey. From the September 2020 (Wave 5) survey onwards, only sample members who had completed at least one partial interview in any of the first four web surveys were invited to participate. From the November 2020 (Wave 6) survey onwards, those who had only completed the initial survey in April 2020 and none since, were no longer invited to participate

The User guide accompanying the data adds to the information here and includes a full variable list with details of measurement levels and links to the relevant questionnaire.

GaechterSchulzDATA&CODEContains all experimental and auxiliary data (institutional and demographic variables) reported in the article and the supplementary information (in both csv and STATA format). A list of variables and their labels is in the ReadMe file.Also contains the STATA codes (do-files) of the analyses reported in the article and the Supplementary Information.

A list of the study’s variables along with an explanation of each measurement.

This dataset contains the data of the survey waves from age 11 to 24 (K4-K9) of the target population. The following documents help to understand the content of the data (partially restricted access):

• Handbook K4-K9 in two versions: Short version (public access) containing general information such as descriptions of questionnaire themes, source, derived constructs, and key publications ("K4-9_Handbook_short") and a long version providing a detailed overview of each scale, and item wordings ("K4-9_Handbook_long”). • Overview of standard variables (e.g., sex, SES, treatment allocation) that are part of every data package on SWISSUbase • Codebooks (questionnaires with question/variable names) in English • Original questionnaires in German • Scale syntaxes (SPSS) for each data collection wave • File info including all variable/value labels and dataset structure • Description of the z-proso project, containing general information on the project, methods and data ("z-proso_ProjectOverview", public access) • Tabular overview on all z-proso project phases, data collections, and questionnaires including information on scales/domains, and page numbers in the original German questionnaires ("z-proso_DataCollectionsInstruments_W1-9", public access) • A publication list with selected z-proso methods publications (public access)

The datafile is available in the CSV, SAV (SPSS), and DTA (STATA) formats.

The data is available with prior agreement of project co-directors (Manuel Eisner, Denis Ribeaud, Lilly Shanahan) only. The project direction will grant access to the data based on a research proposal. The research proposal needs to be in the form of a project description with the following components: research questions and hypotheses, operationalisation, planned publications, linking with other project or other data (if planned). If you have questions or need more detailed information or additional documentation, do not hesitate to contact the project direction (z-proso@jacobscenter.uzh.ch). The research proposal is part of the application form.

If you, as a data user, are or were a z-proso participant yourself (focal participant, primary caregiver, or teacher), you are required to contact us before submitting a proposal.

If you require further data from earlier data collections, or from other informants (parent, teacher), or from add-on data collections that are not (yet) available on SWISSUbase, please provide a brief outline of your research questions along with a rationale for your specific data requirements.

This dataset contains the data of the survey waves from age 11 to 20 (K4-K8) of the target population. The following documents help to understand the content of the data (partially restricted access):

• Handbook K4-K8 in two versions: Short version (public access) containing general information such as descriptions of questionnaire themes, source, derived constructs, and key publications ("K4-8_Handbook_short") and a long version providing a detailed overview of each scale, and item wordings ("K4-8_Handbook_long”). • Overview of standard variables (e.g., sex, SES, treatment allocation) that are part of every data package on SWISSUbase • Codebooks (questionnaires with question/variable names) in English • Original questionnaires in German • Scale syntaxes (SPSS) for each data collection wave • File info including all variable/value labels and dataset structure • Description of the z-proso project, containing general information on the project, methods and data ("z-proso_ProjectOverview", public access) • Tabular overview on all z-proso project phases, data collections, and questionnaires including information on scales/domains, and page numbers in the original German questionnaires ("z-proso_DataCollectionsInstruments_W1-9", public access) • A publication list with selected z-proso methods publications (public access)

The datafile is available in the CSV, SAV (SPSS), and DTA (STATA) formats.

The data is available with prior agreement of project co-directors (Manuel Eisner, Denis Ribeaud, Lilly Shanahan) only. The project direction will grant access to the data based on a research proposal. The research proposal needs to be in the form of a project description with the following components: research questions and hypotheses, operationalisation, planned publications, linking with other project or other data (if planned). If you have questions or need more detailed information or additional documentation, do not hesitate to contact the project direction (z-proso@jacobscenter.uzh.ch). The research proposal is part of the application form.

If you, as a data user, are or were a z-proso participant yourself (focal participant, primary caregiver, or teacher), you are required to contact us before submitting a proposal.

If you require further data from earlier data collections, or from other informants (parent, teacher), or from add-on data collections that are not (yet) available on SWISSUbase, please provide a brief outline of your research questions along with a rationale for your specific data requirements.

This replication package contains the raw data and code to replicate the findings reported in the paper. The data are licensed under a Creative Commons Attribution 4.0 International Public License. The code is licensed under a Modified BSD License. See LICENSE.txt for details.

Software requirements

All analysis were done in Stata version 16:

• Add-on packages are included in scripts/libraries/stata and do not need to be installed by user. The names, installation sources, and installation dates of these packages are available in scripts/libraries/stata/stata.trk.

Instructions

1. Save the folder ‘replication_PLOS’ to your local drive.
2. Open the master script ‘run.do’ and change the global pointing to the working direction (line 20) to the location where you save the folder on your local drive
3. Run the master script ‘run.do’ to replicate the analysis and generate all tables and figures reported in the paper and supplementary online materials

Datasets

• Wave 1 – Survey experiment: ‘wave1_survey_experiment_raw.dta’
• Wave 2 – Follow-up Survey: ‘wave2_follow_up_raw.dta'
• Map: shape-files ‘plz2stellig.shp’ ‘OSM_PLZ.shp’, area codes ‘Postleitzahlengebiete-_OSM.csv’_, (all links to the sources can be found in the script ‘04_figure2_germany_map.do’)
• Pretest: ‘pre-test_corona_raw.dta’
• For Appendix S7: ‘alter_geschlecht_zensus_det.xlsx’, ‘vaccination_landkreis_raw.dta’, ‘census2020_age_gender.csv’ (all links to the sources can be found in the script ‘06_AppendixS7.do’)
• For Appendix S10: ‘vaccination_landkreis_raw.dta’ (all links to the sources can be found in the script ‘07_AppendixS10.do’)

Descriptions of scripts

1_1_clean_wave1.do
This script processes the raw data from wave 1, the survey experiment
1_2_clean_wave2.do
This script processes the raw data from wave 2, the follow-up survey
1_3_merge_generate.do
This script creates the datasets used in the main analysis and for robustness checks by merging the cleaned data from wave 1 and 2, tests the exclusion criteria and creates additional variables
02_analysis.do
This script estimates regression models in Stata, creates figures and tables, saving them to results/figures and results/tables
03_robustness_checks_no_exclusion.do
This script runs the main analysis using the dataset without applying the exclusion criteria. Results are saved in results/tables
04_figure2_germany_map.do
This script creates Figure 2 in the main manuscript using publicly available data on vaccination numbers in Germany.
05_figureS1_dogmatism_scale.do
This script creates Figure S1 using data from a pretest to adjust the dogmatism scale.
06_AppendixS7.do
This script creates the figures and tables provided in Appendix S7 on the representativity of our sample compared to the German average using publicly available data about the age distribution in Germany.
07_AppendixS10.do
This script creates the figures and tables provided in Appendix S10 on the external validity of vaccination rates in our sample using publicly available data on vaccination numbers in Germany.

Abstract

The high-frequency phone survey of refugees monitors the economic and social impact of and responses to the COVID-19 pandemic on refugees and nationals, by calling a sample of households every four weeks. The main objective is to inform timely and adequate policy and program responses. Since the outbreak of the COVID-19 pandemic in Ethiopia, two rounds of data collection of refugees were completed between September and November 2020. The first round of the joint national and refugee HFPS was implemented between the 24 September and 17 October 2020 and the second round between 20 October and 20 November 2020.

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was drawn using a simple random sample without replacement. Expecting a high non-response rate based on experience from the HFPS-HH, we drew a stratified sample of 3,300 refugee households for the first round. More details on sampling methodology are provided in the Survey Methodology Document available for download as Related Materials.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The Ethiopia COVID-19 High Frequency Phone Survey of Refugee questionnaire consists of the following sections:

• Interview Information
• Household Roster
• Camp Information
• Knowledge Regarding the Spread of COVID-19
• Behaviour and Social Distancing - Access to Basic Services
• Employment
• Income Loss
• Coping/Shocks
• Social Relations
• Food Security
• Aid and Support/ Social Safety Nets.

A more detailed description of the questionnaire is provided in Table 1 of the Survey Methodology Document that is provided as Related Materials. Round 1 and 2 questionnaires available for download.

Cleaning operations

DATA CLEANING At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes. Data cleaning carried out is detailed below.

Variable naming and labeling: • Variable names were changed to reflect the lowercase question name in the paper survey copy, and a word or two related to the question. • Variables were labeled with longer descriptions of their contents and the full question text was stored in Notes for each variable. • “Other, specify” variables were named similarly to their related question, with “_other” appended to the name. • Value labels were assigned where relevant, with options shown in English for all variables, unless preloaded from the roster in Amharic.

Variable formatting: • Variables were formatted as their object type (string, integer, decimal, time, date, or datetime). • Multi-select variables were saved both in space-separated single-variables and as multiple binary variables showing the yes/no value of each possible response. • Time and date variables were stored as POSIX timestamp values and formatted to show Gregorian dates. • Location information was left in separate ID and Name variables, following the format of the incoming roster. IDs were formatted to include only the variable level digits, and not the higher-level prefixes (2-3 digits only.)
• Only consented surveys were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset. • Roster data is separated from the main data set and kept in long-form but can be merged on the key variable (key can also be used to merge with the raw data). • The variables were arranged in the same order as the paper instrument, with observations arranged according to their submission time.

Backcheck data review: Results of the backcheck survey are compared against the originally captured survey results using the bcstats command in Stata. This function delivers a comparison of variables and identifies any discrepancies. Any discrepancies identified are then examined individually to determine if they are within reason.

Data appraisal

The following data quality checks were completed: • Daily SurveyCTO monitoring: This included outlier checks, skipped questions, a review of “Other, specify”, other text responses, and enumerator comments. Enumerator comments were used to suggest new response options or to highlight situations where existing options should be used instead. Monitoring also included a review of variable relationship logic checks and checks of the logic of answers. Finally, outliers in phone variables such as survey duration or the percentage of time audio was at a conversational level were monitored. A survey duration of close to 15 minutes and a conversation-level audio percentage of around 40% was considered normal. • Dashboard review: This included monitoring individual enumerator performance, such as the number of calls logged, duration of calls, percentage of calls responded to and percentage of non-consents. Non-consent reason rates and attempts per household were monitored as well. Duration analysis using R was used to monitor each module's duration and estimate the time required for subsequent rounds. The dashboard was also used to track overall survey completion and preview the results of key questions. • Daily Data Team reporting: The Field Supervisors and the Data Manager reported daily feedback on call progress, enumerator feedback on the survey, and any suggestions to improve the instrument, such as adding options to multiple choice questions or adjusting translations. • Audio audits: Audio recordings were captured during the consent portion of the interview for all completed interviews, for the enumerators' side of the conversation only. The recordings were reviewed for any surveys flagged by enumerators as having data quality concerns and for an additional random sample of 2% of respondents. A range of lengths were selected to observe edge cases. Most consent readings took around one minute, with some longer recordings due to questions on the survey or holding for the respondent. All reviewed audio recordings were completed satisfactorily. • Back-check survey: Field Supervisors made back-check calls to a random sample of 5% of the households that completed a survey in Round 1. Field Supervisors called these households and administered a short survey, including (i) identifying the same respondent; (ii) determining the respondent's position within the household; (iii) confirming that a member of the the data collection team had completed the interview; and (iv) a few questions from the original survey.

The data was gathered with the aid of a well-structured questionnaire administered within the cities of Abuja, Ibadan, Port Harcourt and Lagos in Nigeria. The data comprised of three thematic areas. First is the social economic characteristics of the household. Secondly, the nature of the quality electricity supply and how it affects households’ welfare. In the third section, a hypothetical scenario of an improved electricity system that conforms to all the dimensions of quality electricity supply was created. Respondents were asked to state the maximum amount they were willing to pay for such an improved quality of electricity supply system. The CVM elicitation format that was employed in the study was the discrete choice with a follow-up approach. A first bid was proposed to each respondent. If the respondent agrees to pay that amount, a higher amount was proposed. If he agrees to that, a third amount, higher than the second was further proposed. If he declined to pay the first bid, the follow up bid proposed to the respondent was lower. After going through the follow up process, all respondents were asked to state after careful thoughts what their maximum WTP for the improved electricity service would be. The amounts each respondent states here were compared to the responses from the follow up process to check for consistency. The Ordered-Probit Model was employed as the main estimation technique for the study. The model estimated using the Ordered Probit regression was: WTP = β1 HSZ + β2 HY + β3 EDL + β4 REL + β5 CRR + β6 CAP + β7 MO + ε

The model investigates the factors that influence consumers’ willingness to pay (WTP) for the improved electricity service in the study area. In the model, the outcome variable is WTP (coded 1, 2; 1 being N41 – N55 and 2 being Above N55) which is an ordered categorical variable. The variables used as predictors are Household Size (HSZ), Monthly Outages (MO) - which are continuous variables, Household Monthly Income (HY), Highest Educational Level (EDL), Reliability of Current Supply (REL), Cost incurred in damage of appliances (CRR) and Cost of Alternative Power Supply (CAP) - which are categorical variables. However, it should be stated that ‘n-1’ (n being the number of categories) dummies were created for each of the categorical variables in the model. The reference category for Household Monthly Income is Below N51,000, Highest Educational Level is no formal education, Reliability of Current Supply is Excellent, Cost of Damage is Below N2,000.00 and Cost of Alternative Supply is Below N2,000. The models were estimated separately for each of the enumerated cities and full sample for easy comparison. Microsoft Excel and the STATA statistical package was used in analyzing the collected data.

What is the relationship between environment and democracy? The framework of cultural evolution suggests that societal development is an adaptation to ecological threats. Pertinent theories assume that democracy emerges as societies adapt to ecological factors such as higher economic wealth, lower pathogen threats, less demanding climates, and fewer natural disasters. However, previous research confused within-country processes with between-country processes and erroneously interpreted between-country findings as if they generalize to within-country mechanisms. In this article, we analyze a time-series cross-sectional dataset to study the dynamic relationship between environment and democracy (1949-2016), accounting for previous misconceptions in levels of analysis. By separating within-country processes from between-country processes, we find that the relationship between environment and democracy not only differs by countries but also depends on the level of analysis. Economic wealth predicts increasing levels of democracy in between-country comparisons, but within-country comparisons show that democracy declines as countries become wealthier over time. This relationship is only prevalent among historically wealthy countries but not among historically poor countries, whose wealth also increased over time. By contrast, pathogen prevalence predicts lower levels of democracy in both between-country and within-country comparisons. Our longitudinal analyses identifying temporal precedence reveal that not only reductions in pathogen prevalence drive future democracy, but also democracy reduces future pathogen prevalence and increases future wealth. These nuanced results contrast with previous analyses using narrow, cross-sectional data. As a whole, our findings illuminate the dynamic process by which environment and democracy shape each other.

Methods Our Time-Series Cross-Sectional data combine various online databases. Country names were first identified and matched using R-package “countrycode” (Arel-Bundock, Enevoldsen, & Yetman, 2018) before all datasets were merged. Occasionally, we modified unidentified country names to be consistent across datasets. We then transformed “wide” data into “long” data and merged them using R’s Tidyverse framework (Wickham, 2014). Our analysis begins with the year 1949, which was occasioned by the fact that one of the key time-variant level-1 variables, pathogen prevalence was only available from 1949 on. See our Supplemental Material for all data, Stata syntax, R-markdown for visualization, supplemental analyses and detailed results (available at https://osf.io/drt8j/).

Abstract

Background: Adolescent girls in Kenya are disproportionately affected by early and unintended pregnancies, unsafe abortion and HIV infection. The In Their Hands (ITH) programme in Kenya aims to increase adolescents' use of high-quality sexual and reproductive health (SRH) services through targeted interventions. ITH Programme aims to promote use of contraception and testing for sexually transmitted infections (STIs) including HIV or pregnancy, for sexually active adolescent girls, 2) provide information, products and services on the adolescent girl's terms; and 3) promote communities support for girls and boys to access SRH services.

Objectives: The objectives of the evaluation are to assess: a) to what extent and how the new Adolescent Reproductive Health (ARH) partnership model and integrated system of delivery is working to meet its intended objectives and the needs of adolescents; b) adolescent user experiences across key quality dimensions and outcomes; c) how ITH programme has influenced adolescent voice, decision-making autonomy, power dynamics and provider accountability; d) how community support for adolescent reproductive and sexual health initiatives has changed as a result of this programme.

Methodology ITH programme is being implemented in two phases, a formative planning and experimentation in the first year from April 2017 to March 2018, and a national roll out and implementation from April 2018 to March 2020. This second phase is informed by an Annual Programme Review and thorough benchmarking and assessment which informed critical changes to performance and capacity so that ITH is fit for scale. It is expected that ITH will cover approximately 250,000 adolescent girls aged 15-19 in Kenya by April 2020. The programme is implemented by a consortium of Marie Stopes Kenya (MSK), Well Told Story, and Triggerise. ITH's key implementation strategies seek to increase adolescent motivation for service use, create a user-defined ecosystem and platform to provide girls with a network of accessible subsidized and discreet SRH services; and launch and sustain a national discourse campaign around adolescent sexuality and rights. The 3-year study will employ a mixed-methods approach with multiple data sources including secondary data, and qualitative and quantitative primary data with various stakeholders to explore their perceptions and attitudes towards adolescents SRH services. Quantitative data analysis will be done using STATA to provide descriptive statistics and statistical associations / correlations on key variables. All qualitative data will be analyzed using NVIVO software.

Study Duration: 36 months - between 2018 and 2020.

Geographic coverage

Narok and Homabay counties

Analysis unit

Households

Universe

All adolescent girls aged 15-19 years resident in the household.

Sampling procedure

The sampling of adolescents for the household survey was based on expected changes in adolescent's intention to use contraception in future. According to the Kenya Demographic and Health Survey 2014, 23.8% of adolescents and young women reported not intending to use contraception in future. This was used as a baseline proportion for the intervention as it aimed to increase demand and reduce the proportion of sexually active adolescents who did not intend to use contraception in the future. Assuming that the project was to achieve an impact of at least 2.4 percentage points in the intervention counties (i.e. a reduction by 10%), a design effect of 1.5 and a non- response rate of 10%, a sample size of 1885 was estimated using Cochran's sample size formula for categorical data was adequate to detect this difference between baseline and end line time points. Based on data from the 2009 Kenya census, there were approximately 0.46 adolescents girls per a household, which meant that the study was to include approximately 4876 households from the two counties at both baseline and end line surveys.

We collected data among a representative sample of adolescent girls living in both urban and rural ITH areas to understand adolescents' access to information, use of SRH services and SRH-related decision making autonomy before the implementation of the intervention. Depending on the number of ITH health facilities in the two study counties, Homa Bay and Narok that, we sampled 3 sub-Counties in Homa Bay: West Kasipul, Ndhiwa and Kasipul; and 3 sub-Counties in Narok, Narok Town, Narok South and Narok East purposively. In each of the ITH intervention counties, there were sub-counties that had been prioritized for the project and our data collection focused on these sub-counties selected for intervention. A stratified sampling procedure was used to select wards with in the sub-counties and villages from the wards. Then households were selected from each village after all households in the villages were listed. The purposive selection of sub-counties closer to ITH intervention facilities meant that urban and semi-urban areas were oversampled due to the concentration of health facilities in urban areas.

Qualitative Sampling

Focus Group Discussion participants were recruited from the villages where the ITH adolescent household survey was conducted in both counties. A convenience sample of consenting adults living in the villages were invited to participate in the FGDS. The discussion was conducted in local languages. A facilitator and note-taker trained on how to use the focus group guide, how to facilitate the group to elicit the information sought, and how to take detailed notes. All focus group discussions took place in the local language and were tape-recorded, and the consent process included permission to tape-record the session. Participants were identified only by their first names and participants were asked not to share what was discussed outside of the focus group. Participants were read an informed consent form and asked to give written consent. In-depth interviews were conducted with purposively selected sample of consenting adolescent girls who participated in the adolescent survey. We conducted a total of 45 In-depth interviews with adolescent girls (20 in Homa Bay County and 25 in Narok County respectively). In addition, 8 FGDs (4 each per county) were conducted with mothers of adolescent girls who are usual residents of the villages which had been identified for the interviews and another 4 FGDs (2 each per county) with CHVs.

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f] for quantitative data collection and Focus Group Discussions and In Depth Interviews for qualitative data collection

Research instrument

The questionnaire covered; socio-demographic and household information, SRH knowledge and sources of information, sexual activity and relationships, family planning knowledge, access, choice and use when needed, exposure to family planning messages and voice and decision making autonomy and quality of care for those who visited health facilities in the 12 months before the survey. The questionnaire was piloted before the data collection and the questions reviewed for appropriateness, comprehension and flow. The questionnaire was piloted among a sample of 42 adolescent girls (two each per field interviewer) 15-19 from a community outside the study counties.

The questionnaire was originally developed in English and later translated into Kiswahili. The questionnaire was programmed using ODK-based Survey CTO platform for data collection and management and was administered through face-to-face interview.

Cleaning operations

The survey tools were programmed using the ODK-based SurveyCTO platform for data collection and management. During programming, consistency checks were in-built into the data capture software which ensured that there were no cases of missing or implausible information/values entered into the database by the field interviewers. For example, the application included controls for variables ranges, skip patterns, duplicated individuals, and intra- and inter-module consistency checks. This reduced or eliminated errors usually introduced at the data capture stage. Once programmed, the survey tools were tested by the programming team who in conjunction with the project team conducted further testing on the application's usability, in-built consistency checks (skips, variable ranges, duplicating individuals etc.), and inter-module consistency checks. Any issues raised were documented and tracked on the Issue Tracker and followed up to full and timely resolution. After internal testing was done, the tools were availed to the project and field teams to perform user acceptance testing (UAT) so as to verify and validate that the electronic platform worked exactly as expected, in terms of usability, questions design, checks and skips etc.

Data cleaning was performed to ensure that data were free of errors and that indicators generated from these data were accurate and consistent. This process begun on the first day of data collection as the first records were uploaded into the database. The data manager used data collected during pilot testing to begin writing scripts in Stata 14 to check the variables in the data in 'real-time'. This ensured the resolutions of any inconsistencies that could be addressed by the data collection teams during the fieldwork activities. The Stata 14 scripts that perform real-time checks and clean data also wrote to a .rtf file that detailed every check performed against each variable, any inconsistencies encountered, and all steps that were taken to address these inconsistencies. The .rtf files also reported when a variable was

Endovascular therapy (EVT) has changed the landscape of acute stroke treatment in the context of large vessel occlusion (LVO). Still, procedural success is typically determined by the degree of large vessel recanalization, despite the fact that large vessel recanalization does not always result in microvascular reperfusion. To address this discrepancy, we performed bedside optical CBF monitoring (with diffuse correlation spectroscopy) during endovascular therapy. This allowed comparison of CBF pre vs post-recanalization.Note 3 files uploaded:The .dta file is a stata file which contains all variables labels which includes all necessary variables details.The .xlsx file database is in numerical format (i.e. without applying text labels).The .xlsx file data dictionary contains all variable names, variable labels, and code to translate the numerical values.

UNI-CEN Standardized Census Data Tables contain Census data that have been reformatted into a common table format with standardized variable names and codes. The data are provided in two tabular formats for different use cases. "Long" tables are suitable for use in statistical environments, while "wide" tables are commonly used in GIS environments. The long tables are provided in Stata Binary (dta) format, which is readable by all statistics software. The wide tables are provided in comma-separated values (csv) and dBase 3 (dbf) formats with codebooks. The wide tables are easily joined to the UNI-CEN Digital Boundary Files. For the csv files, a .csvt file is provided to ensure that column data formats are correctly formatted when importing into QGIS. A schema.ini file does the same when importing into ArcGIS environments. As the DBF file format supports a maximum of 250 columns, tables with a larger number of variables are divided into multiple DBF files. For more information about file sources, the methods used to create them, and how to use them, consult the documentation at https://borealisdata.ca/dataverse/unicen_docs. For more information about the project, visit https://observatory.uwo.ca/unicen.

Integrated Postsecondary Education Data System (IPEDS) Complete Data Files from 1980 to 2023. Includes data file, STATA data file, SPSS program, SAS program, STATA program, and dictionary. All years compressed into one .zip file due to storage limitations.Updated on 2/14/2025 to add Microsoft Access Database files.From IPEDS Complete Data File Help Page (https://nces.ed.gov/Ipeds/help/complete-data-files):Choose the file to download by reading the description in the available titles. Then, click on the link in that row corresponding to the column header of the type of file/information desired to download.To download and view the survey files in basic CSV format use the main download link in the Data File column.For files compatible with the Stata statistical software package, use the alternate download link in the Stata Data File column.To download files with the SPSS, SAS, or STATA (.do) file extension for use with statistical software packages, use the download link in the Programs column.To download the data Dictionary for the selected file, click on the corresponding link in the far right column of the screen. The data dictionary serves as a reference for using and interpreting the data within a particular survey file. This includes the names, definitions, and formatting conventions for each table, field, and data element within the file, important business rules, and information on any relationships to other IPEDS data.For statistical read programs to work properly, both the data file and the corresponding read program file must be downloaded to the same subdirectory on the computer’s hard drive. Download the data file first; then click on the corresponding link in the Programs column to download the desired read program file to the same subdirectory.When viewing downloaded survey files, categorical variables are identified using codes instead of labels. Labels for these variables are available in both the data read program files and data dictionary for each file; however, for files that automatically incorporate this information you will need to select the Custom Data Files option.

To all,

This dataset was generated in order to fullfill a requirement for a graduate class in applied econometrics. I originally wanted to collect data on the effect of COVID-19 on student performance from a school district, but was unable to given that our local district was already conducting their own research.

The set contains a panel dataset, meant to emulate 6 semesters/trimesters with the first three taking place before the COVID-19 lockdowns, and the final three coming after the lockdowns. It also contains a cross-sectional dataset that is meant to be a single semester/trimester after the COVID-19 lockdowns. Variables were included and manipulated to model real world trends, or local demographics in Portland Oregon. There is a full list of variables at the end of this markdown.

It should be noted that student performance has greatly been diminished as a result of online education.

Feel free to reach out about the Stata code. It ended up being about 1500 lines to generate and manipulate, but I'm happy to share it with the same Public Domain license.

// VARIABLES used in the program // // NAME DATATYPE PURPOSE // // PERSONAL INFORMATION // // studentID into Number assigned to student. // school dummy 0/1, bool 1=school B (poor), 0= school A (wealthy) // gradelevel int Determine grade level of child. // gender dummy 0/1, bool 1=male, 0=female // covidpos dummy 0/1 1=child had Covid, 0=null // freelunch dummy 0/1 1=takes free and reduced lunch, 0=null // timeperiod categorical {0,1,2}=in-person learning, {3,4,5}=online learning // numcomputers into Defines number of computers in child's home. // familysize int Defines size of family, parents and siblings // householdincome float Household income for child. // fathereduc categorical System of values for highest level of father education // /* // no HS diploma 0 -- // High School diploma 1 Highest level of education is High School. // Bachelor degre 2 " " bachelors degree. // Master's Degree 3 " " masters degree. // Doctoral Degree 4 " " PhD. // \ // \ // then, if fathereduc=0, father did not finish High School. // / // mothereduc categorical System of values for highest level of mother education // / // no HS diploma 0 -- // High School diploma 1 Highest level of education is High School. // Bachelor degre 2 " " bachelors degree. // Master's Degree 3 " " masters degree. // Doctoral Degree 4 " " PhD. // \ // \ // then, if mothereduc=0, mother did not finish High School. // */ // // SCHOOL PERFORMANCE INFORMATION // // readingscore float Score for "reading" test in school. // writingscore float Score for "writing" test in school. // mathscore float Score for "math" test in school.
// // STATE PERFORMANCE INFORMATION // // readingscoreSL float Score for "reading" test at state level. // writingscoreSL float Score for "writing" test at state level. // mathscoreSL float Score for "math" test at state level. */