The dataset includes the Ct values for the 4 channels (HPV16, HPV18, HPV other, and beta-globin) as well as the result of the clinical HPV test and where available, the cytology and histology. (DTA)

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.

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 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.

Stata file with all variables used in the logistic regression models (DTA format).

Recent studies have suggested that high levels of social support can encourage better health behaviours and result in improved cardiovascular health. In this study we evaluated the association between social support and ideal cardiovascular health among urban Jamaicans. We conducted a cross-sectional study among urban residents in Jamaica’s south-east health region. Socio-demographic data and information on cigarette smoking, physical activity, dietary practices, blood pressure, body size, cholesterol, and glucose, were collected by trained personnel. The outcome variable, ideal cardiovascular health, was defined as having optimal levels of ≥5 of these characteristics (ICH-5) according to the American Heart Association definitions. Social support exposure variables included number of friends (network size), number of friends willing to provide loans (instrumental support) and number of friends providing advice (informational support). Principal component analysis was used to create a social support score using these three variables. Survey-weighted logistic regression models were used to evaluate the association between ICH-5 and social support score. Analyses included 841 participants (279 males, 562 females) with mean age of 47.6 ± 18.42 years. ICH-5 prevalence was 26.6% (95%CI 22.3, 31.0) with no significant sex difference (male 27.5%, female 25.7%). In sex-specific, multivariable logistic regression models, social support score, was inversely associated with ICH-5 among males (OR 0.67 [95%CI 0.51, 0.89], p = 0.006) but directly associated among females (OR 1.26 [95%CI 1.04, 1.53], p = 0.020) after adjusting for age and community SES. Living in poorer communities was also significantly associated with higher odds of ICH-5 among males, while living communities with high property value was associated with higher odds of ICH among females. In this study, higher level of social support was associated with better cardiovascular health among women, but poorer cardiovascular health among men in urban Jamaica. Further research should explore these associations and identify appropriate interventions to promote cardiovascular health.

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.

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.

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.

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 2017.

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: 2012-2017 - data service: 2006-2017 - datorium: 2014-2017 - histat: 2004-2017

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_2017 ´downloads/orders from all sources combined in 2017´ d_2004_dbk ´downloads from source dbk in 2004´ [up to ...] d_2017_dbk ´downloads from source dbk in 2017´ d_2004_histat ´downloads from source histat in 2004´ [up to ...] d_2017_histat ´downloads from source histat in 2017´ d_2004_dataservice ´downloads/orders from source dataservice in 2004´ [up to ...] d_2017_dataservice ´downloads/orders from source dataservice in 2017´

More information is available within the codebook.

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2023, is designed for analysts to conduct cross-sectional analysis for the 2023 calendar year. The Calendar Year datasets combine data collected in a specific year from across multiple waves and these are released as separate calendar year studies, with appropriate analysis weights, starting with the 2020 Calendar Year dataset. Each subsequent year, an additional yearly study is released.

The Calendar Year data is designed to enable timely cross-sectional analysis of individuals and households in a calendar year. Such analysis can however, only involve variables that are collected in every wave (excluding rotating content which is only collected in some of the waves). Due to overlapping fieldwork the data files combine data collected in the three waves that make up a calendar year. Analysis cannot be restricted to data collected in one wave during a calendar year, as this subset will not be representative of the population. Further details and guidance on this study can be found in the xxxx_main_survey_calendar_year_user_guide_2023.

These calendar year datasets should be used for cross-sectional analysis only. For those interested in longitudinal analyses using Understanding Society please access the main survey datasets: Safeguarded (End User Licence) version or Safeguarded/Special Licence version.

Understanding Society: the UK Household Longitudinal Study, started in 2009 with a general population sample (GPS) of UK residents living in private households of around 26,000 households and an ethnic minority boost sample (EMBS) of 4,000 households. All members of these responding households and their descendants became part of the core sample who were eligible to be interviewed every year. Anyone who joined these households after this initial wave, were also interviewed as long as they lived with these core sample members to provide the household context. At each annual interview, some basic demographic information was collected about every household member, information about the household is collected from one household member, all 16+ year old household members are eligible for adult interviews, 10-15 year old household members are eligible for youth interviews, and some information is collected about 0-9 year olds from their parents or guardians. Since 1991 until 2008/9 a similar survey, the British Household Panel Survey (BHPS), was fielded. The surviving members of this survey sample were incorporated into Understanding Society in 2010. In 2015, an immigrant and ethnic minority boost sample (IEMBS) of around 2,500 households was added. In 2022 a GPS boost sample (GPS2) of around 5,700 households was added. To know more about the sample design, following rules, interview modes, incentives, consent, questionnaire content please see the study overview and user guide.

Co-funders

In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:

There are two versions of the Calendar Year 2023 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see document '9471_eul_vs_sl_variable_differences' for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (Safeguarded (EUL)) and 6931 (Safeguarded/SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2023 dataset, subject to SL access conditions. See the User Guide for further details.

Suitable data analysis software

These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,800 variables.

Heterogeneity-related variables for utilization of NASG in the current meta-analysis (based on meta-regression).

The data set is in STATA V12 format and has the variables case/control, age group, alcohol consumption, engine capacity, having a driving permit, length of experience of riding motorcycle, changing motorcycle in previous year, hours driven in a day, sharing a motorcycle and matched case identification number. The data set has been uploaded in the public repository Harvard dataverse and its URL is https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/RSOQ5E. (DTA)

The Panel Study of Income Dynamics–Social, Health, and Economic Longitudinal File (PSID-SHELF) provides an easy-to-use and harmonized longitudinal file for the Panel Study of Income Dynamics (PSID), the longest-running nationally representative household panel survey in the world.PSID-SHELF accentuates the PSID's strengths through (1) its household panel structure that follows the same families over multiple decades; and (2) its multigenerational genealogical design that follows the descendants of panel families that were originally sampled in 1968, with immigrant sample refreshers in 1997–1999 and 2017. Every individual who has ever been included in the PSID's main study is included in the PSID-SHELF data, with over 80,000 people observed, some of them across more than 40 survey waves (1968–present). The current version of PSID-SHELF includes 41 waves of survey data, ranging from 1968 to 2019.The file contains measures on a wide range of substantive topics from the PSID's individual and family files, including variables on demographics, family structure, educational attainment, family income, individual earnings, employment status, occupation, housing, and wealth—as well as the essential administrative variables pertaining to key survey identifiers, panel status, sample weights, and household relationship identifiers. PSID-SHELF thus covers some of the most central variables in PSID that have been collected for many years. PSID-SHELF can easily be merged with other PSID data products to add other public-use variables by linking variables based on a survey participant’s individual and family IDs.Despite a focus on longitudinally consistent measurement, many PSID variables change over waves, e.g., thanks to new code frames, topcodes, question splitting, or similar. PSID-SHELF provides harmonized measures to increase the ease of using PSID data, but by necessity this harmonization involves analytic decisions that users may or may not agree with. These decisions are described at a high level in the PSID-SHELF User Guide and Codebook, but only a close review of the Stata code used to construct variables in the data will fully reveal each analytic decision. The Stata code underlying PSID-SHELF is openly accessible not only to allow for such review but also to encourage users, as they become more comfortable with PSID, to use and alter the full code or selected code snippets for their own analytic purposes. PSID-SHELF is entirely based on publicly released data and therefore can be recreated by anyone who has registered for PSID data use.Despite careful and multiple code reviews, it is possible that the code used to produce PSID-SHELF contains errors. The authors therefore encourage users to review the codes carefully, to report any mistakes and errors to us (psidshelf.help@umich.edu), and take no responsibility for any errors arising from the provided codes and files. Current VersionPSID-SHELF, 1968–2019, Beta Release 2023.01Recommended CitationsPlease cite PSID-SHELF in any product that makes use of the data. Anyone who uses PSID-SHELF should cite the data or the PSID-SHELF User Guide and Codebook—and, as required by the PSID user agreement, the main PSID data.PSID-SHELF data:Pfeffer, Fabian T., Davis Daumler, and Esther M. Friedman. PSID-SHELF, 1968–2019: The PSID’s Social, Health, and Economic Longitudinal File (PSID-SHELF), Beta Release. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor],

BackgroundWorldwide, there is a growing concern about the rising number of people with declining cognitive functioning. However, findings on this phenomenon are inconclusive. Our study aimed to assess the prevalence of cognitive impairment and the associated factors in women with a history of pregnancy complications in rural southwestern Uganda.MethodsThis was a cross-sectional study carried out among women above 40 years of age in the greater Kabale district of southwestern Uganda between March and April 2022. Study participants were identified using a consecutive sampling method. Predictor variables included pregnancy complications and other social demographic factors that were assessed by semi-structured interviews while cognitive functioning as an outcome variable was assessed by Montreal Cognitive Assessment (MoCA-B) tool. Data were analyzed using STATA at a 95% Confidence level. Logistic regression analyses were selected for statistical modelling while odds ratios were calculated to assess the strength of associations between the predictor and outcome variables.ResultsIn total, 75% (212/280) of participants had some form of cognitive impairment, with 45% (123/280) falling into mild CI, 31% (86/280) moderate CI and 4% (10/280) severe CI. Twenty-three percent (68/280) of participants fell into category of normal cognitive functioning. Participants with >65 years of age had higher odds of developing cognitive impairment (OR = 2.94; 95%CI: 0.96–9.04, p = 0.06) than those with < 65 years of age. Protective factors to cognitive impairment include delivering from a health facility (OR = 0.31,95% CI:0.16–0.60, p = < .001), primary and post primary levels of education (OR = 0.05; 95% CI: 0.02–0.13, p

Description

This dataset contains data from an online experiment designed to test whether economically equivalent penalties—fees (paid before taking) and fines (paid after taking)—influence prosocial behaviour differently. Participants played a modified dictator game in which they could take points from another participant.

The dataset is provided in Excel format (Full-data.xlsx), along with a Stata do-file (submit.do) that reshapes, cleans, and analyses the data.

Data Collection

• Platform: oTree

• Recruitment: Prolific

• Sample size: 201 participants

• Design: Each participant played 20 rounds: 10 in the control condition and 10 in one treatment condition (fee or fine). Order of blocks was randomised.

• Payment: 200 points = £1. One round was randomly selected for payment.

Variables

Identification

• session – Session number

• id – Participant ID

• treatment – Assigned treatment (1 = Fee, 2 = Fine)

• order – Order of blocks (0 = Control first, 1 = Treatment first)

Decision Rounds

For each round, participants made decisions in both control (c) and treatment (t) conditions.

• c1, t1, c2, t2, … – Tokens available and/or allocated across control and treatment rounds.

• takeX – Amount taken from the other participant in case X.

Norm Elicitation

Social norms were elicited after the taking task. Variables include empirical, normative, and responsibility measures at both extensive and intensive margins:

• eyX, etX – Empirical expectations (beliefs about what others do)

• nyX, ntX – Normative expectations (beliefs about what others think is appropriate)

• ryX, rtX – Responsibility measures

• casenormX – Case identifier for norm elicitation

Demographics

From survey responses:

• Sex – Gender

• Ethnicitysimplified – Simplified ethnicity category

• Countryofresidence – Participant’s country of residence

Other

• order, session – Experimental setup metadata

Stata Do-File (analysis.do)

The .do file performs the following steps:

1. Data Preparation

   • Import raw Excel file

   • Reshape from wide to long format (cases per participant)

   • Declare panel data (xtset id)

2. Variable Generation

   • Rename variables for clarity (e.g., take for amount taken)

   • Generate treatment dummies (treat)

   • Construct demographic dummies (gender, race, nationality)

3. Analysis Preparation

   • Create extensive and intensive margin variables

   • Generate expectation and norm measures

4. Output

   • Ready-to-analyse panel dataset for regression and statistical analysis

These files are for replication of "Resistance as Role Model: Disillusionment and Protest Among American Adolescents After 2016" There are three files: a. codebook.docx: Word document with details regarding the dataset and variables b. replication do file.do: STATA do file with complete code for the analysis and predicted values used in the paper, including the appendix c. Resistance_RoleModel.dta: STATA dataset, with variables labeled

Percentage and frequency of number of deaths of under-age five children per mother by explanatory variables.

Bi-variable and multivariable mixed-effect robust Poisson regression analysis to identify factors associated with HTC during ANC visits, EDHS 2016.

Respondents’ knowledge of a community-based health insurance scheme.