This publication provides all the information required to understand the PISA 2003 educational performance database and perform analyses in accordance with the complex methodologies used to collect and process the data. It enables researchers to both reproduce the initial results and to undertake further analyses. The publication includes introductory chapters explaining the statistical theories and concepts required to analyse the PISA data, including full chapters on how to apply replicate weights and undertake analyses using plausible values; worked examples providing full syntax in SAS®; and a comprehensive description of the OECD PISA 2003 international database. The PISA 2003 database includes micro-level data on student educational performance for 41 countries collected in 2003, together with students’ responses to the PISA 2003 questionnaires and the test questions. A similar manual is available for SPSS users.

IntroductionA required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.MethodsThe system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.ResultsThe system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.DiscussionMedical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

Descriptive statistics of the SAS-SV [total (male, female)].

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

The OECD Programme for International Student Assessment (PISA) surveys collected data on students’ performance in reading, mathematics and science, as well as contextual information on students’ background, home characteristics and school factors which could influence performance. This publication includes detailed information on how to analyse the PISA data, enabling researchers to both reproduce the initial results and to undertake further analyses. In addition to the inclusion of the necessary techniques, the manual also includes a detailed account of the PISA 2006 database. It also includes worked examples providing full syntax in SAS

Note. BA = body acceptance, SP = self-protection from social stigmas, FB = feeling and believing in one's capacities, M = mean, SD =  standard deviation, λ = item-factor loading, θ = error term.Brazilian Portuguese original version of the items are given in brackets.Item-factor loadings and descriptive statistics of SAS-EB item scores in Study 3.

In 2020, SAS Institute (short for Statistical Analysis System) generated ** percent of its software revenue in the Americas region. The company's revenue in that year was three billion U.S. dollars, of which SAS reinvested ** percent in research and development. This share is significantly higher than with most other software companies.

SAS is one of the leading analytics software providers

With a share of almost ** percent, SAS Institute is the leading vendor in the global advanced and predictive analytics software market, where SAS competes with IBM, MathWorks, Alteryx, and Microsoft for market share. SAS also holds single to double-digit percentages in all other segments of the overall big data and analytics software market, including BI and analytics tools, artificial intelligence software platforms, analytic data integration and integrity software, and customer relationship analytics applications. The company's share of the overall big data and analytics software market stands at around five percent, putting SAS amongst the top vendors in the market behind Oracle, Microsoft, SAP, IBM, and Salesforce.

90 percent of Fortune *** companies use SAS products

The SAS Institute developed out of a project at the agricultural department at the North Carolina State University in the *****. The company's current CEO James Goodnight and other participants of the project established it as a private company in 1976. As of 2020, the company had ****** employees worldwide, selling and providing its software analytics products and services to customers in *** countries. The company's software is installed at more than ** thousand government, university, and business sites and is used by more than ** percent of the top companies on the 2020 Fortune *** list.

Summary data for the studies used in the meta-analysis of local adaptation (Table 1 from the publication)This table contains the data used in this published meta-analysis. The data were originally extracted from the publications listed in the table. The file corresponds to Table 1 in the original publication.tb1.xlsSAS script used to perform meta-analysesThis file contains the essential elements of the SAS script used to perform meta-analyses published in Hoeksema & Forde 2008. Multi-factor models were fit to the data using weighted maximum likelihood estimation of parameters in a mixed model framework, using SAS PROC MIXED, in which the species traits and experimental design factors were considered fixed effects, and a random between-studies variance component was estimated. Significance (at alpha = 0.05) of individual factors in these models was determined using randomization procedures with 10,000 iterations (performed with a combination of macros in SAS), in which effect sizes a...

Multienvironment trials (METs) enable the evaluation of the same genotypes under a v ariety of environments and management conditions. We present META (Multi Environment Trial Analysis), a suite of 31 SAS programs that analyze METs with complete or incomplete block designs, with or without adjustment by a covariate. The entire program is run through a graphical user interface. The program can produce boxplots or histograms for all traits, as well as univariate statistics. It also calculates best linear unbiased estimators (BLUEs) and best linear unbiased predictors for the main response variable and BLUEs for all other traits. For all traits, it calculates variance components by restricted maximum likelihood, least significant difference, coefficient of variation, and broad-sense heritability using PROC MIXED. The program can analyze each location separately, combine the analysis by management conditions, or combine all locations. The flexibility and simplicity of use of this program makes it a valuable tool for analyzing METs in breeding and agronomy. The META program can be used by any researcher who knows only a few fundamental principles of SAS.

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

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

no abstract provided

Mortality rates were calculated as defined in the text.Summary statistics Black cervical cancer mortality by year in thirteen U.S. states.

Descriptive statistics and distribution of PCOC SAS total scores and individual items, including by distress status.

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

SAS Code for Spatial Optimization of Supply Chain Network for Nitrogen Based Fertilizer in North America, by type, by mode of transportation, per county, for all major crops, using Proc OptModel. the code specifies set of random values to run the mixed integer stochastic spatial optimization model repeatedly and collect results for each simulation that are then compiled and exported to be projected in GIS (geographic information systems). Certain supply nodes (fertilizer plants) are specified to work at either 70 percent of their capacities or more. Capacities for nodes of supply (fertilizer plants), demand (county centroids), transhipment nodes (transfer points-mode may change), and actual distance travelled are specified over arcs.

These SAS files are sample code used for the Monte Carlo studies in a manuscript on statistical properties of four effect size measures for the mediated effect.Citation:Miočević, M., O’Rourke, H. P., MacKinnon, D. P., & Brown, H. C. (2016). The bias and efficiency of five effect size measures for mediation models. Under review at Behavior Research Methods.

Exercise data set for the SAS book by Uehlinger. Sample of individual variables and cases from the data set of ZA Study 0757 (political ideology).

Topics: most important political problems of the country; political interest; party inclination; beha

This item has been replaced by the one which can be found at https://doi.org/10.7488/ds/3411 ## GWAS summary statistics from 7,491 critically ill patients from COVID-19 and 48,400 population controls: European(EUR) 5,989/42,891; South Asian(SAS) 788/3,793; African(AFR) 440/1,350; East Asian(EAS) 274/366. GWAS models were calculated with SAIGE using a logisitic mixed-model regression. A trans-ancestry meta-analysis was performed using inverse-variant weighted fixed-effects. Ancestry-specific and trans-ancestry summary statistics are available. TWAS was performed using GTEx v8 gene expression data for lung and blood and an all-tissue meta-analysis. Summary statistics for tissue-specific and meta-TWAS are available.

Exercise data set for the SAS book by Uehlinger. Sample of individual variables and cases from the data set of ZA Study 0757 (political ideology). Topics: most important political problems of the country; political interest; party inclination; behavior at the polls in the Federal Parliament election 1972; political participation and willingness to participate in political protests. Demography: age; sex; marital status; religious denomination; school education; interest in politics; party preference. Übungsdatensatz zum SAS-Buch von Uehlinger. Auswahl einzelner Variablen und Fälle aus dem Datensatz der ZA-Studie 0757 (Politische Ideologie). Themen: Wichtigste politische Probleme des Landes; politisches Interesse; Parteineigung; Wahlverhalten bei der Bundestagswahl 1972; politische Partizipation und Teilnahmebereitschaft an politischen Protesten. Demographie: Alter; Geschlecht; Familienstand; Konfession; Schulbildung; Politikinteresse; Parteipräferenz. Random selection Zufallsauswahl Oral survey with standardized questionnaire