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

Sas Var Trading Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

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 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.

Var And Trans Sas Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Credit report of Var & Trans Sas contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

This SAS code extracts data from EU-SILC User Database (UDB) longitudinal files and edits it such that a file is produced that can be further used for differential mortality analyses. Information from the original D, R, H and P files is merged per person and possibly pooled over several longitudinal data releases. Vital status information is extracted from target variables DB110 and RB110, and time at risk between the first interview and either death or censoring is estimated based on quarterly date information. Apart from path specifications, the SAS code consists of several SAS macros. Two of them require parameter specification from the user. The other ones are just executed. The code was written in Base SAS, Version 9.4. By default, the output file contains several variables which are necessary for differential mortality analyses, such as sex, age, country, year of first interview, and vital status information. In addition, the user may specify the analytical variables by which mortality risk should be compared later, for example educational level or occupational class. These analytical variables may be measured either at the first interview (the baseline) or at the last interview of a respondent. The output file is available in SAS format and by default also in csv format.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study sought to apply current and advanced Y-STR DNA technology in forensic laboratories to a large in vivo population of proxy-couples, to provide groundwork for future inquiry about the conditions affecting DNA recovery in the living patient, to determine timing for evidence collection, and to attempt to identify variables influencing DNA recovery. The objective of this research was to create the evidence base supporting or limiting the expansion of the 72-hour period for evidence collection. Another objective was to identify conditions that might influence the recovery of DNA, and therefore influence policies related to sample collection from the complex post-coital environment. The collection includes 6 SPSS data files: AlleleRecovery Jun 2014 Allrec.sav (n=70; 34 variables) AlleleRecovery Jun 2014 Used for descriptve analysis.sav (n=66; 58 variables) Condom_collections-baseline-d9-Jun2014 Allrec without open-ended-ICPSR.sav (n=70; 66 variables) DNADemogFemalesJun2014- without open-ended AllRec-ICPSR.sav (n=73; 67 variables) DNADemogFemalesJun2014- without open-ended -For analysis with group variables-ICPSR.sav (n=66; 73 variables) DNADemogMalesJun2014- without open-ended AllRec-ICPSR.sav (n=73; 46 variables) and 1 SAS data file (dnalong.sas7bdat (n=264; 7 variables)). Data from a focus group of subject matter experts which convened to identify themes from their practice are not included with this collection.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study evaluates the impacts of re-entry programs developed by seven grantees awarded funds under the Second Chance Act (SCA) Adult Demonstration Program to reduce recidivism by addressing the challenges faced by adults returning to their communities after incarceration. The collection contains 3 SAS data files: admin30.sas(n=966; 111 variables), MIS.sas(n=606; 48 variables), and survey.sas(n=789; 273 variables) and 1 SAS syntax file.

Var Sas Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.This study sought to examine any major changes in schools in the past two years as an evaluation of the Safe and Civil Schools Initiative. Students, faculty, and administrators were asked questions on topics including school safety, climate, and the discipline process.This collection includes 6 SAS data files: "psja_schools.sas7bdat" with 66 variables and 15 cases, "psja_schools_v01.sas7bdat" with 104 variables and 15 cases, "psja_staff.sas7bdat" with 39 variables and 2,921 cases, "psja_staff_v01.sas7bdat" with 202 variables and 2,398 cases, "psja_students.sas7bdat" with 97 variables and 4,382 cases, and "psja_students_v01.sas7bdat" with 332 variables and 4,267 cases. Additionally, the collection includes 1 SAS formats catalog "formats.sas7bcat", and 10 SAS syntax files.

Mark Variable Print Sas Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

SAS code to reproduce the simulation study and the analysis of the urine osmolarity example. (ZIP)

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

The appraisals of treatment-covariate interaction have theoretical and substantial implications in all scientific fields. Methodologically, the detection of interaction between categorical treatment levels and continuous covariate variables is analogous to the homogeneity of regression slopes test in the context of ANCOVA. A fundamental assumption of ANCOVA is that the regression slopes associating the response variable with the covariate variable are presumed constant across treatment groups. The validity of homogeneous regression slopes accordingly is the most essential concern in traditional ANCOVA and inevitably determines the practical usefulness of research findings. In view of the limited results in current literature, this article aims to present power and sample size procedures for tests of heterogeneity between two regression slopes with particular emphasis on the stochastic feature of covariate variables. Theoretical implications and numerical investigations are presented to explicate the utility and advantage for accommodating covariate properties. The exact approach has the distinct feature of accommodating the full distributional properties of normal covariates whereas the simplified approximate methods only utilize the partial information of covariate variances. According to the overall accuracy and robustness, the exact approach is recommended over the approximate methods as a reliable tool in practical applications. The suggested power and sample size calculations can be implemented with the supplemental SAS and R programs.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. Teenage adolescent females residing in Baltimore, Maryland who were involved in a relationship with a history of violence were sought after to participate in this research study. Respondents were interviewed and then followed through daily diary entries for several months. The aim of the research was to understand the context regarding teen dating violence (TDV). Prior research on relationship context has not focused on minority populations; therefore, the focus of this project was urban, predominantly African American females. The available data in this collection includes three SAS (.sas7bdat) files and a single SAS formats file that contains variable and value label information for all three data files. The three data files are:

final_baseline.sas7bdat (157 cases / 252 variables) final_partnergrid.sas7bdat (156 cases / 76 variables) hart_final_sas7bdata (7004 cases / 23 variables)

Abstract (en): The fourth round of Central and Eastern Euro-Barometer surveys was carried out in Albania, Armenia, Belarus, Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, the former Yugoslav Republic of Macedonia (FYROM), Poland, Romania, European Russia, Slovakia, Slovenia, and Ukraine. The surveys assessed public support for the European Union as well as for political and economic change by asking respondents to comment on conditions within their own countries, the financial situation of their households as compared to 12 months ago and their expectations for the next year, and their attitudes toward a market economy, general economic reforms, and the process of privatizing state enterprises. Respondents were also queried about their level of satisfaction with the way democracy was developing in their country, how much respect there was for human rights, and the nations or organizations they felt the future of their country was most closely tied to. Demographic data collected on participants varied from country to country and included information such as age of the household head, education, age when education finished, occupation, marital status, employment status, religion, mother tongue, ethnic background, vote intention, political party preference, union membership, left/right political placement, sex, household composition, region, and income. Citizens of 16 Central and Eastern European countries, aged 15 and over, residing in Albania, Armenia, Belarus, Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Macedonia, Poland, Romania, Russia, Slovakia, Slovenia, and the Ukraine. Multistage national probability samples. 2005-06-09 This data collection has been reformatted and updated in conformance with current Eurobarometer processing standards. Many variable names, variable labels, and value labels have been revised to current naming conventions. Some combined variables have been broken out into several variables. Revised SAS and SPSS setup files and an updated documentation file are now available as well.2002-05-21 SAS setup files are now available and the SPSS setup files have been updated. Also, a complete version of the questionnaire is now available. face-to-face interviewData processing for this collection was performed at the Zentralarchiv fur Empirische Sozialforschung in Cologne, Germany.

SAS data set "neuro_morbi_indep.sas7bdat" contains data for publication "Neurologic morbidity and functional independence in adult survivors of childhood cancer". Variables and formats are in the file "Variables and formats for neuro_morbi_indep.docx".

This directory contains analytic code used to build cohorts, dependent variables, and covariates, and run all statistical analyses for the study, "Changes in care associated with integrating Medicare and Medicaid for dual eligible individuals: Examination of a Fully Integrated Special Needs Plan."The code files enclosed in this directory are:SAS_Cohorts_Outcomes 23-9-30.sas. This SAS code file builds study cohorts, dependent variables, and covariates. This code produced a person-by-month level database of outcomes and covariates for individuals in the integration and comparison cohorts.STATA_Models_23-6-5_weight_jama.do. This Stata program reads in the person-by-month level database (output from SAS) and conducts all statistical analyses used to produce the main and supplementary analyses reported in the manuscript.We have provided this code and documentation to disclose our study methods. Our Data Use Agreements prohibit publishing of row-level data for this study. Therefore, researchers would need to obtain Data Use Agreements with data providers to implement these analyses. We also note that some measures reference macros with proprietary code (e.g., Medispan® files) which require a separate user license to run. Interested readers should contact the study PI, Eric T. Roberts (eric.roberts@pennmedicine.upenn.edu) for further information.

The existence of interactive effects of a dichotomous treatment variable on the relationship between the continuous predictor and response variables is an essential issue in biological and medical sciences. Also, considerable attention has been devoted to raising awareness of the often-untenable assumption of homogeneous error variance among treatment groups. Although the procedures for detecting interactions between treatment and predictor variables are well documented in the literature, the corresponding problem of power and sample size calculations has received relatively little attention. In order to facilitate interaction design planning, this article describes power and sample size procedures for the extended Welch test of difference between two regression slopes under heterogeneity of variance. Two different formulations are presented to explicate the implications of appropriate reliance on the predictor variables. The simplified method only utilizes the partial information of predictor variances and has the advantages of statistical and computational simplifications. However, extensive numerical investigations showed that it is relatively less accurate than the more profound procedure that accommodates the full distributional features of the predictors. According to the analytic justification and empirical performance, the proposed approach gives reliable solutions to power assessment and sample size determination in the detection of interaction effects. A numerical example involving kidney weigh and body weigh of crossbred diabetic and normal mice is used to illustrate the suggested procedures with flexible allocation schemes. Moreover, the organ and body weights data is incorporated in the accompany SAS and R software programs to illustrate the ease and convenience of the proposed techniques for design planning in interactive research.