Part 1 of the course will offer an introduction to SPSS and teach how to work with data saved in SPSS format. Part 2 will demonstrate how to work with SPSS syntax, how to create your own SPSS data files, and how to convert data in other formats to SPSS. Part 3 will teach how to append and merge SPSS files, demonstrate basic analytical procedures, and show how to work with SPSS graphics.

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

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 data set comprises responses to a questionnaire survey with a wide range of items concerning finances, legal and relationship issues in non-traditional, cohabiting heterosexual couples. It is in the format of an SPSS .sav file with an N of 235 (having excluded a small number of respondents who did not meet the study criteria). The bulk of the data were obtained by means of an on-line survey with the remaining few obtained from a paper version of the questionnaire. Owing to the format imposed by the software used for the on-line version (PHPSurveyor) there are some instances where the precise response format for the items differed between the two versions. In order to merge the data into SPSS, some minor adjustments had to be made to make them compatible, such as combining 2 separate items in the on-line version on cohabitation length to obtain a single measure. To clarify, in the on-line version, two separate responses asked for the number of years of cohabitation and the number of months. These were combined in the final SPSS file into a single measure of overall cohabitation length in months. Thus, a respondent who had cohabited for 2 years and 6 months would receive a value in the final data set of 30 months’ cohabitation length. To indicate in full detail how some of the items have been combined for certain measures, an Excel file has been provided. At the top of the Excel file are the actual question items from the hard copy version of the questionnaire. Underneath in the purple band are the respective variable labels as they appear in the SPSS file. Below this, in blue, can be found the labels for the composite or recorded items that combine information from more than one of the original variables (for example, ‘household income combines information from the items asking for respondent’s own and partner’s income). The labels for the variable values can be found in the SPSS file in the conventional way. Studies of the monetary practices of (mainly) married couples have revealed gender-associated asymmetries in access to household resources. However, theory development has been restricted because gender issues are easily confounded with the ideological meaning(s) of ‘marriage’. In other words, is it being a ‘wife’ or ‘husband’ that produces such asymmetries, rather than gender per se? The proposed project aims to disentangle this conflation of ‘gender’ with ‘marriage’ by focusing on money management in non-traditional (ie unmarried cohabiting or non-cohabiting) heterosexual couples, The research will be in two phases: (1) in-depth qualitative interviews with individual partners in 15 non-traditional heterosexual (NTH) couples, including some that have specifically rejected the notion of marriage on ideological grounds, and (2) a larger scale survey of 300 NTH couples. Our main aims are to: (1) Provide a detailed analysis of how NTH couples organise their finances and compare this with existing data on married couples; (2) Develop theories of household financial management that are grounded in a more inclusive definition of ‘household’ or ‘family’; (3) Explore NTH couples’ understandings of their financial rights and responsibilities and consider the implications of their financial management practices for the proposed law reform governing financial provision on cohabitation breakdown. Our main aims are to: (1) Provide a detailed analysis of how NTH couples organise their finances and compare this with existing data on married couples; (2) Develop theories of household financial management that are grounded in a more inclusive definition of ‘household’ or ‘family’; (3) Explore NTH couples’ understandings of their financial rights and responsibilities and consider the implications of their financial management practices for the proposed law reform governing financial provision on cohabitation breakdown. Data was collected mainly via on-line questionnaire with 267 individual respondents; majority (235) were cohabiting and the rest married.

analyze the area resource file (arf) with r the arf is fun to say out loud. it's also a single county-level data table with about 6,000 variables, produced by the united states health services and resources administration (hrsa). the file contains health information and statistics for over 3,000 us counties. like many government agencies, hrsa provides only a sas importation script and an as cii file. this new github repository contains two scripts: 2011-2012 arf - download.R download the zipped area resource file directly onto your local computer load the entire table into a temporary sql database save the condensed file as an R data file (.rda), comma-separated value file (.csv), and/or stata-readable file (.dta). 2011-2012 arf - analysis examples.R limit the arf to the variables necessary for your analysis sum up a few county-level statistics merge the arf onto other data sets, using both fips and ssa county codes create a sweet county-level map click here to view these two scripts for mo re detail about the area resource file (arf), visit: the arf home page the hrsa data warehouse notes: the arf may not be a survey data set itself, but it's particularly useful to merge onto other survey data. confidential to sas, spss, stata, and sudaan users: time to put down the abacus. time to transition to r. :D

Abstract copyright UK Data Service and data collection copyright owner.The Crime Survey for England and Wales (CSEW) asks a sole adult in a random sample of households about their, or their household's, experience of crime victimisation in the previous 12 months. These are recorded in the victim form data file (VF). A wide range of questions are then asked, covering demographics and crime-related subjects such as attitudes to the police and the criminal justice system (CJS). These variables are contained within the non-victim form (NVF) data file. In 2009, the survey was extended to children aged 10-15 years old; one resident of that age range was also selected from the household and asked about their experience of crime and other related topics. The first set of children's data covered January-December 2009 and is held separately under SN 6601. From 2009-2010, the children's data cover the same period as the adult data and are included with the main study.The Telephone-operated Crime Survey for England and Wales (TCSEW) became operational on 20 May 2020. It was a replacement for the face-to-face CSEW, which was suspended on 17 March 2020 because of the coronavirus (COVID-19) pandemic. It was set up with the intention of measuring the level of crime during the pandemic. As the pandemic continued throughout the 2020/21 survey year, questions have been raised as to whether the year ending March 2021 TCSEW is comparable with estimates produced in earlier years by the face-to-face CSEW. The ONS Comparability between the Telephone-operated Crime Survey for England and Wales and the face-to-face Crime Survey for England and Wales report explores those factors that may have a bearing on the comparability of estimates between the TCSEW and the former CSEW. These include survey design, sample design, questionnaire changes and modal changes.More general information about the CSEW may be found on the ONS Crime Survey for England and Wales web page and for the previous BCS, from the GOV.UK BCS Methodology web page.History - the British Crime SurveyThe CSEW was formerly known as the British Crime Survey (BCS), and has been in existence since 1981. The 1982 and 1988 BCS waves were also conducted in Scotland (data held separately under SNs 4368 and 4599). Since 1993, separate Scottish Crime and Justice Surveys have been conducted. Up to 2001, the BCS was conducted biennially. From April 2001, the Office for National Statistics took over the survey and it became the CSEW. Interviewing was then carried out continually and reported on in financial year cycles. The crime reference period was altered to accommodate this. Secure Access CSEW dataIn addition to the main survey, a series of questions covering drinking behaviour, drug use, self-offending, gangs and personal security, and intimate personal violence (IPV) (including stalking and sexual victimisation) are asked of adults via a laptop-based self-completion module (questions may vary over the years). Children aged 10-15 years also complete a separate self-completion questionnaire. The questionnaires are included in the main documentation, but the data are only available under Secure Access conditions (see SN 7280), not with the main study. In addition, from 2011 onwards, lower-level geographic variables are also available under Secure Access conditions (see SN 7311).New methodology for capping the number of incidents from 2017-18The CSEW datasets available from 2017-18 onwards are based on a new methodology of capping the number of incidents at the 98th percentile. Incidence variables names have remained consistent with previously supplied data but due to the fact they are based on the new 98th percentile cap, and old datasets are not, comparability has been lost with years prior to 2012-2013. More information can be found in the 2017-18 User Guide (see SN 8464) and the article ‘Improving victimisation estimates derived from the Crime Survey for England and Wales’. CSEW Historic back series – dataset update (March 2022)From January 2019, all releases of crime statistics using CSEW data adopted a new methodology for measuring repeat victimisation (moving from a cap of 5 in the number of repeat incidents to tracking the 98th percentile value for major crime types). To maintain a consistent approach across historic data, all datasets back to 2001 have been revised to the new methodology. The change affects all incident data and related fields. A “bolt-on” version of the data has been created for the 2001/02 to 2011/12 datasets. This “bolt-on” dataset contains only variables previously supplied impacted by the change in methodology. These datasets can be merged onto the existing BCS NVF and VF datasets. A template ‘merge’ SPSS syntax file is provided, which will need to be adapted for other software formats.For the seventh edition (March 2022), “bolt-on” datasets for the NVF and VF files, example merge syntax and additional documentation have been added to the study to accommodate the latest CSEW repeat victimisation measurement methodology. See the documentation for further details. Main Topics: The dataset includes information from two sections of the survey, the non-victim form questionnaire and the victim form questionnaire. The non-victim form questionnaire gathers respondent-level data: topics covered include fear of crime, perception of antisocial behaviour, victimisation screener questions, performance of the criminal justice system (CJS), experiences of the police, attitudes to the CJS, crime prevention and security, victims and the CJS, demographic information, and technology crime. The victim form contains offence-level data. Up to six different incidents are asked about for each respondent - each of these constitutes a separate victim form and can be matched back to the respondent-level data through the variable ROWLABEL. Topics covered include the nature and circumstances of the incident, details of offenders, security measures, costs, emotional reactions, contact with the CJS and outcomes where known. From October 2016, the self-completion questionnaire modules covering dcovering drug use, drinking behaviour, and domestic violence, sexual victimisation and stalking are subject to Controlled data access conditions - see SN 7280. Multi-stage stratified random sample Face-to-face interview 2004 2005 ADMINISTRATION OF J... ADOLESCENTS ADVICE AGE AGGRESSIVENESS AIRPORTS ALCOHOL USE ALCOHOLISM AMPHETAMINES ANABOLIC STEROIDS ANGER ASSAULT ATTITUDES BICYCLES BURGLARY CANNABIS CAR PARKING AREAS CHILDREN CHRONIC ILLNESS CLUBS COCAINE COMBATIVE SPORTS COMMUNITIES COMMUNITY ACTION COMMUNITY BEHAVIOUR COMMUNITY SAFETY COMMUNITY SERVICE P... COMPUTERS COSTS COUNSELLING COURT CASES CREDIT CARD USE CRIME AND SECURITY CRIME PREVENTION CRIME VICTIMS CRIMINAL COURTS CRIMINAL DAMAGE CRIMINAL INVESTIGATION CRIMINAL JUSTICE SY... CRIMINALS CULTURAL GOODS CULTURAL IDENTITY Crime and law enfor... DAMAGE DEBILITATIVE ILLNESS DISCIPLINE DOGS DOMESTIC RESPONSIBI... DOMESTIC VIOLENCE DOORS DRINKING BEHAVIOUR DRIVING DRUG ABUSE ECONOMIC ACTIVITY ECONOMIC VALUE ECSTASY DRUG ELECTRONIC MAIL EMERGENCY AND PROTE... EMOTIONAL DISTURBANCES EMOTIONAL STATES EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY ETHNIC CONFLICT ETHNIC GROUPS EVERYDAY LIFE EXPOSURE TO NOISE England and Wales FAMILIES FAMILY MEMBERS FEAR FEAR OF CRIME FINANCIAL COMPENSATION FINANCIAL RESOURCES FIRE FIRE DAMAGE FIRE SAFETY MEASURES GENDER HARASSMENT HEADS OF HOUSEHOLD HEALTH HEALTH PROFESSIONALS HEROIN HOME CONTENTS INSUR... HOME OWNERSHIP HOSPITALIZATION HOURS OF WORK HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S OC... HOUSEHOLD INCOME HOUSEHOLDS HOUSING AGE HOUSING TENURE INDUSTRIES INFORMATION MATERIALS INFORMATION SOURCES INJURIES INSURANCE CLAIMS INTERNET ACCESS INTERNET USE INTERPERSONAL COMMU... INTERPERSONAL CONFLICT INTERPERSONAL RELAT... INTRUDER ALARM SYSTEMS JUDGES JUDGMENTS LAW JURIES JUVENILE DELINQUENCY LANDLORDS LAW ENFORCEMENT LEARNING DISABILITIES LEAVE LEGAL PROCEDURE LIGHTING LOCAL GOVERNMENT SE... LOCKS LSD DRUG MAGISTRATES MARITAL STATUS MEDIATION MEDICAL CARE MOTOR VEHICLES NEIGHBOURHOODS NEIGHBOURS NEWSPAPER READERSHIP NEWSPAPERS OFFENCES OFFENSIVE TELEPHONE... ONLINE SHOPPING PAYMENTS PERSONAL CONTACT PERSONAL FASHION GOODS PERSONAL IDENTIFICA... PERSONAL SAFETY POLICE OFFICERS POLICE SERVICES POLICING POLITICAL PARTICIPA... PORNOGRAPHY PRISON SENTENCES PROBATION PROSECUTION SERVICE PUBLIC HOUSES PUBLIC OPINION PUNISHMENT QUALIFICATIONS QUALITY OF LIFE RECIDIVISM REFUSE RENTED ACCOMMODATION RESIDENTIAL MOBILITY RESPONSIBILITY RISK ROBBERY SECURITY SYSTEMS SELF EMPLOYED SEXUAL ASSAULT SEXUAL HARASSMENT SEXUAL OFFENCES SHARED HOME OWNERSHIP SICK LEAVE SLEEP DISORDERS SMALL BUSINESSES SMOKING SOCIAL ACTIVITIES L... SOCIAL HOUSING SOCIAL PARTICIPATION SOCIAL SUPPORT SOLVENT ABUSE SORROW SPOUSE S ECONOMIC A... SPOUSES STUDENTS SUBSTANCE USE SUPERVISORY STATUS Social behaviour an... THEFT TIED HOUSING TRAFFIC OFFENCES TRAINING TRAINING COURSES TRANQUILLIZERS UNEMPLOYMENT UNWAGED WORKERS VAGRANTS VISITS PERSONAL VOLUNTARY WELFARE O... WEAPONS WINDOWS WITNESS INTIMIDATION WITNESSES WORKPLACE YOUTH YOUTH COURTS