******************************************************************* * Stata "do-file" file with labels and missing data specifications * Created by ddltox on Apr 08, 2009 (Wed 01:48 PM PDT) * DDL source file: "ocs02/ocs02.ddl". * * Note that the data dictionary is given at the end of this file. * Put the dictionary into a separate file (by editing this file). * Then specify below the name of the dictionary file. * * DDL file gives the following dataset description: * Records per case: 1 * Record length: 109 ******************************************************************* label data "Special Survey of Orange County 2002" #delimit ; label define Q61 1 "18-24" 2 "25-34" 3 "35-44" 4 "45-54" 5 "55-64" 6 "65 and older" 9 "Refuse" ; label define SEX 1 "Male" 2 "Female" ; label define Q01 1 "Single-family detached home" 2 "Attached home" 3 "Apartment" 4 "Another type of dwelling" 5 "Mobile home or trailer" 8 "Don't know" 9 "Refuse" ; label define Q02 1 "Less than 5 years" 2 "5 to 10 years" 3 "10 to 20 years" 4 "More than 20 years" 8 "Don't know" 9 "Refuse" ; label define Q03 1 "Own" 2 "Rent" 3 "Neither" 8 "Don't know" 9 "Refuse" ; label define Q04 1 "Very satisfied" 2 "Somewhat satisfied" 3 "Somewhat dissatisfied" 4 "Very dissatisfied" 8 "Don't know" 9 "Refuse" ; label define Q05 1 "Very satisfied" 2 "Somewhat satisfied" 3 "Somewhat dissatisfied" 4 "Very dissatisfied" 8 "Don't know" 9 "Refuse" ; label define Q06 1 "Excellent" 2 "Good" 3 "Fair" 4 "Poor" 8 "Don't know" 9 "Refuse" ; label define Q07 1 "Excellent" 2 "Good" 3 "Fair" 4 "Poor" 8 "Don't know" 9 "Refuse" ; label define Q08 1 "Excellent" 2 "Good" 3 "Fair" 4 "Poor" 8 "Don't know" 9 "Refuse" ; label define Q09 1 "Excellent" 2 "Good" 3 "Fair" 4 "Poor" 8 "Don't know" 9 "Refuse" ; label define Q10 1 "Excellent" 2 "Good" 3 "Fair" 4 "Poor" 5 "Don't live in a city" 8 "Don't know" 9 "Refuse" ; label define Q11 1 "Big problem" 2 "Somewhat of a problem" 3 "Not a problem" 8 "Don't know" 9 "Refuse" ; label define Q12 1 "Big problem" 2 "Somewhat of a problem" 3 "Not a problem" 8 "Don't know" 9 "Refuse" ; label define Q13 1 "Big problem" 2 "Somewhat of a problem" 3 "Not a problem" 8 "Don't know" 9 "Refuse" ; label define Q14 1 "Big problem" 2 "Somewhat of a problem" 3 "Not a problem" 8 "Don't know" 9 "Refuse" ; label define Q15 1 "Big problem" 2 "Somewhat of a problem" 3 "Not a problem" 8 "Don't know" 9 "Refuse" ; label define Q16 1 "Schools and education" 2 "Crime and gangs" 3 "Jobs, the economy, and unemployment" 4 "Traffic and transportation" 5 "Population growth, development, and sprawl" 6 "Terrorism and security" 7 "Immigration" 8 "Health care and HMO reform" 9 "Taxes" 10 "Local government" 11 "Electricity and energy" 12 "Air pollution and air quality" 13 "Beach and water pollution" 14 "Environment (general)" 15 "Government regulations" 16 "Poverty and homelessness" 17 "Guns and gun control" 18 "Housing costs" 19 "Drugs and drug abuse" 20 "Race relations and race issues" 21 "El Toro Marine air base" 22 "Great Park" 97 "Other (specify)" 98 "Don't know" 99 "Refuse" ; label define Q17 1 "Very well" 2 "Somewhat well" 3 "Somewhat badly" 4 "Very badly" 8 "Don't know" 9 "Refuse" ; label define Q18 1 "Better place" 2 "Worse place" 3 "No change" 8 "Don't know" 9 "Refuse" ; label define Q19 1 "Excellent" 2 "Good" 3 "Fair" 4 "Poor" 8 "Don't know" 9 "Refuse" ; label define Q20 1 "A lot" 2 "Some" 3 "Very little" 4 "No attention" 8 "Don't know" 9 "Refuse" ; label define Q21 1 "Waste a lot" 2 "Waste some" 3 "Waste very little" 4 "Waste none" 8 "Don't know" 9 "Refuse" ; label define Q22 1 "Yes, a lot" 2 "Yes, a little" 3 "Yes, don't know much about it" 4 "No" 8 "Don't know" 9 "Refuse" ; label define Q23 1 "Favor" 2 "Oppose" 8 "Don't know" 9 "Refuse" ; label define Q24 1 "Favor" 2 "Oppose" 8 "Don't know" 9 "Refuse" ; label define Q25 1 "Favor" 2 "Oppose" 8 "Don't know" 9 "Refuse" ; label define Q26 1 "Yes, often" 2 "Yes, sometimes" 3 "Yes, rarely" 4 "No" 8 "Don't know" 9 "Refuse" ; label define Q27 1 "Yes, a lot" 2 "Yes, a little" 3 "Yes, don't know very much" 4 "No" 8 "Don't know" 9 "Refuse" ; label define Q28 1 "Surface transportation" 2 "Water systems" 3 "School facilities" 4 "Sewer systems" 5 "Airports" 6 "Other" 8 "Don't know" 9 "Refuse" ; label define Q29 1 "Freeways and highways" 2 "Local streets and roads" 3 "Light rail system" 4 "Public bus system" 5 "Carpool lanes" 6 "Other" 8 "Don't know" 9 "Refuse" ; label define Q30 1 "Very important" 2 "Somewhat important" 3 "Not important" 8 "Don't know" 9 "Refuse" ; label define Q31 1 "Does" 2 "Does not" 8 "Don't know" 9 "Refuse" ; label define Q32 1 "Good thing" 2 "Bad thing" 8 "Don't know" 9 "Refuse" ; label define Q33 1 "Yes" 2 "No" 8 "Don't know" 9 "Refuse" ; label define Q34 1 "Very satisfied" 2 "Somewhat satisfied" 3 "Not satisfied" 8 "Don't know" 9 "Refuse" ; label define Q35 1 "Yes" 2 "No" 8 "Don't know" 9 "Refuse" ; label define Q36 1 "Yes" 2 "No" 8 "Don't know" 9 "Refuse" ; label define Q37 1 "Better off" 2 "Worse off" 3 "Same" 8 "Don't know" 9 "Refuse" ; label define Q38 1 "Better off" 2 "Worse off" 3 "Same" 8 "Don't know" 9 "Refuse" ; label define Q39 1 "Good times" 2 "Bad times" 8 "Don't know" 9 "Refuse" ; label define Q40 1 "Good times" 2 "Periods of unemployment and depression" 8 "Don't know" 9 "Refuse" ; label define Q41 1 "Good time" 2 "Bad time" 8 "Don't know" 9 "Refuse" ; label define Q42 1 "Excellent" 2 "Good" 3 "Fair" 4 "Poor" 8 "Don't know" 9 "Refuse" ; label define Q43 1 "Very satisfied" 2 "Somewhat satisfied" 3 "Not satisfied" 8 "Don't know" 9 "Refuse" ; label define Q44 1 "More than enough" 2 "Just enough" 3 "Not enough" 8 "Don't know" 9 "Refuse" ; label define Q45 1 "Very often" 2 "Fairly often" 3 "Not too often" 4 "Never" 8 "Don't know" 9 "Refuse" ; label define Q46 1 "Excellent" 2 "Good" 3 "Fair" 4 "Poor" 8 "Don't know" 9 "Refuse" ; label define Q47 1 "Yes, serious recession" 2 "Yes, moderate recession" 3 "Yes, mild recession" 4 "No" 8 "Don't know" 9 "Refuse" ; label define Q48 1 "Yes, very concerned" 2 "Yes, somewhat concerned" 3 "No" 8 "Don't know" 9 "Refuse" ; label define Q49 1 "Divided into haves and have nots" 2 "Not divided that way" 8 "Don't know" 9 "Refuse" ; label define Q50 1 "Haves" 2 "Have nots" 8 "Don't know" 9 "Refuse" ; label define Q51 1 "People have equal opportunity" 2 "Government should do more" 3 "Both" 4 "Neither" 8 "Don't know" 9 "Refuse" ; label define Q52 1 "Democrat" 2 "Republican" 3 "Other" 4 "Independent" 5 "Refuse to say q52" 6 "Not registered" 8 "Don't know" 9 "Refuse" ; label define Q53 1 "Very liberal" 2 "Somewhat liberal" 3 "Middle-of-the-road" 4 "Somewhat conservative" 5 "Very conservative" 8 "Don't know" 9 "Refuse" ; label define Q54 1 "Approve" 2 "Disapprove" 8 "Don't know" 9 "Refuse" ; label define Q55 1 "Approve" 2 "Disapprove" 8 "Don't know" 9 "Refuse" ; label define Q56 1 "Approve" 2 "Disapprove" 8 "Don't know" 9 "Refuse" ; label define Q57 1 "Approve" 2 "Disapprove" 8 "Don't know" 9 "Refuse" ; label define Q58 1 "Better place to live" 2 "Worse place to live" 3 "Doesn't make much difference either way" 8 "Don't know" 9 "Refuse" ; label define Q59 1 "Very often" 2 "Fairly often" 3 "Once in a while" 4 "Hardly ever" 8 "Don't know" 9 "Refuse" ; label define Q60 1 "Would be fine with it" 2 "Would bother you, but would come to accept it" 3 "Would not be able to accept it" 8 "Don't know" 9 "Refuse" ; label define Q62 1 "Yes, children in a public school" 2 "Yes, no children in a public school" 3 "No" 9 "Refuse" ; label define Q63 1 "Full-time employed" 2 "Part-time employed" 3 "Not employed, student" 4 "Not employed, homemaker" 5 "Not employed, retired" 6 "Not employed, looking for work" 7 "Not employed, not looking for work" 9 "Refuse" ; label define Q64 1 "Drive alone" 2 "Carpool" 3 "Public bus or transit" 4 "Other" 5 "Work at home" 8 "Don't know" 9 "Refuse" ; label define Q65 1 "Very satisfied" 2 "Somewhat satisfied" 3 "Somewhat dissatisfied" 4 "Very dissatisfied" 8 "Don't know" 9 "Refuse" ; label define Q66 1 "Some high school" 2 "High school graduate" 3 "Some college" 4 "College graduate" 5 "Post graduate" 9 "Refuse" ; label define Q67 1 "Under $20,000" 2 "$20,000 to $39,999" 3 "$40,000 to $59,999" 4 "$60,000 to $79,999" 5 "$80,000 to $99,999" 6 "$100,000 or more" 9 "Refuse" ; label define Q68 1 "Asian" 2 "Black or African-American" 3 "Hispanic or Latino" 4 "Caucasian or White" 5 "Other" 9 "Refuse" ; label define Q68a 1 "Asian" 2 "Black or African-American" 3 "Hispanic or Latino" 4 "Caucasian or White" 5 "Other" 9 "Refuse" ; label define Q69 1 "Yes" 2 "No, US citizen" 3 "No, not a US citizen" 9 "Refuse" ; label define Q70 1 "Aliso Viejo" 2 "Anaheim" 3 "Anaheim Hills" 4 "Atwood" 5 "Brea" 6 "Bristol" 7 "Buena Park" 8 "Capistrano Beach" 9 "Corona del Mar" 10 "Costa Mesa" 11 "Coto de Caza" 12 "Cypress" 13 "Dana Point" 14 "El Toro" 15 "Fountain Valley" 16 "Fullerton" 17 "Garden Grove" 18 "Huntington Beach" 19 "Irvine" 20 "Laguna Beach" 21 "Laguna Hills" 22 "Laguna Niguel" 23 "Laguna Woods" 24 "La Habra" 25 "Lake Forest" 26 "La Palma" 27 "Los Alamitos" 28 "Midway City" 29 "Mission Viejo" 30 "Modjeska" 31 "Newport Beach" 32 "Orange" 33 "Placentia" 34 "Portola Hills" 35 "Rancho Santa Margarita" 36 "Rossmoor" 37 "San Clemente" 38 "San Juan Capistrano" 39 "Santa Ana" 40 "Seal Beach" 41 "Silverado Canyon" 42 "Stanton" 43 "Sunset Beach" 44 "Trabuco" 45 "Trabuco Canyon" 46 "Tustin" 47 "Tustin Foothills" 48 "Villa Park" 49 "Westminster" 50 "Yorba Linda" 97 "Other (specify)" 99 "Not ascertained" ; label define Q71 1 "Yes" 2 "No" 9 "Refuse" ; label define LANGUAGE 0 "English" 1 "Spanish" ; label define COUNTY 1 "Alameda" 3 "Alpine" 5 "Amador" 7 "Butte" 9 "Calaveras" 11 "Colusa" 13 "Contra Costa" 15 "Del Norte" 17 "El Dorado" 19 "Fresno" 21 "Glenn" 23 "Humboldt" 25 "Imperial" 27 "Inyo" 29 "Kern" 31 "Kings" 33 "Lake" 35 "Lassen" 37 "Los Angeles" 39 "Madera" 41 "Marin" 43 "Mariposa" 45 "Mendocino" 47 "Merced" 49 "Modoc" 51 "Mono" 53 "Monterey" 55 "Napa" 57 "Nevada" 59 "Orange" 61 "Placer" 63 "Plumas" 65 "Riverside" 67 "Sacramento" 69 "San Benito" 71 "San Bernadino" 73 "San Diego" 75 "San Francisco" 77 "San Joaquin" 79 "San Luis Obispo" 81 "San Mateo" 83 "Santa Barbara" 85 "Santa Clara" 87 "Santa Cruz" 89 "Shasta" 91 "Sierra" 93 "Siskiyou" 95 "Solano" 97 "Sonoma" 99 "Stanilaus" 101 "Sutter" 103 "Tehama" 105 "Trinity" 107 "Tulare" 109 "Tuolomne" 111 "Ventura" 113 "Yolo" 115 "Yuba" ; label define OCNS 1 "North" 2 "South" ; #delimit cr ******************************************************************* infile using X * Replace 'X' with the name of the dictionary file. * * The contents of the dictionary are given at the end of this file. * Put the dictionary into a separate file (by editing this file). * Then specify here the name of the dictionary file. ******************************************************************* * The md, min and max specifications were translated * into the following "REPLACE...IF" statements: replace Q61 = . if (Q61 == 9) replace Q01 = . if (Q01 == 9) replace Q02 = . if (Q02 == 9) replace Q03 = . if (Q03 == 9) replace Q04 = . if (Q04 == 9) replace Q05 = . if (Q05 == 9) replace Q06 = . if (Q06 == 9) replace Q07 = . if (Q07 == 9) replace Q08 = . if (Q08 == 9) replace Q09 = . if (Q09 == 9) replace Q10 = . if (Q10 == 9) replace Q11 = . if (Q11 == 9) replace Q12 = . if (Q12 == 9) replace Q13 = . if (Q13 == 9) replace Q14 = . if (Q14 == 9) replace Q15 = . if (Q15 == 9) replace Q16 = . if (Q16 == 99) replace Q17 = . if (Q17 == 9) replace Q18 = . if (Q18 == 9) replace Q19 = . if (Q19 == 9) replace Q20 = . if (Q20 == 9) replace Q21 = . if (Q21 == 9) replace Q22 = . if (Q22 == 9) replace Q23 = . if (Q23 == 9) replace Q24 = . if (Q24 == 9) replace Q25 = . if (Q25 == 9) replace Q26 = . if (Q26 == 9) replace Q27 = . if (Q27 == 9) replace Q28 = . if (Q28 == 9) replace Q29 = . if (Q29 == 9) replace Q30 = . if (Q30 == 9) replace Q31 = . if (Q31 == 9) replace Q32 = . if (Q32 == 9) replace Q33 = . if (Q33 == 9) replace Q34 = . if (Q34 == 9) replace Q35 = . if (Q35 == 9) replace Q36 = . if (Q36 == 9) replace Q37 = . if (Q37 == 9) replace Q38 = . if (Q38 == 9) replace Q39 = . if (Q39 == 9) replace Q40 = . if (Q40 == 9) replace Q41 = . if (Q41 == 9) replace Q42 = . if (Q42 == 9) replace Q43 = . if (Q43 == 9) replace Q44 = . if (Q44 == 9) replace Q45 = . if (Q45 == 9) replace Q46 = . if (Q46 == 9) replace Q47 = . if (Q47 == 9) replace Q48 = . if (Q48 == 9) replace Q49 = . if (Q49 == 9) replace Q50 = . if (Q50 == 9) replace Q51 = . if (Q51 == 9) replace Q52 = . if (Q52 == 9) replace Q53 = . if (Q53 == 9) replace Q54 = . if (Q54 == 9) replace Q55 = . if (Q55 == 9) replace Q56 = . if (Q56 == 9) replace Q57 = . if (Q57 == 9) replace Q58 = . if (Q58 == 9) replace Q59 = . if (Q59 == 9) replace Q60 = . if (Q60 == 9) replace Q62 = . if (Q62 == 9) replace Q63 = . if (Q63 == 9) replace Q64 = . if (Q64 == 9) replace Q65 = . if (Q65 == 9) replace Q66 = . if (Q66 == 9) replace Q67 = . if (Q67 == 9) replace Q68 = . if (Q68 == 9) replace Q68a = . if (Q68a == 9) replace Q69 = . if (Q69 == 9) replace Q71 = . if (Q71 == 9) dictionary using Y { ***************************************************************** * Replace 'Y' with the name of the data file. * The default suffix is '.raw'. * * Put this dictionary into a separate file (with the suffix .dct). * (The first line of the dictionary file must contain the * 'dictionary' command.) * * Then edit the name of that dictionary file into the 'do-file' * portion of the STATA definitions. ******************************************************************* * Records per case: _lines(1) _line(1) _column(21) int CASEID %4f "Respondent ID" _column(1) int Q61 :Q61 %1f "Age (range)" _column(2) int SEX :SEX %1f "Gender (sex)" _column(3) double WEIGHT %18.2f "Weight" _column(25) int Q01 :Q01 %1f "Residence type" _column(26) int Q02 :Q02 %1f "Years of residence (range)" _column(27) int Q03 :Q03 %1f "Own current residence?" _column(28) int Q04 :Q04 %1f "Satisfied with your residence?" _column(29) int Q05 :Q05 %1f "Satisfied with your neighborhood?" _column(30) int Q06 :Q06 %1f "Satisfied with local freeways, streets, roads?" _column(31) int Q07 :Q07 %1f "Satisfied with local parks?" _column(32) int Q08 :Q08 %1f "Satisfied with local police protection?" _column(33) int Q09 :Q09 %1f "Satisfied with local public schools?" _column(34) int Q10 :Q10 %1f "City government performance" _column(35) int Q11 :Q11 %1f "Traffic congestion" _column(36) int Q12 :Q12 %1f "Population growth and development?" _column(37) int Q13 :Q13 %1f "Availability of affordable housing?" _column(38) int Q14 :Q14 %1f "Lack of job opportunities?" _column(39) int Q15 :Q15 %1f "Air pollution?" _column(40) int Q16 :Q16 %2f "Most important issue facing Orange County?" _column(42) int Q17 :Q17 %1f "Quality of life?" _column(43) int Q18 :Q18 %1f "Oramge County a better place to live?" _column(44) int Q19 :Q19 %1f "County government performance" _column(45) int Q20 :Q20 %1f "County's attention paid to people" _column(46) int Q21 :Q21 %1f "County wasting taxes?" _column(47) int Q22 :Q22 %1f "County & El Toro" _column(48) int Q23 :Q23 %1f "Favor El Toro Great Park?" _column(49) int Q24 :Q24 %1f "Favor funding El Toro Great Park with tax?" _column(50) int Q25 :Q25 %1f "Favor affordable housing in El Toro?" _column(51) int Q26 :Q26 %1f "Use El Toro Great Park?" _column(52) int Q27 :Q27 %1f "Know about infrastructure?" _column(53) int Q28 :Q28 %1f "Top infrastructure priority" _column(54) int Q29 :Q29 %1f "Top transportation priority in Orange County" _column(55) int Q30 :Q30 %1f "Roads affect Orange County quality of life?" _column(56) int Q31 :Q31 %1f "Local government has funding for projects?" _column(57) int Q32 :Q32 %1f "Supermajority requirement" _column(58) int Q33 :Q33 %1f "Two-thirds majority to 55 percent?" _column(59) int Q34 :Q34 %1f "Satisfied with Measure M?" _column(60) int Q35 :Q35 %1f "Extend Measure M?" _column(61) int Q36 :Q36 %1f "Include other infrastructure in Measure M?" _column(62) int Q37 :Q37 %1f "Financially better off than last year?" _column(63) int Q38 :Q38 %1f "Better off next year?" _column(64) int Q39 :Q39 %1f "U.S. business conditions next 12 months" _column(65) int Q40 :Q40 %1f "U.S. business conditions next 5 years" _column(66) int Q41 :Q41 %1f "Major household purchases" _column(67) int Q42 :Q42 %1f "Investing in homes in Orange County" _column(68) int Q43 :Q43 %1f "Satisfied with financial situation?" _column(69) int Q44 :Q44 %1f "Enough income to meet needs?" _column(70) int Q45 :Q45 %1f "Worry about money often?" _column(71) int Q46 :Q46 %1f "Orange County economy" _column(72) int Q47 :Q47 %1f "Orange County in economic recession" _column(73) int Q48 :Q48 %1f "Concerned about losing job?" _column(74) int Q49 :Q49 %1f "Separation of wealth" _column(75) int Q50 :Q50 %1f "Have or have not?" _column(76) int Q51 :Q51 %1f "Equal opportunity in California" _column(77) int Q52 :Q52 %1f "Party registration" _column(78) int Q53 :Q53 %1f "Political identification" _column(79) int Q54 :Q54 %1f "Bush as president?" _column(80) int Q55 :Q55 %1f "Bush on economy" _column(81) int Q56 :Q56 %1f "Davis as governor?" _column(82) int Q57 :Q57 %1f "Davis on jobs" _column(83) int Q58 :Q58 %1f "Ethnic diversity in Orange County" _column(84) int Q59 :Q59 %1f "Contact with other racial groups?" _column(85) int Q60 :Q60 %1f "Cross-race marriage" _column(86) int Q62 :Q62 %1f "Have kids in public school" _column(87) int Q63 :Q63 %1f "Current employment status" _column(88) int Q64 :Q64 %1f "How do you commute to work?" _column(89) int Q65 :Q65 %1f "Satisfied with commute to work?" _column(90) int Q66 :Q66 %1f "Last grade of school completed" _column(91) int Q67 :Q67 %1f "Income" _column(92) int Q68 :Q68 %1f "Race and ethnicity" _column(93) int Q68a :Q68a %1f "Secondary race and ethnicity" _column(94) int Q69 :Q69 %1f "U.S. born?" _column(95) int Q70 :Q70 %2f "City or community of residence?" _column(97) int Q71 :Q71 %1f "Willing to talk about these questions?" _column(98) int LANGUAGE :LANGUAGE %1f "Language of interview" _column(99) int COUNTY :COUNTY %2f "County" _column(101) int OCNS :OCNS %1f "Region" _column(102) int DATE %4f "Interview date" _column(106) int YEAR %4f "Year of interview" }