Version 01 Codebook ------------------- CODEBOOK VARIABLE DOCUMENTATION 1960 MINOR ELECTION STUDY (1960.S) NES 1960 MINOR STUDY VARIABLE ENTRIES This file should be opened in fixed font, 10 pitch or smaller. Codebook introductory material and appendix material are in separate files. ---------------------------------------------------------------- NOTE ON VARIABLE NAMING: The variable name references used in NES Study codebooks do not include the "V" prefix found in all variable names used within the released SAS and SPSS data definition files (.sas and .sps files). For example, "VAR 920001" and "VAR VERSION" in Study codebooks refer to V920001 and VVERSION in the study data definition files. VARIABLE 'NUMBERING': 'Numbered' variables in NES timeseries datasets and in Pilot datasets (except the 1998 Pilot) comply with the following format: 2-digit year prefix + 4 digits + [opt] 1-char suffix. Examples: 1978 Post variable V780002; 1983 Pilot variable V832101. Note that for datasets including data from multiple studies, the 2-digit year prefix corresponds to the appropriate study year to which the variable is associated; for example, the 1983 Pilot dataset also includes 1982 timeseries variables, for which the 2-digit prefix is 82. Non-time-series studies other than Pilots use 2-character mnemonic prefixes, for example variable VPS0048 in the 1988-1990-1992 Pooled Senate Study dataset. SPECIAL NOTE ON COLUMN LOCATIONS: Some numeric variables use coding schemes that allow for code values having a varying number of digits. In such instances, the number of of columns corresponding to the variable in the data file [.dat file] and in the column specifications will be the width of the maximum value occurring in the actual data, rather than the maximum width allowable by the coding scheme. For example, if codes 01-12 are allowed for a numeric variable but all values in the data are less than 10, then the number of columns corresponding to the variable within the ASCII data file will be 1. ============================== VAR VERSION NES VERSION NUMBER COLUMNS 1 - 2 NUMERIC NO MISSING DATA CODES NES VERSION NUMBER .................. ============================== VAR DSETNO NES DATASET NUMBER COLUMNS 3 - 8 CHARACTER NO MISSING DATA CODES NES DATASET NUMBER .................. This is dataset 1960.S ============================== VAR MN0001 NAME-ICPSR ARCHIVE NUMBER COLUMNS 9 - 12 CHARACTER NO MISSING DATA CODES ICPSR ARCHIVE NUMBER ============================== VAR MN0002 NAME-INTERVIEW NUMBER COLUMNS 13 - 16 NUMERIC NO MISSING DATA CODES INTERVIEW NUMBER ============================== VAR MN0003 NAME-PRIMARY SAMPLING UNIT COLUMNS 17 - 19 NUMERIC NO MISSING DATA CODES PRIMARY SAMPLING UNIT (PSU) SEE APPENDIX NOTE /THE PSU CODE IS THE FIRST AND SECOND DIGIT PLUS EITHER THE THIRD OR FOURTH DIGIT LISTED BELOW, E.G. BALTIMORE CITY MAY BE CODED AS 301 OR 302./ 301-2. BALTIMORE CITY INDEPENDENT CITY 303-4. BAITIMORE SUBURBS BALTINORE, MD. 271-2. BOSTON CITY SUFFOLK, MASS. 273-4. BOSTON SUBURBS HIDDLESEX, Mass. 273-4. BOSTON SUBURBS NORFOLK, MASS. 101-2. CHICAGO CITY, NORTH COOK, ILL. 111-2. CHICAGO CITY, SOUTH COOK, ILL. 103-4. CHICAGO SUBURBS COOK, ILL. 103-4. CHICAGO SUBURBS KANES, ILL. 103-4. CHICAGO SUBURBS LAKE, IND. 103-4. CHICAGO SUBURBS LAKE, ILL. 121-2. CLEVELAND CITY CUYAHOGA, OHIO 123-4. CLEVELAND SUBURBS CUYAHOGA, OHIO 131-2. DETROIT CITY WAYNE, MICH. 133-4. DETROIT SUBURBS WAYNE, MICH. 133-4. DETRCIT SUBURBS OAKLAND, MICH. 133-4. DETROIT SUBURBS MACOMB, MICH. 001-2. LOS ANGELES CITY LOS ANGELES, CAL. 003-4. LOS ANGELES SUBURBS LOS ANGELES, CAL. 003-4. LOS ANGELES SUBURBS ORANGE, CAL. 201-2. NEW YORK CITY-BRONX BRONX, N.Y. 211-2. NEW YORK CITY-BROOKLYN KINGS, N.Y. 221-2. NEW YORK CITY-MANHATTAN NEW YORK, N.Y. 201-2. NEW YORK CITY-QUEENS QUEENS, N.Y. 231-2. NEW YORK CITY-RICHMOND RICHMOND, N.Y. 241-2. JERSEY CITY, N.J. HUDSON, N.J. 241-2. NEWARK, N.J. ESSEX, N.J. 233-4. NEW YORK SUBURBS NASSAU, N.Y. 233-4. NEW YORK SUBURBS WESTCHESTER, N.Y. 233-4. NEW YORK SUBURBS FAIRFIELD, CONN. 233-4. NEW YORK SUBURBS SUFFOLK, N.Y. 243-4. NEN JERSEY SUBURBS UNION, N.J. 243-4. NEW JERSEY SUBURBS HUDSON, N.J. 243-4. NEW JERSEY SUBURBS ESSEX, N.J. 243-4. NEW JERSEY SUBURBS BERGEN, N.J. 281-2. PHILADELPHIA CITY PHILADELPHIA, PA. 283-4. PHILADELPHIA SUBURBS DELAWARE, PA. 283-4. PHILADELPHIA SUBURBS BUCKS, PA. 283-4. PHILADELPHIA SUBURBS MONTGOMERY, PA. 283-4. PHILADELPHIA SUBURBS CAMDEN, N.J. 291-2. PITTSBURGH CIIY ALLEGHENY, PA. 293-4. PITTSBURGH SUBURBS ALLEGHENY, PA. 293-4. PITTSBURGH SUBURBS BEAVER, PA. 293-4. PITTSBURGH SUBURBS WASHINGTON, PA 141-2. ST. LOUIS CITY INDEPENDENT CITY 143-4. ST. LOUIS SUBURBS ST. CLAIR, ILL. 143-4. ST. LOUIS SUBURBS ST. LOUIS, MO. 143-4. ST. LOUIS SUBURBS MADISON, ILL 011-2. SAN FRANCISCO CITY SAN FRANCISCO, CAL. 011-2. OAKLAND CITY ALAMEDA, CAL. 013-4. SAN FRANCISCO SUBURBS ALAMEDA, CAL. 013-4. SAN FRANCISCO SUBURBS SAN HATED, CAL. 013-4. SAN FRANCISCO SUBURBS CONTRA COSTA, CAL. 311-2. WASHINGTON CITY DISTRICT OF COLUMBIA 313-4. WASHINGTON SUBURB PRINCE GEORGES, MD. 313-4. WASHINGTON SUBURB FAIRFAX, VA. 313-4. WASHINGTON SUBURB MONTGOMERY, MD. 313-4. WASHINGTON SUBURB ARLINGTON, VA. 677-8. ADAIR ADAIR, MO. 627-8. BARRON BARRON, WIS. 725-6. BLACK HAWK BLACK HAWK, IOWA 725-6. BLACK HAWK BUCHANAN, IOWA 477-8. BOYD BOYD, KY. 477-8. BOYD GREENUP, KY. 715-6. BUTLER BUTLER, OHIO 715-6. BUTLER WARREN, OHIO 527-8. CLARK CLARK, ARK. 917-8. CORTLAND CORTLAND, N.Y. 637-8. CRANFORD CRANFORD, IOWA 507-8. EASI CARROLL EAST CARROLL, LA. 497-8. ERATH ERATH, TEX 975-6. FAIRFIELD FAIRFIELD, CONN. 425-6. FORSYTH FORSYTH, N.C 425-6. FORSYTH STOKES, N.C. 617-8. FRANKLIN FRANKLIN, NEB. 735-6. GENESSEE GENESEE, MICH. 547-8. GNINNETT GWINNETT, GA. 395-6. HARRIS HARRIS, TEX 765-6. HENNEPIN HENNEPIN, MINN. 765-6. HENNEPIN RAMSEY, MINN. 577-8. HICKMAN HICKMAN, TENN. 857-8. IDAHO IDAHO, ID. 587-8. JEFF DAVIS JEFF DAVIS, GA. 405-6. JEFFERSON JEFFERSON, KY. 687-8. KNOX KNOX, OHIO 487-8. LEON LEON, FLA. 705-6. LORAIN LORAIN, OHIO 965-6. LUZERNE LUZERNE, PA. 937-8. LYCOMING LYCOMING, PA. 755-6. MARION MARION, IND. 837-8. MERCED MERCED, CAL. 695-6. MINNEHAHA MINNEHAHA, S.D. 695-6. MINNEHAHA TURNER, S.D. 647-8. MITCHELL MITCHELL, KAN. 445-6. MONTGOMERY MONTGOMERY, ALA. 445-6. MONTGOMERY ELMORE, ALA. 745-6. MONTGOMERY, OH MONTGOMERY, OHIO 517-8. MUHLENBERG MUHLENBERG, KY. 947-8. NEW LONDON NEW LONDON, CONN. 995-6. ONONDAGA ONONDAGA, N.Y. 657-8. PAGE PAGE, IOWA 815-6. PIERCE PIERCE, WASH. 467-8. PITT PITT, N.C. 825-6. PUEBLO PUEBLO, COLO. 415-6. PULASKI, ARK. PULASKI, ARK. 415-6. PULASKI, ARK. SALINE, ARK. 557-8- PULASKI, VA. PULASKI, VA. 955-6. RENSSELAER RENSSELAER, N.Y 435-6. RICHLAND RICHLAND, S.C. 435-6. RICHLAND LEXINGTON, S.C. 667-8. ST. JOSEPH ST. JOSEPH, MICH. 805-6. SAN DIEGO SAN DIEGO, CAL. 537-8. SARASOTA SARASOTA, FLA. 537-8. SARASOTA MANATEE, FLA. 847-8. SOCORRO SOCORRO, N. M. 907-8. SUSQUEHANNA SUSQUEHANNA, PA. 457-8. TAYLOR TAYLOR, TEX 457-8. TAYLOR JONES, TEX 607-8. TRAVERSE TRAVERSE, MINN. 567-8. WALTHALL WALTHALL, MISS. 597-8. WATAUGA WATAUGA, N.C. 985-6. WORCESTER WORCESTER, MASS. 927-8. YORK YORK, MAINE 999. NOT ASCERTAINED ============================== VAR MN0004 NAME-SIZE OF PLACE COLUMNS 20 - 20 NUMERIC NO MISSING DATA CODES SIZE OF PLACE CODE SELF-REPRESENTING PSUs 195 1. CENTRAL CITIES 27 2. SUBURBS 50,000 POPULATION AND OVER 125 3. SUBURBS 2,500 THROUGH 49,999 PLUS OTHER PLACES IN CENSUS 'URBANIZED AREAS' 58 4. ALL OTHER SUBURBS (RURAL) NON-SELF-REPRESENTING PSU's 209 5. CITIES OF 50,000 AND OVER 82 6. PLACES 2,500 THROUGH 49,999 POPULATION, PLUS ANY OTHER PLACES CLASSIFIED AS URBAN BY THE CENSUS BUREAU 214 7. PLACES 2,500 THROUGH 49,999 64 8. CENSUS NAME PLACES UNDER 2,500 115 9. REMAINDER OF SAMPLE COUNTY IN SMA 301 0. REMAINDER OF PSU NOT IN SMA ============================== VAR MN0005 NAME-BELT CODE COLUMNS 21 - 21 NUMERIC NO MISSING DATA CODES DK 1 COL 13 BELT CODE 195 1. CENTRAL CITIES OF 12 LARGEST SMAs 201 2. CENTRAL CITIES OF OTHER SMA'S 191 3. SUBURBAN AREAS OF 12 LARGEST SMA'S 214 4. SUBURBAN AREAS OF OTHER SMA'S 258 5. ADJACENT AREAS 331 6. OUTLYING AREAS ============================== VAR MN0006 NAME-DATE OF INTERVIEW COLUMNS 22 - 22 NUMERIC MD 9 DATE OF INTERVIEW 174 1. OCTOBER 24TH OF EARLIER 300 2. OCTOBER 25TH - 31ST 307 3. NOVEMBER 1ST - 7TH 159 4. NOVEMBER 8TH - 14TH 214 5. NOVEMBER 15TH - 21ST 160 6. NOVEMBER 22ND - 28TH 64 7. NOVEMBER 29TH OR AFTER 12 9. NA ============================== VAR MN0007 NAME-WHO IS RESPONDENT COLUMNS 23 - 23 NUMERIC MD 9 WHO IS R. 840 1. R IS HEAD OF FAMILY 525 2. R IS WIFE OF HEAD OF FAMILY 3 7. OTHER 22 9. NA ============================== VAR MN0008 NAME-NUMBER OF ADULTS IN FAM COLUMNS 24 - 24 NUMERIC MD 9 NUMBER OF ADULTS IN FAMILY 241 1. ONE 968 2. TWO 140 3. THREE 28 4. FOUR 13 5. FIVE 0 6. SIX 0 7. SEVEN 0 8. EIGHT OR MORE 0 9. NA ============================== VAR MN0009 NAME-AGE OF RESPONDENT COLUMNS 25 - 26 NUMERIC MD 99 AGE OF RESPONDENT SEE APPENDIX NOTE 16 99. NA ============================== VAR MN0010 NAME- AGE OF HEAD COLUMNS 27 - 27 NUMERIC MD 9 AGE OF HEAD 58 1. 18-24 249 2. 25-34 319 3. 35-44 285 4. 45-54 241 5. 55-64 224 6. 65 AND OVER 14 9. NA ============================== VAR MN0011 NAME- R BETTER FINANCIALLY THAN LAST YEAR COLUMNS 28 - 28 NUMERIC MD GE 8 Q1. WE ARE INTERESTED IN HOW PEOPLE ARE GETTING ALONG FINANCIALLY THESE DAYS. WOULD YOU SAY THAT YOU AND YOUR FAMILY ARE BETTER OFF OR WORSE OFF FINANCIALLY THAN YOU WERE A YEAR AGO. 373 1. BETTER 653 3. SAME, PRO-CON 337 5. WORSE 20 8. UNCERTAIN 7 9. NA ============================== VAR MN0012 NAME- MAKE MORE MONEY THAN LAST YEAR COLUMNS 29 - 29 NUMERIC MD 9 Q2. ARE YOU PEOPLE MAKING AS MUCH MONEY NOW AS YOU WERE A YEAR AGO, OR MORE, OR LESS. 447 1. MORE NOW 650 3. ABOUT THE SAME 278 5. LESS NOW 15 9. NA DK ============================== VAR MN0013 NAME- PEOPLE WORRY ABOUT NEXT YEAR COLUMNS 30 - 30 NUMERIC MD GE 8 Q3. DO YOU THINK PEOPLE ABOUND HERE HAVE ANY WORRIES AB0UT HOW THEY'LL GET ALONG IN THE NEXT YEAR OR SO. I'M SPEAKING OF PEOPLE LIKE YOURSELF AND YOUR FRIENDS. 576 1. YES, THERE ARE WORRIES 60 3. A FEW PEOPLE HAVE WORRIES (MANY OR MOST PEOPLE DON'T) 647 5. NO, THERE ARE NO WORRIES AT ALL 94 8. DK 13 9. NA ============================== VAR MN0014 NAME- PEOPLE BETTER FINANCIALLY NEXT YEAR COLUMNS 31 - 31 NUMERIC MD GE 8 Q4. NOW LOOKING AHEAD - DO YOU THINK THAT A YEAR FROM NOW YOU PEOPLE WILL BE BETTER OFF FINANCIALLY, OR WORSE OFF, OR JUST ABOUT THE SAME AS NOW. 426 1. BETTER OFF 652 3. SAME 73 5. WORSE OFF 232 8. UNCERTAIN 7 9. NA ============================== VAR MN0015 NAME- IN FEW YES R HAVE BETTER INCOME COLUMNS 32 - 32 NUMERIC MD GE 6 Q5. A FEW YEARS FROM NOW, DO YOU THINK YOU AND YOUR FAMILY WILL HAVE A BETTER INCOME THAN YOU HAVE NOW, OR WILL YOU BE IN ABOUT THE SAME SITUATION, OR A LESS SATISFACTORY SITUATION. 471 1. BETIER OFF THEN 66 2. BETTER WITH QUALIFICATIONS. THE SAME OR BETTER 471 3. SAME. MAYBE BETTER OFF, MAYBE WORSE. BETTER IN SOME WAYS, WORSE IN OTHERS 34 4. WORSE WITH QUALIFICATIONS. THE SAME OR WORSE 77 5. WORSE OFF THEN 77 6. DEPENDS 29 7. R SPEAKS ONLY OF WHAT HE HOPES, NO INDICATIONS OF WHAT HE REALLY EXPECTS 133 8. CAN'T SAY, WOULDN'T SAY, IMPOSSIBLE TO KNOW 32 9. NA ============================== VAR MN0016 NAME- R OWN HOME OR RENT COLUMNS 33 - 33 NUMERIC MD 7 Q6. DO YOU OWN THIS HOME, OR PAY RENT, OR WHAT. 910 1. OWN HOME 418 2. PAY RENT 3 3. LIVE WITH FAMILY, FRIENDS, ETC. 59 7. OTHER ============================== VAR MN0017 NAME-LENGTH OF RES (HOME) COLUMNS 34 - 34 NUMERIC MD 9 Q7. HOW LONG HAVE YOU PEOPLE BEEN LIVING IN THIS HOUSE (APARTMENT). 210 1. LESS THAN ONE YEAR 258 2. 1-2 YEARS {INCLUDES 2 YEARS AND 6 MONTHS) 202 3. 3-4 YEARS (INCLUDES 4 YEARS AND 6 MONTHS) 274 4. 5-6 YEARS (INCLUDES 9 YEARS AND 6 MONTHS) 244 5. 10-19 YEARS (INCLUDES 19 YEARS AND 6 MONTHS) 200 6. 20 YEARS OR MORE 2 9. NA ============================== VAR MN0018 REF 0018 COLUMNS 35 - 35 NUMERIC NAME- PAST YEAR PRICES ROSE, FELL MD GE 8 Q19. WE'D LIKE TO KNOW WHAT'S HAPPENED TO PRICES ON HOUSEHOLD IIENS AND CLOTHING HERE IN (COMMUNITY NAME) DURING THE PAST YEAR. HAVE THEY STAYED ABOUT THE SAME, GONE UP OR GONE DOWN. (GROCERIES WERE EXCLUDED) 1. PRICES WENT UP 2. SOME WENT UP, SOME STAYED THE SAME. SOME WENT UP, NA, DK OTHERS 3. PRICES STAYED WHERE THEY WERE. SOME WENT UP, SOME WENT DOWN. SOME STAYED THE SAME, NA, DK OTHERS 4. SOME WENT DOWN, SOME STAYED THE SAME. SOME WENT DOWN, NA, DK OTHERS 5. PRICES WENT DOWN 8. DO, UNCERTAIN, DEPENDS 9. NA ============================== VAR MN0019 NAME- NEXT YR PRICES RISE/FALL COLUMNS 36 - 36 NUMERIC MD GE 8 Q20. WHAT DO YOU EXPECT PRICES OF HOUSEHOLD ITEMS AND CL0THING WILL DO DURING THE NEXT YEAR OR SO - STAY WHERE THEY ARE, GO UP, OR GO DOWN. ONE YEAR HH PRICE EXPECTATIONS 549 1. PRICES WILL (PROBABLY) GO UP 36 2. PRICES WILL EITHER STAY THE SAME OR GO UP. SOME WILL GO UP, SOME WILL STAY THE SANE 456 3. PRICES WILL STAY ABOUT THE SAME. SOME WILL GO UP, SOME WILL GO DOWN, LEVEL OFF, EVEN OFF 11 4. PRICES WILL EITHER STAY THE SAME OR GO DOWN. SOME WILL GO DOWN, SOME WILL STAY THE SAME 83 5. PRICES WILL (PROBABLY) GO DOWN 236 8. DK, UNCERTAIN, DEPENDS 19 9. NA ============================== VAR MN0020 NAME- EXPECTS PRICE CHANGE TO BE GOOD/BAD COLUMNS 37 - 37 NUMERIC MD 0 OR GE 6 Q21. WOULD YOU SAY THAT THESE (RISING PRICES) (FALLING PRICES) (UNCHANGED PRICES) WOULD BE TO THE GOOD, OR TO THE BAD, OR WHAT. 459 1. GOOD (GOOD FOR US, GOOD FOR THE AVERAGE PERSON) 10 2. GOOD, IF CHANGE IS SLOW, GRADUAL, SMALL 22 3. MAKES NO DIFFERENCE 443 5. BAD (BAD FOR US, BAD FOR AVERAGE PERSON) 67 6. PRO-CON -- GOOD FOR SOME PEOPLE, BAD FOR OTHERS. DEPENDS WHETHER PRODUCER OR CONSUMER 60 8. DR 74 9. NA 255 0. INAP., CODED 8 OR 9 ON REF. NO. 19 ============================== VAR MN0021 NAME- ELECTION OUTCOME EFFECT ON PRICES COLUMNS 38 - 38 NUMERIC MD GE 8 Q23. DO YOU THINK THE OUTCOME OF THE ELECTION WILL HAVE ANY EFFECT ON WHAT PRICES WILL DO IN THE NEXT FEW YEARS. 683 1. YES 83 2. YES, WITH QUALIFICATIONS 19 3. PRO-CON 42 4. NO, WITH QUALIFICATIONS 341 5. NO 195 8. DK, DEPENDS WHO WINS 27 9. NA ============================== VAR MN0022 NAME- WHICH PARTY HAVE PRICE INCREASES COLUMNS 39 - 39 NUMERIC MD 0 OR GE 8 Q23A. (IF YES) UNDER WHICH OF THE TWO MAJOR PARTIES, IN YOUR OPINION, WOULD BE MORE LIKELY TO HAVE PRICE INCREASES - THE REPUBLICANS OR DEMOCRATS. 240 1. REPUBLICANS 46 3. NOT MUCH DIFFERENCE, NO DIFFERENCE 407 5. DEMOCBATS 54 8. DK 38 9. NA 605 0. INAP., CODED 4, 5, 8 OR 9 ON REF. NO. 21 ============================== VAR MN0023 NAME- BUSINESS CONDITIONS GOOD NEXT YEAR COLUMNS 40 - 40 NUMERIC MD GE 8 Q45. NOW TURNING TO BUSINESS CONDITIONS IN THE COUNTRY AS A WHOLE-- DO YOU THINK THAT DURING THE NEXT TWELVE MONTHS WE'LL HAVE GOOD TIMES FINANCIALLY, OR BAD TIMES, OR WHAT. SEE APPENDIX NOTE 502 1. GOOD TIMES 212 2. GOOD, WITH QUALIFICATIONS 128 3. PRO-CON 49 4. BAD, WITH QUALIFICATIONS 87 5. BAD 382 8. UNCERTAIN, DK 30 9. NA ============================== VAR MN0024 NAME- PRESENT BUSINESS CONDITIONS BTR/WRS COLUMNS 41 - 41 NUMERIC MD GE 8 Q46. WOULD YOU SAY THAT AT PRESENT BUSINESS CONDITIONS ARE BETTER OR WORSE THAN THEY WERE A YEAR AGO. 254 1. BETTER 594 3. ABOUT THE SAME 495 5. WORSE 43 8. DK, DEPENDS 4 9. NA ============================== VAR MN0025 NAME- HEARD ABOUT PRICE OF STOCK COLUMNS 42 - 42 NUMERIC MD GE 9 Q47B, 47C. HAVE YOU HEARD OF ANYTHING ABOUT THE STOCK MARKET. I MEAN, WHAT HAS BEEN HAPPENING TO THE PRICES OF COMMON STOCK DURING THE LAST FEW MONTHS. (IF YES) WHAT HAVE YOU HEARD? 868 0. HEARD NOTHING 41 1. YES, STOCK MARKET WENT UP, GENERAL FAVORABLE COMMENTS 117 2. YES, STOCK MARKET WENT UP AND DOWN, ZIGZAGGED, FLUCTUATED 14 3. YES, NOT MUCH HAPPENED, REMAINED STABLE 278 4. YES, STOCK MARKET WENT DONN. GENERAL UNFAVORABLE COMMENTS 16 5. YES, REFERS ONLY TO MOVEMENT OF SPECIFIC STOCKS (EITHER UP OR DOWN) 8 6. YES, NO FURTHER COMMENT. NA WHAT HEARD 48 9. NA. DK ============================== VAR MN0026 NAME- BUSINESS CONDITIONS BTR NEXT YEAR COLUMNS 43 - 43 NUMERIC MD GE 8 Q48. AND HOW ABOUT A YEAR FROM NOW, DO YOU EXPECT THAT IN THE COUNTRY AS A WHOLE BUSINESS CONDITIONS WILL BE BETTER OR WORSE THAN THEY ARE AT PRESENT, OR JUST AB0UT THE SAME. 429 1. BETTER 738 3. ABOUT THE SANE 58 5. WORSE 147 8. DK, DEPENDS 18 9. NA ============================== VAR MN0027 NAME- WORLD RELATIONS AFFECT BUSINESS COND. COLUMNS 44 - 44 NUMERIC MD O OR GE 6 Q49, 49A. HOW DO YOU THINK THE WAY THINGS ARE GOING IN THE WORLD TODAY - I MEAN THE COLD WAR AND OUR RELATIONS WITH RUSSIA - ARE AFFECTING BUSINESS CONDITIONS HERE AT HOME. DO YOU THINK THEY MAKE FOR GOOD TIMES, OR BAD TIMES, OR WHAT. 283 0. NO EFFECT ON BUSINESS 181 1. GOOD TIMES 52 2. GOOD TINES, QUALIFIED 27 3. PRO-CON. ABOUT AVERAGE. FAIR TIMES 59 4. BAD TIMES, QUALIFIED 409 5. BAD TIMES 12 6. TEMPORARY GOOD TIMES. ARTIFICIAL GOOD TIMES (BROUGHT ABOUT BY COLD WAR, GOVERNMENT SPENDING). GOOD AT PRESENT, BUT BAD IN LONG RUN 3 7. TEMPORARY BAD TIMES 277 8. DK, DEPENDS 87 9. NA ============================== VAR MN0028 NAME- NEXT 5 YRS HAVE GOOD TIMES COLUMNS 45 - 45 NUMERIC MD GE 5 Q50, 50A. LOOKING AHEAD, WHICH WOULD YOU SAY IS MORE LIKELY - THAT IN THE COUNTRY AS A WHOLE WE WILL HAVE CONTINUOUS GOOD TIMES DURING THE NEXT FIVE YEARS OR SO, OR THAT WE WILL HAVE PERIOD OF WIDESPREAD UNEMPLOYMENT OR DEPRESSION, OR WHAT. (IF DON'T KNOW) ON WHAT DOES IT DEPEND IN YOUR OPINION. SEE APPENDIX NOTE EXPECTED BUSINESS CONDITIONS FIVE YEARS HENCE 310 0. (CONTINUOUS) GOOD TINES, BOOM PROSPERITY 140 1. GOOD TINES, QUALIFIED (NOT BAD) 178 2. PRO-CON, SOME UNEMPLOYMENT AND DEPRESSION 65 3. BAD TIMES, QUALIFIED (NOT GOOD) (RECESSION) 169 4. BAD TIMES, DEPRESSION, WIDESPREAD UNEMPLOYMENT 36 5. THINGS WILL BE ABOUT THE SAME. THINGS GET BACK TO NORMAL (NA WHETHER 'SAME' OR 'NORMAL' MEANS GOOD TIMES 0R NOT) "DEPENDS" 66 6. DEPENDS ON COLD WAR, DEFENSE PROGRAM, AID TO ALLIES, INTERNATIONAL SITUATION, WAR 145 7. DEPENDS ON GOVERNMENT POLICY (NO REFERENCES TO WAR OR DEFENSE), POLITICS, POLICIES OF ADMINISTRATION, OUTCOME OF ELECTION 177 8. DEPENDS ON OTHER. DK, CAN'T TELL 104 9. NA (R SPEAKS ONLY OF HOPES AND WISHES) (BETTER OR WORSE THAN NOW-- COMPARATIVE ANSWERS) ============================== VAR MN0029 NAME- MUCH UNEMP. IN THIS COMMUNITY COLUMNS 46 - 46 NUMERIC MD GE 6 Q51, 51A. IS THERE ANY UNEMPLOYMENT HERE IN (COMMUNITY NAME) THAT YOU HAVE HEARD OF. (IF YES) DOES IT INVOLVE MANY PEOPLE OR JUST A FEW? 315 1. YES, UNEMPLOYMENT, MANY PEOPLE INVOLVED 172 2. YES, UNEMPLOYMENT, QUITE A FEW PEOPLE INVOLVED-- R'S ANSWER SOMEWHERE BETWEEN MANY AND FEW. MENTIONS ONLY SPECIFIC INSTANCE 227 3. YES, UNEMPLOYMENT, A FEW PEOPLE INVOLVED 50 4. YES, UNEMPLOYMENT, NA HOW MANY PEOPLE INVOLVED 546 5. NO UNEMPLOYMENT. NONE THAT R KNOWS OF 13 6. UNCERTAIN, BUT THINKS THERE MAY BE SOME UNEMPLOYMENT 55 8. DK 12 9. NA ============================== VAR MN0030 NAME- PAST MONTHS UNEMP. INCREASE COLUMNS 47 - 47 NUMERIC MD 0 OR GE 8 Q51B. WOULD YOU SAY UNEMPLOYMENT HERE HAS BEEN INCREASING OB DECREASING IN THE PAST FEW MONTHS, OR WAS THERE NO CHANGE? 410 1. INCREASING 173 3. NO CHANGE 94 5. DECREASING 65 8. DK 35 9. NA 613 0. INAP., CODED 5, 8 OR 9 ON REF. NO. 29 ============================== VAR MN0031 NAME- NEXT YR WILL UNEMP INCREASE COLUMNS 48 - 48 NUMERIC MD GE 8 Q52. AND HOW ABOUT NEXT YEAR, 1961-- DO YOU THINK THAT THERE WILL BE MORE UNEMPLOYMENT THAN NOW, ABOUT THE SAME, OR LESS. 143 1. BORN IN 1961 780 3. ABOUT THE SAME 297 5. LESS IN 1961 153 8. DK. DEPENDS 17 9. NA ============================== VAR MN0032 NAME- WHO ELECTED PRES NEXT YR COLUMNS 49 - 49 NUMERIC MD 0 0R GE 8 Q53. WHO DO YOU THINK WILL BE ELECTED PRESIDENT IN NOVEMBER? 247 1. NIXON (REPUBLICANS) 341 2. KENNEDY (DEMOCRATS) 182 8. DK (R ONLY SPEAKS OF HOPES) 16 9. NA 604 0. INAP. (INTERVIEW TAKEN AFTER ELECTION) ============================== VAR MN0033 NAME- HOW PRES ELCTN AFFECT BUSINESS COLUMNS 50 - 50 NUMERIC MD 0 OR GE 6 Q54, 54A. WOULD YOU SAY THAT THE OUTCOME OF THE PRESIDENTIAL ELECTION WILL HAVE ANY EFFECT ON BUSINESS CONDITIONS IN THIS COUNTRY. (IF EFFECT) WILL IT IMPROVE 0R WILL IT SLOW DOWN BUSINESS? 278 O. NO EFFECT, IT WON'T MAKE ANY DIFFERENCE 606 1. BUSINESS WILL OR MAY IMPROVE (STAY GOOD) 25 2. THINGS ALWAYS PICK UP THE YEAR AFTER AN ELECTION. IMPROVE REGARDLESS OF WHO IS ELECTED 86 3. YES, IT WILL MAKE A DIFFERENCE (NA WHETHER GOOD OR BAD) 22 4. THINGS ARE ALWAYS SLOW THE YEAR AFTER AN ELECTION, DETERIORATE REGARDLESS OF WHO IS ELECTED 69 5. BUSINESS WILL OR MAY DETERIORATE (STAY BAD) 10 6. FAVORS SOME GROUPS, NOT OTHERS 55 7. DEPENDS ON OUTCOME 154 8. DK 85 9. NA ============================== VAR MN0034 NAME- HOW PRES ELCTN CHANGE TAXES COLUMNS 51 - 51 NUMERIC MD GE 8 Q55. HOW ABOUT TAXES-- DO YOU EXPECT THE NEXT PRESIDENT TO INCREASE OR DECREASE THE TAXES WE PAY, OR WILL THERE BE NO CHANGE IN YOUR OPINION. 580 1. INCREASE TAXES. PROBABLY INCREASE 61 2. NO CHANGE OR INCREASE. MAY INCREASE 419 3. NO CHANGE. PROBABLY NO CHANGE 8 4. NO CHANGE OR DECREASE. MAY DECREASE 62 5. DECREASE TAXES. PROBABLY DECREASE 202 8. DK, DEPENDS 58 9. NA ============================== VAR MN0035 NAME- MILITARY EXPENDITURES INCRS COLUMNS 52 - 52 NUMERIC MD GE 8 Q56. IN YOUR OPINION SHOULD MllITARY EXPENDITURES, WHAT WE SPEND ON ARMS AND DEFENSE, BE INCREASED OR DECREASED NEXT YEAR, OR STAY ABOUT THE SAME. 587 1. MILITARY EXPENDITURES SHOULD BE INCREASED 445 3. MILITARY EXPENDITURES SHOULD STAY ABOUT THE SAME 63 5. MILITARY EXPENDITURES SHOULD BE DECREASED 214 8. DK, DEPENDS 81 9. NA ============================== VAR MN0036A NAME- IF DISARMAMENT USE OF MONEY SAVED COLUMNS 53 - 53 NUMERIC MD 0 OR GE 7 Q57. SOME PEOPLE SAY THAT THERE QILL BE SOME DISARMAMENT AND THEREFORE OUR GOVERNMENT WILL SPEND LESS ON ARMS AND DEFENSE. SUPPOSE THIS IS THE CASE, WHAT WOULD YOU SAY SHOULD BE DONE WITH THE MONEY SAVED? 40 1. SHOULD BE USED TO INCREASE FINANCIAL HELP TO OTHER COUNTRIES 176 2. SHOULD BE USED TO REDUCE GOVERNMENT DEBT 150 3. SHOULD BE USED TO REDUCE INCOME TAXES 138 4. SHOULD BE USED TO BUILD SCHOOLS, HIGHWAYS, DEVELOP NATURAL RESOURCES, OTHER PUBLIC WORKS 250 5. SHOULD BE USED FOB PUBLIC WELFARE PROGRAMS - TO HELP NEEDY PEOPLE IN THE U.S., HELP THE AGED 131 6. EDUCATION (OTHER THAN SCHOOL BUILDING) 213 7. OTHER. VAGUE ANSWER. SHOULD BE SPREAD OVER ALL FIELDS 233 8. DK 190 9. NA 2649 0. R DOES NOT BELIEVE LESS WILL BE SPENT ON ARMS AND DEFENSE. NO SECOND OR THIRD MENTION ============================== VAR MN0036B NAME- IF DISARMAMENT USE OF MONEY SAVED COLUMNS 54 - 54 NUMERIC MD 0 OR GE 7 Q57. SOME PEOPLE SAY THAT THERE QILL BE SOME DISARMAMENT AND THEREFORE OUR GOVERNMENT WILL SPEND LESS ON ARMS AND DEFENSE. SUPPOSE THIS IS THE CASE, WHAT WOULD YOU SAY SHOULD BE DONE WITH THE MONEY SAVED? 40 1. SHOULD BE USED TO INCREASE FINANCIAL HELP TO OTHER COUNTRIES 176 2. SHOULD BE USED TO REDUCE GOVERNMENT DEBT 150 3. SHOULD BE USED TO REDUCE INCOME TAXES 138 4. SHOULD BE USED TO BUILD SCHOOLS, HIGHWAYS, DEVELOP NATURAL RESOURCES, OTHER PUBLIC WORKS 250 5. SHOULD BE USED FOB PUBLIC WELFARE PROGRAMS - TO HELP NEEDY PEOPLE IN THE U.S., HELP THE AGED 131 6. EDUCATION (OTHER THAN SCHOOL BUILDING) 213 7. OTHER. VAGUE ANSWER. SHOULD BE SPREAD OVER ALL FIELDS 233 8. DK 190 9. NA 2649 0. R DOES NOT BELIEVE LESS WILL BE SPENT ON ARMS AND DEFENSE. NO SECOND OR THIRD MENTION ============================== VAR MN0036M3 NAME- IF DISARMAMENT USE OF MONEY SAVED MD 0 OR GE 7 Q57. SOME PEOPLE SAY THAT THERE QILL BE SOME DISARMAMENT AND THEREFORE OUR GOVERNMENT WILL SPEND LESS ON ARMS AND DEFENSE. SUPPOSE THIS IS THE CASE, WHAT WOULD YOU SAY SHOULD BE DONE WITH THE MONEY SAVED? 40 1. SHOULD BE USED TO INCREASE FINANCIAL HELP TO OTHER COUNTRIES 176 2. SHOULD BE USED TO REDUCE GOVERNMENT DEBT 150 3. SHOULD BE USED TO REDUCE INCOME TAXES 138 4. SHOULD BE USED TO BUILD SCHOOLS, HIGHWAYS, DEVELOP NATURAL RESOURCES, OTHER PUBLIC WORKS 250 5. SHOULD BE USED FOB PUBLIC WELFARE PROGRAMS - TO HELP NEEDY PEOPLE IN THE U.S., HELP THE AGED 131 6. EDUCATION (OTHER THAN SCHOOL BUILDING) 213 7. OTHER. VAGUE ANSWER. SHOULD BE SPREAD OVER ALL FIELDS 233 8. DK 190 9. NA 2649 0. R DOES NOT BELIEVE LESS WILL BE SPENT ON ARMS AND DEFENSE. NO SECOND OR THIRD MENTION ============================== VAR MN0037 NAME- XRANK: INCRS FOREIGN AID COLUMNS 56 - 56 NUMERIC MD GE 6 Q57A. HERE ARE SOME SUGGESTIONS THAT HAVE BEEN MADE. PLEASE TELL ME WHICH USE OF THE MONEY APPEARS BEST TO YOU, WHICH IS SECOND BEST, THIRD, ETC. SEE APPENDIX NOTE A. SHOULD BE USED TO INCREASE FINANCIAL HELP TO OTHER COUNTRIES. 43 1. RANKED AS FIRST CHOICE 65 2. RANKED AS SECOND CHOICE 136 3. RANKED AS THIRD CHOICE 141 4. RANKED AS FOURTH CHOICE 769 5. RANKED AS FIFTH CHOICE 2 6. TIED FOR FIRST AND SECOND CHOICE 0R SECOND AND THIRD CHOICE 8 7. TIED FOR THIRD AND FOURTH CHOICE OR FOURTH AND FIFTH CHOICE 226 9. NA ============================== VAR MN0038 NAME- XRANK: REDUCE GOVT DEBT COLUMNS 57 - 57 NUMERIC MD GE 6 B. SHOULD BE USED TO REDUCE GOVERNMENT DEBT 275 1. RANKED AS FIRST CHOICE 195 2. RANKED AS SECOND CHOICE 223 3. RANKED AS THIRD CHOICE 398 4. RANKED AS FOURTH CHOICE 127 5. RANKED AS FIFTH CHOICE 5 6. TIED FOR FIRST AND SECOND CHOICE OR SECOND AND THIRD CHOICE OR FIRST, SECOND AND THIRD PLACE 9 7. TIED FOR THIRD AND FOURTH CHOICE OR FOURTH AND FIFTH CHOICE OR THIRD, FOURTH AND FIFTH PLACE 158 9. NA ============================== VAR MN0039 NAME- XRANK: REDUCE INCOME TAXES COLUMNS 58 - 58 NUMERIC MD GE 6 C. SHOULD BE USED TO REDUCE INCOME TAXES 220 1. RANKED AS FIRST CHOICE 281 2. RANKED AS SECOND CHOICE 330 3. RANKED AS THIRD CHOICE 275 4. RANKED AS FOURTH CHOICE 126 5. RANKED AS FIFTH CHOICE 3 6. TIED FOR FIRST AND SECOND CHOICE OR SECOND AND THIRD CHOICE 10 7. TIED FOR THIRD AND FOURTH CHOICE OR FOURTH AND FIFTH CHOICE 145 9. NA ============================== VAR MN0040 NAME- XRANK: BUILD SCHOOLS, HIWYS COLUMNS 59 - 59 NUMERIC MD GE 6 D. SHOULD BE USED TO BUILD SCHOOLS, HIGHWAYS, AND THE LIKE. 332 1. RANKED AS FIRST CHOICE 423 2. RANKED AS SECOND CHOICE 304 3. RANKED AS THIRD CHOICE 150 4. RANKED AS FOURTH CHOICE 33 5. RANKED AS FIFTH CHOICE 4 6. TIED FOR FIRST AND SECOND CHOICE OR SECOND AND THIRD CHOICE 3 7. TIED FOR THIRD AND FOURTH CHOICE OR FOURTH AND FIFTH CHOICE 141 9. NA ============================== VAR MN0041 NAME- XBANK: PUB WELFARE PROGRAMS COLUMNS 60 - 60 NUMERIC MD GE 6 E. SHOULD BE USED FOR PUBLIC HELPARE PROGRAMS - TO HELP NEEDY PEOPLE IN THE U.S. 426 1. RANKED AS FIRST CHOICE 317 2. RANKED AS SECOND CHOICE 237 3. RANKED AS THIRD CHOICE 192 4. RANKED AS FOURTH CHOICE 74 5. RANKED AS FIFTH CHOICE 4 6. TIED FOR FIRST AND SECOND CHOICE OR SECOND AND THIRD CHOICE 6 7. TIED FOR THIRD AND FOURTH CHOICE OR FOURTH AND FIFTH CHOICE 134 9. NA ============================== VAR MN0042 NAME- IF $200 EXTRA, HOW TO SPEND COLUMNS 61 - 61 NUMERIC MD GE 7 Q58. SUPPOSE IN THE NEXT YEAR YOUR FAMILY HAD SOME EXTRA MONEY-- SAY A FEW HUNDRED MORE THAN YOU HAD THIS PAST YEAR. WHAT WOULD YOU DO WITH THIS MONEY? 853 1. SAVE ALL OF IT (INCLUDE DEBT REPAYMENT) 125 2. SPEND ON ASSET (HOUSE, FARM, BUSINESS) 330 3. SPEND ALL OF IT ON CONSUMER GOODS OR SERVICES, GIFTS, DONATIONS 45 4. SAVE SOME AND SPEND SOME 5 7. OTHER COMBINATIONS 6 8. DK 26 9. NA ============================== VAR MN0043 NAME- HEAD'S OCCUPATION COLUMNS 62 - 62 NUMERIC MD 0 OR GE9 Q60, 60A. WHAT IS (HEAD'S) OCCUPATION. (IF UNEMPLOYED) WHAT KIND OF WORK DOES (HEAD) USUALLY DO. 123 1. PROFESSIONAL, TECHNICAL OR KINDRED WORKERS 195 2. SELF-EMPLOYED BUSINESSMEN AND ARTISANS. MANAGERS, OFFICIALS AND PROPRIETORS 150 3. CLERICAL AND KINDRED WORKERS, SALES PERSONNEL 398 4. CRAFTSMEN, FOREMEN AND KINDRED WORKERS. OPERATIVES AND KINDRED WORKERS 69 5. LABORERS (INCLUDES FARM LABORERS) 80 6. SERVICE WORKERS 81 7. PARK OPERATORS, FARM MANAGERS, FARM FOREMAN 12 8. MEMBERS OF THE ARMED FORCES 34 9. NA 248 0. INAP. (E.G., RETIRED, HOUSEWIVES, STUDENTS AND THOSE WHO HAVE NO JOB DUE TO PROLONGED ILLNESS) ============================== VAR MN0044 NAME- DOES HEAD WORK FOR SELF COLUMNS 63 - 63 NUMERIC MD 0 OR GE 9 Q6OB. DOES (HEAD) WORK FOR HIMSELF OR SOMEONE ELSE. 225 1. SELF 885 2. SOMEONE ELSE 3 3. BOTH, MAJOR JOB IS FOR SELF 2 4. BOTH, MAJOR JOB IS FOR SOMEONE ELSE 4 5. BOTH, DK WHICH IS MAJOR J08 23 9. NA 248 0. INAP., CODED 0 ON REF. NO. 43 ============================== VAR MN0045 NAME- HEAD UNEMP SINCE JANUARY COLUMNS 64 - 64 NUMERIC MD 0 OR GE 9 Q62. HAS (HEAD) BEEN UNEMPLOYED OR LAID OFF AT ANY TIME SINCE JANUARY 0F THIS YEAR. 134 1. YES, SOME UNEMPLOYMENT 741 5. NO, NOT LAID OFF AT ALL 16 9. NA 499 0. INAP., CODED 1, 3, 9, OR 0 ON REF. NO. 44 ============================== VAR MN0046 NAME- PARTY IDENTIFICATION COLUMNS 65 - 65 NUMERIC MD GE 7 Q.P1, 1A. GENERALLY SPEAKING, DO YOU USUALLY THINK OF YOURSELF AS A REPUBLICAN, A DEMOCRAT, AN INDEPENDENT, OR WHAT. (IF INDEPENDENT OR OTHER) DO YOU THINK OF YOURSELF AS CLOSER TO REPUBLICAN OR DEMOCRATIC PARTY 658 1. DEMOCRAT 135 2. INDEPENDENT CLOSER TO DEMOCRATS (YES, DEMOCRAT TO Q. P1A.) 95 3. INDEPENDENT (NO, NEITHER TO Q. P1A) 92 4. INDEPENDENT CLOSER TO REPUBLICANS (YES, REPUBLICANS TO Q. P1A) 343 5. REPUBLICAN 4 7. OTHER. MINOR PARTY AND REFUSED TO SAY 44 8. APOLITICAL 19 9. NA ============================== VAR MN0048 NAME- R EXPECT TO VOTE IN NOV. COLUMNS 67 - 67 NUMERIC MD O OR GE 8 QP2. SO FAR AS YOU KNOW NOW, DO YOU EXPECT TO VOTE IN NOVEMBER OR NOT. 73 1. YES, DEFINITELY 529 2. YES 12 3. YES, QUALIFIED (I THINK SO, GUESS SO, PROBABLY WILL) 3 4. PRO-CON, DEPENDS (I MAY, I MIGHT) 7 5. NO, QUALIFIED (PROBABLY NOT, I MIGHT BUT PROBABLY WON'T) 103 6. NO 42 7. NO, DEFINITELY 4 8. DK 13 9. NA 604 0. INAP. (RESPONDENT INTERVIEWED AFTER PRESIDENTIAL ELECTION) ============================== VAR MN0049 NAME- R VOTE FOR PRES & FOR WHOM COLUMNS 68 - 68 NUMERIC MD 0 OR GE 6 QP2A, 2B. HOW DO YOU THINK YOU WILL VOTE FOR PRESIDENT IN THIS ELECTION. (IF PLANNING NOT TO VOTE OR UNDECIDED) IF YOU WERE GOING TO VOTE, HOW DO YOU THINK YOU WOULD VOTE FOR PRESIDENT IN THIS ELECTION. 338 1. WILL VOTE DEMOCRATIC, FOR KENNEDY 7 2. WILL VOTE DEMOCRATIC, FOR KENNEDY, WITH QUALIFICATIONS 46 3. UNDECIDED, DEPENDS 12 4. WILL VOTE REPUBLICAN, FOR NIXON, WITH QUALIFICATIONS 285 5. WILL VOTE REPUBLICAN, FOR NIXON 3 6. WILL VOTE FOR OTHER PARTY OR CANDIDATE 16 7. REFUSED TO ANSWER 47 8. DK 15 9. NA 621 0. INAP., CODED 8, 9 OR 0 ON REF. NO 48 ============================== VAR MN0050 NAME- POST ELCTN-R VOTE FOR PRES COLUMNS 69 - 69 NUMERIC MD 0 OR GE 8 Q.P3, 3A. DO YOU REMEMBER WHETHER OR NOT YOU VOTED IN THE NOVEMBER ELECTION. (IF 'YES, DID VOTE') HOW DID YOU VOTE FOR PRESIDENT. 218 1. VOTED - DEMOCRATIC 220 2. VOTED - REPUBLICAN 2 3. VOTED - OTHER 12 4. VOTED - REFUSED TO SAY 2 5. VOTED - DK FOR WHOM 2 6. VOTED - NA FOR WHOM 131 7. DID NOT VOTE 4 8. DON'T REMEMBER IF VOTED 13 9. NA WHETHER VOTED 786 0. INAP, (RESPONDENT INTERVIEWED BEFORE PRESIDENTIAL ELECTION) ============================== VAR MN0051 NAME- CHURCH PREFERENCE COLUMNS 70 - 70 NUMERIC MD 9 QP4. IS YOUR CHURCH PREFERENCE PROTESTANT, CATHOLIC, OR JEWISH? 9 0. NONE 1000 1. PROTESTANT 302 2. CATHOLIC 56 3. JEWISH 11 4. OTHER 12 9. NA ============================== VAR MN0052 NAME- R GO TO CHUBCH REGULARLY COLUMNS 71 - 71 NUMERIC MD 9 QP5. WOULD YOU SAY YOU GO TO CHURCH REGULARLY, OFTEN, SELDOM, OR NEVER. 608 1. REGULARLY 231 2. OFTEN 447 3. SELDOM 93 4. NEVER 11 9. NA ============================== VAR MN0053 NAME- MARITAL STATUS OF HEAD COLUMNS 72 - 72 NUMERIC MD 9 QP6. MARITAL STATUS OF HEAD 341 1. SINGLE, WIDOWED, DIVORCED, SEPARATED 1046 2. MARRIED 3 9. NA ============================== VAR MN0054 NAME- SEX OF RESPONDENT COLUMNS 73 - 73 NUMERIC MD 9 QP7. SEX OF RESPONDENT 591 1. MALE 781 2. FEMALE 18 9. NA ============================== VAR MN0055 NAME- LIFE CYCLE COLUMNS 74 - 74 NUMERIC MD 9 LIFE CYCLE (BASED ON REF. NO. 53, AGE OF CHILDREN AND AGE OF HEAD) 53 0. YOUNG (UNDER 45), SINGLE 61 1. YOUNG [UNDER 45), MARRIED, NO CHILDREN 153 2. MARRIED, CHILDREN, YOUNGEST 1-1/2 YEARS OR LESS 162 3. MARRIED, CHILDREN, YOUNGEST OVER 1-1/2 UP TO AND INCLUDING 4-1/2 YEARS 285 4. MARRIED, CHILDREN, YOUNGEST OVER 4-1/2 YEARS UP TO AND INCLUDING 14-1/2 YEARS 65 5. MARRIED, CHILDREN, YOUNGEST OVER 14-1/2 YEARS (UNDER 18) 322 6. OLDER (45 OR OVER), MARRIED, NO CHILDREN 195 7. OLDER (45 OR OVER), SINGLE 83 8. OTHER (INCLUDES DIVORCED, WIDOWED OR OTHER UNMARRIED PERSONS NITH CHILDREN) 11 9. NA ============================== VAR MN0056 NAME- EDUCATION OF HEAD COLUMNS 75 - 75 NUMERIC MD 9 Q.P9, A, B. C. EDUCATION OF HEAD SEE APPENDIX NOTE 453 1. GRADE SCHOOL (1-8). NONE 195 2. SOME HIGH SCHOOL 71 3. SOME HIGH SCHOOL PLUS NON-ACADEMIC 242 4. COMPLETED HIGH SCHOOL 98 5. COMPLETED HIGH SCHOOL PLUS NON-ACADEMIC 159 6. SOME COLLEGE 156 7. HAS COLLEGE DEGREE 16 9. NA ============================== VAR MN0057 NAME- INC OF FAMILY IN 1960 COLUMNS 76 - 77 NUMERIC MD 99 Q.P10. WOULD YOU TELL ME HOW MUCH INCOME YOU AND YOUR FAMILY WILL BE MAKING THIS CALENDAR YEAR, 1960. I MEAN BEFORE TAXES. DOES THAT INCLUDE THE INCOME OF EVERYONE IN THE FAMILY. 87 10. UNDER $1,000 122 11. $l,000-1,999 132 12. $2,000-2,999 148 13. $3,000-3,999 154 14. $4,000-4,999 188 15. $5,000-5,999 176 16. $6,000-7,499 162 17. $7,500-9,999 119 18. $10,000-14,999 25 19. $15,000-19,999 24 20. $20,000 AND OVER 53 99. NA ============================== VAR MN0058 NAME- RACE OF RESPONDENT COLUMNS 78 - 78 NUMERIC MD 9 Q.P11. RACE OF RESPONDENT 1214 1. WHITE 130 2. NEGRO 17 7. OTHER. PUERTO RICAN, MEXICAN 29 9. NA ==============================