The Practice of Social Research by Earl R. Babbie (z-lib.org)

526 ■ Appendix A: Using the Library • Public Affairs Information Service Bulletin In this case, we’re given several choices of • Education Index libraries to search (Figure A-3). We’ll choose the • Applied Science and Technology Index Leatherby Libraries, which are located on campus. • A Guide to Geographic Periodicals • General Science Index Selecting the “Leatherby Libraries Catalog” • Biological and Agricultural Index presents us with the screen shown in Figure A-4, • Nursing and Applied Health Index which provides for several ways of searching: by • Nursing Studies Index author, by title, by subject, and so forth. • Index to Little Magazines • Popular Periodical Index Clicking “AUTHOR” will present a screen (not • Biography Index shown) that asks for the author’s name. Searching • Congressional Quarterly Weekly Report for the name “Babbie” in this system eventually • Library Literature presents a list of books with that author-name, • Bibliographic Index including the one we’re looking for, as shown in Figure A-5. Using the Stacks You’ll notice two entries for the title we’re Serious research usually involves using the stacks, looking for. These represent two editions of the where most of the library’s books are stored. This same book. Let’s click on the more recent edition, section provides information about finding books published in 1994. Figure A-6 is an electronic cata- in the stacks. log record for the desired edition this book. The Electronic Catalog Notice the adjoining bars marked “­ LOCATION,” “CALL#,” and “STATUS.” Just below the bar, we Your library’s catalog of holdings will be available learn that the book is on the second floor, in the electronically; you can access an electronic catalog social science collection. More specifically, the through a computer search, on either a library Library of Congress number (or call number)— computer or your personal computer. Online HM51.B164 1994—will help us locate the book catalog systems vary, but the following illustration on the shelves, which we see is, in fact, available from Chapman University’s Leatherby Libraries rather than checked out. will probably resemble what you’ll find in our own library. Here’s a useful strategy to use when you’re researching a topic. Once you’ve identified the call To start, let’s look up a book I wrote: The Socio­ number for a particular book in your subject area, logical Spirit. The library home page is shown in go to the stacks, find that book, and look over the Figure A-2, and we begin our search by clicking on other books on the shelves near it. Because the “Find Books . . .” books are arranged by subject, this method will help you locate relevant books you didn’t know about. Alternatively, you may want to go directly to the stacks and look at books in your subject area. In most libraries, books are arranged by the Library of Congress numbers. (Some follow the Dewey decimal system.) The following is a selected list of Library of Congress categories.

© 2007, Chapman University Figure A-2 Library Home Page

© 2007, Chapman University Figure A-3 A Choice of Libraries to Search

© 2007, Chapman University Figure A-4 © 2007, Chapman University Search Options Figure A-5 A Research Summary from Sociological Abstracts. Used by permission.

530 ■ Appendix A: Using the Library © 2007, Chapman University Figure A-6 Electronic Catalog “Card” Library of Congress J POLITICAL SCIENCE Classifications (partial) JK United States JN Europe A GENERAL WORKS JQ Asia, Africa B PHILOSOPHY, PSYCHOLOGY, RELIGION JX International relations B–BD Philosophy K LAW BF Psychology L EDUCATION BL–BX Religion M MUSIC C HISTORY-AUXILIARY SCIENCES N FINE ARTS D HISTORY (except America) NA Architecture DA–DR Europe NB Sculpture DS Asia NC Graphic arts DT Africa ND Painting E–F HISTORY (America) NE Engraving E United States NK Ceramics, textiles E51–99 Indians of North America P LANGUAGE AND LITERATURE E185 Negroes in the United States RE English language F101–1140 Canada PG Slavic language F1201–3799 Latin America PJ–PM Oriental language G GEOGRAPHY-ANTHROPOLOGY PN Drama, oratory, journalism G–GF Geography PQ Romance literature GC Oceanology and oceanography PR English literature GN Anthropology PS American literature GV Sports, amusements, games PT Germanic literature H SOCIAL SCIENCES Q SCIENCE H62.B2 The Practice of Social Research QA Mathematics HB–HJ Economics and business QB Astronomy HM–HX Sociology QC Physics

Searching the Periodical Literature ■ 531 AU Kinloch-Graham-C. Tl The Changing Definition and Content of Sociology in Introductory Textbooks, 1894–1981. SO International Review of Modern Sociology. 1984, 14, 1, spring, 89–103. DE Sociology-Education; (D810300). Textbooks; (D863400). AB An analysis of 105 introductory sociology textb­ ooks published between 1894 & 1981 reveals historical changes in definitions of the discipline & major topics in relation to professional factors & changing societal contexts. Pred­ ominant views of sociology in each decade are discussed, with the prevailing view being that of a “scientific study of social struc- ture in order to decrease conflict & deviance, thereby increasing social control.” Consistencies in this orientation over time, coupled with the textbooks’ generally low sensitivity to social issues, are explored in terms of their authors’ relative homogeneity in age & educational backgrounds. 1 Table, 23 References. Modified HA. Figure A-7 A Research Summary from Sociological Abstracts. Used by permission. QD Chemistry may help you limit the number of titles related to a specific keyword. Make sure you narrow your QE Geology search by limiting, for instance, the language or period of the publication. Once you identify the QH–QR Biology articles you’re interested in, the computer will print out their abstracts. R MEDICINE Of particular value to social science researchers, RK Dentistry the publications Sociological Abstracts and Psychologi­ cal Abstracts present summaries of books and ar- RT Nursing ticles—often prepared by the original authors—so that you can locate a great many relevant refer- S AGRICULTURE—PLANT AND ANIMAL ences easily and effectively. As you find relevant references, you can track down the original works INDUSTRY and see the full details. The summaries are avail- able in both written and computerized forms. T TECHNOLOGY Figure A-7 contains the abstract of an article ob- TA–TL Engineering tained in a computer search of Sociological Abstracts. I began by asking for a list of articles dealing with so- TR Photography ciology textbooks. After reviewing the list, I asked to see the abstracts of each of the listed articles. Here’s U MILITARY SCIENCE an example of what I received seconds later: an arti- cle by the sociologist Graham C. Kinloch, published V NAVAL SCIENCE in the International Review of Modern Sociology. Z BIBLIOGRAPHY AND LIBRARY SCIENCE In case the meaning of the abbreviations in Figure A-7 isn’t immediately obvious, I should ex- Searching the Periodical plain that AU is author; TI is title; SO is the source Literature or location of the original publication; DE indicates classification codes under which the abstract is ref- Sometimes you will want to search the articles erenced; and AB is the abstract. The computerized published in academic journals and other periodi- availability of resources such as Sociological Abstracts cals. Electronic library systems make this process provides a powerful research tool for modern social very powerful indeed. scientists. You’ll have the option to download or Many college libraries now have access to the Education Resources Information Center (ERIC). (See the link on your Sociology CourseMate at www.cengagebrain.com.) This computer-based system allows you to search through hundreds of major journals to find articles published in the subject area of your interest. As a rule, each library website should have a list of the databases that you can visit; they also list them by discipline, which

532 ■ Appendix A: Using the Library print, with or without the abstract, any title you Contains a step-by-step guide for writing research find through the library’s browsers. papers; chapters on periodicals, abstract and indexing services, bibliographies, bibliographic If a document is not available in the library aids, and other secondary sources; and a com- itself or via the web, you always have the resource plete guide to government and nongovernment of interlibrary loans, which often are free. Libraries sources of data. Special section on sex roles and don’t own every document or multimedia material women’s studies. (CD-ROMs, videocassettes, DVDs, films), but many have loan agreements that can serve your needs. Li, Tze-chung. 2000. Social Science Reference Sources: A You need to be aware of how much time it will Practical Guide. Westport, CT: Greenwood Press. actually take for you to receive the book or article Lists and describes all types of reference materi- from the time you made your request. In the case als, including databases and archives as well as of a book that is located in another library close published sources. Organized into two parts: social by, for example, it may be faster for you to get it sciences in general and by discipline. directly yourself. The key to a good library search is to become well informed. So start networking with Richlin-Klonsky, Judith, and Ellen Strenski, eds. librarians, faculty, and peers! 1998. A Guide to Writing Sociology Papers. New York: St. Martin’s Press. This is a great little Additional Readings book with good advice on doing research. It’s particularly useful for those who are new to Bart, Pauline, and Linda Frankel. 1986. The Student sociology or other social science disciplines and Sociologist’s Handbook. New York: Random House. have to learn about the most rudimentary as- A survival kit for doing sociological research. pects of research.

Searching the Periodical Literature ■ 533 APPENDIX B GSS Household Enumeration Questionnaire The National Opinion Research Center (NORC) interviewers in collecting basic demographic data at the University of Chicago is one of the primary about households selected in the sample. Question- ­social science research centers in the world. Its naires such as these are an important part of the General Social Survey (GSS), moreover, has be- scientific equipment used by social scientists. Just come a major source of social science data for re- as a microscope is an appropriate instrument for searchers. You may have noticed that many of the observing cells and molecules and a telescope for examples in this book have been taken from the observing distant planets and stars, a questionnaire GSS. It is fitting, therefore, that we turn to the GSS such as this one is often the best instrument for for an example of an interview questionnaire. The o­ bserving the subject matter of social science. following pages present the questionnaire used by

INTERVIEWER NAME INTERVIEWER ID# 5325 NORC JANUARY 1990 HOUSEHOLD ENUMERATION FOLDER HEF COMPLETE SAMPLING REPORT INTRODUCTION: Hello, I’m (YOUR NAME) from the National Opinion Research Center at the University of Chicago (SHOW ID CARD). (We recently sent you a letter explaining that) Your household has been selected to take part in this year’s GSS: America’s Social Survey. We’ve been conducting this study all over the country for more than fifteen years, learning about how people feel about issues like schools, crime, government spend- ing, and the military. This year several households in your community will be par- ticipating in this important research. First, I’d like to make sure that I have your street address described correctly. Is it (READ FROM ASSIGNMENT LABEL OR BOX BELOW: STREET NUMBER AND NAME, APARTMENT NUMBER OR OTHER DESCRIPTION OF HU.) IF EXACTLY THE SAME, CHECK BOX: n(GO TO Q.1, P.2) IF DIFFERENT IN ANY WAY, EXAMINE SEGMENT PRINTOUT AND RECONCILE. EXPLAIN THE DIF- FERENCE HERE: IF DIFFERENCE CAN’T BE RESOLVED, CALL YOUR FIELD MANAGER BEFORE CONTINUING. (CASE #) 01–06/ (PSU) 07–09/ (SEG) 10–12/ (PT) 13/ (LINE #) 14–23/ (A, B, or C) 24/ (X or Y) 25/ (INTID) 26–31/

1. Only one member of your household will be eligible for this survey. In order to TIME AM scienti­fically select that person, first I need to list the names of the people who BEGAN: PM usually live here. 2. Please tell me the names of the people who usually 9. Are any of the people we have listed live in this household. Let’s start with the head of staying somewhere else right now? the household. LIST ON LINES 01-10 BELOW. If NO, CHECK BOX AND SKIP TO Q. 12  n IF YES, GO TO Q. 10 BELOW. 3. Have we forgotten anyone: such as babies or small children; roomers; people who usually live here, but 5. 6. 7. 8. 10. 11. are away temporarily—on business trips, vacations, What is CODE SEX How old IF 13 YRS OR Who is ASK FOR EACH PERSON CHECKED (√) at school, temporarily in a hospital, and so on? (PERSON)’s (ASK IF was (HEAD/ OLDER ASK: staying IN Q. 10: relationship NOT PERSON) Is (PERSON) now some- Where is (PERSON) staying right Yes n LIST ADDITIONAL PERSONS ON LINES to (HEAD OF OBVIOUS) on (his/ married, where now: Is (PERSON) staying at 01-10 BELOW. HOUSEHOLD)? her) last widowed, else another household; Is (he/she) M   F birthday? divorced, right traveling; Is (he/she) in some No n GO TO Q.4. separated, or now? Institution or dormitory—like has (he/she) CHECK at college, or in a hospital or 4. Are there any people currently staying here— never been (√) something; or what? visitors, friends or relatives—who do not usually married? LINE OF live here? EACH CIRCLE CODE, FOLLOW INSTRUCTIONS Ma Wi Di Se NM PERSON Yes n LIST VISITORS ON LINES 11–14 BELOW. WHO IS Another Travel- Insti- Other No n GO TO Q.5. AWAY. House- ing tution DK AFTER QS. 1-4, ASK QS. 5–8 FOR EACH PERSON. hold First Name Last name 49 50 51-52 53 54 1 2 3 55 CROSS LEAVE CROSS 4* 01 HEAD 1  2 1  2  3  4  5 OUT IN OUT 56 57 58-59 60 61 1 2 3 62 LEAVE CROSS 4* 02 1  2 1  2  3  4  5 CROSS IN OUT OUT 63 64 65-66 67 68 1 2 3 69 LEAVE CROSS 4* 03 1  2 1  2  3  4  5 CROSS IN OUT OUT BEGIN DECK 03 07 08 09-10 11 12 1 2 3 13 LEAVE CROSS 4* 04 1  2 1  2  3  4  5 CROSS IN OUT OUT

14 15 16-17 18 19 1 2 3 20 LEAVE CROSS 4* 05 1  2 1  2  3  4  5 CROSS IN OUT 27 OUT 4* 21 22 23-24 25 26 1 2 3 34 CROSS LEAVE CROSS 4* 06 1  2 1  2  3  4  5 OUT IN OUT 41 4* 1 2 3 CROSS LEAVE CROSS 48 4* 28 29 30-31 32 33 OUT IN OUT 55 07 1  2 1  2  3  4  5 1 2 3 4* CROSS LEAVE CROSS 62 OUT IN OUT 4* 1 2 3 69 35 36 37-38 39 40 CROSS LEAVE CROSS 4* 08 1  2 1  2  3  4  5 OUT IN OUT 13 4* 20 4* 42 43 44-45 46 47 09 1  2 1  2  3  4  5 49 50 51-52 53 54 1 2 3 LEAVE CROSS 10 1  2 1  2  3  4  5 CROSS IN OUT OUT 56 57 58-59 60 61 1 2 3 LEAVE CROSS 11 1  2 1  2  3  4  5 CROSS IN OUT OUT 63 64 65-66 67 68 1 2 3 CROSS LEAVE CROSS 12 1  2 1  2  3  4  5 OUT IN OUT 1 2 3 CROSS LEAVE CROSS BEGIN DECK 04 07 08 09-10 11 12 OUT IN OUT 13 1  2 1  2  3  4  5 14 15 16-17 18 19 1 2 3 LEAVE CROSS 14 1  2 1  2  3  4  5 CROSS IN OUT OUT F MORE THAN 10 USUAL PERSONS AND/OR MORE THAN 4 AFTER Q.8 FOR *PROBE FOR DETAILS AND CHECK INTERVIEWER ISITORS, USE A BLANK HEF FOR ADDITIONAL LISTING. LAST PERSON MANUAL FOR HELP IF NECESSARY 13 OR OLDER, ASK Q.9.

SAMPLING TABLE . Now I’m going to scientifically select the one person in STEP 3: USE SAMPLING TABLE TO NUMBER OF PERSON ON this household chosen for this study. By interviewing THE RIGHT TO SELECT ELIGIBLE LINE NUMBER only the person picked in this way, we can be sure the WHICH ELIGIBLE PERSON PERSONS STICK views we find do accurately represent the views of the TO INTERVIEW. country as a whole. LISTED ABOVE SAMPLING PART OF ASSIGNMENT LABEL HERE. (If this is EP 1: ELIGIBLE PEOPLE: 18 OR OVER TWO a “missed HU” added to your NAME NOT CROSSED OUT ON HOUSEHOLD assignment, copy sampling ENUMERATION (P.2) THREE numbers from original case.) IF ONLY ONE ELIGIBLE PERSON, GO TO Q.13 AND MAKE FOUR APPOINTMENT TO INTERVIEW THAT PERSON. IF MORE THAN ONE, CONTINUE WITH STEPS 2-6 BELOW. EP 2: LIST NAMES OF ELIGIBLE PEOPLE IN SUMMARY BOX, IN FIVE ORDER OF AGE. SUMMARY BOX SIX OR MORE INTERVIEW LINE # AGE NAMES OF ELIGIBLE PERSONS OLDEST 1 (Not 2 ecessarily STEP 4: CIRCLE SELECTED R’S LINE # IN SUMMARY BOX. the head) STEP 5: PRINT SELECTED R’S NAME HERE: 3 4 5 YOUNGEST 6 STEP 6: ARRANGE TO INTERVIEW THIS PERSON. REQUEST PHONE AND MAILING INFORMATION (Qs.13 AND 14) TO HELP IN FOLLOW-UP.

. If I have to talk with (SELECTED RESPONDENT), what phone IMMEDIATELY AFTER LEAVING THE HOUSEHOLD, FILL OUT A-D BELOW numbers should I use? AND COMPLETE THE SAMPLING REPORT. Telephone number given: ( ) AREA CODE NUMBER A. Date HEF administered: 23-25/ A. Code location of phone: MONTH DAY YEAR In household................................. 1.22/ In home of neighbor.......................... 2 B. Name of HEF informant: Other (SPECIFY)                         ...... 3 B. If no number given, code C. List line number of HEF informant 26-27/ No phone..................................... 4 from Household Enumeration (P.2, Q.4)... 22) Refused...................................... 5 (If non-household informant, line # = . If I have to mail a note to (SELECTED RESPONDENT), what D. Race of household (by observation) would be the best mailing address to use? White....................................... 1 28/ # AND STREET/BOX #/RFD # Black/Negro................................. 2 American Indian............................. 3 POST OFFICE, TOWN Asiatic, Oriental........................... 4 Other, mixed, not able to observe........... 5 STATE ZIP CODE E. List line number of selected respondent from Household Enumeration (P.2, Q.4)... 29-30/ ANK RESPONDENT FOR THEIR TIME AND HELP. TIME AM HEF PM ENDED: LENGTH MIN OF TIME FOR HEF:

HEF-4 HEF-5 NIR NON-INTERVIEW REPORT (NIR) A. Describe your efforts to obtain information 38- 39/ about the occupants. Name (if known): Telephone number, (if available): 4. IF REFUSED OR BREAKOFF: Please Circle Appropriate Code: Did the respondent give the refusal? HEF not completed..................................... 1 31/ Yes.................................... (Ans. A).... 1 40/ HEF complete/interview incomplete..................... 2 No..................................... (Ans. B).... 2 1. Why were you unable to complete HEF/Interview at this address? DK, HEF not complete.............. (Skip to Q.6).... 8 NOT AN HU:........................................ 32 -33/ A. IF YES: Why did the respondent (refuse/breakoff)?  Condemned............................. (Ans. Q.2). 01  Demolished............................ (Ans. Q.2). 02 (Report verbatim remarks and reasons.).. 41 -42  Place of business..................... (Ans. Q.2). 03  No such address/no such HU............ (Ans. Q.2). 04 (SKIP TO Q. 6)  Group quarters........................ (Ans. Q.2). 05  Vacation cabin........................ (Ans. Q.2). 06 B. IF NO: Why were you unable to speak with  Not usable as permanent residence..... (Ans. Q.2). 07  Transient use (less than one month)... (Ans. Q.2). 08 the respondent? 43-44/  Not an HU for other reason............ (Ans. Q.2). 09  Still under construction.............. (Ans. Q.2). 10 (SKIP TO Q.6) VACANT.............................. (Skip to Q.7). 11 REFUSED............................. (Skip to Q.4). 12 5. IF TEMPORARILY UNAVAILABLE FOR THE ENTIRE PERIOD: BREAKOFF............................ (Skip to Q.4). 13 What is the reason for this status? NOT HOME AFTER 4 CALLS.............. (Skip to Q.3). 14 RESPONDENT IS UNAVAILABLE FOR ENTIRE 45-46/ FIELD PERIOD....................... (Skip to Q.5). 15 LANGUAGE PROBLEM (SPECIFY LANGUAGE SPOKEN) A. When will R be available? .................................... (SKIP TO Q.7). 16 TOO ILL (DESCRIBE SITUATION ON PAGE 7) 47-48/ .................................... (Skip to Q.7). 17 OTHER (DESCRIBE SITUATION ON PAGE 7) 6. Was a follow-up letter sent to the respondent? .................................... (Skip to Q.7). 18 Yes.................................... (Ans. A).... 1 49/ 2. IF NOT AN HU: No..................................... (Ans. B).... 2 DK................................................. 8 Describe the reason for this NIR fully, then go to Q.19. 34 -35/ A. IF YES: Did you speak with R after receipt of letter? 3. IF NOT AT HOME AFTER 4 CALLS: Yes................................................ 1 50/ No..................................... (Ans. B).... 2 Why do you think it has been so hard to find the DK................................................. 8 occupants of this housing unit at home? 36 -37/ B. IF NO: Why not? ANSWER Q.7 UNLESS NOT AN HU 7. Were you ever able to talk with someone at this HU (not necessarily a resident)?

Yes................................. (GO TO Q.8).... 1 51/ No................................................. 2 No..................................... (Ans. A).... 2 Don’t know......................................... 3 A. Why not? 52-53/ 15. What is your estimate of the age of the Household Head? 8. On any calls were there people in the HU who did not Under 30........................................... 1 63/ answer the door? 30-64.............................................. 2 65 or older........................................ 3 Yes, definitely..................................... 1 54/ Don’t know......................................... 8 Suspect so......................................... 2 No reason to think so.............................. 3 16. Type of structure. ANSWER Qs 9-17 FROM INFORMATION OBTAINED BY OBSERVATION 64-65 AND/OR CONTACTS WITH HM MEMBERS, NEIGHBORS AND OTHER SOURCES. Trailer........................................... 01 Detached single family house...................... 02 9. What is the estimated income of R’s family? (NOTE: IF 2 family house, 2 units side by side.............. 03 MULTIPLE FAMILIES AND R NOT DETERMINED, ESTIMATE FOR 2 family house, 2 units one above the other....... 04 PRIMARY FAMILY.) Detached 3-4 family house......................... 05 Rowhouse (3 or more units in an attached row)..... 06 Low................................................ 1 55/ Apartment house (5 or more units, Medium............................................. 2   3 stories or less).............................. 07 High............................................... 3 Apartment house (5 or more units, Don’t know/unable to observe....................... 8   4 stories or more).............................. 08 Apartment in a partly commercial structure........ 09 0. Were you able to complete the household listing? Other (SPECIFY)__________________________ ......... 10 Yes.............................. (Skip to Q.16).... 1 56/ 17. Compared to house/apartments in the neighborhood, No............................. (Ans. Qs. 11-20).... 2 would you say the house/apartment was . . . 1. What is the race of the residents? Far above average.................................. 1 66/ Above average...................................... 2 White............................... (definitely).... 1 57/ Average............................................ 3 White................................ (probably).... 2 Below average...................................... 4 Black............................... (definitely).... 3 Far below average.................................. 5 Black................................ (probably).... 4 Hispanic........................................... 5 18. ADDITIONAL COMMENTS: Asiatic/Oriental................................... 6 Other (DESCRIBE)................................... 7 Could not determine................................ 8 2. Estimate the number of adults living in the HU. # of adults: 58-59/ 19. Interviewer Name: 3. Estimate the number of adult males living in the HU. 20. Interviewer Number: 21. Enter Supervisor’s name. # of adults: 60-61/ 4. Is there a married (or living as married) couple living - in the HU? Yes................................................ 1 62/

HEF-6 RECORD OF CALLS ON HOUSEHOLD USE PENCIL D. Time G. Refusal Description H. Results/Reason for I. Purpose J. Int’r A. Day M.1 Tu.2 W.3 Pre3..1 3-6..2 Post 6..3 Refusal. Give verbatim of Initials Th.4 F.5 Sa.6 Su.7 E. Type reasons and explain in Contact B. Month C. Date Per..1 Tel..2 Mail..3 all circumstances. A. F. Outcome of Contact B. If 35,37,45,47,82,92 Do G C. 32/R D. 37/R G. M.....F | Age_____________ H. I. 41/R J. 33-34/R E. 38/R Other char: A. 35-36/R F. 39-40/R HEF Line # when complete_____ HEF......... 1 B. Quex........ 2 C. 42/R D. 47/R G. M.....F | Age_____________ H. I. 51/R J. 43-44/R E. 48/R Other char: A. 45-46/R F. 49-50/R HEF Line # when complete_____ HEF......... 1 B. Quex........ 2 C. 52/R D. 57/R G. M.....F | Age_____________ H. I. 61/R J. 53-54/R E. 58/R Other char: A. 55-56/R F. 59-60/R HEF Line # when complete_____ HEF......... 1 B. Quex........ 2 C. 62/R D. 67/R G. M.....F | Age_____________ H. I. 71/R J. 63-64/R E. 68/R Other char: BEGIN DECK 02 65-66/R F. 69-70/R HEF Line # when complete_____ HEF......... 1 Quex........ 2

A. 07/R D. 12/R G. M.....F | Age_____________ H. I. 16/R J. B. 08-09/R E. 13/R Other char: C. 10-11/R F. 14-15/R HEF Line # when complete_____ HEF......... 1 Quex........ 2 A. 17/R D. 22/R G. M.....F | Age_____________ H. I. 26/R J. B. 18-19/R E. 23/R Other char: C. 20-21/R F. 24-25/R HEF Line # when complete_____ HEF......... 1 Quex........ 2 A. 27/R D. 32/R G. M.....F | Age_____________ H. I. 36/R J. B. 28-29/R E. 33/R Other char: C. 30-31/R F. 34-35/R HEF Line # when complete_____ HEF......... 1 Quex........ 2 A. 37/R D. 42/R G. M.....F | Age_____________ H. I. 46/R J. B. 38-39/R E. 43/R Other char: HEF......... 1 C. 40-41/R F. 44-45/R HEF Line # when complete_____ Quex........ 2 47-48 STATUS CODES FOR OUTCOME (COLUMN F) TEMPORARY FINAL (Requires supervisor’s approval) HEF QUEX HEF QUEX 31 No action 43 R not home/unavailable 10 No HU* 13 R does not speak English* 32 No one home/No answer/Busy 45 Temp refusal/breakoff 11 Vacant* 92 Refusal/breakoff* 35 Temporary refusal/breakoff 46 Appointment 12 HH speaks no English* 94 R absent entire field period* 36 Appointment 47 Broken appointment 82 Refusal/breakoff* 97 Other* 37 Broken appointment 49 Other 84 Not home/unavailable* 60 Ballot Quex only 38 HH not accessible 85 HH not accessible* 65 Ballot Quex & some, not all documents 39 Other 87 Other* 67 Ballot Quex & all documents EVERYONE COMPLETE SAMPLING REPORT USE CONTINUATION SHEET IF NECESSARY *FILL OUT NIR

Searching the Periodical Literature ■ 543 APPENDIX C Random Numbers 10480 15011 01536 02011 81647 91646 69179 14194 62590 36207 20969 99570 91291 90700 22368 46573 25595 85393 30995 89198 27982 53402 93965 34095 52666 19174 39615 99505 24130 48360 22527 97265 76393 64809 15179 24830 49340 32081 30680 19655 63348 58629 42167 93093 06243 61680 07856 16376 39440 53537 71341 57004 00849 74917 97758 16379 37570 39975 81837 16656 06121 91782 60468 81305 49684 60672 14110 06927 01263 54613 77921 06907 11008 42751 27756 53498 18602 70659 90655 15053 21916 81825 44394 42880 99562 72905 56420 69994 98872 31016 71194 18738 44013 48840 63213 21069 10634 12952 96301 91977 05463 07972 18876 20922 94595 56869 69014 60045 18425 84903 42508 32307 89579 14342 63661 10281 17453 18103 57740 84378 25331 12566 58678 44947 05585 56941 85475 36857 53342 53988 53060 59533 38867 62300 08158 17983 16439 11458 18593 64952 28918 69578 88231 33276 70997 79936 56865 05859 90106 31595 01547 85590 91610 78188 63553 40961 48235 03427 49626 69445 18663 72695 52180 20847 12234 90511 33703 90322 09429 93969 52636 92737 88974 33488 36320 17617 30015 08272 84115 27156 30613 74952 10365 61129 87529 85689 48237 52267 67689 93394 01511 26358 85104 20285 29975 89868 07119 97336 71048 08178 77233 13916 47564 81056 97735 85977 29372 74461 28551 90707 51085 12765 51821 51259 77452 16308 60756 92144 49442 53900 70960 63990 75601 40719 02368 21382 52404 60268 89368 19885 55322 44819 01188 65255 64835 44919 05944 55157 01011 54092 33362 94904 31273 04146 18594 29852 71585 85030 51132 01915 92747 64951 52162 53916 46369 58586 23216 14513 83149 98736 23495 64350 94738 17752 35156 35749 07056 97628 33787 09998 42698 06691 76988 13602 51851 46104 88916 19509 25625 58104 48663 91245 85828 14346 09172 30168 90229 04734 59193 22178 30421 61666 99904 32812 54164 58492 22421 74103 47070 25306 76468 26384 58151 06646 21524 15227 96909 44592 32639 32363 05597 24200 13363 38005 94342 28728 35806 06912 17012 64161 18296 22851 29334 27001 87637 87308 58731 00256 45834 15398 46557 41135 10367 07684 36188 18510 02488 33062 28834 07351 19731 92420 60952 61280 50001 67658 32586 86679 50720 94953 81525 72295 04839 96423 24878 82651 66566 14778 76797 14780 13300 87074 79666 95725 29676 20591 68086 26432 46901 20849 89768 81536 86645 12659 92259 57102 80428 25280 00742 57392 39064 66432 84673 40027 32832 61362 98947 96067 64760 64584 96096 98253 05366 04213 25669 26422 44407 44048 37397 63904 45766 66134 75470 66520 34693 90449 91921 26418 64117 94305 26766 25940 39972 22209 71500 64568 91402 42416 07844 69618 00582 04711 87917 77341 42206 35126 74087 99547 81817 42607 43808 76655 62028 76630 00725 69884 62797 56170 86324 88072 76222 36086 84637 93161 76038 65855 77919 88006 69011 65795 95876 55293 18988 27354 26575 08625 40801 59920 29841 80150 12777 48501 25976 57948 29888 88604 67917 48708 18912 82271 65424 69774 33611 54262 85963 03547 09763 83473 73577 12908 30883 18317 28290 35797 05998 41688 34952 37888 38917 88050 91567 42595 27958 30134 04024 86385 29880 99730 55536 84855 29080 09250 79656 73211 17955 56349 90999 49127 20044 59931 06115 20542 18059 02008 73708 83517 36103 42791 46503 18584 18845 49618 02304 51038 20655 58727 28168 15475 56942 53389 20562 87338 92157 89634 94824 78171 84610 82834 09922 25417 44137 48413 25555 21246 35509 20468 14577 62765 35605 81263 39667 47358 56873 56307 61607 49518 89656 20103 77490 18062 98427 07523 33362 64270 01638 92477 66969 98420 04880 45585 46565 04102 46880 45709 34914 63976 88720 82765 34476 17032 87589 40836 32427 70002 70663 88863 77775 69348 70060 28277 39475 46473 23219 53416 94970 25832 69975 94884 19661 72828 00102 66794 53976 54914 06990 67245 68350 82948 11398 42878 80287 88267 47363 46634 06541 97809 76072 29515 40980 07391 58745 25774 22987 80059 39911 96189 41151 14222 60697 59583 90725 52210 83974 29992 65831 38857 50490 83765 55657 14361 31720 57375 56228 41546 64364 67412 33339 31926 14883 24413 59744 92351 97473 89286 35931 04110 23726 51900 08962 00358 31662 25388 61642 34072 81249 35648 56891 69352 48373 45578 78547 81788 95012 68379 93526 70765 10592 04542 76463 54328 02349 17247 28865 14777 62730 92277 15664 10493 20492 38391 91132 21999 59516 81652 27195 48223 46751 22923 32261 85653 16408 81899 04153 53381 79401 21438 83035 92350 36693 31238 59649 91754 72772 02338 18629 81953 05520 91962 04739 13092 97662 24822 94730 06496 35090 04822 86774 98289 73115 35101 47498 87637 99016 71060 88824 71013 18735 20286 23153 72924 35165 43040 57491 16703 23167 49323 45021 33132 12544 41035 80780 45393 44812 12515 98931 91202 30405 83946 23792 14422 15059 45799 22716 19792 09983 74353 68668 30429 70735 25499

16631 35006 85900 98275 32388 52390 16815 69298 82732 38480 73817 32523 41961 44437 96773 20206 42559 78985 05300 22164 24369 54224 35083 19687 11052 91491 60383 19746 38935 64202 14349 82674 66523 44133 00697 35552 35970 19124 63318 29686 03387 59846 31624 76384 17403 53363 44167 64486 64758 75366 76554 31601 12614 33072 60332 92325 78919 19474 23632 27889 47914 02584 37680 20801 72152 39339 34806 08930 85001 87820 03931 33309 57047 74211 63445 17361 62825 39908 05607 91284 68833 25570 38818 46920 74426 33278 43972 10119 89917 15665 52872 73823 73144 88662 88970 74492 51805 99378 09066 00903 20795 95452 92648 45454 09552 88815 16553 51125 79375 97596 16296 66092 42238 12426 87025 14267 20979 04508 64535 31355 86064 29472 47689 05974 52468 16834 16153 08002 26504 41744 81959 65642 74240 56302 00033 67107 77510 70625 28725 34191 21457 40742 29820 96783 29400 21840 15035 34537 33310 06116 95240 15957 16572 06004 21581 57802 02050 89728 17937 37621 47075 42080 97403 48626 68995 43805 33386 21597 55612 78095 83197 33732 05810 24813 86902 60397 16489 03264 88525 42786 05269 92532 44657 66999 99324 51281 84463 60563 79312 93454 68876 25471 93911 25650 12682 73572 91340 84979 46949 81973 37949 61023 43997 15263 80644 43942 89203 71795 99533 50501 91227 21199 31935 27022 84067 05462 35216 14486 29891 68607 41867 14951 91696 85065 50001 38140 66321 19924 72163 09538 12151 06878 91903 18749 34405 56087 82790 70925 65390 05224 72958 28609 81406 39147 25549 48542 42627 45233 57202 94617 23772 07896 27504 96131 83944 41575 10573 08619 64482 73923 36152 05184 94142 25299 84387 34925 37169 94851 39117 89632 00959 16487 65536 49071 39782 17095 02330 74301 00275 48280 11508 70225 51111 38351 19444 66499 71945 05422 13442 78675 84081 66938 93654 59894 37449 30362 06694 54690 04052 53115 62757 95348 78662 11163 81651 50245 34971 52924 46515 70331 85922 38329 57015 15765 97161 17869 45349 61796 66345 81073 49106 79860 30986 81223 42416 58353 21532 30502 32305 86482 05174 07901 54339 58861 74818 46942 63798 64995 46583 09785 44160 78128 83991 42865 92520 83531 80377 35909 81250 54238 82486 84846 99254 67632 43218 50076 21361 64816 51202 88124 41870 52689 51275 83556 21885 32906 92431 09060 64297 51674 64126 62570 26123 05155 59194 52799 28225 85762 60336 98782 07408 53458 13564 59089 26445 29789 85205 41001 12535 12133 14645 23541 43937 46891 24010 25560 86355 33941 25786 54990 71899 15475 95434 98227 21824 19585 97656 63175 89303 16275 07100 92063 21942 18611 47348 20203 18534 03862 78095 50136 03299 01221 05418 38982 55758 92237 26759 86367 21216 98442 08303 56613 91511 75928 79626 06486 03574 17668 07785 76020 79924 25651 83325 88428 85076 72811 22717 50585 85636 68335 47539 03129 65651 11977 02510 26113 99447 68645 34327 15152 55230 93448 18039 14367 61337 06177 12143 46609 32989 74014 64708 00533 35398 58408 13261 47908 08362 15656 60627 36478 65648 16764 53412 09013 07832 41574 17639 82163 60859 75567 79556 29068 04142 16268 15387 12856 66227 38358 22478 73373 88732 09443 82558 05250 92608 82674 27072 32534 17075 27698 98204 63863 11951 34648 88022 56148 34925 57031 23982 25835 40055 67006 12293 02753 14827 23235 35071 99704 37543 11601 35503 85171 09915 96306 05908 97901 28395 14186 00821 80703 70426 75647 76310 88717 37890 40129 59037 33300 26695 62247 69927 76123 50842 43834 86654 70959 79725 93872 28117 19233 42488 78077 69882 61657 34136 79180 97526 43092 04098 73571 80799 76536 71255 64239 46764 86273 63003 93017 31204 36692 40202 35275 57306 55543 53203 18098 47625 88684 03237 45430 55417 63282 90816 17349 88298 90183 36600 78406 06216 95787 42579 90730 86591 81482 52667 61582 14972 90053 89534 76036 49199 43716 97548 04379 46370 28672 38534 01715 94964 87288 65680 43772 39560 12918 86537 62738 19636 51132 25739 56947 Abridged from Handbook of Tables for Probability and Statistics, 2nd ed., edited by William H. Beyer (Cleveland, OH: The Chemical Rubber Company, 1968). Used by permission of The Chemical Rubber Company.

Searching the Periodical Literature ■ 545 APPENDIX D Distribution of Chi Square Probability df .99 .98 .95 .90 .80 .70 .50   1 .03157 .03628 .00393 .0158 .0642 .148 .455   2 .0201 .0404 .713 1.386   3 .115 .103 .211 .446 1.424 2.366   4 .297 .185 .352 .584 1.005 2.195 3.357   5 .554 .429 .711 1.064 1.649 3.000 4.351 .752 1.145 1.610 2.343   6 .872 1.134 1.635 2.204 3.070 3.828 5.348   7 1.239 1.564 2.167 2.833 3.822 4.671 6.346   8 1.646 2.032 2.733 3.490 4.594 5.528 7.344   9 2.088 2.532 3.325 4.168 5.380 6.393 8.343 10 2.558 3.059 3.940 4.865 6.179 7.267 9.342 11 3.053 3.609 4.575 5.578 6.989 8.148 10.341 12 3.571 4.178 5.226 6.304 7.807 9.034 11.340 13 4.107 4.765 5.892 7.042 8.634 9.926 12.340 14 4.660 5.368 6.571 7.790 9.467 10.821 13.339 15 5.229 5.985 7.261 8.547 10.307 11.721 14.339 16 5.812 6.614 7.962 9.312 11.152 12.624 15.338 17 6.408 7.255 8.672 10.085 12.002 13.531 16.338 18 7.015 7.906 9.390 10.865 12.857 14.440 17.338 19 7.633 8.567 10.117 11.651 13.716 15.352 18.338 20 8.260 9.237 10.851 12.443 14.578 16.266 19.337 21 8.897 9.915 11.591 13.240 15.445 17.182 20.337 22 9.542 10.600 12.338 14.041 16.314 18.101 21.337 23 10.196 11.293 13.091 14.848 17.187 19.021 22.337 24 10.856 11.992 13.848 15.659 18.062 19.943 23.337 25 11.524 12.697 14.611 16.473 18.940 20.867 24.337 26 12.198 13.409 15.379 17.292 19.820 21.792 25.336 27 12.879 14.125 16.151 18.114 20.703 22.719 26.336 28 13.565 14.847 16.928 18.939 21.588 23.647 27.336 29 14.256 15.574 17.708 19.768 22.475 24.577 28.336 30 14.953 16.306 18.493 20.599 23.364 25.508 29.336 continued For larger values of df, the expression 22  2df  1 may be used as a normal deviate with unit variance, remembering thχat the probability of χ2 corresponds with that of a single tail of the normal curve. Source: I am grateful to the Literary Executor of the late Sir Ronald A. Fisher, F.R.S., to Dr. Frank Yates, F.R.S., and to Longman Group Ltd., London, for permission to reprint Table IV from their book Statistical Tables for Biological, Agricultural, and Medical

Probability df .30 .20 .10 .05 .02 .01 .001   1 1.074 1.642 2.706 3.841 5.412 6.635 10.827   2 2.408 3.219 4.605 5.991 7.824 9.210 13.815   3 3.665 4.642 6.251 7.815 9.837 11.341 16.268   4 4.878 5.989 7.779 9.488 11.668 13.277 18.465   5 6.064 7.289 9.236 11.070 13.388 15.086 20.517   6 7.231 8.558 10.645 12.592 15.033 16.812 22.457   7 8.383 9.803 12.017 14.067 16.622 18.475 24.322   8 9.524 11.030 13.362 15.507 18.168 20.090 29.125   9 10.656 12.242 14.684 16.919 19.679 21.666 27.877 10 11.781 13.442 15.987 18.307 21.161 23.209 29.588 11 12.899 14.631 17.275 19.675 22.618 24.725 31.264 12 14.011 15.812 18.549 21.026 24.054 26.217 32.909 13 15.119 16.985 19.812 22.362 25.472 27.688 34.528 14 16.222 18.151 21.064 23.685 26.873 29.141 36.123 15 17.322 19.311 22.307 24.996 28.259 30.578 37.697 16 18.841 20.465 23.542 26.296 29.633 32.000 39.252 17 15.511 21.615 24.769 27.587 30.995 33.409 40.790 18 20.601 22.760 25.989 28.869 32.346 34.805 42.312 19 21.689 23.900 27.204 30.144 33.687 36.191 43.820 20 22.775 25.038 28.412 31.410 35.020 37.566 45.315 21 23.858 26.171 29.615 32.671 36.343 38.932 46.797 22 24.939 27.301 30.813 33.924 37.659 40.289 48.268 23 26.018 28.429 32.007 35.172 38.968 41.638 49.728 24 27.096 29.553 33.196 36.415 40.270 42.980 51.179 25 28.172 30.675 34.382 37.652 41.566 44.314 52.620 26 29.246 31.795 35.563 38.885 42.856 45.642 54.052 27 30.319 32.912 36.741 40.113 44.140 46.963 55.476 28 31.391 34.027 37.916 41.337 45.419 48.278 56.893 29 32.461 35.139 39.087 42.557 46.693 49.588 58.302 30 35.530 36.250 40.256 43.773 47.962 50.892 59.703

Searching the Periodical Literature ■ 547 APPENDIX E Normal Curve Areas z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09 0.0 .0000 .0040 .0080 .0120 .0160 .0199 .0239 .0279 .0319 .0359 0.1 .0398 .0438 .0478 .0517 .0557 .0596 .0636 .0675 .0714 .0753 0.2 .0793 .0832 .0871 .0910 .0948 .0987 .1026 .1064 .1103 .1141 0.3 .1179 .1217 .1255 .1293 .1331 .1368 .1406 .1443 .1480 .1517 0.4 .1554 .1591 .1628 .1664 .1700 .1736 .1772 .1808 .1844 .1879 0.5 .1915 .1950 .1985 .2019 .2054 .2088 .2123 .2157 .2190 .2224 0.6 .2257 .2291 .2324 .2357 .2389 .2422 .2454 .2486 .2517 .2549 0.7 .2580 .2611 .2642 .2673 .2704 .2734 .2764 .2794 .2823 .2852 0.8 .2881 .2910 .2939 .2967 .2995 .3023 .3051 .3078 .3106 .3133 0.9 .3159 .3186 .3212 .3238 .3264 .3289 .3315 .3340 .3365 .3389 1.0 .3413 .3438 .3461 .3485 .3508 .3531 .3554 .3577 .3599 .3621 1.1 .3643 .3665 .3686 .3708 .3729 .3749 .3770 .3790 .3810 .3830 1.2 .3849 .3869 .3888 .3907 .3925 .3944 .3962 .3980 .3997 .4015 1.3 .4032 .4049 .4066 .4082 .4099 .4115 .4131 .4147 .4162 .4177 1.4 .4192 .4207 .4222 .4236 .4251 .4265 .4279 .4292 .4306 .4319 1.5 .4332 .4345 .4357 .4370 .4382 .4394 .4406 .4418 .4429 .4441 1.6 .4452 .4463 .4474 .4484 .4495 .4505 .4515 .4525 .4535 .4545 1.7 .4554 .4564 .4573 .4582 .4591 .4599 .4608 .4616 .4625 .4633 1.8 .4641 .4649 .4656 .4664 .4671 .4678 .4686 .4693 .4699 .4706 1.9 .4713 .4719 .4726 .4732 .4738 .4744 .4750 .4756 .4761 .4767 2.0 .4772 .4778 .4783 .4788 .4793 .4798 .4803 .4808 .4812 .4817 2.1 .4821 .4826 .4830 .4834 .4838 .4842 .4846 .4850 .4854 .4857 2.2 .4861 .4864 .4868 .4871 .4875 .4878 .4881 .4884 .4887 .4890 2.3 .4893 .4896 .4898 .4901 .4904 .4906 .4909 .4911 .4913 .4916 2.4 .4918 .4920 .4922 .4925 .4927 .4929 .4931 .4932 .4934 .4936 2.5 .4938 .4940 .4941 .4943 .4945 .4946 .4948 .4949 .4951 .4952 2.6 .4953 .4955 .4956 .4957 .4959 .4960 .4961 .4962 .4963 .4964 2.7 .4965 .4966 .4967 .4968 .4969 .4970 .4971 .4972 .4973 .4974 2.8 .4974 .4975 .4976 .4977 .4977 .4978 .4979 .4979 .4980 .4981 2.9 .4981 .4982 .4982 .4983 .4984 .4984 .4985 .4985 .4986 .4986 3.0 .4987 .4987 .4987 .4988 .4988 .4989 .4989 .4989 .4990 .4990 Abridged from Table I of Statistical Tables and Formulas, by A. Hald (New York: John Wiley & Sons, Inc., 1952). Used by permission of John Wiley & Sons, Inc.

548 ■ Chapter 17: Reading and Writing Social Research APPENDIX F Estimated Sampling Error How to use this table: Find the intersection between The sampling error is estimated at plus or minus the sample size and the approximate percentage 4.9 percentage points. The confidence interval, distribution of the binomial in the sample. The then, is between 55.1 percent and 64.9 percent. number appearing at this intersection represents We would estimate (95 percent confidence) that the estimated sampling error, at the 95 percent con- the proportion of the total population who would fidence level, expressed in percentage points (plus say yes is somewhere within that interval. or minus). Example: In the sample of 400 respondents, 60 percent answer yes and 40 percent answer no. Sample Binomial Percentage Distribution Size   100 50/50 60/40 70/30 80/20 90/10   200 10 9.8 9.2 8 6   300 6.9 6.5 5.7 4.2   400 7.1 5.7 5.3 4.6 3.5   500 5.8 4.9 4.6 4 3   600 5 4.4 4.1 3.6 2.7   700 4.5 4 3.7 3.3 2.4   800 4.1 3.7 3.5 3 2.3   900 3.8 3.5 3.2 2.8 2.1 1000 3.5 3.3 3.1 2.7 2 1100 3.3 3.1 2.9 2.5 1.9 1200 3.2 3 2.8 2.4 1.8 1300 3 2.8 2.6 2.3 1.7 1400 2.9 2.7 2.5 2.2 1.7 1500 2.8 2.6 2.4 2.1 1.6 1600 2.7 2.5 2.4 2.1 1.5 1700 2.6 2.4 2.3 2 1.5 1800 2.5 2.4 2.2 1.9 1.5 1900 2.4 2.3 2.2 1.9 1.4 2000 2.4 2.2 2.1 1.8 1.4 2.3 2.2 2 1.8 1.3 2.2

Glossary abstract  (1) A summary of a research article. The biased in that it would generally encourage more abstract usually begins the article and states the favorable responses. See Chapter 8. (2) The thing purpose of the research, the methods used, and inside you that makes other people or groups the major findings. See Chapter 17. (2) An expen‑ seem consistently better or worse than they re‑ sive painting you may not understand but may ally are. (3) What a nail looks like after you hit it need to appreciate if you want to impress people crooked. (If you drink, don’t drive.) at the art museum. bivariate analysis  The analysis of two variables agreement reality  Those things we “know” as simultaneously, for the purpose of determining part and parcel of the culture we share with those the empirical relationship between them. The around us. See Chapter 1. construction of a simple percentage table or the computation of a simple correlation coefficient are analysis of variance (ANOVA)  Method of analysis examples of bivariate analyses. See Chapter 14 for in which cases under study are combined into more on this topic. groups representing an independent variable, and the extent to which the groups differ from one Bogardus social distance scale  (1) A measure‑ another is analyzed in terms of some dependent ment technique for determining the willingness variable. Then, the extent to which the groups of people to participate in social relations—of differ is compared with the standard of random varying degrees of closeness—with other kinds distribution. See Chapter 16. of people. It is an especially efficient technique in that one can summarize several discrete an‑ anonymity  Anonymity is achieved in a research swers without losing any of the original details project when neither the researchers nor the read‑ of the data. See Chapter 7. (2) The distance you ers of the findings can identify a given response might be prepared to travel to see a rarely shown with a given respondent. See Chapter 2. black‑and‑white movie of good ol’ Humphrey.* attributes  Characteristics of people or things. case study  The in-depth examination of a single See variables and Chapter 1. instance of some social phenomenon, such as a vil‑ lage, a family, or a juvenile gang. See Chapter 11. average  An ambiguous term generally suggest‑ ing typical or normal—a central tendency. The case-oriented analysis  (1) An analysis that aims to mean, median, and mode are specific examples of understand a particular case or several cases by look‑ m­ athematical averages. See Chapter 14. ing closely at the details of each. See Chapter 13. (2) A private investigator’s billing system. axial coding  A reanalysis of the results of open ­coding in the Grounded Theory Method, aimed closed-ended questions  Survey questions in at identifying the important, general concepts. which the respondent is asked to select an See also selective coding and Chapter 13. answer from among a list provided by the re‑ searcher. Popular in survey research because they bias  (1) That quality of a measurement device that tends to result in a misrepresentation of what is *Supplemental definitions marked with an aster‑ being measured in a particular direction. For ex‑ isk have been provided courtesy of James Instone, ample, the questionnaire item “Don’t you agree U­ niversity of New England, Armidale, NSW, Australia. that the president is doing a good job?” would be

550 ■ Glossary provide a greater uniformity of responses and are formulation of theory. See Chapter 13. (2) A mas‑ more easily processed than open-ended questions. culine technique for finding locations by logic and See Chapter 8. will, without asking for directions. cluster sampling  (1) A multistage sampling in conceptualization  (1) The mental process whereby which natural groups (clusters) are sampled ini‑ fuzzy and imprecise notions (concepts) are made tially, with the members of each selected group more specific and precise. So you want to study being subsampled afterward. For example, you prejudice. What do you mean by “prejudice”? Are might select a sample of U.S. colleges and uni‑ there different kinds of prejudice? What are they? versities from a directory, get lists of the students See Chapter 6, which is all about conceptualiza‑ at all the selected schools, then draw samples of tion and its pal, operationalization. (2) Sexual students from each. See Chapter 5. (2) Pawing reproduction among intellectuals. around in a box of macadamia nut clusters to take all the big ones for yourself. confidence interval  (1) The range of values within which a population parameter is estimated to codebook  (1) The document used in data process‑ lie. A survey, for example, may show 40 percent ing and analysis that tells the location of different of a sample favoring Candidate A (poor devil). data items in a data file. Typically, the codebook A­ lthough the best estimate of the support exist‑ identifies the locations of data items and the ing among all voters would also be 40 percent, meaning of the codes used to represent different we would not expect it to be exactly that. We attributes of variables. See Chapter 14. (2) The might, therefore, compute a confidence interval document that cost you 38 box tops just to learn (such as from 35 to 45 percent) within which that Captain Marvelous wanted you to brush your the actual percentage of the population probably teeth and always tell the truth. (3) The docu‑ lies. Note that we must specify a confidence level ment that allows CIA agents to learn that Captain in ­connection with every confidence interval. M­ arvelous wants them to brush their teeth. See Chapters 5 and 16. (2) How close you dare to get to an alligator. coding  (1) The process whereby raw data are transformed into a standardized form suitable for confidence level  (1) The estimated probability machine processing and analysis. See Chapter 10. that a population parameter lies within a given (2) A strong drug you may take when you hab a confidence interval. Thus, we might be 95 percent bad code. confident that between 35 and 45 percent of all voters favor Candidate A. See Chapters 5 and 16. cohort study  A study in which some specific (2) How sure you are that the ring you bought subpopulation, or cohort, is studied over time, from a street vendor for $10 is really a three-carat although data may be collected from different diamond. members in each set of observations. For example, a study of the occupational history of the class of confidentiality  A research project guarantees con‑ 1970 in which questionnaires were sent every five fidentiality when the researcher can identify a years would be a cohort study. See Chapter 4 for given person’s responses but promises not to do so more on this topic (if you want more). See also publicly. See Chapter 2. longitudinal study, panel study, and trend study. conflict paradigm  A paradigm that views human comparative and historical research  The behavior as attempts to dominate others or avoid e­ xamination of societies (or other social units) being dominated by others. See Chapter 3. over time and in comparison with one another. See Chapter 10. constant comparative method  (1) A component of the Grounded Theory Method in which obser‑ completion rate  See response rate. vations are compared with one another and with the evolving inductive theory. See Chapter 13. computer-assisted telephone interviewing (2) A blind-dating technique. (CATI)  A data-collection technique in which a telephone-survey questionnaire is stored in a construct validity  The degree to which a measure computer, permitting the interviewer to read the relates to other variables as expected within a sys‑ questions from the monitor and enter the answers tem of theoretical relationships. See Chapter 6. on the computer keyboard. See Chapter 8. content analysis  The study of recorded human concept mapping  (1) The graphic display of communications, such as books, websites, concepts and their interrelations, useful in the ­paintings, and laws. See Chapter 10.

Glossary ■ 551 content validity  The degree to which a measure by its expense (both financial and other). See covers the range of meanings included within a Chapter 12. concept. See Chapter 6. criterion-related validity  The degree to which a contingency question  A survey question intended measure relates to some external criterion. For ex‑ for only some respondents, determined by their ample, the validity of College Board tests is shown responses to some other question. For example, in their ability to predict the college success of stu‑ all respondents might be asked whether they dents. Also called predictive validity. See Chapter 6. b­ elong to the Cosa Nostra, and only those who said yes would be asked how often they go to critical race theory  A paradigm grounded in race company meetings and picnics. The latter would awareness and an intention to achieve racial jus‑ be a contingency question. See Chapter 8. tice. See Chapter 3. contingency table  (1) A format for presenting the critical realism  A paradigm that holds things are relationships among variables as percentage dis‑ real insofar as they produce effects. See Chapter 3. tributions. See Chapter 14. (2) The card table you keep around in case your guests bring their seven cross-case analysis  An analysis that involves an kids with them to dinner. examination of more than one case; this can be e­ ither a variable-oriented or case-oriented analy‑ continuous variable  A variable whose attributes sis. See Chapter 13. form a steady progression, such as age or income. Thus, the ages of a group of people might in‑ cross-sectional study  A study based on ob‑ clude 21, 22, 23, 24, and so forth and could even servations representing a single point in time. be broken down into fractions of years. Contrast C­ ontrasted with a longitudinal study. See Chapter 4. this with discrete variables, such as sex or religious affiliation, whose attributes form discontinuous curvilinear regression analysis  A form of regres‑ chunks. See Chapter 14. sion analysis that allows relationships among vari‑ ables to be expressed with curved geometric lines control group  (1) In experimentation, a group of instead of straight ones. See Chapter 16. subjects to whom no experimental stimulus is administered and who should resemble the ex‑ debriefing  (1) Interviewing subjects to learn about perimental group in all other respects. The com‑ their experience of participation in the project. parison of the control group and the experimental Especially important if there’s a possibility that group at the end of the experiment points to the they have been damaged by that participation. effect of the experimental stimulus. See Chapter 9. See Chapter 2. (2) Pulling someone’s shorts down. (2) American Association of Managers. Don’t do that. It’s not nice. control variable  See test variable. deduction  The logical model in which specific expectations of hypotheses are developed on conversation analysis (CA)  A meticulous analysis the basis of general principles. Starting from of the details of conversation, based on a complete the general principle that all deans are mean‑ transcript that includes pauses, hems, and also ies, you might anticipate that this one won’t let haws. See Chapter 13. you change courses. This anticipation would be the r­esult of deduction. See also induction and correlation  (1) An empirical relationship between Chapters 1 and 3. (2) What the Internal Revenue two variables such that (a) changes in one are Service said your good-for-nothing moocher of a a­ ssociated with changes in the other or (b) partic‑ brother-in-law technically isn’t. (3) Of a duck. ular attributes of one variable are associated with particular attributes of the other. Thus, for exam‑ dependent variable  (1) A variable assumed to ple, we say that education and income are correlated d­ epend on or be caused by another (called the in that higher levels of education are associated independent variable). If you find that income with higher levels of income. Correlation in and is partly a function of amount of formal education, of itself does not constitute a causal relationship ­income is being treated as a dependent variable. between the two variables, but it is one criterion See Chapter 1. (2) A wimpy variable. of causality. See Chapter 4. (2) Someone you and your friend are both related to. descriptive statistics  Statistical computations describing either the characteristics of a sample cost-benefit studies  Studies that determine or the relationship among variables in a sample. whether the results of a program can be justified Descriptive statistics merely summarize a set of sample observations, whereas inferential statistics

552 ■ Glossary move beyond the description of specific observa‑ emancipatory research  Research conducted for tions to make inferences about the larger popula‑ the purpose of benefiting disadvantaged groups. tion from which the sample observations were See Chapter 11. drawn. See Chapter 16. epistemology  The science of knowing; systems of dimension  A specifiable aspect of a concept. knowledge. See Chapter 1. ­“Religiosity,” for example, might be specified in terms of a belief dimension, a ritual dimension, a EPSEM (equal probability of selection method)  devotional dimension, a knowledge dimension, A sample design in which each member of a pop‑ and so forth. See Chapter 6. ulation has the same chance of being selected into the sample. See Chapter 5. discrete variable  (1) A variable whose at‑ tributes are separate from one another, or ethnography  A report on social life that focuses d­ iscontinuous, as in the case of sex or religious on detailed and accurate description rather than affiliation. Contrast this with continuous vari- ­explanation. See Chapter 11. ables, in which one attribute shades off into the next. Thus, in age (a continuous variable), the ethnomethodology  An approach to the study of attributes progress steadily from 21 to 22 to 23, social life that focuses on the discovery of implicit, and so forth, whereas there is no ­progression usually unspoken assumptions and agreements; from male to female in the case of sex. See this method often involves the intentional break‑ C­ hapter 14. (2) A variable that doesn’t undress ing of agreements as a way of revealing their exis‑ in public.* tence. See Chapter 11. discriminant analysis  Method of analysis similar evaluation research  Research undertaken for the to multiple regression, except that the dependent purpose of determining the impact of some social variable can be nominal. See Chapter 16. intervention, such as a program aimed at solving a social problem. See Chapter 12. dispersion  The distribution of values around some central value, such as an average. The range is ex post facto hypothesis  A hypothesis created a simple example of a measure of dispersion. after confirming data have already been collected. Thus, we may report that the mean age of a It is a meaningless construct because there is no group is 37.9, and the range is from 12 to 89. way for it to be disconfirmed. See Chapter 15. See Chapter 14. experimental group  In experimentation, a group distorter variable  In the elaboration model, a test of subjects to whom an experimental stimulus variable that reverses the direction of a zero-order is administered. Compare with control group. See relationship. See Chapter 15. Chapter 9. double-blind experiment  An experimental design explanation  (1) An elaboration model outcome in which neither the subjects nor the experiment‑ in which the original relationship between two ers know which is the experimental group and variables is revealed to have been spurious, be‑ which is the control. See Chapter 9. cause the relationship disappears when an ante‑ cedent test variable is introduced. See Chapter 15. ecological fallacy  Erroneously drawing conclusions (2) “My little sister ate my homework.” about individuals solely from the observation of groups. See Chapter 4. extended case method  A technique developed by Michael Burawoy in which case study observa‑ elaboration model  A logical model for under‑ tions are used to discover flaws in and to improve standing the relationship between two variables existing social theories. See Chapter 11. by controlling for the effects of a third. Principally developed by Paul Lazarsfeld. The various out‑ external invalidity  Refers to the possibility that comes of an elaboration analysis are replication, conclusions drawn from experimental results specification, explanation, and interpretation. See may not be generalizable to the “real” world. Chapter 15. See Chapter 9 and also internal invalidity. element  (1) That unit of which a population is external validation  The process of testing the composed and which is selected in a sample. v­ alidity of a measure, such as an index or scale, by Distinguished from units of analysis, which are examining its relationship to other, presumed indi‑ used in data analysis. See Chapter 5. (2) What an cators of the same variable. If the index really mea‑ e­ lephant eats when it has bad breath.* sures prejudice, for example, it should correlate with other indicators of prejudice. See Chapter 7.

Glossary ■ 553 face validity  (1) That quality of an indicator that hypothesis  A specified testable expectation about makes it seem a reasonable measure of some vari‑ empirical reality that follows from a more gen‑ able. That the frequency of attendance at religious eral proposition; more generally, an expectation services is some indication of a person’s religiosity about the nature of things derived from a theory. seems to make sense without a lot of explanation. It is a statement of something that ought to be It has face validity. See Chapter 6. (2) When your observed in the real world if the theory is correct. face looks like your driver’s license photo (rare See d­ eduction and Chapter 3. and perhaps unfortunate). idiographic  An approach to explanation in which factor analysis  A complex algebraic method for we seek to exhaust the idiosyncratic causes of a ­determining the general dimensions or factors particular condition or event. Imagine trying to list that exist within a set of concrete observations. all the reasons why you chose to attend your par‑ See Chapter 16. ticular college. Given all those reasons, it’s difficult to imagine your making any other choice. By feminist paradigms  Paradigms that (a) view and ­contrast, see nomothetic. See also Chapter 1. understand society through the experiences of women and/or (b) examine the generally de‑ independent variable  (1) A variable with values prived status of women in society. See Chapter 3. that are not problematic in an analysis but are taken as simply given. An independent variable focus group  A group of subjects interviewed to‑ is presumed to cause or determine a dependent gether, prompting a discussion. The technique ­variable. If we discover that religiosity is partly a is frequently used by market researchers, who function of gender—women are more religious ask a group of consumers to evaluate a product than are men—gender is the independent variable or ­discuss a type of commodity, for example. and religiosity is the dependent variable. Note that See Chapter 11. any given variable might be treated as indepen‑ dent in one part of an analysis and dependent frequency distribution  (1) A description of the in another part of it. Religiosity might become an number of times the various attributes of a vari‑ independent variable in the explanation of crime. able are observed in a sample. The report that See dependent variable and Chapter 1. (2) A vari‑ 53 percent of a sample were men and 47 percent able that refuses to take advice. were women would be a simple example of a f­requency distribution. See Chapter 14. (2) A index  A type of composite measure that summa‑ radio dial. rizes and rank-orders several specific observations and represents some more general dimension. Geographic Information Systems (GIS)  Analytic Contrasted with scale. See Chapter 7. technique in which researchers map quantitative data that describe geographic units for a graphic indicator  An observation that we choose to con‑ display. See Chapter 16. sider as a reflection of a variable we wish to study. Thus, for example, attending religious services grounded theory  (1) An inductive approach to might be considered an indicator of religiosity. See the study of social life that attempts to generate a Chapter 6. theory from the constant comparing of unfolding observations. This is very different from hypoth‑ induction  (1) The logical model in which general esis testing, in which theory is used to generate principles are developed from specific observa‑ hypotheses to be tested through observations. See tions. Having noted that Jews and Catholics are Chapter 11. (2) A theory that is not allowed to fly. more likely to vote Democratic than Protestants are, you might conclude that religious minorities Grounded Theory Method (GTM)  An induc‑ in the United States are more affiliated with the tive approach to research, introduced by Barney Democratic party and then your task is to explain Glaser and Anselm Strauss, in which theories why. This would be an example of induction. are generated solely from an examination of See also deduction and Chapters 1 and 3. (2) The data rather than being derived deductively. See culinary art of stuffing ducks. ­Chapter 13. inferential statistics  The body of statistical com‑ Guttman scale  (1) A type of composite measure putations relevant to making inferences from used to summarize several discrete observations findings based on sample observations to some and to represent some more-general variable. larger population. See also descriptive statistics and See Chapter 7. (2) The device Louis Guttman weighs himself on.

554 ■ Glossary Chapter 16. (Not to be confused with infernal item analysis  An assessment of whether each of statistics, a characterization sometimes invoked by the items included in a composite measure makes frustrated statistics students.) an independent contribution or merely duplicates the contribution of other items in the measure. informant  Someone who is well versed in the See Chapter 7. social phenomenon that you wish to study and who is willing to tell you what he or she knows latent content  (1) In connection with content about it. If you were planning participant observa‑ analysis, the underlying meaning of communica‑ tion among the members of a religious sect, you tions, as distinguished from their manifest content. would do well to make friends with someone who See Chapter 10. (2) What you need to make a already knows about them—possibly a member of latent. the sect—who could give you some background information about them. Not to be confused with level of significance  (1) In the context of tests of a respondent. See Chapter 5. statistical significance, the degree of likelihood that an observed, empirical relationship could informed consent  A norm in which subjects base be attributable to sampling error. A relationship their voluntary participation in research proj‑ is significant at the .05 level if the likelihood of ects on a full understanding of the possible risks its being only a function of sampling error is no i­nvolved. See Chapter 2. greater than 5 out of 100. See Chapter 16. (2) Height limits on outdoor advertising. institutional ethnography  A research technique in which the personal experiences of individuals Likert scale  A type of composite measure devel‑ are used to reveal power relationships and other oped by Rensis Likert, in an attempt to improve characteristics of the institutions within which the levels of measurement in social research they operate. See Chapter 11. through the use of standardized response cat‑ egories in survey questionnaires, to determine interest convergence  The thesis that majority the relative intensity of different items. Likert group members will only support the interests of items are those using such response categories minorities when those actions also support the as strongly agree, agree, disagree, and strongly ­interests of the majority group. See Chapter 3. ­disagree. Such items may be used in the construc‑ tion of true Likert scales as well as other types of internal invalidity  (1) Refers to the possibility that composite measures. See Chapter 7. the conclusions drawn from experimental results may not accurately reflect what went on in the linear regression analysis  A form of statistical experiment itself. See Chapter 9 and also external analysis that seeks the equation for the straight invalidity. (2) What my grandad has and why he line that best describes the relationship between wears special “nappies.”* two ratio variables. See Chapter 16. interpretation  A technical term used in connec‑ log-linear models  Data-analysis technique based tion with the elaboration model. It represents the on specifying models that describe the interrela‑ research outcome in which a control variable is tionships among variables and then comparing discovered to be the mediating factor through expected and observed table-cell frequencies. which an independent variable has its effect on a See Chapter 16. dependent variable. See Chapter 15. longitudinal study  A study design involving the interval measure  A level of measurement describ‑ collection of data at different points in time, as ing a variable whose attributes are rank-ordered contrasted with a cross-sectional study. See also and have equal distances between adjacent at‑ Chapter 4 and cohort study, panel study, and trend tributes. The Fahrenheit temperature scale is an study. example of this, because the distance between 17 and 18 is the same as that between 89 and 90. macrotheory  A theory aimed at understanding See also Chapter 6 and nominal measure, ordinal the “big picture” of institutions, whole societies, measure, and ratio measure. and the interactions among societies. Karl Marx’s examination of the class struggle is an example of interview  A data-collection encounter in which macrotheory. By contrast, see microtheory. See also one person (an interviewer) asks questions Chapter 3. of ­another (a respondent). Interviews may be c­ onducted face-to-face or by telephone. See manifest content  (1) In connection with con‑ C­ hapter 8. tent analysis, the concrete terms contained in

Glossary ■ 555 a communication, as distinguished from latent multiple time-series designs  The use of more content. See Chapter 10. (2) What you have after a than one set of data that were collected over manifest bursts. time, as in accident rates over time in several states or cities, so that comparisons can be made. matching  In connection with experiments, the pro‑ See Chapter 12. cedure whereby pairs of subjects are matched on the basis of their similarities on one or more vari‑ multivariate analysis  The analysis of the simul‑ ables, and one member of the pair is assigned to taneous relationships among several variables. the experimental group and the other to the control E­ xamining simultaneously the effects of age, sex, group. See Chapter 9. and social class on religiosity would be an example of multivariate analysis. See Chapters 14, 15, mean  (1) An average computed by summing the and 16. values of several observations and dividing by the number of observations. If you now have a naturalism  An approach to field research based grade point average of 4.0 based on 10 courses, on the assumption that an objective social reality and you get an F in this course, your new grade e­ xists and can be observed and reported accu‑ point (mean) average will be 3.6. See Chapter 14. rately. See Chapter 11. (2) The quality of the thoughts you might have if your instructor did that to you. needs assessment studies  Studies that aim to determine the existence and extent of problems, median  (1) An average representing the value of typically among a segment of the population, such the “middle” case in a rank-ordered set of ob‑ as the elderly. See Chapter 12. servations. If the ages of five men are 16, 17, 20, 54, and 88, the median would be 20. (The mean nominal measure  A nominal variable has attri‑ would be 39.) See Chapter 14. (2) The dividing butes that are merely different, as distinguished line between safe driving and exciting driving. from ordinal, interval, or ratio measures. Sex is an example of a nominal measure. All a nominal memoing  Writing memos that become part of the variable can tell us about two people is if they are data for analysis in qualitative research such as the same or different. See Chapter 6. grounded theory. Memos can describe and define concepts, deal with methodological issues, or offer nomothetic  An approach to explanation in which initial theoretical formulations. See Chapter 13. we seek to identify a few causal factors that gener‑ ally impact a class of conditions or events. Imagine methodology  The science of finding out; proce‑ the two or three key factors that determine which dures for scientific investigation. See Chapter 1. colleges students choose—proximity, reputation, and so forth. By contrast, see idiographic. See also microtheory  A theory aimed at understanding Chapter 1. social life at the intimate level of individuals and their interactions. Examining how the play nonequivalent control group  A control group behavior of girls differs from that of boys would that is similar to the experimental group but is be an example of microtheory. By contrast, not created by the random assignment of subjects. see ­macrotheory. See also Chapter 3. This sort of control group differs significantly from the experimental group in terms of the dependent mode  (1) An average representing the most variable or variables related to it. See Chapter 12. ­frequently observed value or attribute. If a sample contains 1,000 Protestants, 275 Catholics, and nonprobability sampling  Any technique in 33 Jews, Protestant is the modal category. which samples are selected in some way not sug‑ See Chapter 14 for more thrilling disclosures gested by probability theory. Examples include about averages. (2) Better than apple pie à la reliance on available subjects as well as purpo- median. sive ­(judgmental), quota, and snowball sampling. See Chapter 5. monitoring studies  Studies that provide a steady flow of information about something of inter‑ nonsampling error  (1) Those imperfections of est, such as crime rates or the outbreak of an data quality that are a result of factors other than e­ pidemic. See Chapter 12. sampling error. Examples include misunderstand‑ ings of questions by respondents and erroneous multiple regression analysis  A form of statistical ­recordings by interviewers and coders. See Chap‑ analysis that seeks the equation representing the ter 16. (2) The mistake you made in deciding to impact of two or more independent variables on a interview everyone rather than selecting a sample. single dependent variable. See Chapter 16.

556 ■ Glossary null hypothesis  (1) In connection with hypothesis variables are held constant, similar to the logic of testing and tests of statistical significance, that the elaboration model. See Chapter 16. (2) A re‑ hypothesis that suggests there is no relationship gression analysis you didn’t have time to finish. among the variables under study. You may con‑ clude that the variables are related after h­ aving partial relationship  (1) In the elaboration model, statistically rejected the null hypothesis. See this is the relationship between two variables ­Chapter 3. (2) An expectation about nulls. when examined in a subset of cases defined by a third variable. Beginning with a zero-order odds ratio  A statistical technique for expressing the relationship between political party and attitudes relationship between variables by comparing the toward abortion, for example, we might want to see odds of different occurrences. See Chapter 16. whether the relationship held true among both men and women (i.e., controlling for sex). The open coding  The initial classification and labeling relationship found among men and the relation‑ of concepts in qualitative data analysis. In open ship found among women would be the partial coding, the codes are suggested by the research‑ relationships, sometimes simply called the partials. ers’ examination and questioning of the data. See See Chapter 15. (2) Someone you would take to Chapter 13. the opera but not to mud wrestling. open-ended questions  Questions for which the participatory action research (PAR)  An ap‑ respondent is asked to provide his or her own proach to social research in which the people answers. In-depth, qualitative interviewing relies being studied are given control over the purpose almost exclusively on open-ended questions. and procedures of the research; intended as a See Chapters 8 and 11. counter to the implicit view that researchers are superior to those they study. See Chapter 11. operational definition  The concrete and specific definition of something in terms of the operations path analysis  (1) A form of multivariate analysis by which observations are to be categorized. The in which the causal relationships among variables operational definition of “earning an A in this are presented in a graphic format. See Chapter 16. course” might be “correctly answering at least (2) Watching your step along a horse trail. 90 percent of the final exam questions.” See Chapter 3. plagiarism  Presenting someone else’s words or thoughts as though they were your own, consti‑ operationalization  (1) One step beyond conceptu‑ tuting intellectual theft. See Chapter 17. alization. Operationalization is the process of de‑ veloping operational definitions, or specifying the population  The theoretically specified aggregation exact operations involved in measuring a variable. of the elements in a study. See Chapter 5. See Chapters 3 and 6. (2) Surgery on intellectuals. positivism  Introduced by Auguste Comte, this ordinal measure  A level of measurement describ‑ philosophical system is grounded on the rational ing a variable with attributes we can rank-order proof/disproof of scientific assertions; assumes a along some dimension. An example is socioeco- knowable, objective reality. See Chapter 3. nomic status as composed of the attributes high, m­ edium, low. See also Chapter 6 and interval postmodernism  A paradigm that questions the as‑ m­ easure, nominal measure, and ratio measure. sumptions of positivism and theories describing an “objective” reality. See Chapter 3. panel study  A type of longitudinal study, in which data are collected from the same set of people (the posttesting  (1) The remeasurement of a dependent sample or panel) at several points in time. See variable among subjects after they’ve been ex‑ Chapter 4 and cohort, longitudinal, and trend study. posed to an independent variable. See Chapter 9. (2) What my younger sister did when she was paradigm  (1) A model or frame of reference learning to drive.* through which to observe and understand. See Chapter 3. (2) (pl.) $0.20. PPS (probability proportionate to size)  (1) This refers to a type of multistage cluster sample in parameter  The summary description of a given which clusters are selected, not with equal prob‑ variable in a population. See Chapter 5. abilities (see EPSEM) but with probabilities propor‑ tionate to their sizes—as measured by the number partial  See partial relationship. of units to be subsampled. See Chapter 5. (2) The odds on who gets to go first: you or the 275-pound partial regression analysis  (1) A form of regres‑ fullback. sion analysis in which the effects of one or more

Glossary ■ 557 predictive validity  See criterion-related validity. quasi experiments  Nonrigorous inquiries some‑ what resembling controlled experiments but lack‑ pretesting  The measurement of a dependent vari‑ ing key elements such as pre- and posttesting and/ able among subjects. See Chapter 9. or control groups. See Chapter 12. probability sampling  The general term for samples questionnaire  A document containing questions selected in accord with probability theory, typically and other types of items designed to solicit infor‑ involving some random-selection mechanism. mation appropriate for analysis. Questionnaires Specific types of probability sampling include are used primarily in survey research but also in EPSEM, PPS, simple random sampling, and systematic experiments, field research, and other modes of sampling. See Chapter 5. observation. See Chapter 8. probe  A technique employed in interviewing to quota sampling  A type of nonprobability sampling solicit a more complete answer to a question. It in which units are selected into a sample on the is a nondirective phrase or question used to en‑ basis of prespecified characteristics, so that the courage a respondent to elaborate on an answer. total sample will have the same distribution of Examples include “Anything more?” and “How is characteristics assumed to exist in the population that?” See Chapter 8. being studied. See Chapter 5. program evaluation/outcome assessment  The random selection  A sampling method in which determination of whether a social intervention is each element has an equal chance of selection producing the intended result. See Chapter 12. independent of any other event in the selection process. See Chapter 5. proportionate reduction of error (PRE)  A logical model for assessing the strength of a relationship random-digit dialing (RDD)  A sampling by asking how much knowing values on one vari‑ t­echnique in which random numbers are able would reduce our errors in guessing values s­elected from within the ranges of numbers as‑ on the other. For example, if we know how much signed to active telephones. See Chapter 8. education people have, we can improve our abil‑ ity to estimate how much they earn, thus indi‑ randomization  A technique for assigning ex‑ cating there is a relationship between the two perimental subjects to experimental and control variables. See Chapter 16. groups randomly. See Chapter 9. purposive (judgmental) sampling  A type of rapport  An open and trusting relationship; espe‑ ­nonprobability sampling in which the units to ­ cially important in qualitative research between be observed are selected on the basis of the researchers and the people they’re observing. ­researcher’s judgment about which ones will See Chapter 11. be the most useful or representative. See Chapter 5. ratio measure  A level of measurement describing a variable with attributes that have all the quali‑ qualitative analysis  (1) The nonnumerical ties of nominal, ordinal, and interval measures e­ xamination and interpretation of observations, and in addition are based on a “true zero” point. for the purpose of discovering underlying mean‑ Age is an example of a ratio measure. See also ings and patterns of relationships. This is most Chapter 6 and nominal measure, interval measure, typical of field research and historical research. and ordinal measure. See Chapter 13. (2) A classy analysis. reactivity  The problem that the subjects of social qualitative interview  Contrasted with survey research may react to the fact of being studied, i­nterviewing, the qualitative interview is based thus altering their behavior from what it would on a set of topics to be discussed in depth rather have been normally. See Chapter 11. than based on the use of standardized questions. See Chapter 11. reductionism  (1) A fault of some researchers: a strict limitation (reduction) of the kinds of con‑ quantitative analysis  (1) The numerical represen‑ cepts to be considered relevant to the phenom‑ tation and manipulation of observations for the enon under study. See Chapter 4. (2) The cloning purpose of describing and explaining the phenom‑ of ducks. ena that those observations reflect. See Chapter 14 especially, and also the remainder of Part 4. regression analysis  (1) A method of data analysis (2) A BIG analysis. in which the relationships among variables are represented in the form of an equation, called a

558 ■ Glossary regression equation. See Chapter 16 for a discus‑ everyone. For probability sampling, the maximum sion of the different forms of regression analysis. error depends on three factors: the sample size, (2) What seems to happen to your knowledge of the diversity of the population, and the confi‑ social research methods just before an exam. dence level. See Chapter 5. reliability  (1) That quality of measurement method sampling frame  That list or quasi list of units com‑ that suggests that the same data would have been posing a population from which a sample is se‑ collected each time in repeated observations of the lected. If the sample is to be representative of the same phenomenon. In the context of a survey, we population, it is essential that the sampling frame would expect that the question “Did you attend include all (or nearly all) members of the popula‑ religious services last week?” would have higher tion. See Chapter 5. reliability than the question “About how many times have you attended religious services in your sampling interval  The standard distance between life?” This is not to be confused with validity. See elements selected from a population for a sample. Chapter 6. (2) Quality of repeatability in untruths. See Chapter 5. replication  (1) Repeating a research study to test sampling ratio  The proportion of elements in the and either confirm or question the findings of an population that are selected to be in a sample. earlier study. See Chapter 1. (2) A technical term See Chapter 5. used in connection with the elaboration model, referring to the elaboration outcome in which the sampling unit  That element or set of elements initially observed relationship between two vari‑ considered for selection in some stage of sampling. ables persists when a control variable is held con‑ See Chapter 5. stant, thereby supporting the idea that the original relationship is genuine. See Chapter 15. scale  (1) A type of composite measure composed of several items that have a logical or empirical representativeness  (1) That quality of a sample structure among them. Examples of scales in‑ of having the same distribution of characteristics clude Bogardus social distance, Guttman, Likert, as the population from which it was selected. By and Thurstone scales. Contrasted with index. implication, descriptions and explanations derived See Chapter 7. (2) One of the less-appetizing from an analysis of the sample may be assumed to parts of a fish. represent similar ones in the population. Repre‑ sentativeness is enhanced by probability sampling search engine  A computer program designed to and provides for generalizability and the use of locate where specified terms appear on websites inferential statistics. See Chapter 5. (2) A notice‑ throughout the World Wide Web. See Chapter 17. able quality in the presentation-of-self of some members of the U.S. Congress. secondary analysis  (1) A form of research in which the data collected and processed by one research monograph  A book-length research ­researcher are reanalyzed— often for a different report, either published or unpublished. This is purpose—by another. This is especially appro‑ distinguished from a textbook, a book of essays, a priate in the case of survey data. Data archives novel, and so forth. See Chapter 17. are repositories or libraries for the storage and ­distribution of data for secondary analysis. respondent  A person who provides data for analy‑ See Chapter 8. (2) Estimating the weight and sis by responding to a survey questionnaire. See speed of an opposing team’s linebackers. Chapter 8. selective coding  In Grounded Method Theory, response rate  The number of people participating this analysis builds on the results of open coding in a survey divided by the number selected in the and axial coding to identify the central concept sample, in the form of a percentage. This is also that organizes the other concepts that have been called the completion rate or, in self-administered identified in a body of textual materials. See also surveys, the return rate: the percentage of ques­ axial coding and Chapter 13. tionnaires sent out that are returned. See Chapter 8. semantic differential  A questionnaire format in which the respondent is asked to rate some‑ return rate  See response rate. thing in terms of two, opposite adjectives (e.g., rate ­textbooks as “boring” or “exciting”), using sampling error  The degree of error to be ex‑ qualifiers such as “very,” “somewhat,” “neither,” pected by virtue of studying a sample instead of “somewhat,” and “very” to bridge the distance b­ etween the two opposites. See Chapter 7.

Glossary ■ 559 semiotics  (1) The study of signs and the meanings send lots of fire trucks to a large fire and a lot of associated with them. This is commonly associated damage is done because of the size of the fire. For with content analysis. See Chapter 13. (2) Anti­ a little fire, they just send a little fire truck, and biotics that only work half of the time.* not much damage is done because it’s a small fire. Sending more fire trucks does not cause more simple random sampling (SRS)  (1) A type of damage. For a given size of fire, in fact, sending probability sampling in which the units compos‑ more trucks would reduce the amount of damage. ing a population are assigned numbers. A set of See Chapter 4. (2) You thought you were going random numbers is then generated, and the units steady but that @#*&@#&* thought you were having those numbers are included in the sample. “just friends.” Although probability theory and the calculations it provides assume this basic sampling method, it’s standard deviation  (1) A measure of dispersion seldom used, for practical reasons. An equivalent around the mean, calculated so that approxi‑ alternative is the systematic sample (with a ran‑ mately 68 percent of the cases will lie within plus dom start). See Chapter 5. (2) A random sample or minus one standard deviation from the mean, with a low IQ. 95 percent will lie within plus or minus two stan‑ dard deviations, and 99.9 percent will lie within snowball sampling  (1) A nonprobability sam‑ three standard deviations. Thus, for example, pling method, often employed in field research, if the mean age in a group is 30 and the stan‑ whereby each person interviewed may be asked dard deviation is 10, then 68 percent have ages to suggest additional people for interviewing. See between 20 and 40. The smaller the standard Chapters 5 and 11. (2) Picking the icy ones to deviation, the more tightly the values are clus‑ throw at your methods instructor. tered around the mean; if the standard d­ eviation is high, the values are widely spread out. See social artifact  Any product of social beings or their ­Chapter 14. (2) Routine rule-breaking. behavior. Can be a unit of analysis. See Chapter 4. statistic  The summary description of a variable in a social indicators  Measurements that reflect the sample, used to estimate a population parameter. quality or nature of social life, such as crime rates, See Chapter 5. infant mortality rates, number of physicians per 100,000 population, and so forth. Social indicators statistical significance  (1) A general term refer‑ are often monitored to determine the nature of ring to the likelihood that relationships observed social change in a society. See Chapter 12. in a sample could be attributed to sampling error alone. See tests of statistical significance and Chapter sociobiology  A paradigm based in the view that 16. (2) How important it would really be if you social behavior can be explained solely in terms flunked your statistics exam. I mean, you could of genetic characteristics and behavior. See always be a poet. ­Chapter 4. stratification  The grouping of the units composing specification  (1) The process through which con‑ a population into homogeneous groups (or strata) cepts are made more specific. See Chapter 6. (2) A before sampling. This procedure, which may be technical term used in connection with the elabo‑ used in conjunction with simple random, systematic, ration model, representing the elaboration out‑ or cluster sampling, improves the representativeness come in which an initially observed relationship of a sample, at least in terms of the stratification between two variables is replicated among some variables. See Chapter 5. subgroups created by the control variable but not among others. In such a situation, you will have structural functionalism  A paradigm that di‑ specified the conditions under which the original vides social phenomena into parts, each of which relationship exists: for example, among men but serves a function for the operation of the whole. not among women. See Chapter 15. See Chapter 3. spurious relationship  (1) A coincidental statistical study population  That aggregation of ­elements correlation between two variables, shown to be from which a sample is actually selected. See caused by some third variable. For example, there Chapter 5. is a positive relationship between the number of fire trucks responding to a fire and the amount suppressor variable  In the elaboration model, a of damage done: the more trucks, the more dam‑ test variable that prevents a genuine r­elationship age. The third variable is the size of the fire. They from appearing at the zero-order level. See C­ hapter 15.

560 ■ Glossary symbolic interactionism  A paradigm that views tolerance for ambiguity  The ability to hold human behavior as the creation of meaning conflicting ideas in your mind simultaneously, through social interactions, with those mean‑ without denying or dismissing any of them. ings conditioning subsequent interactions. See Chapter 1. See Chapter 3. trend study  A type of longitudinal study in which systematic sampling  (1) A type of probability a given characteristic of some population is moni‑ ­sampling in which every kth unit in a list is se‑ tored over time. An example would be the series lected for inclusion in the sample—for example, of Gallup Polls showing the electorate’s prefer‑ every 25th student in the college directory of stu‑ ences for political candidates over the course of a dents. You compute k by dividing the size of the campaign, even though different samples were in‑ population by the desired sample size; k is called terviewed at each point. See Chapter 4 and cohort, the sampling interval. Within certain constraints, longitudinal, and panel study. systematic sampling is a functional equivalent of simple random sampling and usually easier triangulation  Choking a triangle (submitted by to do. Typically, the first unit is selected at ran‑ Wendy Ogden, Mount Royal College, Calgary, dom. See Chapter 5. (2) Picking every third one Canada). whether it’s icy or not. See snowball sampling (2). typology  (1) The classification (typically nominal) test variable  A variable that is held constant in an of observations in terms of their attributes on two attempt to clarify further the relationship between or more variables. The classification of newspapers two other variables. Having discovered a relation‑ as liberal-urban, liberal-rural, conservative-urban, ship between education and prejudice, for example, or conservative-rural would be an example. See we might hold sex constant by examining the Chapter 7. (2) Apologizing for your neckwear. r­elationship between education and prejudice among men only and then among women only. units of analysis  The what or whom being studied. In this example, sex would be the test variable. In social science research, the most typical units of See Chapter 15 to find out how important the analysis are individual people. See Chapter 4. proper use of test variables is in analysis. univariate analysis  The analysis of a single vari‑ tests of statistical significance  (1) A class of sta‑ able, for purposes of description. Frequency tistical computations that indicate the likelihood distributions, averages, and measures of disper‑ that the relationship observed between variables sion would be examples of univariate analysis, in a sample can be attributed to sampling error as ­distinguished from bivariate and multivariate only. See inferential statistics and Chapter 16. (2) A analysis. See Chapter 14. determination of how important statistics have been in improving humankind’s lot in life. (3) An unobtrusive research  Methods of studying social examination that can radically affect your grade in behavior without affecting it. Such methods can this course and your GPA as well. be qualitative or quantitative. See Chapter 10. theory  A systematic explanation for the observa‑ URL  (1) Web address, typically beginning with tions that relate to a particular aspect of life: juve‑ “http://”; stands for “uniform resource locator” nile delinquency, for example, or perhaps social or “universal resource locator.” See Chapter 17. stratification or political revolution. See Chapter 1. (2) Phonetic spelling of “Earl.” (3) What my mum used to say to me when I sounded like I was Thurstone scale  A type of composite measure, ­getting a cold.* constructed in accord with the weights assigned by “judges” to various indicators of some vari‑ validity  A term describing a measure that accu‑ ables. See Chapter 7. rately reflects the concept it is intended to mea‑ sure. For example, your IQ would seem a more time-series analysis  An analysis of changes in valid measure of your intelligence than the a variable (such as crime rates) over time. See number of hours you spend in the library would. Chapter 16. Though the ultimate validity of a measure can never be proved, we may agree to its relative time-series design  A research design that involves validity on the basis of face validity, criterion-related measurements made over some period, such as validity, construct validity, content validity, internal the study of traffic accident rates before and after ­validation, and external validation. Validity must lowering the speed limit. See Chapter 12. not be confused with reliability. See Chapters 6 and 7.

Glossary ■ 561 variable-oriented analysis  An analysis that probability of selection. When all cases have the d­ escribes and/or explains a particular variable. same chance of selection, no weighting is neces‑ See Chapter 13. sary. See Chapter 5. variables  Logical sets of attributes. The variable sex zero-order relationship  (1) In the elaboration is made of up of the attributes male and female. model, this is the original relationship between See Chapter 1. two variables, with no test variables controlled for. See Chapter 15. (2) A blind date that just didn’t weighting  Assigning different weights to cases that work out. Hang in there. You can always turn to were selected into a sample with different prob‑ social research methods. abilities of selection. In the simplest scenario, each case is given a weight equal to the inverse of its



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