-Earl_Babbie-_The_Practice_of_Social_Research(BookFi)

HEF-6 RECORD OF CALLS ON HOUSEHOLD USE PENCIL D. 'l'1-lIle C. I{E~ [U:jit1 De:jc r i.pt j.OIl 11. He~~u Lu-;j HeZl~3011 [01: I[.1.)Ul.po~;e J < Tnt j r P~e3 .. L 3-6 .. ~ Post G.. 3 Ln.Lt.ld 1,:3 1\\. Day E. Type He(u!-;d!. CIve vel\"iJdtim of M. I '['u.::! W.3 Per\" L 'I'e.i . . :~ t<lJLt .. 3 ,J 'I'll. II F. 5 Sd. () F. OULcome of: Contact rCi1SOW3 imet exp 1d til III COIlLdct Suo '/ d. 13. i'vientll 35,3?,45,~1J82,92 Do G citJ I llTUI1l!';tdIlCl~[-;. C. Ddte d. 32/E uD. 3'1 I [1 G. H . . . . I\" I 1I([e [[. [. 41/H u1\\. 33 ··3'1 IE uE. 3BIR Otllt:!r chdt~ •• J .~[ EJ-36/R IlEF L LIl(--' H whel1 comp! (:,tt' 111':1\" . B. UU F. UU 39-'10/1{ Oucx. 5 l/H C. UU ,I:U[{ 'I 'II H G. ~1. . . . I\" I IIge _ _ _ _ __ II. I. .1 43-'1,1/1<. uD. 'Itl/H OUIer eila r : uA. Ii 5-4 (J IH. uE. liEF LinE' when complete: liEF .~ !)uex. U UIJ. 52/1\\ F. UU 49-50/H C. t1 . . . <.F I t\\(jC _ _ _ _ __ II. GI/H C. UU :)3 ..\":)4 IH :)7/1{ OU1Cl' chtll\" [. uD. ')[l/H HEF iL 11(-' II \\'Jhcn complete 71/1{ uII. ~):.i-·~)6/H uE. liEF Quex. .2 B. UU G;;/l{ F UU :)9--GO/H C. UU 63-6'111{ 1. G5-GG/R uD. 67/H G. !·1 •. , .1\" I i\\(le _ _ _ _ __ uA. uE. Gil/[{ Other Chdl~; liEF Quex. , B. UU I\" UU 69-'/0/1< liEF Line II when complete _ _ C. UU BEGIN DECK 07. uA. 07 Ii{ I D. u 7.!HIG.t·1. . • 1\" I IIge III. J. J lo/H I d, OB-09/H J F u liEF B. UU 13/[{ I Othel' char .J 10-Jl/i{1 F. UU j/l-]5/H I liEF 1.Jne 1/ \\vhcn complete (lUE)X. C. UU , .) 'j IH I D. u uII. iO-19/R I E. u 7.2/H I G. ~l.\" .. F I IIge_._.... ________ I II. I. 26/H I d. 2D-2J IE I F 23/H I Other cllilL U Ull. UU /\"l-/.S/H I liEF Llnc !I \\'/11011 complete liEF. .1 7.7/R I D. C. UU 28-29/H I E. u Quex. , . .:.: u uA. 3D-3111l I F 32/H I G. M\" .. I\" I t\\CJe I II. 3[,/Hld, UU 33/H I Other char .1 B. UU 37/H I D. 3 /1-35/1{ I HEF I,inc II wilen compJete liEF, 38-3911{ I E. u . :~ C. UU '10-4J/H I 1\", u Ouox. \" uA. UU 'IU[{ I G. M .. ,.1\" IAge_______ III. liEF, 46/H I J. 43/H I Otbet: clta,., Quex. .1 B. UU 4tl-'15/H I liEF Li.lle II when complete _ _ . .7. C. UU 4'/-48 S'I'ATUS CODES FOE OU'[,CONE (COLUMN F) '['EMPOHAHY FINALI (Hequj reLi superv i sor' s approval) liEF QUEX liEF QUEX 31 No .:J.ctiOIl '13 H not home/unavai J abJ e 10 No IIU\" 13 H cloes !lot .speak Enq 1 i sh * t15 Temp refusa.1/bl'Cc1koff 1 J Vacant·k 92 l{efusaJ/breakoff* 37. No one home/No aflsw(\"!r/Bw::;y '1 G Appo i.ntment I? 1111 ~~peaks no EllglJ sIl* 9'} H absellt ent:J.re Held peLiod* 35 'l'cmporary refusal/breakoff '17 Broken appointment B2 RefusaJ/breakoff* 97 OUter* 36 Appointment 49 Other BiJ Not\" home/unava lJable* 60 Ballot Quex ollJy 3'7 Broken dPPo.lntrnent 85 HI! not accessible* 65 Ballot Quex & some, not {.\\J 1 document.!:.; 87 Other* 67 Ballot Quex & all documeIlU, 38 Ill-! not accessible 39 Other EVEI{YONE COMPLETE SAt1Pl.ING I{EPOH'I' USE CON'l'lNUIITION SIIEE'l' IF NECESSARY *FlI.!., OU'l' NIH

Random Numbers 10480 all 01536 816 14194 25 207 20 00 573 25595 309 53402 39 095 52 22527 763 93 081 30 as 360 06243 078 70659 13 00 81837 061 18738 96 15053 29 77921 06907 42751 16376 18602 56869 48840 21916 7 44394 79 72905 11008 27756 91782 71194 84378 90655 60045 63213 06927 10634 13 85475 91977 10281 98872 9-1595 62300 -14013 12565 18.;25 4.2508 14342 56·no 53988 18876 20922 57740 69014 17983 58678 11458 05585 42380 28918 36357 17453 18103 38867 92144 25331 15439 85590 18593 12952 63553 05463 33276 53060 59533 44819 08158 31595 90511 32307 09<i29 69578 63661 03427 56865 29852 20847 01547 27156 91610 56941 10365 40961 53342 92737 70997 33488 18663 98736 90106 08272 1223.4 20285 33703 64352 07119 93969 85689 49626 52267 36320 13602 52180 26358 3H15 74451 30613 61129 88231 08178 88974 13916 67689 30015 85977 85104 29975 78183 51085 97336 48235 48237 47564 04734 01511 29372 61665 28551 90322 02368 52635 51259 77233 16308 2633-1 97735 53900 15227 74952 01011 12765 87529 60268 19885 60756 28728 65255 70960 75601 89868 52162 21382 71048 9490'; 77452 04146 55322 15398 49442 85030 64835 86679 05944. 90707 07036 54092 58586 89368 14513 185% 61280 01188 54350 51132 87074 92747 53916 51821 09998 31273 06691 83149 71585 .;6104 94733 35155 ';0719 48663 97628 52404 23216 76988 14778 23-i95 88916 09250 25625 55157 5'-±164 33362 1-13~6 42698 30168 81536 51851 22178 83517 64.951 32639 92.245 46369 25306 90229 61362 06646 30421 53389 99904 35749 29334 58492 33787 74103 09172 38005 76468 63904 59193 06912 21524 21246 58104 02488 32363 47070 00256 94342 22209 58151 41135 17012 20103 36188 27001 85828 77341 13363 92420 45834 35806 67658 10367 04102 50720 32812 81525 33062 22421 70 58731 60952 99547 46557 32586 -14592 29676 05597 19731 82651 36086 50001 14780 57375 79666 22851 00712 72295 87637 30134 20849 66566 05625 12659 13300 80·;28 18510 05366 20591 28834 49127 24878 40027 89768 82271 76797 96067 14.777 96095 9.;953 91921 57392 49618 4404.3 32832 35797 86645 66134 75-i70 22923 34693 04213 04839 78171 44407 25940 37397 64552 91402 91 ()i8.;4 95725 00582 26418 68086 81263 26765 39972 99730 71500 72 25280 00725 39064 35126 205-i2 42607 29841 12 62028 98253 69011 0·;711 25669 64270 42206 88072 74087 81817 93161 33611 30 77919 90 .149 25976 69884 64117 82765 86324 27354 76222 98420 84637 59920 34952 12777 69618 09763 65795 46473 18988 42708 26575 40836 40801 69774 85963 57948 87917 67245 67917 18317 18912 25832 65424 41688 9080 33917 76630 91567 83473 62797 07391 30883 28290 42878 05998 3708 83006 1 95876 85385 80059 84855 6942 79 56 ';8501 -12595 29888 29992 04024 59931 23880 55536 02008 5555 36 03 03547 14577 56349 73577 31925 20044 51038 06115 83765 18059 15475 9556 20 62 33050 18584 25388 02304 82834 20655 92351 28168 43413 35 09 98427 89634 27958 70765 84610 47358 09922 35648 44137 49518 5565 77 90 73211 34914 62765 90999 38391 39667 56873 54328 61607 0663 .;2791 18845 92477 81652 45585 9661 46 80 87338 90725 07523 94824 3381 01638 17032 66969 04880 70002 7363 77 75 204.68 6';364 63976 35605 1962 34476 53416 87589 92350 32427 94884 1151 00 02 18062 08962 28277 7637 23219 82948 94970 24822 69975 88267 06 41 95012 54914 33362 9323 68350 25774 11398 80287 96189 1720 50 97 709 15664 29515 88720 4422 58745 22987 39911 5931 348 39475 38857 14361 8373 794 15408 52210 06990 831 24413 50490 55657 3865 18629 674.12 40980 883 34072 97473 17247 6751 5-i6 00358 642 04542 76463 56891 48223 900 30405 68379 83974 592 21999 59516 02349 49 758 10';93 33339 132 27195 31238 90 277 31662 21438 8303 53 653 899 93526 ,,0 9766 36693 45393 12 953 20492 73 33132 8882 94730 74353 68 338 101 01 45799 18735 239 703 53 02 125~ 80780 040 94.6 20 05 09983 98 2271 67 92 A18

16631 32 88 16815 2732 848 817 32523 4196 44\";37 95773 05 00 5083 968 052 91491 6038 i974.6 66 23 5970 912 318 29686 0338 78919 33309 14.349 67 44 67 17361 37680 39908 6554 160 514 33072 6033 87820 33278 1 36 15665 62825 73823 2152 933 806 08930 8500 46920 03931 57047 88 534\";5 45454 52872 88815 99378 74.426 08002 39917 04508 31355 05607 91284 68833 25570 38818 66092 09066 26504 74.212. 92648 65642 35216 56302 7314'; 88652 88970 74492 51805 16834 42238 40742 20979 12151 16553 51125 79375 97596 16296 34191 16153 57802 4.6949 96783 81959 2:'340 25549 3\";537 86064. 29472 47689 05974 52468 0600'; 78095 89723 37621 64482 Q080 00033 67107 77510 70625 28725 22.';S7 66999 39117 33732 29400 24.813 65536 60397 50501 2::.581 84979 51281 :. 7937 60563 71945 93454 33310 06116 95240 15957 16572 85065 55612 46583 S1973 05810 61023 52757 2.5263 97403 48526 68995 43805 33386 70925 4.-i657 2:.:99 84';63 97:'61 0326'; 88525 42786 05269 07896 91340 32140 07403 27022 31949 054.62 32305 :4486 80644 25471 25650 12682 05224 2';010 19924 09538 83991 06878 43942 41867 71795 99533 54.238 91227 96131 89303 28609 84067 391'; 7 21361 4.8542 29891 34405 83556 50001 94851 05418 \";1575 72163 73923 91903 45233 57202 14951 91696 85762 55390 89632 81';06 66499 25786 ,;9071 42627 05184 94142 56087 82790 2750'; 70225 04142 10573 53115 21942 36152 17095 02330 94617 23772 75928 37169 30352 27072 38351 00959 15765 26759 05422 39782 25299 8';387 50585 70331 30502 79924 95343 78675 80377 7,;)01 00275 93448 11508 81223 ~0055 5~690 0'; 052 78128 02510 17869 13442 21163 47908 37449 64.995 57015 32989 86482 78662 61796 41870 66938 93654 75567 4.6515 05908 33329 22.532 50076 53412 ';2865 45349 07901 59194 50245 34971 05250 30986 8.1846 26695 58353 ';4160 5167,; 66227 05174 83531 12535 81073 49106 63798 32906 09785 59089 648:.6 92520 95434 58861 74818 71 98782 52567 \";3218 33941 97526 62570 41001 18534 35909 81250 82486 46891 94964. 67632 64297 92063 40202 51202 15475 21385 63175 09060 13564 88298 86367 26123 20203 88732 52689 51275 60336 53.:l58 86355 92237 89534 25651 85205 88022 52799 28225 ';3937 01221 25560 07100 76020 39560 2612.3 71899 98442 37543 12133 14645 97656 06486 16275 11977 74014 47348 76310 98227 21824 68335 55758 46609 09013 73373 79725 03852 78095 85636 33982 07785 16764 21216 34648 2.8039 1~367 17568 65651 38358 83325 99704 80799 56613 91511 03362 03129 12856 63863 99447 75647 53203 72811 22717 15656 06177 15387 27698 23235 64708 70959 06216 15152 55230 79556 36..;78 17075 02753 80703 07832 97548 58408 13261 92508 29068 12293 14186 43834 7357:' 19636 82163 60859 23932 8267'; 16268 28395 76123 22478 55543 09915 25835 32534 69927 43092 1],951 78406 09443 82558 59037 96306 67006 79180 35275 35071 56148 33300 97901 90816 36692 90183 70426 11601 3~925 03237 62247 14972 17349 76036 86654 88717 86591 78077 65680 90053 12918 93872 35503 38534 86273 6:'657 43772 0,,098 37890 ,;5430 93017 57306 76536 28117 81;;82 63282 36600 18098 01715 61582 95787 71255 87288 ~9199 04379 47625 51132 42579 86537 46370 25739 Abridged from Handbook afTabIes for Probability andStatistics, 2nd ed\" edited by William K Beyer (Cleveland:The Chemical Rubber Company, 1968), Used by permission ofThe Chemical Rubber Company,

Distribution of Chi Square Probability df 99 .98 .95 .90 .80 70 50 1 .03157 .0404 .00393 0158 0642 148 455 2 0201 185 .103 .211 446 713 1.386 3 115 429 .352 584 1424 2. 366 4 .297 .752 .711 1064 1.005 2.195 3.357 5 .554 1 610 1. 649 3. 000 4.351 1 145 2.343 6 872 1 134 1.635 2.204 3070 3828 5.348 7 1.239 1564 2.167 2. 833 3.822 4671 6.346 8 1.646 2032 2.733 3.490 4.594 5528 7.344 9 2.088 2532 3. 325 4.168 5.380 6.393 8.343 10 2.558 3.059 3.940 4865 6.179 7.267 9.342 11 3053 3.609 4.575 5578 6989 8.148 10341 12 3571 4.178 5.226 6.304 7807 9034 11340 13 4.107 4.765 5.892 7042 8.634 9926 12.340 14 4. 660 5.368 6.571 7790 9467 10.821 13339 15 5.229 5. 985 7..261 8547 10. 307 11 . 721 14.339 16 5812 6.614 7962 9. 312 11.152 12. 624 15.338 17 6408 7.255 8672 10. 085 12002 13.531 16338 18 7015 7.906 9.390 10865 12857 14440 17338 19 7.633 8.567 10. 117 11 651 13.716 15. 352 18.338 20 8. 260 9.237 10.851 12.443 14578 16.266 19337 21 8.897 9.915 11 591 13.240 15.445 17. 182 20.337 22 9542 10600 12338 14.041 16.314 18101 21.337 23 10.196 11 293 13091 14.848 17187 19.021 22337 24 10.856 11 . 992 13.848 15659 18062 19943 23337 25 11524 12. 697 14.611 16.473 18. 940 20. 867 24.337 26 12.198 13409 15379 17.292 19. 820 21 . 792 25336 27 12879 14125 16.151 18.114 20.703 22.719 26336 28 13565 14847 16.928 18.939 21588 23.647 27.336 29 14.256 15.574 17.708 19.768 22.475 24.577 28336 30 14.953 16306 18.493 20.599 23.364 25.508 29.336 continued 2/ - \\For larger values of df, the expression \\ 2df 1 may be used as a normal deviate with unit variance, remembering that the probability of -/ corresponds with that of a single tail of the normal curve A20 Source I am grateful to the Literary Executor of the late Sir Ronald A. Fisher, FRS, to Dr. Frank Yates, FRS, and to Longman Group Ltd, London, for permission to reprint Table IV from their book Statistical Tables for Biological, Agricultural, and Medical Research (6th Edition, 1974)

Probability df 30 .20 10 05 02 01 001 1 1. 074 1.642 2706 3.841 5412 6. 635 10. 827 2 2408 3.219 4605 5.991 7.824 9.210 13.815 3 3665 4.642 6251 7.815 9837 11.341 16268 4 4878 5.989 7779 9488 11 668 13.277 18465 5 6064 7289 9236 11070 13.388 15086 20. 517 6 7231 8558 10645 12592 15033 16812 22457 7 8383 9.803 12017 14067 16622 18475 24.322 8 9.524 11030 13362 15.507 18.168 20090 29. 125 9 10. 656 12242 14.684 16. 919 19. 679 21666 27.877 10 11.781 13.442 15987 18. 307 21 161 23209 29.588 11 12899 14.631 17275 19675 22618 24. 725 31.264 12 14011 15812 18549 21.026 24054 26217 32.909 13 15.119 16.985 19.812 22. 362 25.472 27688 34.528 14 16222 18.151 21064 23.685 26.873 29. 141 36.123 15 17322 19.311 22307 24996 28.259 30.578 37..697 16 18. 841 20465 23542 26.296 29. 633 32000 39.252 17 15.511 21..615 24.769 27.587 30.995 33409 40.790 18 20601 22760 25989 28869 32346 34 . 805 42 . 312 19 21.689 23.900 27..204 30.144 33.687 36191 43.820 20 22..775 25038 28.412 31410 35020 37.566 45.315 21 23858 26. 171 29615 32 . 671 36343 38 . 932 46 . 797 22 24939 27301 30813 33924 37.659 40.289 48.268 23 26.018 28.429 32007 35.172 38.968 41.638 49.728 24 27.096 29.553 33196 36415 40.270 42 . 980 51179 25 28.172 30.675 34382 37652 41566 44.314 52.620 26 29.246 31.795 35563 38.885 42.856 45642 54052 27 30.319 32.912 36741 40 . 113 44.140 46.963 55.476 28 31.391 34.027 37916 41.337 45419 48.278 56.893 29 32461 35.139 39087 42.557 46693 49.588 58.302 30 35530 36250 40.256 43.773 47.962 50892 59.703

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 02 .0793 .0832 .0871 .0910 0948 0987 .1026 .1064 1103 .1141 0.3 .1179 1217 .1255 .1293 .1331 1368 .1406 .1443 1480 .1517 04 1554 1591 1628 1664 .1700 .1736 1772 1808 1844 .1879 0.5 1915 1950 ,1985 2019 .2054 .2088 2123 2157 .2190 .2224 06 2257 .2291 2324 .2357 .2389 2422 2454 .2486 .2517 2549 07 .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 .3389 1.0 3413 3438 .3461 3485 .3531 3554 3577 3365 .3621 11 3643 . 3665 .3686 3708 .3508 . 3749 .3770 3790 3599 3830 1 2 .3849 .3869 3888 .3907 .3729 3944 . 3962 .3980 .3810 4015 1.3 4032 4049 4066 4082 .3925 4115 4131 4147 3997 4177 14 4192 4207 4222 4236 4099 4265 4279 4292 4162 4319 1.5 4332 4345 4357 4370 4251 4394 4406 .4418 4306 4441 1.6 4452 4463 4474 4484 4382 4505 4515 4525 4429 4545 1.7 4554 4564 4573 4582 4495 4599 4608 4616 4535 4633 1.8 4641 4649 4656 4664 4591 4678 4686 4693 4625 4706 19 4713 4719 4726 4732 4671 4744 4750 4756 4699 4767 2.0 4772 4778 4783 4788 4738 4798 4803 4808 4761 4817 2.1 4821 4826 4830 4834 4793 4842 4846 4850 4812 4857 2.2 4861 4864 4868 4871 4838 4878 4881 4884 4854 4890 2,3 4893 4896 4898 4901 4875 4906 4909 4911 4887 4916 24 4918 4920 4922 4925 4904 4929 4931 4932 4913 4936 25 4938 4940 4941 4943 4927 4946 4948 4949 4934 4952 2.6 4953 4955 4956 4957 4945 4960 4961 4962 4951 4964 2. 7 4965 4966 4967 4968 4959 4970 4971 4972 4963 4974 2,8 4974 4975 4976 4977 4969 4978 4979 4979 4973 4981 2.9 4981 4982 4982 4983 4977 4984 4985 4985 4980 4986 30 4987 4987 4987 4988 4984 4989 4989 4989 4986 4990 4988 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. A22

Estimated Sampling Error How to use this table: Find the intersection be- The sampling error is estimated at plus or minus tween the sample size and the approximate per- 4,9 percentage points. The confidence interval. centage distribution of the binomial in the sample. then, is between 55.1 percent and 64.9 percent. The number appearing at this intersection repre- We would estimate (95 percent confidence) that sents the estimated sampling error, at the 95 per- the proportion of the total population who would cent confidence level, ex'Pressed in percentage say yes is somewhere within that interval. points (plus or minus) . Example: In the sample of 400 respondents, 60 percent answer yes and 40 percent answer no. Sample 50/50 60/40 Binomial Percentage Distribution 80/20 90/10 Size 10 9. 8 8 6 100 71 6.9 70/30 5.7 42 200 58 5.7 9.2 4.6 3.5 300 5 4.9 65 4 3 400 45 44 53 36 27 500 41 4 46 33 24 600 38 3.7 41 3 23 700 3.5 3.5 37 28 21 800 3.3 33 35 27 2 900 3.2 31 32 25 19 3 3 31 24 1.8 1000 29 28 2.9 23 1.7 1100 2.8 27 2.8 2.2 1.7 1200 2.7 26 26 21 1.6 1300 2.6 25 2.5 2.1 1.5 1400 2.5 24 24 2 1.5 1500 24 24 24 1.9 1.5 1600 24 2.3 2.3 19 14 1700 2.3 2.2 2.2 1.8 14 1800 2.2 2.2 2.2 1.8 1.3 1900 2.1 2000 2 A23

Twenty Questions aJournalist Should Ask about Poll Results* Sheldon R. Gawiser G. Evans Witt For journalists and for pollsters, questions are the many people in addition to those interviewed- most frequently used tool for gathering informa- even the opinions of all Americans, The results of tion. Here are 20 questions for the journalist to ask an unscientific poll tell you nothing more than sim- the pollster before reporting poll results. This publi- ply what those respondents say. cation is designed to help the working journalist do a thorough, professional job covering polls; it is not With these 20 questions in hand, the journalist a primer on how to conduct a public opinion survey. can seek the facts to decide just how to handle every poll that comes across the news desk This work is about polls that are \"scientific.\" each day. A number of the questions will help you decide 1. Who did the poll? What poIling firm, re- whether or not a poll is a \"scientific\" one worthy search house, political campaign, corporation, or of coverage-or an unscientific survey that may be other group conducted the poll? This is always the entertaining, but meaningless, first question to ask. Of the scientific polls, the Gallup Poll is proba- If you don't know who did the poll, you can't bly the best known and one of the oldest. There are get the answers to all the other questions listed many other excellent surveys conducted by repu- here. And if the person providing poll results can't table firms, as well. or won't tell you who did it, serious questions must be raised about the reliability and truthfulness of The unscientific surveys are less well-known, if the results being presented. quite widespread. There are 900-number call-in polls, man-on-the-street surveys, shopping mall In most cases, reputable poIling firms will pro- polls, and even the classic toilet tissue poll featuring vide you with the information you need to evaluate pictures of the candidates on each sheet. the survey. And because reputation is important to a quality firm, a professionally conducted poll will The major distinguishing difference between avoid many errors. scientific and unscientific polls is who picks the re- 2. Who paid for the poll and why was it done? spondents for the survey. In a scientific poll, the You must know who paid for the survey, because pollster identifies and seeks out the people to be in- that tells you-and your audience-who thought terviewed. In an unscientific poll, the respondents these topics were important enough to spend usually \"volunteer\" their opinions, selecting them- money finding out what people think. And that selves for the polL goes to the whole issue of why the poll was done. The results of the well-conducted scientific poll Polls usually are not conducted for the good can provide a reliable guide to the opinions of of the world. They are conducted for a reason- either to gain helpful information or to advance a * Published as a brochure by the National Council on particular cause, Public Polls and reprinted here with permission, A24

Twenty Questions aJoumalist Should Ask about Poll Results III A25 It may be the news organization wants to de- 4. How were those people chosen? The key velop a good story, It may be the politician wants to reason that some polls reflect public opinion accu- be re-elected, It may be that the corporation is try- rately and other polls are unscientific junk is how ing to push sales of its new product. Or a special in- the people were chosen to be interviewed. terest group may be trying to prove that its views are the views of the entire country. In scientific polls, the pollster uses a specific method for picking respondents. In unscientific AIl are legitimate reasons for doing a poll. polls, the person picks himself to participate, The important issue for you as a journalist is whether the motive for doing the poll CTeates such The method pollsters use to pick interviewees serious doubts about the validity of the poll results relies on the bedrock of mathematical reality: when that the results should not be publicized. the chance of selecting each person in the target An example of suspect polls are private polls population is known, then and only then do the re- conducted by a political campaign. These polls are sults of the sample survey reflect the entire popula- conducted solely to help the candidate win-and tion. This is called a random sample or a probability for no other reason. The poll may have terrifically sample, This is the reason that interviews with 1000 slanted questions or a strange sampling methodol- American adults can accurately reflect the opinions ogy, all with a tactical campaign purpose. For ex- of more than 185 million American adults. ample, the campaign may be testing out new slo- gans or a new stance on a key issue or a new attack Most scientific samples use special techniques on the opponent. to be economically feasible. For example, some But since accurately gauging the general pub- sampling methods for telephone interviewing do lic's sentiments is not the goal of the candidate's not just pick randomly generated telephone num- poll, the results should be reported with great care, bers, Only telephone exchanges that are known to Likewise, reporting on a survey by a special in- contain working residential numbers are selected- terest group is tricky. For example, an environmen- to reduce the number of wasted calls, tal group trumpets a poll saying the American people support strong measures to protect the envi- But even a random sample cannot be purely ronment. That may be true, but the poll may have random in practice since some people don't have been conducted for a group with definite views. phones, refuse to answer, or aren't home, That may have swayed the question wording, the 5. What area: nation, state, or region-or timing of the poll, the group interviewed, and the what group: teachers, lawyers, Democratic order of the questions, You should examine the voters, etc.-were these people chosen from? poll to be certain that it accurately samples public Although the results of probability samples can be opinion-and does not simply push a single projected to represent the larger population from viewpoint. which they were selected. the characteristics of the 3. How many people were interviewed for larger population must be specified; for example, the survey? This is another basic piece of infor- you should know if a sample represents all people mation you should have. Because polls give ap- in the United States or just those in one state or proximate answers, the more people interviewed in one city. In another example. the case of telephone a scientific poll, the smaller the error due to the size samples, the population is that of people living in of the sample, all other things being equaL households with telephones. For most purposes. However, a common trap to avoid is that \"more telephone households may be similar to the gen- is automatically better.\" It is absolutely true that the eral population. But. if you were reporting a poll more people interviewed in a reliable survey, the on what it was like to be poor or homeless, this smaller the sampling error-all other things being would not be the appropriate sample. Remem- equal. But, other factors may be more inlportant in ber, the use of a scientific sampling technique judging the quality of a survey. does not mean that the correct population was interviewed. It is absolutely critical to know from which group the interviewees were chosen.

A26 Appendix G: Twenty Questions aJournalist Should Ask about Poll Results For example, a survey of business people can viewed were a random sample of 1000 delegates as reflect the opinions of business people-but not of compared to their being the only 100 out of the all adults. Only if the interviewees were chosen 1000 vvilling to participate. The same potential for from among all American adults can the poll reflect distorted results occurs if some of the delegates who the opinions of all American adults. were in the sample were never actually contacted. 8. When was the poll done? Events have a Political polls are especially sensitive to this dramatic impact on poll results. Your interpretation issue. of a poll should depend on when it was conducted relative to key events. Even the freshest poll results In pre-primary and pre-election polls, how me can be overtaken by subsequent events. The Presi- people are chosen as the base for poll results is criti- dent may have given a stirring speech to the na- caL A poll of all adults, for example, is not very tion, the stock market may have crashed, or an oil useful on a primary race where only 25 percent of tanker may have sunk, spilling millions of gallons the registered voters actually turn out. So look for of crude on beautiful beaches. polls based on registered voters, \"likely voters,\" pre- vious primary voters, and such. These distinctions Poll results that are several weeks or months are important and should be included in the story. old may be perfectly valid as history, but are not al- ways newsworthy. One of the most variable aspects of polling is 9. How were the interviews conducted? trying to figure out who actually is going to vote. There are three main possibilities: in person at 6. Are the results based on the answers of all home, by telephone, or by maiL the people interviewed? One of the easiest ways to misrepresent the results of a poll is to re- Most surveys are now conducted by telephone, port the answers of only a subgroup. For example, with the calls made from a central interviewing there is usually a substantial difference between center. However, some surveys are still conducted the opinions of Democrats and Republicans on by sending interviewers into people's homes to campaign-related matters. Reporting the opinions conduct the interviews. of only Democrats in a poll purported to be of all adults would substantially misrepresent the results. Some surveys are conducted by mail. In sci- entific polls, tlle pollster picks the people to receive Poll results based on Democrats must be the mail questionnaires. The respondent fills out identified as such and should be reported as repre- the questionnaire and returns it. senting only Democratic opinions. Mail surveys can be excellent sources of infor- Of course, reporting on just one subgroup mation, but it takes weeks to do a mail survey, can be exactly the right course. In polling on a Re- meaning that the results cannot be as timely as publican prin1ary contest, it is the opinions of the a telephone survey. And mail surveys can be sub- Republicans who can vote in the primary that ject to other kinds of errors, particularly low re- count-not those of Democrats who cannot vote sponse rates. In many mail surveys, more people in that GOP contest. fail to participate than do. This makes the results 7. Who should have been interviewed suspect. and was not? You ought to know how many people refused to answer the surveyor were never Surveys done in shopping mails, in stores or contacted. restaurants or on the sidewalk may have their uses for their sponsors, but publishing the results in the The non-response rate is the percentage of media is not among them. These \"man in the people contacted who should have been inter- street\" approaches may yield interesting human in- viewed, but were not. They may have refused at- terest stories, but they should never be treated as if tempts to interview them. Or interviews may not they represent a public opinion poll. have been attempted if people were not home 10. Is this a dial-in poll, a mail-iII poll, or a when the interviewer called. subscriber coupon poll? If the poll you are looking at is a dial-in, mail-in, or coupon poll, don't The results of a survey should be judged very differently if the 100 convention delegates inter-

Twenty Questions aJournalist Should Ask about Poll Results A27 report the results because the respondents are self- Pollsters express the size of the uncertainty selected. These pseudo-polls have no validity. Re- caused by using a sample at a \"confidence level.\" member, the purpose of a poll is to draw conclu- This means a sample is likely to be within so many sions about the population, not about the sample. points of the results one would have gotten if an In these pseudo-polls there is no way to project the interview was attempted with the entire target results to any larger group. Scientific polls usually population. They usually say this with 95% show different results than pseudo-polls. confidence. The 900-number dial-in polls may be fine for Thus, for example, a \"3 percentage point mar- deciding whether or not Larry the Lobster should gin of error\" in a national poll means that if the be cooked on Saturday Night Live or even for dedi- attempt was made to interview every adult in the cated fans to express their opinions on who is the nation with the same questions in the same way at greatest quarterback in the National Football about the same time as the poll was taken, the poll's League, but they have only entertainment value. answers would fall vvithin plus or minus 3 per- There is no way to tell who actually called in, how centage points of the complete count result 95% old they are, or how many tinles each person of the time. called. Please note that this does not address the issue Never be fooled by the number of responses. In of whether or not people cooperate with the sur- some cases, a few people call in thousands of times. vey, or if the questions are understood, or if any Even if 500,000 calls are tallied, no one has any other methodological issue exists. The sampling er- real knowledge of what the results mearL If big ror is only the portion of the potential error in a numbers impress you, remember that the Literary survey introduced by using a sample rather than Digest's non-scientific sample of 12,000,000 people the entire population. Sampling error tells us said Landon would beat Roosevelt. nothing about the refusals or those consistently unavailable for interview; it also tells us nothing The subscriber coupon polls are just as bad. In about the biasing effects of a particular question these cases, the magazine or newspaper includes a wording or the bias a particular interviewer may coupon to be mailed in with the answers to the inject into the interview situation. questions. Again, there is no way to know who re- sponded and how many times. These results are Remember that the sampling error margin not projectable even to the subscribers of the publi- applies to each figure in the results-it is at least cation that includes the coupon. 3 percentage points plus or minus for each one. 11. What is the sarnpliIlg error for the poll Thus, in a poll question matching two candidates results? Interviews with a scientific sample of for President, both figures are subject to sampling 1000 adults can accurately reflect the opinions of error. more than 185 million American adults. That means interviews attempted with all 185 million This raises one of the thorniest problems in the adults-if such were possible-would give ap- presentation of poll results. For a horse-race poll, proximately the same results as a well-conducted when is one candidate really ahead of the other? survey. Certainly, when the gap between the two can- But what happens if another carefully done didates is more than twice the error margin- poll of 1000 adults gives slightly different results 6 percentage points in our example-you can say from the first survey? Neither of the polls is with confidence that the poll says Candidate A is \"wrong.\" This range of results is called the error leading Candidate B. due to sampling, often called the margin of error. And just as certainly, if the gap between the This is not an \"error\" in the sense of making a two candidates is less than error margin then you mistake. Rather, it is a measure of the possible should not say that one candidate is ahead of the range of approximation in the results because a other. Then, the race is \"close\"; the race is \"roughly sample was used. even\"; or there is \"little difference between the candidates.\"

A28 \" Appendix G: Twenty Questions aJournalist Should Ask about Poll Results And bear in mind that when subgroup results 14. In what order were the questions asked? are reported-women or blacks, or young people- Sometimes the very order of the questions can the sampling error margin for those figures is have an impact on the results. Often that impact is greater than for results based on the survey as a intentional; sometimes, it is not. The impact of or- whole. der can often be subtle, 12. What other kinds of mistakes can skew poll results? The margin of sampling error is just During troubled economic times, for example, one source of inaccuracy in a poll and not necessar- if people are asked what they think of the economy ily the greatest source of error; we use it because before they are asked their opinion of the presi- it's the only one that can be quantified. Question dent, the presidential popularity rating will proba- phrasing and ordering are also a likely source of bly be lower than if you had reversed the questions, flaws, That's why you need to examine poll ques- And in good economic times, the opposite is true, tions for bias. In political polls, campaign consultants often You should always ask if the poll results have ask a series of questions about various issue posi- been \"weighted.\" This process is usually used to tions of the candidates-or various things that account for unequal probabilities of selection and could be said about the candidates. After these to correct demographics in the sample, However, questions are asked, the horse-race question is you should be aware that a poll can also be unduly asked, usually for the second time in the poll. This manipulated by weighing to produce some desired second horse-race question is then examined to see result. if the questions about issues and positions swayed any opinions, This may be a good way to test is- And there are other possible sources of error. sues, It is a poor way to test the candidates' true These include issues such as inadequate inter- standings in the public's mind, viewer training and supervision, data processing errors, and other operational problems. Profes- What is important here is whether the ques- sional polling operations are less subject to these tions that went before the important question affect problems than volunteer-conducted polls, which the results. If the poll asks questions about abortion are usually less trustworthy. just before a question about an abortion ballot 13. What questions were asked? You must measure, those previous questions could sway the find out the exact wording of the poll questions. results. Why? Because the very wording of questions can 15. What other polls have been done on this make major differences in the results, topic? Do they say the same thing? If they are different, why are they different? Results of other Perhaps the best test of any poll question is polls-a candidate's opponent. public polls, media your reaction to it. On the face of it. does the ques- polls, or whatever-should be used to check and tion seem fair and unbiased? Does it present a contrast poll results you have in hand. balanced set of choices? Would people you know be able to answer the question? If the polls differ. first check the timing of when the intervievving was done. The different poll re- On sensitive questions-such as abortion-the sults may demonstrate a swing in public opinion, complete wording of the question should probably be included in your story But at the very least, you If the polls were done about the same time, and must have the exact wording as you are preparing no other factor seems to explain the disagreement, the story. go to each poll sponsor and ask for an explanation of the differences. Conflicting polls often make It may well be worth\\·vhile to compare the re- good stories. sults of several different polls from different organi- 16. So, the poll says the race is all over. What zations on these sensitive questions, In that case, now? No matter how good the poll, no matter you should be careful to compare both the results how wide the margin, no matter how big the and the exact wording of the questions. sample, a pre-election poll does not show that

Twenty Questions aJournalist Should Ask about Poll Results III A29 one candidate has the race \"locked up.\" Things 18. So I've asked all the questions. The an- change-often and dramatically in politics, swers sound good. The poll is coneet, right? 17. Was the poll part of a fund-raising effort? Usually, yes. However, remember that the laws of This is another example of a pseudo-poll. An orga- chance alone say that the results of one poll out of nization sends out a survey form to a large list of 20 may be skewed away from the public's real people. The last question usually asks for a contri- views just because of sampling error. bution from the respondent. He or she is expected 19. With all these potential problems, should to send money to support the organization or pay we ever report poll results? Yes. Because repu- for tabulating the survey. table polling organizations consistently do good work In spite of the difficulties, the public opinion The people who respond to these types of sur- survey, correctly conducted, is still the best objec- veys are likely to be those who agree with the or- tive measure of the state of the views of the public ganization's goals. Also, the questions are usually 20. Is this poll worth reporting? If the poll loaded and the results meaningless. was conducted correctly, and you have been able to obtain the information outlined here, your news This technique is used by a wide variety of judgment and that of your editors should be ap- organizations from political parties and special- plied to polls, as it is to every other element of a interest groups to charitable organizations. Again, story. if the poll in question is part of a fund-raising pitch, pitch it-in the waste basket.



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abstract (1) A summary of a research article. The seem consistently better or worse than they really abstract usually begins the article and states the areo (3) What a nail looks like after you hit it purpose of the research, the methods used, and crooked. (If you drink don't drive.) the major findingso See Chapter 17. (2) An expen- sive painting you may not understand but may bivariate analysis The analysis of two variables si- need to appreciate if you want to impress people multaneously, for the purpose of determining the at the art museum. empirical relationship between them. The con· struction of a simple percentage table or the com- analysis of variance (ANOVA) Method of analysis putation of a simple correlation coefficient are ex- in which cases under study are combined into amples of bivariate analyses. See Chapter 14 for groups representing an independent variable, and more on this topic. the extent to which the groups differ from one another is analyzed in terms of some dependent Bogardus social distance scale (1) A measure- variable. Then, the extent to which the groups do ment technique for determining the vvillingness of differ is compared vvith the standard of random people to participate in social relations-of vary- distribution. See Chapter 16. ing degrees of closeness-vvith other kinds of people. It is an especially efficient technique in anonymity Anonymity is guaranteed in a research that one can summarize several discrete answers project when neither the researchers nor the vvithout lOSing any of the original details of the readers of the findings can identify a given reo datao See Chapter 60 (2) The distance you might sponse vvith a given respondent. See Chapter 3. be prepared to travel to see a rarely shown black- and-white movie of good a!' Humphrey.* attributes Characteristics of people or things. See variables and Chapter 1. mse-oriented analysis (1) An analysis that aims to understand a partkular case or several cases by average An ambiguous term generally suggesting looking closely at the details of each. See Chapter typical or normal-a central tendencyo The mean, 13. (2) A private investigator'S billing systemo median, and mode are specific examples of math· ematical averages. See Chapter 14. case study The in-depth examination of a single in- stance of some social phenomenon, such as a vil- axial coding A reanalysis of the results of open cod· lage, a family, or a juvenile gang. See Chapter 10. ing in Grounded Theory Method, aimed at identi- fying the important, general concepts. See also closed-ended questions Survey questions in which selective coding and Chapter 13. the respondent is asked to select an answer from among a list provided by the researcher. Popular bias (1) That quality of a measurement device that in survey research because they provide a greater tends to result in a misrepresentation of what is uniformity of responses and are more easily pro- being measured in a partkular direction. For ex- cessed than open-ended questions. See Chapter 9. ample, the questionnaire item \"Don't you agree that the president is doing a good job?\" would be * Supplemental definitions marked vvith an asterisk biased in that it would generally encourage more have been provided courtesy of James Instone, Uni- favorable responseso See Chapter 90 (2) The thing versity of New England, Armidale, NSW, Australia. inside you that makes other people or groups Gl

G2 Glossary cluster sampling (I) A multistage sampling in tion and its pal, operationalization. (2) Sexual re- which natural groups (clusters) are sampled ini- production among intellectuals. tially, vvith the members of each selected group being subsampled aftervvard. For example, you confidence interval (1) The range of values vvithin might select a sample of US. colleges and univer- which a population parameter is estimated to lie. sities from a directory, get lists of the students at A survey, for example, may show 40 percent of a all the selected schools, then draw samples of sample favoring Candidate A (poor devil). Al- students from each. See Chapter 7. (2) Pavving though the best estimate of the support existing around in a box of macadamia nut clusters to take among all voters would also be 40 percent, we all the big ones for yourself. would not eX1lect it to be exactly that. We might, therefore, compute a confidence interval (such as codebook (I) The document used in data process- from 35 to 45 percent) within which the actual ing and analysis that teIls the location of different percentage of the population probably lies. Note data items in a data file. Typically, the codebook that we must specify a confidence level in connec- identifies the locations of data items and the tion vvith every confidence interval. See Chapters meaning of the codes used to represent different 7 and 16. (2) How close you dare to get to an attributes of variables. See Chapter 14. (2) The alligator. document that cost you 3S box tops just to learn that Captain Marvelous wanted you to brush your confidence level (1) The estimated probability that teeth and always tell the truth. (3) The document a population parameter lies vvithin a given confi- that allows CIA agents to learn that Captain Mar- dence interval. Thus, we might be 95 percent velous wants them to brush their teeth. confident that between 35 and 45 percent of all voters favor Candidate A. See Chapters 7 and 16. coding (I) The process whereby raw data are trans- (2) How sure you are that the ring you bought formed into standardized form suitable for ma- from a street vendor for $10 is really a three-carat chine processing and analysis. See Chapter I L diamond. (2) A strong drug you may take when you hab a bad code. confidentiality A research project guarantees confidentiality when the researcher can identify a cohort study A study in which some specific sub- given person's responses but promises not to do so population, or cohort, is studied over time, al- publicly. See Chapter 3. though data may be collected from different members in each set of observations. For example, constant comparative method (1) A component a study of the occupational history of the class of of the Grounded Theory Method in which obser- 1970, in which questionnaires were sent every vations are compared vvith one another and with five years would be a cohort study. See Chapter 4 the evolving inductive theory. See Chapter 13. for more on this topic (if you want more) . See also (2) A blind-dating technique. longitudinal study. panel study, and trend study. construct validity The degree to which a measure comparative and historical research The exami- relates to other variables as expected vvithin a sys- nation of societies (or other social units) over time tem of theoretical relationships. See Chapter 5. and in comparison with one another. See Chapter I L content analysis The study of recorded human communications, such as books, websites, paint- completion rate See response rate. ings, and laws. See Chapter 1L concept mapping (I) The graphical display of con- content validity The degree to which a measure cepts and their interrelations, useful in the formu- covers the range of meanings included vvithin a lation of theory. See Chapter 13. (2) A masculine concept. See Chapter 5. technique for finding locations by logic and vvill, without asking for directions. contingency question A survey question intended for only some respondents, determined by their conceptualization (1) The mental process whereby responses to some other question. For example, fuzzy and imprecise notions (concepts) are made all respondents might be asked whether they be- more specific and precise. So you want to study long to the Cosa Nostra, and only those who said prejudice. What do you mean by \"prejudice\"? Are yes would be asked how often they go to com- there different kinds of prejudice? What are they? pany meetings and pimics. The latter would be a See Chapter 5, which is all about conceptualiza- contingency question. See Chapter 9.

Glossary G3 contingency table (I) A format for presenting the cross-sectional study A study based on observa- relationships among variables as percentage distri- tions representing a single point in time. Con- butions. See Chapter 14. (2) The card table you trasted vvith a longitudinal study. See Chapter 4. keep around in case your guests bring their seven kids with them to dinner. curvilinear regression analysis A form of regres- sion analysis that allows relationships among vari- continuous variable A variable whose attributes ables to be expressed vvith curved geometric lines form a steady progression, such as age or income. instead of straight ones. See Chapter 16. Thus, the ages of a group of people might include 21, 22, 23, 24, and so forth and could even be debriefing (1) Interviewing subjects to learn about broken down into fractions of years. Contrast this their experience of participation in the project. Es- vvith discrete variables, such as gender or religious pecially important if there's a possibility that they affiliation, whose attributes form discontinuous have been damaged by that participation. See chunks. See Chapter 14. Chapter 3. (2) Pulling someone's shorts down. Don't do that. It's not nice. control group (1) In experimentation, a group of subjects to whom no experimental stimulus is ad- deduction (1) The logical model in which specific rninistered and who should resemble the experi- eX1lectations of hypotheses are developed on the mental group in all other respects. The compari- basis of general principles. Starting from the gen- son of the control group and the experimental eral principle that all deans are meanies, you might group at the end of the experiment points to the anticipate that this one won't let you change effect of the experimental stimulus. See Chapter courses. This anticipation would be the result of S. (2) American Association of Managers. deduction. See also induction and Chapters 1 and 2. (2) What the Internal Revenue Service said your control variable See test variable. good-for-nothing moocher of a brother-in-law technically isn't. (3) Of a duck. conversation analysis (CA) A meticulous analysis of the details of conversation, based on a complete dependent variable (1) A variable assumed to de- transcript that includes pauses, hems, and also pend on or be caused by another (called the inde- haws. See Chapter 13. pendent variable). If you find that income is partly a function of amoum offorlllal education, income is correlation (1) An empirical relationship between being treated as a dependent variable. See Chap- two variables such that (a) changes in one are as- ter L (2) A vvimpy variable. sociated vvith changes in the other or (b) particular attributes of one variable are associated with par- descriptive statistics Statistical computations de- ticular attributes of the other. Thus, for example, scribing either the characteristics of a sample or we say that education and income are correlated in the relationship among variables in a sample. that higher levels of education are associated vvith Descriptive statistics merely summarize a set of higher levels of income. Correlation in and of itself sample observations, whereas inferential statistics does not constitute a causal relationship between move beyond the description of specific observa- the two variables, but it is one criterion of causal- tions to make inferences about the larger popula- ity. See Chapter 4. (2) Someone you and your tion from which the sample observations were friend are both related to. drawn. See Chapter 16. cost-benefit studies Studies that determine dimension A specifiable aspect of a concept. \"Reli- whether the results of a program can be justified giosity,\" for example, might be specified in terms by its expense (both financial and other). See of a belief dimension, a ritual dimension, a devo- Chapter 12. tional dimension, a knowledge dimension, and so forth. See Chapter 5. criterion-related validity The degree to which a measure relates to some external criterion. For ex- discrete variable (1) A variable whose attributes ample, the validity of College Board tests is shown are separate from one another, or discontinuous, in their ability to predict the college success of stu- as in the case of gender or religiolls affiliation. Con- dents. Also called predictive validity. See Chapter 5. trast this with continuous variables, in which one attribute shades off into the next. Thus, in age cross-case analysis An analysiS that involves an (a continuous variable), the attributes progress examination of more than one case; this can be steadily from 21 to 22 to 23, and so forth, whereas either a variable-oriented or case-oriented analy- there is no progression from male to female in the sis. See Chapter 13.

G4 Glossary case of gendeL See Chapter 14. (2) A variable that is a meaningless construct because there is no way doesn't undress in public* for it to be disconfirmed. See Chapter IS. discriminant analysis Method of analysis similar experimental group In experimentation, a group to multiple regression, except that the dependent of subjects to whom an experimental stimulus is variable can be nominaL See Chapter 16. administered. Compare with control group. See Chapter 8. dispersion The distribution of values around some central value, such as an average. The range is a explanation (1) An elaboration model outcome simple example of a measure of dispersion. Thus, in which the original relationship between two we may report that the mean age of a group is 37.9, variables is revealed to have been spurious, be- and the range is from 12 to 89. See Chapter 14. cause the relationship disappears when an ante- cedent test variable is introduced. See Chapter IS. distorter variable In the elaboration modeL a test (2) \"My little sister ate my homework.\" variable that reverses the direction of a zero-order relationship. See Chapter IS. extended case method A technique developed by Michael Burawoy in which case study observa- double-blind experiment An experimental design tions are used to discover flaws in and to improve in which neither the subjects nor the experi- existing social theories. See Chapter 10. menters know which is the experimental group and which is the controL See Chapter 8. external invalidity Refers to the possibility that conclusions drawn from experimental results may ecological fallacy Erroneously drawing conclusions not be generalizable to the \"real\" world. See about individuals solely from the observation of Chapter 8 and also internal invalidity. groups. See Chapter 4. external validation The process of testing the va- elaboration model A logical model for understand- lidity of a measure, such as an index or scale, by ing the relationship between two methods by con- examining its relationship to other, presumed in- trolling for the effects of a third. Developed princi- dicators of the same variable. If the index really pally by Paul Lazarsfeld. The various outcomes of measures prejudice, for example, it should an elaboration analysis are replication, specification, correlate with other indicators of prejudice. See explanation, and interpretation. See Chapter 15. Chapter 6. element (I) That unit of which a population is com- face validity (1) That quality of an indicator that prised and which is selected in a sample. Distin- makes it seem a reasonable measure of some vari- guished from ullits ofanalysis, which are used in able. That the frequency of attendance at religious data analysiS. See Chapter 7. (2) What an ele- services is some indication of a person's religiosity phant eats when it has bad breath.* seems to make sense without a lot of explanation. It has face validity. See Chapter 5. (2) When your EPSEM (equal probability of selection method) face looks like your driver's license photo (rare A sample design in which each member of a and perhaps unfortunate). population has the same chance of being selected into the sample. See Chapter 7. factor analysis A complex algebraic method for de- termining the general dimensions or factors that ethnography A report on social life that focuses on exist within a set of concTete observations. See detailed and accurate description rather than ex- Chapter 16. planation. See Chapter 10. focus group A group of subjects interviewed to- ethnomethodology An approach to the study of gether, prompting a discussion. The technique is social life that focuses on the discovery of implicit, frequently used by market researchers, who ask a usually unspoken assumptions and agreements; group of consumers to evaluate a product or this method often involves the intentional break- discuss a type of commodity, for example. See ing of agreements as a way of revealing their exis- Chapter 10. tence. See Chapter 10. frequency distribution (1) A description of the evaluation research Research undertaken for the number of times the various attributes of a vari- purpose of determining the impact of some social able are observed in a sample. The report that 53 intervention, such as a program aimed at solving a percent of a sample were men and 47 percent social problem. See Chapter 12. were women would be a simple example of a fre- quency distribution. Another example would be ex post facto hypothesis A hypothesis created af- ter confirming data have already been collected. It

Glossary G5 the report that 15 of the cities studied had popula- variable in the explanation of cTime. See dependent tions under 10,000; 23 had populations between variable and Chapter L (2) A variable that refuses 10,000 and 25,000; and so forth. See Chapter 14. to take advice. (2) A radio dial. index A type of composite measure that summa- Geographic Information Systems (GIS) Analyti- rizes and rank-orders several specific observations cal technique in which researchers map quantita- and represents some more general dimension. tive data that descTibe geographic units for a Contrasted \"vith scale. See Chapter 6. graphical display. See Chapter 16. indicator An observation that we choose to consider grounded theory (I) An inductive approach to the as a reflection of a variable we wish to study. study of social life that attempts to generate a the- Thus, for example, attending religious services ory from the constant comparing of unfolding ob- might be considered an indicator of religiosity. See servations. This is very different from hypothesis Chapter 5. testing, in which theory is used to generate hy- potheses to be tested through observations. See induction (1) The logical model in which general Chapter 10. (2) A theory that is not allowed to fly. principles are developed from specific observa- tions. Having noted that Jews and Catholics are Grounded Theory Method (GTM) An inductive more likely to vote Democratic than Protestants approach to research, introduced by Barney are, you might conclude that religious minorities Glaser and Anselm Strauss, in which theories are in the United States are more affiliated with the generated solely from an examination of data DemocTatic party and then your task is to explain rather than being derived deductively. See why. This would be an example of induction. See Chapter 13. also dedudion and Chapters I and 2. (2) The culi- nary art of stuffing ducks. Guttman scale (I) A type of composite measure used to summarize several discrete observations inferential statistics The body of statistical compu- and to represent some more-general variable. See tations relevant to making inferences from find- Chapter 6. (2) The device Louis Guttman weighs ings based on sample observations to some larger himself on. population. See also descriptive statistics and Chapter 16. (Not to be confused with infernal statistics, a hypothesis A specified testable expectation about characterization sometimes invoked by frustrated empirical reality that follows from a more general statistics students.) proposition; more generally, an expectation about the nature of things derived from a theory. It is a informant Someone who is well versed in the so- statement of something that ought to be observed cial phenomenon that you wish to study and who in the real world if the theory is correct. See deduc- is willing to tell you what he or she knows about tion and Chapter 2. it. If you were planning participant observation among the members of a religious sect, you would idiographic An approach to explanation in which do well to make friends with someone who al- we seek to exhaust the idiosyncratic causes of a ready knows about them-possibly a member of particular condition or event. Imagine trying to list the sect-who could give you some background all the reasons why you chose to attend your par- information about them. Not to be confused with ticular college. Given all those reasons, it's difficult a respondent. See Chapter 7. to imagine your making any other choice. By con- trast, see nomothetic. See also Chapter L informed consent A norm in which subjects base their voluntary participation in research projects independent variable (I) A variable with values on a full understanding of the possible risks in- that are not problematical in an analysis but are volved. See Chapter 3. taken as simply given. An independent variable is presumed to cause or determine a dependent vari- institutional ethnography A research technique able. If we discover that religiosity is partly a func- in which the personal experiences of individuals tion of gender-women are more religious than are used to reveal power relationships and other are men-gender is the independent variable and characteristics of the institutions within which religiosity is the dependent variable. Note that any they operate. See Chapter 10. given variable might be treated as independent in one part of an analysis and dependent in another internal invalidity (1) Refers to the possibility that part of it. Religiosity might become an independent the conclusions drawn from experimental results may not accurately reflect what went on in the

G6 Glossary experiment itself. See Chapter 8 and also exte171al agree. Such items may be used in the construction invalidity. (2) What my grandad has and why he of true Likert scales as well as other types of com- wears special \"nappies.\"* posite measures. See Chapter 6. interpretation A technical term used in connection linear regression analysis A form of statistical with the elaboration model. It represents the re- analysiS that seeks the equation for the straight search outcome in which a control variable is dis- line that best descTibes the relationship between covered to be the mediating factor through which two ratio variables. See Chapter 16. an independent variable has its effect on a depen- dent variable. See Chapter 15. log-linear models Data-analysis technique based on specifying models that descTibe the interrela- interval measure A level of measurement descTib- tionships among variables and then comparing ing a variable whose attributes are rank-ordered expected and observed table-cell frequencies. and have equal distances between adjacent attri- See Chapter 16. butes. The Fahrenheit temperature scale is an ex- ample of tlns, because the distance between 17 longitudinal study A study design involving the and 18 is the same as that between 89 and 90. See collection of data at different points in time, as also Chapter 5 and nominalmeasllre, ordinal mea· contrasted with a cross-sectional study. See also sure, and ratio measure. Chapter 4 and cohort study, panel study, and trend study. interview A data-collection encounter in which one person (an interviewer) asks questions of an- macrotheory A theory aimed at understanding the other (a respondent). Interviews may be conducted \"big picture\" of institutions, whole societies, and face-to-face or by telephone. See Chapter 9. the interactions among societies. Karl Marx's ex- amination of the class struggle is an example of item analysis An assessment of whether each of macrotheory. By contrast, see microtheory. See also the items included in a composite measure makes Chapter 2. an independent contribution or merely duplicates the contribution of other items in the measure. manifest content (I) In connection with content See Chapter 6. analysis, the concTete terms contained in a com- munication, as distinguished from latent content. judgmental sampling (I) See purposive sampling See Chapter 1L (2) What you have after a mani- and Chapter 7. (2) A sampling of opinionated fest bursts. people. matching In connection with experiments, the pro- latent content (I) As used in connection with con- cedure whereby pairs of subjects are matched on tent analysis, the underlying meaning of commu- the basis of their sinillarities on one or more vari- nications, as distinguished from their manifest con- ables, and one member of the pair is assigned to tent. See Chapter I L (2) What you need to make a the experimental group and the other to the control latent. group. See Chapter 8. level of significance (I) In the context of tests of mean (I) An average computed by summing the statistical significance, the degree of likelihood values of several observations and dividing by the that an observed, empirical relationship could be number of observations. If you now have a grade attributable to sampling erroL A relationship is point average of 4.0 based on 10 courses, and you significant at the .05 level if the likelihood of its get an F in this course, your new grade point being only a function of sampling error is no (mean) average will be 3.6. See Chapter 14. greater than 5 out of 100. See Chapter 16. (2) The quality of the thoughts you nlight have (2) Height limits on outdoor advertising. if your instructor did that to you. Likert scale A type of composite measure devel- median (I) An average representing the value of the oped by Rensis Likert in an attempt to improve \"nliddle\" case in a rank-ordered set of observa- the levels of measurement in social research tions. If the ages of five men are 16, 17, 20, 54, through the use of standardized response cate- and 88, the median would be 20. (The mean gories in survey questionnaires to deternline the would be 39.) See Chapter 14. (2) The dividing relative intensity of different items. Likert items line between safe driving and exciting drivitlg. are those using such response categories as strongly agree, agree, disagree, and strongly dis- memoing Writing memos that become part of the data for analysis in qualitative research such as

Glossary G7 grounded theory. Memos can describe and define ally impact a class of conditions or events. Imagine concepts, deal with methodological issues, or offer the nvo or three key factors tlIat determine which initial theoretical formulations. See Chapter 13. colleges students choose-proxinlity, reputation, and so forth. By contrast, see idiographic See also nUcrotheory A theory aimed at understanding so- Chapter 1. ciallife at the intimate level of individuals and their interactions. Examining how the play behav- nonequivalent control group A control group ior of girls differs from that of boys would be an that is similar to the experimental group but is not example of nlicrotheory. By contrast, see macro- created by the random assignment of subjects. theOl}'. See also Chapter 2. This sort of control group differs significantly from the experimental group in terms of the dependent mode (1) An average representing the most fre- variable or variables related to it. See Chapter 12. quently observed value or attribute. If a sample contains LOOO Protestants, 275 Catholics, and 33 nonprobability sampling Any technique in which Jews, Protestant is the modal category. See Chap- samples are selected in some way not suggested by ter 14 for more thrilling disclosures about aver- probability theory. Examples include reliance on available subjects as well as pllTposive (judgmen- ages. (2) Better than apple pie ala median. tal), quota, and snowball sampling. See Chapter 7. monitoring studies Studies that provide a steady nonsampling error (I) Those imperfections of data flow of information about something of interest, quality that are a result of factors other than sam- such as crime rates or the outbreak of an epi- pling errol. Examples include nlisunderstandings demic See Chapter 12. of questions by respondents, erroneous recordings by interviewers and coders, and keypunch errors. multiple regression analysis A form of statistical See Chapter 16. (2) The nlistake you made in de- analysis that seeks the equation representing the ciding to interview everyone rather than selecting impact of two or more independent variables on a a sample. single dependent variable. See Chapter 16. null hypothesis (I) In connection with hypothesis multiple time-series designs The use of more testing and tests of statistical significance, that hy- than one set of data that were collected over time, pothesis that suggests there is no relationship as in accident rates over time in several states or among the variables under study. You may con- cities, so that comparisons can be made. See clude that the variables are related after having Chapter 12. statistically rejected the null hypothesis. See Chap- ter 2. (2) An expectation about nulls. multivariate analysis The analysis of the simulta- neous relationships among several variables. Ex- open coding The initial classification and labeling of amining simultaneously the effects of age, gender, concepts in qualitative data analysis. In open cod- and sodal class on religiosiry would be an example of ing, the codes are suggested by the researchers' multivariate analysis. See Chapters 14, 15, and 16. examination and questioning of the data. See Chapter 13. naturalism An approach to field research based on the assumption that an objective social reality ex- open-ended questions Questions for which the ists and can be observed and reported accurately. respondent is asked to provide his or her own an- See Chapter 10. swers. In-depth, qualitative interviewing relies al- most exclusively on open-ended questions. See needs assessment studies Studies that aim to de- Chapters 9 and 10. termine the existence and extent of problems, typically among a segment of the population, such operational definition The concrete and specific as the elderly. See Chapter 12. definition of something in terms of the operations by which observations are to be categorized. The nominal measure A variable whose attributes have operational definition of \"earning an A in this only the characteristics of exhaustiveness and mu- course\" might be \"correctly answering at least tual exclusiveness. In other words, a level of mea- 90 percent of the final exam questions.\" See surement describing a variable that has attributes Chapter 2. that are merely different, as distinguished from OT- dinal, inte/val, or ratio measures. Gender is an ex- operationalization (I) One step beyond conceptu- ample of a nominal measure. See Chapter 5. alization. Operationalization is the process of de- nomothetic An approadl to explanation in which we seek to identify a few causal factors that gener-

G8 \" Glossary veloping operational definitions, or specifying the plagiarism Presenting someone else's words or exact operations involved in measuring a variable, thoughts as though they were your own, consti- See Chapters 2 and 5, (2) Surgery on intellectuals, tuting intellectual theft. See Chapter 17, ordinal measure A level of measurement describ- population The theoretically specified aggregation ing a variable with attributes we can rank-order of the elements in a study. See Chapter 7. along some dimension, An example is sodoeco- l10mic status as composed of the attributes high, me- posttesting (1) The remeasurement of a dependent dium, low, See also Chapter 5 and il1terval measure, variable among subjects after they've been ex- nominal measure, and ratio measure. posed to an independent variable, See Chapter 8, (2) What my younger sister did when she was panel study A type of longitudinal study, in which learning to drive.* data are collected from the same set of people (the sample or panel) at several points in time. See PPS (probability proportionate to size) (1) This Chapter 4 and cohort, longitudinal, and trend study, refers to a type of multistage cluster sample in which clusters are selected, not with equal proba- paradigm (1) A model or framework for observa- bilities (see EPSEM) but vvith probabilities propor- tion and understanding, which shapes both what tionate to their sizes-as measured by the number we see and how we understand it. The conflict of units to be subsampled. See Chapter 7. (2) The paradigm causes us to see social behavior one way, odds on who gets to go first: you or the 275- the interactionist paradigm causes us to see it dif- pound fullback ferently. See Chapter 2. (2) (pl.) $0,20, predictive validity See criteriol1-related validity, parameter The summary description of a given variable in a population, See Chapter 7. pretesting The measurement of a dependent vari- able among subjects. See Chapter 8. partial See partial relationship. probability sampling The general term for samples partial regression analysis (1) A form of regres- selected in accord with probability theory, typically sion analysis in which the effects of one or more involving some random-selection mechanism. variables are held constant, similar to the logic of Specific types of probability sampling include the elaboration model. See Chapter 16, (2) A re- EPSEM, PPS, simple ral1dom samplil1g, and systematic gression analysis you didn't have time to finish. samplil1g. See Chapter 7, partial relationship (1) In the elaboration model, probe A technique employed in interviewing to so- this is the relationship between two variables licit a more complete answer to a question. It is a when examined in a subset of cases defined by a nondirective phrase or question used to encour- third variable, Beginning with a zero-order rela- age a respondent to elaborate on an answer. Ex- tionship between political party and attitudes toward amples include \"Anything more?\" and \"How is abortion, for example, we might want to see that?\" See Chapter 9. whether the relationship held true among both men and women (Le., controlling for gender). The program evaluation/outcOIue assessment The relationship found among men and the relation- determination of whether a social intervention is ship found among women would be the partial producing the intended result. See Chapter 12. relationships, sometimes simply called the partials, See Chapter 15. (2) Someone you would take to proportionate reduction of error (PRE) A logical the opera but not to mud wrestling. model for assessing the strength of a relationship by asking how much knowing values on one vari- participatory action research An approach to so- able would reduce our errors in guessing values cial research in which the people being studied are on the other, For example, if we know how much given control over the purpose and procedures of education people have, we can improve our ability the research; intended as a counter to the implicit to estimate how much they earn, thus indicating view that researchers are superior to those they there is a relationship between the two variables. study See Chapter 10, See Chapter 16. path analysis (1) A form of multivariate analysis in purposive sampling A type of nonprobability sam- which the causal relationships among variables pling in which the units to be observed are se- are presented in a graphical format. See Chapter lected on the basis of the researcher'S judgment 16. (2) Watching your step along a horse trail. about which ones will be the most useful or rep-

Glossary G9 resentative, Another name for this is judgmental reactivity The problem that the subjects of social re- samplil1g. See Chapter 7. search may react to the fact of being studied, thus altering their behavior from what it would have qualitative analysis (1) The nonnumerical exami- been normally. See Chapter 10. nation and interpretation of observations, for the purpose of discovering underlying meanings and reductionism (1) A fault of some researchers: a patterns of relationships. This is most typical of strict limitation (reduction) of the kinds of con- field research and historical research, See Chapter cepts to be considered relevant to the phenome- 13, (2) A classy analysis. non under study. See Chapter 4. (2) The cloning of ducks . qUalitative interview Contrasted with survey in- terviewing, the qualitative interview is based on a regression analysis (1) A method of data analysis set of topics to be discussed in depth rather than in which the relationships among variables are based on the use of standardized questions, See represented in the form of an equation, called a Chapter 10, regression equation, See Chapter 16 for a discus- sion of the different forms of regression analysis. quantitative analysis (1) The numerical represen- (2) What seems to happen to your knowledge of tation and manipulation of observations for the social research methods just before an exam, purpose of describing and explaining the phe- nomena that those observations reflecr. See Chap- reliability (1) That quality of measurement method ter 14 especially, and also the remainder of Part 4. that suggests that the same data would have been (2) A BIG analysis. collected each time in repeated observations of the same phenomenon. In the context of a survey, we quasi experiments Nonrigorous inquiries some- would expect that the question \"Did you attend what resembling controlled experiments but lack- religious services last week?\" would have higher ing key elements such as pre- and posttesting reliability than the question \"About how many and/or control groups. See Chapter 12. times have you attended religious services in your life?\" This is not to be confused with validity. See questionnaire A document containing questions Chapter 5, (2) Quality of repeatability in untruths. and other types of items designed to solicit infor- mation appropriate for analysis, Questionnaires replication (1) Repeating a research study to test are used primarily in survey research but also in and either confirm or question the findings of an experiments, field research, and other modes of earlier study See Chapter L (2) A technical term observation. See Chapter 9. used in connection with the elaboration model, referring to the elaboration outcome in which the quota sampling A type of nonprobability sampling initially observed relationship between two vari- in which units are selected into a sample on the ables persists when a control variable is held con- basis of prespecified characteristics, so that the to- stant, thereby supporting the idea that the original tal sample will have the same distribution of char- relationship is genuine. See Chapter 15, acreristics assumed to exist in the population be- ing studied. See Chapter 7. representativeness (1) That quality of a sample of having the same distribution of characteristics as random selection A sampling method in which the population from which it was selected. By im- each element has an equal chance of selection in- plication, descriptions and explanations derived dependent of any other event in the selection pro- from an analysis of the sample may be assumed to cess. See Chapter 7. represent similar ones in the population. Repre- sentativeness is enhanced by probability sampling randomization A technique for assigning experi- and provides for generalizability and the use of in- mental subjects to experimental and control ferential statistics. See Chapter 7. (2) A noticeable groups randomly See Chapter 8. quality in the presentation-of-self of some mem- bers of the U.S, Congress. ratio measure A level of measurement descTibing a variable with attributes that have all the qualities research monograph A book-length research of nominal, ordinal, and interval measures and in report, either published or unpublished. This is addition are based on a \"true zero\" point. Age is distinguished from a textbook, a book of essays, a an example of a ratio measure. See also Chapter 5 novel, and so forth, See Chapter 17, and l1omil1almeasure, il1terval measure, and ordil1al measure,

G10 Glossary respondent A person who provides data for analy- ial codillg to identify the central concept that or- sis by responding to a survey questionnaire. See ganizes the other concepts that have been identi- Chapter 9, fied in a body of textual materials. See also axial codillg and Chapter 13.. response rate The number of people participating in a survey divided by the number selected in the semantic differential A questionnaire format in sample, in the form of a percentage, This is also which the respondent is asked to rate something called the completioll rate or, in self-administered in terms of two, opposite adjectives (e.g., rate text- surveys, the retll171 rate: the percentage of question- books as \"boring\" or \"exciting\"), using qualifiers naires sent out that are returned . See Chapter 9. such as \"very,\" \"somewhat\" \"neither,\" \"some- what,\" and \"very\" to bridge the distance between return rate See response rate. the two opposites. See Chapter 6. sampling error The degree of error to be expected semiotics (I) The study of signs and the meanings in probability sampling. The formula for determin- associated with them. This is commonly associated ing sampling error contains three factors: the pa- with content analysis. See Chapter 13. (2) Anti- rameter, the sample size, and the standard errOL biotics that only work half of the time. * See Chapter 7. simple random sampling (I) A type of probability sampling frame That list or quasi list of units com- sampling in which the units composing a popula- posing a population from which a sample is se- tion are assigned numbers. A set of random num- lected, If the sample is to be representative of the bers is then generated, and the units having those popUlation, it is essential that the sampling frame numbers are included in the sample. Although include all (or nearly all) members of the popula- probability theory and the calculations it provides tion. See Chapter 7.. assume this basic sampling method, it's seldom used, for practical reasons. An equivalent alter- sampling interval The standard distance between native is the systematic sample (with a random elements selected from a population for a sample. start). See Chapter 7. (2) A random sample with See Chapter 7. a 10wIQ. sampling ratio The proportion of elements in the snowball sampling (I) A nonprobability sampling population that are selected to be in a sample. See method often employed in field research whereby Chapter 7. each person interviewed may be asked to suggest additional people for interviewing. See Chapters sampling unit That element or set of elements con- 7 and 10. (2) Picking the icy ones to throw at your sidered for selection in some stage of sampling. methods instructor. See Chapter 7. social artifact Any product of social beings or their scale (I) A type of composite measure composed of behavior. Can be a unit of analysis. See Chapter 4. several items that have a logical or empirical struc- ture among them. Examples of scales include Bo- social indicators Measurements that reflect the gardus social distance, Guttman, Likert and Thur- quality or nature of social life, such as crime rates, stone scales. Contrasted with index. See Chapter 6. infant mortality rates, number of physicians per (2) One of the less appetizing parts of a fish. 100,000 population, and so forth. Social indicators are often monitored to determine the nature of search engine A computer program designed to social change in a society. See Chapter 12. locate where specified terms appear on websites throughout the World Wide Web. See Chapter 17. sociobiology A paradigm based in the view that social behavior can be explained solely in terms secondary analysis (I) A form of research in of genetic characteristics and behavior. See which the data collected and processed by one re- Chapter 4. searcher are reanalyzed-often for a different purpose-by another. This is especially appropri- specification (1) The process through which con- ate in the case of survey data. Data archives are cepts are made more specific. See Chapter 5. (2) A repositories or libraries for the storage and distri- technical term used in connection vvith the elabo- bution of data for secondary analysis. See Chapter ration model, representing the elaboration out- 9. (2) Estimating the weight and speed of an op- come in which an initially observed relationship posing team's linebackers. between two variables is replicated among some subgroups created by the control variable but not selective coding In Grounded Method Theory, this analysis builds on the results of open codillg and ax-

Glossary G11 among others. In such a situation, you will have study population That aggregation of elements specified the conditions under which the original from which a sample is actually selected. See relationship exists: for example, among men but Chapter 7. not among women. See Chapter 15. suppressor variable In the elaboration model, a spurious relationship (1) A coincidental statistical test variable that prevents a genuine relationship correlation between two variables, shown to be from appearing at the zero-order level. See caused by some third variable. For example, there Chapter 15. is a positive relationship between the number of fire trucks responding to a fire and the amount of systematic sampling (1) A type of probability sam- damage done: the more trucks, the more damage. pling in which every A.1h unit in a list is selected The third variable is the size of the fire. They send for inclusion in the sample-for example, every lots of fire trucks to a large fire and a lot of damage 25th student in the college directory of students. is done because of the size of the fire. For a little You compute k by dividing the size of the popula- fire, they just send a little fire truck, and not much tion by the desired sample size; k is called the sam- damage is done because it's a small fire. Sending pling interval. Within certain constraints, system- more fire trucks does not cause more damage. atic sampling is a functional equivalent of simple For a given size of fire, in fact, sending more random sampling and usually easier to do. Typi- trucks would reduce the amount of damage. cally, the first unit is selected at random. See See Chapter 4. (2) You thought you were going Chapter 7. (2) Picking every third one whether it's steady but that @#*&@#&* thought you were icy or not. See snowball sampling (2). \"just friends.\" test variable A variable that is held constant in an standard deviation (1) A measure of dispersion attempt to clarify further the relationship between around the mean, calculated so that approxi- two other variables. Having discovered a relation- mately 68 percent of the cases will lie within plus ship between education and prejudice, for ex- or minus one standard deviation from the mean, ample, we might hold gender constant by examin- 95 percent will lie within plus or minus two stan- ing the relationship between education and prejudice dard deviations, and 99.9 percent will lie within among men only and then among women only. In three standard deviations. Thus, for example, if this example, gender would be tlle test variable. See the mean age in a group is 30 and the standard Chapter 15 to find out how important the proper deviation is 10, then 68 percent have ages be- use of test variables is in analysis. tween 20 and 40. The smaller the standard devia- tion, the more tightly the values are clustered tests of statistical significance (1) A class of statis- around the mean; if the standard deviation is high, tical computations that indicate the likelihood that the values are widely spread out. See Chapter 14. the relationship observed between variables in a (2) Routine rule-breaking. sample can be attributed to sampling error only. See inferential statistics and Chapter 16. (2) A deter- statistic The summary description of a variable in a mination of how important statistics have been in sample, used to estimate a population parameter. improving humankind's lot in life. (3) An exami- See Chapter 7. nation that can radically affect your grade in this course and your GPA as well. statistical significance (1) A general term referring to the likelihood that relationships observed in theory A systematic explanation for the observa- a sample could be attributed to sampling error tions that relate to a particular aspect of life: juve- alone. See tests ofstatistical significance and Chapter nile delinquency, for example, or perhaps sodal 16. (2) How important it would really be if you stratification or political revolution. See Chapter 1. flunked your statistics exam. I mean, you could always be a poet. Thurstone scale A type of composite measure, con- structed in accord with the weights assigned by stratification The grouping of the units composing \"judges\" to various indicators of some variables. a population into homogeneous groups (or strata) See Chapter 6. before sampling. This procedure, which may be used in conjunction with simple random, systematic, time-series analysis An analysis of changes in or cluster sampling, improves the representativeness a variable (e.g., crime rates) over time. See of a sample, at least in tenus of the stratification Chapter 16. variables. See Chapter 7. time-series design A research design that involves measurements made over some period, such as

G12 \" Glossary the study of traffic accident rates before and after (2) Phonetic spelling of \"EarL\" (3) What my mum lowering the speed limiL See Chapter 12. used to say to me when I sounded like I was get- trend study A type of longitudinal study in which a ting a cold.* given characteristic of some population is moni- tored over time. An example would be the series validity A term describing a measure that accurately of Gallup Polls showing the electorate's prefer- reflects the concept it is intended to measure. For ences for political candidates over the course of a example, your IQ would seem a more valid mea- campaign, even though different samples were in- sure of your intelligence than the number of hours terviewed at each point. See Chapter 4 and cohort, you spend in the library would. Though the ulti- longitudinal. and panel study. mate validity of a measure can never be proved, we may agree to its relative validity on the basis offace triangulation Choking a triangle (submitted by validity, criten'on-related validity, content validity, con- Wendy Ogden, Mount Royal College, Calgary, struct validity, external validation, and internal valida- Canada). tion. Yalidity must not be confused with reliability. See Chapters 5 and 6. typology (1) The classification (typically nominal) of observations in terms of their attributes on two variable-oriented analysis An analysis that de- or more variables. The classification of newspapers scribes and/or explains a particular variable. See as liberal-urban, liberal-rural. conservative-urban, Chapter 13. or conservative-rural would be an example. See Chapter 6. (2) Apologizing for your neckwear. variables Logical groupings of attributes. The vari- able gender is made up of the attributes male and units of analysis The what or whom being studied. female. See Chapter 1. In social science research, the most typical units of analysis are individual people. See Chapter 4. weighting Assigning different weights to cases that were selected into a sample with different proba- univariate analysis The analysis of a single vari- bilities of selection. In the simplest scenario, each able, for purposes of desc.Tiption. Frequency distri- case is given a weight equal to the inverse of its butions, averages, and measures of dispersion probability of selection. When all cases have the would be examples of univariate analysis, as dis- same chance of selection, no weighting is neces- tinguished from bivariate and multivariate analysis. sary. See Chapter 7. See Chapter 14. zero-order relationship (1) In the elaboration unobtrusive researdl Methods of studying social model. this is the original relationship between two behavior without affecting it. Such methods can variables, with no test variables controlled for. See be qualitative or quantitative. See Chapter II. Chapter 15. (2) A blind date that just didn't work out. Hang in there. You can always turn to social URL (1) Web address, typically beginning with research methods. ''http://\"; stands for \"uniform resource locator\" or \"universal resource locator.\" See Chapter 17.

AAOPR 71, 72 Durkheim's study of suicide, 331- Average, 411 AAOPR code of ethics, 72 332 Axial coding, 386 Abdollahyan, Hamid, 210 Axioms, 43 Abdulhadi, Rabab, 107 globalization, 332-333 Azadarmaki, Taghi. 210 Abstract. 490 reliability, 334-335 Aburdene, Patricia, 320 sources of existing statistics, 335- Babbie, Earl, 29, 48, 64, 77, 79, 159, Academic change, 297 209,282,287,342,428,438- Accuracy, 143 338 440,486 Ackroyd, Dan, 235 units of analysis, 333 Adler, Patricia A., 315 validity, 333-334 Bailey, William, 370 Adler, Peter, 315 Analyzing Social Settings (Lofland/ Baker, Reginald P., 272 African Americans. See Race and Baker, Vern, 346 Lofland), 287, 311 Ball-Rokeach, Sandra, 239 ethnicity Anderson, Andy B., 150, 177, 217, Baron, James N., 117, 163 Age differences, 106 Bart, Pauline, 117,361-362, A7 Aggregates, 13-14 283 Basics of Qualitative Research (Strauss/ AIDS prevention, 299-300 Anderson, Leon, 293, 294 Aldea, Andra, 297 Anderson, W., 8 Corbin), 381 Alexander, Jan, 511 Andorka, Rudolf, 351 Basilevsky, Alexander, 177 Alford, Krista, 362 AneshenseL CarolS., 105 Becerra, Rosina, 105 American Almanac, The, 335 Anomia, 130-131 Becker, Howard 5., 29, 298 American Association for Public Anomie suicide, 332 Becker, Penny, 66 Anomie, 130,331 Bednarz, Marlene, 245 Opinion Research (AAOPR), Anonymity, 64-65 Behrens, William W., III. 371 71,72 ANOVA. 476-478 Belenky, Mary Field, 38, 39 American democracy, 341 Anti-Semitism, 471 Bell, Derrick, 39 Aminzade, Ron, 343 AoIR 71 Bell Curve (Herrnstein/Murray), 77 Analysis and reporting, 68-69 Applied research, 25-26 Bellah, Robert. 291,339-340,342 Analysis of data, 374-51 L See also Approximately longitudinal studies, Benton, J. Edwin, 255 individual subject headings Berbrier, Mitch, 107 elaboration modeL 430-448 105-106 Berg, Bruce L., 153,322,326, 328, qualitative data analysis, 377-403 Approximating observations, 106 quantitative data analysis, 404- Army studies (Stouffer), 431-433 346, 402 429 Article (academic journal), 504 Berg, Richard, 372 reading and writing social re- Asch, Solomon, 40 Berkman, MichaeL 104 search, 488-511 Asher, Ramona, 102 Bernstein, Ira H., 428 statistical analysis, 449-487 Association between variables, 451 Best college in U.S . ?, 164 Analysis of variance (ANOYA), 476- Association of Internet Researchers Beyer, William H., A19 478 Bian, Yanjie, 249 Analytic induction, 328 (AoIR),71 Bias, 143 Analyzing, 308 Attribute, 15,43, 136 Analyzing existing statistics, 330- Attribution process, 235 defined, 250 338 Auster, Carol], 326 double-blind experiment, 224- Austin, James, 366 Authority, 5-6 225 Auto-kinetic effect, 41 sampling, 188-189 Autoethnography, 294 11

12 Index Bias (continued) Carpini, Michael. 103 Collins, G. C, 214 selection, 231 Carr, C Lynn, 106, 107 Collum-Swan, Betsy, 381 survey research, 250-251 Carroll, Lewis, 45 Columbia school method, 43 L See Case-oriented analysis, 379 Bibliographic citations, 502 Case study, 298-300 also Elaboration model Bickart, Barbara, 275 CASt 272 Comfort hypothesis, 48, 424 Bickman, Leonard, 353, 372, 373 CASL,187 Common Cause, 337 Biddle, Stuart 1. H, 25 Casley, D. J., 117 Community Adaptation and Sustain- Bielby, Denise, 140 Categorical Data Analysis, 429 Bielby, William, 140 CAT!. 270-271 able Livelihoods (CASL) Pro- Birchfield, R. W, 503, 511 Causal relationship, 90-93 gram, 187 Birthrate, 13, 14 Causal time order, 231 Comparative and historical research, Bishop, George E, 249, 255 Cause and effect indicators, 158 338-345 Bivariate analysis, 419-423 Census, 77-78,151 analytical techniques, 342-345 Bivariate relationships, 157-160 Central tendency, 411-414 defined, 338 Black, Donald, 334 Central tendency theorem, 197 examples, 338-341 Blacks. See Race and ethnicity Cernat, Constantin, 297 Japanese religion and capitalism, Blair, Johnny, 257, 275 Chafetz, Janet, 58 339-340 Blake, Judith, 282 Chambliss, William], 370 sources of information, 341-342 Blalock, Hubert M., Jr., 486 Channeling, 88 Weber, Max, 339 Blaunstein, Albert, 76 Chatzisarantis, Nikos L D., 25 Comparative case study method, 300 Blocking a demolition, 303 Chawla, A., 177 Compensation, 231 Blodgett, Timothy, 214 Chen, Huey-Tsyh, 372 Compensatory rivalry, 231 Bobo, Lawrence, 177 Chi square, 466-468 Complete causation, 92 Bogardus, Emory, 169 Childers, Terry, 263 Completion rate, 262 Bogardus social distance scale, 168- Chirot, DanieL 126 Composite measures, 153. See also Chisolm, Rupert, 238 Index; Scale 169 Chossudovsky, Michel. 35 Compulsory heterosexuality, 301 Bohrnstedt. George W, 150 Christianity, 340-341 Computer-assisted personal inter- Bollen, Kenneth, 158 Citing Internet materials, 502 viewing (CAPI), 272 Bolstein, Richard, 262 Clark, Roger, 97 Computer-assisted self-interviewing Book,504 Classical experiment, 222-225, 232 (CASI),272 Books in Print, A2 Clinchy, McVicker. 38, 39 Computer-assisted telephone inter- Book's web site, 29 Clinton, Bill. 43 viewing (CATI), 270-271 Boruch, Robert, 353 Clipp, Elizabeth C, 282 Computer simulation, 371 Bottomore, T. B., 339 Closed-ended questions, 246-247 Computerized library files, A6-A7 Bradburn, Norman M., 282, 283 Cluster sampling, 209. See also Multi- Computerized self-administered Branch Davidians, 294 questionnaire (CSAQ), 272 Breaching experiments, 295 stage cluster sampling Comstock, Donald, 117 Brown, L Dave, 360, 361 Code notes, 388 Comte, Auguste, 33-34, 338 Brown v. Board of Education, 39, 76 Code of ethics, 71. 72 Conan Doyle, Arthur, 51 Burawoy, Michael. 298,316 Codebook,408-409 Concept, 43, 122-124 Bureau of Labor Statistics, 406 Coding, 325-328, 384-388,405- Concept mapping, 389-390 Bureaucracy, 343, 344 Conception, 122 Burton, A. A., 298 409 Conceptual order, 128-129 Bush, George W., 180, 181 Coding units, 384 Conceptualization, 110, 124-132 Coefficient of reproducibility, 174 conceptual order, 128-129 CA,383 Cohen, Maurie ], 65 defined, 124 Call-in polls, 189 Cohen-Mansfield, ], 21 example (anomie), 130-132 Calvin, John, 339 Cohort, 103 indicators/dimensions, 125-127 Calvinism, 339 Cohort study, 103-104 interchangeability of indicators, Campbell. Donald T., 228, 230, 233, Coin flipping, 191 127 Cole, Stephen, 29 nominal definition, 128 234, 241. 319, 346 Coleman, James S., 76, 447 operational definition, 128 Campbell. M. L, 300 Collapsing response categories, 416- real definition, 128 Campbell. Stephen, 263 Confidence interval. 197-198 CAPI. 272 418 Confidence level. 197-198 Card catalog, A4-A5 Collective regularities, 13 Conflict paradigm, 34-35 Carmines, Edward Go, 177 College performance, 141

Index 13 Conrad, Clifton, 297 Current Population Survey (CPS), Dimension, 126, 135-136 Constant comparative method, 380 247 Direct observables, 123 Construct, 124 DiscovelY ofGrollllded Theory, The (Gla- Construct validity, 147 Curtin, Richard, 272 Constructed knowledge, 39 Curvilinear regression, 458-459 ser/Strauss), 296 Consumer price index (CPI), 178 Discrete variable, 415 Content analysis, 320-330 Daly, John, 255 Discriminant analysis, 478-482 D'Andrade, Roy, 283 Dispersion, 414-415 coding, 325-328 Dannemeyer. William, 77 Disproportionate sampling and counting/record keeping, 326- Dannemiller. James E., 271 Darwin, Charles, 12, 34 weighting, 213-215 328 Data analysis. See Analysis of data Distorter variable, 443, 444 defined, 320 Data-dredging techniques, 470 Distribution of chi-square, A20-A21 illustration (gender stereotyping in Data entry, 409 Distributive justice, 52-54 Data processing, 111-112, 384-390 Documentary sociology, 303 TV), 328-330 Data reduction, 450-451 Dole, Robert, 43 qualitative data analysis, 328 Davern, Michael. 263 Domestic terrorism, 294 sampling, 321-324 Davis, Fred, 290 Don't know/no opinion, 418-419 strengths/weaknesses, 330 Davis, James A., 103, 104,278,428 Double-barreled questions, 247, 248 units of analysis, 321-324 de Seve, Michel. 443 Double-blind experiment, 224-225 uses, 320-321 Death penalty, 370 Douvan, Elizabeth, 105 Content validity, 147 Debriefing, 67 Drivers' education programs, 351, Contingency questions, 252-254 Deception, 67-68 Contingency table, 254, 423 Deduction, 22, 46-51 366 Continuous variable, 415 Deductive theory construction, 51- Drug arrests, 334 Control group, 223-224 DuBois, W E. B., 39 Control variable, 435 54 Dunlap, Rill' Eo, 47 Conversation analysis (CA), 383 Deegan, Mary Jo, 38 Durkheim, Emile, 37, 51. 130, 131, Conversation clarification experi- Definition, 132-134 Deflem, Mathieu, 341 330-332, 338 ment, 295 DeFleur, Lois, 334 Durkheim's study of suicide, 331- Convert, 290 Defronzo, James, 37 Convict code, 295-296 Degrees of freedom, 467-468 332 Cook, Elizabeth, 443 Delgado, Richard, 39, 58 Cook, Thomas D., 230, 241 Democracy ill America (Tocqueville), Early positivism, 33-34 Cooley, Charles Horton, 35,234 Eastman, Crystal. 26 Cooney, Margaret, 302 341 Ecological fallacy, 100 Cooper, Harris M., 118 Demographic Yearbook, 336 Economic determinism, 339 Cooper-Stephenson, Cynthia, 184 Demoralization, 231 Economic Ethic ofthe World Religiolls Copernicus, 365 Denzin, Norman K., 58, 316, 402 Corbin, Juliet, 316, 378, 381. 385, Dependent variable, 18,222 (Bellah), 340 Descriptive statistics, 450-459 EcolVarriors: Understanding rhe Radi- 388,402 Descriptive studies, 89 Cornerville, 293 Design . See Research design cal Envirollmental Movement Correlation, 90-91 Designing, 308 (Scarce), 66 Correlation matrix, 455 DeVault, Marjorie L, 58 Edangiu, Eugenia, 297 Correlation squared (r2), 457 Dewey, Thomas E., 182 Educational Resources Information Cost-benefit studies, 350 Dewey-Truman presidential election, Center (ERIC), 373, A6 Couper, Mick P, 273, 282 Edwards, Jennifer, 126 Cox, James, 181-182 182-183, 185 Eifler, Deborah, 97 CPI. 178 Dialectics of social science, 19-26 Elaboration model. 430-448 CPS, 247 defined,431 Craig, R Stephen, 328-329 ideographic/nomothetic explana- distorter variable, 443, 444 Crawford, Kent, 363 tion, 19-21 elaboration paradigm, 436-444 Criterion-related validity, 146-147 ex post facto hypothesizing, 444- Critical race theory, 39 inductive/deductive theory, 22-23 445 Cross-case analysis, 380 pure/applied research, 25-26 explanation, 437-439 Cross-sectional study, 102 quantitative/qualitative data, 23- interpretation, 439 Cross-tabulations, 162 origins, 431-435 CSAQ,272 25 replication, 437 Cunningham, J. Barton, 372 Dickson, W ], 224 specification, 439-442 Diffusion, 231 suppressor variable, 442, 443 Dillman, Don A, 263, 270, 282 web sites, 448

14 \" Index Elaboration paradigm, 436-444 measurement, 351-356 pretesting, 222-223 Elder, Glen K, JL, 282 motivating Navy personnel, 363 probability sampling, 225-226 Election polls, 180-183 multiple time-series design, 359- randomization, 226 Element, 190 selecting subjects, 225-228 Elemental memo, 389 361 Solomon four-group design, 233 Elements of Style, Tize (Strunk/White), new vSo existing measures, 354- static-group comparison, 228, 229 strengths/weaknesses, 239-240 503, 511 355 uses, 221 Elitist modeL 301 nonequivalent control groups, 359 validity, 230-234 Ellison, Christopher G\" 117 outcomes, 352-353 web-based,237 Emerson, Robert, 36 population, 354 web sites, 242 Emic perspective, 291 qualitative evaluations, 361-362 Experimental group, 223-224 Empirical relationships, 157 quasi-experimental design, 357- Experimental mortality, 231 Epistemology, 4 Expert witnesses, 78-79 EPSEM samples, 189 361 Explained variation, 457 ERIC, 373, A6 rape reform legislation, 367-368 Explanation, 19-21,437-439 Errors in inquiry, 6-7 research design, 356-362 Explanatory bivariate analyses, 420 Eskenazi, Brenda, 238 social context, 362-369 Explanatory studies, 89-90 Established measures, 145 social indicators research, 369- Exploratory studies, 88-89 Estimated sampling error, A23 Extended case method, 298 Ethical issues, 26-27, 62-74 371 External invalidity, 231-234 success/failure, 355 External validation, 166-167 analysis and reporting, 68-69 Tanzanian soap opera, 349, 353, Extrapolation, 459 anonymity, 64-65 Exxon Valdez oil spill, 65 code of ethics, 71, 72 362 confidentiality, 65-67, 68 time-series design, 357-359 Face validity, 146, 156 deception, 67-68 types of studies, 350 Fact, 43 evaluation research, 364-365 use of research results, 365-369 Factor, 474 field research, 312 web sites, 373 Factor analysis, 474-476 homosexual behavior (Tearoom wife battering, 368-369 Factor loadings, 474 Evans, William, 346 Fausto-Sterling, Anne, 77 Trade), 71, 73 Ex post facto hypothesizing, 444- Fax surveys, 272-273 human obedience (Milgram ex- FedStats, 502 445 Feick, Lawrence F., 282 periments),73-74 Exceptional cases, 92 Feinberg, Barry, 270 informed consent, 64, 68 Existing statisticso See Analyzing ex- Feminist paradigms, 37-39 IRB,69-71 Ferguson, k k, 298 no harm to participants, 27, 63- isting statistics Ferrante, Joan, 428 Expectations communication model, Ferris, Kerry, 36 64, 68 Festinger, Leon, 103 protection from harm, 27, 63-64, 235 Fictive stories, 294 Expectations-states theory, 235 Field research, 54. See also Qualita- 68 Experiment, 220-242 voluntary participation, 26-27, tive field research classical, 222-225, 232 Fielder, Eve, 105 62-63 control group, 223-224 Fielding, Nigel, 280 Ethnography, 293-294 double-blind, 224-225 Fine, Gary, 102 Ethnomethodology, 36, 294-296 elaboration model, 432 Fink, Jeffrey, 363 Etic perspective, 291 evaluation research, 356 Fisher, Patricia, 134 Evaluation Exchange, 373 experimental group, 223-224 Fisher, Sir Ronald Ao, A20 Evaluation research, 348-373 illustration (Pygmalion), 234-237 Flipping a coin, 191 independent/dependent variables, Focus group, 308-309 administrative control, 364 Follow-up mailings, 261-262 computer simulation, 371 222 Fool's experiment, 12 death penalty, 370 matching, 226-227 Ford, David, 368-369 defined, 350 matching and randomization, Forslund, Morris, 474, 475 drivers' education programs, 351, FoschL Martha, 235 227-228 366 natural, 237-239 ethics, 364-365 one-group pretest-posttest design, experimental contexts, 353 experimental design, 356 228,229 Hungarian economy, 351 one-shot case study, 228, 229 interventions, 354 posttest-only control group design, logistical problems, 363-364 234 posttesting, 222-223 preexperimental research designs, 228-230

Index f!l IS Foster, Bruce D\" 472 Globalization, 332-333 Hedrick, Terry E, 353, 373 Foundations of social science, 10-19 Glock, Charles Y, 48, 49, 209, 440, Helms, Jesse, 77 Hempel, Carl, 128 aggregates, 13-14 441, 445, 447, 472 Heritage, Johen, 36 social regularities, 11-13 Godfrey, Mary, 316 theory, 10-11 Goffman, Erving, 54, 382-383 Herrnstein, Richard L 77 variables, 14-19 Going native, 290 Fowler, Floyd L JL, 282 Goldberger, Nancy Rule, 38, 39 Higginbotham, k Leon, JL, 342 Fox, Katherine, 298, 299 Gottlieb, Bruce, 164 Hill, Lewis, 7 Frankel, Linda, 117, A7 Gould, Roger V\" 346 Hilts, Philip, 351, 366 Frankel, Mark So, 71 Government Printing Office, 336 Historical researcho See Comparative Frankfort-Nachmias, Chava, 217, Government research sites, 502 Graham, Laurie, 117 and historical research 486 Greatbatch, David, 36 History, 230 Freeman, Howard E\" 373 Green, Melanie C, 283 Hite, Shere, 245 Frequency distribution, 411 Greenwood, Do Jo, 301, 302 Hite Reports on human sexuality, Freud, Sigmund, 382 Greenwood, Peter We' 366 Functionalist paradigm, 37 Griffin, Larry, 344-345 245 Griffith, Alison, 300-301 Hogan, Richard, 107, 117 Gallup, George, 182, 183, 185,244, Grimes, Michael D\" 150 Hokada, Elizabeth, 97 337 Grounded theory, 54,296-300, 317 Holbrook, AIJyson L, 283 Grounded theory method (GTM), Holocaust Revisionist movement, Gallup Poll, A24 Gambler's faIJacy, 7 380-381 250 Gamma, 453-455 Groups as units of analysis, 96-97, Holstein, James Ao, 293-295, 316 Gamson, 1\" 298 Homan, Roger, 82 Gamson, William, 309 98 Homeless people, 293, 294 Gans, Herbert Jo, 80, 316 Groupthink, 309 Homosexual behavior (Tearoom Garant, Carol, 313 Grove, Janet Belew, 319, 346 Gard, Greta, 38 Groves, Robert M\" 282 Trade), 71, 73 Gardner, Carol, 36 Grube, Joel, 239 Hoover, Kenneth K, 29 Garfinkel, Harold, 36, 295 GSS, 19,278 Horney, Julie, 367-368 Gartrell, R, 298 GSS codebook, 151,278 Howard, Edward, 352 Gaventa, ], 30I GSS household enumeration ques- Howell, Joseph, 313 Gawiser, Sheldon K, A24 Huberman, A. Michael, 379-380 GDI, 167 tionnaire, A8-A17 Hughes, Michael, 117 GEM,167 GTM,380-381 Hum, Derek P. J\" 177 Gender Advertisements (Goffman), 382 Gubrium, Jaber F, 293-295,316 Human obedience (Milgram experi- Gender empowerment measure Guided conversation, 307 Guttman, Louis, 172 ments), 73-74 (GEM),167 Guttman scale, 172-174 Human suffering, 336-337 Gender-related development index Humphreys, Laud, 71, 73, 77 Hald, Ao, A22 Hungarian economy, 351 (GDI), 167 Haley, Alex, 239 Hunt, Morton, 118 Gender stereotyping in TV, 328-330 Halley, Fred, 428, 486 Hurst, Leslie, 298-299 General population survey, 273 Hamnett, Michael P, 82 Hutchby, Ian, 402 General Social Survey (GSS), 19, Hannon, Lance, 37 Hypothesis, 44, 47 Harding, Warren, 181, 182 Hypothesis construction, 47 278 Hart, Stephen, 235 Hypothesis testing, 46 General Social Survey Codebook Harvard Test of Inflected Acquisition, Iannacchione, Vincent, 209 website, 278 235 Ibrahim, Saad Eddin, 25, 26 Generalized other, 36, 38, 235 Harvey, David, 58 Icolisan, Ioana-Minerva, 297 Genetics, 13 Hatchett, Shirley, 105 Ideal types, 343 Genocide, 126-127 Havemann, Ernest, 440 Idealistic, 339 Geographic information system Havig, Paul, 428 Ideational, 339 Hawking, Stephen, 42 Ideology, 75-78 (GIS), 483, 484 Hawthorne effect, 224, 290 Idiographic, 19 Gilboard, Mark], 362 \"Heady Days Are Here Again\" (Tay- Idiographic explanation, 19, 21 Gilbourne, David, 25 Illogical reasoning, 7 Gill, Duane Ao, 65 lor/Terhanian),273 Imitation of treatments, 231 GIS, 483, 484 Healey, Joseph F, 487 Inaccurate observations, 6 Glaser, Barney, 54, 296, 328, 380- Income inequality, 333 381, 385,402

16 Index Independent variable, 18, 222 searching the Web, 496-498 Kebede, Alemseghed, 56 Index, 153-168 web sites. See Web sites Keeter, Scott. 103 Internet Public Library, 118 Kelle, Udo, 316 bad index vs. bad validators, 167 Interpolation, 459 Kendall. Diana, 344 bivariate relationships, 157-160 Interpretation, 439 Kendall. Patricia L, 432-435, 440, construction logic, 155 Interpretation method, 431. See also defined, 154 447 empirical relationships, 157 Elaboration model Kentor, Jeffrey, 332-333 example (status women), 167-168 Interquartile range, 415 Keohane, Robert 0 .. 402 external validation, 166-167 Interval. 137-138 Kerensky, Alexander. 338 item analysis, 165-166 Interval variables, 455 Khayatt Didi. 301 item selection, 156-157 Interview missing data, 163-165 Kilburn, John c., Jr., 107 multivariate relationships, 160- defined, 264 qualitative, 305-308 Killen, Joel. 359 162 survey, 264-269 King, Gary, 402 scale, contrasted, 153-156 Interview survey, 264-269 Kinnell. Ann Marie, 383 scoring, 162-163 appearance/demeanor, 265-266 Kinsey, Alfred, 77 validation, 165-167 coordination and control. 267- Kish, Leslie, 190, 217 Index scoring, 162-163 Knottnerus, J David, 56 Index validation, 165-167 269 Koresh, David, 294 Indicators familiarity with questionnaire, Kosovo, 127 cause and effect. 158 Krosnick, Jon A.. 283 defined, 125 266 Krueger, Richard, 309 interchangeability, 127 follow question wording, 266 Krushat W Mark. 145 multiple, 141. 153 probing for responses, 267 Kubrin, Charis, 95 single vs. multiple, 140-141 recording responses, 266-267 Kuhn, Ralph, 275 social. 369 response rate, 271-272 Kuhn, Thomas, 32, 58 world development. 332 specifications, 268 Kumar, Krishna, 82 Indirect observables, 123 survey interviewer, 264-265 Kumar, R, 177 Individuals as units of analysis, 96, Interviewing, 308 Iordache, Gabriel. 297 Kurzman, c., 298 98 IQ test results, 76 Indrayan, A., 177 IRB,69-71 Kvale, Steinar, 129, 306, 308, 316 Induction, 22, 46-51 Irwin, John, 366 Inductive theory construction, 54- Isaac Larry, 344-345 Labor laws, 11 Item analysis, 165-166 Laboratory experiment 237. See also 55 Item selection, 156-157 Inescapable subjectivity, 41 Iversen, Gudmund R., 118 Experiment Inferential statistics, 459-460 Ivy League schools, 440 Lambda, 452-453 Informant, 186-187 Lambert, Charles, 346 Information sources. See Sources of Jackman, Mary R, 199 Lamias, Mark J., 282 Jacobs, Bruce A., 107 Landon, AlL 181. 182,245 information Jacobson, David, 402 Language alld Social Reality.: The Case of Informed consent, 64, 68 Jacobson, Lenore, 235 InfoTrac College Edition, 489 Japanese religion and capitalism, Telling the Convict Code (Wieder), Innovator. 44 295 Inquiry, 4-7 339-340 Laslett, Barbara, 343 Insider understanding, 291 Jasso, Guillermina, 52, 53 Latent content, 325 Institutional ethnography, 294, 300- Jaywalking, 311 Laumann, Edward 0., 77 Jensen, Arthur, 76 Laws, 43 301 Lazarsfeld, Paul E, 151. 177, 375, Institutional review board (IRB), 69- Jobes, Patrick c., 297 433-435,447 Lazarsfeld method, 431. See also 71 Johnson, Jeffrey, 186, 316 Elaboration model Instrumentation, 230-231 Johnston, Hank. 107, 117 Lazeru, Sabastian, 297 Integrating memo, 389 Jones, James R, 365 Lazes, Peter. 301. 302 Interest convergence, 39 Jones, Stephen R G.. 241 Lee, Motoko, 169 Internal invalidity, 230-233 Judgmental sampling, 184 Lee, Raymond, 82 International policing, 341 Lengermann, Patricia Madoo, 26 Internet. 496-503 Kalton, Graham, 217 Lennon, Rachel. 97 Kaplan, Abraham, 43, 123, 124 Leon-Guerrero, Anna, 217, 486 citing materials, 502-503 KasL Stanislav, 238 Level of significance, 465 evaluating quality of materials, KasoL Joseph, 251 Levels of measurement, 136-140 498-502

Index 17 Lever, Janet 38 Manning, Peter, 381 Menjivar, Cecilia, 305 Leviticus example, 386-388, 391- Marginal correlations, 441 Merton, Robert K., 12,44, 130,447 Marginal frequencies, 411 Methodology, 4 393 Marginals, 411 MFA,483 Lewis-Beck, Michael. 428 Marijuana, 54-55, 334 MicroCase, 162,405 U Tze-chung, A7 Markland, David, 25 Microtheory, 33 Libin, A., 21 Marsden, Peter V., 278 Miles, Matthew, 379-380 Library Marshall. Catherine, 128, 289, 290 Milgram, Stanley, 73, 74, 237 Martian, 290 Military police (MPs), 12 card catalog, A4-A5 Martin, David W, 241 Miller. Delbert, 118, 151. 177, 262, computerized library files, A6-A7 Martin, Jean, 272 getting help, A2 Martin, Patricia Yancey, 56, 107 283 Library of Congress classification, Marx, Karl. 34-35, 241. 338, 339 Miller, Jodi. 107 Mass media, 321 Minnesota Multiphasic Personality A5 Matching, 226-227 reference sources, A2-A4. See also Matrix questions, 254-255 Inventory (MMPI), 145 Maturation, 230 Mirola, William, 320 Sources of information Maxwell. Joseph A., 118 Missing data, 163-165 stacks, A4-A5 Maynard, Douglas, 383 Mitchell, Richard, 249, 294 Library of Congress, 489 Maynard, Judy L, 199 Mitofsky, Warren, 273 Library of Congress classification, A5 McAlister, Alfred, 359 MMPI. 145 McCormack, Coralie, 402 Mode, 412, 413 Licari. Frederick c., 177 McGrane, Bernard, 36n, 58 Model. Karyn, 366 McIver, John P., 177 Modes of observation, 218-373. See Life history interviews, 106 McKinney, Kathleen, 68 Likert, Rensis, 170 McVeigh, Timothy, 294 also individual subject headings Likert scale, l70-l71 Mead, George Herbert, 35-36, 38, evaluation research, 348-373 Lincoln, Yvonna S., 58, 316,402 experiments, 220-242 Linear regression, 456-457 235 qualitative field research, 285-317 Linking theory and research, 55-56 Meadows, Dennis L, 371 survey research, 243-284 Linton, Ralph, 37-38 Meadows, Donella, 371 unobtrusive research, 318-347 Lists of individuals, 200 Mean, 411-412, 413 Mohr, Lawrence B.. 487 Literary Digest presidential poll. 181- Measurement Monitoring studies, 350 Montoya, Rima, 362 182 composite measures, 153 Moore, David W., 283 Literature review, 489-490, 505 defined, 121 Morgan, David L, 309, 316 Lloyd Warner, William, 131. 298 established measures, 145 Morgan, Lewis, 338 Lofland, John, 58, 99, 187,287,291. evaluation research, 351-356 Morris, Leana, 97 interval. 137-138 Moskowitz, Milt 200 303, 307-308, 312, 316, 378, levels oL 136-140 Mothering, schooling, child develop- 385, 389 nominal measures, 136-137 Lofland, Lyn R, 58, 99, 187,287, ordinal measures, 137 ment, 300-301 291,307-308,312,316,378, precision/accuracy, 143 Motivating Navy personnel. 363 385, 389 ratio, 138-139 Moynihan, Daniel. 76 Log-linear models, 482-483 reliability, 143-146. See also MPs, 12 Logical inferences, 106 Multi-way frequency analysis Logical reasoning, 334 Reliability Logit 483 validity, 146-149 (MFA),483 Long, Andrew E, 316 Measures of association, 451-455 Multiple-correlation coefficient, 458 Longhurst, Terri, 302 \"Measures of Association and Levels Multiple indicators, 141. 153 Longitudinal study, 102-105 Multiple regression, 458 Looking-glass self, 35, 234-235 of Measurement\" (Nardi), 460 Multiple time-series design, 359- Lopata, Helena, 454-455 Measures of central tendency, 411- Lury, D. A., 117 361 Lynchings, 320 414 Multistage cluster sampling, 208- Lynd, Helen, 298 Median, 412-414 Lynd, Robert, 298 Medical research ethics, 62. See also 215 cluster design, 209-211 Maccoby, Nathan, 359 Ethical issues content analysis, 324 Macrotheory, 33 Megatrends (Naisbitt/Aburdene), 320 disproportionate sampling and Madison, Anna-Marie, 352 Membership lists, 200 Mahoney, James, 338 Memoing, 388-389 weighting, 213-215 Majority of cases, 92-93 Menard, Scott. 118 PPS sampling, 211-213 Maltz, Michael D., 400 sampling error. 210 Manifest content, 325 steps, 209

IS liI Index Multistage cluster sampling Oklahoma City bombing, 294 Peer review, 509 (continued) 011 the Origin of Species (Darwin), 34 Percentaging a table, 421, 422 One-group pretest-posttest design, Perinelli, Phillip, 189 stratification, 211 Periodicity, 205 uses, 209 228,229 Perrow, Charles, 341 Multivariate analysis, 424-425 One-shot case study, 228, 229 Perrucci, Carolyn Co, 107 Multivariate relationships, 160-162 One-way ANOVA, 476-478 Perry, CheryL 359 Murphy, Eddie, 235 O'NeilL Harry, 270, 272 Personal Responsibility and Work Murray, Charles, 77 Online Grounded Theory Articles, My Lai massacre, 73 Opportunity Act (PRWORA), MyrdaL Gunnar, 76 317 302 Online Social Psychology Studies, Petersen, Larry R, 199 Nader, Ralph, 337 Phenomenology, 294-295 Naisbitt, John, 320 237 Picou,l Steven, 65, 66 Naive realism, 7 Online surveys, 273-275 Placebo, 224 Nanking, 126 Open coding, 385 Plagiarism, 505-507 Nardi. Peter, 429, 460 Open-ended questions, 246-247 Plutzer, Eric, 104 Natural experiments, 237-239 Operational definition, 45, 128 PoL Louis G\" 373 Naturalism, 293-294 Operational notes, 389 Political party affiliation, 121 Nazi Holocaust, 127,250 Operationalization, 44-45, Ill, Political polling, 180-183 Necessary cause, 93 Politics of social research, 74-80 Needs assessment studies, 350 134-142 census, 77-78 Neuman, W. Lawrence, 56 continuation of, 142 children, 79 Newman, Jeffrey, 145 dimensions, 135-136 expert witnesses, 78-79 Newton, Rae R, 429 illustrations, 141-142 lessons learned, 79-80 Nicholls, William L, 272 levels of measurement, 136-140 objectivity/ideology, 75-78 Niebrugge-Brantley, Jill, 26 range of variation, 134-135 race, 75-77 Nixon, Richard, 365 single vs, multiple indicators, 140- sexual research, 77 No harm to participants, 27, 63-64, PolL 244-245, See also Survey 141 research 68 variables/attributes, 136 call-in, 189 Nominal definition, 128 variations between extremes, 135 election, 180-183 Nominal measures, 136-137 Ordinal measures, 137 push, 245 Nominal variables, 452-453 Ordinal variables, 453-455 questions to ask, A24-A29 Nomothetic, 21 Ordinary human inquiry, 4-5 Polling the Nations, 278 Nomothetic explanation, 21, 90-93 Organizations as units of analysis, 98 Population, 190 Nonequivalent control groups, 359 Outcome assessment, 35 L See also Population Bulletil1, 337 Nonhuman entities as units of analy- Population Reference Bureau, 337 Evaluation research Population size, 198 sis, 98 Overgeneralization, 6 Pornography, 365 Nonprobability sampling, 183-187 0yen, Else, 346 Porter, Douglas J\" 82 Porter, Stephen, 274 available subjects, 183-184 Panel study, 104-105 Positivism, 33-34,40,41 defined, 183 PAR, 75, 301-303, 317 Postmodernism, 42 informants, 186-187 Paradigm, 31-33, See also Social sci- Posttest-only control group design, purposive sampling, 184 234 quota sampling, 185-186 ence paradigms Posttesting, 222-223 snowball sampling, 184-185 Parameter, 191 Postulates, 43 Nonsampling error, 461 Parametric tests of significance, 461 Pot, PoL 127 Normal curve, 195 Partial regression, 458 PowelL Elwin, 130 Normal curve areas, A22 Partial relationships, 436, 441 PPS sampling, 211-213 Norman, Darlene, 352 Partial tables, 435 PRE,452 NUD*IST, 391-393, 396 Partials, 436 Precision, 143 Null hypothesis, 47, 466 Participant observation, 289 Predestination, 339 NVivo, 393-398 Participatory action research (PAR), Predictive validity, 146 Preexperimental research designs, Objective reality, 41 75,301-303,317 228-230 Objectivity, 40-42, 41, 75-78 Pasanella, Ann, 177,447 O'Brien, Patricia, 361-362 Path analysis, 471 Observation, 43, 45-46 Path coefficients, 471 Patterns, 378-380 Pavalko, Eliza K., 282 Pearson's product-moment correla- tion (r), 455

Index 19 Prejudice, 122 Public opinion research, See Survey preparation, 304-305 Presidential elections, 180-183 research qualitative interviewing, 305-308 Presser, Stanley, 257, 272 reactivity, 290 Pretesting, 222-223 Public Perspective, 278 recording observations, 309-312 Pretesting the questionnaire, 257 Public sociologist, 80 relations with subjects, 291-293 Prewitt, Kenneth, 78 Pure research, 25 reliability, 314 Primary group, 35 Purposes of research, 87-90 role of observer, 289-291 Principles, 43 Purposive sampling, 184 shopping in Romania, 297 Privacy (confidentiality), 65-67, Push polL 245 strengths/weaknesses, 312-313 Pygmalion (Shaw), 234 taking notes, 311 68 Pygmalion effect, 235 teacher-student negotiations, 298- Probability proportionate to size Qualitative data, 24 299 (PPS) sampling, 211-213 Qualitative data analysis, 328, 377- uses, 286-289 Probability sampling, 187-215 validity, 313-314 403 web sites, 317 assumptions, 198 analysis tools/computer software, welfare policy, 302 cautions, 198-199 Qualitative interviewing, 305-308 cluster sampling, 209. See also 390-398 Qualitative Report, The, 511 coding, 384-388 Quantification of data, 405-409 Multistage cluster sampling concept mapping, 389-390 Quantiphrenia, 375 confidence intervaL 197-198 conversation analysis (CA), 383 Quantitative data, 23-24 confidence leveL 197-198 defined, 378 Quantitative data analysis, 404-429 defined, 187 grounded theory method (GTM), bivariate analysis, 419-423 disproportionate sampling and codebook, 408-409 380-381 coding, 405-409 weighting, 213-215 Leviticus example, 386-388, 391- collapsing response categories, EPSEM samples, 189 experiments, 225-226 393 416-418 illustration (sampling university linking theory and analysis, 378- data entry, 409 defined,405 students),208 384 don't know/no opinion, 418-419 PPS sampling, 211-213 memoing, 388-389 multivariate analysis, 424-425 probability theory, 191-197 NUD*IST, 391-393, 396 numerical descriptions in qualita- random selection, 190-191 numerical descriptions, 419 representativeness, 189-190 NVivo, 393-398 tive research, 419 sampling bias, 188-189 patterns, 378-380 percentaging a table, 421, 422 sampling distribution, 191-196 qualitative data processing, 384- quantification of data, 405-409 sampling error, 196-197 sociological diagnostics, 425-427 sampling frame, 199-202 390 subgroup comparisons, 416-419 simple random sampling, 202 quantitative data, 398-400 univariate analysis, 409-416, See stratified sampling, 205-207 semiotics, 381-383 systematic sampling, 202-205, web sites, 403 also Univariate analysis Qualitative data processing, 384-390 Quasi-experimental design, 357-361 207-208 Qualitative evaluations, 361-362 Question underlying concept, 187 Qualitative field research, 285-317 Probability theory, 191-197 academic change, 297 c1osed-ended,246-247 Probe, 267 AIDS prevention, 299-300 contingency, 252-254 Procedural knowledge, 39 blocking a demolition, 303 double-barreled, 247, 248 Professional codes of ethics, 71, 72 case study, 298-300 matrix, 254-255 Professional papers, 504 compulsory heterosexuality, 30 I open-ended,246-247 Program evaluation, 351. See also ethics, 312 Questionnaire, 245-257 ethnography, 293-294 bias, 250-251 Evaluation research ethnomethodology, 294-296 clarity, 247 Proportionate reduction of error extended case method, 298 closed-ended questions, 246-247 focus group, 308-309 contingency questions, 252-254 (PRE),452 grounded theory, 296-300 defined, 246 Propositions, 43-44 institutional ethnography, 300- double-barreled questions, 247, Protection from harm, 27, 63-64, 301 248 68 mothering, schooling, child devel- format, 252 Protestal1t Ethic and the Spirit of Capital- instructions, 256-257 opment, 300-301 ism (Weber), 339 naturalism, 293-294 PRWORA, 302 PAR 301-303 Psychological Abstracts, A6 Public opinion call-in polls, 189

110 Index Questionnaire (continued) comparative and historical re- Report writing, 503-509 matrix questions, 254-255 search, 494-495 aim of report, 504-505 negative items, 250 analysis/interpretation, 507 open-ended questions, 246-247 content analysis, 494 audience, 503 ordering of items, 255-256 data analysis, 495 details, 508 pretesting, 257 evaluation research, 495 formilength, 503-504 questions and statements, 246 experiments, 493 general guidelines, 503 relevancy of questions, 249 field research, 494 plagiarism, 505-507 sample, 258-259 journal article, 490 publication of the report, 508-509 sample questionnaire, A-8 to measurement, 492 purpose/overview, 505 A-17 questions to ask, 491-495 qualification/conditions, 508 self-administeredo See Self- reporting, 495 reporting analyses, 507-508 administered questionnaire research design, 491-492 review of literature, 505 short items, 249-250 sampling, 492 sponsors of research, 503-504 survey research, 493 study design/execution, 507 Quinley, Harold E, 472 theoretical orientation, 491 style, 503, 508 Quoss, Bernita, 302 Real definition, 128 summary and conclusions, 507, Quota frame, 185 Reality, 4-10 508 Quota matrix, 226, 227 authority, 5-6 supporting materials, 508 Quota sampling, 185-186 errors in inquiry, 6-7 modern view, 8 Reporting, 308 R. 458 ordinary human inquiry, 4-5 Reporting results, 68-69 r,455 postmodern view, 8-10 Representativeness, 189-190 r 2,457 premodern view, 8 Reprints, 509 Race and ethnicity tradition, 5 Research . See Social research Received knowledge, 38 Research design, 86-119 achievement, 76-77 Redden, David, 209 history of race relations, 342 Redfield, Robert, 338 approximately longitudinal stud- IQ test results, 76 Reductionism, 100-101 ies, 105-106 lynchings, 320 Reference group, 433 separate but equal. 75-76 Reference group theory, 12 cohort study, 103-104 televised historical sagas, 239 Reference sources, A2-A4. See also cross-sectional study, 102 Tuskegee research deception, 365 longitudinal study, 102-105 Radcliffe-Brown, Alfred, 131 Sources of information necessary and sufficient causes, Radio call-in polls, 189 Reflexivity, 292 Ragin, Charles, 298 Regoli, Mary Jean, 368-369 93-94 Randers, J0rgen, 371 Regression analysis, 455-459 nomothetic explanation, 90-93 Random -digit dialing, 191 Regression equation, 456-457 panel study, 104-105 Random numbers, 202-204, A18- Regression line, 456, 457 research proposal. 113-115 Regression to the mean, 231 research strategies, 107 A19 Reicker, Henry, 103 steps in process, 107-113 Random selection, 190-191, 198 Reification, 124 time dimension, 10 1 Randomization, 226 Reifman, Alan, 215 trend study, 103 Range of variation, 134-135 Reinharz, Shulamit, 58, 320 units of analysis, 94-101 . See also Rao, Nagesh, 349, 353 Relative deprivation, 433 Rape crisis centers, 56 Reliability, 143-146 Units of analysis Rape reform legislation, 367-368 Research ethicso See Ethical issues Rasinski. Kenneth A, 251. 283 analyzing existing statistics, 334- Research monograph, 491 Rastafarianism, 56 335 Research process, 107-1130 See also Ratio, 138-139 Ratio variables, 455 defined, 143 Social research process Rational objectivity, 40-42 ensuring, 146 Research proposal. 113-115, 118- Ray, Melvin, 169 established measures, 145 Ray, William Jo, 241 field research, 314 119 Reactivity, 290 research workers, as, 146 Research report. See Report writing Readers' Guide to Periodical Literatl/re, split-half method, 145 Research strategies, 107 test-retest method, 145 Respondent, 244, 248-249 A2, A3 validity, and, 148-149 Response rate, 262-263, 271-272 Reading social research, 490-496 web site, 178 Response variable, 352 Religious affiliation, 121 Return rate, 262 analyzing existing statistics, 494 Replication, 6, 146, 334,437 Reverse social distance scale, 169 book /research monograph, 491 Review of literature, 489-490, 505 Reynolds, H. T, 482 Richlin-Klonsky, Judith, A7

Index 111 Riecken, Henry, 353 probability. See Probability Secondary analysis, 277-280 Ringer. Benjamin So, 48, 209, 440 sampling Selection biases, 231 Ritzer, George, 59 Selective coding, 386 Rockwood, Todd H., 263 purposive, 184 Selective observation, 7 Roethlisberger, E J.. 224 quota, 185-186 Self-administered questionnaire, Rog, Debra Jo, 353, 372, 373 random selection, 190-191 Rogers, Everett Mo, 349, 353, 362 representativeness, 189-190 257-264 Rokeach, Milton, 239 simple random, 202 case study, 263-264 Romanian shopping, 297 snowball. 184-185 follow-up mailings, 261-262 Roosevelt, Franklin, 182,245 stratified, 205-207 mail distribution and return, 260- Roots, 239 systematic, 202-205 Roors.: The Next Generation, 239 theoretical. 297 261 Roper Center for Public Opinion Re- web sites, 217 monitoring returns, 261 Sampling bias, 188-189 response rate, 262-263 search,278 Sampling distribution, 191-196 Self-weighting sample, 213 Rosenau, Pauline Marie, 59 Sampling error, 196-197,210, A23 Selvin, Hanan, 192,470 Rosenbaum, W. L, 469, 470 Sampling frame, 199-202 Semantic differential. 171-172 Rosenberg, Morris, 99, 151. 177, Sampling interval. 204 Semiotics, 381-383 Sampling ratio, 204 Sensate, 339 442-444,447 Sampling unit, 191 Senter, Mary Scheuer, 199 Rosenthal. Robert, 235 Samurai. 340 Separate but equal. 75-76 Rosenthal-Jacobson study, 235 Sanders, William, 89-90 SES, 128 Ross, Catherine, 97 Sapp, Stephen, 169 Sexual research, 77 Ross, Jeffrey, 87 SAT, 145 Shaffir, William B, 291. 316 Rossi, Peter He, 150,217,283,372, Scale, 153, 168-174 Shapiro, Lilian L A4 Bogardus social distance scale, Shaw, George Bernard, 234 373,447 Shea, Christopher, 70 Rossman, Gabriel. 321 168-169 Sheatsley, Paul E, 283 Rossman, Gretchen, 128, 289, 290 construction logic, 155 Sherif. Muzafer, 41 Rothman, Ellen, 341 defined, 154 Sherkat, Darren E, 117 Rubel. Maximillien, 339 Guttman scaling, 172 -1 74 Sherrod, Randy, 263 Rubin, Herbert, 305, 307 index, contrasted, 153-156 Shingaku, 340, 342 Rubin, Riene, 305, 307 Likert scaling, l70-l71 Shopping in Romania, 297 Ruby Ridge, Idaho, 294 semantic differential. 171-172 Shostak. Arthur, 316 Rudestam, Kjell Erik, 429 Thurston scaling, 169-170 Siang, Sanyin, 71 Rueschemeyer, Dietrich, 338 Scale types, 173 Sieber. Joan, 73 Rwanda, 127 \"Scandal in Bohemia, p.:' (Conan Signs, 382 Rydell. C Peter. 366 Silence, 38 Rynbrandt, Linda J., 38 Doyle). 51 Silverman, David, 151. 316, 383, Scarce, Rik, 66 Sacks, Jeffrey, 145 Scattergram, 456, 457 419 Salzinger. L. 298 Schachter. Stanley, 103 Silverman, George, 309 Sample of convenience, 188 Schiffman, ]\" 298 Simmel. Georg, 35 Sample questionnaire, 258-259 Schiflett, Kathy L, 117 Simple random sampling, 202 Sample size, 197, 198 Schmitt, Frederika E., 56, 107 SINET' A Quarterly Review ofSocial Re- Sampling, 179-217 Scholastic Assessment Test (SAT), ports and Research on Social Indica- bias, 188-189 145 tors, Social 'Trmds, and the Quality cluster, 209. See also Multistage Schuler, Roberleigh, 105 of Life, 337 Schutz, Alfred, 294 Singer. Eleanor, 272 cluster sampling Schwartz, Richard D., 319, 346 Singh, Amarjit, 82 content analysis, 320-330 Scientific observation, 6 Singh, N., 177 defined, 180 Scientific paradigms, 32 Six degrees of separation, 237 Dewey-Truman presidential elec- Scientific polls, 244-245, A24-A29. Skedsvold, Paula, 70 Skinner, Steven, 263 tion, 182-183, 185 See also Survey research Skocpol. Theda, 341 EPSEM, 189 Scientific theory, 11 Slinkard, Lee Ann, 359 frame, 199-202 Scientism, 375 Small-group interaction, 221 judgmental. 184 Scott, W. Richard, 282 Small World Phenomenon, 237 Literary Digest presidential poll. Search engine, 497 Smith, Andrew, 249, 255 Sechrest, Lee, 319, 346 Smith, Dorothy, 300 181-182 Second economy, 351 nonprobability, 183 -187 PPS, 211-213

112 !:l Index Smith, Eric R\" A N, 283 Socioeconomic status (SES), 128 gamma, 453-455 Smith, Tom W, 278 Sociological Abstracts, A6 GIS, 483, 484 Smith, vickL 107 Sociological diagnostics, 425-427 inferential statistics, 459-460 Snow, David A\", 107, 293, 294 Solomon four-group design, 233 lambda, 452-453 Snowball sampling, 184-185 Sood, SuruchL 349, 353 linear regression, 456-457 Soap opera research, 349, 353, 362 Sorokin, Pitirim A\", 338-339 log-linear models, 482-483 Social Darwinism, 34 Sorting memo, 389 measures of association, 451-455 Social desirability, 251 Souls ofthe Black Folk, The (DuBois), multiple regression, 458 Social groups, 96 partial regression, 458 Social indicators, 369, 371 39 path analysis, 471 Social indicators research, 369-371 Sources of information PRE. 452 Social interactions, 97, 99 r,455 Social interactions as units of analy- comparative and historical re- r2,457 search, 341-342 regression analysis, 455-459 sis, 97, 99 statistical significance, 461-469 Social Organization of Sexuality 77 evaluating sources, 343 time-series analysis, 471-473 Social organizations, 97 existing statistics, 335-338 univariate inferences, 460-461 Social regularities, 11-13 InfoTrac College Edition, 489 Statistical regression, 231 Social research library, A2-A7 Statistical significance, 461-469 Library of Congress, 489 Stearns, Cindy A\", 107 dialectics, 19-26 web sites\" See Web sites Stebbins, Robert A, 291. 316 ethics, 26-27\" See also Ethical Sparkes, Andrew C, 25 Stefancic. Jean, 58 Specification, 128,439-442 Stein, Gertrude, 8 issues Spencer, Herbert, 34, 35 Sterling, To D, 107,469,470 foundations, 10-19 Split-half method, 145 Stimulus, 223 politics oL See Politics of social Spohn, Cassie, 367-368 Stoica, Catalin, 297 Spoiled identity, 54 StosseL John, 335 research Sponsors of research paper. 503-504 Stouffer. Samuel. 12, 375,431-433, purposes, 87-90 race, 75-77 SPSS 445 reading, 490-496 bar chart. 411 Stouffer'S army studies, 431-433 writing, 503-509 GSS attendance at religious ser- Strang, David, 117 Social Research Online, 511 vices, 410 Stratification, 205, 207-208, 211 Social research process, 107-113 multivariate relationships, 162 Stratified sampling, 205-207,324 analysis, 112 quantitative analysis, 405 Strauss, Anselm, 54, 296, 316, 328, application, 112 choice of research method, 110 Spurious relationship, 91. 92 378-381,385,388,402 conceptualization, 110 Squire, PeverilL 283 Street Corner Society (Whyte). 293 data processing, 111-112 Srole, Leo, 131 Street directories, 201 observations, III Staab, Jennifer. 209 Street gangs, 97 operationalization, III Stacks, A4-A5 Strenski, Ellen, A7 overview (flowchart), 108 Standard deviation, 414-415 Structural functionalism, 37 population and sampling, III Standard error, 196-197,461 Structure of Scientific Revolutions, The starting up, 109-110 Standards, 235-236 Social Science Data Archives, 151 Stanford Prison Experiment, 242 (Kuhn),33 Social science paradigms, 31-42 Stanley, Julian, 230, 233, 234, 241 Structuring of inquiry, 84-217 conflict paradigm, 34-35 Stark, Rodney, 472 critical race theory, 39 State transformation theory, 56 conceptualization, 124-132 early positivism, 33-34 Static-group comparison, 228, 229 index, 153-168 ethnomethodology, 36 Statistic, 195 measurement\" See Measurement feminist paradigms, 37-39 Statistical Abstract of the United States, operationalization, 134-142 microtheory/macrotheory, 33 research design, 86-119\"\" See also rational objectivity, 40-42 335 social Darwinism, 34 Statistical analyses, 449-451 Research design structural functionalism, 37 sampling, 179-2170 See also symbolic interactionism, 35-36 ANOVA,476-478 \"Social Structure and Anomie\" chi square, 466-468 Sampling curvilinear regression, 458-459 scale, 168-174 (Merton), 130 data reduction, 450-451 typology, 175-176 Social theory, 10,43-44 degrees of freedom, 467-468 Strunk, William, Jr\" 503 Sociobiology, 101 descriptive statistics, 450-459 Student term papers, 504 discriminant analysis, 478-482 Study ofMan, The (Linton), 37 factor analysis, 474-476