Russel-Research-Method-in-Anthropology

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324 Chapter 12 those three cases would be responsible for six errors—you’d have to stick in two pluses for each of the cases to make them come out according to the hypothesis. Yes, you could make it just three, not six errors, by sticking a minus sign in column 3. Some researchers use this scoring method, but I prefer the more conservative method of scoring more errors. It keeps you on your toes. Finally, we don’t expect that minus sign in column 2 of respondent 16’s data. That case creates just one error (you only need to put in one plus to make it come out right). All together, that makes 3 6 1 10 errors in the attempt to reproduce a perfect scale. For table 12.1, the CR is 1 (10/48) .79 which is to say that the data come within 21% of scaling perfectly. By convention, a coefficient of reproducibility of .90 or greater is accepted as a significant approximation of a perfect scale (Guttman 1950). I’m willing to settle for around .85, especially with the conservative method for scoring errors, but .79 just isn’t up to it, so these data fail the Guttman test for unidimensionality. Some Examples of a Guttman Scale Robert Carneiro (1962, 1970) had an idea that cultural evolution is orderly and cumulative. If he is right, then cultures evolve by adding certain traits in an orderly way and should show a Guttman-scale-like pattern. Carneiro coded 100 cultures for 354 traits and looked at the pattern. Table 12.2 shows a sample of 12 societies and 11 traits. When you collect data on cases, you don’t know what (if any) pattern will emerge, so you pretty much grab cases and code them for traits in random order. The 12 societies and traits in Table 12.2a are in random order. The first thing to do is arrange the pluses and minuses in their ‘‘best’’ possible order—the order that conforms most to the perfect Guttman scale—and compute the CR. We look for the trait that occurs most frequently (the one with the most pluses across the row) and place that one at the bottom of the matrix. The most frequently occurring trait is the existence of special religious practitioners. Then we look for the next most frequent trait and put it on the next to the bottom row of the matrix. We keep doing this until we rearrange the data to take advantage of whatever underlying pattern is hiding in the matrix. The best arrangement of the pluses and minuses is shown in table 12.2b. Now we can count up the ‘‘errors’’ in the matrix and compute Guttman’s coefficient of reproducibility. For these 12 societies and 11 traits, the coefficient is a perfect 1.0. Of course, it’s one thing to find this kind of blatant pattern in a matrix of
Scales and Scaling 325 TABLE 12.2a Carneiro’s Matrix Showing the Presence () or Absence () of 11 Culture Traits among 12 Societies. The Order of Both the Traits and the Societies Is Random. Society 1 2 3 4 5 6 7 8 9 10 11 12 Political leader has considerable authority Sumptuary laws Headman, chief, or king Surplus of food regularly produced Trade between communities Ruler grants audiences Special religious practitioners Paved streets Agriculture provides 75% of subsistence Full-time service specialists Settlements 100 persons Societies: 1 Iroquois, 2 Marquesans, 3 Tasmanians, 4 Yahgan, 5 Dahomey, 6, Mundurucú, 7 Ao Naga, 8 Inca, 9 Semang, 10 Tanala, 11 Vedda, 12 Bontoc SOURCE: A Handbook of Method in Cultural Anthropology by Raoul Naroll and Ronald Cohen, eds., copyright 1970 by Raoul Naroll and Ronald Cohen. Used by permission of Doubleday, a division of Random House, Inc. TABLE 12.2b The Data in Table 12.2a Rearranged: The Data Form a Perfect Guttman Scale Society 1 2 3 4 5 6 7 8 9 10 11 12 Paved streets Sumptuary laws Full-time service specialists Ruler grants audiences Political leader has considerable authority Surplus of food regularly produced Agriculture provides 75% of subsistence Settlements 100 persons Headman, chief, or king Trade between communities Special religious practitioners Societies: 1 Tasmanians, 2 Semang, 3 Yahgan, 4 Vedda, 5 Mundurucú, 6 Ao Naga, 7 Bontoc, 8 Iroquis, 9 Tanala, 10 Marquesans, 11 Dahomey, 12 Inca
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H. Russell Bernard FOURTH EDITION R
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Research Methods in Anthropology Fo
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Contents Preface vii 1. Anthropolog
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Preface Since 1988, when I wrote th
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Preface ix sampling: why we do it a
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Preface xi 21, you will be able to
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Preface xiii testimony. There is an
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Preface xv begin writing the second
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1 ◆ Anthropology and the Social S
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Anthropology and the Social Science
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The Foundations of Social Research
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3 ◆ Preparing for Research Settin
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Preparing for Research 71 research
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Preparing for Research 73 morning u
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Preparing for Research 75 the AAA b
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Preparing for Research 77 showed wh
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Preparing for Research 79 That will
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Preparing for Research 81 mothers.
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Preparing for Research 83 gathering
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Preparing for Research 85 hypothesi
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Preparing for Research 87 I mention
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Preparing for Research 89 A Guide t
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Preparing for Research 91 people re
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Preparing for Research 93 Observed
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Preparing for Research 95 dent. (Re
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The Literature Search 97 too busy t
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The Literature Search 99 year, who
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The Literature Search 101 like demo
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The Literature Search 103 reports e
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The Literature Search 105 gists, in
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The Literature Search 107 Women Sca
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5 ◆ Research Design: Experiments
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Research Design: Experiments and Ex
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Sampling 147 ried around here deliv
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Sampling 149 the potential sampling
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Sampling 151 they’re joined by al
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Sampling 153 116. If the first rand
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Sampling 155 off base in our thinki
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Sampling 157 Cluster Sampling and C
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Sampling 159 Probability Proportion
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Sampling 161 you will properly surv
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Sampling 163 the cluster, the large
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Sampling 165 TABLE 6.2 Comparison o
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Sampling 167 25 Number of Household
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7 ◆ Sampling Theory Distributions
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Sampling Theory 171 In normal distr
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Sampling Theory 173 a lot of inform
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Sampling Theory 175 TABLE 7.2 All S
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Sampling Theory 177 12 10 Number of
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Sampling Theory 179 Calculating Sam
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Sampling Theory 181 Look up what’
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Sampling Theory 183 and we solve fo
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Sampling Theory 185 ables combine t
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Nonprobability Sampling and Choosin
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TABLE 8.4 Matches, Proportions of M
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Interviewing: Unstructured and Semi
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10 ◆ Structured Interviewing I: Q
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Structured Interviewing I: Question
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Participant Observation 375 Besides
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Participant Observation 377 Thomas
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Participant Observation 379 But not
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Participant Observation 381 baby’
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Participant Observation 383 into fo
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Participant Observation 385 in Mace
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14 ◆ Field Notes: How to Take The
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Field Notes: How to Take Them, Code
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15 ◆ Direct and Indirect Observat
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Direct and Indirect Observation 415
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16 ◆ Introduction to Qualitative
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Introduction to Qualitative and Qua
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START: SEARCH WHOLESALE MARKET FOR
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17 ◆ Qualitative Data Analysis I:
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Qualitative Data Analysis I: Text A
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Qualitative Data Analysis II: Model
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19 ◆ Univariate Analysis The next
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Univariate Analysis 551 But if you
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Univariate Analysis 553 TABLE 19.1
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Univariate Analysis 555 Notice in t
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Univariate Analysis 559 TABLE 19.3b
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Univariate Analysis 561 possible an
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Univariate Analysis 563 people hang
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Univariate Analysis 573 Number of C
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Univariate Analysis 575 some basic
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Univariate Analysis 577 interquarti
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Univariate Analysis 579 countries t
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Univariate Analysis 581 Both groups
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Univariate Analysis 585 would be no
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Univariate Analysis 587 rejecting H
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Univariate Analysis 589 lation, 68.
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Univariate Analysis 591 of Shipibo
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TABLE 19.13 Chi-Square for a Univar
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Bivariate Analysis: Testing Relatio
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TABLE 20.20 Correlation Matrix of A
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21 ◆ Multivariate Analysis Most o
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Multivariate Analysis 651 environme
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Multivariate Analysis 653 TABLE 21.
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TABLE 21.11 Wealth by Education, Co
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Multivariate Analysis 657 it is hig
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Multivariate Analysis 659 ideas abo
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Multivariate Analysis 661 You can a
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Multivariate Analysis 663 which mea
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Multivariate Analysis 667 In fact,
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Multivariate Analysis 669 under plo
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Multivariate Analysis 671 fr .968 R
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Multivariate Analysis 673 earity pr
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Multivariate Analysis 675 BTU and O
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Multivariate Analysis 677 Factor An
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Multivariate Analysis 679 columns h
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Multivariate Analysis 681 4. Across
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Multivariate Analysis 683 instead o
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Multivariate Analysis 687 1.71 1.32
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Multivariate Analysis 689 ‘‘chi
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Multivariate Analysis 691 6, 1, 4.
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Multivariate Analysis 693 Lambros C
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Multivariate Analysis 695 just beca
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698 Appendix A 32179 00597 87379 25
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APPENDIX B Table of Areas under a N
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702 Appendix B (A) (B) (C) (A) (B)
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APPENDIX D Chi-Square Distribution
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APPENDIX E F Table for the .05 Leve
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708 Appendix E F Table for the .01
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APPENDIX F Resources for Fieldworke
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712 References obesity in Saudi Ara
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714 References Beautrais, A. L., P.
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716 References the self-assessment
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718 References Bradburn, N. M. 1983
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720 References ments by considering
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722 References ing in reflected glo
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724 References village. In Self, se
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726 References Duncan, O. D. 1966.
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728 References achieve high respons
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730 References Gatz, M., and M. Hur
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732 References C2KBR/01-1. Washingt
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734 References study of relationshi
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736 References Hornik, J., and S. E
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738 References esses in survey rese
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740 References Kessler, R. C., and
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742 References Laban Moogi Gwako, E
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744 References Lewis, R. B. 2004. N
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746 References McCall, G. 1978. Obs
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748 References Morgan, D. L. 1997.
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750 References Oakhill, J., and A.
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752 References Piliavin, I. M., J.
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754 References nicity-of-interviewe
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756 References Rotton, J., and M. S
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758 References Schwarts, N. A. 2004
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760 References Smith, L. D. 1986. B
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762 References Stricker, L. J. 1988
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764 References pharmacists in HIV/S
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766 References Webb, E. J., D. T. C
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768 References Williams, T. 1996. E
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Subject Index Numbers in italics re
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Subject Index 773 Campos, Alberto,
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Subject Index 775 data analysis, 45
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Subject Index 777 external states,
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Subject Index 779 individual attrib
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Subject Index 781 median, 562, 563-
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Subject Index 783 parsimony, princi
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Subject Index 785 random digit dial
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Subject Index 787 simple scales, 31
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Subject Index 789 T-scores, 591 t-t
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Author Index Numbers in italics ref
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Author Index 793 Choi, N., 33 Choms
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Author Index 795 Gregson, S., 93 Gr
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Author Index 797 Leap, W., 376 LeCo
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Author Index 799 Rashid, R. G., 121
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Author Index 801 Vadez, V., 55, 93
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