Russel-Research-Method-in-Anthropology

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542 Chapter 18 TABLE 18.5 A Componential Analysis of Four Things with Two Features Feature 1 Feature 2 Thing 1 Thing 2 Thing 3 Thing 4 , and thing 4 can be . Each bundle of features is different and defines each of the four things. With three binary features, you can distinguish 8 things; with four, 16; with five, 32; and so on. When componential analysis was introduced into cultural anthropology, it was applied to the set of English kinship terms (Goodenough 1956) and it continues to be used for understanding kinship systems (Rushforth 1982; Kendall 1983; Hedican 1986; Pericliev and Valdez-Perez 1998). A ‘‘daughter’’ in English, for example, is a consanguineal, female, descending generation person. So is a niece, but a niece is through a sibling or a spouse. Table 18.6 shows the distinctive feature principle applied to a list of 22 barnyard animals. Stallions are adult male horses and foals are baby horses. Notice that there is no column in table 18.6 labeled ‘‘male’’ and no column labeled ‘‘juvenile.’’ A parsimonious set of features for distinguishing among these animals does not require all that information. A stallion is a nonfemale, gendered (that is, not neutered), adult horse. Any horse that is not female and gendered and not an adult must be a colt. A barrow is a neutered, adult hog, and a wether is a neutered, adult sheep. Actually, if we wanted the most parsimonious set of features we would drop one of the last four columns in table 18.6. Those columns identify the general class of animals—horses, cattle, sheep, and swine—to which each named animal belongs. If the domain of barnyard animals comprised just those four classes, then a not-horse, not-cattle, not-sheep animal must be a swine. I’ve left all four animal classes in table 18.6 because there are other classes of barnyard animals not yet represented (chickens, rabbits, goats, etc.). Componential analysis can be applied to any domain of a language where you are interested in understanding the semantic features that make up the domain. Table 18.7 shows a componential analysis of seven cars, using three features elicited from Jack (he of the taxonomy shown in figure 18.6). A Corvette is an expensive car, not very practical, and not foreign; a Mercedes is an expensive, practical, foreign car; and so on. Each of the seven cars is uniquely defined by the three features Jack mentioned.
Qualitative Data Analysis II: Models and Matrices 543 TABLE 18.6 A Componential Analysis of 17 Barnyard Animals Female Neuter Adult Horses Cattle Sheep Swine Cow Bull Steer Calf Heifer Mare Stallion Gelding Foal Filly Colt Sow Boar Barrow Piglet Gilt Shoat Sheep Ram Ewe Wether Lamb TABLE 18.7 Minimal Componential Analysis for Seven Cars, According to Jack Expensive Practical Foreign Corvette Firebird MG Maserati Mercedes Jeep Dodge Van
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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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11 ◆ Structured Interviewing II:
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Structured Interviewing II: Cultura
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Scales and Scaling 319 10 times, or
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Scales and Scaling 321 students’
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Scales and Scaling 323 points. The
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Scales and Scaling 325 TABLE 12.2a
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Scales and Scaling 327 wealth in vi
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Scales and Scaling 329 You may obse
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Scales and Scaling 331 interitem co
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Scales and Scaling 333 ality of a s
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Scales and Scaling 335 TABLE 12.7 T
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Scales and Scaling 337 Having a Col
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Scales and Scaling 339 The Cantril
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Scales and Scaling 341 and has been
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Participant Observation 343 Romanci
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Participant Observation 345 sion, s
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Participant Observation 347 doses o
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Participant Observation 349 great s
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Participant Observation 351 less—
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Participant Observation 353 oped by
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Participant Observation 355 Graham
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Participant Observation 357 ters fr
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Participant Observation 359 washing
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Participant Observation 361 that yo
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Participant Observation 363 ‘‘c
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Participant Observation 365 hall wh
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Participant Observation 367 Maintai
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Participant Observation 369 not to
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Participant Observation 371 pletely
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Participant Observation 373 changed
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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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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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