Russel-Research-Method-in-Anthropology

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TABLE 21.28 Similarity Matrix from One Person’s Pile Sorting of the 18 Fruits 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 AP OR PA MA PE BL WA PI PE ST LE CA GR PL BA AV FI CH 1 APPLE 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 2 ORANGE 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 3 PAPAYA 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 4 MANGO 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 5 PEACH 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 6 BLUEBERRY 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 7 WATERMELON 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 8 PINEAPPLE 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 9 PEAR 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 10 STRAWBERRY 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 11 LEMON 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 12 CANTELOPE 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 13 GRAPEFRUIT 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 14 PLUM 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 15 BANANA 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 16 AVOCADO 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 17 FIG 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 18 CHERRY 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1
Multivariate Analysis 687 1.71 1.32 GRAPEFRUIT LEMON ORANGE 0.94 0.55 0.17 -0.22 CANTELOPE STRAWBERRY WATERMELON BLUEBERRY PLUM APPLE FIG PEACH CHERRY PEAR -0.60 -0.98 MANGO PAPAYA PINEAPPLE BANANA -1.37 AVOCADO -1.75 -2.14 -2.14 -1.37 -0.60 0.17 0.94 1.71 Figure 21.5. Two-dimensional MDS for 18 fruits sorted by one informant. mant sees the similarities among these 18 fruits. It is, in other words, a mental map. Make no mistake here. The MDS graph is simply a representation of what the informant was thinking when he pile sorted those fruits. No one claims that MDS graphs are one-for-one maps of what’s happening inside people’s heads. Now, suppose we collect a whole set of pile sort data from, say, 20 informants. That would produce 20 individual similarity matrices. If we stacked those matrices on top of one another we could look down through each cell, count up the number of 1s, and divide by 20. That would tell us the percentage of times that the 20 informants put a pair of items (fruits, for example) together in a pile. This would create an aggregate proximity matrix—a summary of percentages, rather than just hits, that tell us how similar, on average
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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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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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Page 673 and 674: TABLE 21.11 Wealth by Education, Co
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Page 685 and 686: Multivariate Analysis 667 In fact,
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Page 693 and 694: Multivariate Analysis 675 BTU and O
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Page 707 and 708: Multivariate Analysis 689 ‘‘chi
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Page 711 and 712: Multivariate Analysis 693 Lambros C
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Page 716 and 717: 698 Appendix A 32179 00597 87379 25
Page 718 and 719: APPENDIX B Table of Areas under a N
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Page 722 and 723: APPENDIX D Chi-Square Distribution
Page 724 and 725: APPENDIX E F Table for the .05 Leve
Page 726 and 727: 708 Appendix E F Table for the .01
Page 728 and 729: APPENDIX F Resources for Fieldworke
Page 730 and 731: 712 References obesity in Saudi Ara
Page 732 and 733: 714 References Beautrais, A. L., P.
Page 734 and 735: 716 References the self-assessment
Page 736 and 737: 718 References Bradburn, N. M. 1983
Page 738 and 739: 720 References ments by considering
Page 740 and 741: 722 References ing in reflected glo
Page 742 and 743: 724 References village. In Self, se
Page 744 and 745: 726 References Duncan, O. D. 1966.
Page 746 and 747: 728 References achieve high respons
Page 748 and 749: 730 References Gatz, M., and M. Hur
Page 750 and 751: 732 References C2KBR/01-1. Washingt
Page 752 and 753: 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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