1. Typology: Language Sampling Anna Siewierska & Dik Bakker

2. Typology: Language Sampling2 Empirical Cycle L L L L L L L L L L L L L L L Definition Categories: C1 … C3 Hypotheses PROVISIONAL DATA

3. Typology: Language Sampling3 Empirical Cycle L L L L L L L L L L L L L L L Definition Categories: C1 … C3 Hypotheses PROVISIONAL DATA

4. Typology: Language Sampling4 Empirical Cycle L L L L L L L L L L L L L L L Definition Categories: C1 … C3 Hypotheses PROVISIONAL DATA TEST

5. Typology: Language Sampling5 Empirical Cycle L L L L L L L L L L L L L L L L Definition Categories: C1 … C3 Hypotheses PROVISIONAL DATA L L L L L L L L L TEST

6. Typology: Language Sampling6 Empirical Cycle L L L L L L L L L L L L L L L L Definition Categories: C1 … C3 Hypotheses PROVISIONAL DATA L L L L L L L L L TEST

7. Typology: Language Sampling7 Overview

8. Typology: Language Sampling8 Overview 1.Collecting language data 2.Why a sample? 3.Types of biases in samples 4.Two strategies 5.Samples in typological literature 6.The DV method

9. Typology: Language Sampling9 Data collecting Languages of the world: n  7000

10. Typology: Language Sampling10 Data collecting Languages of the world: n  7000 S A M P L E (50 – 500)

11. Typology: Language Sampling11 Data collecting Why not all languages in our database ? - Too many - Only <1000 well described (grammar) <2000 partial sketch - Not (always) necessary - Sometimes even wrong - Impossible even in principle

12. Typology: Language Sampling12 All Languages: impossible Extant languages:7000

13. Typology: Language Sampling13 All Languages: impossible Extant languages:7000 Extinct languages: 500 (Ruhlen 1991)

14. Typology: Language Sampling14 All Languages: impossible Extant languages:7000 Extinct languages: 500 (Ruhlen 1991) - Latin, Cl. Greek, Gothic, Hebrew, Hittite, …

15. Typology: Language Sampling15 All Languages: impossible Extant languages:7000 Extinct languages: 500 (Ruhlen 1991) - Latin, Cl. Greek, Gothic, Hebrew, Hittite, … - Cl. Turkic, Cl.Tibetan, Archaic Chinese, …

16. Typology: Language Sampling16 All Languages: impossible Extant languages:7000 Extinct languages: 500 (Ruhlen 1991) - Latin, Cl. Greek, Gothic, Hebrew, Hittite, … - Cl. Turkic, Cl.Tibetan, Archaic Chinese, … - Manx, Cornish, …

17. Typology: Language Sampling17 All Languages: impossible Extant languages:7000 Extinct languages: 500 (Ruhlen 1991) - Latin, Cl. Greek, Gothic, Hebrew, Hittite, … - Cl. Turkic, Cl.Tibetan, Archaic Chinese, … - Manx, Cornish, … Problem?

18. Typology: Language Sampling18 All Languages: impossible Extant languages:7000 Extinct languages: 500 (Ruhlen 1991) - Latin, Cl. Greek, Gothic, Hebrew, Hittite, … - Cl. Turkic, Cl.Tibetan, Archaic Chinese, … - Manx, Cornish, … No native speaker intuitions …

19. Typology: Language Sampling19 All Languages: impossible Extant languages:7000 Extinct languages: 500 (Ruhlen 1991) - Latin, Cl. Greek, Gothic, Hebrew, Hittite, … - Cl. Turkic, Cl.Tibetan, Archaic Chinese, … - Manx, Cornish, … - Illinois, Mohican, Massachusett, Carolina, …

20. Typology: Language Sampling20 All Languages: impossible Extant languages:7000 Extinct languages: 500 (Ruhlen 1991) - Latin, Cl. Greek, Gothic, Hebrew, Hittite, … - Cl. Turkic, Cl.Tibetan, Archaic Chinese, … - Manx, Cornish, … - Illinois, Mohican, Massachusett, Carolina, … - X1, X2, X3, …, Xn

21. Typology: Language Sampling21 All Languages: impossible Extant languages:7000 Extinct languages: 500 (Ruhlen 1991) X1, X2, X3, …, Xn????

22. Typology: Language Sampling22 All Languages: impossible Homo Sapiens200,000 BP Geat Leap Forward 40,000 BP Average n of lgs6000 Diachronic change1000 year X lgs: (40,000 / 1000) * 6000 = 240,000

23. Typology: Language Sampling23 All Languages: impossible Extant languages: 7000 Extinct languages: 500 X1, X2, X3, …, Xn240,000 Human languages247,500

24. Typology: Language Sampling24 All Languages: impossible Extant languages: 7000 Extinct languages: 500 X1, X2, X3, …, Xn240,000 Human languages247,500 3.0%

25. Typology: Language Sampling25 All Languages: impossible Extant Documented: 1500 Extinct languages: 500 X1, X2, X3, …, Xn240,000 Human languages247,500 0.6%

26. Typology: Language Sampling26 All Languages: impossible Extant Documented: 1500 Extinct languages: 500 X1, X2, X3, …, Xn240,000 Human languages247,500 0.6% spoken anno 2000

27. Typology: Language Sampling27 All Languages: impossible Extant Documented: 1500 Extinct languages: 500 X1, X2, X3, …, Xn240,000 Human languages247,500 0.6% spoken anno 2000

28. Typology: Language Sampling28 All Languages: impossible Extant Documented: 1500 Extinct languages: 500 X1, X2, X3, …, Xn240,000 Human languages247,500 0.6% spoken anno 2000 Typology: Universals of Human Language

29. Typology: Language Sampling29 All Languages: impossible Extant Documented: 1500 Extinct languages: 500 X1, X2, X3, …, Xn240,000 Human languages247,500 0.6% “Human Language” spoken anno 2000

30. Typology: Language Sampling30 All Languages: impossible Extant Documented: 1500 Extinct Documented: <100 X1, X2, X3, …, Xn240,000 Human languages247,500 “Human Language” spoken anno 2000

31. Typology: Language Sampling31 All Languages: impossible Extant Documented: 1500 Extinct Documented: <100 X1, X2, X3, …, Xn240,000 Human languages247,500 “Human Language” spoken anno 2000 Uniformi- tarianism (Lass 1997)

32. Typology: Language Sampling32 All Languages: impossible Extant Documented: 1500 Extinct Documented: <100 X1, X2, X3, …, Xn240,000 Human languages247,500 “Human Language” spoken anno 2000 Uniformi- tarianism (Lass 1997)

33. Typology: Language Sampling33 All Languages: impossible Extant Documented: 1500 Extinct Documented: <100 X1, X2, X3, …, Xn240,000 Human languages247,500 “Human Language” spoken anno 2000 Uniformi- tarianism (Lass 1997)

34. Typology: Language Sampling34 All Languages: impossible Extant Documented: 1500 Extinct languages: 500 X1, X2, X3, …, Xn240,000 Human languages247,500 0.6% Typology: Variety among human languages spoken anno 2000

35. Typology: Language Sampling35 All Languages: impossible Extant Documented: 1500 Extinct languages: 500 X1, X2, X3, …, Xn240,000 Human languages247,500 0.6% Variety among human languages spoken anno 2000

36. Typology: Language Sampling36 Variety: rare types Variety:

37. Typology: Language Sampling37 Variety: rare types Variety: Clicks (only in one family – Khoisan: 30 lgs) Active nominal marking (Pomo, Laz) Opposite person hierarchy Acc-Erg (Tib.Burm.) Tripartite agreement on ditransitives Syntactic ergativity (Aus, Maya) Adverbial agreement with focal (Aus, Cauc) OSV main clause order (S.Am) N.B. combination of (rare) features (cf. Greenberg)

38. Typology: Language Sampling38 Variety: rare types Variety: Clicks (only in one family – Khoisan: 30 lgs) Active nominal marking (Pomo, Laz) Opposite person hierarchy Acc-Erg (Tib.Burm.) Tripartite agreement on ditransitives Syntactic ergativity (Aus, Maya) Adverbial agreement with focal (Aus, Cauc) OSV main clause order (S.Am)  “Rara et Rarissima”

39. Typology: Language Sampling39 Data collecting Why not all languages in our database ? - Too many - Only <1000 well described (grammar) <2000 partial sketch - Not (always) necessary - Sometimes even wrong - Impossible even in principle  Problematic for variety  Possibly not for universality

40. Typology: Language Sampling40 Data collecting Why not all languages in our database ? - Too many - Only <1000 well described (grammar) <2000 partial sketch - Not (always) necessary - Sometimes even wrong

41. Typology: Language Sampling41 Data collecting Why not all languages in our database ? - Too many - Only <1000 well described (grammar) <2000 partial sketch - Not (always) necessary - Sometimes even wrong

42. Typology: Language Sampling42 Too many languages Samples in the typological literature: Greenberg (1963) – Word order30 Hawkins (1983) – Word order225 Tomlin (1986) – Word order402 Nichols (1992) – Head/Dep marking174 Bybee (1994) – Tense/Aspect/Mood76 Siewierska & Bakker (1990-) – Pers.Agr.450 Dryer (1985-) – Word order1200 Typical PhD project (1 person, 3 years): 50 - 100

43. Typology: Language Sampling43 Data collecting Why not all languages in our database ? - Too many - Only <1000 well described (grammar) <2000 partial sketch - Not (always) necessary - Sometimes even wrong

44. Typology: Language Sampling44 Data collecting Why not all languages in our database ? - Too many  sample inevitable - Only <1000 well described (grammar) <2000 partial sketch - Not (always) necessary - Sometimes even wrong

45. Typology: Language Sampling45 Data collecting Why not all languages in our database ? - Only <1000 well described (grammar) <2000 partial sketch - Not (always) necessary - Sometimes even wrong

46. Typology: Language Sampling46 Data collecting Why not all languages in our database ? - Only <1000 well described (grammar) <2000 partial sketch - Not (always) necessary - Sometimes even wrong

47. Typology: Language Sampling47 Lack of material Bibliographic bias: - (very) old - scarce - theory specific (Tagmemics; GG) - restricted to phonology and morphology - biased selection of the world’s languages:

48. Typology: Language Sampling48 Lack of material Further types of bias:

49. Typology: Language Sampling49 Lack of material Further types of bias: - Genetic

50. Typology: Language Sampling50 Lack of material Further types of bias: - Genetic Indo-European, Ugric, Bantu ++ Australian, Amerindian, Papuan - -

51. Typology: Language Sampling51 Lack of material Further types of bias: - Genetic - Areal

52. Typology: Language Sampling52 Lack of material Further types of bias: - Genetic - Areal Sprachbund: Balkan Circum-Baltic C.America S.E.Asia …

53. Typology: Language Sampling53 Lack of material Further types of bias: - Genetic - Areal - Typological

54. Typology: Language Sampling54 Lack of material Further types of bias: - Genetic - Areal - Typological Parametric variables (Hawkins 1983):

55. Typology: Language Sampling55 Lack of material Further types of bias: - Genetic - Areal - Typological Parametric variables (Hawkins 1983): Adposition

56. Typology: Language Sampling56 Lack of material Further types of bias: - Genetic - Areal - Typological Parametric variables (Hawkins 1983): Prep

57. Typology: Language Sampling57 Lack of material Further types of bias: - Genetic - Areal - Typological Parametric variables (Hawkins 1983): Prep  [ Dem Num Adj Gen Rel N ] NP

58. Typology: Language Sampling58 Lack of material Further types of bias: - Genetic - Areal - Typological Parametric variables (Hawkins 1983): PRepNounModHierarchy:

59. Typology: Language Sampling59 Lack of material Further types of bias: - Genetic - Areal - Typological Parametric variables (Hawkins 1983): PRepNounModHierarchy: Prep  ((NDem OR NNum  NA) AND (NA  NGen) AND (NGen  NRel))

60. Typology: Language Sampling60 Lack of material Further types of bias: - Genetic - Areal - Typological - Cultural

61. Typology: Language Sampling61 Lack of material Further types of bias: - Genetic - Areal - Typological - Cultural Linguistic relativity (Sapir; Whorf)

62. Typology: Language Sampling62 Lack of material Further types of bias: - Genetic - Areal - Typological - Cultural Linguistic relativity (Sapir; Whorf) Lucy (1992): count nouns vs classifiers ~ counting tasks

63. Typology: Language Sampling63 Lack of material Further types of bias: - Genetic - Areal - Typological - Cultural - Community size

64. Typology: Language Sampling64 Lack of material Further types of bias: - Genetic - Areal - Typological - Cultural - Community size Small  high genetic drift (Kimura 1983)

65. Typology: Language Sampling65 Lack of material Further types of bias: - Genetic - Areal - Typological - Cultural - Community size Small  high genetic drift (Kimura 1983) Also linguistic drift? (Dahl: hunter/gatherer)

66. Typology: Language Sampling66 Lack of material Further types of bias: - Genetic - Areal - Typological - Cultural - Community size Small  high genetic drift (Kimura 1983) Also linguistic drift? (Dahl: hunter/gatherer) N.B. OSV/OVS only in < 3000 languages

67. Typology: Language Sampling67 Lack of material Further types of bias: - Genetic - Areal - Typological - Cultural - Community size - Language contact

68. Typology: Language Sampling68 Lack of material Further types of bias: - Genetic - Areal - Typological - Cultural - Community size - Language contact Borrowed phenomenon measured twice

69. Typology: Language Sampling69 Lack of material Further types of bias: - Genetic - Areal - Typological - Cultural - Community size - Language contact BUT: contact may also create new types

70. Typology: Language Sampling70 Lack of material Further types of bias: - Genetic - Areal - Typological - Cultural - Community size - Language contact BUT: contact may also create new types

71. Typology: Language Sampling71 Data collecting Why not all languages in our database ? - Only <1000 well described (grammar) <2000 partial sketch (= bibliographical bias) - Not (always) necessary - Sometimes even wrong

72. Typology: Language Sampling72 Data collecting Why not all languages in our database ? - Only <1000 well described (grammar) < 2000 partial sketch  Cater for biases by stratifying for the relevant dimensions - Not (always) necessary - Sometimes even wrong

73. Typology: Language Sampling73 Data collecting Why not all languages in our database ? - Not (always) necessary - Sometimes even wrong

74. Typology: Language Sampling74 Small is beautiful A good sample may be better than a large sample: Sample type and size depends on goal of project: Establish the probability of a language type (e.g. prepositional vs postpositional)  Probability sample Explore the existing variety on a certain dimension (e.g. case systems; combination of order patterns)  Variety sample

75. Typology: Language Sampling75 Small is beautiful 1. Probability sample - Only independent cases Control for: - genetic relations - language contact But: relative stability of relevant variables - Reflexive passive (Romance vs Slavic)

76. Typology: Language Sampling76 Small is beautiful Samples in the typological literature: Greenberg (1963) – Word order30 Hawkins (1983) – Word order225 Tomlin (1986) – Word order402 Nichols (1992) – Head/Dep marking174 Bybee (1994) – Tense/Aspect/Mood76 Siewierska & Bakker (1990-) – Pers.Agr.450 Dryer (1985-) – Word order1200 probab

77. Typology: Language Sampling77 Large may be better 2. Variety sample - Maximum (all?) different cases Cater for: - variation in genetic/areal groups - typically cyclical - stop when no new cases found Research parameters typically unknown !

78. Typology: Language Sampling78 Probability vs Variety Probability sample: - relatively small (30 – 150) - may be too large (double cases) Variety sample: - relatively large (> 200) - can not be too large (just superfluous)

79. Typology: Language Sampling79 Sampling in the literature Introductions to Typology: Comrie (1981) 9-12(4) Croft (1990)18-26(9) Whaley (1997)36-43(8) Song (2001)17-38(22)

80. Typology: Language Sampling80 Probability sampling Bell (1978) - genetic, areal and typological bias - 478 genetic groups (> 3000 year depth) - per family: n of lgs proportional to n of groups - problems: sample < 478: selection small families ‘disappear’

81. Typology: Language Sampling81 Probability sampling Perkins (1980) - Bell stratified for culture (Murdock 1967) - 50 languages with optimal genetic and cultural distance - good for probability, too small for variety

82. Typology: Language Sampling82 Probability sampling Dryer (1989) - ~ Bell, but: - 322 established genera, 3500 – 4000 years deep - variable values established per genus not language (mainly stable, else the most frequent) - 5 macro-areas, counting genera per area:

83. Typology: Language Sampling83 Probability sampling AfricaEurasiaAus-NGN.AmerS.Amer SOV2226292618 SVO2119665  SOV > SVO

84. Typology: Language Sampling84 Probability sampling AfricaEurasiaAus-NGN.AmerS.Amer SOV2226292618 SVO2119665 Good for universal preferences on stable variables Unclear how to generalize to other types of sampling, with languages central

85. Typology: Language Sampling85 Variety sampling Characteristics: Create variety samples of any size Free choice of classification used (Gen/Ar/Typ) Stratification on other parameter (Gen: Ar/Typ) Generate new samples + evaluate existing samples Fully formalized and computer implemented

86. Typology: Language Sampling86 Variety sampling Central idea: - classifications express linguistic (dis)similarities between languages - established on the basis of expert knowledge - subject to cyclical improvement and refinement - best starting point for explorative research into variation among languages

87. Typology: Language Sampling87 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian

88. Typology: Language Sampling88 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian Mimimum sample: 1 language per family

89. Typology: Language Sampling89 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian HBRARBQUEGUA GEOCHEKANTAM

90. Typology: Language Sampling90 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian HBRARBQUEGUA GEOCHEKANTAM

91. Typology: Language Sampling91 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian Select language with the best description (for the purpose) HBRARBQUEGUA GEOCHEKANTAM

92. Typology: Language Sampling92 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian Includes all ISOLATES: Basque, Burushaski, Ket, Nahali, … HBRARBQUEGUA GEOCHEKANTAM

93. Typology: Language Sampling93 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian Mimimum sample: 1 language per family Ruhlen (1991): 27 Ethnologue (2005): 120 Basic Sample Murdock (1967): 50

94. Typology: Language Sampling94 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian Extending the Basic Sample to preferred size: e.g. extend Ruhlen-based sample from 27  50 KEY: relative complexity of family tree DV=3DV=6 DV=2

95. Typology: Language Sampling95 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian Adjusting DV values to full tree structure: Recursively down the trees Lower levels contribute relatively less to DV DV=3DV=6 DV=2

96. Typology: Language Sampling96 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian Formula for weight per level: C k = C k-1 + ( N k - N k-1 ) * ( MAX – (k-1) ) / MAX ) DV=3DV=6 DV=2 See Rijkhoff & Bakker (1998)

97. Typology: Language Sampling97 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian Formula for weight per level: C k = C k-1 + ( N k - N k-1 ) * ( MAX – (k-1) ) / MAX ) DV=55.5DV=178.4 DV=8.5

98. Typology: Language Sampling98 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian Formula for weight per level: C k = C k-1 + ( N k - N k-1 ) * ( MAX – (k-1) ) / MAX ) DV=55.5DV=178.4 DV=8.5 362

99. Typology: Language Sampling99 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian DV=55.5DV=178.4 DV=8.5 Computer program:

100. Typology: Language Sampling100 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian DV=55.5DV=178.4 DV=8.5 Computer program:  Number of lgs per family given sample size

101. Typology: Language Sampling101 Variety sampling SampleSize Family (n=5273) 3050100250 Afro-Asiatic (258)12616 Amerind (854)271851 Austric (1186)251439 Caucasian (38)1113 Chukchi (5)1111 Indo-European (180)12411 RUHLEN (1991)

102. Typology: Language Sampling102 Variety sampling SampleSize Family (n=5273) 3050100250 Afro-Asiatic (258)12616 Amerind (854)271851 Austric (1186)251439 Caucasian (38)1113 Chukchi (5)1111 Indo-European (180)12411 5.9% 3.3% 6.1%

103. Typology: Language Sampling103 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian DV=55.5DV=178.4 DV=8.5 Computer program:  Number of lgs per family given sample size

104. Typology: Language Sampling104 Variety sampling Afro-Asiatic Amerindian Caucasian Dravidian DV=55.5DV=178.4 DV=8.5 Computer program:  Number of lgs per family given sample size  Optimal distribution over subbranches (maximum distance  maximum variety)

105. Typology: Language Sampling105 Variety sampling Amerind Main Branch (n=854) TotalDVin 250 sample (n = 51) Central6019.16 Ge-Pano Carib19329.39 Northern23245.514 Eq-Tucanoan26845.014 Chibchan-Paezan7116.95 Andean309.93

106. Typology: Language Sampling106 Variety sampling Amerind Main Branch (n=854) TotalDVin 250 sample (n = 51) Central6019.16 Ge-Pano Carib19329.39 Northern23245.514 Eq-Tucanoan26845.014 Chibchan-Paezan7116.95 Andean309.93

107. Typology: Language Sampling107 Variety sampling Amerind Main Branch (n=854) TotalDVin 250 sample (n = 51) Central6019.16 Ge-Pano Carib19329.39 Northern23245.514 Eq-Tucanoan26845.014 Chibchan-Paezan7116.95 Andean309.93

108. Typology: Language Sampling108 Variety sampling Amerind Main Branch (n=854) TotalDVin 250 sample (n = 51) Central6019.16 Ge-Pano Carib19329.39 Northern23245.514 Eq-Tucanoan26845.014 Chibchan-Paezan7116.95 Andean309.93

109. Typology: Language Sampling109 Variety sampling Amerind Main Branch (n=854) TotalDVin 250 sample (n = 51) Central6019.16 Ge-Pano Carib19329.39 Northern23245.514 Eq-Tucanoan26845.014 Chibchan-Paezan7116.95 Andean309.93

110. Typology: Language Sampling110 Variety sampling Amerind Main Branch (n=854) TotalDVin 250 sample (n = 51) Central60 19.1 6 = 10% Ge-Pano Carib19329.39 Northern23245.514 Eq-Tucanoan26845.014 Chibchan-Paezan7116.95 Andean309.93

111. Typology: Language Sampling111 Variety sampling Andean (3 / 30) Amerind (51 / 854)

112. Typology: Language Sampling112 Variety sampling Andean ( 3 / 30) NORTHSOUTHAYMAQUECHCAHUAURA Amerind (51 / 854)

113. Typology: Language Sampling113 Variety sampling NORTHSOUTHAYMAQUECHCAHUAURA Andean ( 3 / 30) Amerind (51 / 854)

114. Typology: Language Sampling114 Variety sampling NORTHSOUTHAYMAQUECHCAHUAURA Andean ( 3 / 30) Amerind (51 / 854)

115. Typology: Language Sampling115 Variety sampling: output Typical output:

116. Typology: Language Sampling116 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273)

117. Typology: Language Sampling117 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273)

118. Typology: Language Sampling118 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273) Afro-Asiatic (55.53/6/258) 6 Altaic (15.07/2/62) 2 Amerind (178.44/6/854) 18 Australian (67.58/30/262) 7 …

119. Typology: Language Sampling119 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273) Afro-Asiatic (55.53/6/258) 6 Altaic (15.07/2/62) 2 Amerind (178.44/6/854) 18 Australian (67.58/30/262) 7 … Na-Dene (9.44/2/41) 1 Niger-Kordofanian (90.38/2/1068) 9 …

120. Typology: Language Sampling120 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273) Afro-Asiatic (55.53/6/258) 6 Altaic (15.07/2/62) 2 Amerind (178.44/6/854) 18 Australian (67.58/30/262) 7 … Na-Dene (9.44/2/41) 1 Niger-Kordofanian (90.38/2/1068) 9 … Basque (1.00/0/0) 1 Etruscan (1.00/0/0) 1 Gilyak (1.00/0/0) 1

121. Typology: Language Sampling121 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273) Afro-Asiatic (55.53/6/258) 6 Altaic (15.07/2/62) 2 Amerind (178.44/6/ 854 ) 18 Australian (67.58/30/262) 7 … Na-Dene (9.44/2/41) 1 Niger-Kordofanian (90.38/2/ 1068 ) 9 … Basque (1.00/0/0) 1 Etruscan (1.00/0/0) 1 Gilyak (1.00/0/0) 1

122. Typology: Language Sampling122 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273) … Niger-Kordofanian (90.38/2/1068) 9 …

123. Typology: Language Sampling123 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273) Niger-Kordofanian (2/1068) 9 Niger-Congo (2/1036) 8 Niger-Congo Proper (2/1007) 7 Central Niger-Congo (2/961) 6 South Central Niger-Congo (3/755) 3 Eastern (9/703) 1 Western (2/47) 1 Ijo-Defaka (2/5) 1 North Central Niger-Congo (4/206) 3 West Atlantic (3/46) 1 Mande (3/29) 1 Kordofanian (2/32) 1

124. Typology: Language Sampling124 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273) Niger-Kordofanian (2/1068) 9 Niger-Congo (2/1036) 8 Niger-Congo Proper (2/1007) 7 Central Niger-Congo (2/961) 6 South Central Niger-Congo (3/755) 3 Eastern (9/703) 1 Western (2/47) 1 Ijo-Defaka (2/5) 1 North Central Niger-Congo (4/206) 3 West Atlantic (3/46) 1 Mande (3/29) 1 Kordofanian (2/32) 1

125. Typology: Language Sampling125 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273) Niger-Kordofanian (2/1068) 9 Niger-Congo (2/1036) 8 Niger-Congo Proper (2/1007) 7 Central Niger-Congo (2/961) 6 South Central Niger-Congo (3/755) 3 Eastern (9/703) 1 Western (2/47) 1 Ijo-Defaka (2/5) 1 North Central Niger-Congo (4/206) 3 West Atlantic (3/46) 1 Mande (3/29) 1 Kordofanian (2/32) 1

126. Typology: Language Sampling126 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273) Niger-Kordofanian (2/1068) 9 Niger-Congo (2/1036) 8 Niger-Congo Proper (2/1007) 7 Central Niger-Congo (2/961) 6 South Central Niger-Congo (3/755) 3 Eastern (9/703) 1 Western (2/47) 1 Ijo-Defaka (2/5) 1 North Central Niger-Congo (4/206) 3 West Atlantic (3/46) 1 Mande (3/29) 1 Kordofanian (2/32) 1

127. Typology: Language Sampling127 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273) Niger-Kordofanian (2/1068) 9 Niger-Congo (2/1036) 8 Niger-Congo Proper (2/1007) 7 Central Niger-Congo (2/961) 6 South Central Niger-Congo (3/755) 3 Eastern (9/703) 1 Western (2/47) 1 Ijo-Defaka (2/5) 1 North Central Niger-Congo (4/206) 3 West Atlantic (3/46) 1 Mande (3/29) 1 Kordofanian (2/32) 1

128. Typology: Language Sampling128 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273) Niger-Kordofanian (2/1068) 9 Niger-Congo (2/1036) 8 Niger-Congo Proper (2/1007) 7 Central Niger-Congo (2/961) 6 South Central Niger-Congo (3/755) 3 Eastern (9/703) 1 Western (2/47) 1 Ijo-Defaka (2/5) 1 North Central Niger-Congo (4/206) 3 West Atlantic (3/46) 1 Mande (3/29) 1 Kordofanian (2/32) 1

129. Typology: Language Sampling129 Variety sampling: output Classification: Ruhlen91 Criterion 1: Diversity Value: dynamic/global/average Sample size: 100 ( 1.90 % of 5273) Niger-Kordofanian (2/1068) 9 Niger-Congo (2/1036) 8 Niger-Congo Proper (2/1007) 7 Central Niger-Congo (2/961) 6 South Central Niger-Congo (3/755) 3 Eastern (9/703) 1 Western (2/47) 1 Ijo-Defaka (2/5) 1 North Central Niger-Congo (4/206) 3 West Atlantic (3/46) 1 Mande (3/29) 1 Kordofanian (2/32) 1

130. Typology: Language Sampling130 Variety sampling Side effect of large (variety) sample: Hidden diachrony

131. Typology: Language Sampling131 Variety sampling Problems: - works only on tree-shaped classifications - time depth in genetic trees: unbalanced - not good for probability samples - Creoles? Extinct languages?

132. Typology: Language Sampling132 Round off

133. Typology: Language Sampling133 Round off Two Sample Strategies:

134. Typology: Language Sampling134 Round off Two Sample Strategies: 1. Probability sample - relatively small - control for Gen/Ar/Typ bias

135. Typology: Language Sampling135 Round off Two Sample Strategies: 1. Probability sample - relatively small - control for Gen/Ar/Typ bias 2. Variety sample - relatively large - may be stratified for bias parameters - may have diachronic dimension

136. Typology: Language Sampling136 Round off Sample Types: 1. Probability sample 2. Variety sample 3. Random sample: when bias is unimportant

137. Typology: Language Sampling137 Round off Sample Types: 1. Probability sample 2. Variety sample 3. Random sample: when bias is unimportant 4. Convenience sample: when bibliographical constraints kick in...

138. Typology: Language Sampling138 ?