Dataset 1: GDP per labour for some small state economies
Date Botswana Cyprus Hong
Kong Malta Mauritius Singapore Taiwan Luxembourg Lesotho Seychelles
1975 3647 7375 9730 9071 7072 13167 7657 2666 1516 3905
1976 4011 8575 10750 10174 7558 13394 8360 2724 1670 4301
1977 3924 9785 11851 10976 7948 13764 8859 2935 1779 4899
1978 4545 10307 12802 11376 8149 14197 9657 2865 1957 5083
1979 4908 11057 12956 11955 8217 14713 10394 2826 1980 5922
1980 5532 11387 14030 12521 7351 15251 10837 2845 2009 6221
1981 5650 11372 14919 13066 7255 16294 11066 2897 2075 5878
1982 5691 12050 15051 13747 7078 16901 11091 2848 2104 6382
1983 5404 12537 15672 13867 7030 17697 11614 2883 2083 6158
1984 5636 13458 16580 14414 7240 18692 12440 3282 2114 6189
1985 6792 13918 16447 15380 7474 17986 12701 3078 2028 7058
1986 7782 14078 17842 15512 7872 18142 13699 3332 1944 7068
1987 6488 14775 20034 15892 8915 19589 15251 3209 1996 7603
1988 7123 15861 21553 17021 9279 21495 16503 5050 2241 7631
1989 6533 16999 21962 18206 10706 23030 17701 7089 2191 8520
1990 6456 18053 22827 19348 10198 24369 18409 7903 2080 9137
Dataset 2: Time series annual data set
Year FDP BC MC MV
1977 524,6 23,9 46,2 82
1978 554,5 30,8 48,6 84,2
1979 483,6 35,8 51 90,8
1980 626,5 44,5 54,4 94,4
1981 731,9 36,9 55,6 104,1
1982 753 39,5 58,3 119,9
1983 737,5 40,7 60,3 145,3
1984 717,9 38,8 61,7 163,6
1985 841,1 46,3 62,9 143
1986 1075,9 52 65,7 153,2
1987 1100,7 55,1 67,4 221
1988 942,9 52,4 68,8 218,1
1989 937,4 55,2 70,9 262,5
1990 1064,9 65,5 72,5 381,5
1991 1018,7 52,5 73 301,1
1992 946,1 54,6 74,9 371,4
1993 1141,2 54,5 76 363,9
1994 971 53,4 76,6 286,6
1995 900,3 67,3 77,3 366,1
1996 1067,3 70,5 81,2 318,4
1997 1486,3 57,7 84 356,6
1998 1487,5 53,4 86 430,5
1999 1604,1 52,4 88,5 412,7
2000 1805,2 51,4 99,9 394,9
Note: FDP (financial development proxy), BC (domestic bank credit), MC (market capitalization), MV (market volatility).
Dataset 3:
Region Temperature Dehydration
1 31.8 67.3
2 34 52.5
3 40.2 68.1
4 42.1 84.6
5 42.3 65.1
6 43.5 72.2
7 44.2 81.7
8 45.1 89.2
9 46.3 78.9
10 47.3 88.6
11 47.8 95
12 48.5 87
13 49.2 95.9
14 49.9 104.5
15 50 100.4
16 51.3 102.5
Dataset 4:
ns aa pe sm ae r g c
1 93 19 1 2 0 1 1
2 46 12 0 0 0 1 0
3 57 15 1 1 0 1 0
4 94 18 2 2 1 2 1
5 82 13 2 1 1 2 1
6 59 12 0 0 2 1 0
7 61 12 1 2 0 1 0
8 29 9 0 0 1 2 0
9 36 13 1 1 0 1 0
10 91 16 2 2 1 2 0
11 55 10 0 0 1 1 0
12 58 11 0 1 0 1 0
13 67 14 1 1 0 2 1
14 77 14 1 2 2 2 0
15 71 12 0 0 2 2 0
16 83 16 2 2 1 1 1
17 96 15 2 2 2 1 1
18 87 12 1 1 0 1 1
19 62 11 0 0 0 1 0
20 52 9 0 1 2 2 0
21 46 10 1 0 0 2 0
22 91 20 2 2 1 1 0
23 85 17 2 1 1 2 1
24 48 11 1 1 2 1 0
25 81 17 1 1 1 2 1
Source: Einspruch (1998). Note: NS: Number of student, AA: Academic ability (exam results), PE: Parents’ education (education level for parents), SM: Student motivation (0=not willing, 1=undecided, 2=willing), AE: Advisor evaluation (0= fail, 1=succeed or fail, 2=succeed), R: Religious affiliation (0= Catholic, 1=Protestant, 2=Jewish), G: Gender (0=male, 1=female), C: Community type (0=urban, 1=rural)
Dataset 5: Data set for the Academic Ability (Wintergreen Study)
ns aa pe sm ae r G c
1 93 19 1 2 0 1 1
2 46 12 0 0 0 1 0
3 57 15 1 1 0 1 0
4 94 18 2 2 1 2 1
5 82 13 2 1 1 2 1
6 59 12 0 0 2 1 0
7 61 12 1 2 0 1 0
8 29 9 0 0 1 2 0
9 36 13 1 1 0 1 0
10 91 16 2 2 1 2 0
11 55 10 0 0 1 1 0
12 58 11 0 1 0 1 0
13 67 14 1 1 0 2 1
14 77 14 1 2 2 2 0
15 71 12 0 0 2 2 0
16 83 16 2 2 1 1 1
17 96 15 2 2 2 1 1
18 87 12 1 1 0 1 1
19 62 11 0 0 0 1 0
20 52 9 0 1 2 2 0
21 46 10 1 0 0 2 0
22 91 20 2 2 1 1 0
23 85 17 2 1 1 2 1
24 48 11 1 1 2 1 0
25 81 17 1 1 1 2 1
26 74 16 2 1 2 2 0
27 68 12 2 1 1 2 1
28 63 12 1 0 0 1 1
29 72 14 0 2 0 1 0
30 99 19 1 1 1 1 0
31 64 13 1 1 0 1 0
32 77 13 1 0 1 2 1
33 88 16 2 2 0 2 0
34 54 9 0 1 1 1 0
35 86 17 1 2 1 1 1
36 73 15 1 1 0 2 0
37 79 15 2 1 0 1 1
38 85 14 2 1 2 2 1
39 96 16 0 1 1 1 1
40 59 12 1 0 0 2 0
41 84 14 1 0 1 1 1
42 71 15 2 1 1 1 0
43 89 15 0 1 0 2 1
44 38 12 1 0 1 2 0
45 62 11 1 1 2 1 1
46 93 16 1 0 1 1 1
47 71 13 2 1 1 1 0
48 55 11 0 1 0 1 0
49 74 15 1 2 0 2 0
50 88 18 1 1 0 2 0
Dataset 6: Impulse Buying1
No ofPeople. Gender Age
Band Portfolio Impulse Rating
1 1 3 1 1
2 1 3 2 4
3 1 2 2 3
4 1 2 1 3
5 1 4 2 4
6 1 5 1 3
7 1 3 1 4
8 2 3 1 3
9 1 4 1 1
10 1 2 3 5
11 1 2 2 3
12 1 5 2 4
13 2 2 2 7
14 1 5 3 6
15 1 2 1 8
16 1 5 1 2
17 2 5 2 1
18 2 5 3 4
19 1 4 3 6
20 1 2 3 5
21 1 2 2 3
22 2 2 1 8
23 1 3 3 9
24 2 2 1 4
25 1 5 3 2
26 2 2 3 1
27 2 3 1 3
28 2 3 3 7
29 2 4 2 2
30 2 4 3 5
31 2 4 2 8
32 2 4 1 6
33 2 4 2 5
34 2 5 3 4
35 2 5 1 6
36 2 5 3 7
37 2 6 1 3
38 2 6 2 1
39 2 7 2 6
40 2 1 1 4
41 2 2 2 6
42 1 3 2 5
43 1 3 3 8
44 1 4 1 6
45 1 4 1 2
46 1 5 2 5
47 2 6 1 5
48 2 5 3 9
49 2 5 2 4
50 1 6 2 5
Dataset 7:
Student IQ Score Student IQ Score
1 110 16 110
2 105 17 117
3 102 18 98
4 112 19 124
5 120 20 107
6 107 21 112
7 99 22 122
8 100 23 104
9 109 24 105
10 103 25 110
11 115 26 120
12 125 27 125
13 115 28 120
14 106 29 100
15 110 30 110
Dataset 8:
Student
Condition ScoreStudent
ConditionScore1 1 87 21 2 82
2 1 95 22 2 72
3 1 89 23 2 95
4 1 74 24 2 60
5 1 73 25 2 90
6 1 92 26 2 87
7 1 63 27 2 89
8 1 90 28 2 86
9 1 94 29 2 76
10 1 84 30 2 74
11 1 91 31 2 85
12 1 90 32 2 75
13 1 75 33 2 90
14 1 93 34 2 91
15 1 87 35 2 88
16 1 85 36 2 63
17 1 90 37 2 70
18 1 89 38 2 72
19 1 87 39 2 84
20 1 85 40 2 60
Note: For condition, 1=integrated course and 2=traditional course.
Dataset 9:
Child Pre Post Child Pre Post
1 31 34 11 31 28
2 26 25 12 27 32
3 32 38 13 25 25
4 38 36 14 28 30
5 29 29 15 32 41
6 34 41 16 27 37
7 24 26 17 37 39
8 35 42 18 29 33
9 30 36 19 31 40
10 36 44 20 27 28
