dat.bourassa1996.RdResults from 47 studies on the association between handedness and eye-dominance.
dat.bourassa1996The data frame contains the following columns:
| study | numeric | study number |
| sample | numeric | sample number |
| author | character | (first) author |
| year | numeric | publication year |
| selection | character | selection of subjects on the basis of eyedness or handedness |
| investigator | character | investigator (psychologist, educationalist, or other) |
| hand_assess | character | method to assess handedness (questionnaire or performance based) |
| eye_assess | character | method to assess eyedness (see ‘Details’) |
| mage | numeric | mean age of sample |
| lh.le | numeric | number of left-handed left-eyed individuals |
| lh.re | numeric | number of left-handed right-eyed individuals |
| rh.le | numeric | number of right-handed left-eyed individuals |
| rh.re | numeric | number of right-handed right-eyed individuals |
| sex | character | sex of the sample (combined, male, or female) |
The 47 studies included in this meta-analysis examined the association between handedness and eye-dominance (ocular dominance or eyedness). Results are given in terms of \(2 \times 2\) tables, indicating the number of left-handed left-eyed, left-handed right-eyed, right-handed left-eyed, and right-handed right-eyed individuals in each sample. Note that some studies included multiple (independent) samples, so that the meta-analysis included 54 samples in total. Also, for some studies, the combined data of the males and females are further broken down into the two subgroups.
In some studies, there was indication that the selection of subjects was not random with respect to handedness and/or eyedness. While this should not influence the size of the association as measured with the odds ratio, this invalidates those studies for assessing the overall percentage of left-eyed and left-handed individuals.
Handedness was assessed in the individual studies either based on a questionnaire or based on task performance. Eyedness was assessed based on various methods: E.1 methods are based on task performance, while E.2.a denotes assessment based on a questionnaire. The performance based methods could be further broken down into: E.1.a.i (monocular procedure with object/instrument held in one hand), E.1.a.ii (monocular procedure with object/instrument held in both hands), E.1.b (binocular procedure), E.1.c (a combination of the previous methods), and E.1.d (some other method).
Bourassa, D. C., McManus, I. C., & Bryden, M. P. (1996). Handedness and eye-dominance: A meta-analysis of their relationship. Laterality, 1(1), 5–34. https://doi.org/10.1080/713754206
psychology, odds ratios, multilevel models
### copy data into 'dat' and examine data
dat <- dat.bourassa1996
head(dat, 10)
#> study sample author year selection investigator hand_assess eye_assess mage lh.le lh.re rh.le
#> 1 1 1 Mills 1925 no other questionnaire E.1.d NA 93 17 130
#> 2 2 2 Downey 1927 yes psychologist questionnaire E.1.b 37 140 91 305
#> 3 2 2 Downey 1927 yes psychologist questionnaire E.1.b 37 74 57 158
#> 4 2 2 Downey 1927 yes psychologist questionnaire E.1.b 37 66 34 147
#> 5 3 3 Miles 1930 no psychologist questionnaire E.1.a.ii 22 16 14 43
#> 6 4 4 Quinan 1931 no other performance E.1.b 25 102 97 597
#> 7 5 5 Jasper 1932 yes psychologist questionnaire E.1.a.ii 20 17 14 38
#> 8 6 6 Lund 1932 no psychologist performance E.1.a.ii 20 10 2 52
#> 9 7 7 Eyre 1933 no psychologist performance E.1.a.ii 15 7 2 15
#> 10 8 8 Updegraff 1933 no psychologist performance E.1.a.ii 4 4 2 15
#> rh.re sex
#> 1 760 combined
#> 2 697 combined
#> 3 427 male
#> 4 270 female
#> 5 114 combined
#> 6 1898 combined
#> 7 80 combined
#> 8 170 combined
#> 9 169 combined
#> 10 38 combined
### load metafor package
library(metafor)
### calculate log(OR) and corresponding sampling variance with 1/2 correction
dat <- escalc(measure="OR", ai=lh.le, bi=lh.re, ci=rh.le, di=rh.re, data=dat, add=1/2, to="all")
head(dat, 10)
#>
#> study sample author year selection investigator hand_assess eye_assess mage lh.le lh.re rh.le
#> 1 1 1 Mills 1925 no other questionnaire E.1.d NA 93 17 130
#> 2 2 2 Downey 1927 yes psychologist questionnaire E.1.b 37 140 91 305
#> 3 2 2 Downey 1927 yes psychologist questionnaire E.1.b 37 74 57 158
#> 4 2 2 Downey 1927 yes psychologist questionnaire E.1.b 37 66 34 147
#> 5 3 3 Miles 1930 no psychologist questionnaire E.1.a.ii 22 16 14 43
#> 6 4 4 Quinan 1931 no other performance E.1.b 25 102 97 597
#> 7 5 5 Jasper 1932 yes psychologist questionnaire E.1.a.ii 20 17 14 38
#> 8 6 6 Lund 1932 no psychologist performance E.1.a.ii 20 10 2 52
#> 9 7 7 Eyre 1933 no psychologist performance E.1.a.ii 15 7 2 15
#> 10 8 8 Updegraff 1933 no psychologist performance E.1.a.ii 4 4 2 15
#> rh.re sex yi vi
#> 1 760 combined 3.4384 0.0768
#> 2 697 combined 1.2544 0.0228
#> 3 427 male 1.2512 0.0395
#> 4 270 female 1.2627 0.0545
#> 5 114 combined 1.0970 0.1613
#> 6 1898 combined 1.2061 0.0222
#> 7 80 combined 0.9257 0.1645
#> 8 170 combined 2.6130 0.5202
#> 9 169 combined 3.4906 0.6037
#> 10 38 combined 1.4976 0.7127
#>
### overall association between handedness and eyedness
res <- rma(yi, vi, data=dat, subset=sex=="combined")
res
#>
#> Random-Effects Model (k = 54; tau^2 estimator: REML)
#>
#> tau^2 (estimated amount of total heterogeneity): 0.4340 (SE = 0.1208)
#> tau (square root of estimated tau^2 value): 0.6588
#> I^2 (total heterogeneity / total variability): 89.08%
#> H^2 (total variability / sampling variability): 9.16
#>
#> Test for Heterogeneity:
#> Q(df = 53) = 355.7748, p-val < .0001
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> 1.3135 0.1098 11.9597 <.0001 1.0982 1.5287 ***
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
predict(res, transf=exp, digits=2)
#>
#> pred ci.lb ci.ub pi.lb pi.ub
#> 3.72 3.00 4.61 1.00 13.77
#>
### multilevel model to account for heterogeneity at the study and sample levels
res <- rma.mv(yi, vi, random = ~ 1 | study/sample, data=dat, subset=sex=="combined")
res
#>
#> Multivariate Meta-Analysis Model (k = 54; method: REML)
#>
#> Variance Components:
#>
#> estim sqrt nlvls fixed factor
#> sigma^2.1 0.2097 0.4579 47 no study
#> sigma^2.2 0.1985 0.4455 54 no study/sample
#>
#> Test for Heterogeneity:
#> Q(df = 53) = 355.7748, p-val < .0001
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> 1.2712 0.1133 11.2169 <.0001 1.0491 1.4934 ***
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
predict(res, transf=exp, digits=2)
#>
#> pred ci.lb ci.ub pi.lb pi.ub
#> 3.57 2.86 4.45 1.00 12.72
#>
### restructure the dataset to keep only the male/female data when it is reported
### separately and the combined data when this is the only data reported
dat <- lapply(split(dat, dat$sample), function(x) {
if (nrow(x) == 3L) {
x[-which(x$sex == "combined"),]
} else {
x
}
})
dat <- do.call(rbind, dat)
rownames(dat) <- NULL
dat
#>
#> study sample author year selection investigator hand_assess eye_assess mage lh.le lh.re
#> 1 1 1 Mills 1925 no other questionnaire E.1.d NA 93 17
#> 2 2 2 Downey 1927 yes psychologist questionnaire E.1.b 37 74 57
#> 3 2 2 Downey 1927 yes psychologist questionnaire E.1.b 37 66 34
#> 4 3 3 Miles 1930 no psychologist questionnaire E.1.a.ii 22 16 14
#> 5 4 4 Quinan 1931 no other performance E.1.b 25 102 97
#> 6 5 5 Jasper 1932 yes psychologist questionnaire E.1.a.ii 20 17 14
#> 7 6 6 Lund 1932 no psychologist performance E.1.a.ii 20 10 2
#> 8 7 7 Eyre 1933 no psychologist performance E.1.a.ii 15 7 2
#> 9 8 8 Updegraff 1933 no psychologist performance E.1.a.ii 4 4 2
#> 10 9 9 Gates 1936 no educationalist performance E.1.c 5 2 3
#> 11 10 10 Witty 1936 no educationalist questionnaire E.1.c 10 3 4
#> 12 11 11 Fink 1938 no other performance E.1.d NA 4 3
#> 13 12 12 Castner 1939 no psychologist performance E.1.a.ii 10 2 1
#> 14 13 13 Schonell 1941 no educationalist performance E.1.a.ii 10 3 2
#> 15 14 14 Lavery 1944 no other questionnaire E.1.d 31 27 0
#> 16 15 15 Hildreth 1945 no psychologist performance E.1.a.ii 9 9 11
#> 17 16 16 Stevenson 1953 no educationalist performance E.1.d 5 5 2
#> 18 17 17 Merrell 1957 no other performance E.1.c 22 20 13
#> 19 18 18 Meuhl 1963 no educationalist performance E.1.a.ii 5 6 8
#> 20 19 19 Harmon 1966 yes other performance E.1.d NA 8 5
#> 21 20 20 Rengstorff 1967 no other questionnaire E.1.a.ii 24 241 211
#> 22 21 21 Stephens 1967 yes educationalist performance E.1.c 6 11 6
#> 23 21 21 Stephens 1967 yes educationalist performance E.1.c 6 8 4
#> 24 21 22 Stephens 1967 no psychologist performance E.1.a.ii 27 2 2
#> 25 21 22 Stephens 1967 no psychologist performance E.1.a.ii 27 0 0
#> 26 22 23 Dawson 1972 no psychologist performance E.1.a.ii 27 4 2
#> 27 22 23 Dawson 1972 no psychologist performance E.1.a.ii 27 4 0
#> 28 23 24 Gur 1974 yes psychologist questionnaire E.1.b 22 9 9
#> 29 23 24 Gur 1974 yes psychologist questionnaire E.1.b 22 4 6
#> 30 24 25 Chaurasia 1976 no other questionnaire E.1.b 25 8 14
#> 31 24 25 Chaurasia 1976 no other questionnaire E.1.b 25 2 6
#> 32 25 26 Hardyck 1976 no psychologist performance E.1.a.ii 9 429 311
#> 33 26 27 Dawson 1977 no psychologist performance E.1.a.ii 17 10 10
#> 34 27 28 Gur 1977 no psychologist questionnaire E.1.c 35 10 6
#> 35 27 28 Gur 1977 no psychologist questionnaire E.1.c 35 1 5
#> 36 28 29 Van-Camp 1977 no psychologist performance E.1.c 7 18 20
#> 37 29 30 Birkett 1979 yes psychologist questionnaire E.1.b 23 11 10
#> 38 29 30 Birkett 1979 yes psychologist questionnaire E.1.b 23 27 4
#> 39 30 31 Hovsepian 1980 no psychologist questionnaire E.1.a.i 22 19 6
#> 40 31 32 Porac 1980 no psychologist performance E.1.a.ii 10 19 12
#> 41 32 33 Porac 1981 no psychologist questionnaire E.2.a 21 247 125
#> 42 32 33 Porac 1981 no psychologist questionnaire E.2.a 21 164 73
#> 43 33 34 Hebben 1981 no psychologist questionnaire E.1.c 8 4 5
#> 44 33 34 Hebben 1981 no psychologist questionnaire E.1.c 8 5 3
#> 45 34 35 Noonan 1981 yes psychologist performance E.1.c 22 26 34
#> 46 35 36 Van-Camp 1981 no psychologist questionnaire E.1.a.ii 75 1 1
#> 47 35 36 Van-Camp 1981 no psychologist questionnaire E.1.a.ii 75 3 1
#> 48 36 37 Combs 1983 yes psychologist questionnaire E.1.b 29 18 19
#> 49 37 38 Rymar 1983 no other performance E.1.c 10 31 8
#> 50 37 39 Rymar 1983 no other questionnaire E.1.b 29 11 21
#> 51 38 40 Nachson 1983 no psychologist performance E.1.a.ii 7 22 16
#> 52 38 40 Nachson 1983 no psychologist performance E.1.a.ii 7 15 11
#> 53 38 41 Nachson 1983 no psychologist performance E.1.a.ii 7 242 156
#> 54 38 41 Nachson 1983 no psychologist performance E.1.a.ii 7 225 144
#> 55 39 42 Shan-Ming 1985 no psychologist performance E.1.a.ii 30 7 10
#> 56 39 42 Shan-Ming 1985 no psychologist performance E.1.a.ii 30 9 5
#> 57 40 43 Hoogmaartens 1987 no other questionnaire E.2.a 23 10 2
#> 58 41 44 Whittington 1987 no other performance E.1.a.i 11 563 320
#> 59 42 45 Costeff 1988 no other performance E.1.a.ii 42 5 1
#> 60 43 46 Brown 1988 no psychologist performance E.1.a.i 23 19 6
#> 61 44 47 Bryden 1988 yes psychologist questionnaire E.1.a.ii 22 46 32
#> 62 45 48 Bryden 1989 yes psychologist questionnaire E.1.b 20 43 44
#> 63 45 48 Bryden 1989 yes psychologist questionnaire E.1.b 20 46 26
#> 64 46 49 Levander 1989 yes psychologist questionnaire E.1.c 22 12 25
#> 65 46 49 Levander 1989 yes psychologist questionnaire E.1.c 22 13 28
#> 66 47 50 Dargent-Pare 1992 no psychologist questionnaire E.2.a 25 61 21
#> 67 47 50 Dargent-Pare 1992 no psychologist questionnaire E.2.a 25 80 33
#> 68 47 51 Dargent-Pare 1992 no psychologist questionnaire E.2.a 22 11 11
#> 69 47 51 Dargent-Pare 1992 no psychologist questionnaire E.2.a 22 12 6
#> 70 47 52 Dargent-Pare 1992 no psychologist questionnaire E.2.a 30 24 12
#> 71 47 52 Dargent-Pare 1992 no psychologist questionnaire E.2.a 30 8 1
#> 72 47 53 Dargent-Pare 1992 no psychologist questionnaire E.2.a 25 11 2
#> 73 47 53 Dargent-Pare 1992 no psychologist questionnaire E.2.a 25 9 10
#> 74 47 54 Dargent-Pare 1992 no psychologist questionnaire E.2.a 25 18 7
#> 75 47 54 Dargent-Pare 1992 no psychologist questionnaire E.2.a 25 12 1
#> rh.le rh.re sex yi vi
#> 1 130 760 combined 3.4384 0.0768
#> 2 158 427 male 1.2512 0.0395
#> 3 147 270 female 1.2627 0.0545
#> 4 43 114 combined 1.0970 0.1613
#> 5 597 1898 combined 1.2061 0.0222
#> 6 38 80 combined 0.9257 0.1645
#> 7 52 170 combined 2.6130 0.5202
#> 8 15 169 combined 3.4906 0.6037
#> 9 15 38 combined 1.4976 0.7127
#> 10 24 39 combined 0.1412 0.7518
#> 11 18 39 combined 0.5072 0.5873
#> 12 29 89 combined 1.3612 0.5530
#> 13 20 49 combined 1.3924 1.1356
#> 14 19 45 combined 1.1838 0.7590
#> 15 101 261 combined 4.9537 2.0500
#> 16 59 98 combined 0.3130 0.2192
#> 17 23 38 combined 1.2821 0.6503
#> 18 137 327 combined 1.2856 0.1332
#> 19 14 34 combined 0.5985 0.3694
#> 20 31 56 combined 1.0196 0.3489
#> 21 1587 3440 combined 0.9061 0.0098
#> 22 11 16 male 0.9316 0.3884
#> 23 16 17 female 0.6948 0.4576
#> 24 27 75 male 1.0099 0.8496
#> 25 6 9 female 0.3795 4.2591
#> 26 19 31 male 1.0674 0.7053
#> 27 10 25 female 3.0845 2.3567
#> 28 4 25 male 1.7346 0.4720
#> 29 13 17 female -0.1082 0.5073
#> 30 69 146 male 0.2116 0.2078
#> 31 28 40 female -0.6041 0.6136
#> 32 2528 4418 combined 0.8794 0.0062
#> 33 12 48 combined 1.3558 0.2911
#> 34 27 57 male 1.2172 0.3028
#> 35 29 65 female -0.5016 0.8977
#> 36 118 155 combined 0.1691 0.1177
#> 37 16 17 male 0.1498 0.2999
#> 38 23 17 female 1.5153 0.3583
#> 39 16 33 combined 1.8068 0.2956
#> 40 172 352 combined 1.1593 0.1399
#> 41 500 1884 male 2.0049 0.0145
#> 42 575 1579 female 1.8152 0.0221
#> 43 22 63 male 0.8369 0.4642
#> 44 29 60 female 1.1702 0.5180
#> 45 42 90 combined 0.4920 0.1013
#> 46 8 11 male 0.3023 1.5379
#> 47 35 54 female 1.2760 0.9989
#> 48 31 75 combined 0.8215 0.1503
#> 49 271 543 combined 2.0040 0.1549
#> 50 180 353 combined 0.0464 0.1418
#> 51 90 131 male 0.6838 0.1237
#> 52 100 129 female 0.5520 0.1691
#> 53 1203 1725 male 0.7982 0.0119
#> 54 1298 1739 female 0.7374 0.0127
#> 55 24 159 male 1.5369 0.2757
#> 56 44 174 female 1.9130 0.3153
#> 57 32 84 combined 2.3906 0.5378
#> 58 2703 6927 combined 1.5052 0.0054
#> 59 14 37 combined 2.2495 0.9441
#> 60 29 132 combined 2.6008 0.2466
#> 61 48 171 combined 1.6212 0.0787
#> 62 78 193 male 0.8795 0.0634
#> 63 65 191 female 1.6351 0.0797
#> 64 4 35 male 1.3525 0.3696
#> 65 12 33 female 0.2386 0.2190
#> 66 150 708 male 2.6002 0.0708
#> 67 224 811 female 2.1617 0.0480
#> 68 50 280 male 1.7146 0.1973
#> 69 39 225 female 2.3959 0.2636
#> 70 92 307 male 1.8742 0.1349
#> 71 36 133 female 3.0314 0.8192
#> 72 70 198 male 2.5612 0.5062
#> 73 93 178 female 0.5465 0.2168
#> 74 56 236 male 2.3346 0.2093
#> 75 73 211 female 3.1772 0.7650
#>
### multilevel model to account for heterogeneity at the study, sample, and subgroup levels
res <- rma.mv(yi, vi, random = ~ 1 | study/sample/sex, data=dat)
res
#>
#> Multivariate Meta-Analysis Model (k = 75; method: REML)
#>
#> Variance Components:
#>
#> estim sqrt nlvls fixed factor
#> sigma^2.1 0.1828 0.4276 47 no study
#> sigma^2.2 0.2259 0.4753 54 no study/sample
#> sigma^2.3 0.0000 0.0000 75 no study/sample/sex
#>
#> Test for Heterogeneity:
#> Q(df = 74) = 386.3457, p-val < .0001
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> 1.2681 0.1130 11.2219 <.0001 1.0466 1.4895 ***
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
predict(res, transf=exp, digits=2)
#>
#> pred ci.lb ci.ub pi.lb pi.ub
#> 3.55 2.85 4.44 1.00 12.69
#>