normTable: Norm table.
In psytabs: Produce Well-Formatted Tables for Psychological Research
Description
Usage
Arguments
Details
Value
Examples
Description
Produces a table of norms.
Usage
1
Arguments
sumscores
The sumscore vector for which norms should be created.
from
numeric value. Lowest possible sumscore as a numeric value.
to
numeric value. Highest possible sumscore as a numeric value.
statistics
character vector that toggles which norm statistics are included in the norm table. Currently Percent ranks "PR", z-Statistic "z", Z-Statistic "Z", IQ-Stiatistc "IQ", T-Statistic "T" and Stanine "Stanine" are supported.
group
List of subgroups by which the norms should be created.
as.list
logical vector that toggles whether the norm table should rather be returned as a (vertical) list than as a table. This option is useful if you have a lot of subgroups and/or norm statistics and the resulting table would not fit on the page horizontally. Note that currently when you set as.list = TRUE the resulting list cannot be saved by the saveTable function.
Details
The different norm statistics (besides the percent ranks) are created by transforming the z-Statistic. This means that at first the sumscores are z-Transformed and then the other statistics are calculated based on the z-Statistic.
One could also choose to first "normalize" the z-Statistic, i.e. calculate the percent ranks and then assign the z-values to the sumscores according to the percent ranks of the standard normal distribution. This option is—albeit it is sometimes called the scientific standard—debatle because if the population does not follow the normal curve then the normalisation will lead to an artificial spreading or shortening of scores. This leads to the scores having only an ordinal scale quality even when the scores in the population distribution had originally metric scale quality. Thus this option should be used with caution and only on a strong theoretical basis. Therefore this option is currently not implemented.
Value
A dataframe constituting the norm table.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
data(mydata)
mydata.sumscore <- rowSums(mydata[,c("item1", "item2", "item3", "item4")])
tab.1 <- normTable(mydata.sumscore, from = 0, to = 12, statistics=c("PR"))
tab.1
tab.2 <- normTable(mydata.sumscore, from = 0,
to = 12, statistics=c("PR", "T", "Stanine"))
tab.2
tab.3 <- normTable(mydata.sumscore, from = 0,
to = 12, statistics=c("PR"), group=mydata$sex)
tab.3
tab.4 <- normTable(mydata.sumscore, from = 0,
to = 12, statistics=c("PR", "T"), group=mydata$employment)
tab.4
tab.5 <- normTable(mydata.sumscore, from = 0,
to = 12, statistics=c("T"), group=list(mydata$sex, mydata$employment))
tab.5
list.5 <- normTable(mydata.sumscore, from = 0,
to = 12, statistics=c("PR", "T", "Z", "z"),
group=list(mydata$sex, mydata$employment), as.list=TRUE)
list.5
#saveTable(tab.2, "normTable.rtf")
Example output
Score PR
1 0 37.6
2 1 63.2
3 2 78.2
4 3 87.0
5 4 93.4
6 5 96.6
7 6 98.6
8 7 99.4
9 8 100.0
10 9 100.0
11 10 100.0
12 11 100.0
13 12 100.0
Score PR T STANINE
1 0 37.6 41.3 3
2 1 63.2 47.3 4
3 2 78.2 53.2 5
4 3 87.0 59.1 6
5 4 93.4 65.1 8
6 5 96.6 71.0 9
7 6 98.6 76.9 9
8 7 99.4 82.9 9
9 8 100.0 88.8 9
10 9 100.0 94.7 9
11 10 100.0 100.7 9
12 11 100.0 106.6 9
13 12 100.0 112.5 9
Female.Score Female.PR Male.Score Male.PR
1 0 41.4 0 33.3
2 1 64.3 1 62.0
3 2 79.1 2 77.2
4 3 86.3 3 87.8
5 4 92.0 4 94.9
6 5 95.8 5 97.5
7 6 99.2 6 97.9
8 7 99.6 7 99.2
9 8 100.0 8 100.0
10 9 100.0 9 100.0
11 10 100.0 10 100.0
12 11 100.0 11 100.0
13 12 100.0 12 100.0
Employed.Score Employed.PR Employed.T Not employed.Score Not employed.PR
1 0 38.4 41.1 0 36.8
2 1 62.0 47.3 1 64.3
3 2 76.9 53.5 2 79.5
4 3 87.6 59.7 3 86.4
5 4 94.6 65.9 4 92.2
6 5 97.5 72.1 5 95.7
7 6 99.2 78.4 6 98.1
8 7 100.0 84.6 7 98.8
9 8 100.0 90.8 8 100.0
10 9 100.0 97.0 9 100.0
11 10 100.0 103.2 10 100.0
12 11 100.0 109.4 11 100.0
13 12 100.0 115.7 12 100.0
Not employed.T
1 41.6
2 47.3
3 53.0
4 58.6
5 64.3
6 70.0
7 75.7
8 81.4
9 87.1
10 92.8
11 98.5
12 104.1
13 109.8
Female.Employed Score Female.Employed T Male.Employed Score Male.Employed T
1 0 41.7 0 40.4
2 1 48.1 1 46.5
3 2 54.5 2 52.6
4 3 60.9 3 58.6
5 4 67.2 4 64.7
6 5 73.6 5 70.8
7 6 80.0 6 76.8
8 7 86.4 7 82.9
9 8 92.8 8 89.0
10 9 99.2 9 95.0
11 10 105.6 10 101.1
12 11 112.0 11 107.2
13 12 118.4 12 113.2
Female.Not employed Score Female.Not employed T Male.Not employed Score
1 0 41.8 0
2 1 47.2 1
3 2 52.6 2
4 3 57.9 3
5 4 63.3 4
6 5 68.7 5
7 6 74.1 6
8 7 79.5 7
9 8 84.9 8
10 9 90.2 9
11 10 95.6 10
12 11 101.0 11
13 12 106.4 12
Male.Not employed T
1 41.3
2 47.4
3 53.5
4 59.6
5 65.7
6 71.8
7 77.9
8 84.1
9 90.2
10 96.3
11 102.4
12 108.5
13 114.6
: Female
: Employed
Score PR T Z z
1 0 45.1 41.7 91.7 -0.8335786
2 1 63.1 48.1 98.1 -0.1939774
3 2 79.5 54.5 104.5 0.4456238
4 3 88.5 60.9 110.9 1.0852249
5 4 95.9 67.2 117.2 1.7248261
6 5 97.5 73.6 123.6 2.3644273
7 6 100.0 80.0 130.0 3.0040284
8 7 100.0 86.4 136.4 3.6436296
9 8 100.0 92.8 142.8 4.2832307
10 9 100.0 99.2 149.2 4.9228319
11 10 100.0 105.6 155.6 5.5624331
12 11 100.0 112.0 162.0 6.2020342
13 12 100.0 118.4 168.4 6.8416354
------------------------------------------------------------
: Male
: Employed
Score PR T Z z
1 0 31.7 40.4 90.4 -0.9555081
2 1 60.8 46.5 96.5 -0.3488363
3 2 74.2 52.6 102.6 0.2578355
4 3 86.7 58.6 108.6 0.8645074
5 4 93.3 64.7 114.7 1.4711792
6 5 97.5 70.8 120.8 2.0778511
7 6 98.3 76.8 126.8 2.6845229
8 7 100.0 82.9 132.9 3.2911947
9 8 100.0 89.0 139.0 3.8978666
10 9 100.0 95.0 145.0 4.5045384
11 10 100.0 101.1 151.1 5.1112103
12 11 100.0 107.2 157.2 5.7178821
13 12 100.0 113.2 163.2 6.3245539
------------------------------------------------------------
: Female
: Not employed
Score PR T Z z
1 0 38.3 41.8 91.8 -0.8207984
2 1 65.2 47.2 97.2 -0.2825073
3 2 78.7 52.6 102.6 0.2557837
4 3 84.4 57.9 107.9 0.7940747
5 4 88.7 63.3 113.3 1.3323657
6 5 94.3 68.7 118.7 1.8706567
7 6 98.6 74.1 124.1 2.4089477
8 7 99.3 79.5 129.5 2.9472387
9 8 100.0 84.9 134.9 3.4855298
10 9 100.0 90.2 140.2 4.0238208
11 10 100.0 95.6 145.6 4.5621118
12 11 100.0 101.0 151.0 5.1004028
13 12 100.0 106.4 156.4 5.6386938
------------------------------------------------------------
: Male
: Not employed
Score PR T Z z
1 0 35.0 41.3 91.3 -0.8722403
2 1 63.2 47.4 97.4 -0.2611498
3 2 80.3 53.5 103.5 0.3499407
4 3 88.9 59.6 109.6 0.9610313
5 4 96.6 65.7 115.7 1.5721218
6 5 97.4 71.8 121.8 2.1832124
7 6 97.4 77.9 127.9 2.7943029
8 7 98.3 84.1 134.1 3.4053935
9 8 100.0 90.2 140.2 4.0164840
10 9 100.0 96.3 146.3 4.6275745
11 10 100.0 102.4 152.4 5.2386651
12 11 100.0 108.5 158.5 5.8497556
13 12 100.0 114.6 164.6 6.4608462
psytabs documentation built on May 29, 2017, 10:52 p.m.
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Description Usage Arguments Details Value Examples
Description
Produces a table of norms.
Usage
1 |
Arguments
sumscores |
The sumscore vector for which norms should be created. |
from |
numeric value. Lowest possible sumscore as a numeric value. |
to |
numeric value. Highest possible sumscore as a numeric value. |
statistics |
character vector that toggles which norm statistics are included in the norm table. Currently Percent ranks "PR", z-Statistic "z", Z-Statistic "Z", IQ-Stiatistc "IQ", T-Statistic "T" and Stanine "Stanine" are supported. |
group |
List of subgroups by which the norms should be created. |
as.list |
logical vector that toggles whether the norm table should rather be returned as a (vertical) list than as a table. This option is useful if you have a lot of subgroups and/or norm statistics and the resulting table would not fit on the page horizontally. Note that currently when you set as.list = TRUE the resulting list cannot be saved by the saveTable function. |
Details
The different norm statistics (besides the percent ranks) are created by transforming the z-Statistic. This means that at first the sumscores are z-Transformed and then the other statistics are calculated based on the z-Statistic.
One could also choose to first "normalize" the z-Statistic, i.e. calculate the percent ranks and then assign the z-values to the sumscores according to the percent ranks of the standard normal distribution. This option is—albeit it is sometimes called the scientific standard—debatle because if the population does not follow the normal curve then the normalisation will lead to an artificial spreading or shortening of scores. This leads to the scores having only an ordinal scale quality even when the scores in the population distribution had originally metric scale quality. Thus this option should be used with caution and only on a strong theoretical basis. Therefore this option is currently not implemented.
Value
A dataframe constituting the norm table.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | data(mydata)
mydata.sumscore <- rowSums(mydata[,c("item1", "item2", "item3", "item4")])
tab.1 <- normTable(mydata.sumscore, from = 0, to = 12, statistics=c("PR"))
tab.1
tab.2 <- normTable(mydata.sumscore, from = 0,
to = 12, statistics=c("PR", "T", "Stanine"))
tab.2
tab.3 <- normTable(mydata.sumscore, from = 0,
to = 12, statistics=c("PR"), group=mydata$sex)
tab.3
tab.4 <- normTable(mydata.sumscore, from = 0,
to = 12, statistics=c("PR", "T"), group=mydata$employment)
tab.4
tab.5 <- normTable(mydata.sumscore, from = 0,
to = 12, statistics=c("T"), group=list(mydata$sex, mydata$employment))
tab.5
list.5 <- normTable(mydata.sumscore, from = 0,
to = 12, statistics=c("PR", "T", "Z", "z"),
group=list(mydata$sex, mydata$employment), as.list=TRUE)
list.5
#saveTable(tab.2, "normTable.rtf")
|
Example output
Score PR
1 0 37.6
2 1 63.2
3 2 78.2
4 3 87.0
5 4 93.4
6 5 96.6
7 6 98.6
8 7 99.4
9 8 100.0
10 9 100.0
11 10 100.0
12 11 100.0
13 12 100.0
Score PR T STANINE
1 0 37.6 41.3 3
2 1 63.2 47.3 4
3 2 78.2 53.2 5
4 3 87.0 59.1 6
5 4 93.4 65.1 8
6 5 96.6 71.0 9
7 6 98.6 76.9 9
8 7 99.4 82.9 9
9 8 100.0 88.8 9
10 9 100.0 94.7 9
11 10 100.0 100.7 9
12 11 100.0 106.6 9
13 12 100.0 112.5 9
Female.Score Female.PR Male.Score Male.PR
1 0 41.4 0 33.3
2 1 64.3 1 62.0
3 2 79.1 2 77.2
4 3 86.3 3 87.8
5 4 92.0 4 94.9
6 5 95.8 5 97.5
7 6 99.2 6 97.9
8 7 99.6 7 99.2
9 8 100.0 8 100.0
10 9 100.0 9 100.0
11 10 100.0 10 100.0
12 11 100.0 11 100.0
13 12 100.0 12 100.0
Employed.Score Employed.PR Employed.T Not employed.Score Not employed.PR
1 0 38.4 41.1 0 36.8
2 1 62.0 47.3 1 64.3
3 2 76.9 53.5 2 79.5
4 3 87.6 59.7 3 86.4
5 4 94.6 65.9 4 92.2
6 5 97.5 72.1 5 95.7
7 6 99.2 78.4 6 98.1
8 7 100.0 84.6 7 98.8
9 8 100.0 90.8 8 100.0
10 9 100.0 97.0 9 100.0
11 10 100.0 103.2 10 100.0
12 11 100.0 109.4 11 100.0
13 12 100.0 115.7 12 100.0
Not employed.T
1 41.6
2 47.3
3 53.0
4 58.6
5 64.3
6 70.0
7 75.7
8 81.4
9 87.1
10 92.8
11 98.5
12 104.1
13 109.8
Female.Employed Score Female.Employed T Male.Employed Score Male.Employed T
1 0 41.7 0 40.4
2 1 48.1 1 46.5
3 2 54.5 2 52.6
4 3 60.9 3 58.6
5 4 67.2 4 64.7
6 5 73.6 5 70.8
7 6 80.0 6 76.8
8 7 86.4 7 82.9
9 8 92.8 8 89.0
10 9 99.2 9 95.0
11 10 105.6 10 101.1
12 11 112.0 11 107.2
13 12 118.4 12 113.2
Female.Not employed Score Female.Not employed T Male.Not employed Score
1 0 41.8 0
2 1 47.2 1
3 2 52.6 2
4 3 57.9 3
5 4 63.3 4
6 5 68.7 5
7 6 74.1 6
8 7 79.5 7
9 8 84.9 8
10 9 90.2 9
11 10 95.6 10
12 11 101.0 11
13 12 106.4 12
Male.Not employed T
1 41.3
2 47.4
3 53.5
4 59.6
5 65.7
6 71.8
7 77.9
8 84.1
9 90.2
10 96.3
11 102.4
12 108.5
13 114.6
: Female
: Employed
Score PR T Z z
1 0 45.1 41.7 91.7 -0.8335786
2 1 63.1 48.1 98.1 -0.1939774
3 2 79.5 54.5 104.5 0.4456238
4 3 88.5 60.9 110.9 1.0852249
5 4 95.9 67.2 117.2 1.7248261
6 5 97.5 73.6 123.6 2.3644273
7 6 100.0 80.0 130.0 3.0040284
8 7 100.0 86.4 136.4 3.6436296
9 8 100.0 92.8 142.8 4.2832307
10 9 100.0 99.2 149.2 4.9228319
11 10 100.0 105.6 155.6 5.5624331
12 11 100.0 112.0 162.0 6.2020342
13 12 100.0 118.4 168.4 6.8416354
------------------------------------------------------------
: Male
: Employed
Score PR T Z z
1 0 31.7 40.4 90.4 -0.9555081
2 1 60.8 46.5 96.5 -0.3488363
3 2 74.2 52.6 102.6 0.2578355
4 3 86.7 58.6 108.6 0.8645074
5 4 93.3 64.7 114.7 1.4711792
6 5 97.5 70.8 120.8 2.0778511
7 6 98.3 76.8 126.8 2.6845229
8 7 100.0 82.9 132.9 3.2911947
9 8 100.0 89.0 139.0 3.8978666
10 9 100.0 95.0 145.0 4.5045384
11 10 100.0 101.1 151.1 5.1112103
12 11 100.0 107.2 157.2 5.7178821
13 12 100.0 113.2 163.2 6.3245539
------------------------------------------------------------
: Female
: Not employed
Score PR T Z z
1 0 38.3 41.8 91.8 -0.8207984
2 1 65.2 47.2 97.2 -0.2825073
3 2 78.7 52.6 102.6 0.2557837
4 3 84.4 57.9 107.9 0.7940747
5 4 88.7 63.3 113.3 1.3323657
6 5 94.3 68.7 118.7 1.8706567
7 6 98.6 74.1 124.1 2.4089477
8 7 99.3 79.5 129.5 2.9472387
9 8 100.0 84.9 134.9 3.4855298
10 9 100.0 90.2 140.2 4.0238208
11 10 100.0 95.6 145.6 4.5621118
12 11 100.0 101.0 151.0 5.1004028
13 12 100.0 106.4 156.4 5.6386938
------------------------------------------------------------
: Male
: Not employed
Score PR T Z z
1 0 35.0 41.3 91.3 -0.8722403
2 1 63.2 47.4 97.4 -0.2611498
3 2 80.3 53.5 103.5 0.3499407
4 3 88.9 59.6 109.6 0.9610313
5 4 96.6 65.7 115.7 1.5721218
6 5 97.4 71.8 121.8 2.1832124
7 6 97.4 77.9 127.9 2.7943029
8 7 98.3 84.1 134.1 3.4053935
9 8 100.0 90.2 140.2 4.0164840
10 9 100.0 96.3 146.3 4.6275745
11 10 100.0 102.4 152.4 5.2386651
12 11 100.0 108.5 158.5 5.8497556
13 12 100.0 114.6 164.6 6.4608462
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