Several descriptive measures for constructs and elements.
Arguments
- x
repgridobject.- index
Whether to print the number of the element.
- trim
The number of characters an element or a construct is trimmed to (default is
20). IfNAno trimming occurs. Trimming simply saves space when displaying correlation of constructs or elements with long names.
Value
A dataframe containing the following measures is returned invisibly (see describe()):
item name
item number
number of valid cases
mean standard deviation
trimmed mean (default
.1)median (standard or interpolated)
mad: median absolute deviation (from the median)
minimum
maximum
skew
kurtosis
standard error
Note
Note that standard deviation and variance are estimations, i.e. including Bessel's correction. For more info
type ?describe.
Examples
statsConstructs(fbb2003)
#>
#> ####################################
#> Desriptive statistics for constructs
#> ####################################
#>
#> vars n mean sd median trimmed mad min max range
#> (1) clever - not bright 1 8 3.75 2.31 4.0 3.75 2.97 1 7 6
#> (2) disorganiz - organized 2 8 4.00 1.77 4.5 4.00 2.22 2 6 4
#> (3) listens - doesn't he 3 8 3.50 2.14 3.0 3.50 2.22 1 7 6
#> (4) no clear v - clear view 4 8 4.38 1.60 4.0 4.38 1.48 3 7 4
#> (5) understand - no underst 5 8 3.50 1.85 2.5 3.50 0.74 2 6 4
#> (6) ambitious - no ambitio 6 8 4.50 1.51 4.5 4.50 2.22 3 7 4
#> (7) respected - not respec 7 8 3.25 1.75 3.0 3.25 1.48 1 6 5
#> (8) distant - warm 8 8 4.12 1.96 4.0 4.12 1.48 1 7 6
#> (9) rather agg - not aggres 9 8 3.62 1.92 3.0 3.62 2.22 1 7 6
#> skew kurtosis se
#> (1) clever - not bright 0.02 -1.84 0.82
#> (2) disorganiz - organized -0.13 -1.96 0.63
#> (3) listens - doesn't he 0.35 -1.40 0.76
#> (4) no clear v - clear view 0.38 -1.68 0.56
#> (5) understand - no underst 0.41 -1.90 0.65
#> (6) ambitious - no ambitio 0.33 -1.58 0.53
#> (7) respected - not respec 0.23 -1.67 0.62
#> (8) distant - warm -0.05 -1.46 0.69
#> (9) rather agg - not aggres 0.36 -1.25 0.68
statsConstructs(fbb2003, trim = 10)
#>
#> ####################################
#> Desriptive statistics for constructs
#> ####################################
#>
#> vars n mean sd median trimmed mad min max range skew
#> (1) cleve - not b 1 8 3.75 2.31 4.0 3.75 2.97 1 7 6 0.02
#> (2) disor - organ 2 8 4.00 1.77 4.5 4.00 2.22 2 6 4 -0.13
#> (3) liste - doesn 3 8 3.50 2.14 3.0 3.50 2.22 1 7 6 0.35
#> (4) no cl - clear 4 8 4.38 1.60 4.0 4.38 1.48 3 7 4 0.38
#> (5) under - no un 5 8 3.50 1.85 2.5 3.50 0.74 2 6 4 0.41
#> (6) ambit - no am 6 8 4.50 1.51 4.5 4.50 2.22 3 7 4 0.33
#> (7) respe - not r 7 8 3.25 1.75 3.0 3.25 1.48 1 6 5 0.23
#> (8) dista - warm 8 8 4.12 1.96 4.0 4.12 1.48 1 7 6 -0.05
#> (9) rathe - not a 9 8 3.62 1.92 3.0 3.62 2.22 1 7 6 0.36
#> kurtosis se
#> (1) cleve - not b -1.84 0.82
#> (2) disor - organ -1.96 0.63
#> (3) liste - doesn -1.40 0.76
#> (4) no cl - clear -1.68 0.56
#> (5) under - no un -1.90 0.65
#> (6) ambit - no am -1.58 0.53
#> (7) respe - not r -1.67 0.62
#> (8) dista - warm -1.46 0.69
#> (9) rathe - not a -1.25 0.68
statsConstructs(fbb2003, trim = 10, index = FALSE)
#>
#> ####################################
#> Desriptive statistics for constructs
#> ####################################
#>
#> vars n mean sd median trimmed mad min max range skew kurtosis
#> cleve - not b 1 8 3.75 2.31 4.0 3.75 2.97 1 7 6 0.02 -1.84
#> disor - organ 2 8 4.00 1.77 4.5 4.00 2.22 2 6 4 -0.13 -1.96
#> liste - doesn 3 8 3.50 2.14 3.0 3.50 2.22 1 7 6 0.35 -1.40
#> no cl - clear 4 8 4.38 1.60 4.0 4.38 1.48 3 7 4 0.38 -1.68
#> under - no un 5 8 3.50 1.85 2.5 3.50 0.74 2 6 4 0.41 -1.90
#> ambit - no am 6 8 4.50 1.51 4.5 4.50 2.22 3 7 4 0.33 -1.58
#> respe - not r 7 8 3.25 1.75 3.0 3.25 1.48 1 6 5 0.23 -1.67
#> dista - warm 8 8 4.12 1.96 4.0 4.12 1.48 1 7 6 -0.05 -1.46
#> rathe - not a 9 8 3.62 1.92 3.0 3.62 2.22 1 7 6 0.36 -1.25
#> se
#> cleve - not b 0.82
#> disor - organ 0.63
#> liste - doesn 0.76
#> no cl - clear 0.56
#> under - no un 0.65
#> ambit - no am 0.53
#> respe - not r 0.62
#> dista - warm 0.69
#> rathe - not a 0.68
statsElements(fbb2003)
#>
#> ##################################
#> Desriptive statistics for elements
#> ##################################
#>
#> vars n mean sd median trimmed mad min max range
#> (1) self 1 9 3.44 1.81 3 3.44 1.48 1 6 5
#> (2) my father 2 9 3.00 1.87 3 3.00 1.48 1 6 5
#> (3) an old flame 3 9 4.89 1.45 5 4.89 1.48 3 7 4
#> (4) an ethical person 4 9 3.11 0.93 3 3.11 0.00 2 5 3
#> (5) my mother 5 9 4.11 1.69 5 4.11 2.97 2 7 5
#> (6) a rejected teacher 6 9 4.33 2.35 5 4.33 2.97 1 7 6
#> (7) as I would love to b 7 9 3.44 2.35 3 3.44 2.97 1 7 6
#> (8) a pitied person 8 9 4.44 1.42 5 4.44 1.48 2 7 5
#> skew kurtosis se
#> (1) self 0.30 -1.60 0.60
#> (2) my father 0.61 -1.22 0.62
#> (3) an old flame -0.05 -1.72 0.48
#> (4) an ethical person 0.65 -0.54 0.31
#> (5) my mother 0.12 -1.44 0.56
#> (6) a rejected teacher -0.15 -1.89 0.78
#> (7) as I would love to b 0.19 -1.82 0.78
#> (8) a pitied person -0.02 -0.75 0.47
statsElements(fbb2003, trim = 10)
#>
#> ##################################
#> Desriptive statistics for elements
#> ##################################
#>
#> vars n mean sd median trimmed mad min max range skew
#> (1) self 1 9 3.44 1.81 3 3.44 1.48 1 6 5 0.30
#> (2) my father 2 9 3.00 1.87 3 3.00 1.48 1 6 5 0.61
#> (3) an old fla 3 9 4.89 1.45 5 4.89 1.48 3 7 4 -0.05
#> (4) an ethical 4 9 3.11 0.93 3 3.11 0.00 2 5 3 0.65
#> (5) my mother 5 9 4.11 1.69 5 4.11 2.97 2 7 5 0.12
#> (6) a rejected 6 9 4.33 2.35 5 4.33 2.97 1 7 6 -0.15
#> (7) as I would 7 9 3.44 2.35 3 3.44 2.97 1 7 6 0.19
#> (8) a pitied p 8 9 4.44 1.42 5 4.44 1.48 2 7 5 -0.02
#> kurtosis se
#> (1) self -1.60 0.60
#> (2) my father -1.22 0.62
#> (3) an old fla -1.72 0.48
#> (4) an ethical -0.54 0.31
#> (5) my mother -1.44 0.56
#> (6) a rejected -1.89 0.78
#> (7) as I would -1.82 0.78
#> (8) a pitied p -0.75 0.47
statsElements(fbb2003, trim = 10, index = FALSE)
#>
#> ##################################
#> Desriptive statistics for elements
#> ##################################
#>
#> vars n mean sd median trimmed mad min max range skew kurtosis
#> self 1 9 3.44 1.81 3 3.44 1.48 1 6 5 0.30 -1.60
#> my father 2 9 3.00 1.87 3 3.00 1.48 1 6 5 0.61 -1.22
#> an old fla 3 9 4.89 1.45 5 4.89 1.48 3 7 4 -0.05 -1.72
#> an ethical 4 9 3.11 0.93 3 3.11 0.00 2 5 3 0.65 -0.54
#> my mother 5 9 4.11 1.69 5 4.11 2.97 2 7 5 0.12 -1.44
#> a rejected 6 9 4.33 2.35 5 4.33 2.97 1 7 6 -0.15 -1.89
#> as I would 7 9 3.44 2.35 3 3.44 2.97 1 7 6 0.19 -1.82
#> a pitied p 8 9 4.44 1.42 5 4.44 1.48 2 7 5 -0.02 -0.75
#> se
#> self 0.60
#> my father 0.62
#> an old fla 0.48
#> an ethical 0.31
#> my mother 0.56
#> a rejected 0.78
#> as I would 0.78
#> a pitied p 0.47
# save the access the results
d <- statsElements(fbb2003)
d
#>
#> ##################################
#> Desriptive statistics for elements
#> ##################################
#>
#> vars n mean sd median trimmed mad min max range
#> (1) self 1 9 3.44 1.81 3 3.44 1.48 1 6 5
#> (2) my father 2 9 3.00 1.87 3 3.00 1.48 1 6 5
#> (3) an old flame 3 9 4.89 1.45 5 4.89 1.48 3 7 4
#> (4) an ethical person 4 9 3.11 0.93 3 3.11 0.00 2 5 3
#> (5) my mother 5 9 4.11 1.69 5 4.11 2.97 2 7 5
#> (6) a rejected teacher 6 9 4.33 2.35 5 4.33 2.97 1 7 6
#> (7) as I would love to b 7 9 3.44 2.35 3 3.44 2.97 1 7 6
#> (8) a pitied person 8 9 4.44 1.42 5 4.44 1.48 2 7 5
#> skew kurtosis se
#> (1) self 0.30 -1.60 0.60
#> (2) my father 0.61 -1.22 0.62
#> (3) an old flame -0.05 -1.72 0.48
#> (4) an ethical person 0.65 -0.54 0.31
#> (5) my mother 0.12 -1.44 0.56
#> (6) a rejected teacher -0.15 -1.89 0.78
#> (7) as I would love to b 0.19 -1.82 0.78
#> (8) a pitied person -0.02 -0.75 0.47
d["mean"]
#>
#> ##################################
#> Desriptive statistics for elements
#> ##################################
#>
#> mean
#> (1) self 3.44
#> (2) my father 3.00
#> (3) an old flame 4.89
#> (4) an ethical person 3.11
#> (5) my mother 4.11
#> (6) a rejected teacher 4.33
#> (7) as I would love to b 3.44
#> (8) a pitied person 4.44
d[2, "mean"] # mean rating of 2nd element
#> [1] 3
d <- statsConstructs(fbb2003)
d
#>
#> ####################################
#> Desriptive statistics for constructs
#> ####################################
#>
#> vars n mean sd median trimmed mad min max range
#> (1) clever - not bright 1 8 3.75 2.31 4.0 3.75 2.97 1 7 6
#> (2) disorganiz - organized 2 8 4.00 1.77 4.5 4.00 2.22 2 6 4
#> (3) listens - doesn't he 3 8 3.50 2.14 3.0 3.50 2.22 1 7 6
#> (4) no clear v - clear view 4 8 4.38 1.60 4.0 4.38 1.48 3 7 4
#> (5) understand - no underst 5 8 3.50 1.85 2.5 3.50 0.74 2 6 4
#> (6) ambitious - no ambitio 6 8 4.50 1.51 4.5 4.50 2.22 3 7 4
#> (7) respected - not respec 7 8 3.25 1.75 3.0 3.25 1.48 1 6 5
#> (8) distant - warm 8 8 4.12 1.96 4.0 4.12 1.48 1 7 6
#> (9) rather agg - not aggres 9 8 3.62 1.92 3.0 3.62 2.22 1 7 6
#> skew kurtosis se
#> (1) clever - not bright 0.02 -1.84 0.82
#> (2) disorganiz - organized -0.13 -1.96 0.63
#> (3) listens - doesn't he 0.35 -1.40 0.76
#> (4) no clear v - clear view 0.38 -1.68 0.56
#> (5) understand - no underst 0.41 -1.90 0.65
#> (6) ambitious - no ambitio 0.33 -1.58 0.53
#> (7) respected - not respec 0.23 -1.67 0.62
#> (8) distant - warm -0.05 -1.46 0.69
#> (9) rather agg - not aggres 0.36 -1.25 0.68
d["sd"]
#>
#> ####################################
#> Desriptive statistics for constructs
#> ####################################
#>
#> sd
#> (1) clever - not bright 2.31
#> (2) disorganiz - organized 1.77
#> (3) listens - doesn't he 2.14
#> (4) no clear v - clear view 1.60
#> (5) understand - no underst 1.85
#> (6) ambitious - no ambitio 1.51
#> (7) respected - not respec 1.75
#> (8) distant - warm 1.96
#> (9) rather agg - not aggres 1.92
d[1, "sd"] # sd of ratings on first construct
#> [1] 2.31455