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Sometimes basic descriptive measures for constructs and elements, e.g. mean, standard deviation, are neeeded. To prompt descriptive statistics for the constructs and elements of a grid use the function statsConstructs and statsElements. The following measures are returned:

  • item name
  • item number
  • number of valid cases
  • mean
  • standard deviation
  • trimmed mean (with trim defaulting to .1)
  • median (standard or interpolated)
  • mad: median absolute deviation (from the median)
  • minimum
  • maximum
  • skew
  • kurtosis
  • standard error

R-Code

The following examples are identical for statsElements. Just replace statsConstructs by statsElements in order to analyze elements.

d <- statsConstructs(fbb2003)
d
# 
# ####################################
# Desriptive statistics for constructs
# ####################################
# 
#                             vars n mean   sd median trimmed  mad min max range  skew kurtosis   se
# (1) clever - not bright        1 8 3.75 2.31    4.0    3.75 2.97   1   7     6  0.02    -1.84 0.82
# (2) disorganiz - organized     2 8 4.00 1.77    4.5    4.00 2.22   2   6     4 -0.13    -1.96 0.63
# (3) listens - doesn't he       3 8 3.50 2.14    3.0    3.50 2.22   1   7     6  0.35    -1.40 0.76
# (4) no clear v - clear view    4 8 4.38 1.60    4.0    4.38 1.48   3   7     4  0.38    -1.68 0.56
# (5) understand - no underst    5 8 3.50 1.85    2.5    3.50 0.74   2   6     4  0.41    -1.90 0.65
# (6) ambitious - no ambitio     6 8 4.50 1.51    4.5    4.50 2.22   3   7     4  0.33    -1.58 0.53
# (7) respected - not respec     7 8 3.25 1.75    3.0    3.25 1.48   1   6     5  0.23    -1.67 0.62
# (8) distant - warm             8 8 4.12 1.96    4.0    4.12 1.48   1   7     6 -0.05    -1.46 0.69
# (9) rather agg - not aggres    9 8 3.62 1.92    3.0    3.62 2.22   1   7     6  0.36    -1.25 0.68

The returned object is a dataframe, so you may access them as usual. E.g. to retrieve the means of the constructs, type

d$mean
# [1] 3.750 4.000 3.500 4.375 3.500 4.500 3.250 4.125 3.625
statsConstructs(fbb2003, trim = 10)
# 
# ####################################
# Desriptive statistics for constructs
# ####################################
# 
#                   vars n mean   sd median trimmed  mad min max range  skew kurtosis   se
# (1) cleve - not b    1 8 3.75 2.31    4.0    3.75 2.97   1   7     6  0.02    -1.84 0.82
# (2) disor - organ    2 8 4.00 1.77    4.5    4.00 2.22   2   6     4 -0.13    -1.96 0.63
# (3) liste - doesn    3 8 3.50 2.14    3.0    3.50 2.22   1   7     6  0.35    -1.40 0.76
# (4) no cl - clear    4 8 4.38 1.60    4.0    4.38 1.48   3   7     4  0.38    -1.68 0.56
# (5) under - no un    5 8 3.50 1.85    2.5    3.50 0.74   2   6     4  0.41    -1.90 0.65
# (6) ambit - no am    6 8 4.50 1.51    4.5    4.50 2.22   3   7     4  0.33    -1.58 0.53
# (7) respe - not r    7 8 3.25 1.75    3.0    3.25 1.48   1   6     5  0.23    -1.67 0.62
# (8) dista - warm     8 8 4.12 1.96    4.0    4.12 1.48   1   7     6 -0.05    -1.46 0.69
# (9) rathe - not a    9 8 3.62 1.92    3.0    3.62 2.22   1   7     6  0.36    -1.25 0.68
statsConstructs(fbb2003, index = F)
# 
# ####################################
# Desriptive statistics for constructs
# ####################################
# 
#                         vars n mean   sd median trimmed  mad min max range  skew kurtosis   se
# clever - not bright        1 8 3.75 2.31    4.0    3.75 2.97   1   7     6  0.02    -1.84 0.82
# disorganiz - organized     2 8 4.00 1.77    4.5    4.00 2.22   2   6     4 -0.13    -1.96 0.63
# listens - doesn't he       3 8 3.50 2.14    3.0    3.50 2.22   1   7     6  0.35    -1.40 0.76
# no clear v - clear view    4 8 4.38 1.60    4.0    4.38 1.48   3   7     4  0.38    -1.68 0.56
# understand - no underst    5 8 3.50 1.85    2.5    3.50 0.74   2   6     4  0.41    -1.90 0.65
# ambitious - no ambitio     6 8 4.50 1.51    4.5    4.50 2.22   3   7     4  0.33    -1.58 0.53
# respected - not respec     7 8 3.25 1.75    3.0    3.25 1.48   1   6     5  0.23    -1.67 0.62
# distant - warm             8 8 4.12 1.96    4.0    4.12 1.48   1   7     6 -0.05    -1.46 0.69
# rather agg - not aggres    9 8 3.62 1.92    3.0    3.62 2.22   1   7     6  0.36    -1.25 0.68