Description Usage Arguments Details Value Calculation Author(s) References See Also Examples
Calculate Hartmann distance
1 2 3 4 5 6 7 8  distanceHartmann(
x,
method = "paper",
reps = 10000,
prob = NULL,
progress = TRUE,
distributions = FALSE
)

x 

method 
The method used for distance calculation, on of

reps 
Number of random grids to generate sample distribution for
Slater distances (default is 
prob 
The probability of each rating value to occur.
If 
progress 
Whether to show a progress bar during simulation
(default is 
distributions 
Wether to additionally return the values of the simulated
distributions (Slater etc.) The default is 
Hartmann (1992) showed in a simulation study that Slater distances (see
distanceSlater
) based on random grids, for which Slater coined
the expression quasis, have a skewed distribution, a mean and a standard
deviation depending on the number of constructs elicited. He suggested a
linear transformation (ztransformation) which takes into account the
estimated (or expected) mean and the standard deviation of the derived
distribution to standardize Slater distance scores across different grid
sizes. 'Hartmann distances' represent a more accurate version of 'Slater
distances'. Note that Hartmann distances are multiplied by 1. Hence,
negative Hartmann values represent dissimilarity, i.e. a big Slater distance.
There are two ways to use this function. Hartmann distances can either be
calculated based on the reference values (i.e. means and standard deviations
of Slater distance distributions) as given by Hartmann in his paper. The
second option is to conduct an instant simulation for the supplied grid
size for each calculation. The second option will be more accurate when
a big number of quasis is used in the simulation.
It is also possible to return the quantiles of the sample distribution and only the element distances consideres 'significant' according to the quantiles defined.
A matrix containing Hartmann distances.
In the attributes several additional parameters can be found:

A list of several parameters
including 

Quantiles for Slater and Hartmann distance distribition. 

List with values of the simulated distributions. 
The 'Hartmann distance' is calculated as follows (Hartmann 1992, p. 49).
D = 1 (D_slater  M_c / sd_c)
Where D_slater denotes the Slater distances of the grid, M_c the sample distribution's mean value and sd_c the sample distributions's standard deviation.
Mark Heckmann
Hartmann, A. (1992). Element comparisons in repertory grid technique: Results and consequences of a Monte Carlo study. International Journal of Personal Construct Psychology, 5(1), 4156.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ## Not run:
### basics ###
distanceHartmann(bell2010)
distanceHartmann(bell2010, method="simulate")
h < distanceHartmann(bell2010, method="simulate")
h
# printing options
print(h)
print(h, digits=6)
# 'significant' distances only
print(h, p=c(.05, .95))
# access cells of distance matrix
h[1,2]
### advanced ###
# histogram of Slater distances and indifference region
h < distanceHartmann(bell2010, distributions=TRUE)
l < attr(h, "distributions")
hist(l$slater, breaks=100)
hist(l$hartmann, breaks=100)
## End(Not run)

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