bronze age environment and economy in the troad - Universität ...

chapter 2: methods
mistaken sample amongst a total of 10 samples results in an
error of 10%, whereas the same sample amongst a total of 344
samples results in an error of only 0.29%.
To exclude multiple sampling, the raw data were examined for
the origin (co-ordinates) of the samples, and on the sample
composition of neighbouring samples. Except for pits filled by
clearly distinguishable events, samples from neighbouring
locations which showed the same composition and the same
proportions of species or categories were summarised as one
sample.
The percentage occurrence of specific plant groups (e.g. crops)
within different periods gives an impression of dominance or
underrepresentation of each species. Dominance within the set
of analysed samples should not be confused with the meaning
of “importance”, which would be an “unjustified assumption
that numerical values reflect importance” as G. Jones (1991)
pointed out (for earlier discussions see also Dennell 1976). But
at least percentage occurrence provides a clear pattern of the
increase or decrease of ubiquities of single species from one
period to another.
The percentage occurrence of some of the crops was calculated
for the sequence Kumtepe, Early Bronze Age Troy, Middle
Bronze Age Troy, Late Bronze Age Troy, Post-Bronze Age
Troy and is presented in Graph 45.
2.2.2.3 Multivariate methods
According to G. Jones (1991) the two main approaches within
statistical analysis are ‛pattern searching’, where groupings of
the data are sought and ‛problem-oriented analysis’, where
rather complicated analyses find application. The different kind
of analyses in this work were mainly used for ‛pattern
searching’.
For the preparation of the input files, those samples were
chosen that were assumed to be representative of the sampled
context, i.e. that had more than 50 records (van der Veen
1992). Fifty-seven percent of the original number of samples
were excluded. A method of reducing the volume of data by
reducing the number of categories (taxa) has been worked out
by van der Veen and Fieller (1982). They have calculated the
number of items required to estimate relative quantities at
different levels of accuracy and confidence, for different sizes
of parent population. For the cut-off point of species
represented in the Troy and Kumtepe samples, ubiquities of
more than 10% were chosen. This lies within the range (5-
20%), assumed to characterise rare species in community
ecology (Gauch 1982a, and 1982b, Lange 1990), and reduces
the number of species categories from 318 to 94, i.e. 266714
seeds or 98,8 % of the total amount of seeds.
Samples or species below the threshholds mentioned were
omitted from the multivariate analysis in order to conduct the
necessary data compression. No other manipulation of the data
was necessary to apply Canoco.
Canonical discriminant analysis is used to discover those
variables that best discriminate between groups, thus allowing
ungrouped units to be assigned to them (Pearsall 1989). It starts
from the assumption that a set of objects (species) belongs to
more than two groups (crop-processing stages), i.e. that the
group to which an individual belongs is known. “Linear
combinations of the variables are sought that display the
difference between the presumed groups as clearly as possible.
The aim may be to confirm that the presumed groups are
indeed distinct or to provide a criterion for allocating
unclassified individuals to a group” (Baxter 1994, p. 186).
Canonical discriminant analysis was used to determine cropprocessing
stages. The problems of comparing samples without
knowing the stage of crop-processing they come from are
pointed out by Jones (1983a, 1983b and 1984) and Hillman
(1984a and 1984b). The statistical method to detect cropprocessing
stages, was worked out by Jones (1987a, 1987b),
and is based on ethnographic research in Greece (Kolofana).
Samples from traditional crop-processing were analysed for
characteristics that best distinguished between the known
groups. New variables were defined (discrimination). The four
groups of remains that result from traditional crop-processing
are: winnowing by-products, coarse-sieve by-products, finesieve
by-products, and fine-sieve products. The characteristics
that best distinguish between the known groups (new variables)
are combinations of three morphological characters of the weed
seed. The characters are seed size (Big/Small), ‛headedness’
(tendency of seeds to remain in heads during crop-processing,
rather than to be dispersed (Free/Headed)), and
‛aerodynamics/lightness’ (Heavy/Light). The combinations of
these three characters in one weed species lead to six overall
categories of weed taxa, the new variables (see appendix 4,
CPS). The application of the new variables to the whole set of
species leads to the allocation of the samples to the four cropprocessing
stages.
The potential weed species of Troy samples were assigned to
categories. The analysis was run and plotted to Jones’ Kolofana
data with SPSS. The results are included in chapter 5 (see
Graph 41).
With the calculation of individual correlation coefficients, the
strength of a relationship between the values of two variables
(species) can be ascertained. Over the whole set of samples the
values of one variable (species) are compared with those of the
second variable (species). If there is any relationship between
the values of the variables, i.e. if the variables vary together,
they are correlated. To visualise the strength of a correlation
one might imagine a plot, with the values of one variable on
the x-axis, the values of the other variable on the y-axis. The
samples (cases) would be the reference points in the plot. The
plotted points would lie for a perfect correlation on an
imaginary straight diagonal. Usually correlations are weaker.
The degree of correlation, or the closeness with which the pairs
of values fit a straight line, is numerically expressed by the
correlation coefficient (r). The standard error of the correlation
coefficient is a measure for the significance of the correlation
coefficient. In the analyses conducted here a probability level
between p=0.00 and p=0.02 was chosen. The standard error
p=0.02 means that there will appear to be a significant
correlation in 2% of the tests, even if there is no actual
correlation between the variables. This factor could only be
excluded by taking p=0.00, but in this case one would have to
exclude too many very probable actual correlations.
21