Biology Reference
In-Depth Information
TABLE 5.5 Classification Matrix for the ORA Two-Group Analysis
Black
White
Total
% Correct
Black
203
15
218
93.12
White
22
124
146
84.93
Total
225
139
364
89.03
2
c
¼
190.709; p
<
0.000
TABLE 5.6 Classification Matrix for the ORA Three-Group Analysis
Amerindian
Black
White
Total
% Correct
Amerindian
206
46
10
262
78.63
Black
59
130
29
218
59.63
White
10
33
103
146
70.55
Total
275
209
142
626
69.60
2
c
¼
287.765; p
0.000
<
predictor variables to assess ancestry for the entire sample. As
Table 5.5
shows, the ORA
works well, separating a sample of American Blacks and Whites (data collected by JTH) in
a two-way analysis correctly nearly 90% of the time.
Table 5.5
also presents the classification
matrix for the two-group analysis.
Multiway ORAs are not as successful. In a three-way analysis the ORA correctly classified
approximately 70% of the sample of American Whites, American Blacks, and Amerindians
(
Table 5.6
). As more groups are added to the model the classification rate is drastically
reduced. This may be because ORAs are somewhat sensitive to sample size. Yet the method
is promising and merits further scrutiny and research.
Logistic Regression
Logistic regression (LR) is a statistical method similar to linear regression since LR finds an
equation that predicts an outcome for a binary variable, Y, from one or more response vari-
ables, X. However, unlike linear regression the response variables can be categorical or
continuous, as the model does not strictly require continuous data. To predict groupmember-
ship, LR uses the log odds ratio rather than probabilities and an iterative maximum likeli-
hood method rather than a least squares to fit the final model. This means the researcher
has more freedomwhen using LR and the method may be more appropriate for nonnormally
distributed data or when the samples have unequal covariance matrices. Logistic regression
assumes independence among variables, which is not always met in morphoscopic datasets.
However, as is often the case, the applicability of the method (and how well it works, e.g., the
classification error) often trumps statistical assumptions. One drawback of LR is that the
method cannot produce typicality probabilities (useful for forensic casework), but these
values may be substituted with nonparametric methods such as ranked probabilities and
ranked interindividual similarity measures (
Ousley and Hefner, 2005
).
