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Unauthorized Border Crosser Deaths in the Southern Arizona Desert: Examining the Relationship between Demographic Characteristics and Cause of Death
Unformatted Document Text:  male, near the age of 30, most likely from the traditional or central region, and having  been recovered during a summer month.   Table 3.  Descriptive Statistics for Independent Variables Ind. Variable (N = 752) Mean/Percent Std. Dev. Min. Max FY 2003.33 2.982 1990 2007 TOPD 0.414 0.493 0 1 Summer 0.772 0.419 0 1 Female 0.233 0.423 0 1 Age 30.59 10.46 3 66 North 0.147 0.354 0 1 Central 0.291 0.455 0 1 Traditional 0.199 0.399 0 1 South-Southeast 0.252 0.434 0 1 Non-Mexican 0.110 0.313 0 1 Multinomial Logistic Regression and Results This section introduces and discusses the inferential analysis used in the study.  Although ideal, estimating the probability of a UBC death occurring would be nearly  impossible given the unavailability of data regarding UBCs who successfully cross as  well as the relatively limited data available on UBC bodies that are actually recovered.  Although I am not able to estimate an overall probability of a UBC death occurring, I am  able to estimate the probability of a UBC having died a specific way given that she or he  died and was recovered in a southern Arizona.  Thus, the focus of this study is to assign  recovered UBC bodies the probability of a confirmed cause of death given a set of  demographic characteristics as well as where, when and by whom the body was  recovered.      As previously mentioned, series of log likelihood ratio tests were utilized to arrive  at the final model.  Each theoretically relevant independent variable was incrementally  added to the model and was tested for overall improvement of the model.  All  independent variables used in the analysis improved overall fit of the model, thus  justifying their inclusion.  Also, a series of interaction effects that were related to  biological sex and region of origin were tested, however, none yielded statistically  significant results.  This was most likely related to the low frequencies of interaction  effect variables.   Table 4 describes the multinomial logistic regression results for the analysis  sample.  Coefficients for three causes of death were estimated: exposure, motor vehicle  11

Authors: Martinez, Daniel.
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male, near the age of 30, most likely from the traditional or central region, and having 
been recovered during a summer month.  
Table 3.  Descriptive Statistics for Independent Variables
Ind. Variable (N = 752)
Mean/Percent
Std. Dev.
Min.
Max
FY
2003.33
2.982
1990
2007
TOPD
0.414
0.493
0
1
Summer
0.772
0.419
0
1
Female
0.233
0.423
0
1
Age
30.59
10.46
3
66
North
0.147
0.354
0
1
Central
0.291
0.455
0
1
Traditional
0.199
0.399
0
1
South-Southeast
0.252
0.434
0
1
Non-Mexican
0.110
0.313
0
1
Multinomial Logistic Regression and Results
This section introduces and discusses the inferential analysis used in the study. 
Although ideal, estimating the probability of a UBC death occurring would be nearly 
impossible given the unavailability of data regarding UBCs who successfully cross as 
well as the relatively limited data available on UBC bodies that are actually recovered. 
Although I am not able to estimate an overall probability of a UBC death occurring, I am 
able to estimate the probability of a UBC having died a specific way given that she or he 
died and was recovered in a southern Arizona.  Thus, the focus of this study is to assign 
recovered UBC bodies the probability of a confirmed cause of death given a set of 
demographic characteristics as well as where, when and by whom the body was 
recovered.     
As previously mentioned, series of log likelihood ratio tests were utilized to arrive 
at the final model.  Each theoretically relevant independent variable was incrementally 
added to the model and was tested for overall improvement of the model.  All 
independent variables used in the analysis improved overall fit of the model, thus 
justifying their inclusion.  Also, a series of interaction effects that were related to 
biological sex and region of origin were tested, however, none yielded statistically 
significant results.  This was most likely related to the low frequencies of interaction 
effect variables.  
Table 4 describes the multinomial logistic regression results for the analysis 
sample.  Coefficients for three causes of death were estimated: exposure, motor vehicle 
11


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