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Copy pathsexual_violence_analysis.m
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sexual_violence_analysis.m
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load filtered_db_2015_01_18
load GENDER
%also load some deomgraphic analysis !
%2. SEXUAL VIOLENCE/EXCHANGE VARIABLES
%%--these first variables are in the "exchange" and "exchange_N" variables
% % for total N for all variables; frequencies, percentages and p-values
% % for all variables by gender.
% % Touched/rubbed in sexual way (sexvio_Note1/sexvio_1) ? this looks correct (binary)
% % Touched/rubbed in sexual way # of times (sexvio_Note1/sexvio_2) ? this looks correct (continuous)
% % Collapse into 3 groups: 1, 2-4, 5 or more
% %
% % Fingers/objects inserted (sexvio_Note1/sexvio_3) ? this looks correct (binary)
% % Fingers/objects inserted # of times (sexvio_Note1/sexvio_4) ? this looks correct (continuous)
% % Collapse into 3 groups: 1, 2-4, 5 or more
% %
% % Sex without consent (sexvio_Note1/sexvio_5) ? this looks correct (binary)
% % Sex without consent # of times (sexvio_Note1/sexvio_6) ? this looks correct (continuous)
% % Collapse into 3 groups: 1, 2-4, 5 or more
% %
% % Felt sex expected because using together (sexvio_Note1/sexvio_9) ? this looks correct (binary)
% % Felt sex expected because using together # of times (sexvio_Note1/sexvio_10) ? this looks correct (continuous), Collapse into 3 groups: 1-4, 5-9, 10 or more
% % Sexually insulted (sexvio_Note1/sexvio_11) ? this looks correct (binary)
% % Sexually insulted # of times (sexvio_Note1/sexvio_12) ? this looks correct (continuous),
% % Collapse into 3 groups: 1-4, 5-9, 10 or more
% %
% % Offered drugs/$ for sex (sexvio_Note1/sexvio_13) ? this looks correct (binary)
% % Offered drugs/$ for sex # of times (sexvio_Note1/sexvio_14) ? this looks correct (continuous),
% % Frequency & % for following groups: 1-4, 5-9, 10 or more, 50 or more
% % Felt sexually violated but don't remember (sexvio_Note1/sexvio_15) ? this looks correct (binary)
% %
% % Witnessed sexual violence? this looks correct (binary, as TRUE/FALSE right now)
% % sexvio_Note1/sexvio_16/1 -- witnessed someone touching/rubbing in a
% sexual way
% % sexvio_Note1/sexvio_16/2 --witnessed oral sex without consent
% % sexvio_Note1/sexvio_16/3 --witnessed finger/object insertion
% % sexvio_Note1/sexvio_16/4 -- witnessed sex without consent
% % sexvio_Note1/sexvio_16/88 --Don't know <----DON'T DO THIS
% %
% % Sexual exchange (LifetimeSex_3) ? this looks correct (binary)
% % Sexual exchange received drugs/$ # times (LifetimeSex_4) ? this looks correct (continuous)
% % Collapse into 3 groups: 1, 2-4, 5 or more
% %
% % Sexual exchange paid drugs/$ # times (LifetimeSex_5) ? this looks correct (continuous)
% % Collapse into 3 groups: 1, 2-4, 5 or more
% %
sex_exchanged={'LifetimeSex_3', 'LifetimeSex_4', 'LifetimeSex_5'};
%THE LIFETIME 5 doesn't have an intital binary
names={'Touched/rubbed in sexual way', 'Fingers/objects inserted', 'Sex without consent', 'Felt sex expected because using together', 'Sexually insulted', 'Offered drugs/$ for sex', 'Felt sexually violated but does not remember', 'Witnessed someone touching/rubbing in sexual way', 'Witnessed oral sex without consent', 'Witnessed finger/object insertion', 'Witnessed sex without consent' 'Sexual exchange received drugs/$', 'Sexual exchange paid drugs/$' };
exchange={'sexvio_Note1/sexvio_1','sexvio_Note1/sexvio_3', 'sexvio_Note1/sexvio_5', 'sexvio_Note1/sexvio_9','sexvio_Note1/sexvio_11', 'sexvio_Note1/sexvio_13', 'sexvio_Note1/sexvio_15', 'sexvio_Note1/sexvio_16/1', 'sexvio_Note1/sexvio_16/2', 'sexvio_Note1/sexvio_16/3', 'sexvio_Note1/sexvio_16/4', 'LifetimeSex_3', 'LifetimeSex_3'};
exchange_N={'sexvio_Note1/sexvio_2','sexvio_Note1/sexvio_4','sexvio_Note1/sexvio_6', 'sexvio_Note1/sexvio_10','sexvio_Note1/sexvio_12','sexvio_Note1/sexvio_14', '', '', '', '', '', 'LifetimeSex_4', 'LifetimeSex_5' };
[r,c]=size(filtered_final);
filtered_data=filtered_final(2:r,:);
headers=filtered_final(1,:);
results=cell.empty;
results{1,1}='Question';
results{1,2}='Code';
results{1,3}='freq-total';
results{1,4}='% total';
results{1,5}='95% CI';
results{1,6}='freq-males';
results{1,7}='% males';
results{1,8}='95% CI';
results{1,9}='freq-females';
results{1,10}='% females';
results{1,11}='95% CI';
results{1,12}='p value';
T={'1', '2-4', '5-9', '10-50' '>50', '1 %', '2-4 %', '5-9 %', '10-50 %', '>50 %', '1', '2-4', '5-9', '10-50' '>50', '1 %', '2-4 %', '5-9 %', '10-50 %', '>50 %', '1', '2-4', '5-9', '10-50' '>50', '1 %', '2-4 %', '5-9 %', '10-50 %', '>50 %'};
results(1, 12:41)=T;
z=1.96;
for i=1:numel(exchange)
E=exchange{i};
indx=find(strcmp(headers,E)==1);
results{i+1,1}=names{i};
E_N=exchange_N{i};
indx_N=find(strcmp(headers,E_N)==1);
n_check=0;
if numel(indx)==1
data_mat=filtered_data(:,indx);
tf=isa(data_mat,'cell');
indx_t=find(strcmp('TRUE',data_mat)==1);
if numel(indx_t)>0%tf==1
indx_t=find(strcmp('TRUE',data_mat)==1);
indx_f=find(strcmp('FALSE',data_mat)==1);
for j=1:numel(indx_t)
data_mat{indx_t(j)}=1;
end
for j=1:numel(indx_f)
data_mat{indx_f(j)}=0;
end
% indx_empty=cellfun('isempty',data_mat);
% data_mat{indx_empty}=NaN;
% indx_nan=find(strcmp('NaN',data_mat)==1);
% for j=1:numel(indx_nan)
% data_mat{indx_nan(j)}=NaN;
% end
isnum = cellfun(@isnumeric,data_mat);
data_mat2 = NaN(size(data_mat));
data_mat2(isnum) = [data_mat{isnum}];
data_mat=data_mat2;
else
data_mat=str2double(data_mat);
end
indx_male=find(GENDER==1);
indx_female=find(GENDER==2);
data_=find(isnan(data_mat)==0);
if numel(indx_N)==1
results{i+1,2}=[E '-' E_N];
data_n=filtered_data(:,indx_N);
data_n=str2double(data_n);
n_check=1;
else
results{i+1,2}=E;
end
%TOTAL------------------------------
N=numel(data_); %total answered
N_m=numel(intersect(data_,indx_male));
N_f=numel(intersect(data_,indx_female));
indx_yes=find(data_mat==1);
results{i+1,3}=numel(indx_yes);
P=numel(indx_yes)/N;
upper=((P+z*sqrt(P*(1-P)/N))*100);
lower=((P-z*sqrt(P*(1-P)/N))*100) ;
p=P*100;
results{i+1,4}=[sprintf('%.1f',p) '%'];
upper=sprintf('%0.1f',round(upper*10)/10);
lower=sprintf('%0.1f',round(lower*10)/10);
results{i+1,5}=[lower '%-' upper '%'];
%Deal with the N
if n_check==1
indx_N=find(data_n>0);
indx_1=find (data_n==1);
indx_2=find(data_n>1 & data_n<5);
indx_5=find(data_n>4 & data_n<10);
indx_10=find(data_n>9 & data_n<50);
indx_50=find(data_n>49);
results{i+1,12}=numel(indx_1);
results{i+1,13}=numel(indx_2);
results{i+1,14}=numel(indx_5);
results{i+1,15}=numel(indx_10);
results{i+1,16}=numel(indx_50);
p_1=numel(indx_1)/numel(indx_N)*100;
p_2=numel(indx_2)/numel(indx_N)*100;
p_5=numel(indx_5)/numel(indx_N)*100;
p_10=numel(indx_10)/numel(indx_N)*100;
p_50=numel(indx_50)/numel(indx_N)*100;
results{i+1,17}=[sprintf('%.1f',p_1) '%'];
results{i+1,18}=[sprintf('%.1f',p_2) '%'];
results{i+1,19}=[sprintf('%.1f',p_5) '%'];
results{i+1,20}=[sprintf('%.1f',p_10) '%'];
results{i+1,21}=[sprintf('%.1f',p_50) '%'];
end
%MALE-------------------------------
indx_M=intersect(indx_male,indx_yes);
results{i+1,6}=numel(indx_M);
P=numel(indx_M)/N_m;
upper=((P+z*sqrt(P*(1-P)/N_m))*100);
lower=((P-z*sqrt(P*(1-P)/N_m))*100) ;
p=P*100;
results{i+1,7}=[sprintf('%.1f',p) '%'];
upper=sprintf('%0.1f',round(upper*10)/10);
lower=sprintf('%0.1f',round(lower*10)/10);
results{i+1,8}=[lower '%-' upper '%'];
if n_check==1
indx_M_N=intersect(indx_male,indx_N);
indx_1=find (data_n==1);
indx_2=find(data_n>1 & data_n<5);
indx_5=find(data_n>4 & data_n<10);
indx_10=find(data_n>9 & data_n<50);
indx_50=find(data_n>49);
indx_1_=intersect(indx_1,indx_male);
indx_2_=intersect(indx_2,indx_male);
indx_5_=intersect(indx_5,indx_male);
indx_10_=intersect(indx_10,indx_male);
indx_50_=intersect(indx_50, indx_male);
results{i+1,22}=numel(indx_1_);
results{i+1,23}=numel(indx_2_);
results{i+1,24}=numel(indx_5_);
results{i+1,25}=numel(indx_10_);
results{i+1,26}=numel(indx_50_);
p_1=numel(indx_1_)/numel(indx_M_N)*100;
p_2=numel(indx_2_)/numel(indx_M_N)*100;
p_5=numel(indx_5_)/numel(indx_M_N)*100;
p_10=numel(indx_10_)/numel(indx_M_N)*100;
p_50=numel(indx_50_)/numel(indx_M_N)*100;
results{i+1,27}=[sprintf('%.1f',p_1) '%'];
results{i+1,28}=[sprintf('%.1f',p_2) '%'];
results{i+1,29}=[sprintf('%.1f',p_5) '%'];
results{i+1,30}=[sprintf('%.1f',p_10) '%'];
results{i+1,31}=[sprintf('%.1f',p_50) '%'];
end
indx_F=intersect(indx_female,indx_yes);
results{i+1,9}=numel(indx_F);
P=numel(indx_F)/N_f;
upper=((P+z*sqrt(P*(1-P)/N_f))*100);
lower=((P-z*sqrt(P*(1-P)/N_f))*100) ;
p=P*100;
results{i+1,10}=[sprintf('%.1f',p) '%'];
upper=sprintf('%0.1f',round(upper*10)/10);
lower=sprintf('%0.1f',round(lower*10)/10);
results{i+1,11}=[lower '%-' upper '%'];
if n_check==1
indx_F_N=intersect(indx_female,indx_N);
indx_1=find (data_n==1);
indx_2=find(data_n>1 & data_n<5);
indx_5=find(data_n>4 & data_n<10);
indx_10=find(data_n>9 & data_n<50);
indx_50=find(data_n>49);
indx_1_=intersect(indx_1,indx_female);
indx_2_=intersect(indx_2,indx_female);
indx_5_=intersect(indx_5,indx_female);
indx_10_=intersect(indx_10,indx_female);
indx_50_=intersect(indx_50, indx_female);
results{i+1,32}=numel(indx_1_);
results{i+1,33}=numel(indx_2_);
results{i+1,34}=numel(indx_5_);
results{i+1,35}=numel(indx_10_);
results{i+1,36}=numel(indx_50_);
p_1=numel(indx_1_)/numel(indx_F_N)*100;
p_2=numel(indx_2_)/numel(indx_F_N)*100;
p_5=numel(indx_5_)/numel(indx_F_N)*100;
p_10=numel(indx_10_)/numel(indx_F_N)*100;
p_50=numel(indx_50_)/numel(indx_F_N)*100;
results{i+1,37}=[sprintf('%.1f',p_1) '%'];
results{i+1,38}=[sprintf('%.1f',p_2) '%'];
results{i+1,39}=[sprintf('%.1f',p_5) '%'];
results{i+1,40}=[sprintf('%.1f',p_10) '%'];
results{i+1,41}=[sprintf('%.1f',p_50) '%'];
end
x=max(numel(indx_male),numel(indx_female));
stat_mat=nan(x,2);
stat_mat(1:numel(indx_male),1)=zeros(numel(indx_male),1);
stat_mat(1:numel(indx_female),2)=zeros(numel(indx_female),1);
stat_mat(1:numel(indx_M),1)=ones(1,numel(indx_M));
stat_mat(1:numel(indx_F),2)=ones(1,numel(indx_F));
[h,p]=ttest2(stat_mat(:,1), stat_mat(:,2));
results{i+1,42}=p;
else
E
end
end