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Phobia for specific situations
#Graphical representation of the given data
"""
Created on Wed Jul 8 20:44:24 2020
@author: Nishant
"""
import pandas
import numpy
import seaborn
import matplotlib.pyplot as plt
data = pandas.read_csv(r"E:\1. courcera\NESARC.csv", low_memory=False)
print ("Number if Rows :",len(data))
print ("Number of columns :",len(data.columns))
print()
print("Graphical representation of variables:")
print()
print("1. S8Q1A12: EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE")
print()
print('I. AGE')
sub1=data[(data["AGE"]>=10) & (data["AGE"]<=25) & (data['S8Q1A12']==1)]
sub2 = sub1.copy()
sub2['S8Q1A12']=sub2['S8Q1A12'].replace(9, numpy.nan)
sub2['S8Q1A12']=sub2['S8Q1A12'].replace(99, numpy.nan)
seaborn.countplot(x="S8Q1A12", data=sub2)
plt.xlabel('people of AGE 10 to 25 that are afraid to go out alone')
plt.title('Number of individual wrt AGE HAVING FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME')
print('II. SEX')
sub1=data[(data["SEX"]>=10) & (data["SEX"]<=25) & (data['S8Q1A12']==1)]
sub2 = sub1.copy()
sub2['S8Q1A12']=sub2['S8Q1A12'].replace(9, numpy.nan)
sub2['S8Q1A12']=sub2['S8Q1A12'].replace(99, numpy.nan)
seaborn.countplot(x="S8Q1A12", data=sub2)
plt.xlabel('people of SEX 10 to 25 that are afraid to go out alone')
plt.title('Number of individual wrt SEX for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME')
print (' S8Q1A12: Number of of individual having fear of going out alone')
print()
desc1 = sub2['S8Q1A12'].describe()
print (desc1)
c1= sub2.groupby('S8Q1A12').size()
print (c1)
print ('describe nicotine dependence')
desc2 = sub2['S8Q1A12'].describe()
print (desc2)
c1= sub2.groupby('S8Q1A12').size()
print (c1)
print ('mode')
mode1 = sub2['S8Q1A12'].mode()
print (mode1)
print ('mean')
mean1 = sub2['S8Q1A12'].mean()
print (mean1)
print ('std')
std1 = sub2['S8Q1A12'].std()
print (std1)
print ('min')
min1 = sub2['S8Q1A12'].min()
print (min1)
print ('max')
max1 = sub2['S8Q1A12'].max()
print (max1)
print ('median')
median1 = sub2['S8Q1A12'].median()
print (median1)
print ('mode')
mode1 = sub2['S8Q1A12'].mode()
print (mode1)
c1= sub2.groupby('S8Q1A12').size()
print (c1)
p1 = sub2.groupby('S8Q1A12').size() * 100 / len(data)
print (p1)
c2 = sub2.groupby('S8Q1A12').size()
print (c2)
p2 = sub2.groupby('S8Q1A12').size() * 100 / len(data)
print (p2)
print ('counts for S8Q1A12 with 99 set to NAN and number of missing requested')
c3 = sub2['S3AQ3C1'].value_counts(sort=False, dropna=False)
print(c3)
#OUTPUT
Number if Rows : 43093
Number of columns : 3010
S8Q1A12: Number of of individual having fear of going out alone
count 1584.000000
mean 520.304361
std 264.436777
min 2.000000
25% 110.000000
50% 380.000000
75% 690.000000
max 3103.000000
Name: NUMCIGMO_EST, dtype: float64
Graphical representation of variables:
1. S8Q1A12: EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE
I. AGE
II. SEX
Summary
As per my taken variables, here comes the summary according to my all findings, the pattern of going through many research papers, codebooks, and also real life examples.
These phobias are often developed at a young age, it can be developed later in life as well or a person can overcome their fears in later life. Each and every individual reacts to a given situation differently. Mostly the children’s that have experienced some kind of abuse physical or mental are seen with these phobia. So, the environment in which a children grows up plays a very important role. Yes there is a significant correlation between phobia and variables like gender and age factor.
0 notes
Text
Phobia for specific situations
#coading out missing data
After looking through the provided Codebook for the NERSAC study, I finally decided that my area of interest is under specific phobia for specific situations. but there are blank spaces in the data provided so here’s something to code out the missing data:
#CODE
“”“
Created on Sun Jul 5 16:16:26 2020
@author: Nishant Sharma
”“”
import pandas
import numpy
data = pandas.read_csv('123.csv', low_memory=False)
print ("Rows :",len(data))
print ("columns :",len(data.columns))
print()
print('1. S8Q1A12: EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE, (yes=1/no=2/9=no answer/Blank=No clue)')
print()
print('i. AGE')
print()
sub1=data[(data['AGE']>=10) & (data['AGE']<=25) & (data['S8Q1A12']==1)]
sub2 = sub1.copy()
print()
print ('-Number of individual wrt age for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)')
c1 = sub2['AGE'].value_counts(sort=False)
print(c1)
sub2['S8Q1A12'] = sub2['S8Q1A12'].replace(9, numpy.nan)
print("counts for S8Q1A12 with 9 set to NAN(age):- EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE ")
ct2=sub2['S8Q1A12'].value_counts(sort= False, dropna= False)
print(ct2)
print('ii. SEX')
print()
sub3= sub2.copy()
print ('-Number of individual wrt gender (1:male, 2:female) for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)')
c1 = sub3['SEX'].value_counts(sort=False)
print(c1)
sub3['S8Q1A12'] = sub3['S8Q1A12'].replace(9, numpy.nan)
print("counts for S8Q1A12 with 9 set to NAN(gender) :- EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE ")
ct2=sub3['S8Q1A12'].value_counts(sort= False, dropna= False)
print(ct2)
print('2. S8Q1A6:- EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE, (yes=1/no=2/9=no answer/Blank=No clue)')
print()
print('i.age')
print()
sub4=data[(data['AGE']>=10) & (data['AGE']<=25) & (data['S8Q1A6']==1)]
sub5 = sub4.copy()
print()
print ('-Number of individual wrt age for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE(S8Q1A6)')
d1 = sub5['AGE'].value_counts(sort=False)
print(d1)
sub5['S8Q1A6'] = sub5['S8Q1A6'].replace(9, numpy.nan)
print("counts for (S8Q1A6) with 9 set to NAN(age):- HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE(S8Q1A6) ")
dt2=sub5['S8Q1A6'].value_counts(sort= False, dropna= False)
print(dt2)
print('ii. SEX')
print()
sub6= sub5.copy()
print ('-Number of individual wrt gender (1:male, 2:female) for Total number of individual reported to HHAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE (S8Q1A6)')
d1 = sub6['SEX'].value_counts(sort=False)
print(d1)
sub6['S8Q1A6'] = sub6['S8Q1A12'].replace(9, numpy.nan)
print("counts for S8Q1A12 with 9 set to NAN(gender) :- HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE ")
dt2=sub6['S8Q1A6'].value_counts(sort= False, dropna= False)
print(dt2)
print("*************AGE***************")
print("frequency distribution of age for HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE ")
sub7=sub5.copy()
print('Age-4 categories- quartiles')
sub7['AGEGROUP41']= pandas.cut(sub7.AGE, 4, labels=["1=25%tile","2=50%tile","3=75%tile","4=100%"])
c3=sub7['AGEGROUP41'].value_counts(sort= False, dropna= True)
print(c3)
sub7['AGEGROUP31']= pandas.cut(sub7.AGE,[17, 20, 22, 25])
print(pandas.crosstab(sub7['AGEGROUP31'], sub7['AGE']))
print("frequency distribution of age for EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE")
sub8=sub3.copy()
print('Age-4 categories- quartiles')
sub8['AGEGROUP42']= pandas.cut(sub8.AGE, 4, labels=["1=25%tile","2=50%tile","3=75%tile","4=100%"])
c4=sub8['AGEGROUP42'].value_counts(sort= False, dropna= True)
print(c4)
sub8['AGEGROUP32']= pandas.cut(sub8.AGE,[17, 20, 22, 25])
print(pandas.crosstab(sub8['AGEGROUP32'], sub8['AGE']))
print()
print("************GENDER***************")
print("frequency distribution of Gender for HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE ")
sub9=sub6.copy()
print('GENDER-2 categories- quartiles')
c5=sub9['SEX'].value_counts(sort= False, dropna= True)
print(c5)
print(pandas.crosstab(sub9['SEX'], sub9['SEX']))
print("frequency distribution of Gender for EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE")
sub10=sub6.copy()
print('GENDER-2 categories- quartiles')
c6=sub10['SEX'].value_counts(sort= False, dropna= True)
print(c6)
print(pandas.crosstab(sub9['SEX'], sub9['SEX']))
#OUTPUT
Rows : 43093
columns : 3010
1. S8Q1A12: EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE, (yes=1/no=2/9=no answer/Blank=No clue)
i. AGE
-Number of individual wrt age for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)
1 647
2 40997
9 1449
Name: AGE, dtype: int64
counts for S8Q1A12 with 9 set to NAN(age):- EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE
9 NAN
1 647
2 40997
Name: S8Q1A12, dtype: int64
ii. SEX
-Number of individual wrt gender (1:male, 2:female) for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)
1 20
2 86
Name: SEX, dtype: int64
counts for S8Q1A12 with 9 set to NAN(gender) :- EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE
1 106
Name: S8Q1A12, dtype: int64
2. S8Q1A6:- EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE, (yes=1/no=2/9=no answer/Blank=No clue)
i. AGE
-Number of individual wrt age for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE(S8Q1A6)
1 1705
2 39944
9 1444
Name: AGE, dtype: int64
-counts for (S8Q1A6) with 9 set to NAN(age):- HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE(S8Q1A6)
9 NAN
1 1705
2 39944
Name: S8Q1A6, dtype: int64
ii.SEX
-Number of individual wrt gender (1:male, 2:female) for Total number of individual reported to HHAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE (S8Q1A6)
1 67
2 128
Name: SEX, dtype: int64
-counts for S8Q1A12 with 9 set to NAN(gender) :- HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE
1 34
2 161
Name: S8Q1A6, dtype: int64
***************AGE***************
frequency distribution of age for HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE
Age-4 categories- quartiles
1=25%tile 30
2=50%tile 49
3=75%tile 47
4=100% 69
Name: AGEGROUP41, dtype: int64
AGE 18 19 20 21 22 23 24 25
AGEGROUP31
(17, 20] 7 23 27 0 0 0 0 0
(20, 22] 0 0 0 22 26 0 0 0
(22, 25] 0 0 0 0 0 21 42 27
frequency distribution of age for EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE
Age-4 categories- quartiles
1=25%tile 22
2=50%tile 33
3=75%tile 20
4=100% 31
Name: AGEGROUP42, dtype: int64
AGE 18 19 20 21 22 23 24 25
AGEGROUP32
(17, 20] 10 12 17 0 0 0 0 0
(20, 22] 0 0 0 16 10 0 0 0
(22, 25] 0 0 0 0 0 10 21 10
***************GENDER***************
frequency distribution of age for HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE
GENDER-2 categories- quartiles
1 67
2 128
Name: SEX, dtype: int64
SEX 1 2
SEX
1 67 0
2 0 128
frequency distribution of age for EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE
GENDER-2 categories- quartiles
1 67
2 128
Name: SEX, dtype: int64
SEX 1 2
SEX
1 67 0
2 0 128
Summary
As per my taken variables, here comes the summary according to my all findings, the pattern of going through many research papers, codebooks, and also real life examples.
These phobias are often developed at a young age, it can be developed later in life as well or a person can overcome their fears in later life. Each and every individual reacts to a given situation differently. Mostly the children’s that have experienced some kind of abuse physical or mental are seen with these phobia. So, the environment in which a children grows up plays a very important role.
0 notes
Text
Phobia for specific situations
#My First Code
After looking through the provided Codebook for the NERSAC study, I finally decided that my area of interest is under specific phobia for specific situations. My first code to shown data managed variables as frequency tables is shown below:
************************************************************
#CODE
"""
Created on Sun Jun 27 18:16:26 2020
@author: Nishant
"""
import pandas
import numpy
data = pandas.read_csv(r'E:\courcera\123.csv', low_memory=False)
print ("Number if Rows :",len(data))
print ("Number of columns :",len(data.columns))
print()
print("1: Number of individual for S8Q1A12:- EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE,{yes=1/no=2/9=no answer/Blank=No clue}")
ct1=data.groupby('S8Q1A12').size()
print(ct1)
print()
print("1.1: percentage of individuals for S8Q1A12:- EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE,{yes=1/no=2/9=no answer/Blank=No clue}")
pt1=data.groupby('S8Q1A12').size() * 100/len(data)
print(pt1)
print()
print("2: Number of individual for S8Q1A6:- EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE, {yes=1/no=2/9=no answer/Blank=No clue}")
ct2=data.groupby('S8Q1A6').size()
print(ct2)
print()
print("2.1: percentage of individuals for S8Q1A6:- EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE, {yes=1/no=2/9=no answer/Blank=No clue}")
pt2=data.groupby('S8Q1A6').size() * 100/len(data)
print(pt2)
print()
print("3: Number of individual for S8Q1A9:- EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS, {yes=1/no=2/9=no answer/Blank=No clue}")
ct3=data.groupby('S8Q1A9').size()
print(ct3)
print()
print("3.1: percentage of individuals for S8Q1A9:- EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS, {yes=1/no=2/9=no answer/Blank=No clue}")
pt3=data.groupby('S8Q1A9').size() * 100/len(data)
print(pt3)
print('*******************************************************')
print('stats related to specific scenario by using variables like Age and Sex:')
print()
print('1. S8Q1A12: EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE')
print()
print('I. AGE')
print()
sub1=data[(data['AGE']>=10) & (data['AGE']<=25) & (data['S8Q1A12']==1)]
sub2 = sub1.copy()
print ('-Number of individual wrt age for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)')
c1 = sub2['AGE'].value_counts(sort=False)
print(c1)
print()
print ('AGE wise percentages for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)')
p1 = sub2['AGE'].value_counts(sort=False, normalize=True)
print (p1)
print()
print ('Total number of individuals that marked yes as there choice for FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)')
c1 = sub2['S8Q1A12'].value_counts(sort=False)
print(c1)
print()
print ('Percentages for S8Q1A12 wrt total data')
p1 = sub2['S8Q1A12'].value_counts(sort=False, normalize=True)
print (p1)
sub3=data[(data['AGE']>=10) & (data['AGE']<=25) & (data['S8Q1A12']==1)]
sub4 = sub3.copy()
print()
print('ii.SEX')
print()
print ('-Number of individual wrt gender (1:male, 2:female) for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)')
c1 = sub4['SEX'].value_counts(sort=False)
print(c1)
print()
print ('Gender wise percentages for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)')
p1 = sub4['SEX'].value_counts(sort=False, normalize=True)
print (p1)
print()
print('2. S8Q1A6:- EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE')
print()
print('I. AGE')
print()
sub5=data[(data['AGE']>=10) & (data['AGE']<=25) & (data['S8Q1A6']==1)]
sub6 = sub5.copy()
print()
print ('-Number of individual wrt age for EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE (S8Q1A6)')
c2 = sub6['AGE'].value_counts(sort=False)
print(c2)
print()
print ('AGE wise percentages for EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE (S8Q1A6)')
p2 = sub6['AGE'].value_counts(sort=False, normalize=True)
print (p2)
print()
print ('Total number of individuals that marked yes as there choice for EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE (S8Q1A6)')
c2 = sub6['S8Q1A6'].value_counts(sort=False)
print(c2)
print()
print ('Percentages for S8Q1A6 wrt total data')
p2 = sub6['S8Q1A6'].value_counts(sort=False, normalize=True)
print (p2)
print()
sub7=data[(data['AGE']>=10) & (data['AGE']<=25) & (data['S8Q1A6']==1)]
sub8 = sub7.copy()
print()
print('ii. SEX')
print()
print ('-Number of individual wrt gender (1:male, 2:female) for Total number of individual reported that they have FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE (S8Q1A6)')
c2 = sub8['SEX'].value_counts(sort=False)
print(c2)
print()
print ('Gender wise percentages for EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE (S8Q1A6)')
p2 = sub8['SEX'].value_counts(sort=False, normalize=True)
print (p2)
print()
print('3.S8Q1A9: EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS')
print()
print('i.age')
print()
sub9=data[(data['AGE']>=10) & (data['AGE']<=25) & (data['S8Q1A9']==1)]
sub10 = sub9.copy()
print()
print ('-Number of individual wrt age for EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS (S8Q1A9)')
c3 = sub10['AGE'].value_counts(sort=False)
print(c3)
print()
print ('AGE wise percentages for EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS (S8Q1A9)')
p3= sub10['AGE'].value_counts(sort=False, normalize=True)
print (p3)
print()
print ('Total number of individuals that marked yes as there choice for EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS (S8Q1A9)')
c3 = sub10['S8Q1A9'].value_counts(sort=False)
print(c3)
print()
print ('Percentages for S8Q1A9 wrt total data')
p3 = sub10['S8Q1A9'].value_counts(sort=False, normalize=True)
print (p3)
print()
sub11=data[(data['AGE']>=10) & (data['AGE']<=25) & (data['S8Q1A9']==1)]
sub12 = sub11.copy()
print()
print('ii. SEX')
print()
print ('-Number of individual wrt gender (1:male, 2:female) for Total number of individual reported that they have FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS (S8Q1A9)')
c3 = sub12['SEX'].value_counts(sort=False)
print(c3)
print()
print ('Gender wise percentages for EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS (S8Q1A9)')
p3 = sub12['SEX'].value_counts(sort=False, normalize=True)
print (p3)
print()
#OUTPUT
Number if Rows : 43093
Number of columns : 3010
1: Number of individual for S8Q1A12:- EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE,{yes=1/no=2/9=no answer/Blank=No clue}
S8Q1A12
1 647
2 40997
9 1449
dtype: int64
1.1: Percentage of individuals for S8Q1A12:- EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE,{yes=1/no=2/9=no answer/Blank=No clue}
S8Q1A12
1 1.501404
2 95.136101
9 3.362495
dtype: float64
2: Number of individual for S8Q1A6:- EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE, {yes=1/no=2/9=no answer/Blank=No clue}
S8Q1A6
1 1705
2 39944
9 1444
dtype: int64
2.1: percentage of individuals for S8Q1A6:- EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE, {yes=1/no=2/9=no answer/Blank=No clue}
S8Q1A6
1 3.956559
2 92.692549
9 3.350892
dtype: float64
3: Number of individual for S8Q1A9:- EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS, {yes=1/no=2/9=no answer/Blank=No clue}
S8Q1A9
1 657
2 40991
9 1445
dtype: int64
3.1: Percentage of individuals for S8Q1A9:- EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS, {yes=1/no=2/9=no answer/Blank=No clue}
S8Q1A9
1 1.524610
2 95.122178
9 3.353213
dtype: float64
*******************************************************
stats related to specific scenario by using variables like Age and Sex:
1. S8Q1A12: EVER HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME ALONE
i.age
-Number of individual wrt age for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)
18 10
19 12
20 17
21 16
22 10
23 10
24 21
25 10
Name: AGE, dtype: int64
AGE wise percentages for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)
18 0.094340
19 0.113208
20 0.160377
21 0.150943
22 0.094340
23 0.094340
24 0.198113
25 0.094340
Name: AGE, dtype: float64
Total number of individuals that marked yes as there choice for FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)
1 106
Name: S8Q1A12, dtype: int64
Percentages for S8Q1A12 wrt total data
1 1.0
Name: S8Q1A12, dtype: float64
ii. SEX
-Number of individual wrt gender (1:male, 2:female) for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)
1 20
2 86
Name: SEX, dtype: int64
Gender wise percentages for Total number of individual reported to HAD FEAR/AVOIDANCE OF BEING OUTSIDE YOUR HOME (S8Q1A12)
1 0.188679
2 0.811321
Name: SEX, dtype: float64
2. S8Q1A6:- EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE
I. AGE
-Number of individual wrt age for EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE (S8Q1A6)
18 7
19 23
20 27
21 22
22 26
23 21
24 42
25 27
Name: AGE, dtype: int64
AGE wise percentages for EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE (S8Q1A6)
18 0.035897
19 0.117949
20 0.138462
21 0.112821
22 0.133333
23 0.107692
24 0.215385
25 0.138462
Name: AGE, dtype: float64
Total number of individuals that marked yes as there choice for EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE (S8Q1A6)
1 195
Name: S8Q1A6, dtype: int64
Percentages for S8Q1A6 wrt total data
1 1.0
Name: S8Q1A6, dtype: float64
ii SEX
-Number of individual wrt gender (1:male, 2:female) for Total number of individual reported that they have FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE (S8Q1A6)
1 67
2 128
Name: SEX, dtype: int64
Gender wise percentages for EVER HAD FEAR/AVOIDANCE OF BEING IN CROWD/STANDING IN LINE (S8Q1A6)
1 0.34359
2 0.65641
Name: SEX, dtype: float64
3.S8Q1A9: EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS
I. AGE
-Number of individual wrt age for EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS (S8Q1A9)
18 9
19 10
20 16
21 8
22 9
23 14
24 10
25 10
Name: AGE, dtype: int64
AGE wise percentages for EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS (S8Q1A9)
18 0.104651
19 0.116279
20 0.186047
21 0.093023
22 0.104651
23 0.162791
24 0.116279
25 0.116279
Name: AGE, dtype: float64
Total number of individuals that marked yes as there choice for EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS (S8Q1A9)
1 86
Name: S8Q1A9, dtype: int64
Percentages for S8Q1A9 wrt total data
1 1.0
Name: S8Q1A9, dtype: float64
ii. SEX
-Number of individual wrt gender (1:male, 2:female) for Total number of individual reported that they have FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS (S8Q1A9)
1 28
2 58
Name: SEX, dtype: int64
Gender wise percentages for EVER HAD FEAR/AVOIDANCE OF TRAVELING IN BUSES/CARS/TRAINS (S8Q1A9)
1 0.325581
2 0.674419
Name: SEX, dtype: float64
Summary
As per my taken variables, here comes the summary according to my all findings, the pattern of going through many research papers, codebooks, and also real life examples.
These phobias are often developed at a young age, it can be developed later in life as well or a person can overcome their fears in later life. Each and every individual reacts to a given situation differently. Mostly the children’s that have experienced some kind of abuse physical or mental are seen with these phobia. So, the environment in which a children grows up plays a very important role.
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Text
Phobia for specific situations
Selection Of My Data Set:
After looking through the provided Codebook for the NERSAC study, I finally decided that my area of interest is under specific phobia for specific situations. But I’m not sure about the variables I’ll be using for representing the data set of specific phobia for example: fear of heights, fear of flying, fear of being in closed space etc. So, for now I’m using any of the variables provided in the codebook.
Reason For My Interest:
As a human being, we all have some kind phobia since our childhood doesn't matter if we recognize it in the early stages or not. So, I found it really fascinating that whenever I observe my friends, acquaintances and even myself I notice some strange behavior due to fear or anxiety triggered by something. So, I’m curious to analyse this data.
Start Of My Research Question and Hypothesis:
I have seen people of different age groups reacting differently to same situations. So, I think that I'm interested in the association between the starting age when an individual feels anxious or fearful while being in a specific scenario and the age at which that same individual becomes comfortable being in that same specific situation, for example: A kid of age 11 was afraid of dogs and after crossing age of 20 that same individual is not afraid of dogs. And further-more, Sex of that individual because I’ve observed different behaviors among females and males of same or different age groups. So, I’ll be adding variables reflecting about these contents in my codebook.
Literature Reviews:
Fear of Being outside your home alone:
Fear of Being outside your home alone:
The term used to denote this phobia is called Agoraphobia, this is an anxiety disorder that involves intense fear and anxiety of any place or situation where escape might be difficult. So, that individual with this phobia feels anxious while going out alone. This phobia is often confused by fear of open spaces but it is much more complex as it involves the following:
rapid heartbeat rapid breathing (hyperventilating)
feeling hot sweaty feeling of being sick.
According to NIMH this phobia is much more common between females of age group 13-18.The number keeps on reducing till age of 35. But the number of males suffering from this phobia are less as compared to females. There are several factors which could increase the risk of developing the condition:
A family history Other mental disorders like anxiety
disorder depression
Social phobia Substance abuse
History of child abuse
History of physical or sexual abuse
An estimated 0.9% of U.S. adults had agoraphobia in the past year. Past year prevalence of agoraphobia among adults was similar for females (0.9%) and males (0.8%).An estimated 1.3% of U.S. adults experience agoraphobia at some time in their lives. The prevalence of agoraphobia among adolescents was higher for females (3.4%) than for males (1.4%).
reference:
https://www.nimh.nih.gov/health/statistics/agoraphobia.shtml#part_155981
https://www.nhs.uk/conditions/agoraphobia/
2. Fear of closed spaces:
The term used to denote this phobia is called Claustrophobia this phobia is triggered when an individual is in a tight and close places. For example: Being stuck in a crowded elevator or in a box, tight and small room without any windows. Triggering this phobia completely depends on the severity the phobia. When experiencing symptoms of claustrophobia, you may feel like you’re having a panic attacks.
For example, adults may develop claustrophobia if, as a child, they:
were trapped or kept in a confined space
were bullied or abused in a closed space
had a parent with claustrophobia
Claustrophobia is relatively common, with a review of specific phobia research estimating that approximately 2.2% of the population experience a fear of enclosed spaces. Claustrophobia prevalence is higher among women and tends to emerge for the first time in adolescence or early adulthood.
References:
https://www.healthline.com/health/claustrophobia#treatment https://www.therecoveryvillage.com/mental-health/claustrophobia/claustrophobia-statistics/
Summary
As per my taken variables, here comes the summary according to my all findings, the pattern of going through many research papers, codebooks, and also real life examples.
These phobias are often developed at a young age, it can be developed later in life as well or a person can overcome their fears in later life. Each and every individual reacts to a given situation differently. Mostly the children's that have experienced some kind of abuse physical or mental are seen with these phobia. So, the environment in which a children grows up plays a very important role.
Developing Hypothesis based on Literature Review
Yes there is a significant correlation between phobia and variables like gender and age factor.
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