shereen-hasan

[shereen-hasan]

Social media has become an integral part of our daily life. Until 5 year ago, social media platforms were used for known social purposes like chatting, posting daily events, sharing personal photos..... etc. Later, social media ads appeared and they popped up here and there. However, they were not traditional ads. Have you ever wondered why after searching for your favorite product, you find it suggested on facebook out of the blue? Usually, you come across tens of ads unconsciously on different platforms in a single session. Futhermore, you may interact instantly without even realising they are ads! Making ads sound realistic, simple, touching or nostalgic is a very important skill any digital marketer should have. Target persona is a model for the perfect customer that a digital marketer should look forward to making him/her satisfied. Since social media channels are where most customers are found, business owners, and accordingly marketers, should pay due attention to market for their products there. That's why digital marketing was found! Actually, I didn't considered how much social media marketing is effective before couple of weeks ago when I came across FWD (future work is digital). FWD is an initiative which offers four courses each in a different track. Digital marketing is one of them. Their social media marketing campaign was one of the reasons why I enrolled in their course. It costs none. To register, I had to answer the online application questions which needed prior knowledge to digital marketing which was the hardest part. Also, I had to submit a soft copy of my governmental ID. Finally, an email arrived, which informed me that I was accepted! In my opinion, learning digital marketing is inevitable if you are looking forward to having your own new business.

For applying to Digital Marketing Nanodegree : https://egfwd.com/digital-marketing-t2-3/

[shereen-hasan]

Does Realationship With Parents Affect Mental Health?

Data from ‘AddHealth In Home Questionnaire’ Code Book along with ‘Addhealth wave 1’ dataset (Questions from Sections: “Feeling scale” and “Relations with parents”) were chosen. The subset chosen concerns unmarried young adults (age 16-21). There were three variables defined:

1. ”DEPSMPT“

2. "RELWPAR"

3. "DEP_WER"

The bivariate graph is C-C graph

As we can see, the adults with “None or Low” depression or anxiety symptoms has the highest proportion around 85% good relationship with parents. Those with the highest depression or anxiety symptoms has the lowest percentage (around 69%).

Code:

https://github.com/Shereen97/Data/blob/main/Code%23!/usr/bin/Code

[shereen-hasan]

The Relationship Between Feeling Scale And Relations With Parents

Below is my python code showing the frequency distribution concerning single young adults (age 16-21). Variables where chosen depending on questions that could(not) indicate depression in addition to questions indicating relations with parents.

The results shows that there is roughly no relationship between depression and relations with parents.

Note:

1. There are two files imported: the first is imported for sub1; the second is imported for sub3.

2. Values the variables can take and information about missing data are showed in the comments before each related section among the code.

Code:

import pandas as pd import numpy as np # any additional libraries would be imported here #Call in data csv file data = pd.read_csv('SUB2.csv', low_memory=False) print (len(data)) #number of observations (rows) print (len(data.columns)) # number of variables (columns) #setting variables you will be working with to numeric data['H1GI1Y'] = pd.to_numeric(data['H1GI1Y']) data['H1GI15'] = pd.to_numeric(data['H1GI15']) #subset data to young single adults age 16 to 21 sub1 = data[(data['H1GI1Y']>=74) & (data['H1GI1Y']<=79)& (data['H1GI15']==0)] #make a copy of my new subsetted data sub2 = sub1.copy() # frequency distritions on new sub2 data frame print ('counts for H1GI1Y') c1 = sub2['H1GI1Y'].value_counts(sort=False, dropna=True) print(c1) print ('percentages for H1GI1Y') p1 = sub2['H1GI1Y'].value_counts(sort=False, normalize=True, dropna=True) print (p1*100) print ('counts for H1GI15') c2 = sub2['H1GI15'].value_counts(sort=False, dropna=True) print(c2) print ('percentages for H1GI15') p2 = sub2['H1GI15'].value_counts(sort=False, normalize=True, dropna=True) print (p2*100) #Frequency distribution of feeling scale section questions #0 never or rarely #1 sometimes #2 a lot of the time #3 most of the time or all of the time #6 refused #8 don’t know #Call in data csv file sub3 = pd.read_csv('SUB4.csv', low_memory=False) #Replacing 6(refused) & 8(don't know) with NaN sub3= sub3.replace([6,8], np.NaN) #Q1 You were bothered by things that usually don’t bother you (indicates depression) print ('counts for H1FS1') c3 = sub3['H1FS1'].value_counts(sort=False, dropna=True) print(c3) print ('percentages H1FS1') p3 = sub3['H1FS1'].value_counts(sort=False, normalize=True, dropna=True) print (p3*100) #Q2 You didn’t feel like eating, your appetite was poor (indicates depression) print ('counts for H1FS2') c4 = sub3['H1FS2'].value_counts(sort=False, dropna=True) print(c4) print ('percentages H1FS2') p4 = sub3['H1FS2'].value_counts(sort=False, normalize=True, dropna=True) print (p4*100) #Q3 You felt that you could not shake off the blues, even with help from your family and your friends (indicates depression) print ('counts for H1FS3') c5 = sub3['H1FS3'].value_counts(sort=False, dropna=True) print(c5) print ('percentages H1FS3') p5 = sub3['H1FS3'].value_counts(sort=False, normalize=True, dropna=True) print (p5*100) #Q4 You felt that you were just as good as other people.(indicates depression) print ('counts for H1FS4') c6 = sub3['H1FS4'].value_counts(sort=False, dropna=True) print(c6) print ('percentages H1FS4') p6 = sub3['H1FS4'].value_counts(sort=False, normalize=True, dropna=True) print (p6*100) #Q5 You had trouble keeping your mind on what you were doing(indicates depression) print ('counts for H1FS5') c7 = sub3['H1FS5'].value_counts(sort=False, dropna=True) print(c7) print ('percentages H1FS5') p7 = sub3['H1FS5'].value_counts(sort=False, normalize=True, dropna=True) print (p7*100) #Q6 You felt depressed( indicates depression) print ('counts for H1FS6') c8 = sub3['H1FS6'].value_counts(sort=False, dropna=True) print(c8) print ('percentages H1FS6') p8 = sub3['H1FS6'].value_counts(sort=False, normalize=True, dropna=True) print (p8*100) #Q7 You felt that you were too tired to do things (indicates depression) print ('counts for H1FS7') c9 = sub3['H1FS7'].value_counts(sort=False, dropna=True) print(c9) print ('percentages H1FS7') p9 = sub3['H1FS7'].value_counts(sort=False, normalize=True, dropna=True) print (p9*100) #Q8 You felt hopeful about the future (negation indicates a depression symptom) print ('counts for H1FS8') c10 = sub3['H1FS8'].value_counts(sort=False, dropna=True) print(c10) print ('percentages H1FS8') p10 = sub3['H1FS8'].value_counts(sort=False, normalize=True, dropna=True) print (p10*100) #Q9 You thought your life had been a failure (indicates depression) print ('counts for H1FS9') c11 = sub3['H1FS9'].value_counts(sort=False, dropna=True) print(c11) print ('percentages H1FS9') p11 = sub3['H1FS9'].value_counts(sort=False, normalize=True, dropna=True) print (p11*100) #Q10 You felt fearful (may be an anxiety symptom) print ('counts for H1FS10') c12 = sub3['H1FS10'].value_counts(sort=False, dropna=True) print(c12) print ('percentages H1FS10') p12 = sub3['H1FS10'].value_counts(sort=False, normalize=True, dropna=True) print (p12*100) #Q11 You were happy (negation indicates a depression symptom) print ('counts for H1FS11') c13 = sub3['H1FS11'].value_counts(sort=False, dropna=True) print(c13) print ('percentages H1FS11') p13 = sub3['H1FS11'].value_counts(sort=False, normalize=True, dropna=True) print (p13*100) #Q12 You talked less than usual. print ('counts for H1FS12') c14 = sub3['H1FS12'].value_counts(sort=False, dropna=True) print(c14) print ('percentages H1FS12') p14 = sub3['H1FS12'].value_counts(sort=False, normalize=True, dropna=True) print (p14*100) #Q13 You felt lonely ( indicates depression) print ('counts for H1FS13') c15 = sub3['H1FS13'].value_counts(sort=False, dropna=True) print(c15) print ('percentages H1FS13') p15 = sub3['H1FS13'].value_counts(sort=False, normalize=True, dropna=True) print (p15*100) #Q14 People were unfriendly to you print ('counts for H1FS14') c16 = sub3['H1FS14'].value_counts(sort=False, dropna=True) print(c16) print ('percentages H1FS14') p16 = sub3['H1FS14'].value_counts(sort=False, normalize=True, dropna=True) print (p16*100) #Q15 You enjoyed life (negation indicates a depression symptom) print ('counts for H1FS15') c17 = sub3['H1FS15'].value_counts(sort=False, dropna=True) print(c17) print ('percentages H1FS15') p17 = sub3['H1FS15'].value_counts(sort=False, normalize=True, dropna=True) print (p17*100) #Q16 You felt sad ( indicates depression) print ('counts for H1FS16') c18 = sub3['H1FS16'].value_counts(sort=False, dropna=True) print(c18) print ('percentages H1FS16') p18 = sub3['H1FS16'].value_counts(sort=False, normalize=True, dropna=True) print (p18*100) #Q17 You felt that people disliked you (depression symptom--Lack of self confidence) print ('counts for H1FS17') c19 = sub3['H1FS17'].value_counts(sort=False, dropna=True) print(c19) print ('percentages H1FS17') p19 = sub3['H1FS17'].value_counts(sort=False, normalize=True, dropna=True) print (p19*100) #Q18 It was hard to get started doing things (depression symptom) print ('counts for H1FS18') c20 = sub3['H1FS18'].value_counts(sort=False, dropna=True) print(c20) print ('percentages H1FS18') p20 = sub3['H1FS18'].value_counts(sort=False, normalize=True, dropna=True) print (p20*100) #Q19 You felt life was not worth living (depression symptom) print ('counts for H1FS19') c21 = sub3['H1FS19'].value_counts(sort=False, dropna=True) print(c21) print ('percentages H1FS19') p21 = sub3['H1FS19'].value_counts(sort=False, normalize=True, dropna=True) print (p21*100) #Frequency distribution for those with one or more depression symptom sub4=sub3[(sub3['H1FS1'] ==3) | (sub3['H1FS2'] ==3) | (sub3['H1FS3'] ==3)    | (sub3['H1FS4'] ==3) | (sub3['H1FS5'] ==3) | (sub3['H1FS6'] ==3)          | (sub3['H1FS7'] ==3) | (sub3['H1FS8'] == 0) | (sub3['H1FS9'] ==3) | (sub3['H1FS10'] ==3) | (sub3['H1FS11'] == 0)          | (sub3['H1FS12'] ==3) | (sub3['H1FS13'] ==3) | (sub3['H1FS14'] ==3) | (sub3['H1FS15'] == 0)          | (sub3['H1FS16'] ==3) | (sub3['H1FS17'] ==3) | (sub3['H1FS18'] ==3)          | (sub3['H1FS19'] ==3) ] #Frequency distribution showing the effect of Family relationship on feeling scale print('Frequency distribution showing the effect of Family relationship on feeling scale') # 1  may indicate good relationship while 0 indicates the opposite #Replacing 6 (refused), 7 (legitimate skip), 8 (don’t know) and 9 (not applicable) with NaN sub4= sub4.replace([6,7,8,9], np.nan) sub5=sub4.copy() #On how many of the past 7 days was at least one of your parents in the room with you while you ate your evening meal? #Assume from 4 to 7 means yes (=1) #Assume from 0 to 3 means no (=0) sub5['H1WP8']= sub5['H1WP8'].replace([4,5,6,7], 1) sub5['H1WP8']= sub5['H1WP8'].replace([0,1,2,3], 0) #Replacing 96 (refused), 97 (legitimate skip) and 98 (don’t know) with NaN sub5['H1WP8']= sub5['H1WP8'].replace([96,97,98], np.nan) print ('counts for H1WP8') c22 = sub5['H1WP8'].value_counts(sort=False, dropna=True) print(c22) print ('percentages H1WP8') p22 = sub5['H1WP8'].value_counts(sort=False, normalize=True, dropna=True) print (p22*100) # Did you have a talk with your mother about a personal problem you were having print ('counts for  H1WP17F') c23 = sub5['H1WP17F'].value_counts(sort=False, dropna=True) print(c23) print ('percentages  H1WP17F') p23 = sub5['H1WP17F'].value_counts(sort=False, normalize=True, dropna=True) print (p23*100) # Did you have a talk with your father about a personal problem you were having   print ('counts for H1WP18F') c24 = sub5['H1WP18F'].value_counts(sort=False, dropna=True) print(c24) print ('percentages H1WP18F') p24 = sub5['H1WP18F'].value_counts(sort=False, normalize=True, dropna=True) print (p24*100) #Did you talk  with your mother about other things you’re doing in school   print ('counts for H1WP17J') c25 = sub5['H1WP17J'].value_counts(sort=False, dropna=True) print(c25) print ('percentages H1WP17J') p25 = sub5['H1WP17J'].value_counts(sort=False, normalize=True, dropna=True) print (p25*100) #Did you talk  with your father about other things you’re doing in school print ('counts for  H1WP18J') c26 = sub5['H1WP18J'].value_counts(sort=False, dropna=True) print(c26) print ('percentages  H1WP18J') p26 = sub5['H1WP18J'].value_counts(sort=False, normalize=True, dropna=True) print (p26*100) #gone shopping with mother? print ('counts for H1WP17A') c27 = sub5['H1WP17A'].value_counts(sort=False, dropna=True) print(c27) print ('percentages H1WP17A') p27 = sub5['H1WP17A'].value_counts(sort=False, normalize=True, dropna=True) print (p27*100) #played a sport with mother? print ('counts for H1WP17B') c28 = sub5['H1WP17B'].value_counts(sort=False, dropna=True) print(c28) print ('percentages H1WP17B') p28 = sub5['H1WP17B'].value_counts(sort=False, normalize=True, dropna=True) print (p28*100) #worked on a project for school with mother? print ('counts for H1WP17I') c29 = sub5['H1WP17I'].value_counts(sort=False, dropna=True) print(c29) print ('percentages H1WP17I') p29 = sub5['H1WP17I'].value_counts(sort=False, normalize=True, dropna=True) print (p29*100) #gone shopping with father?   print ('counts for H1WP18A') c30 = sub5['H1WP18A'].value_counts(sort=False, dropna=True) print(c30) print ('percentages H1WP18A') p30 = sub5['H1WP18A'].value_counts(sort=False, normalize=True, dropna=True) print (p30*100) #played a sport with father? print ('counts for H1WP18B') c31 = sub5['H1WP18B'].value_counts(sort=False, dropna=True) print(c31) print ('percentages H1WP18B') p31 = sub5['H1WP18B'].value_counts(sort=False, normalize=True, dropna=True) print (p31*100) #worked on a project for school with father? print ('counts for H1WP18I') c32 = sub5['H1WP18I'].value_counts(sort=False, dropna=True) print(c32) print ('percentages H1WP18I') p32 = sub5['H1WP18I'].value_counts(sort=False, normalize=True, dropna=True) print (p32*100) # Frequency distribution of those with no potential depression symptoms sub6=sub3[(sub3['H1FS1'] ==0) | (sub3['H1FS2'] ==0) | (sub3['H1FS3'] ==0)    | (sub3['H1FS4'] ==0) | (sub3['H1FS5'] ==0) | (sub3['H1FS6'] ==0)          | (sub3['H1FS7'] ==0) | (sub3['H1FS8'] == 3) | (sub3['H1FS9'] ==0) | (sub3['H1FS10'] ==0) | (sub3['H1FS11'] == 3)          | (sub3['H1FS12'] ==0) | (sub3['H1FS13'] ==0) | (sub3['H1FS14'] ==0) | (sub3['H1FS15'] == 3)          | (sub3['H1FS16'] ==0) | (sub3['H1FS17'] ==0) | (sub3['H1FS18'] ==0)          | (sub3['H1FS19'] ==0) ] #Replacing 6 (refused), 7 (legitimate skip), 8 (don’t know) and 9 (not applicable) with NaN sub6= sub6.replace([6,7,8,9], np.nan) sub7=sub6.copy() print('Frequency distribution of those with no potential depression symptoms') #On how many of the past 7 days was at least one of your parents in the room with you while you ate your evening meal? #Assume from 4 to 7 means yes (=1) #Assume from 0 to 3 means no (=0) sub7['H1WP8']= sub7['H1WP8'].replace([4,5,6,7], 1) sub7['H1WP8']= sub7['H1WP8'].replace([0,1,2,3], 0) #Replacing 96 (refused), 97 (legitimate skip) and 98 (don’t know) with NaN sub7['H1WP8']= sub7['H1WP8'].replace([96,97,98], np.nan) print ('counts for H1WP8') c33 = sub7['H1WP8'].value_counts(sort=False, dropna=True) print(c33) print ('percentages H1WP8') p33 = sub7['H1WP8'].value_counts(sort=False, normalize=True, dropna=True) print (p33*100) # Did you have a talk with your mother about a personal problem you were having print ('counts for  H1WP17F') c34 = sub7['H1WP17F'].value_counts(sort=False, dropna=True) print(c34) print ('percentages  H1WP17F') p34 = sub7['H1WP17F'].value_counts(sort=False, normalize=True, dropna=True) print (p34*100) # Did you have a talk with your father about a personal problem you were having   print ('counts for H1WP18F') c35 = sub7['H1WP18F'].value_counts(sort=False, dropna=True) print(c35) print ('percentages H1WP18F') p35 = sub7['H1WP18F'].value_counts(sort=False, normalize=True, dropna=True) print (p35*100) #Did you talk  with your mother about other things you’re doing in school   print ('counts for H1WP17J') c36 = sub7['H1WP17J'].value_counts(sort=False, dropna=True) print(c36) print ('percentages H1WP17J') p36 = sub7['H1WP17J'].value_counts(sort=False, normalize=True, dropna=True) print (p36*100) #Did you talk  with your father about other things you’re doing in school print ('counts for  H1WP18J') c37 = sub7['H1WP18J'].value_counts(sort=False, dropna=True) print(c37) print ('percentages  H1WP18J') p37 = sub7['H1WP18J'].value_counts(sort=False, normalize=True, dropna=True) print (p37*100) #gone shopping with mother? print ('counts for H1WP17A') c38 = sub7['H1WP17A'].value_counts(sort=False, dropna=True) print(c38) print ('percentages H1WP17A') p38 = sub7['H1WP17A'].value_counts(sort=False, normalize=True, dropna=True) print (p38*100) #played a sport with mother? print ('counts for H1WP17B') c39 = sub7['H1WP17B'].value_counts(sort=False, dropna=True) print(c39) print ('percentages H1WP17B') p39 = sub7['H1WP17B'].value_counts(sort=False, normalize=True, dropna=True) print (p39*100) #worked on a project for school with mother? print ('counts for H1WP17I') c40 = sub7['H1WP17I'].value_counts(sort=False, dropna=True) print(c40) print ('percentages H1WP17I') p40 = sub7['H1WP17I'].value_counts(sort=False, normalize=True, dropna=True) print (p40*100) #gone shopping with father?   print ('counts for H1WP18A') c41 = sub7['H1WP18A'].value_counts(sort=False, dropna=True) print(c41) print ('percentages H1WP18A') p41 = sub7['H1WP18A'].value_counts(sort=False, normalize=True, dropna=True) print (p41*100) #played a sport with father? print ('counts for H1WP18B') c42 = sub7['H1WP18B'].value_counts(sort=False, dropna=True) print(c42) print ('percentages H1WP18B') p42 = sub7['H1WP18B'].value_counts(sort=False, normalize=True, dropna=True) print (p42*100) #worked on a project for school with father? print ('counts for H1WP18I') c43 = sub7['H1WP18I'].value_counts(sort=False, dropna=True) print(c43) print ('percentages H1WP18I') p43 = sub7['H1WP18I'].value_counts(sort=False, normalize=True, dropna=True) print (p43*100) #upper-case all DataFrame column names - place afer code for loading data above data.columns = map(str.upper, data.columns) # bug fix for display formats to avoid run time errors - put after code for loading data above pd.set_option('display.float_format', lambda x:'%f'%x)

Output:

6504 2 counts for H1GI1Y 74      17 76     376 78    1165 75      43 77    1133 79    1159 Name: H1GI1Y, dtype: int64 percentages for H1GI1Y 74    0.436681 76    9.658361 78   29.925507 75    1.104547 77   29.103519 79   29.771385 Name: H1GI1Y, dtype: float64 counts for H1GI15 0    3893 Name: H1GI15, dtype: int64 percentages for H1GI15 0   100.000000 Name: H1GI15, dtype: float64 counts for H1FS1 1.000000    2068 0.000000    3913 2.000000     385 3.000000     116 Name: H1FS1, dtype: int64 percentages H1FS1 1.000000   31.903733 0.000000   60.367171 2.000000    5.939525 3.000000    1.789571 Name: H1FS1, dtype: float64 counts for H1FS2 0.000000    4192 1.000000    1744 2.000000     410 3.000000     141 Name: H1FS2, dtype: int64 percentages H1FS2 0.000000   64.621551 1.000000   26.884538 2.000000    6.320333 3.000000    2.173578 Name: H1FS2, dtype: float64 counts for H1FS3 1.000000    1296 0.000000    4668 2.000000     372 3.000000     144 Name: H1FS3, dtype: int64 percentages H1FS3 1.000000   20.000000 0.000000   72.037037 2.000000    5.740741 3.000000    2.222222 Name: H1FS3, dtype: float64 counts for H1FS4 3.000000    2345 2.000000    2070 0.000000     715 1.000000    1353 Name: H1FS4, dtype: int64 percentages H1FS4 3.000000   36.171526 2.000000   31.929662 0.000000   11.028845 1.000000   20.869968 Name: H1FS4, dtype: float64 counts for H1FS5 1.000000    2768 3.000000     277 0.000000    2624 2.000000     816 Name: H1FS5, dtype: int64 percentages H1FS5 1.000000   42.683115 3.000000    4.271396 0.000000   40.462606 2.000000   12.582884 Name: H1FS5, dtype: float64 counts for H1FS6 1.000000    1853 0.000000    3994 2.000000     444 3.000000     193 Name: H1FS6, dtype: int64 percentages H1FS6 1.000000   28.578038 0.000000   61.597779 2.000000    6.847625 3.000000    2.976558 Name: H1FS6, dtype: float64 counts for H1FS7 1.000000    2934 0.000000    2755 2.000000     630 3.000000     168 Name: H1FS7, dtype: int64 percentages H1FS7 1.000000   45.228919 0.000000   42.469554 2.000000    9.711731 3.000000    2.589795 Name: H1FS7, dtype: float64 counts for H1FS8 3.000000    2003 2.000000    2185 1.000000    1567 0.000000     720 Name: H1FS8, dtype: int64 percentages H1FS8 3.000000   30.934363 2.000000   33.745174 1.000000   24.200772 0.000000   11.119691 Name: H1FS8, dtype: float64 counts for H1FS9 1.000000     782 0.000000    5451 3.000000      80 2.000000     164 Name: H1FS9, dtype: int64 percentages H1FS9 1.000000   12.073491 0.000000   84.159333 3.000000    1.235140 2.000000    2.532036 Name: H1FS9, dtype: float64 counts for H1FS10 0.000000    4714 1.000000    1545 2.000000     163 3.000000      65 Name: H1FS10, dtype: int64 percentages H1FS10 0.000000   72.668414 1.000000   23.816864 2.000000    2.512718 3.000000    1.002004 Name: H1FS10, dtype: float64 counts for H1FS11 3.000000    2397 2.000000    2690 0.000000     172 1.000000    1230 Name: H1FS11, dtype: int64 percentages H1FS11 3.000000   36.939436 2.000000   41.454770 0.000000    2.650640 1.000000   18.955155 Name: H1FS11, dtype: float64 counts for H1FS12 1.000000    2206 0.000000    3642 2.000000     476 3.000000     161 Name: H1FS12, dtype: int64 percentages H1FS12 1.000000   34.016962 0.000000   56.160370 2.000000    7.340015 3.000000    2.482652 Name: H1FS12, dtype: float64 counts for H1FS13 1.000000    1787 0.000000    4157 2.000000     401 3.000000     140 Name: H1FS13, dtype: int64 percentages H1FS13 1.000000   27.555898 0.000000   64.101773 2.000000    6.183500 3.000000    2.158828 Name: H1FS13, dtype: float64 counts for H1FS14 0.000000    4307 1.000000    1839 2.000000     256 3.000000      87 Name: H1FS14, dtype: int64 percentages H1FS14 0.000000   66.373863 1.000000   28.340268 2.000000    3.945138 3.000000    1.340730 Name: H1FS14, dtype: float64 counts for H1FS15 3.000000    3141 1.000000    1043 2.000000    2047 0.000000     255 Name: H1FS15, dtype: int64 percentages H1FS15 3.000000   48.427382 1.000000   16.080789 2.000000   31.560284 0.000000    3.931545 Name: H1FS15, dtype: float64 counts for H1FS16 1.000000    2629 0.000000    3405 2.000000     336 3.000000     120 Name: H1FS16, dtype: int64 percentages H1FS16 1.000000   40.508475 0.000000   52.465331 2.000000    5.177196 3.000000    1.848998 Name: H1FS16, dtype: float64 counts for H1FS17 0.000000    4246 1.000000    1859 3.000000     105 2.000000     276 Name: H1FS17, dtype: int64 percentages H1FS17 0.000000   65.464076 1.000000   28.661733 3.000000    1.618871 2.000000    4.255319 Name: H1FS17, dtype: float64 counts for H1FS18 0.000000    3124 1.000000    2814 2.000000     462 3.000000      84 Name: H1FS18, dtype: int64 percentages H1FS18 0.000000   48.180136 1.000000   43.399136 2.000000    7.125231 3.000000    1.295497 Name: H1FS18, dtype: float64 counts for H1FS19 1.000000     545 0.000000    5728 2.000000     149 3.000000      63 Name: H1FS19, dtype: int64 percentages H1FS19 1.000000    8.404009 0.000000   88.326908 2.000000    2.297610 3.000000    0.971473 Name: H1FS19, dtype: float64 Frequency distribution showing the effect of Family relationship on feeling scale counts for H1WP8 0.000000    1884 Name: H1WP8, dtype: int64 percentages H1WP8 0.000000   100.000000 Name: H1WP8, dtype: float64 counts for  H1WP17F 1.000000    1354 0.000000    2037 Name: H1WP17F, dtype: int64 percentages  H1WP17F 1.000000   39.929224 0.000000   60.070776 Name: H1WP17F, dtype: float64 counts for H1WP18F 0.000000    1934 1.000000     470 Name: H1WP18F, dtype: int64 percentages H1WP18F 0.000000   80.449251 1.000000   19.550749 Name: H1WP18F, dtype: float64 counts for H1WP17J 1.000000    1750 0.000000    1641 Name: H1WP17J, dtype: int64 percentages H1WP17J 1.000000   51.607196 0.000000   48.392804 Name: H1WP17J, dtype: float64 counts for  H1WP18J 0.000000    1350 1.000000    1054 Name: H1WP18J, dtype: int64 percentages  H1WP18J 0.000000   56.156406 1.000000   43.843594 Name: H1WP18J, dtype: float64 counts for H1WP17A 1.000000    2418 0.000000     973 Name: H1WP17A, dtype: int64 percentages H1WP17A 1.000000   71.306399 0.000000   28.693601 Name: H1WP17A, dtype: float64 counts for H1WP17B 0.000000    3080 1.000000     311 Name: H1WP17B, dtype: int64 percentages H1WP17B 0.000000   90.828664 1.000000    9.171336 Name: H1WP17B, dtype: float64 counts for H1WP17I 0.000000    2917 1.000000     474 Name: H1WP17I, dtype: int64 percentages H1WP17I 0.000000   86.021822 1.000000   13.978178 Name: H1WP17I, dtype: float64 counts for H1WP18A 0.000000    1758 1.000000     646 Name: H1WP18A, dtype: int64 percentages H1WP18A 0.000000   73.128120 1.000000   26.871880 Name: H1WP18A, dtype: float64 counts for H1WP18B 0.000000    1682 1.000000     722 Name: H1WP18B, dtype: int64 percentages H1WP18B 0.000000   69.966722 1.000000   30.033278 Name: H1WP18B, dtype: float64 counts for H1WP18I 0.000000    2140 1.000000     264 Name: H1WP18I, dtype: int64 percentages H1WP18I 0.000000   89.018303 1.000000   10.981697 Name: H1WP18I, dtype: float64 Frequency distribution of those with no potential depression symptoms counts for H1WP8 0.000000    3363 Name: H1WP8, dtype: int64 percentages H1WP8 0.000000   100.000000 Name: H1WP8, dtype: float64 counts for  H1WP17F 1.000000    2348 0.000000    3729 Name: H1WP17F, dtype: int64 percentages  H1WP17F 1.000000   38.637486 0.000000   61.362514 Name: H1WP17F, dtype: float64 counts for H1WP18F 0.000000    3645 1.000000     866 Name: H1WP18F, dtype: int64 percentages H1WP18F 0.000000   80.802483 1.000000   19.197517 Name: H1WP18F, dtype: float64 counts for H1WP17J 1.000000    3160 0.000000    2917 Name: H1WP17J, dtype: int64 percentages H1WP17J 1.000000   51.999342 0.000000   48.000658 Name: H1WP17J, dtype: float64 counts for  H1WP18J 0.000000    2534 1.000000    1977 Name: H1WP18J, dtype: int64 percentages  H1WP18J 0.000000   56.173797 1.000000   43.826203 Name: H1WP18J, dtype: float64 counts for H1WP17A 1.000000    4429 0.000000    1648 Name: H1WP17A, dtype: int64 percentages H1WP17A 1.000000   72.881356 0.000000   27.118644 Name: H1WP17A, dtype: float64 counts for H1WP17B 0.000000    5537 1.000000     540 Name: H1WP17B, dtype: int64 percentages H1WP17B 0.000000   91.114037 1.000000    8.885963 Name: H1WP17B, dtype: float64 counts for H1WP17I 0.000000    5272 1.000000     805 Name: H1WP17I, dtype: int64 percentages H1WP17I 0.000000   86.753332 1.000000   13.246668 Name: H1WP17I, dtype: float64 counts for H1WP18A 0.000000    3316 1.000000    1195 Name: H1WP18A, dtype: int64 percentages H1WP18A 0.000000   73.509200 1.000000   26.490800 Name: H1WP18A, dtype: float64 counts for H1WP18B 0.000000    3142 1.000000    1369 Name: H1WP18B, dtype: int64 percentages H1WP18B 0.000000   69.651962 1.000000   30.348038 Name: H1WP18B, dtype: float64 counts for H1WP18I 0.000000    4004 1.000000     507 Name: H1WP18I, dtype: int64 percentages H1WP18I 0.000000   88.760807 1.000000   11.239193 Name: H1WP18I, dtype: float64

[shereen-hasan]

The Relationship Between Adolescents' Feeling Scale And Their Relations With Parents: A Literature Review

      Depressive feelings and suicidal ideation in a non-clinical sample of adolescents in Estonia were analysed in the context of family structure, mutual relationships amongst family members and schoolchildren’s preferences regarding intimate personal contacts with particular family members (Samm, A., Tooding, L., Sisask, M. et al, 2010). Data from ‘AddHealth In Home Questionnaire’ Code Book along with ‘Addhealth wave 1’ dataset (Sections: “Feeling scale” and “Relations with parents”) were chosen.

Related Published Academic Works

1. Samm, A., Tooding, L., Sisask, M. et al. Suicidal thoughts and depressive feelings amongst Estonian schoolchildren: effect of family relationship and family structure. Eur Child Adolesc Psychiatry 19, 457–468 (2010). https://doi.org/10.1007/s00787-009-0079-7

2.   Fumiko Kakihara, Lauree Tilton-Weaver, Margaret Kerr, Håkan Stattin. The Relationship of Parental Control to Youth Adjustment: Do Youths’ Feelings About Their Parents Play a Role?. J Youth Adolescence (2010) 39:1442–1456 DOI 10.1007/s10964-009-9479-8