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Data Management and Visualization Week 4

1. Python Code

2. Output of the Program

3. Explanations

I have restricted my analysis to those participants of the NESARC study whose income is less than $ 250000. This applies to most of the participants and makes the analysis of the income distribution somewhat easier (as the yearly income could be millions of dollars, but only for very few participants). Again, I am interested in low self esteem, which I regard with respect to gender, age and income. The univariate bar graph shows that about 14 % of the participants have low self esteem. The average income is $ 26703. But the standard deviation of the income is quite large with $ 26760. The bivariate bar graph for low self esteem and gender shows that females have a slightly higher rate of low self esteem. There seems to be a correlation between age and self esteem: with increasing age, low self esteem decreases. Regarding income, people with higher income tend to have more self esteem. But there is no strong correlation.

Definition of the income groups used in the last graph:

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Course Data Management and Visualization Week 3

1. Python Code

2. Output of the program

3. Explanations

First, I reduced the NESARC data set to the variables of interest: IDNUM, SEX, AGE, S10Q1A4 (OFTEN WORRY ABOUT BEING CRITICIZED OR REJECTED IN SOCIAL SITUATIONS), S10Q1A5 (BELIEVE THAT YOU ARE NOT AS GOOD, AS SMART, OR AS ATTRACTIVE AS MOST PEOPLE) and S10Q1A6 (USUALLY QUIET OR HAVE VERY LITTLE TO SAY WHEN MEET NEW PEOPLE BECAUSE YOU BELIEVE THEY ARE BETTER THAN YOU ARE). I replaced the value '9' of the variables S10Q1A4, S10Q1A5 and S10Q1A6 with NaN and the value 2 (= no) with 0. In this way, I find the data values more meaningful. I combined the variables S10Q1A4, S10Q1A5 and S10Q1A6 to a new variable called 'LESTEEM' that should reflect properties of low self esteem. I checked frequencies and percentages for all respondents and again for women only. The variable LESTEEM has the value 1 (meaning True) for 15.8 % when taking all respondents and 17.6 % when taking female respondents only. So, women seem to have slightly lower self esteem than men. I was also interested in the age distribution of the respondents based on the age groups 17 - 20, 21 - 39, 40 - 59, 60 and over. Only 0.05 % of the respondents are younger than 21 years (note: previously, I checked that there are no respondents younger than 18 years in the data set).

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Course Data Management and Visualization Week 2

1) Python Program:

2) Output of Program:

3) Remarks

I have analyed the following three variables from the NESARC dataset:

S10Q1A4 - OFTEN WORRY ABOUT BEING CRITICIZED OR REJECTED IN SOCIAL SITUATIONS

S10Q1A5 - BELIEVE THAT YOU ARE NOT AS GOOD, AS SMART, OR AS ATTRACTIVE AS MOST PEOPLE

S10Q1A6 - USUALLY QUIET OR HAVE VERY LITTLE TO SAY WHEN MEET NEW PEOPLE BECAUSE YOU BELIEVE THEY ARE BETTER THAN YOU ARE

All three variables have the value 1 for yes, the value 2 for no and the value 9, if the respondent did not answer the question for the respective variable.  For the three variables, the rate of missing data (i. e. value 9) is between 3.2 and 3.3 %.

For the variable  S10Q1A4, I have found out that 8.92 % of the respondents often worry about being criticized or rejected in social situations. Among women, the rate is slightly higher: 10.08 %.

Regarding the variable  S10Q1A5, 8.13 % of the respondents believe that they are not as good, as smart or as attractive as most people. Among women, the rate is again slightly higher: 9.73 %.

Regarding the variable  S10Q1A6, 5.51 % of the respondents are usually quiet or have very little to say when meet new people. Among women, the rate is again slightly higher: 5.92 %. 

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Course Data Management and Visualization Week 1

I am interested in working with the NESARC data, specifically with the section about “PERSONALITY DISORDERS (USUAL FEELINGS/ACTIONS)”. I am particularly interested in the topic “low self-esteem”. Therefore, I focus on the corresponding variables in the NESARC section “PERSONALITY DISORDERS (USUAL FEELINGS/ACTIONS)”.

I intend to pick a few of the variables related to low self-confidence and try to find if they are associated with gender.

I have performed some literature research using the terms “low self-esteem, self-confidence, gender”.

In the study “Self-esteem and the intersection of age, class, and gender” by JA McMullin and J Cairney (Journal of aging studies, 2004 – Elsevier), the authors have found out that “in all age groups, women have lower levels of self-esteem than do men.”

In the study “Age and Gender Differences in Self-Esteem—A Cross-Cultural Window” by Wiebke Bleidorn, Ruben C. Arslan et al., which encompasses 48 nations, it is also stated that there are “significant gender gaps, with males consistently reporting higher self-esteem than females”.

In the study "Is a lack of self‐confidence hindering women entrepreneurs?" by Jodyanne Kirkwood (2009), it is shown that "Women exhibit a lack of self‐confidence in their own abilities as entrepreneurs compared to men."

Numerous other studies also conclude that men usually have higher levels of self-confidence than women.

My hypothesis is that women are more frequently suffering from lower self-esteem than men and that this is also confirmed in the NESARC study.

At the beginning of my research project, I will focus on the following variables which are related to low self-esteem:

OFTEN WORRY ABOUT BEING CRITICIZED OR REJECTED IN SOCIAL SITUATIONS

BELIEVE THAT YOU ARE NOT AS GOOD, AS SMART, OR AS ATTRACTIVE AS MOST PEOPLE

USUALLY QUIET OR HAVE VERY LITTLE TO SAY WHEN MEET NEW PEOPLE BECAUSE YOU BELIEVE THEY ARE BETTER THAN YOU ARE

I will check, if there is an association between these variables and gender.

I have extracted the data of the sections 1 (Background information) and 10 (personality disorders) from the NESARC data to build a new dataset, which I will work with.