In the last few weeks of class, we focused on analyzing the raw data provided by the Equality of Opportunity Project. My group specifically concentrated on gender. We looked to see if there were gender differences between college tiers and parental income levels. We had expected to see a greater mean parental income for females than males across all college tiers because there was generally a greater proportion of females attending colleges than males. However, this was not the case. From the graph we made, it was clear that the data was right skewed. The students attending ivy plus schools had the greatest mean parental incomes for both genders and this decreased as you went down the tiers. One surprising observation was that males had a higher mean parental income than females within all of the college tiers, with the biggest gender gap seen in ivy plus students. This observation went against our original hypothesis, which I believe goes to show that there are many other factors involved that we are unable to take into account due to the scope of the data.

One of the main goals of this project was to be able to present our results in a manner that was clear and concise using Tableu. I had never used Tableu before and it was very frustrating at first. I realized while writing my draft that our graph was not right. It was saying that students in ivy plus schools had the lowest mean parental incomes while students at selective schools had the greatest mean parental incomes. After working with Katie Rawson, we were able to fix our graph. With this new graph, we were able to take the raw data and condense it into something that was easily understood.

Overall, I found all of the groups results very interesting and some surprising. I think that if we had more time, we would have been able to get a better understanding of the big picture. There was so much raw data available and so many factors that influence social mobility that we were only able to look at one facet. By exploring these other variables, we can see trends and work towards solving these issues.

In the graph, the blue bars represents the average female parental income and the orange bars represent the average male parental income for each college tier. The data was right skewed and from this, we were able to observe one major trend that followed our original predictions. The students at ivy plus schools had the highest mean parental incomes for both genders, with the average female mean parental income at $412,212 and the average male parental income at $450,376. The mean parental incomes declined as you moved down the college tiers. Students at two-year (public and private not-for-profit), four-year for-profit, and two-year for-profit had the lowest mean parental incomes for both genders, with females having a mean parental income of $58,156 and males $70,848 at two-year for-profit schools.

One thing that we did find surprising was that across the board, males had a greater average parental income than female students within every college tier. This was something that we did not expect to see because there are a number of surveys and data released by universities about their male to female ratio on campus, where there are typically more female students enrolled than male students. As a result, we anticipated that the mean parental income for female students would be greater than males because there were generally a greater proportion of female students at colleges. This was not the case. The mean parental incomes of male students exceeded those of female students, with the greatest difference seen in ivy plus students.

There are many factors that contribute to these results. Some being the difference between public and private universities and the persistent gender pay gap and family structure at these schools. Students who come from single parent households will have a lower mean parental income than students who come from two-parent households where both parents are contributing to the mean parental income. In addition, students raised by with single-mothers or have absentee fathers may have a lower mean parental income due to factors, such as a gender pay gap or a decrease in college enrollment, particularly among males. There are many factors that contribute to these findings, and therefore, we cannot point to one particular reason for as the basis of our conclusions. However, we explore some of these issues further within our topic.

In the graph, the blue bars represents the average female parental income and the orange bars represent the average male parental income for each college tier. The data was right skewed and from this, we were able to observe one major trend that followed our original predictions. The students at ivy plus schools had the highest mean parental incomes for both genders, with the average female mean parental income at $412,212 and the average male parental income at $450,376. The mean parental incomes declined as you moved down the college tiers. Students at two-year (public and private not-for-profit), four-year for-profit, and two-year for-profit had the lowest mean parental incomes for both genders, with females having a mean parental income of $58,156 and males $70,848 at two-year for-profit schools.

One thing that we did find surprising was that across the board, males had a greater average parental income than female students within every college tier. This was something that we did not expect to see because there are a number of surveys and data released by universities about their male to female ratio on campus, where there are typically more female students enrolled than male students. As a result, we anticipated that the mean parental income for female students would be greater than males because there were generally a greater proportion of female students at colleges. This was not the case. The mean parental incomes of male students exceeded those of female students, with the greatest difference seen in ivy plus students.

There are many factors that contribute to these results. Some being the difference between public and private universities and the persistent gender pay gap and family structure at these schools. Students who come from single parent households will have a lower mean parental income than students who come from two-parent households where both parents are contributing to the mean parental income. In addition, students raised by with single-mothers or have absentee fathers may have a lower mean parental income due to factors, such as a gender pay gap or a decrease in college enrollment, particularly among males. There are many factors that contribute to these findings, and therefore, we cannot point to one particular reason for as the basis of our conclusions. However, we explore some of these issues further within our topic.

Since the Census Bureau started to collect information on higher education attainment, women are now more likely to obtain a bachelor’s degree than men. In 2015, 30.2% of women had a bachelor’s degree while 29.9% of men did. In 2005, only 26% of women had bachelor’s degree while 28.5% of men did. The reason for this increase is due to a rise in young women pursuing higher education. In females between the ages of 25-34, 37.5% of women have a bachelor’s degree or higher, whereas only 29.5% of men do. However, for people over the age of 65, only 20.3% of women compared to 30.6% of men have bachelor degrees or higher. This shift in gender indicates that as times are changing, more women than men are obtaining bachelor degrees.

Many have looked into factors that could have contributed to this growing gender disparity and concluded that gender and family-structure and socioeconomic status differences may have impacted the widening gender gap. According to a study by William Doherty, Brian Willoughby, and Jason Wilde, changes in family structures have led to the growing gender gap in higher education attainment. Both genders of children that grew up with stably married parents were equally likely to succeed at school, However, the absence of a father seemed to lead to less boys obtaining a college education. The researchers obtained longitudinal data on more than 15,000 people who were in 7th to 12th grade in the 1994-1995 school year from Add Health. The results illustrated that out of the participants whose fathers were present, 63.1% of men and 72.1% of women had some college education. From those who had an absent father, 49.2% of men and 61.3% of women had some college education. In addition, socioeconomic status can play a role in the increasing gender gap. As stated by Dr. Vedder, people who have had the most contact with the welfare state through public assistance policies, have also seen a decrease in males obtaining a college education. From these observations, it is likely that family structure and socioeconomic status impacts the widening gender disparity in higher education attainment. Other aspects that would be interesting to look at further would be to see the gender ratios at public and private universities and across different states.

Although this episode was our second episode, it was both easier and challenging than producing the first episode. It was easier in the sense that this was our second time producing a podcast episode so we knew what to expect and the most effective way to go about actually recording our episode. However, it was more challenging in two aspects. The first was that I was very unfamiliar with the website howstuffworks.com. I had never even heard of it before until Ruth brought up this topic for her podcast episode. After looking into it, I got the general idea of what the website was about; however, there were parts about the website I still wasn’t as familiar with. The second difficulty was that we decided to make our podcast a little more scripted this time around. For my podcast on WestWorld, we wrote down points of conversations and just talked about them freely. However, that had its pros and cons. The advantage of talking freely was that it didn’t sound scripted; however, the con was that we didn’t talk about our points as eloquently and coherently as we wanted to have. We were often organizing our thoughts as we were speaking and had to rerecord several times. To avoid the problems we faced in the first podcast, we decided to make this one scripted.

With this episode, we struggled with trying to make it sound like it was not scripted. We added pauses in our points and inflections to our tones to make it seem like we were thinking on the spot. However, after hearing it play back it still sounded like it was very scripted. I think one of the reasons why it sounded like this was because in the beginning it seemed more like an interview. I would ask Ruth questions about howstuffworks.com and she would answer them. In class, we discussed how scripted podcasts sounded scripted and conversational podcasts flowed more smoothly. However, from my experience in producing the two different types of podcasts, I think there is a fine balance between the two. In the future, I believe we should script some of our lines, but also leave it open ended so that it can generate follow-up conversations and questions. Towards the middle of the podcast, we found a good balance between the two styles. We scripted some parts, but would go off script as we brought up questions or points we hadn’t considered before when writing out our lines.

Our primary goal in this podcast was to examine who the general audience was and to analyze the idea of convergence culture and how it applies to Howstuffworks.com. In planning the podcast, we made a Google doc with main points we wanted to discuss on the episode. We divided the tasks by each researching information about the main points we wanted to bring up, and then coming together and talking about them.

This podcast was different than the ones that were produced before in the sense that we weren’t necessarily analyzing what made this website new media. Instead, we were looking at it from the perspective of who the targeted audience was, the purpose of the website, and the idea of convergence culture. In most of the other podcasts, the interactive aspect of the topic was something that made it more new media “friendly.” However, we looked at how this website was not as interactive as we thought it could potentially be. As a result, I think this episode focused more on rhetorical composition. We analyzed what audience howstuffworks.com was targeting and what the purpose of the website was. There are a variety of categories on a bunch of random topics and seeing how everything was organized was also a point we discussed.

Although college enrollment rates have risen throughout the years, there is a growing gap between the enrollment rates of men and women. More women are now enrolling in colleges than men. This study conducted by Pew Research Center examines the gender gaps in college enrollment across 4 different ethnicities (Hispanic, black, white, and Asian) and compares data from 1994 and 2012. They found that in 1994, three out of four groups had more men enrolled in college than women. The only outlier was that among young white men and women who were enrolled in college immediately after graduation, young white women (66%) exceeded the men (62%) by 4%. By 2012, women enrolling in college immediately after graduating high school was greater than men across all groups.

I think that this is also an interesting aspect to look into concerning our question. Although it lacks the income levels, it includes gender differences in college enrollment between different ethnicities. It also examines possible reasons for the widening gender gap in college enrollments, with some scholars indicating that barriers to entering the labor market for women have been lower while others point to the higher incidence of disciplinary problems among boys in school as a factor. This study also takes note of the changes in demographics in the public school student population. Compared to 1994, there is an increase in diversity in public schools by 2012.

]]> http://eng221.janetkim.org/gender/increasing-gender-gap-in-colleges/ Wed, 12 Apr 2017 20:30:05 +0000 http://eng221.janetkim.org/?p=187 Although college enrollment rates have risen throughout the years, there is a growing gap between the enrollment rates of men and women. More women are now enrolling in colleges than men. This study conducted by Pew Research Center examines the gender gaps in college enrollment across 4 different ethnicities (Hispanic, black, white, and Asian) and compares […]]]>

Although college enrollment rates have risen throughout the years, there is a growing gap between the enrollment rates of men and women. More women are now enrolling in colleges than men. This study conducted by Pew Research Center examines the gender gaps in college enrollment across 4 different ethnicities (Hispanic, black, white, and Asian) and compares data from 1994 and 2012. They found that in 1994, three out of four groups had more men enrolled in college than women. The only outlier was that among young white men and women who were enrolled in college immediately after graduation, young white women (66%) exceeded the men (62%) by 4%. By 2012, women enrolling in college immediately after graduating high school was greater than men across all groups.

I think that this is also an interesting aspect to look into concerning our question. Although it lacks the income levels, it includes gender differences in college enrollment between different ethnicities. It also examines possible reasons for the widening gender gap in college enrollments, with some scholars indicating that barriers to entering the labor market for women have been lower while others point to the higher incidence of disciplinary problems among boys in school as a factor. This study also takes note of the changes in demographics in the public school student population. Compared to 1994, there is an increase in diversity in public schools by 2012.

]]> http://eng221.janetkim.org/uncategorized/equality-of-opportunity/ Thu, 06 Apr 2017 14:23:46 +0000 http://eng221.janetkim.org/?p=174 At the beginning of the project, our group was struggling to come up with a question that could be answered with the data we had. We were thinking of very broad questions that had many variables within it that made it difficult to answer. After tossing around ideas, we eventually agreed that we were interested in whether there was a gender difference in college tiers within each income level.

Before making the graphs, we went on the site and looked at the data. There were two tables that contained data on gender, and one of them contained the information we needed. The hard part came next. I am not a data driven person and it was challenging to see which variables went where in making the graphs. After playing around with the variables and the different graphs, I eventually made 3 graphs. All three measured male vs. female, mean parental income, and college tier. The first was a double bar graph that contained both male and female on the same graph next to each other. The other two were the same; however, one was for females and one was for males. For the purpose of our question, I think that the first one that had measured male and female students side by side is more appropriate to the question that we are asking.

From looking at the data, I had expected that there would be more females attending college overall as you went down the income brackets; however, this was not the case. In most instances, both genders were about equally present at the colleges. In the highest income bracket, there were more females attending selectively private colleges than males, which differed from a NY Times article I had read that stated that at the highest income level, there were more males attending colleges than females. After looking at the data and seeing the trends, I think that in the future it would be interesting to look at other factors, such as race, and see how this affects the data.

In this technology literacy narrative, I analyze the development of my media literacy skills. It was challenging at first trying to think of why I am the way I am and how what I do on a daily basis has influenced the way I refined my skills. I realized that my habits and interactions I thought nothing of actually influenced the way I interact with technology.

One habit in particular I considered a bad trait of mine was getting easily distracted while I was online. When I am reading an article and I find a particular point I am not familiar with, I will go to Google or YouTube to learn more instead of finishing the article (although I do finish reading once I have quenched my curiosity). In looking at this habit through a critical lens, I can see that this habit helps me get a more comprehensive understanding of the topic at hand and is the root of my ability to navigate transmedia effectively. I understand now that it is traits like these and my personality in general that has shaped my interactions with technology today.

I was particularly interested in looking at the children’s income distribution in relation to the parent’s income distribution. I thought it was interesting that most children don’t earn as much as their parents since many people go to school and work hard so that the next generation can have an easier life. In the graph above, students who went to higher-tiered schools earned more. Although the only variables in the graph are college tier and the median child individual earnings in 2014, I think it would be interesting to also examine whether region impacts the data and the probability that the child will end up in the same percentile as their parents or even move into a different percentile than their parents. By looking at these variables, we can see whether children are in the same or different income percentile as their parents and how college tiers and region can impact this.