Blog 2 | Catalano Ap Research

Since my last blog post, I have been working on collecting and analyzing my data. I began to read my plays and start analyzing them in order to come closer to a conclusion. But, before I dive into what challenges I faced and what I discovered, let's take a look into the background of my research to refresh the context.

Scholarly Conversation and Purpose Refresher

In the past couple of decades, there has been an abundance of research on the portrayal of women in literature. These studies have found that women are riddled with stereotypical characteristics and there is an abundance of sexism. However, plays have not been a primary source of this research. Although there have been brief studies of Shakespeare, there is a lack of research regarding the portrayal of women over a wide period of time, and scoping between many different playwrights. Additionally, there is a lack of research regarding how the male and female playwrights' portrayal of women may differ. In the past few months, I have dedicated my research in an attempt to bring society closer to these answers. It is my goal that this research will help to bridge the gap in the understanding of this topic. Answering some of these questions is essential for today's society. Sexism in everyday life is incredibly prevalent for many women and young girls, and what we witness in the theatre may have more of an impact than what is initially believed. Dudley Nichols explains that what we see in the theatre can have a direct impact on what we believe as a society. For example, he explains that if one sees an act of violence without any persecution or penalty, it may affect the subconscious of the viewer's mind to believe that violence is acceptable. In many ways, the “theatre” that Nichols speaks of goes beyond the stage. This is also applicable to television and movies. However, the staged theatre was the first primary form of entertainment and the foundation for all that came after it, thus it is imperative to look into what is being presented to its audience.

Methodology Review and Updates

In total, I read and analyzed 20 plays, written between the 1920s and 2010s. For each decade, I randomly selected 2 plays, 1 written by a male playwright and 1 written by a male playwright. I then created a spreadsheet containing all 20 plays and the characteristics and subjects that previous researchers have found to be prevalent in their research (Figure A). I assigned each category with a coding color and annotation (ie blue highlight, green underline, orange square, etc.) to help me remain organized. At each occurrence, I marked the area of the spreadsheet that accounted for it. The order in which I read the plays was determined with a random number generator to avoid any bias that might come with reading in a particular order. This is a copy of my blank spreadsheet I initially started with before any changes were made:

I know this spreadsheet is incredibly busy, and possibly overwhelming, but try not to focus on it too much. This is just so you can get an initial idea of what I was working with!

Figure A: Initial Blank Spreadsheet

Throughout my research, my methodology remained fairly consistent, although there are a few specifics worth mentioning. As I collected data, I altered my spreadsheet to create a final, comprehensive sheet based on changes I faced as I read plays (Figure B). First of all, as I began to read the first few plays, I noticed some themes that were not mentioned in past research that I added to my spreadsheet. For example, when I read “Reasons to be Pretty” by Neil Labute, there was a prevalence of psychological mentions such as phrases like “Are you insane?” This was a topic that ended up resurfacing in my data. One limitation to this is that I was unable to continue adding categories with later plays. As I added categories, I had to go back and reread plays that I had already completed to analyze for the new category. Once I was toward the end of my research, there would not have been enough time to reread all of the plays I had already finished, so I could not add any new categories based on later plays. Because the plays were read in a randomly selected order, this should not have a significant impact on any analysis over time, however, it still presents a slight gap in overall understanding that is worth mentioning.

Additionally, there were a few categories that ended up being too difficult to analyze consistently between all of the plays. This mainly included the 5 main stereotypes defined by Cynthia Griffin Wolff, because characters in plays are not as fully developed and described as they are in novels, thus placing each female character into a category was not possible to do without personal bias via my own interpretation of the character. Another category that ended up not being analyzed was the “gentle” characteristic as it fell too closely with being motherly/caretaking, so the two categories were combined. This is a blank copy of my spreadsheet after changes were made, and what I inevitably used in my data collection:

Here are some more specifics to help understand my methodology:

For any sort of action, including, sexual harassment/assault, mentions of marriage, physical harm, cooking, and cleaning, it had to be an active action to be included in my data. For example, If it was inferred that a meal was made by a woman, it would not count as an occurrence of cooking and was only marked if a woman was actively cooking.

Some categories were not marked when they were being spoken of literally including psychological mentions, mentions of aging, and mentions of fertility/childbirth. An example of this is for psychological mentions, if a character was defined to have some sort of mental illness, mentions of this were not marked and were only recorded when the mention was more so meant as an insult or judge of character.

For all categories, there were two main requirements. The first is that if an entire conversation was based on a category, it was only marked once, versus at every individual mention. The reasoning for this is that marking every single mention in one category could cause a significant difference in data that would not be representative of how much the play focused on that subject in general. The other main specific was that women had to be subjects of all the occurrences. Although doing a similar study focused on men might be a lucrative subject for this topic, the scope of my research was focused on the portrayal of women only.

Once again, I know this spreadsheet is overwhelming! It is just to illustrate changes. (Removal of the 5 stereotypes and gentle category, and addition of neutral and sexual appearance, uncontrollable anger, psychological mentions, mentions of childbirth/fertility, mentions of aging, physical harms, and sexist sentences.

Figure B: Final Blank Spreadsheet

Data Collection

At this point, my data collection is complete and my next steps include finishing my analysis to advance my knowledge of the implications of my data and complete my paper and presentation. But first, let's take a look at some of the most prevalent data and my collection. After I was done reading all of these plays, I marked up all of the total occurrences. Between all 20 plays, there were a total of 469 Occurrences. 243 of these occurrences came from plays written by men while 2196 of these plays come from plays written by women. Additionally, over time there does seem to be some change. Each individual category was different in how male and female playwrights incorporated them and how they changed over time. Let's go to my data analysis to find out what some of these results show.

Data Analysis

As soon as I began looking at my data and attempting to begin data analysis, I realized that there were two different parts to my research. The first part is how the total amount of occurrences and types of occurrences have changed over time. The second is how men and women portrayed women differently and how that portrayal changed over time.

Let's start with the total change over time. For change over time incorporating all 20 plays, the two plays in each decade were averaged together (Figure 1.1). For change over time for both plays written by men (Figure 1.2) and women (Figure 1.3) individually, the total values were used for the corresponding play and decade. Here is a look into total change over time.

Total Occurrences Change Over Time (Avg. per Decade)

Occurrences (Frequency)

Decade

Figure 1.1: Total Occurances Change Over Time (Avg. per Decade)

Total Occurrences Change Over Time (Plays Written By Men)

Occurrences (Frequency)

Decade

Figure 1.2: Total Occurrences Change Over Time (Plays Written By Men)

Total Occurrences Change Over Time (Plays Written By Women)

Occurrences (Frequency)

Decade

Figure 1.3: Total Occurrences Change Over Time (Plays Written By Women)

As you can see, this data reveals that there has been some change over time. In the middle of the graph for total change between plays written by men and women, the total number of occurrences per decade appears to be decreasing at a constant rate before it increases quickly, caused by a very large outlier in the data as seen by the graph displaying change over time based only on plays written by men. Overall, there seems to be the least amount of change/a regular pattern in plays written by men, and the most noticeable decrease can be seen in the graph representing the change over time in plays written by women.

Next, let's look specifically at the distribution of the proportion of occurrences in plays written by men vs women (Figure 2.1) and the proportion of each specific different occurrence (Figure 2.2). Overall, between all of the stereotypes/characteristics the most common, between both men and women, were sexual mentions (16.7%), positive appearance (15%), wearing a dress/skirt (9.3%), motherly/caretaking (8.3%), mentions of marriage (6.1%), sexist sentences (5.4%), cleaning (5.4%), and neutral appearance (4.3%).

Total Occurrences Men vs. Women (Relative Frequency)

Figure 2.1: Total Occurrences Male Vs. Female Playwrights (Relative Frequency)

Total Occurrences Proportion (Relative Frequency)

Figure 2.2: Total Occurrences Proportion (Relative Frequency)

To wrap up some of the conversation about my data, there are a few more patterns that are worth mentioning:

On average the categories that were significantly more prominent in plays written by men were motherly/caretaking (68.4% of occurrences), cooking (66.7% of occurrences), mentions of marriage (64.3% of occurrences), positive appearance (55.1% of occurrences), sexual mentions (57.1% of occurrences), and mentions of aging (72.2% of occurrences).

On average, the categories that were significantly more prominent in plays written by women were wearing a dress/skirt (62.8% of occurrences), cleaning (56% of occurrences), neutral appearance (65% of occurrences), psychological mentions (57.9% of occurrences), sexist sentences (60% of occurrences), mentions of childbirth/fertility (62.5Z% of occurrences).

The categories that demonstrated the largest decrease of occurrences over time, when each decade was averaged together to represent total change for both plays written by men and women, were wearing a dress/skirt, motherly/caretaking, and negative appearance.

The categories that showed the greatest decrease of occurrences over time in plays written by men were limited to what was also seen in the average of all plays, being wearing a dress/skirt and motherly/caretaking.

The categories that showed the greatest decrease of occurrences over time in plays written by women were wearing a dress/skirt, motherly/caretaking, mentions of marriage, total appearance, positive appearance, negative appearance, negative appearance, and sexist sentences.

The categories that demonstrated the greatest increase of occurrences over time, when each decade was averaged together to represent total change for plays written by men and women, were total appearance, sexual appearance, and sexual mentions.

The categories that demonstrated the greatest increase of occurrences over time in plays written by men were total appearance, positive appearance, neutral appearance, sexual appearance, and sexist sentences.

The categories that demonstrated the greatest increase of occurrences over time in plays written by women were sexual appearances and sexual mentions.

Although many other categories changed over time, these are the ones that revealed the most defined pattern.

Discussion

Overall, the results of my study have many different layers and aspects that help to bring us closer to the answer to my research question. There are many different parts of my data including the change over time in total, the change over time from plays written by men and women especially, the many different categories of stereotypes/characteristics/instances, how those have changed over time, and how male and female playwrights utilized these things in their plays. Firstly, overall there has been a change over time in the way that plays have portrayed women. There seems to be a decrease in total instances of sexism or gender prejudices both overall and in plays written by men and women individually. However, the change in plays written by women is more significant. Additionally, some categories have witnessed an increase in frequency over time, partially categories that are related to something sexual or appearance. Nonetheless, regardless of where the most significant changes and differences were seen, it is equally important to look at where no change has been witnessed.

My research contributes to the larger academic conversation as Ira Horowitz says, sexism is a prolonged pattern in society that is sustained due to gender conditioning. What we see in everyday life affects the way that we look at ourselves based on our gender. Women are conditioned to believe that we are the victims of society, and men are conditioned to believe that they are the perpetrators. Horowitz explains how this conditioning comes from what we see in everyday life and how we see our mentors, teachers, and parents act. But there is another aspect that is a party of conditions and that is conditioning. From lying in bed while our parents read us a bedtime story, to reading a book for school, to watching a play in the theater, literature is a massive part of our lives from the moment we are born. However, while sexism in novels and plays by specific playwrights has been examined, there is a lack of a larger scope of research purely based on plays. My research helps to contribute to the scholarly conversation as it helped to discover what some of the worst and most prevalent types of sexism appear in the theatre, the difference between how men and women portray women, and whether or not as a whole, the literature in theatre has progressed and whether we are taking a step in a positive direction, which is it seems like we slowly are.

Conclusion

In a world where sexism and sexual prejudices have had a long-standing presence in society, it is important to look at where some of these ideas come from. Dudley Nichols explains how what we see in the theatre can directly impact how we live in how we live our day to day lives, by subconsciously affecting our stream of consciousness. Although the data that I have collected in the past couple of months does not necessarily prove that this is the cause of sexism in society, it does reveal that there are many struggles prominent in the literature of the theatre which may be affecting us, or perhaps representing us. Overall, plays have shown that the way we portray women in our writing has slowly become less and less centered around some of the typical stereotypes and interoperation of women. However, there are also many things that are important to take into account, as although overall there has been a decrease in total sexism in plays, specific areas in theatre have become more prominent in the past 100 years. Additionally, despite the change witnessed over time, there are some areas that plays have focused on in general that are more prominent. My initial research question was how has the portrayal of women by American male and female playwrights compared and changed between the 1920s to 2010s in dramatic plays? Based on my data, it is clear that overall there has been a decrease in the total amount of sexism in plays, however, this decrease is seen much more steadily and with an identifiable pattern in plays written by women compared to men. Additionally, there is a difference in how men and women portray women. Men seem to be overall more focused on the sexual and appearance aspects of women. There are also some areas where female playwrights portrayed women with more of a specific category, however, there is not a specific theme or pattern in these areas. Additionally, some areas have increased in occurrence over time, as well as areas that have remained relatively constant or have no discernible pattern. Either way, all of these things are important to look at. Increase, decrease, or no change at all, understanding how playwrights are portraying women is essential in understanding the sexism seen in the world today.

Roadblocks & Recovery

Through my data collection, I have reached two major roadblocks. The first was the acquiring of my materials. Initially, I randomly selected 22 plays to read and analyze. Within the collection of plays at my school, I was about to find seven of these plays but had to look for the rest online. Very quickly it came to my attention that I did not have access to some of the plays I had randomly selected. Because of this, I had to go back into my original spreadsheet, randomly select other plays, and attempt to find those online. Additionally, for some decades, there were not any more options on my original spreadsheet so I had to do further research on playwrights and plays. In the end, I had to completely eliminate the 1910s decade because there were no plays available to me that fit all the criteria. This caused a huge delay in my data collection. This led to my second roadblock, which was time. The time I ended up having for data collection was very limited. Ideally, I would have preferred to spend a bit more time on each play, and possibly reread them so I could make sure I captured everything, but time did not account for that. In order to complete my data collection, I spent large amounts of time in single days reading the plays, however, I still had to stretch into my time for data analysis. There were some other roadblocks already mentioned, such as not being able to code for the 5 stereotypes, and other categories, however, these two were the most significant. Here is a presentation from many weeks ago that represents how behind I was in my data collection (note: there is nothing in it about my data).

https://docs.google.com/presentation/d/1vDLrT9ZvmuDc6Hkto9OUQQS5bhI4PttamYJom_04dqk/edit?usp=sharing

Limitations

There are some limitations in my research and my results. First of all, because of time, I was not able to read that large of a selection of plays. Typically for statistical tests and studies, the ideal sample size is 30 or larger. However, because of time, I was only able to read 20 plays. Additionally, there are some differences between these plays that could have affected my research. The first is that although all of these plays were multi-act, the length of the plays differed greatly, which may have affected my research since I am looking at quantitative values. Additionally, the number of female characters in these plays also differed greatly for some plays. In one play, “Top Dog/Underdog” there were no female characters. This has an impact on data because characteristics and actions such as cooking, cleaning, wearing a dress/skirt, and many more could not be accounted for, since the subject had to be a woman. However, there were still some categories that could be analyzed such as sexual mentions, mentions of marriage, and more as conversations were still held about women. Additionally, there were a few outliers in the plays, the largest of which was “Reasons to be Pretty” by Neil LaBute. The subject of this play is focused on the idea that women have to be pretty and how men sexualize women, so the occurrences of many categories were way above the average. However, I could not account for this in my research, as that would create bias. In my conclusion and discussion, all of these things will be taken into account.

Here is a link to my final presentation that also cites all sources used:

https://docs.google.com/presentation/d/1l885PxicEK_biEc7ZkETA1LSx1Wkqz69UVTl4lhcTgI/edit?usp=sharing