Welcome to my third blog post! While my research and methods have not changed since my last post, I have some exciting updates to share! 

A recap from my last blog post: 

My research question asks: How does the protagonist's portrayal of healthy vs. unhealthy coping mechanisms correlate to prevalent themes within teen films over time?

My hypothesis answers this question directly: Due to increased awareness and conversation regarding teen struggles and mental health concerns, the evolution of unhealthy coping strategies has become increasingly prevalent over time. 

Throughout my research process, I coded 14 popular teen films based on the occurrence of healthy vs. unhealthy coping mechanisms. I kept a handwritten journal of my data collection, and later organized my findings onto a google doc, and created multiple graphs to represent my final results! The google docs are linked in my second blog post, and the graphs/charts are posted and explained below. 

Results : 

The three graphs above are just a few examples of my findings! Going from left to right, the first graph represents the amount of healthy vs. unhealthy coping mechanisms I observed throughout the every film. The release date of each of the films are listed on the x-axis, increasing chronologically until present day. Each film has it's own column pair; the green column is the amount of healthy coping mechanisms, and the red is the amount of unhealthy coping mechanisms that were seen throughout the film. The next two graphs are a breakdown of the firsts graph; I chose to separate the data so it was easier to observe an increase of coping mechanisms over time. However, there is no observable difference between the occurrence of either healthy or unhealthy coping strategies; while there is a spike in healthy coping mechanisms in 1960, and a spike in unhealthy coping mechanisms in 2012, these graphs do not portray a steady increase of decrease over time. 

The 10 graphs above represent the amount of each coping mechanism observed within each theme! The details regarding the collection of the themes are described in my previous blog post, but you are also able to see each of the themes listed out in the title of each graph. The coping mechanisms are also listed out on the x-axis; you can see the specific list in my first blog post! The graphs are split down the middle; the left side of the graph has the healthy coping mechanisms listed, and the right side has the unhealthy listed. Every single time a coping strategy was observed from the main character, it was correlated to a theme and then quantified accordingly. Anytime there is a blank spot on the graph simply means that there was no occurrence of that specific coping mechanism within that theme. Coming of Age, for example, was the theme that had the most coping mechanisms correlated to it. Coming of Age is defined as an event that evokes a greater sense of maturity from any given person, and includes things such as graduation, parenthood, and death, etc (Webster). Each time a character was observed coping with graduating high school, for example, the theme would be categorized as coming of age, and the coping mechanisms would be added to the graph any time they were present within that theme.

To test if my data was statistically significant, I ran my results through a Sample Z test, which is known as the statistical test that specifically evaluates the significance of one’s hypothesis to their data (Chen). Through this test, I had to solve for a p-value, otherwise known as a probability value, which shows you how likely your data would have occurred under your null hypothesis (Bevans). In this case, my null hypothesis states that there is no difference between the fluctuation of healthy and unhealthy coping mechanisms over time. Contradicting this, my alternative hypothesis states that there is a noticeable difference between the fluctuation of healthy and unhealthy coping. You are trying to reject your null hypothesis to show the significance of your data findings. The baseline for a statistically significant p-value is less than 0.05; the lower the p-value the more accurate your data is (Legeforen). However, after plugging my results regarding the number healthy vs. unhealthy coping incidences into the test, my p-value came out to 0.248 (or 25%), meaning that 25% of my data was likely to have happened by chance and not by real occurrences. My data supports my null hypothesis by failing to reject it, showing that my data is not statistically significant as it does not give convincing data to show a proper evolution between healthy vs. unhealthy coping mechanisms. 

Limitations/Discussion 

My results were able to answer my research question : Coping mechanisms correlate greatly to prevalent themes in teen films, relating most to Coming of Age, Love, Conflict with Parents, and Angst. 

While my results answer my research question, my results do not support my initial hypothesis. While my initial statement suggests a steady increase of unhealthy coping mechanisms over time, my data does not reflect this claim as it does not represent a steady increase or decrease over time. While I was able to collect and reflect cohesive data points, there were a few limitations that potentially inhibited my research findings. 

The main limitations for my project included : 

- Limited sample size

- Time constraints

- On the Loose (1951)

- 2020's decade

- Movies with 2 main protagonists

- No coping mechanisms in 1966

- Potential Bias

My limited sample size was most likely the reason as to why my data was statistically insignificant; while there is no guarantee that this would have altered my results in any way, increasing my movie inventory per decade would have potentially given me a smaller margin of error, encouraging more accurate results. Time constraints were another limitation regarding this project; while I wouldn’t have been able to watch multiple movies from each year of each decade, this was most likely the main reason as to why my data was insignificant. 

Additionally, I had to adapt my research when I was forced to eliminate a movie from the 1950's decade. The movie that had originally fit my movie selection criteria for the first five years of the 1950’s was On the Loose, released in 1951. However, this movie was so obscure that after looking through an absurd amount of streaming services, public libraries, and databases, the film was unable to be found anywhere. Since the 1950’s was such a pivotal decade for the teen film genre, I knew needed to include it in my research, and decided to move on without the film, continuing on with one movie released within the last 5 years of the decade. 

The 2020’s decade presented a similar challenge; obviously we are only four years in to the 2020’s decade (2020, 2021, 2022, 2023), so I was only able to choose one movie. However, it was important to include as my research was observing an increase in coping mechanisms as close to present day as possible, where cutting out the 2020’s decade would have eliminated the past four years of film. My next research limitation was my movies with two main protagonists. I had two movies where this instance occurred: Blue Denim (1959), and Too Soon to Love (1960). Everywhere I looked the synopses said that there were two main characters in the films. While I continued with these movies, this altered the amount of coping mechanisms I was collecting, based on the fact I was observing double the data since I was looking at two characters as opposed to one. Another limitation was the film, Lord Love a Duck, released in 1966. The main character had no observable coping mechanisms throughout the entirety of this film, even after watching it twice. I decided it would be worse to collect no data from the last five years of this decade, so I watched this movie a third time and collected data based on the secondary protagonist, and that was the data reflected for 1960. 

My last project limitation was any potential bias. While I tried to eliminate as much bias as possible by randomizing my movies, obtaining a specific list of coping mechanisms, looking at the methodologies of other studies, and more, I was the only person who was watching these films, and was also the only person categorizing coping mechanisms, themes, categorizing data, etc, and may have been slightly different had another researcher been involved or conducting similar methods. 

Conclusion/Implications : 

The portrayal of healthy vs. unhealthy coping mechanisms in film are extremely important elements to consider. The representation of these ideals have been shown to have both positive and negative effects; while it can increase awareness, conversation, and relatability among viewers (Wolpert), it can adversely foster inaccurate representations of mental illness, ways of coping, and can “ultimately influence” the audience by “promoting bad behaviors” (Katz). Teen Futures Media Network, an organization brought about by the Washington State Department of Health, states that“[t]he average teenager spends more time in front of the television than any other activity besides sleeping.” They also disclose that younger adolescents tend to spend approximately 20% of their “waking hours” watching television (Teen). In my previous blog posts, I mentioned a study conducted by the American Academy of Child and Adolescent Psychiatry. They recorded a 28.9% increase in suicide rates among ages 10-17 in the months following the release of Netflix's 13 Reasons Why. Suicide is considered to be a way of coping, categorized as self-harm, in which it has been proven that teenagers are considerably vulnerable when exposed to explicit graphics or representations in mainstream media (Bridge). For this reason, it is imperative that films represent and include portrayals of healthy coping strategies, so teens are able to observe healthy ways of handling their life stressors, rather than turning to dangerous, harmful, or otherwise life threatening pursuits.

Reflection

1. If you could revisit your research process, what would you do differently and why?

2. What was the most important research skill you developed as a result of this process, and how might you apply it to your future endeavors?

1. If I could revisit my research process, I would absolutely consider limiting my films to a particular streaming service, similar to how Savannah Marie Carter did in her research : Portrayals of Mental Illness of Teens in Popular TV Shows : 13 Reasons Why and Atypical. Locating the films were the biggest challenge I encountered throughout the entirety of my research process, and I believe limiting the movies to a particular streaming service or database would help greatly with locating the films. I would also reform my movie selection criteria to only allow one main protagonist per film; as previously mentioned, I had two instances where there were two main protagonists, which in turn affected the amount of data reflected based on the fact that I was observing double the characters. 

2. The most important research skill I learned throughout this process is asking questions! My counselor (Mrs. Metzger) and Mrs. Dobos both helped greatly in the development in my research. Their guidance was extremely valuable, as they are both knowledgable in my field of study and have conducted previous research projects. Being able to utilize their skills and knowledge were extremely beneficial to me and my project, where being able to ask questions will also prove extremely valuable in my future endeavors. By inquiring and receiving clarification not only allows you to have consistent communication between you and your advisors, but it improves the strength of your research, and increases your overall knowledge as a student. 

Thank you all for tuning in to Blog Post #3! I will see you in the next one! :)

Sources : 

• Bevans, Rebecca. “Understanding P-Values | Definition and Examples.” Scribbr, July 16, 2020. https://www.scribbr.com/statistics/p-value/#:~:text=The%20p%20value%2C%20or%20probability,statistical%20test%20using%20your%20data. 

• Bridge, Jeffrey A, Joel B Greenhouse, Donna Ruch, Lisa M Horowitz, Kelly J Kelleher, John V Campo, John Ackerman, Jack Stevens, and Arielle H Sheftall. “Association Between the Release of Netflix’s 13 Reasons Why and Suicide Rates in the United States: An Interrupted Time Series Analysis.” American Academy of Child and Adolescent Psychiatry, April 28, 2019. https://www.jaacap.org/article/S0890-8567(19)30288-6/fulltext. 

• Carter, Savannah Marie. “Portrayals of Mental Illness of Teens in Popular TV Shows : 13 Reasons Why and Atypical.” The University of Arizona University Libraries . UA Campus Repository , May 2020. https://repository.arizona.edu/bitstream/handle/10150/650934/azu_etd_hr_2020_0032_sip1_m.pdf?sequence=1. 

• Chen, James. “Z-Test Definition: Its Uses in Statistics Simply Explained With Example.” Investopedia, September 5, 2022. https://www.investopedia.com/terms/z/z-test.asp#:~:text=A%20z%2Dtest%20is%20used,30%20data%20points%20or%20larger. 

• Legeforen, Tidsskr Nor. “Why the P-Value Is Significant.” Tidsskrift, September 8, 2015. https://tidsskriftet.no/en/2015/09/why-p-value-significant-0#:~:text=A%20low%20p%2Dvalue%20shows,the%20null%20hypothesis%20is%20true. 

• Katz, Maxine. “The Impact of Teen Drama Shows.” Herderzeitung, December 12, 2019. https://herderzeitung.de/blog/the-impact-of-teen-drama-shows/. 

• Merriam Webster Dictionary. “Coming-of-Age Definition & Meaning.” Merriam-Webster Dictionary, March 8, 2023. https://www.merriam-webster.com/dictionary/coming-of-age. 

• Teen Futures, Media Network. “Fast Facts.” Teen Health and the Media, 2022. https://depts.washington.edu/thmedia/view.cgi?section=medialiteracy&page=fastfacts. 

• Wolpert, Stuart. “Can TV Shows Help with Teen Mental Health?” University of California, May 27, 2021. https://www.universityofcalifornia.edu/news/can-tv-shows-help-teen-mental-health. 

Image Links : 

https://www.imdb.com/title/tt0043881/ 

https://www.imdb.com/title/tt0052639/ 

https://www.imdb.com/title/tt0054392/ 

https://www.imdb.com/title/tt0060636/