Game Related Spending and Age
I recently conducted a brief study that had the purpose of determining if the age of a video game player has any bearing on the amount of money they spend a year on video games. The population I used for this study is all video game players between the ages of 12 and 45, I divided this population into four segments: Ages 12-16, Ages 17- 21, Ages 22-29, and Ages 30-45. I conducted surveys to determine an average monthly spending on video games.
In this project I was seeking to determine if the age of a video game player has any bearing on the amount of money they spend on video games and gaming equipment. I had gamers fill out surveys online, and I then collected the data for my project and compiled it into a Microsoft Excel file, which I am attaching. I broke the results into the four bins of ages mentioned; I also included a bin for all of the data. I also included a distribution line graph on the Excel spreadsheet. In looking over the data collected I noticed a few interesting things. The mean amount of money spent on video games goes up as the respondent’s age increased, but the percentage of income spent on video games goes down as the respondents got older.
The Excel spreadsheet also includes a scatter gram. The two variables I used for my scatter gram are the major ones I am focusing on for my experiment. These are age and percentage of salary spent on gaming. These variables were the overall purpose of this study. I have drawn in the best-fit line for my scatter gram. This line lies the closest to the most points possible. This line allows me to make educated estimates at to where another point may fall on the scatter gram if I were to add respondents. The correlation coefficient for these two variables is -.440, with 70 samples taken. There is a weak negative correlation between these two factors. This is about what I expected based on some of my preliminary conversations with other game players. I calculated the 95% confidence interval for the group data in whole (for the mean percentage of income spent) and it ranges from 3.325 to 4.075. One interesting fact about this is the only bin of data that has a group mean within that range is the 22-25 year olds.
    In conclusion my study is fairly close to what I would have expected. If you look at sheer money the amount goes up as the gamers get older, but the players are also making more money as they get older as well. The mean percentage of income spent goes down as the players get older. In the two youngest bins there is not a very significant change (only .1%) but the two older bins have a large difference (1.6%).
Research Study