An article by the NPD Group, a leading market research company, states that today, 91 percent of kids in the United States aged 2-17 play video games. This is an increase of 9 percentage points when compared to their 2009 survey results. Is this a statistically significant increase?
I did some research and was able to find the methodology used for NPD’s results. 2011’s survey utilized 4,136 individuals ages 2-17, whereas 2009’s results cited "over 5,000 members." Hmm. Well, I guess I have to assume that it’s a number close to 5,000, so we’ll round down to 5,000. Because the articles provide the total participants and the end percentages, I needed to back-calculate to get the numerator of this fraction. The results are displayed below:
With this information in hand, I was ready to see if this represented a statistically significant increase. In Minitab, I chose Stat->Basic Statistics->2 Proportions. I filled out the dialog as shown below:
I got the following results:
Sample X N Sample p
1 4100 5000 0.820000
2 3764 4136 0.910058
Difference = p (1) - p (2)
Estimate for difference: -0.0900580
95% CI for difference: (-0.103821, -0.0762949)
Test for difference = 0 (vs not = 0): Z = -12.82 P-Value = 0.000
Fisher's exact test: P-Value = 0.000
Because our P-value is below our alpha (set at 0.05), we reject the null hypothesis that both proportions are equal. In other words, there is a statistically significant difference in the results of this year's survey.
Let’s say that another study is done a couple of years down the road. What proportion would you need to achieve to call it statistically significant against a baseline proportion of 0.91? With those 2011 survey statistics in mind, let’s fill out the dialog box for Power and Sample Size for 2 Proportions. I am assuming a sample size of 4136 again and a power of 0.95 (It's probably unrealistic that they’d use precisely the same sample size, but let’s assume for the sake of argument, anyway.)
I got the following results:
Test for Two Proportions
Testing comparison p = baseline p (versus not =)
Calculating power for baseline p = 0.91
Alpha = 0.05
Size Power Comparison p
4136 0.95 0.931411
4136 0.95 0.886019
I found this statement from the 2009 survey results very interesting:
What happened in 2 years? The article talks about how mobile devices may have had a stake in this, due to the sheer availability in both smartphones and content (apps). Most notable in this 2011 survey is the percentage increase of kids gaming from ages 2-5: 12.68 percent. This does make some sense to me though. More smart phones = more video games to be played on smart phones. However, these games aren’t as complex as the ones you find on a console system like a Nintendo or Sony PlayStation. These games might be at the level which a 2 to 5 year old child can comprehend, thus the spike in video game use for that age group. This is all speculation, however.
Whether you're a gamer or not, I hope that you enjoyed this article!