Understanding Social Media Effects Across Age Groups

Method of Data Collection

Discuss About The Understanding Social Media Effects Across?

The social media is taking the place of traditional media. Astonishingly, people are being more willing to spend time in social media than traditional media. However, as digital media consumption continues to increase, platform of social media also develops. Different types of gadgets such as Smart phones, mobiles, iPod/iPad, Laptop and PC are getting easily available in market. Technological facilities are also getting cheap and within the range of common people.

Social media networking sites has attracted many people for sharing post, images, video conferencing and all other things. A predicted percentage of active social media users in case of the global point of view are 28% (Asur and Huberman, 2010). Although people have hectic schedule, they are still spending a trivial amount of time on the sites of social media. People are getting more advantages to express their thinking in the platform of social media by attracting and engaging new audiences. The initiation of live-streaming features and 360-degree photos or videos is creating new platforms together with Snapchat, Instagram and twitter (Tuten and Solomon 2017).

The quantity of time people squandered on social media is continuously enhancing. Teenagers are also getting attracted to use social media and websites such as Facebook, twitter, Google buzz, Flickr etc. People are being habituated to spend their time online with the interaction of social media. From eight to eighty, corporate to senior citizen, everybody is indulged in social media in current days (Rapp et al. 2013). The main reason is that it is difficult to deny social media and its scopes that permit a number of people across the globe. The different age groups of students spend their valuable time to connect with known or unknown people rather than paying time for playing or studying. The different age group majorly utilises the social media sites for enhancing their circle of network and keep themselves updated with the current occurrences of associated interests (Saravanakumar and SuganthaLakshmi 2012).

To provide readers an improved consideration of the landscape of social media, we are surveying age wise time tired across the platforms of most popular social medias anticipated within the lifetime of a consumer.  Surprisingly, total time spent on social media is greater than time spent in eating, drinking, studying, socializing and grooming (Kaplan and Haenlein 2010). Though surfeit of anything is harmful, an intemperance use of social media has also bought many fresh and unique problems. This problem is known as addiction. In recent days, people are paying deficit time on social media websites accounting 28% percent of online time spent.  

Summary of the Data Set

One of the primary methods collects the data used for research study and therefore the data is primary data. The data is collected by questionnaire method. It is a very easy, cheap and less time consuming method (Warwick and Lininger 1975). First, a questionnaire of 10 questions is formed and possible answers are provided with each question to choose. Hence, all the questions are close-ended questions. Secondly, the questionnaire is circulated via e-mail and 20 complete samples were granting. No missing valued sample was permitted. The data is authentic and true. Not sampling bias is present while collecting the dataset.

The data set have 20 samples with 10 variables. No missing data is present in the dataset. The responses of the dataset are gathered from questions of the questionnaire. The questions and responses are collected as per the following table:

According to the responses of 10 variables, it is observed that “age” and “Average number of friends & followers in social networking sites” are numerical variables. Rest of all are categorical variables. Some of them are nominal (example-gender) and some are ordinal (example-no. of social networking sites are you member of).

The descriptive statistics of both the quantitative variables are measured. Gender wise equality of means is tested by two-sample z-test method. A simple linear regression model is structured assuming “age” independent variable and “Average number of friends & followers in social networking sites” dependent variable to find their linear relationship.

With the help of qualitative variables, necessary graphical summaries and frequency distributions are executed.

The mean value of ages of social network users is 26.5 years. The mean value of average number of friends and followers in social networking sites is 317.75. The standard deviations of these two variables are respectively 5.236 years and 351.884 years. The variance of age is 27.421 and variance of “average number of friends and followers in social networking sites” is 123822.0921 (Oja 1983). The minimum and maximum values of age of social network sites users are 19 and 37. The median age is calculated as 26.5. The minimum and maximum values of average number of friends and followers in social networking sites are 21 and 1270 with a median value 214.5.

The dependent variable is taken as average number of friends & followers in social networking sites and independent variable is the age of the users. A scatter plot is created according to that.

Quantitative Data Analysis

Now, we would like to find the difference of means of ages for male and female social networking sites users. The calculated z-statistic is 0.958687588. The p-value of two-tail z-statistic is 0.337716153. Therefore, we accept the null hypothesis of equality of averages of ages of both the genders at 95% confidence interval.

The pie chart indicates that number of social networking sites is highest for 3-4 and lowest for 8-10. The line diagram infers that maximum people (7) are using social networking sites for more than one year. Minimum number of people (3) is using social networking sites for 1-6 months. The main reasons behind the use of social networking site are chatting (4) and sharing the experience (4). The minor reasons behind social networking site are the use of on-line buying (1) and play games (1). Most of the people (6) spend time daily on social networking sites for 1 to 3 hours per day. Minimum number of people (4) spends time daily on social networking sites for less than 1 hour per day.

The preferable social media according to survey is found to be Facebook (7) followed by twitter (4). 65% people consider that their use of social sites has increased in last three months and only 35% people incur that consideration. Among 20 people, 6 people consider that they mostly share pictures followed by videos (5 people). Only 1 people uses social network for contacts sharing.       

The linear regression model is given as-

y = β₀ + β₁ * x.

(Zou, Tuncali and Silverman 2003)

Dependent variable (y): average number of friends & followers in social networking sites

Independent variable (x): age of the users

(Montgomery, Peck and Vining 2012)

The intercept is β₀ = 559.2255278 and slope is β₁ = (-9.112284069).

The linear regression model is found to be-

“Average number of friends & followers in social networking sites” = 559.2255278 – 9.112284069* “age”.

The correlation between independent and dependent variables is 0.135603295, which refers an insignificant correlation between them.

The multiple R-square is found to be 0.018388254 (1.8%). It refers an insignificant linear association between dependent and independent variable. The p-value is 0.568655989(>0.05). It helps us to reject the null hypothesis of linear correlation coefficient between these two variables.

Conclusion:

The influence of social media addiction is affecting our life style. The impact of social media makes our life easy and fulfilled. Independent of age, people are losing their relationship, sufficient sleep and time for work purpose due to addiction of heavy social media. A huge amount of time, people are wasting behind social media necessarily or unnecessarily. The analysis of sampled data reflects that every day from “less than 1 hour” to “more than 6 hours”, all types of time spending on social networking were significantly observed in this case.

References:

Asur, S. and Huberman, B.A., 2010, August. Predicting the future with social media. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01 (pp. 492-499). IEEE Computer Society.

Kaplan, A.M. and Haenlein, M., 2010. Users of the world, unite! The challenges and opportunities of Social Media. Business horizons, 53(1), pp.59-68.

Montgomery, D.C., Peck, E.A. and Vining, G.G., 2012. Introduction to linear regression analysis (Vol. 821). John Wiley & Sons.

Oja, H., 1983. Descriptive statistics for multivariate distributions. Statistics & Probability Letters, 1(6), pp.327-332.

Rapp, A., Beitelspacher, L.S., Grewal, D. and Hughes, D.E., 2013. Understanding social media effects across seller, retailer, and consumer interactions. Journal of the Academy of Marketing Science, 41(5), pp.547-566.

Saravanakumar, M. and SuganthaLakshmi, T., 2012. Social media marketing. Life Science Journal, 9(4), pp.4444-4451.

Tuten, T.L. and Solomon, M.R., 2017. Social media marketing. Sage.

Warwick, D.P. and Lininger, C.A., 1975. The sample survey: Theory and practice. McGraw-Hill.

Zou, K.H., Tuncali, K. and Silverman, S.G., 2003. Correlation and simple linear regression. Radiology, 227(3), pp.617-628.

Order an Essay Now & Get These Features For Free:

Turnitin Report

Formatting

Title Page

Citation

Outline

Place an Order