Title: Binge Watching Boom or Bane
Name of Researchers:
Arjya Chandra – 80012100761
Deepansh Krishna – 80012100942
Divya Jhunjhunwala – 80012100714
Sajal Maheshwari – 80012100781
Introduction:
With easy access to the internet and cheap subscriptions to online streaming applications. and prevailing covid situation when everyone is stuck at home. there had been an increase of watch hours which had drastically increased the after effect of it to everyone stuck with it.
Our research revolves around understanding how binge-watching affects the health of students.
Objectives:
Main objective of this particular study is to determine whether binge watching (for many hours) is affecting the well-being of students or not. Starting from sleeping schedule to eating habits, how are these factors being affected by the habit of binge watching?
Hypothesis for all 5 statements of our problem can be described as below:
Statement 1:
H0: People may or may not have headache due to increased screen time
Ha: People do have headaches due to increased screen time
Ha: People do not have headaches due to increased screen time
Statement 2:
H0: People may or may not eat a lot while binge watching
Ha: People eat a lot during binge watching
Ha: People do not eat a lot during binge watching
Statement 3:
H0: People might or might not have gained weight because of binge watching
Ha: People have gained weight because of binge watching
Ha: People have not gained weight because of binge watching
Statement 4:
H0: People may or may not sleep less due to binge watching
Ha: People sleep less due to binge watching
Ha: People do not sleep less due to binge watching
Statement 5:
H0: People may or may not prefer binge watching over physical exercise
Ha: People prefer binge watching over physical exercise
Ha: People do not prefer binge watching over physical exercise
Now we analyze the mean responses and Z-scores of the statements we designed, based on which we will either accept or reject the mentioned hypothesis.
Data Collection:
To find out the answer to our problem statement, we designed a questionnaire, following the Likert scale model. Where the students had five different options for each question:
• Strongly Agree
• Agree
• Neutral
• Disagree
• Strongly Disagree
The statements included in the survey are as follows:
My head hurts because of increased screen time.
I eat a lot while binge-watching.
I have gained weight because of binge-watching.
I sleep less because of binge-watching.
I prefer binge-watching over physical exercise.
Our main objective here is to analyze the available data and to determine whether there is a trend among the given options. We collected a total of 100 responses from the survey and we further analyzed the available data to conclude the survey. Our goal is to find out whether majority of the people are agreeing with the statements or not.
Data Analysis:
We downloaded the Excel file, containing responses from the Google form and we further analyze the available data using excel.
Step 1: We calculate the statement wise response of specific scales. For example, for statement 1, we calculate the number of people responding strongly agree, agree, neutral and so on. We do the same for all the 5 statements.
Step 2: In the next step, we assign certain weightage to the 5 responses of scale (strongly agree, agree…). In this case, we assigned the weight of 5 to strongly agree, 4 to agree, 3 to neutral, 2 to disagree and 1 to strongly disagree.
Step 3: In this step, we calculate the mean of responses of each statement. To calculate the same, we multiply the weightage of a response to the number of people selecting that response, e.g., in statement 1, 25 people selected strongly agree and the weightage assigned to it is 5, so we multiply 25 with 5 and do the same for all other responses (multiply 4 with 55 and so on) and add the available values. Then we divide the derived number by total number of responses to obtain the mean for that particular statement.
Example of calculation for statement 1:
Mean response of statement 1= (25*5+55*4+12*3+7*2+1*1)/100=3.96
Now we calculate weighted mean of all other statements, following the same formula.
Step 4: In this step we calculate the mean 2. We square the weightage of the responses and follow the same procedure as the previous step.
Example of calculation for statement 1:
Mean 2= (25*25+55*16+12*9+7*4+1*1)/100=16.42
In the same way we calculate mean 2 for all other statements.
Step 5: In this step we find out the standard deviation of our responses. To find out the same, we calculate the square root of (mean 2-mean^2), e.g., for statement 1, standard deviation=sqrt (16.42-3.96*3.96) =0.8593…
Following the same procedure, we calculated standard deviation for all the statements.
Step 6: After calculating the standard deviation, we have to find out the standard error (S.E.) of the responses. To calculate standard error, we divide standard deviation by the square root of number of responses, e.g., for statement 1, standard error=0.8593…/sqrt (100) = 0.08593… and so on.
Step 7: Last but not the least, we calculate the z scores for each statements using the parameters calculated before.
We know that, Z=(X-µ)/S.E. Here we take the mean of respective statements as X and mean of the Likert scale weightage as µ. Here µ = (1+2+3+4+5)/3=3.
So, Z score of first statement= (3.96-3)/0.08593…=11.1719
In the same way, we found out Z scores for all the 5 statements.
Now that we are done with all the calculations, we have to analyze the available data. We can do the same from 2 different point of views, Mean of each statement and Z score of each statement.
To analyze the data using mean, we compare the respective means of statements with the Likert scale weightage. For example, mean for the first statement is 3.96. If we compare that with the weightage of Likert scale, it turns out that the mean falls between Neutral and Agree. But it exceeds 3.5, so we can deduce that majority of the people are inclined towards agreeing with Statement 1. We can analyze all other statements in the same manner.
To analyze the data with Z score, we define some conditions for positively agreeing/denying the statements.
We define the same as:
• If Z > 1.96, accept positively
• If Z is between 1.96 and -1.96, people are neutral
• If Z < – 1.96 accept negatively
• For statement 1, Z=11.1719; which is greater than 1.96, so we can deduce people accept the statement 1 positively.
• For statement 2, Z=4.364358; which is greater than 1.96, so we can deduce people accept the statement 2 positively.
• For statement 3, Z=0.088048503; which falls under the range of -1.96 to 1.96, so we can say people are neutral about statement 3.
• For statement 4, Z=6.893346; which is greater than 1.96, so we can deduce people accept the statement 4 positively.
• For statements 5, Z =1.5242; which falls under the range of -1.96 to 1.96, so we can say people are neutral about statement 5.
However, Z score analysis is more accurate as compared to analysing the statements with respect to means. If we compare the results of our analysis with simple numerical breakdown of data, we can say that they are aligned in the same direction:
Findings:
• For statement 1, Z=11.1719; people accept the statement 1 positively.
As we can see that people are not neutral about statement 1, we reject H0 and we fail to reject Ha.
People’s head hurt because of increased screen time.
For statement 2, Z=4.364358; people accept the statement 2 positively. As we can see that people are not neutral about statement 2, we reject H0 and we fail to reject Ha.
People eat a lot while binge-watching.
• For statement 3, Z=0.088048503; people are neutral about statement 3.
Therefore, we accept H0.
People might or might not have gained weight because of binge-watching.
• For statement 4, Z=6.893346; people accept the statement 4 positively.
As we can see that people are not neutral about statement 4, we reject H0 and we fail to reject Ha.
People sleep less because of binge-watching.
• For statements 5, Z =1.5242; people are neutral about statement 5.
Therefore, we accept H0,
People may or may not prefer binge-watching over physical exercise.