Title: Binge Watching Boom or Bane<\/p>\n
Name of Researchers:
\nArjya Chandra – 80012100761
\nDeepansh Krishna – 80012100942
\nDivya Jhunjhunwala – 80012100714
\nSajal Maheshwari \u2013 80012100781<\/p>\n
Introduction:
\nWith 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.
\nOur research revolves around understanding how binge-watching affects the health of students.<\/p>\n
Objectives:
\nMain 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?
\nHypothesis for all 5 statements of our problem can be described as below:
\nStatement 1:
\nH0: People may or may not have headache due to increased screen time
\nHa: People do have headaches due to increased screen time
\nHa: People do not have headaches due to increased screen time
\nStatement 2:
\nH0: People may or may not eat a lot while binge watching
\nHa: People eat a lot during binge watching
\nHa: People do not eat a lot during binge watching<\/p>\n
Statement 3:
\nH0: People might or might not have gained weight because of binge watching
\nHa: People have gained weight because of binge watching
\nHa: People have not gained weight because of binge watching
\nStatement 4:
\nH0: People may or may not sleep less due to binge watching
\nHa: People sleep less due to binge watching
\nHa: People do not sleep less due to binge watching
\nStatement 5:
\nH0: People may or may not prefer binge watching over physical exercise
\nHa: People prefer binge watching over physical exercise
\nHa: People do not prefer binge watching over physical exercise
\nNow 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.<\/p>\n
Data Collection:
\nTo 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:
\n\u2022 Strongly Agree
\n\u2022 Agree
\n\u2022 Neutral
\n\u2022 Disagree
\n\u2022 Strongly Disagree
\nThe statements included in the survey are as follows:
\nMy head hurts because of increased screen time.
\nI eat a lot while binge-watching.
\nI have gained weight because of binge-watching.
\nI sleep less because of binge-watching.
\nI prefer binge-watching over physical exercise.<\/p>\n
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.<\/p>\n
Data Analysis:
\nWe downloaded the Excel file, containing responses from the Google form and we further analyze the available data using excel.
\nStep 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.
\nStep 2: In the next step, we assign certain weightage to the 5 responses of scale (strongly agree, agree\u2026). 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.
\nStep 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.
\nExample of calculation for statement 1:
\nMean response of statement 1= (25*5+55*4+12*3+7*2+1*1)\/100=3.96
\nNow we calculate weighted mean of all other statements, following the same formula.<\/p>\n
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.
\nExample of calculation for statement 1:
\nMean 2= (25*25+55*16+12*9+7*4+1*1)\/100=16.42
\nIn the same way we calculate mean 2 for all other statements.
\nStep 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\u2026
\nFollowing the same procedure, we calculated standard deviation for all the statements.
\nStep 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\u2026\/sqrt (100) = 0.08593\u2026 and so on.
\nStep 7: Last but not the least, we calculate the z scores for each statements using the parameters calculated before.
\nWe know that, Z=(X-\u00b5)\/S.E. Here we take the mean of respective statements as X and mean of the Likert scale weightage as \u00b5. Here \u00b5 = (1+2+3+4+5)\/3=3.
\nSo, Z score of first statement= (3.96-3)\/0.08593\u2026=11.1719
\nIn the same way, we found out Z scores for all the 5 statements.
\nNow 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.
\nTo 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.
\nTo analyze the data with Z score, we define some conditions for positively agreeing\/denying the statements.
\nWe define the same as:
\n\u2022 If Z > 1.96, accept positively
\n\u2022 If Z is between 1.96 and -1.96, people are neutral
\n\u2022 If Z < \u2013 1.96 accept negatively<\/p>\n
\u2022 For statement 1, Z=11.1719; which is greater than 1.96, so we can deduce people accept the statement 1 positively.
\n\u2022 For statement 2, Z=4.364358; which is greater than 1.96, so we can deduce people accept the statement 2 positively.
\n\u2022 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.
\n\u2022 For statement 4, Z=6.893346; which is greater than 1.96, so we can deduce people accept the statement 4 positively.
\n\u2022 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.
\nHowever, 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:<\/p>\n
Findings:
\n\u2022 For statement 1, Z=11.1719; people accept the statement 1 positively.
\nAs we can see that people are not neutral about statement 1, we reject H0 and we fail to reject Ha.
\nPeople\u2019s head hurt because of increased screen time.
\nFor 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.
\nPeople eat a lot while binge-watching.
\n\u2022 For statement 3, Z=0.088048503; people are neutral about statement 3.
\nTherefore, we accept H0.
\nPeople might or might not have gained weight because of binge-watching.<\/p>\n
\u2022 For statement 4, Z=6.893346; people accept the statement 4 positively.
\nAs we can see that people are not neutral about statement 4, we reject H0 and we fail to reject Ha.
\nPeople sleep less because of binge-watching.
\n\u2022 For statements 5, Z =1.5242; people are neutral about statement 5.
\nTherefore, we accept H0,
\nPeople may or may not prefer binge-watching over physical exercise.<\/p>\n","protected":false},"excerpt":{"rendered":"
Title: Binge Watching Boom or Bane Name of Researchers: Arjya Chandra – 80012100761 Deepansh Krishna – 80012100942 Divya Jhunjhunwala – 80012100714 Sajal Maheshwari \u2013 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… Continue reading Binge Watching Boom or Bane — Group 2, Division B(MBA, NMIMS Navi Mumbai)<\/span><\/a><\/p>\n","protected":false},"author":91463,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19,4],"tags":[],"class_list":["post-15838","post","type-post","status-publish","format-standard","hentry","category-health-fitness","category-movies-tv","entry"],"_links":{"self":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/15838","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/users\/91463"}],"replies":[{"embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=15838"}],"version-history":[{"count":1,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/15838\/revisions"}],"predecessor-version":[{"id":15912,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/15838\/revisions\/15912"}],"wp:attachment":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15838"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15838"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15838"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}