University of Florida The Impact of Social Networking Sites on The Youth Paper

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The basic idea of the project is to find a study “in the wild” and evaluate it according to the appropriate six step program (theoretical, statistical, or causal). Decision problems are excluded because they are generally too complex to evaluate without a lot of manipulation and preparation of the information you can easily find in the wild in popular media reports.

The report can be done in either of two possible formats. Which you choose should be clearly stated in your report.

  1. an instructive example properly worked out in detail like the examples in the textbook chapters that teach readers how to understand that example as a particular kind of study (theoretical, statistical, causal) and how to evaluate it.
  2. a “homework” problem to be solved by the reader, like the homework problems at the ends of the textbook chapters. In this style, the report would be a briefly summarized “episode” (like the homework problems), followed by a detailed answer key that goes through the steps of the evaluation and gives properly constructed diagrams of the study and (if appropriate) the data analysis.

The report should take about 2 pages plus diagrams. It should be written in clear, well- crafted English up to the standard of our textbook. The clarity, spelling, grammar, and structure of your writing counts in this assignment. College students should be able to write at a professional level, worthy of publication and reading by people who pay for your ideas.

The study you identify can be theoretical, statistical, or causal and the evaluation must be correct in all steps and details.

Since I claimed that any scientific study can be evaluated as a study of a theoretical hypothesis, there may be more than one way to evaluate the study you find. Note that studies with statistical data would only be suitable candidates for theoretical rather than statistical or causal evaluation if the statistical data analysis in the report is far beyondthe correlation tools we have learned in class or there is too little information to follow the format of the statistical 6-step program.

I strongly suggest you search for a report of the sort we have looked at in class or "in the wild" – a news report of a scientific study that gives the gist of the study and possibly enough to evaluate on its own, but for which you might need to dip into the original technical scientific report for a few clues or bits of information (like sample sizes). I don't recommend going straight to the technical scientific literature because the methods used there are likely beyond our tools and abilities.

You are NOT permitted to use any of the studies discussed in class, that you have submitted as homework or Quizzes before, or that you found in the textbook, or posted at the “Science in the News” section of the course Google Site. The project is to find a new example or case to evaluate.

You should meet with your group (if you are going to work in a group) during 8th and 9th weeks (Tuesday November 12 - Wednesday November 27) before Thanksgiving to get started because there is not much time after Thanksgiving this quarter before the end of classes. You need to be set up and launched on your project soon so you can work independently during Thanksgiving break and beyond.

Caution: projects slapped together in the last week of the quarter are not likely to get a good grade because it takes time to find a good report of scientific reasoning to evaluate. You may even need to try to evaluate a case that you then give up on as too hard and have to find a back-up study to do. You also have to figure out how best to evaluate the report, how to describe the episode in an appropriate way, and to write up the evaluation in a suitable format.

During 8th week and 9th week of classes, I suggest you work to: find a study, distribute it to all group members (if you are working with a group), and begin a discussion to identify what kind it is and work on understanding the reported study, which means work through Steps 1-4 of the appropriate six step program.

You might want to use Google Docs or other online methods of collaboration if you choose to work in a small group. You can also use email or other communication methods you can all agree on. No one in a group should be asked to sign up for a social media service they don't want, such as Facebook or Twitter.

Be careful developing documents online since anyone in your group may be able to delete all of the content in your document or the whole file by accident. It is wise to save copies of your work in progress offline to your own computer from time to time as you work, just in case of an accident. Every quarter, someone discovers the hard way that they should make offline copies and they should back up their laptop. Google retains versions of your edited cloud documents, so it is possible to recover from such accidents if you are using Google Docs, but I find it is extra insurance to make an offline copy.

During 9th and 10th weeks, complete an evaluation of your study, now that you understand it. Write up a brief report (up to 2 pages or so, plus an extra page for diagrams) presenting your evaluation in six steps, including an appropriate diagram or

Revised Nov 18, 2019

diagrams of the sort we use for theoretical, statistical, or causal studies. Note that for causal studies, we generally need two diagrams: one of the study design (RED, PRO, or RET) and one of the data (like correlation data).

Your report should be detailed enough that readers of your report get the gist of the study without having to read the original report for themselves, but it should not be a copy and paste job from the report you find into the report you make. And you must submit a copy of the original study (not a link) with your report submission to Canvas.

You must cite and give credit to the original report so a reader can find the original report. This requires proper bibliography. You may choose the format for your reference/ bibliography, but it must be possible for a reader to find the original report from the content of your bibliographic information. A web link is NOT sufficient for this purpose, because links can break or be taken down or exist behind a paywall. So you MUST include full bibliographic information: author, title, date, publisher, page numbers, URL (if online), and date you consulted the item (if online).

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6.6 A PROGRAM FOR EVALUATING STATISTICAL HYPOTHESES We now set out our program for evaluating statistical hypotheses and apply it to some of the other data from the HBSC. The Program Step 1. The Real-World Population. Identify the real-world population actually sampled in carrying out the study. Note any important differences between the population sampled and the population of interest. Step 2. The Sample Data. Identify the real-world sample and the particular data from that sample whose relevance for hypotheses about the population you wish to evaluate. Be sure to include sample sizes when available. Step 3. The Statistical Model. Identify the relevant variables and the values of these vari- ables. Then identify the statistical model of the population that is appropriate for evaluation in the light of the data already identified. If the data have the form of a correlation, give a clear statement of the statistical hypothesis asserting the existence of the corresponding cor- relation in the real-world population. Step 4. Random Sampling. How well does a random sampling model represent the actu- al process by which the sample was selected from the population? Possible answers: (a) very well, (b) moderately well, (e) somewhat well, or (d) not very well. Explain the factors relevant to your answer. Step 5. Evaluating the Hypothesis. Assuming a random sampling model is applicable, what can you reasonably conclude about the real-world population? If a correlation is possible, is there good evidence for the hypothesis stated in Step 3? Estimate the strength of the correlation. Step 6. Summary. In the light of your answers in all previous steps, give a summary state- ment of how well the statistical data support your evaluation in Step 5. Possible answers: (a) very well, (b) moderately well, (e) somewhat well, or (d) not very well. Note the major fac- tors supporting your answer. While you work through the program, it is useful simultaneously to diagram the study following the examples of Figures 6.1, 6-2, 6.3, or 6.4. This might even be done on a separate sheet of paper. During Step 1, draw a box representing the population sam- pled. If relevant, surround this box with a larger box representing the population of inter- est. Label your boxes. During Step 2, add a smaller box below the first one to represent the sample. Label this box with relevant parts of the data such as sample sizes and observed frequencies. For Step 3, add the details to your picture of the population necessary to rep- resent the appropriate statistical hypothesis. At Step 5, draw in the appropriate intervals representing estimates of population ratios. Where appropriate, indicate statistically sig- nificant differences. At the end of your analysis, you should have a complete diagram of the study as it applies to the specific data you are considering. Nationality and Bullying Now let us use this program to evaluate other statistical hypotheses suggested by the HBSC. In particular, let us look at some data on nationality and reported bullying. Among that data 6.6 A PROGRAM FOR EVALUATING STATISTICAL HYPOTHESES We now set out our program for evaluating statistical hypotheses and apply it to some of the other data from the HBSC. The Program Step 1. The Real-World Population. Identify the real-world population actually sampled in carrying out the study. Note any important differences between the population sampled and the population of interest. Step 2. The Sample Data. Identify the real-world sample and the particular data from that sample whose relevance for hypotheses about the population you wish to evaluate. Be sure to include sample sizes when available. Step 3. The Statistical Model. Identify the relevant variables and the values of these vari- ables. Then identify the statistical model of the population that is appropriate for evaluation in the light of the data already identified. If the data have the form of a correlation, give a clear statement of the statistical hypothesis asserting the existence of the corresponding cor- relation in the real-world population. Step 4. Random Sampling. How well does a random sampling model represent the actu- al process by which the sample was selected from the population? Possible answers: (a) very well, (b) moderately well, (e) somewhat well, or (d) not very well. Explain the factors relevant to your answer. Step 5. Evaluating the Hypothesis. Assuming a random sampling model is applicable, what can you reasonably conclude about the real-world population? If a correlation is possible, is there good evidence for the hypothesis stated in Step 3? Estimate the strength of the correlation. Step 6. Summary. In the light of your answers in all previous steps, give a summary state- ment of how well the statistical data support your evaluation in Step 5. Possible answers: (a) very well, (b) moderately well, (e) somewhat well, or (d) not very well. Note the major fac- tors supporting your answer. While you work through the program, it is useful simultaneously to diagram the study following the examples of Figures 6.1, 6-2, 6.3, or 6.4. This might even be done on a separate sheet of paper. During Step 1, draw a box representing the population sam- pled. If relevant, surround this box with a larger box representing the population of inter- est. Label your boxes. During Step 2, add a smaller box below the first one to represent the sample. Label this box with relevant parts of the data such as sample sizes and observed frequencies. For Step 3, add the details to your picture of the population necessary to rep- resent the appropriate statistical hypothesis. At Step 5, draw in the appropriate intervals representing estimates of population ratios. Where appropriate, indicate statistically sig- nificant differences. At the end of your analysis, you should have a complete diagram of the study as it applies to the specific data you are considering. Nationality and Bullying Now let us use this program to evaluate other statistical hypotheses suggested by the HBSC. In particular, let us look at some data on nationality and reported bullying. Among that data was the fact that 61% of American youth (boys and girls, all age groups combined) reported not bullying any other schoolmates over the previous school term, while 85% of Swedish youth reported not bullying. Step 1. The Real-World Population. The population sampled consisted of school-attending children, both boys and girls, aged 11, 13, and 15 years, who were capable of completing the entire HBSC questionnaire translated into their primary language and whose school classes (or class equivalents) were chosen randomly to receive the HBSC questionnaire. There should be little difference between the population actually sampled and the population of all school- attending children (both sexes) at these ages in the participating countries. Step 2. The Sample Data. Among the young people surveyed, 61% of American youth reported not bullying any schoolmates during the previous school term, while 85% of Swedish youth reported not bullying. A total of 5168 American young people completed the survey; 3802 Swedish youths completed it. Step 3. The Statistical Model. The data can be understood as treating two variables. The first variable is nationality of the youths sampled with values American and Swedish. The sec- ond variable is self-reported bullying of other schoolmates at any time during the previous school term, with values None and one or more times. The data suggest a weak positive cor- relation between being a Swedish youth and self-reporting no instances of bullying other schoolmates during the previous school term. Step 4. Random Sampling. The HBSC employed a cluster sampling procedure that sam- pled from all students expected to be in the target age groups in school classes or class equiv- alents. This method, though administratively practical for a study of this scope, is not as precise as simple random sampling for equal sample sizes. However, the HBSC study designers used elaborate statistical techniques to generate larger sample sizes in order to ensure 95% confi- dence levels for each participating country or region. They also chose school classes or class equivalents using simple random sampling. Schools of all types in each participating nation or region were included in the population from which classes or class equivalents were drawn. Steps were taken to ensure accurate and consistent translations of the questionnaire into each home language. With these procedures, the sampling methods employed approach the preci- sion of a simple random sample. Step 5. Evaluating the Hypothesis. The rule-of-thumb margins of error for the sample of 5168 American youths and 3802 Swedish youths are both about 2%. (This value includes correcting for the less precise method of cluster sampling.) So the observed difference is sta- tistically significant. The margins of error are both 0.02, so their sum is 0.04. The difference in observed frequencies is.85 - .61 = 24. So the difference in sample frequencies is statisti- cally significant and we have evidence of a correlation between being a Swedish school- attending youth and not bullying, as compared to being a school-attending American youth. Its minimal estimated strength is only 2, however [(-85 – 02) – (.61 +.02)—a relatively weak correlation. Step 6. Summary. Given the fit between the design of the HBSC and a random sampling model (with the appropriate corrections in place for the cluster sampling technique), and given the difference between the two observed sample frequencies, we can be confident that the population sampled exhibits a weak but statistically significant positive correlation between being a Swedish school-attending youth and not bullying schoolmates, as compared to being a school-attending American youth. The same judgment applies to the population of interest. Population of interest U.S. and Sweden youth aged 11-15 U.S. Sweden (6122)% (852)% Population sampled U.S. Sweden 85% SSD % no bullying- 61% n> 5100 ΝΣ 3800 ME = .02 ME = .02 FIGURE 6.7 Analysis of the HBSC survey data indicating a weak positive correlation between being a Swedish youth and not bullying classmates compared to being an American youth. Figure 6.7 exhibits a diagram corresponding to this analysis. In reviewing the analysis, you should note especially that the sample size associated with any subgroup in the sample is the size of that subgroup, not that of the whole sam- ple. This means that the relevant margins of error may differ for the two parts of the sample used in evaluating a correlation. Both margins of error will be larger than the margin of error associated with the sample as a whole.
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please find the attached files. i look forward to working with you again. good bye

Running head: STUDY EVALUATION

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STUDY EVALUATION
A study was done by a research scholar Khurana N of Amity University the impact of
social networking sites on the youth. The target age was 15 -24 years. 75% of the participants
stated that they use Facebook more often while 15% said Instagram, 6% Twitter and 4 %
LinkedIn. This proved that young people are more interested in social media platforms that
professional media platforms.
Step one – the real-world population. The population that was sampled consisted of young
people aged 15 to 24 years who were either in high school or in college. The participants were
able to discern the questionnaire given to come up with the appropriate answers concerning the
study. They were chosen randomly through the Delhi and the NCR region of the country. The
population chosen is not any different from any other population in the same area with similar
demographics.
Step two – the sample data. The young people who were surveyed stated they use Facebook
more t...


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