1.2 MEDIA REPRESENTATION

Media Data Representation

For this task I had to represent my recorded data on Illustrator using the bar graph and pie chart tool. 
The data I recorded was of my personal media consumption over the period of 12 days. This included TV, Cinema, Netflix and Youtube. The wisdom part of my triangular graph was to reflect on my media consumption and what I believe I should limit too. The wisdom, knowledge, information and data is explained on my triangular graph. These charts represent my media consumption graphically, which visually breaks down the different components that make up the representation.

Primary Data 

Data collected by a researcher from first hand sources, it is data collected with a research project in mind.

Positives 

- It is legitimately from the source
- Better accuracy
- Easily accessible

Negatives 

- Time consuming
-  Feasibility
-  Cost, can be expensive as lot of marketing is involved in collection the data.

Secondary Data 

 Data collected from someone other than the user,

Positives 

- Economical 
- Easy to access as it can be previously used in other research 

Negatives

- Vague (as it may not answer all the information you want)
- Dated (the data can be dated and not current to the time you are reseraching from)

The difference between primary data is that it comes directly from the source. Whether secondary data is something or someone other than the original source.

Different ways I collected my data 

ONE - Record my own notes by documenting using time, dates etc. 
TWO - Articles and Youtube (researching secondary data)
THREE - Conversing with peers on their data and my own (discussing, writing, communicating)

I rate data ONE as a 6/10 in terms of how successful it was to use a data source. This is because it was primary source that was easy to access, however it was hard to repeatedly document. 

I rate data TWO as a 7/10 in terms of how successful it was to use as a data source. This is because it was clear and easy to access, however it was from a secondary source and can be untrustworthy. 

I rate data THREE as a 5/10 in terms of how successful it was to use as a data source. This is because it was primary data and easy to access. 

Triangular Chart Graph 


Netflix - 3hrs 30 mins
Youtube - 11hrs
TV - 1hr 50 mins
Cinema - 4 hrs
Total = 20hrs 20 mins

Bar Chart Graph

This Bar Chart is another visual/graphic way of representing my media consumption. The numbers on the side represent how many hours of media I have consumed, over the period of 12 days. 





Netflix - 3hrs 30 mins
Youtube - 11hrs
TV - 1hr 50 mins
Cinema - 4 hrs
Total = 20hrs 20 mins


Pie Chart Graph

This pie chart graph represents another way I can display my media consumption in a graphic graphical visual form, using Illustrator. 

Netflix - 3hrs 30 mins
Youtube - 11hrs
TV - 1hr 50 mins
Cinema - 4 hrs

Total = 20hrs 20 mins


Class Pie Chart 

For this task I had to collect data from my peers and present it in a graphic bar chart form, using Illustrator. By doing this it allowed us to workout the average and create a comparison between the genders. For example, the information displayed shows how more boys consumer media than girls in the class. It also shows the reasoning for this as there are more boys populating the class than girls. 
The yellow bar is my personal media consumption, this graphs shows that i am on the lower end of media consumption than my peers. 


Media consumption in hours

Boys - 25 hrs, 27 hrs, 32 hrs, 38 hrs, 39 hrs, 41 hrs, 42 hrs, 45 hrs, 64 hrs, 70 hrs, 105 hrs.

Girls - 11 hrs, 20 hrs, 21 hrs, 38 hrs, 39 hrs.

Average Boys - 36 hrs.

Average Girls - 48 hrs.

Average class total - 41 hrs.

7 Day Graph Data 

Sleep Schedule Graph

For this task, I had to gather data from my own sleeping schedule. The sleeping schedule data I used was recorded for over a period of a week. Using the times I woke up and went to sleep, I used two line graphs to represent both datas. To create this data into a visual/graphical graph I used Adobe Illustrator. 


Amount of Hours I Have Slept Bar Graph

For this task, I had to represent the amount of sleep I had over the period of a week. Using this data I then had to represent it in a graphical/visual form using Adobe illustrator. For this task I used the bar graph tool in order to clearly show the data I had collected.



The correlation between the amount of hours I slept and the time I went to bed was that when I slept for a longer period of time, I woke up earlier. 

Fruit Consumption Graph

For this task I had to show my fruit consumption over a period of a week. This was to represent my personal data that I collected in a graphical/visual form. For this task, I created the shapes and graph using adobe Illustrator. 


What correlations can you deduce? do you get healthier mid-week?

I can deduce the correlation between the uneven gaps between my consumption of fruit to the amount of fruit that I have. I do slightly improve in being healthier as mid week I consumed two pears. However, I do not continue after this and therefore only applies to that one sunday. 

Exercise Graph

For this graph, I had to represent the amount of exercise I had done over the period of a week. This was to show exercise through a visual/graphic representation using Adobe Illustrator. For this task I used the pie chart tool to help input my data. In comparison between my own amount of exercise complete to other that of my peers (as they documented their results too). I was quite healthy in comparison as I cycle majority of the week. To conclude, gathering my own research to others allowed me to see that I do quite a lot fo exercise during the week than those of my peers,
however I was not the healthiest as some other peers did more exercise than me. 




Research on Global Media Consumption

Media- Television (excluding Netflix and Amazon Prime)
Published - Finder, 2019


19 hrs 17minutes of TV consumed by every British citizen, per week. 

I believe the statistic shown isn't valid as they don't include Amazon Prime or Netflix. This is because both are highly streamed services and if included would show a more accurate consumption of Television as a whole. I believe the data is useful when representing just Television consumption without the streaming services. As you can use this statistic to compare the increase/decrease in Television consumption over a period of time. 


Media- Television 

Young people watch a third less broadcast TV as they move online. 

I believe the statistic shown is somewhat valid as it includes the streaming devices in television. However, it lacks in clear statistics. The statistics/data shown are given little information on where they were extracted from. So it is unclear whether the statistics resemble a fair figure. I believe the data is quite useful as it represents an increase in online Television streaming over a period of 10 years (2010-2016). 

Media - Netflix 
Published - Global web index, 2015.


58 percent of Teens in US are watching Netflix.

I believe the statistic shown is invalid as it only gives you data but doesn't show the information on how the data was received / calculated. It also lacks in detail. For example, it doesn't represent which states consume more Netflix. The statistic/data I feel isn't useful because it only states the statistic without any proof. If given proof it would be a more suitable statistic to compare with other data. 

Media - Social Media 
Published - Pew Research, 2018.


Fully 95% of US teens have access to a smartphone, and 45% say they are online 'almost constantly'.

I believe the statistics shown are quite valid as they represents the most popular platforms of social media, which will give a more equal statistic. However, they don't give clear information of how they extracted the data. This would help breakdown the data to see whether it is fair. The data/statistics is only useful if compared with other global statistics on teenage smartphone usage. 

Media - Television
Published - World Atlas, 2018.


The USA consumes the most amount of Television a week.

I believe this data is slightly valid as it represents the types of Television watched by the USA and compares the data with other global highly populated countries. However, it doesn't show where the statistic/data was found from. This would make the data more reasonable to compare. The data is is useful when comparing with other global countries between the same period of time. Although the data isn't fully useful because the information from where the data is located isn't stated. 

Media - Television/ Broadcasting
Published - The Guardian, 2018.


16-24 year olds spend more time on Netflix in a week than they will on all BBC services.


I believe the data shown is valid as it shows the age range and the data is sourced from the official company, BBC. The data's comparison is spread over the period of a week, this gives a clear and informative representation to compare with other statistics. The data is quite useful as it shows how streaming services are becoming more popular overtime. Although the data is only compared with one streaming company (Netflix) and not others. This would give a broader spectrum of how many streaming services are increasing in the youth's media consumption.

Media - Television 
Published - Ofcom, 2017. 

16-34 year olds watch -34% of Broadcast Television in 2017 than in 2010.  

I believe these statistics are quite valid as the Television representation only includes the decrease in Broadcast Television in Scotland, specifically teens to young adults. Which therefore is a more accurate statistic. However, the data fails to show the information of how the data created/located, which weakens it's validation in representing 16-34 year old's watching Broadcast Television in Scotland. The data is partly useful, this is because it only represents a certain age gap. However, the data is useful when comparing that age gap with other global countries. 

Media - Social Media 
Published - Pew Research, 2018.


78% of 18-24 year olds use Snapchat in USA.

I believe this data quite valid as the raw data was gathered from a survey online. However the survey doesn't state the amount of people took part, which dilutes the amount of validation the data holds. The data is quite useful, as you can compare this data with other global countries by using the same  the survey with the same amount of people. Although, the data is restricted when comparing as you have to keep the same age gap between those who take the survey. In order for a fair/equal comparison. 

Media -  Social media
Published - The Washington post, 2019.

Link - https://www.washingtonpost.com/technology/2019/10/29/survey-average-time-young-people-spend-watching-videos-mostly-youtube-has-doubled-since/?


The average time kids spend watching videos has doubled in 4 years.

I believe this data is very valid as it is represented through a survey by a company (Common Sense Media) that collect data from children's technology habits. However, the data is not given an age range for the 'kids' representing this data. By doing this it would give a more specific range that would make it easier for other to compare their data with. The data is slightly useful it can be globally compared with other countries if their 'kids' (youth) took the survey too.  Although if the data is compared the survey would have to be given t to the same amount of children, and then data won't be fair as it doesn't show the amount of people who took the survey or age gap between kids. Therefore, the data becomes less useful because it can't be accurately compared.

Media - Social
Published - BBC News, 2017.

Link- https://www.bbc.co.uk/news/technology-42153694

28% of 10 year olds now have a social media profile.

I believe this data is not valid as it doesn't clarify how the data was formed e.g survey or polls. It only gives a statistic. The data is slightly useful if comparing percentages of 10 year olds social media profiles with other counties globally, as this data is representing those in the UK. However, the comparison wouldn't be fair/equal as the data is given no information on how it was sourced.

Class Height Graph

For this task, i had to create a height graph in order of tallest to smallest students in my class. I then had to copy my symbol self portrait on the position/number at which I am marked for on height. I used the open tool to create the graph and the text box to represent the numbers and letters. 


Graph of Class - Height in cm

For this task, I had to visually represent my results from the class height data. This meant I had to calculate the mean, mode and median for each of the boys, girls and total (both boys and girls combined). In my bar graph I had to also represent my own personal height within the data. 


Correlation vs Causation and Relationships (in data)

Correlation - Two variables related to each other, but one doesn't cause the other. Sometimes correlation can include a third party. Correlation also includes data dreading. Data dredging is when you have patterns within your data, shown statistically.

Causation - When you claim that one action or event has caused a second event to happen. Causation is dependent on the strength of a relationship between the two subjects/topics in the claim.

Correlation and Causation differ from each other and shouldn't be confused because correlation does not form causation. For example, when analysing data from crime increase with ice cream increase. The ice cream is not the causation for the crime rates, but the correlation. The temperature increase which causes an increase in ice cream sales and homicides. This example shows how correlation and causation are different and that there is a correlation between ice cream sales and homicides, not an causation.

Positive relationship - A positive relationship when describing data is when two variables move in the same direction, almost mirroring one another. This relationship occurs when one variable is decreased, so that the other variables increase.

Inverse relationship - An inverse relationship is when two variables move in opposite directions, this is known as a negative correlation.

The graphical cycle below represents how correlation and causation are different, using the same topics. The sun is the cause of the increase in ice cream and homicides. The correlation is that both ice creams and homicides increase when temperatures increase.





Comments

Post a Comment