Design
In my experiment , I will be testing which brand of microwave popcorn brand yields fewer un-popped kernels.
I will be testing three brands of Microwave butter flavoured Popcorn, one higher priced name brand, one mid-range name brand and one sure fine generic brand. I will be pop three bags of each brand and then I will collect my quantitative data as I count each individual un-popped and popped kernels and record my results on a data sheet. Following this I will add up the popped and un-popped kernels to gain a sum of the kernels that are held in the bag and also record this information on the data sheet. Once every single bag is popped, I will go on to calculate the sum totals of each brand I will do this by adding the popped kernels in the three bags, then adding the un-popped kernels in the three bags for each brand and also adding this to the first table. Next, recording the average number of popped, un-popped kernels , total kernels and then calculating the total percentage average of popped kernels by dividing the total sum of popped kernels for each of the three brands by the total kernels and recording all this data in the second table. Finally I will compare and determine the brand of microwave popcorn brand that yields fewer un-popped kernels by observing my column graph, where I then can draw a conclusion.
The equipment underneath is not my final equipment list as I might add more equipment that I find that I need for the experiment and will update you in future posts if I have made alterations to this list :
Equipment
- 3 bags of UNCLE TOBYS butter flavoured microwave popcorn
- 3 bags of Poppin butter flavoured Microwave Popcorn
- 3 bags of Coles butter flavoured Microwave Popcorn
- 1 microwave oven
- 1 large bowl
- 1 small bowl
- 1 data sheet
- Gloves
* For the popcorn packets , if you can you can get them prepackaged in boxes that contain three individual packets , this would be a more economical approach as it is cheaper than buying three separate bags of each brand.
Variables
Dependant Variable(What is being measured):
Independent Variable (What changes in the experiment):
Controlled Variables(What stays the same):
There can be a possible problem for my internal validity of my test if I decide to incorporate anything besides the independent variable, this could have an affect on the result of the dependant variable in my experiment. A solution for this problem I will carry out is that I will undertake to decrease the threat of internal validity by using the following strategies to keep my experiment valid and reliable:
Method of Collecting Data
The method I will be using to collect data for the purpose of this study will be that any single kernel that either fits in the criteria of either being:
Kernels that fit in this criteria will be considered un-popped. Other kernels must be fully popped to be counted as a popped kernel. Once all the bags are popped, I will then individually count the popped and un-popped kernels, placing the un-popped kernels in a small bowl and the popped kernels in a larger bowl. After this I will record the raw data onto two separate data tables on a data sheet.Finally to collaborate my results, I divide the total number of popped kernels in each brand by the total kernels for each brand, obtaining a percentage of popped kernels produced by each brand to graph. By counting the total amount of both popped and un-popped kernels I can easily account for any difference between each brand. Using the measurement of percentages of popped kernels will allow myself to have a more accurate comparison of the brand's overall performance. After much deliberation between constructing a pie graph or column (bar) graph , I have decided to go ahead with the column graph to record my results as not only are they easy to understand , because they consist of rectangular bars that differ in height or length according to their value, They also will be easier to identify any trends and patterns in the results. . This method will be easy to follow along with and repeat.
Table 1
Table 2
Column Graph
*Both tables were constructed on a "Pages" document and the column graph was constructed here.
Additionally, I have written up my aim, equipment , method and safety precautions to my report on neoOffice. You will see my work in the next post.
BUBBL.US UPDATE:
Dependant Variable(What is being measured):
- Percentage of popped kernels per brand of microwave popcorn.
Independent Variable (What changes in the experiment):
- The brand of microwave popcorn
Controlled Variables(What stays the same):
- Same microwave oven used throughout testing
- Same temperature for all samples
- Same cooking times for all samples
- Same amount of waiting and microwave cooking time between the popping of each bag
- Same flavour of popcorn for each sample
- Same amount of test samples for each brand of microwave popcorn
Validity and Reliability of Test
There can be a possible problem for my internal validity of my test if I decide to incorporate anything besides the independent variable, this could have an affect on the result of the dependant variable in my experiment. A solution for this problem I will carry out is that I will undertake to decrease the threat of internal validity by using the following strategies to keep my experiment valid and reliable:
- For consistent results I will pop three bags of each brand.
- I will wait a time period of 10 minutes between each popping in order to eliminate oven pre-heating , this can be a possible variable.
- The same microwave oven,cooking temperature and cooking time will be used to cook each bag of microwave popcorn.
- To terminate any discrepancy among the total sum of kernel in each bran, I will use a percentage of popped kernels to total sum of kernels for each brand.
- All popcorn bags will be purchased at exactly the same time.
- I will try my best to keep expiration dates as close to each other as possible,therefore terminating the age of popcorn as variable.
Method of Collecting Data
The method I will be using to collect data for the purpose of this study will be that any single kernel that either fits in the criteria of either being:
- Partially broken
- unbroken but inedible
- Not fully popped
Kernels that fit in this criteria will be considered un-popped. Other kernels must be fully popped to be counted as a popped kernel. Once all the bags are popped, I will then individually count the popped and un-popped kernels, placing the un-popped kernels in a small bowl and the popped kernels in a larger bowl. After this I will record the raw data onto two separate data tables on a data sheet.Finally to collaborate my results, I divide the total number of popped kernels in each brand by the total kernels for each brand, obtaining a percentage of popped kernels produced by each brand to graph. By counting the total amount of both popped and un-popped kernels I can easily account for any difference between each brand. Using the measurement of percentages of popped kernels will allow myself to have a more accurate comparison of the brand's overall performance. After much deliberation between constructing a pie graph or column (bar) graph , I have decided to go ahead with the column graph to record my results as not only are they easy to understand , because they consist of rectangular bars that differ in height or length according to their value, They also will be easier to identify any trends and patterns in the results. . This method will be easy to follow along with and repeat.
Table 1
BRAND | POPPED KERNELS | UN-POPPED KERNELS | TOTAL KERNELS |
Uncle Toby’s 1 | |||
Uncle Toby’s 2 | |||
Uncle Toby’s 3 | |||
Poppin 1 | |||
Poppin 2 | |||
Poppin 3 | |||
Coles 1 | |||
Coles 2 | |||
Coles 3 |
Table 2
BRAND | AVERAGE NUMBER OF POPPED KERNELS | AVERAGE NUMBER OF UN-POPPED KERNELS | AVERAGE TOTAL KERNELS | TOTAL PERCENTAGE OF POPPED KERNELS(%) |
Uncle Toby’s | ||||
Poppin | ||||
Coles |
Column Graph
Stacked Column Graph
Additionally, I have written up my aim, equipment , method and safety precautions to my report on neoOffice. You will see my work in the next post.
BUBBL.US UPDATE:
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