HEY EVERYONE!
Data collection is finally over, and even though I have a lot more stuff to do, it's really nice knowing that I have everything that I need for my ultimate conclusions. That being said, the numbers that I've obtained aren't the best for what my original question was. The percent errors for almost all of my multiple estimates are close to 100, and only a handful are below 30 percent.
As mentioned before, this likely indicative of a more general characteristic of social media firms, which I plan to develop for my discussion section.
For now, I'm planning on developing exactly what I plan to do to analyze the data that I have collected.
First, I will compute the average error of the multiples for each firm. I have already done this, and plan on including this in a table for my data section. Thus, my data section will have five different tables with four columns and five rows. Each column for a single row will be the estimation error for a specific multiple and a specific firm. This will allow me to most directly compare the effectiveness of multiples for a given firm.
Next, I will need to compare the accuracy of multiples across the five firms I am analyzing. To do this, I will have a table for each multiple with the average error of each method for every firm. This allows me to determine which multiples perform the best for which firms, allowing me to draw conclusions for why specific multiples work better for specific firms.
Finally, I will determine which multiple is best overall across all five firms. To do this I will draw five box plots for each multiple representing the different errors for all five firms. I can then analyze the spreads for the box plots, and their average accuracy to compare how accurate they are in general, and how much they differ in their accuracy. The box plots also give a visual representation of the pricing errors, which allows me readers to more quickly grasp the differences between the multiples. To further explain the box plots, I intend to include a brief description of the box plots below them, summarizing important features of the plot such as the mean error of the box plot, which represents the mean error of the multiples.
I may also do the same box plots for each of the three methods that I used so that I can provide better comparative analysis of the data.
I think what I have right now is a pretty good start, but it might be hard to graphically fit five box plots side by side to ultimately compare them. If anyone has suggestions on how I can more nicely compare the data for the methods, it would be really useful. Anyways, thats it! (476)
Signing off,
Akash
Data collection is finally over, and even though I have a lot more stuff to do, it's really nice knowing that I have everything that I need for my ultimate conclusions. That being said, the numbers that I've obtained aren't the best for what my original question was. The percent errors for almost all of my multiple estimates are close to 100, and only a handful are below 30 percent.
As mentioned before, this likely indicative of a more general characteristic of social media firms, which I plan to develop for my discussion section.
For now, I'm planning on developing exactly what I plan to do to analyze the data that I have collected.
First, I will compute the average error of the multiples for each firm. I have already done this, and plan on including this in a table for my data section. Thus, my data section will have five different tables with four columns and five rows. Each column for a single row will be the estimation error for a specific multiple and a specific firm. This will allow me to most directly compare the effectiveness of multiples for a given firm.
Next, I will need to compare the accuracy of multiples across the five firms I am analyzing. To do this, I will have a table for each multiple with the average error of each method for every firm. This allows me to determine which multiples perform the best for which firms, allowing me to draw conclusions for why specific multiples work better for specific firms.
Finally, I will determine which multiple is best overall across all five firms. To do this I will draw five box plots for each multiple representing the different errors for all five firms. I can then analyze the spreads for the box plots, and their average accuracy to compare how accurate they are in general, and how much they differ in their accuracy. The box plots also give a visual representation of the pricing errors, which allows me readers to more quickly grasp the differences between the multiples. To further explain the box plots, I intend to include a brief description of the box plots below them, summarizing important features of the plot such as the mean error of the box plot, which represents the mean error of the multiples.
I may also do the same box plots for each of the three methods that I used so that I can provide better comparative analysis of the data.
I think what I have right now is a pretty good start, but it might be hard to graphically fit five box plots side by side to ultimately compare them. If anyone has suggestions on how I can more nicely compare the data for the methods, it would be really useful. Anyways, thats it! (476)
Signing off,
Akash
There are for sure a lot of combinations of things that need to be conveyed, and I echo your instinct that all together it may be a little overwhelming. You may want to first just convey what the overall conclusion is and then explore some of the nuances of that conclusion afterwards looking at the the smaller ways you got to that larger conclusion.
ReplyDeleteHi Akash! I'm glad to hear that you seem to have a clear idea for the steps and methods necessary for analyzing your data. I agree with Ms. Haag that presenting ALL the information and box plots may get a little overwhelming for the reader, so maybe just draw out the most important bits and pieces for answering your question. Also, you may want to address the high percent errors not just in your discussion, but at the beginning of the results, and articulate what you can actually reasonably conclude with such a high percent error (it may be a little more limited than what you originally intended). Good luck narrowing everything down and getting all of your data analyzed!
ReplyDeleteHey Akash! I hope you had a good weekend. I am glad that you have finished your data collection. However, I am immediately worried about the percent error you achieved. You stated that you had a close to 100% percent error for most of your multiples. Is this a good thing or a bad thing? Explain please. In addition, I agree with Audrey and Mrs. Haag that you might want to find a better way to represent your data. Box Plots my be easy for you as an expert in the field to understand, but I feel as though other graphs such as a bar graph or a scatter plot will provide a variety and diversity of data representation to your project.
ReplyDeleteThanks,
Ved Narayan
127 words
Hey Akash!
ReplyDeleteIt’s always good to know that you have everything that you need for your conclusions and discussions. Congratulations on finishing your methods and gathering results! That was probably the most tedious part (it was for me). I really like that you have a clear plan for how you are going to analyze the data. You definitely have a lot of information and numbers and stuff to look at and analyze, so don’t throw all of it into the paper even though it doesn’t affect word count, just include and draw attention to and discuss the most important parts that prove your research question (Mrs. Haag told me this too!!)
Akash -- I agree with Yash, that you've gotten a lot better at conveying your ideas to a lay audience. However, it's going to be important that you transition between the the different sections of the results to show the relationship between them. In other words, you need to show how each of the parts build off of each other.
ReplyDelete