Introduction To Research Methods
Chopsticks
A few researchers set out to determine the optimal length of chopsticks for children and adults. They came up with a measure of how effective a pair of chopsticks performed, called the "Food Pinching Performance." The "Food Pinching Performance" was determined by counting the number of peanuts picked and placed in a cup (PPPC).
An investigation for determining the optimum length of chopsticks.
Link to Abstract and Paper
the abstract below was adapted from the link
Chopsticks are one of the most simple and popular hand tools ever invented by humans, but have not previously been investigated by ergonomists. Two laboratory studies were conducted in this research, using a randomised complete block design, to evaluate the effects of the length of the chopsticks on the food-serving performance of adults and children. Thirty-one male junior college students and 21 primary school pupils served as subjects for the experiment to test chopsticks lengths of 180, 210, 240, 270, 300, and 330 mm. The results showed that the food-pinching performance was significantly affected by the length of the chopsticks, and that chopsticks of about 240 and 180 mm long were optimal for adults and pupils, respectively. Based on these findings, the researchers suggested that families with children should provide both 240 and 180 mm long chopsticks. In addition, restaurants could provide 210 mm long chopsticks, considering the trade-offs between ergonomics and cost.
The analysis below is based only on the part of the experiment analyzing the thirty-one adult male college students.
The dataset can be found here.
Independent variable in the experiment:
Chopstick Length
Dependent variable in the experiment:
Food Pinching Efficiency
How is the dependent variable operationally defined?
By counting the number of peanuts picked and placed in a cup (PPPC)
Variables that were controlled:
Age and Gender
# Pandas is a software library for data manipulation and analysis
# We commonly use shorter nicknames for certain packages. Pandas is often abbreviated to pd
import pandas as pd
# Change the path to the location where the chopstick-effectiveness.csv file is located on your computer
path = '~/Documents/Projects/IntroToResearchMethods_Chopsticks/chopstick-effectiveness.csv'
# Read and print data
dataFrame = pd.read_csv(path)
dataFrame
Food.Pinching.Efficiency | Individual | Chopstick.Length | |
---|---|---|---|
0 | 19.55 | 1 | 180 |
1 | 27.24 | 2 | 180 |
2 | 28.76 | 3 | 180 |
3 | 31.19 | 4 | 180 |
4 | 21.91 | 5 | 180 |
5 | 27.62 | 6 | 180 |
6 | 29.46 | 7 | 180 |
7 | 26.35 | 8 | 180 |
8 | 26.69 | 9 | 180 |
9 | 30.22 | 10 | 180 |
10 | 27.81 | 11 | 180 |
11 | 23.46 | 12 | 180 |
12 | 23.64 | 13 | 180 |
13 | 27.85 | 14 | 180 |
14 | 20.62 | 15 | 180 |
15 | 25.35 | 16 | 180 |
16 | 28.00 | 17 | 180 |
17 | 23.49 | 18 | 180 |
18 | 27.77 | 19 | 180 |
19 | 18.48 | 20 | 180 |
20 | 23.01 | 21 | 180 |
21 | 22.66 | 22 | 180 |
22 | 23.24 | 23 | 180 |
23 | 22.82 | 24 | 180 |
24 | 17.94 | 25 | 180 |
25 | 26.67 | 26 | 180 |
26 | 28.98 | 27 | 180 |
27 | 21.48 | 28 | 180 |
28 | 14.47 | 29 | 180 |
29 | 28.29 | 30 | 180 |
... | ... | ... | ... |
156 | 26.18 | 2 | 330 |
157 | 25.93 | 3 | 330 |
158 | 28.61 | 4 | 330 |
159 | 20.54 | 5 | 330 |
160 | 26.44 | 6 | 330 |
161 | 29.36 | 7 | 330 |
162 | 19.77 | 8 | 330 |
163 | 31.69 | 9 | 330 |
164 | 24.64 | 10 | 330 |
165 | 22.09 | 11 | 330 |
166 | 23.42 | 12 | 330 |
167 | 28.63 | 13 | 330 |
168 | 26.30 | 14 | 330 |
169 | 22.89 | 15 | 330 |
170 | 22.68 | 16 | 330 |
171 | 30.92 | 17 | 330 |
172 | 20.74 | 18 | 330 |
173 | 27.24 | 19 | 330 |
174 | 17.12 | 20 | 330 |
175 | 23.63 | 21 | 330 |
176 | 20.91 | 22 | 330 |
177 | 23.49 | 23 | 330 |
178 | 24.86 | 24 | 330 |
179 | 16.28 | 25 | 330 |
180 | 21.52 | 26 | 330 |
181 | 27.22 | 27 | 330 |
182 | 17.41 | 28 | 330 |
183 | 16.42 | 29 | 330 |
184 | 28.22 | 30 | 330 |
185 | 27.52 | 31 | 330 |
186 rows × 3 columns
# Basic statistical calculations
# Mean
dataFrame['Food.Pinching.Efficiency'].mean()
25.005591397849461
This number is helpful, but the number doesn't let us know which of the chopstick lengths performed best for the thirty-one male junior college students. Let's break down the data by chopstick length. The next block of code will generate the average "Food Pinching Effeciency" for each chopstick length.
# Reset_index() changes Chopstick.Length from an index to column.
# Instead of the index being the length of the chopsticks, the index is the row numbers 0, 1, 2, 3, 4, 5
meansByChopstickLength = dataFrame.groupby('Chopstick.Length')['Food.Pinching.Efficiency'].mean().reset_index()
meansByChopstickLength
Chopstick.Length | Food.Pinching.Efficiency | |
---|---|---|
0 | 180 | 24.935161 |
1 | 210 | 25.483871 |
2 | 240 | 26.322903 |
3 | 270 | 24.323871 |
4 | 300 | 24.968065 |
5 | 330 | 23.999677 |
# Plot the data
# Causes plots to display within the notebook rather than in a new window
%pylab inline
# Import library
import matplotlib.pyplot as plt
# Create and display scatter plot
plt.scatter(x=meansByChopstickLength['Chopstick.Length'], y=meansByChopstickLength['Food.Pinching.Efficiency'])
plt.xlabel("Length in mm")
plt.ylabel("Efficiency in PPPC")
plt.title("Average Food Pinching Efficiency by Chopstick Length")
plt.show()
Populating the interactive namespace from numpy and matplotlib
There is a linear relationship between the Length and Efficiency in PPPC when the length is less than 260mm. When the length becomes greater than 260mm, there is no relationship between the Length and Efficiency of PPPC.
In the abstract the researchers stated that their results showed food-pinching performance was significantly affected by the length of the chopsticks, and that chopsticks of about 240 mm long were optimal for adults. Based on that data, I agree with this claim because the efficiency in PPPC is greatest when the Length is 240mm.