Introduction to Research Methods

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

png

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.


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