Need of a Sizing System for Indian Apparel Industry

One of the biggest challenges in the apparel segment is that the countries like UK, US, Europe have their sizing system but India still doesn’t have it. The purpose of the study is to identify the need for a sizing system for India. This research paper shows the lack of an accurate and standardized sizing system for readymade garments in India. Every brand in India follows its sizing chart which is based on the international sizing chart of readymade garments. Since our Indian body shapes are different from the international figures, international sizing systems-based garments fail to fulfil the need of our Indian customers.

Each brand has its understanding regarding Indian build and they create variation in the sizes according to their interpretation hence there are inconsistencies in sizes between brands which makes it difficult for the consumer to get their right fit and creates confusion regarding their right size. Consumers are forced to have alternations done every time they purchase cloth or else wear ill-fitting clothing. Research shows that the unavailability of a correct and standardized sizing system, targeting Indian people is a major issue.

Introduction

The sizing system is a numerical table that represents the number of key values ​​used to classify the bodies encountered in the population. An anthropometric data-based measurement system (human measurement) is known as an anthropometric sizing system. Research into anthropometric sizing systems has been comprehensively done around the world for efficiency in apparel manufacturing. The structure of the sizing system is based on the division of targeted people into groups with similar body measurements.

Researchers in the field of clothing have long recognized the need for consistent and standard sizes for the varied population. Such a program aims to provide each individual with the appropriate balance of clothing. In addition, people in each size group are considered to have similar physical features and therefore can share the same size of clothing.

To develop a good measurement system, key sizes should take one measurement from a horizontal body size set (e.g., belt) and a set of precise body sizes (e.g., length or length).

Since the beginning of the dress code in the mid-1900s, there has been a need for definitions of clothing that differentiate body shape and size into different categories. This requires a systematic and scientific system for measuring and classifying human bodies, which is the purpose of creating a clothing scale. In addition, a good measurement system that leads to appropriate clothing should be based on anthropometric data. Clothing balance is defined as clothing with the right size and size. Having said that, the definition of clothing equality includes two items of size and shape. To get the right fit, you need to choose a dress that is the right size and shape. Sizes are based on the measurements of the human body. In addition, body composition is made by the depth, width, and length of the body.

The purpose of creating a bodybuilding program is to find a large number of size groups that will determine the shape and size of the most encountered people and to accommodate as many people in each situation and group as possible with the source – Clothing worn while allowing the manufacturer to make a profit.

Traditional measurement system

No two people have ever been alike in all of their measurable aspects. This difference has been a matter of curiosity and research for over 200 years. In the past, different people were more likely to express their limited body image. This method is called anthropometry. Anthropometry describes the size of a human body. The name is derived from Anthropos, meaning a person, and metrics, meaning or related to the equation.

It was not until the 1950s that anthropometrics became a popular discipline. Anthropometry usage settings include cars, workplaces, equipment, aeroplane cockpits, and clothing. For years, anthropometry has been used in national research as a measure of health status. Step-by-step testing has been the subject of research for a long time.

Reliability is defined as the degree to which a measure can be recovered over time. Reliability of scale has elements of accuracy and reliability. In both cases, accuracy is more important than reliability. However, issues of fidelity are often overlooked in problem-oriented research because of the impact of measurement error. Viewer error is the most troublesome source of anthropometric error. It includes ambiguity in the notable area, setting the title, and the use of the instrument. This error can be emphasized by the use of multiple observers even if they are trained by the same person and work collaboratively. Error limits are usually set before data collection while measurement performance is considered throughout the process against pre-set standards. Viewing errors in anthropometry are unplanned and uncommon. Therefore, traditional methods of measurement bodies need to be significantly improved.

Ancient anthropometric data provides details on the static size of the human body in a typical position. The science of anthropometry is one of the most accurate. Experienced workers in the field are best able to use this method. Most of the measurements taken from the article are considered to be in the most desirable position. Measurements are measured, if possible, in the morning. The human body usually loses its height during the day and is usually more relaxed in the morning. It is best if the subject is not fully clothed or has as little clothing as possible.

The traditional method of measurement

In most cases, the body of the title is placed in a fixed vertical position, the body parts being about 180, 0, or 90 degrees to each other. For example, a title may need to be “straight; heels together; buttocks, shoulder blades, and back of the head touching the wall; arms straight, fingers straight…”: This is close to the so-called “anatomical position” used in anatomy. The head is placed on “Frankfurt Flight”; Pupils are at the same level of horizontal, right traction (thought of ear piercing), and the lowest point of the right orbit (eye socket) is also placed in the same horizontal plane. When taking steps on a sitting head, the area (flat and straight) of the seat and foot support is arranged in such a way that the thighs are straight, the legs are straight and the feet are flat on their horizontal support. The subject is naked, however, and not clean.

Instruments have been used since Richer began using weapons in 1890. Simple, fast, non-invasive tools include a weight scale, a camera, a measuring tape, an anthropometer, a scattering calliper, a navigation compass, and a head panel.

3D Scanning Measurement System

Even after basic training, it is difficult for the average retailer to come up with specific rates. This is where 3D technology proves to be a blessing. The development of three-dimensional body scanning technologies has great potential for use in the clothing industry, especially to create custom or quantity techniques to be used.

3D body scanning is a technology that produces a 3D model by scanning. A man stands given the scanner while taking a picture of his body and producing 3D images in seconds. The scanner uses a series of light sensors to produce a 3D image. Images are taken in 360 degrees within a short time as well as body measurements and the human body image. This data is archived or processed according to need.

3D body scans and computer-generated images are used to customize many outfits, in which the consumer is measured in size, and with a computer-generated image displayed on a computer screen, he can choose a dress that suits his or her wishes.

Three-body scanning technologies are used in a variety of fields. A popular application for this scanning process is in the field of customization. People come in many shapes and sizes. This has created an equality problem. Manufacturers needed precise details to produce suitable clothing. Customizing clothes to fit well with the consumer depends on the availability of complete measurements. Advances in the field of Information Technology assists retailers and manufacturers with the 3D Body Scanning process. In this process, individual measurements can be obtained accurately and quickly. 3D scanned data contains standard measurements such as chest size, body size, circle, and complete 3-D data for each individual. These new technologies are changing the characteristics of the clothing industry.

NIFT LAUNCHES STANDARD SIZE INITIATIVE

The National Institute of Fashion Technology (NIFT) has launched a national study to measure the size of the development of the first chart for India, which will provide Indians with better clothing.

Mapping research will include basic data such as gender, location and age. The first phase of the study will look at a size chart for 15–65-year-olds, and the second phase will look at the size of the children, as well as the shoes. The whole scan process will take approximately 15 minutes for each scan.

Indian Size Project will use three 3D body scanners in NIFT – New Delhi (North), Mumbai (West), Kolkata (East), Hyderabad (Centre), Bengaluru (South) and Shillong (North East), for ceremonial and rural areas. combined. These human-powered scanners are enabled to extract fully 3D body maps in less than 10 seconds, thus reducing the amount of time required to perform anthropometric research.

The details will be treated as assets of the Ministry of Textiles. It will need to be reviewed after 10 years, although further research can be done at a much smaller scale.

LITERATURE SURVEY

In the most general terms, the purpose of an apparel sizing system is to divide a varied population into homogeneous subgroups. Members of a subgroup are similar to each other in body size and shape so that a single garment can adequately fit each of them. Members of different subgroups are dissimilar and would therefore require different garments (McCulloch, Paal & Ashdown, 1996).

Due to the lack of Indian sizing surveys, retailers/manufacturers are forced to use the size charts of other countries i.e. the US and/or the UK where the anthropometric make of the population is very different from that of India.

The knowledge of anthropometric dimensions is necessary for the development of garment patterns. In countries like India, where retailing is expected to see unprecedented growth in the next few years, the success or failure of a brand depends on the fit of clothing that is offered to consumers (Deepti Gupta, 2010).

Appavoo & Jeyasingh (2012) proposed a new approach of a two-stage data mining procedure for labelling the shirt types exclusively for Indian men. In the first stage, the clustering technique was applied to the original dataset, to categorise the size labels. Then these clusters are used for supervised learning in the second stage for classification

The  anthropometric  data  collected  and  analysed  will  be used  to  develop  comprehensive  bottom-wear  body  size charts for Indian male youth.    The body size charts maybe used by mass manufacturers of bottom-wears.  It  is recommended  that  to  include  people  of  other  age  groups, genders  and  product  categories  as  an  extension  of  this research Manoj Tiwari& Noopur Anand (2019) used anthropometric data to develop comprehensive bottom-wear body size charts for Indian male youth.

Kausher& Srivastava (2019) collected anthropometric data of females between the ages of 16 to 80 years from some states of India to produce the sizing system suitable for clothing manufacture and retailing. Factor analysis technique was used to filter the control body dimensions from a large number of variables. Decision tree-based data mining was used to cluster the data.

Throughout the literature, there have been consistent efforts to develop an Indian sizing system by using several different methodologies. However, researchers did not include people of all age groups and gender. As we know, the body shapes within the country are not the same, so people from every region of India should be included in the proper sizing system of all body types.

RESEARCH DESIGN

The research design is developed to provide an acceptable framework for the study. The choice to be made regarding the research method is a very significant decision in the research design process since it determines how relevant information can be gathered for a study.

The following decisions were made while conducting this study:

Sample Design

Universe	Population of India
Measuring tool	Questionnaire
Sampling Method	Convenience sampling
Sample Size	165
Data Analysis Method	Tables, Graphs and Descriptive Statistics
Hypothesis Testing Tools	1.     Chi square Goodness of Fit 2.     Z test for Proportion, 3.     One Way Anova, 4.     Pearson Correlation Coefficient, 5.     Fisher’s Exact Method.

 

Data collection

Data collecting is an important approach of research design and it helps in collecting relevant information with the help of which the researcher can predict, analyze, and evaluate different outcomes.

The data collection in this particular study includes the following two types of data:

1. Primary Data: Questionnaire
2. Secondary Data: Similar studies, articles, journals, and research papers.

TOOLS FOR HYPOTHESES TESTING AND DATA ANALYSIS

•  A google form has been made to develop the questionnaire and collect the responses.
• Chi-square goodness of fit test – Chi-Square goodness of fit test is a non-parametric test that is used to find out how the observed value of a given phenomenon is significantly different from the expected value.  In Chi-Square goodness of fit test, the term goodness of fit is used to compare the observed sample distribution with the expected probability distribution.
• Z test for proportion – This test for a difference in proportions. Z-test allows you to compare two proportions to see if they are the same. The null hypothesis (H₀) for the test is that the proportions are the same while the alternate hypothesis (H₁) is that the proportions are not the same.
• One-way Anova – The one-way ANOVA compares the means between the groups you are interested in and determines whether any of those means are statistically significantly different from each other.
• Pearson’s Correlation Coefficient – The Pearson product-moment correlation coefficient (or Pearson correlation coefficient, for short) is a measure of the strength of a linear association between two variables and is denoted by r. Basically, a Pearson product-moment correlation attempts to draw a line of best fit through the data of two variables, and the Pearson correlation coefficient, r, indicates how far away all these data points are to this line of best fit.
• Fisher’s Exact Method test – Fisher’s exact test examines the relationship between the two dimensions of the table (classification into rows vs. classification into columns). The null hypothesis is that these two classifications are not different.

DATA ANALYSIS AND INTERPRETATION 

TOTAL NUMBER OF RESPONDENTS

165

 

Data breakdown of the survey on different parameters:

Break-up category	Category Item	Participants	Category-wise Participants Total
Gender-wise	Male	60	165
	Female	105
	Non-binary	0
	Prefer not to say	0
Age-wise	18-24	149	165
	25-34	12
	35-48	4
	48 & above	0
Demographics-wise	North	108	165
	South	6
	East	45
	West	6
Occupation-wise	Student	141	165
	Data Analyst	1
	Cyber admin	1
	Doctor	2
	Banking Professional	1
	Education and Teaching	4
	Engineering	4
	Government Service	8
	Private Service	1
	Music Producer	1
	UX Designer	1
Awareness about the Indian Sizing system	Yes (Aware)	107	165
	No (Not aware)	58

 

FREQUENCY OF RESPONDENTS BUYING NEW CLOTHES

Category of Clothes	1Month	3 to 3 Months	More than 6 Months	9-10 Months	After 12 Months	Total
Formals	3	23	41	30	68	165
Jackets	1	8	31	43	82	165
Denim Jeans	4	40	47	38	36	165
Casual Wear	49	70	30	12	4	165
Party Wear	8	42	39	36	40	165

 

CHANNELS THROUGH WHICH RESPONDENTS BOUGHT CLOTHES

CHANNELS	RESPONDENTS
Online	108
In – Store	6
Custom Made	45
Total	165

 

RESPONDENT EXPERIENCES IN TERMS OF COMFORTNESS WITH THEIR CLOTHING ARTICLES 

Type of Clothes	Bust/Chest	Waist	Hips	Fits Perfectly	Total
Formals	56	40	20	70	165
Jackets	35	13	9	116	165
Denim Jeans	0	58	31	88	165
Casual Wear	32	20	7	106	165
Party Wear	42	42	24	84	165

 

RESPONDENTS VIEWS ON UNIVERSAL SIZING SYSTEM

RESPONSE	RESPONDENTS
Uncomfortable	13
Somewhat Comfortable	82
Comfortable	55
Extremely Comfortable	12

 

RESPONDENTS VIEWS ON INDIA NEEDING ITS OWN SIZING SYSTEM 

RESPONSE	RESPONDENTS
Yes	136
No	3
May be	26
Total	165

 

DATA ON RESPONDENTS WILLING TO VOLUNTEER FOR INDIAN SIZING SYSTEM OR NOT 

RESPONSE	RESPONDENTS
Yes	83
No	15
May be	67
Total	165

 

RESPONDENTS COMFORTNESS TOWARDS DIFFERENT SIZING METHODS

Type of Methods	Comfortable	Not Comfortable	Total
Traditional Methods	106	59	165
3D Body Scanning	138	27	165

 

CALCULATION OF MEAN, MEDIAN, MODE AND STANDARD DEVIATION

 

OBJECTIVE: Calculation of the mean, median, mode and standard deviation of the age group of the participants.

Age Interval	Frequency
18-21	118
22-25	34
26-29	4
30-33	4
34-37	1
38-41	2
42-45	1
46-48	1

 

Solution:

Age interval	Frequency(f)	Mid value (m)	f * m	Cumulative frequency (cf)
18-21	118	19.5	2301.00	118
22-25	34	23.5	799.00	152
26-29	4	27.5	110.00	156
30-33	4	31.5	126.00	160
34-37	1	35.5	35.50	161
38-41	2	39.5	79.00	163
42-45	1	43.5	43.50	164
46-49	1	47.5	47.50	165
	n = 165		f *m = 3541.50

 

To find Median Class

= value of (n/2)th observation
= value of (165/2)th observation
= value of 82nd observation

From the column of cumulative frequency cf, we find that the 82nd observation lies in the class 18-21.

∴ The median class is 18-21.

Now,
L=lower boundary point of median class =18
n=Total frequency =165
cf=Cumulative frequency of the class preceding the median class =0
f=Frequency of the median class =118
c=class length of median class =3

To find Mode Class

Here, maximum frequency is 118.

The mode class is 17.5-21.5.

L=lower boundary point of mode class =17.5
f₁= frequency of the mode class =118
f₀= frequency of the preceding class =0
f₂= frequency of the succeeding class =34
c= class length of mode class =4

 

=19.8366

 

For Standard Deviation 

Age interval	f	m	Mean	m –	(m – )2	f *(m-x)2
18-21	118	19.5	21.46	-1.96	3.84	453.12
22-25	34	23.5	21.46	2.04	4.16	141.44
26-29	4	27.5	21.46	6.04	36.48	145.92
30-33	4	31.5	21.46	10.04	100.80	403.20
34-37	1	35.5	21.46	14.04	197.12	197.12
38-41	2	39.5	21.46	18.04	325.44	650.88
42-45	1	43.5	21.46	22.04	485.76	485.76
46-49	1	47.5	21.46	26.04	678.08	678.08
	n = 165					3155.52

 

MEAN
MEDIAN	20.0975
MODE	19.8366
STANDARD DEVIATION	4.386

HYPOTHESIS TESTING

TEST 1: CHI SQUARE GOODNESS OF FIT TEST

Question: Do you think India needs its own sizing system?

Response collected

 

RESEARCH HYPOTHESIS

In this study, the following hypothesis has been assumed to find out that whether the null hypothesis stand true or not.

• Null Hypothesis (H₀):
  • There is an equal probability of people needing the Indian sizing system and satisfied with present sizing system.
• Alternate Hypothesis (H_a):
  • People are not satisfied with present sizing system and need their own Indian sizing system.

Analysis

As we are assuming the null hypothesis, where the outcomes have equal probability so the expected result would be 1/3^rd of total sample (i.e., 165) as there are 3 choices.

Therefore, expected outcome can be

	Yes	No	Maybe
Observed	136	3	26
Expected	55	55	55

 

where, O_i = Observed value

E_i= Expected value

Degree of freedom = (rows-1) (columns -1) = (2-1) (3-1) = 2

 

	Observed	Expected	Difference	Difference Square	Difference Square/ Expected value
Yes	136	55	81.00	6561.00	119.29
No	3	55	-52.00	2704.00	49.16
Maybe	26	55	-29.00	841	15.29
Chi squared (χ2)					183.745

 

Significance level (α) = 0.05(indicates a 5% risk of concluding that a difference exists when there is no actual difference)

Chi squared is equal to 183.745, therefore its p-value is .00001.

• p-value > 0.1: No evidence
• p-value between 0.05 and 0.1: Weak evidence
• p-value between 0.01 and 0.05: Evidence
• p-value between 0.001 and 0.01: Strong evidence
• p-value < 0.001: Very strong evidence

 

TEST 2: Z test for proportion

Proportion of Gender

 

RESEARCH HYPOTHESIS

In this study, the following hypothesis has been assumed to find out that whether the null hypothesis stand true or not.

• Null Hypothesis:
  • H₀ = Probability that number of male participants are equal to female participants.
• Alternate Hypothesis:
  • H_a = Probability that number of male participants are not equal to female participants.

• z = calculated critical value.
• x = Observed success.
• n = Size of samples.
• p = Probability of success
• q or 1-p= Probability of failure

 

Here,

• n = 165
• X = 60 or 105
• p = 1/2
• 1-p = 1/2

Significance level = 0.05

At 0.05 Significance level Z is 1.96.

RESULT

Our observed value of Z is 3.51 which is greater than the critical value of 1.96. We therefore reject null hypothesis H_0.

---

Question: Which part of the body feels uncomfortable in your ready-made garments?

Response collected

 

RESEARCH HYPOTHESIS

In this study, the following hypothesis has been assumed to find out that whether the null hypothesis stand true or not.

• Null Hypothesis:
  • H₀ = There is no difference between the size issue in formal, casual and party wear clothing.
• Alternate Hypothesis:
  • H_a = There is difference between the size issue in formal, casual and party wear clothing.

Analysis

	Formal wear (A)	Casual wear (B)	Party wear (C)
Bust	56	32	42
Waist	40	20	42
Hip	20	7	24

 

Solution

Data table

Step-1: sum of squares between samples

Step-2: sum of squares within samples

Step-3: Total sum of squares

SST= SSB + SSW
=1069.5556+744.6667
=1814.2222

Step-4: variance between samples

 

MSB =SSB / k-1
=1069.5556/2
=534.7778

Step-5: variance within samples

MSW=SSW/ n–k
=744.6667/ 9-3
=744.6667/ 6
=124.1111

Step-6: test statistic F for one way ANOVA test

F=MSB / MSW
=534.7778 / 124.1111
=4.3089

• The degree of freedom between samples= k-1=2
• degree of freedom within samplesn–k = 9-3 = 6

ANOVA table

Source of Variation	Sums of Squares SS	Degrees of freedom DF	Mean Squares MS	F	p-value
Between samples	SSB = 1069.5556	k-1 = 2	MSB = 534.7778	4.3089	0.0692
Within samples	SSW = 744.6667	n–k = 6	MSW = 124.1111
Total	SST = 1814.2222	n-1 = 8

 

RESULT

As calculated Fis 4.3089 which is smaller than 5.1433.
So, H₀ is accepted, hence there is no difference between the size issue in formal, casual and party wear clothing.

F (2,6) at 0.05 level of significance=5.1433

 

---

TEST 4: Pearson Correlation Coefficient test

Question: If a sizing survey is initiated, will you be willing to volunteer?

Response collected

 

Age	No. of people willing to volunteer
19	3
22	15
31	1
39	2
23	4
20	23
45	1
24	3
21	27
26	1
32	2

 

 

RESEARCH HYPOTHESIS

In this study, the following hypothesis has been assumed to find out that whether the null hypothesis stand true or not.

• Null Hypothesis:
  • H₀ = There is no correlation between age of participants to number of participants willing to volunteer
• Alternate Hypothesis:
  • H_a = There is correlation between age of participants to number of participants willing to volunteer.

Solution:

Let, X: Age of participants, Y: No. of people, M_x: Mean of X Values and M_y: Mean of Y Values

X	Y
19	3
22	15
31	1
39	2
23	4
20	23
45	1
24	3
21	27
26	1
32	2
Sum = 302	Sum= 82
Mean (Mx)= 27.455	My= 7.455

 

X – M_x & Y – M_y: Deviation scores
(X – M_x)² & (Y – M_y)²: Deviation Squared
(X – M_x) (Y – M_y): Product of Deviation Scores

X- Mx	Y-My	(X- Mx)2	(Y-My)2	(X- Mx) (Y-My)
-8.455	-4.455	71.479	19.843	37.661
-5.455	7.545	29.752	56.934	-41.157
3.545	-6.455	12.570	41.661	-22.884
11.545	-5.455	133.298	29.752	-62.975
-4.455	-3.455	19.843	11.934	15.388
-7.455	15.545	55.570	241.661	-115.884
17.545	-6.455	307.843	41.661	-113.248
-3.455	-4.455	11.934	19.843	15.388
-6.455	19.545	41.661	382.025	-126.157
-1.455	-6.455	2.116	41.661	9.388
4.545	-5.455	20.661	29.752	-24.793
		Sum: 706.727	Sum: 916.727	Sum: -429.273

 

The value of r = -0.5333

Degree of correlation:

1. Perfect: If the value is near ± 1, then it said to be a perfect correlation: as one variable increases, the other variable tends to also increase (if positive) or decrease (if negative).
2. High degree: If the coefficient value lies between ± 0.50 and ± 1, then it is said to be a strong correlation.
3. Moderate degree: If the value lies between ± 0.30 and ± 0.49, then it is said to be a medium correlation.
4. Low degree: When the value lies below + .29, then it is said to be a small correlation.

RESULT

As r value is -0.5333, there is a negative correlation, which means there is a tendency for high X variable scores to go with low Y variable scores (and vice versa). Therefore, null hypothesis H₀ is rejected.

---

TEST 5: Fisher’s Exact Method test

Question: Do you know that India does have their own sizing system?

Response collected

 

Response with respect to gender of participants

 

	Male	Female	Total
Yes	36	71	107
No	24	34	58
Total	60	105	165

 

RESEARCH HYPOTHESIS

In this study, the following hypothesis has been assumed to find out that whether the null hypothesis stand true or not.

• Null Hypothesis (H₀):
  • The gender of the participants has no association with having awareness aboutIndian sizing system.
• Alternate Hypothesis (H_a):
  • The gender of the participants has association with having awareness about Indian sizing system.

Assigning alphabets to each data

	Male	Female	Total
Yes	a = 36	b = 71	a +b = 107
No	c = 24	d = 34	c +d = 58
Total	a +c = 60	b +d = 105	N = 165

 

For Fisher’s Exact Test we use a mathematical method called Combination which helps us find out the value of p (two tailed) so that it can be compared with significance level (α) of 0.05 to check our hypothesis.

Similarly, we need to calculate p₁, p₂, p₃, p₄ 3-4 times in which the data is altered by +1 and -1 alternatively in each row which will give the p-value (p₁+p₂+p₃+p₄).

 

 

Table 2

	Male	Female	Total
Yes	32	75	107
No	28	30	58
Total	60	105	165

 

Assigning alphabets to each data

	Male	Female	Total
Yes	a = 32	b = 75	a+b = 107
No	c = 28	d = 30	c+d = 58
Total	a+c = 60	b+d = 105	N = 165

 

Table 3

	Male	Female	Total
Yes	33	74	107
No	27	31	58
Total	60	105	165

 

Assigning alphabets to each data-

	Male	Female	Total
Yes	a = 33	b = 75	a+b = 107
No	c = 27	d = 31	c+d = 58
Total	a+c = 60	b+d = 105	N = 165

 

Table 4

	Male	Female	Total
Yes	34	73	107
No	26	32	58
Total	60	105	165

 

Assigning alphabets to each data –

	Male	Female	Total
Yes	a = 34	b = 73	a+b = 107
No	c = 26	d = 32	c+d = 58
Total	a+c = 60	b+d = 105	N = 165

 

Table 5

	Male	Female	Total
Yes	35	72	107
No	25	33	58
Total	60	105	165

 

Assigning alphabets to each data –

	Male	Female	Total
Yes	a = 35	b = 72	a+b = 107
No	c = 25	d = 33	c+d = 58
Total	a+c = 60	b+d = 105	N = 165

 

Calculating p-value for the Fisher’s Exact Test: –

p-value = p₁ + p_{2 +} p_{3 +} p_{4 +} p₅

= 0.165064951 + 0.018119212 + 0.037194863 + 0.068304538 +0.112244166

= 0.40092773

 

RESULT

As p value is greater than significance level of 0.05, H₀ is accepted. “The gender of the participants has no association with the knowledge of sizing system”, stands true.

CONCLUSION

We all know that India does not have its own sizing system. So, keeping this in mind I conducted survey about the knowledge of Indian sizing system, problem faced due to current sizing system, knowing the mindset to volunteer for the anthropometric data collection and so on. The survey included people from different ages, occupations, genders, geographic regions and so on.

Through this survey I proved my hypothesis, “India needs its own sizing system”.

I collected the data of the citizens and ran 5 hypothesis tests: –

1. CHI SQUARE GOODNESS OF FIT TEST
2. Z TEST FOR PROPORTION
3. ONE WAY ANOVA TEST
4. PEARSON CORRELATION COEFFICIENT TEST
5. FISHER’S EXACT METHOD TEST

The tests concluded several results like

All the tests came out with the results in favor of our hypothesis that “India needs its own sizing system”.

---

REFERENCES

1. Dey, S. (2019). Why India needs its own size chart for the right fit. Retrieved 26 May 2021, from https://www.livemint.com/industry/manufacturing/why-india-needs-its-own-size-chart-for-the-right-fit-1548305046228.html
2. Gupta, D. (2010). Anthropometric Study of Young Indian Men for Garment Sizing. Research Journal Of Textile And Apparel, 14(1), 82-89. doi: 10.1108/rjta-14-01-2010-b009
3. Jeyasingh, M., &Appavoo, K. (2012). Retrieved 26 May 2021, from http://www.mecs-press.com/ijitcs/ijitcs-v4-n6/IJITCS-V4-N6-2.pdf
4. Kausher, H., & Srivastava, S. (2007). Developing Structured Sizing Systems for Manufacturing Ready-Made Garments of Indian Females Using Decision Tree-Based Data Mining. Retrieved 26 May 2021, from https://publications.waset.org/10010963/developing-structured-sizing-systems-for-manufacturing-ready-made-garments-of-indian-females-using-decision-tree-based-data-mining
5. Mcculloch, C., Paal, B., & Ashdown, S. (2007). An optimisation approach to apparel sizing. Retrieved 26 May 2021, from https://www.tandfonline.com/doi/abs/10.1057/palgrave.jors.2600533
6. Media coverage on INDIASize | NIFT. Retrieved 26 May 2021, from https://nift.ac.in/india-size

7. Vasesi, A. (2018). India Sizing soon to be a reality; set to boost Indian retail | Apparel Resources. Retrieved 26 May 2021, from https://in.apparelresources.com/business-news/retail/india-sizing-soon-reality-2/

---

ANNEXURE – QUESTIONNAIRE

SIZING SYSTEM

A sizing system is a table of numbers that represents the value of key dimensions used to classify the measurement of bodies encountered in the population. It is based on anthropometric data (measurement of humans) and is known as an anthropometric sizing system.

PURPOSE

This survey is for educational purpose only.

1. Name: –

 

2. Age: –

 

3. Gender: –
   1. Male
   2. Female
   3. Non binary
   4. Prefer not to say

 

4. Which part of India are you from?
   1. North
   2. South
   3. East
   4. West

 

5. Occupation: –

 

6. Do you know that India does not have their own sizing system?
   1. Yes
   2. No

 

7. How regular do you need to buy new clothes?

Within a monthIn 3-4 months     After 6 months     In 9-10 months     After 1 year

1. Formals
2. Jackets
3. Denim Jeans
4. Casual Wear
5. Party Wear

 

8. From where do you buy your clothes?
   1. Online
   2. In stores
   3. Custom Made

 

9. Which part of the body feels uncomfortable in your ready-made garments?

Bust/Chest     Waist     Hips     Fits perfectly

1. Formals
2. Jackets
3. Denim Jeans
4. Casual Wear
5. Party Wear

 

10. On a scale of 1 to 5, how comfortable are you with the universal sizing system?

1 = Extremely comfortable; 5 = Extremely uncomfortable

Extremely comfortable     Comfortable     Neutral     Uncomfortable     Extremely uncomfortable

1. 1
2. 2
3. 3
4. 4
5. 5

 

11. Do you think India needs its own sizing system?
    1. Yes
    2. No
    3. Maybe

 

12. If a sizing survey is initiated, will you be willing to volunteer?
    1. Yes
    2. No
    3. Maybe

 

13. For taking your whole-body measurements, which method would you find most comfortable?

Comfortable     Not comfortable

1. Traditional
2. 3-D Body scanning

Aryan Rathore

Aryan Rathore is a fashion technology graduate with a strong foundation in textile and apparel production, spanning both theoretical knowledge and hands-on industry experience. He is driven by a deep commitment to innovation, imagination, and creative problem-solving—qualities that shape his approach to learning and execution.

His exposure to the textile value chain includes spinning, weaving, dyeing, finishing, inspection, and quality control, gained through professional training at leading organizations such as OCM Mills and Raymond. His academic journey is complemented by strategic project work in fashion retail and supply chain environments.

Aryan is also a published contributor, having authored numerous articles focused on textile processes, apparel manufacturing, and fashion technology—bringing clarity and insight to complex industry topics.