Chapter 5 Data Overview
Here we shall explore the data we have collected from the 23 college students of India as a response to a survey form given to them. First we shall look at the survey form, then look at the actual data we have collected and as the last part of this section we shall see the different variables and domains involved in this study.
5.1 Survey Form
To conduct the face recognition study, we circulated a survey form among college students of India through Google Forms. The Survey form comprised of 4 sections, each section pertaining to a particular domain of face recognition ability. Each section comprised of 5-7 questions framed to test the response towards that particular aspect of face recognition ability among students of India. Let’s look at the sections and the questions asked in the form itself.
5.1.1 Domains
These can also be referred to as the study variables and we have collected data on the following 4 domains of face recognition ability. These domains comprise of the overall face recognition ability of an individual. The domains are as follow:
Memory(abbv. as MEM): It deals with the aspect of how humans remember familiar and unfamiliar faces. It also keeps a record of whether we can track the information about the identity of a person. We also see how well we remember our past friends and people we have met.
Attention & Perception(ATTNPER): It deals with how attentive we are to various parts of the face. How human faces are perceived by the society. It also deals with how human brain treat face as an important aspect of information processing.
Social Understanding(SOC): It deals with how human face plays an important role in communication and interacting with the society. It also deals with how humans tend to prefer some pre-set standards of beauty in terms of facial look.
Facial Features(FAC): It deals with what preferences in terms of external facial features prevails in the society. It also deals with the aspect of how a person looks at external beauty like shape, size, eyes, facial hair, smile, etc of a person’s face.
5.1.2 Questions
Here we will see the actual questions asked in the Survey Questionnaire Form. Each of these items club together to form the study variables, which further club together to form the overall pro-environmental attitude.
MEM
MEM1: I can easily remember unfamiliar faces.
MEM2: I can easily recall familiar faces.
MEM3: I find it easy to recognize my childhood friends.
MEM4: I find it easy to identify people based on their facial features.
MEM5: Overall I possess a good face-recognition ability.
MEM6: I find it easier to recognize familiar faces but am often unable to trace their identity.
ATTNPER
ATTNPER1: I pay more attention to the human face as a whole in general.
ATTNPER2: I tend to pay more attention to external features of the face.
ATTNPER3: The face is a n important tool for information processing.
ATTNPER4: I tend to focus more on the lower part of the face.
ATTNPER5: I tend to attend faster to familiar faces than unfamiliar faces.
SOC
SOC1: I consider the human face as an important tool for daily communication.
SOC2: I am easily attracted to beautiful and fair-skinned faces.
SOC3: I am more keen to get involved with a person who is good-looking.
SOC4: I feel that attractive people are able to get better opportunities.
SOC5: I consider myself lucky that I have attractive facial features.
SOC6: I get easily carried away by good-looks.
FAC
FAC1: I am more drawn towards the eyes of a person.
FAC2: I dislike people with too much facial hair.
FAC3: I like to keep my face clean and glowing.
FAC4: I am easily attracted towards toned cheek-bones.
FAC5: I prefer make up on my face/partner’s face.
FAC6: I like people with sharp facial features, e.g.: sharp nose.
FAC7: I like to mix with people who smile wholeheartedly.
END
5.1.3 Response
All the items accepted response in the form of a Likert Scale having 5 levels, which are
Strongly Disagree(Score: 1)
Disagree(Score: 2)
Neutral(Score: 3)
Strongly Agree(Score: 4)
Agree(Score: 5)
We also asked the same question in different manner to check the consistency of individual towards face recognition ability.
5.2 Population
The Data is collected from only the college and university students of India, who are in the age range of approximately 18 to 25. We have students participating from various colleges and universities and various majors or areas of study fill this Survey Questionnaire Form. We are hence, targeting to study the face recognition ability of this population using our sample.
5.3 Collection
The data was collected from the 23 college students of India via google forms. The sample is not a random sample and we used a method known as the snowball sampling to gather more students to fill the Survey form. The survey form had sections referring to each domain and data was stored in Google Sheets from where it was obtained as a CSV file to conduct our analysis.
5.4 Data
Here we present the collected data from 23 college students.
| Sex | MEM1 | MEM2 | MEM3 | MEM4 | MEM5 | MEM6 | ATTNPER1 | ATTNPER2 | ATTNPER3 | ATTNPER4 | ATTNPER5 | SOC1 | SOC2 | SOC3 | SOC4 | SOC5 | SOC6 | FAC1 | FAC2 | FAC3 | FAC4 | FAC5 | FAC6 | FAC7 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Female | 3 | 5 | 3 | 1 | 1 | 4 | 3 | 1 | 2 | 3 | 5 | 5 | 1 | 1 | 4 | 2 | 1 | 5 | 1 | 4 | 2 | 1 | 2 | 5 |
| Male | 3 | 4 | 4 | 4 | 4 | 4 | 4 | 3 | 4 | 2 | 3 | 4 | 4 | 4 | 3 | 1 | 1 | 5 | 3 | 3 | 4 | 3 | 4 | 4 |
| Male | 3 | 4 | 4 | 2 | 3 | 4 | 5 | 2 | 4 | 2 | 4 | 4 | 3 | 4 | 5 | 2 | 2 | 4 | 3 | 4 | 3 | 4 | 4 | 4 |
| Male | 2 | 4 | 3 | 1 | 3 | 4 | 2 | 3 | 3 | 3 | 4 | 4 | 4 | 4 | 3 | 2 | 4 | 4 | 3 | 2 | 3 | 3 | 4 | 4 |
| Male | 3 | 5 | 5 | 5 | 4 | 4 | 3 | 5 | 5 | 2 | 5 | 5 | 3 | 3 | 4 | 4 | 2 | 2 | 4 | 5 | 3 | 2 | 2 | 5 |
| Female | 3 | 4 | 3 | 3 | 3 | 5 | 5 | 4 | 4 | 2 | 5 | 3 | 5 | 4 | 5 | 2 | 3 | 5 | 2 | 3 | 3 | 1 | 3 | 5 |
| Male | 5 | 1 | 1 | 5 | 1 | 5 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 5 | 1 |
| Male | 5 | 3 | 3 | 3 | 5 | 4 | 3 | 3 | 5 | 4 | 4 | 5 | 4 | 4 | 4 | 4 | 3 | 3 | 2 | 3 | 3 | 2 | 4 | 5 |
| Male | 2 | 5 | 3 | 2 | 4 | 1 | 5 | 4 | 5 | 1 | 5 | 5 | 3 | 3 | 4 | 1 | 2 | 5 | 1 | 3 | 2 | 1 | 4 | 5 |
| Male | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
| Female | 2 | 5 | 3 | 3 | 3 | 4 | 4 | 4 | 4 | 3 | 4 | 4 | 3 | 2 | 2 | 3 | 3 | 4 | 4 | 4 | 3 | 2 | 4 | 4 |
| Male | 2 | 3 | 3 | 4 | 3 | 4 | 5 | 4 | 5 | 5 | 5 | 5 | 4 | 3 | 3 | 4 | 3 | 4 | 2 | 3 | 3 | 4 | 4 | 5 |
| Male | 2 | 4 | 3 | 3 | 3 | 4 | 5 | 4 | 5 | 2 | 5 | 3 | 3 | 2 | 5 | 1 | 2 | 3 | 2 | 2 | 4 | 1 | 3 | 4 |
| Male | 2 | 4 | 4 | 4 | 4 | 2 | 5 | 3 | 5 | 3 | 3 | 5 | 3 | 4 | 4 | 3 | 3 | 5 | 3 | 5 | 4 | 1 | 3 | 5 |
| Male | 3 | 4 | 5 | 4 | 4 | 2 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| Male | 2 | 2 | 1 | 1 | 1 | 5 | 5 | 1 | 5 | 2 | 4 | 5 | 3 | 3 | 4 | 4 | 1 | 5 | 1 | 3 | 5 | 2 | 3 | 5 |
| Female | 2 | 4 | 4 | 4 | 4 | 1 | 2 | 2 | 3 | 1 | 4 | 1 | 1 | 1 | 4 | 3 | 1 | 4 | 1 | 5 | 1 | 2 | 3 | 5 |
| Male | 3 | 4 | 4 | 5 | 4 | 4 | 4 | 4 | 4 | 5 | 4 | 4 | 4 | 4 | 2 | 1 | 5 | 4 | 1 | 4 | 3 | 3 | 3 | 5 |
| Male | 3 | 5 | 2 | 3 | 4 | 4 | 4 | 3 | 5 | 3 | 5 | 4 | 3 | 3 | 2 | 3 | 3 | 4 | 5 | 4 | 3 | 1 | 3 | 5 |
| Male | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
| Male | 3 | 4 | 4 | 4 | 4 | 4 | 5 | 3 | 5 | 3 | 5 | 4 | 3 | 3 | 4 | 3 | 3 | 4 | 3 | 2 | 2 | 2 | 2 | 5 |
| Male | 2 | 5 | 2 | 4 | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 | 4 | 5 | 4 | 3 | 4 | 1 | 4 | 3 | 3 | 3 | 5 |
| Male | 3 | 5 | 5 | 5 | 4 | 4 | 3 | 5 | 5 | 2 | 5 | 5 | 3 | 3 | 4 | 4 | 2 | 2 | 4 | 5 | 3 | 2 | 2 | 5 |
Since, we are going to conduct statistical analysis with this data, we proceed with the above data after converting each column to their respected score i.e. numerical in nature and hence makes sense to carry forward with all sorts of statistical tools.
The data collected for the study is available on the github repository for this project(here).