Katz_Jonathan-IS (3)

The College of Wooster
All is Fair in Love and Work:
A Comparison of Job Search and Dating Websites
by
Jonathan Katz
Presented in Partial Fulfillment of the
Requirements of Independent Study Thesis Research
Supervised by
Dr. Barbara Thelamour
Department of Psychology
2015-2016

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A COMPARISON OF JOB SEARCH AND DATING WEBSITES
Table of Contents
Acknowledgments …………………………………………………………………………….......5
Abstract……………………………………………………………………………………………6
Introduction………………………………………………………………………………………..7
Social Media………………………………………………………..……..........................8
Uses of social media………………………………………………………………8
Traits of users……………………………………………………………………...9
Learning from companies’ branding on social media……………………………10
Attraction………………………………………………………………………………...13
Attraction and Arousal…………………………………………………………………...18
Measure of arousal……………………………………………………………….18
Attraction and eye tracking………………………………………………………20
Factors that influence arousal and attraction online……………………………………..22
Gender....................................................................................................................23
Race………………………………………………………………………………24
Picture professionalism…………………………………………………………..26
Current Study…………………………………………………………………………….27
Hypotheses……………………………………………………………………………….27
Method…………………………………………………………………………………………..30
Participants……………………………………………………………………………….30
Materials…………………………………………………………………………………30
Procedure...………………………………………………………………………………31

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Results……………………………………………………………………………………………33
LinkedIn……………………………….............................................................................33
Professionalism of target profile……………………………………………...….33
Race of target profile……………………………….……………………………34
Gender of target profile…………………………………………………………..35
Hiring of job seekers……………………………………………………………..36
Match.com…………………………………………………………..…………………...37
Professionalism of target profile……………………………………………...….37
Race of target profile……………………………….……………………………43
Gender of target profile…………………………………………………………..47
Comments on profiles…………………………………..………………………..50
Discussion…………………………………………………………….………………………….64
LinkedIn…………………..……………………………………………………………...65
Match.com…………………………………………………………………………….…70
Limitations and Future Research …………………………………...………….………..73
Sample…………..…………………………………………...…………………...73
Materials……………………………………………………………….………..74
Missing Data Cell……………………………………….…………….…………75
Future Research………………………………….………………………………75
Conclusion……………………………………………………………..………………...77
References………………………………………………………………………………………..78
Appendix A: Demographic Information Form…………………………...……………………...92
Appendix B: Recruiter Survey………………………………………………..………………….93

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Appendix C: Relationship Seeker Survey…………………………..……………………………94
Appendix D: Example of Match.com Profile………………………………...………………….95
Appendix E: Example of LinkedIn Profile………………………..……………………………..96

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Acknowledgments
I would like to thank my advisor, Dr. Barbara Thelamour, for her invaluable help during the
process of creating this study. I would also like to thank Dr. Bryan Karazsia of the College of
Wooster’s Psychology Department, as well as Dr. Gary Katz of the California State University of
Northridge Psychology Department for their significant contributions to the project. Lastly, I
would like to thank my family and friends for their support during the process.

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Abstract
This study analyzes what attracts/influences recruiters and relationship seekers when viewing
LinkedIn.com and Match.com profiles respectively. It will also look to see if the gender, race, or
professionalism of the person in the profile has an effect on how much time is spent looking at
the profile and how attractive the participants find the individuals in the profile. Visual behavior
(e.g., what participants look at most) was investigated through the use of MH100 Smart Glasses.
Additionally, participants were asked to complete a survey based upon the strengths/weaknesses
of 15 Match.com profiles and the 10 LinkedIn profiles. Attractiveness to the profiles for
Match.com and the willingness to hire the profiles for LinkedIn was also measured. Participants
had 20 minutes to look through 10 LinkedIn profiles or through 15 Match.com profiles. The
specific numbers of profiles were chosen based upon the need for variety in race, gender, and
professionalism capacities. A series of 2 X 2 Analyses of Variance (ANOVAs) was used to
analyze any differences between the viewing of different LinkedIn profiles and Match.com
profiles. A series of Chi-squared test of independence were tested to see if there was a
relationship between the LinkedIn target profiles and the gender of the recruiter. Participants
(recruiters) spent significantly more time viewing the professionally styled profile pictures for
LinkedIn in comparison to the unprofessional LinkedIn profile pictures. In addition,
unprofessional minority male and unprofessional minority females were more likely to be hired
by female recruiters in comparison to the male recruiters. Lastly. the professional profiles for
Match.com were rated as more attractive than the unprofessional profiles for Match.com.

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Introduction
So much of today’s world happens in a digital space. Whether it is job searching, online
dating, or news scanning, the extent to which information is accessible in a matter of seconds is
endless. Images and videos stream online at rates higher than ever in history. The sheer amount
of content that is available to skim, read, or summarize is constantly growing at significant rates
every day and the search engines such as Google, Yahoo, or Bing are shifting through this data.
Rayner, Smith, Malcolm, and Henderson (2009) found that viewers can distinguish and extract
the main points of a visual scene within a mere 40 to 100 milliseconds of exposure. That means
that job recruiters or relationship seekers usually only take less than a second to let the content
on the screen grab their attention. Every website designer is attempting to grab the attention of
visitors. The more frequently a website is visited and the longer the interaction, the more likely
the page will appear in the top links on search engines as well. Getting viewers to “click” on a
website is the job of website designers or marketers, but every individual who has a LinkedIn
profile or Match.com are their own marketing team for themselves.
Based on these uses of the internet to attract people, a few questions arise: Can the image
of an attractive individual take the attention away from the rest of a web page? Do gender or
racial biases affect hiring or dating trends? In this empirical study, recruiters and relationship
seekers will use an eye-tracker to predict whether there are similarities or differences between
what they most often view on a LinkedIn.com and Match.com profile page. This study will
additionally analyze whether professional attire, gender, or race influences desirability by a
recruiter or relationship seeker as well as the how an image on a profile influences the time spent

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on an “about me” section of a profile. Lastly, the study will examine whether there are
similarities between a professional profile and a casual dating profile.
Social Media
Uses of social media. Research has identified that the increase in Internet usage has
made everything people look to do (e.g., relationship seeking, product buying and selling,
television shows) accessible via online resources. Jones and Fox (2009) found that social
networking sites and instant messaging are being utilized for communication purposes and more
than a third of all Internet users are using these resources. The current study’s definition of social
media use is “the discrete intake of digital media or Internet that focuses on areas outside
traditional media use” (Buettner, 2016, p. 1). It provides a mechanism for society or an
individual to connect and communicate via social networking or instant messaging sites. In one
study, college undergraduates developed a diary-like response to their daily usage on the popular
social media site Facebook (Pempek, Yermolayeva & Calvert, 2009). Users in Pempek’s study
spent an average of 30 minutes on Facebook per day. The authors found that during these 30
minutes, users spent more time observing content on Facebook compared to posting content
while also expressing their personal identity through posts or articles.
Online social networking is not being used exclusively by the millennial generation.
There has been a significant increase in adult social media usage over the past several years and
this type of internet use has spread across generations (Lenhart, 2009). Over a third of adult
internet users report having a social networking site (Lenhart, 2009). Research indicates
similarities in online (e.g., texting, posting, tweeting) and offline (e.g. everyday vocabulary)
communication for emerging adults with their peers and family members (Subrahmanyam,
Reich, Waechter, & Espinoza, 2008). Use of these online media helped emerging adults

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reconnect with relationships that may have splintered since going off to separate colleges after
high school graduation. Many of the older students in the study stated that they would use social
networking sites like Facebook to stay in touch with friends who go separate ways after college
graduation. After understanding which age groups are currently utilizing social media, it is
important to analyze the type of social media user.
Traits of users. Social media outlets such as Facebook, LinkedIn, or YouTube have
expanded to reach hundreds of millions of users. Psychological research behind the effects of
social media on current users is a new topic and has the potential to evolve into a separate field
of study. Extraversion has been found to be positively correlated with social media usage, but a
more emotionally unstable male was more likely to be a regular user (Correa, Hinsley, & Zuniga,
2009). Another study found that extraversion, neuroticism, and openness to experience were the
big three personality traits the majority of social media users embodied. Later research by Correa
et al. (2009) examined not only the gender and age of these users but also investigated factors
such as life satisfaction and socio-economic background.
Studies have also analyzed the amount of time that Generation Z (i.e. “millennials,” born
after the year 1994) have interacted online. Results of these studies indicate that more than one-
third of millennial users check their profile page daily and almost another 25% visit every few
days (Correa et al. 2009). In addition to constantly checking their social networking sites, these
young adults are also more open to experiences (Ross et al., 2009) which correlates with higher
curiosity levels. Having higher curiosity and novelty levels will allow them to stay online even
longer and search for a greater range of information as their curiosity continues to grow with
each click of the mouse.

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Overall emotional well-being greatly influences social media as well. Research indicates
that participants who were high in positive collective self-esteem were more likely to utilize
social media to communicate with their groups (Barker, 2009). In addition, individuals who
identified with negative collective self-esteem communicated with their social groups as an
alternative form to communication compared with the uses of the high collective self-esteem
group. Gratification and social compensation are two other emotional rewards associated with
social media use (Barker, 2009). Online usage and the connection of these online resources to
social well-being has significantly impacted perceived gratification levels by users. Previous
literature indicates that individuals are devoted, engaged, and highly motivated to spend effort
and time in uploading content to social media sites (Boyd and Heer, 2006).
Learning from companies’ branding on social media. Social media has been found to
be beneficial for businesses, provided that they use it strategically. By avoiding negative
comments, questions (which cause forced answers), and entertainment/call to action messages on
brand fan pages, a company can increase their visibility and popularity on social networking sites
(Vries et al. 2012). Having positive news about the company and strategic content placement as
well as sharing positive customer feedback are all ways to increase attractiveness of the
company’s website (Vries et al. 2012). This in turn is a direct reflection of the company itself.
One could suggest that the more attractive a company’s website is to a user, the more likely
customers will purchase products from that company.
Social media sites are categorized by different functionalities and capabilities. For
instance, Friendster, Sixdegrees, and Hi5, all pre-Facebook social media sites, focused their
business platforms on creating friend lists and profiles while also having the ability to connect

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with friends-of-friends (Boyd and Ellison, 2008). Myspace, YouTube, and Flickr focused on
photos and shared music. Integrated marketing communications, a term developed by Boone and
Kurtz, (2007), describes how individuals are able to advertise through their own personal public
relations outlet and sell themselves through direct marketing strategies which potentially could
lead to employment promotions. A few functions that major social media sites offer for users
when presenting themselves include wide-ranged “word-of-mouth” blogs, discussion boards, and
chat rooms that can be hosted by companies/organizations, product/service ratings, as well as
social networking sites (Mangold & Faulds, 2009). Companies have now utilized social
networking sites during business to business (B2B) branding in order to connect with other
organizations. Thanks to the advent of sites such as Web 2.0, there are new and more efficient
ways to share, upload, and communicate content online (Enders, Hungenberg, Denker, & Mauch,
2008). Social networking sites such as Facebook, who has the most unique visitors globally, as
well as Twitter and LinkedIn connect individuals, groups, and organizations through
contemporary commercial online interaction (NielsenWire, 2010). According to Michaelidou,
Siamagka, and Christodoulides (2011), over a quarter of business to business interactions were
utilizing social networking sites to achieve their brand objectives with attracting new customers
as their number one priority. Increased market spending on this specific channel also illustrates
the importance of utilizing social networking sites for businesses on a non-business to consumer
platform. (Michaelidou, Siamagka, & Christodoulides, 2011). This modern form of business-to-
business interaction translates to a person-to-person online platform as well.
Individuals also have the ability to promote both their professional and personal selves.
Vries, et al. (2012) studied how specific page content influences website’s popularity. By
observing how a company brands itself on social media, a person can learn how to “brand”

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themselves via social networking sites. Results from Vries’ study specifically looked at location
of brand posts on pages as well as the type of content that should be posted, but one could
suggest that these type of results could relate to an individual’s own personal fan page (e.g.,
Facebook, LinkedIn, Match.com). Yet, the most important finding by Subrahmanyam et al.
(2008) which was later replicated by Pempek et al. (2009) is the fact that individuals would
express their personal identity on these social networking platforms. By having the ability to
self-promote on the internet, there are possibilities for increased engagement of content and
information that is out there for people to research.
The most common use of social networking sites such as Facebook is uploading and
sharing of photos. Many individuals utilize photo sharing for both intrinsic and extrinsic needs
(Nov, Naaman, Ye, 2010). Yet individual differences have been found for reasons behind social
media usage. Depending on an individual’s perceived identity, users will either depict
themselves as “self-revealing” or “responding to others” (Chang, 2015). Self-revealers will be
more likely to disclose personal information such as political opinions, day-to-day activities, or
personal feelings. When an individual self-reveals on social media, they are projecting their
thoughts, ideas, passions, and opinions on a world-wide platform. From there, viewers of profiles
are able to get a sense of who a person is and what he/she represents.
When viewing online profiles, the different types of content being viewed have an impact
on the viewers of the profiles. One study conducted by Burkell and Saginur (2015) compared
how rural vs. urban girls and young woman viewed online social networking sites differently.
There findings were consistent with earlier research conducted by Fischer (1982) which
suggested that individuals living in urban settings had fewer relatives in their social network and
less densely connected networks. This is contrasted with the smaller, but highly dense social

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networks of individuals living in rural areas. Fischer (1982) also pointed out that individuals
from urban backgrounds are more tolerant of deviations from traditional norms such as religion
or sexuality compared to people with rural backgrounds.
Outside of an individual’s setting and where people live, factors such as race and gender
also impact how individuals view online profiles. Research suggests that whites are least open to
dating outside their race (i.e. out-dating) (Robnett & Feliciano, 2011). Yet an interesting
discovery by Robnett and Feliciano (2011) illustrated how Asians and Latinos are adopting this
type of racial exclusion that is similar to whites. Early research suggests that in addition to the
economic and structural organization of society, racial boundaries are created due to intimacy
preferences (Blumer, 1958). Racial boundaries between whites and minorities has been
extensively studied (Robnett and Feliciano, 2011) and research has shown that most people
prefer to date within their own racial group (Gordon, 1964; Kalmijn, 1998). In the present study,
attractiveness is being measured alongside strengths/weaknesses of online dating profiles
(specifically Match.com). Whether racial/gender boundaries are portrayed through the survey
responses and how these potential boundaries may influence attractiveness level of these online
profiles has yet to be researched.
Attraction
Physical appearance and sexual identity are the most accessible characteristics to others
in social interaction (Dion, Berscheid, & Walster, 1972). Attractiveness has been a key influence
on recognition and memory as well. Social psychology defines attraction as the natural feeling of
being drawn to other individuals and desiring their company (Psychology Dictionary, n.d.).
Beauty or attractiveness levels are two common characteristics used when describing a person’s
physical attributes. Whether arousal and attractiveness are related may depend on how

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memorable one individual is to another individual based upon attraction and arousal levels.
According to Singer (1984), men and women differ in how they recognize personal sexual
arousal with men being better able to identify their sexual arousal than women. Sexual arousal
recognition is how individuals are able to comprehend and come to the realization that they have
become sexually aroused. In addition, research suggests that “neutral” faces, not negative or
“unattractive” faces are the least memorable (Cross, Cross, & Daly, 1971).
First impressions are powerful in that having a positive primacy effect on another
individual can greatly increase your chances of corresponding with that individual again. First
impressions and attractiveness tend to have a corresponding impact. In connection with primacy
effects (i.e. reasons such as being a juror on a court case), people tend to stand true to primacy
effects and hold to their original impressions of an individual (Tetlock, 1983). This is why it is
essential to make a strong, positive first impression when meeting with someone online or
offline. This research dates back to Asch (1964) and the findings that suggest that a multitude of
characteristics are weighed into how a person perceives another individual. In addition, when
characteristics are disassociated, the disassociated perception tends to hinder our ability to form
an accurate depiction and representation of an individual.
Forming an attractive first impression that is recognizable is crucial when developing
one’s personal brand. Commonality and similarity are more easily forgotten in facial recognition
(Hunt, 1995) but distinctiveness of objects do create more significant memories. Displaying
characteristics that separate oneself from other people (e.g., competition in the job market) in a
positive manner will make that individual more memorable. Furthermore, distinctiveness
positively correlates with actual and predicted recognition of faces (Sommer, Leuthold, Matt, &
Schweinberger, 1995). This finding further reiterates the importance of creating a unique

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external image of oneself. Attractiveness has been attributed as a distinctive feature when
observing and commenting on people’s faces. For instance, Cross, Cross, and Daly (1971) found
that after a surprise test on facial recognition, the faces that were rated as more attractive were
more likely to be remembered. Later research found that not only attractive, but unattractive
faces were the most memorable (Fleishman, Buckley, Klosinky, Smith, & Tuck, 1976). Both of
these findings suggest that attractiveness or unattractiveness are distinct features of an individual
and these distinct features, whether positive or negative, will be memorable.
Commonality, or the state of sharing features or attributes, has an effect on distinguishing
between images since the more common a type of image is, the less likely it will be to
distinguish between common images. A more gender-specific study suggested that attractive
females and unattractive males were the most easily recognized (Yarmey, 1979). A meta-analytic
review was performed concerning the relationship between physical attractiveness and
intellectual competence (Jackson, Hunter, & Hodge, 1995). Two major findings by Jackson et al.
(1995) concluded that attractiveness effects were stronger for males than for females and even
stronger when explicit information about competence was absent than when it was present.
These findings coincide with the theory that direct measures of competence, compared with
indirect measures of competence were influenced strongly by attractiveness (Jackson et al., 1995.
Feingold’s (1992) earlier findings suggest more attractive participants are not distinguishable
from less attractive participants when analyzing personal characteristics, skills, or attributes.
Studies have found that more attractive individuals are perceived to have more socially
desirable characteristics, regardless of sex (Dion, Berscheid, & Walster 1972). Socially desirable
characteristics range from a perceived sense of humor to intelligence. Aside from the
professional benefits of being attractive, physically attractive individuals may have advantages

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outside on regular dating benefits. The material benefits and happiness that are associated with
being beautiful attract individuals toward attractive marriage partners. Yet, the professional
benefits that are associated with beautiful people could directly affect people in the job market.
This past research shows that more attractive people perceived to be happier, live happier lives,
and be more successful than unattractive people. Additionally, teachers favor better looking
students (Clifford & Walster, 1973), voter preferences in political elections lean toward more
beautiful people (Efran and Patterson, 1974), and jury judgements favor the more attractive in
pseudo trials (Efran, 1974). All of these professional, judicial, and educational benefits relate
with a person’s attractiveness level. Job interviewing is another area where attractiveness has
affected screening decisions (Watkins & Johnston, 2000). These advantages may greatly
influence whether an individual receives a job opportunity.
A number of studies have found that attractiveness affects the hiring process with job
occupations with decreased exposure and person-to-person interaction (Dipboye, Arvey &
Terpstra, 1977; Dipboye, Fromkin & Wiback, 1975). Some even have found that hiring based
upon attractiveness level negatively impacts overall organizational performance (Shahani-
Denning, Andreoli, Snyder, Tevet, and Fox, 2011). On the contrary to Feingold (1992) findings,
studies on attraction have found that similarity (Byrne and Nelson, 1965), proximity (Festinger,
Schachter, and Back, 1950), and misattribution of arousal (Dutton and Aron, 1974) are all
significant reasons behind attraction between people. More attractive people were also rated as
being more socially pleasing (e.g., kind, outgoing, modest) as well as more successful
professionally. The biggest influence that Dion, Berscheid, and Walster (1972) found on
attraction was physical attractiveness.

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Could external attractiveness, such as beauty, relate with internal personality
characteristics? Research findings have found that more attractive individuals are perceived to
have more socially desirable characteristics. Research has also indicated that the cause of
attraction is a major influencer. Arousal and attraction are two concepts that have been
interconnected through past research findings. In this study, attractiveness level will be measured
through behavioral responses to the viewing of online profiles. Attraction is an essential portion
of viewing online profiles as people make behavioral responses (i.e. messaging the person they
are viewing, liking the photo) based upon attraction level.
Personality traits of potential job candidates in the recruiting world are also assessed by
traits outside of attractiveness level (Cole, Feild, Giles, & Harris, 2009). Company recognition,
previous experience, and education are other areas that are receiving attention by recruiters,
especially when reviewing LinkedIn.com profiles (Benjamin, 2015). Based upon an applicant’s
personality traits, besides extraversion and openness, the Big 5 personality traits usually lacked
validity when the personality traits and Big 5 were correlated together (Cole et al., 2009). This
finding illustrates that recruiters should not assume the personality traits of applicants based
solely off of the written resume. Personality trait evaluations should be judged solely from in-
person interviews instead of the “dispositional characteristics” formed from the resumes (Cole et
al., 2009, pg. 5; Cole, Field, & Giles, 2003).
For online dating sites, the attractiveness of the profile picture has a large influence on
the overall quality of an individual’s online profile (10 things women look for in dating profiles,
2014). Yet, there are other factors and sections of an online dating profile that have been found
to have an impact on a relationship seeker viewing dating profiles. Dating websites state that
although the profile picture is the essential piece of an online profile, dating headlines (first

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words on a profile, usually an “about me” section) and then biography and background (usually
demographics of the person) are other pieces that influence whether a relationship seeker pursues
another individual online (Oasis, 2012). With increases in online dating from age groups under
25 (due to increases in mobile dating apps) and individuals over the ages of 50/60, understanding
what aspects of an online dating profile is crucial in the quest to find love (Anderson & Smith,
2016).
Attraction and Arousal
Research indicates that arousal, (i.e. sexual, physical, physiological arousal), has been
linked with attraction. Although studies have defined arousal in terms of: “the external incentive
and the state of arousability of the nervous system” (Toates, 1986, p. 97) or as alternatives for
“sexual excitation” (Kinsey, Pomeroy, & Martin, 1948) and “sexual arousability” (Whalen,
1966), this study will define arousal similarly to Sachs and Barfield (1976): the level of
excitation relative to a threshold. Arousal will be analyzed to see if this type of emotional
excitation will influence an individual’s behavioral actions.
When analyzing aspects of visual behavior, myriads of studies have published
information pertaining to people’s perspectives and biases as influenced by a person’s level of
arousal. Once aroused, the behaviors that follow the arousal can be measured as a way to
understand how that specific action affected that individual. Research has measured the various
types of arousal ranging from physical to cognitive arousal.
Measure of arousal. Past research has shown that glances, fixations, or avoidances are
forms of nonverbal communication, which are often related to emotional reactions perceived
from visual interactions. For instance, Gibson and Pick (1963) concluded that individuals are
able to sense when another individual is looking at him/her fully in the face, resulting in the

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notion that “signs” or nonverbal communication can be perceived. There are emotional responses
to looking people fully in the face (Exline et al., 1965). These emotional responses have been
measured by the amount of avoidant or fixated visual tendencies participants performed when
discussing potentially arousing or embarrassing topics. Results indicated that participants were
more likely to become fixated in the conversation when they were the “listener” compared with
the “speaker” in the interview setting. In addition, others have found that due to a female’s
tendency to portray more affection and inclusive relationships, females would be more willing to
fixate their gaze during an uncomfortable or arousing situation (Parsons, 1955; Exline et al.,
1965).
Embarrassment and concealment were the main measurements of Exline et al. (1965) and
these two emotions have often been associated with feelings of physical attraction and arousal.
Results of participant’s concealment during embarrassing situations concluded that although the
uncomfortableness created avoidant tendencies by the participants, but Exline et al. (1965) could
not confidently state that this was solely due to the desire to conceal information. Although
factors such as fatigue or distractions may have affected the results, men did look at female
instructors more frequently when they were uninstructed. In contrast, females did not look at
male instructors as frequently when uninstructed compared to instructed.
Selective attention is also affected by arousal. According to Portas, Rees, Howseman,
Josephs, Turner, and Frith (1998) factors such as consciousness are directly affected by circadian
changes in arousal, which selective attention influences. This may result in the reduction of the
impact of specific stimuli. In effect, when an individual experiences changes in arousal due to
selectively focusing on a specific object or stimuli, other stimuli may be excluded from their
vision. Selective attention, defined as focusing attention to a single source of information for a

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significant period of time, is affected by level of arousal (Portas et al., 1998; Das, Naglieri,
Kirby, 1994). The other type of attention focused on in previous research is sustained attention,
which is defined as excluding other information while focusing on one source of information
also is affected by level of arousal. Further research indicates that although moderate levels of
arousal do not deter attention performance, high levels of arousal excitement significantly
impacts performance (Easterbrook, 1959). Later research by La Berge (1995) identified the
connection between the pulvinar and the mediodorsal nuclei of the thalamus and the effect that
these two systems have on prefrontal voluntary control of attention. In other words, levels of
arousal may be regulated by the thalamus and the specific level of arousal elicited directly affects
the attention systems. When an individual views online profiles, selective attention has a
significant effect on both arousal level and behavioral responses. For the present study,
participants will be viewing profiles of people’s faces. The fact that the majority of online
profiles have individuals viewing other individual’s faces may influence how individuals react to
each profile. In addition, the present study features images of profiles of both sexes regardless of
the participant’s gender or sexual orientation. According to past research (Exline et al. 1965),
this may influence the behavioral responses of the participants. Lastly, where an individual
focuses their attention was recorded via an eye-tracking camera and measurements of arousal
will be analyzed by the responses to the 3 question survey.
Attraction and eye-tracking. One way of measuring arousal is via eye movements.
Overall, eye movements can be separated into four different types of movement: pursuit,
vergence, vestibular, and saccadic (Rayner & Pollatsek, 1992). Each of these eye movements
have a different effect on perceiving visual stimuli. Pursuit eye movements are used to track
something that is moving and vergence eye movements are used when an individual is looking

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from a distant object to a near object (or vice-versa). Vestibular eye movements are used when a
person is tracking an object and the viewer is in motion while saccadic eye movements are
largely seen as voluntary eye movements towards stimuli that garner one’s attention.
This study will focus on vestibular and saccadic eye movements. When the head or body moves,
vestibular eye movements help compensate this type of action (head/body movement). Saccadic
eye movements facilitate rapid movements that serve to position the eye for visual processing
(Rayner & Pollatsek, 1992; Rayner & Pollatsek, 1994). For infants, saccadic eye movements
have been measured when analyzing preferential looking. Preferential looking, defined as the
discrimination between two stimuli, has been found to imply early knowledge of physics,
memory, visual attention, numbering in infants (Spelke, Katz, Purcell, Ehrlich, & Breinlinger
1994; Hirsh-Pasek & Golinkoff, 1996). Arousal has also been indicated as having a significant
effect on eye movement activities. Previous research has indicated that decreases in arousal
result in the reduction of microsaccade (type of fixation eye movement) rate and pupil
fluctuation (how often a pupil enlarges or minimizes) during continuous gaze of a fixated target
(Honda, Kohama, Tanaka, & Yoshida, 2013). These findings suggest that monitoring eye
movements can create precise evaluations of arousal levels.
Monitoring eye movements and pupil fluctuations may also become more significant
when restricted to initial slow eye movements. While other studies have analyzed pupil
fluctuations and pupil size in response to increases in blood pulsation (Koprowski, Szmigiel,
Kasprzak, Wrobel, & Wilczynski, 2015), studies on eye movement have also been associated
with increased blood pulsation caused by arousal. Arousal levels have been measured during
slow eye movement activities such as the wake-sleep transition while driving a vehicle. Although
auditory alarms would awaken a drowsy driver, eye movements would not be fully enabled by

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the auditory signals and reaction times were still poor. (i.e. the individual would be cognitively
awake, but vision was still not optimal) (Sakai, Shin, Uchiyama, Terashima, & Wakita, 2011).
This would infer that auditory arousal (e.g., an alarm), does not fully arouse an individual to
make advanced behavior movements, such as driving a car.
Factors that Influence Arousal and Attraction Online
As social media continues to evolve, different facets of everyday life will make the
transition to the digital world. Dating websites are one area where self-presentation is key in
grabbing the attention of a potential suitor. Presenting oneself as being liked, perceiving
competence or high status, varies among the type of audience (Guadagno, Okdie, & Kruse,
2012). For a particular situation, such as a job interview, one would want to focus on the
perception of competence over being liked. On the other hand, a person on a date would value
likability over competence. Factors such as deception may also have an underlying impact on
online self-presentations due to the lack in face-to-face interaction.
However, this lack of face-to-face interaction does not seem to deter individuals from
evaluating attractiveness online. As stated earlier in Jackson et al. (1995) findings, high levels of
attractiveness have been found to elicit perceived levels of high competence. This perception of
competence stems back to the notion that high levels of attractiveness in an individual lead to
assumed intelligence levels. Past research indicates that attractive profile photos have not only
been found to be more favorable overall (Fiore, Lindsay, Mendelsohn, & Hearst, 2008), but the
notion “what is beautiful is good” (Dion et al., 1972, p. 1108) has also been attributed to
possessing higher qualities in social skills and intelligence. Originating from Dion et al. (1972)
study, the physical attractiveness stereotype states that more attractive people are more popular

Katz 23
and socially skilled, while also being perceived as more intelligent (Langlois, Kalakanis,
Rubenstein, Larson, Hallam, & Smoot (2000).
A job candidate’s characteristics can influence his or her attractiveness. Much research
has highlighted racial and gender discrimination, especially for hiring purposes, but less has
focused on discrimination according to these characteristics for dating. Because the present study
will be conducted with social media in mind, the image the job-seeker portrays may also impact
his/her attractiveness to a job recruiter.
Gender. In the recruiting world, there is pressure to fulfill certain types of employees in
specific roles in order to move the business forward and create profit. The people who work for
the company are the ones directly responsible for the bottom-line profit and without a solid core
of employees, the business cannot succeed. Smith (1978) studied overseers or gatekeepers,
people who set the standards and values of the field and directly influence who is admitted into a
system. Essentially, Smith looked to see how past hiring managers went about either bringing in
certain employees or keeping certain employees out. Yoder, Crumpton, and Zipp (1989)
expanded upon Smith’s (1978) initial research and explored whether gatekeepers would favor
same-sex candidates and their contributions to the field. Both studies concluded that same-sex
candidates were favored by the gatekeepers. Bielby and Baron (1986) argued that the best
practices focusing on deterring sex discrimination in the workplace settings involved firms who
published examples of how to establish minimal levels of sex discrimination.
How attractiveness effects hiring with gender as a moderator of the relationship between
attractiveness level and hiring tendencies has also been closely researched. When a male-
dominated employment opportunity was undergoing hiring, attractive applicants were seen as
more qualified than unattractive applicants (Cash, Gillen, and Burns, 1977). The same type of

Katz 24
hiring decision was found for female-dominated job industries employment. Early research
supports the hypothesis that recruiters will be more likely to hire attractive applicants as Heilman
and Saruwatari (1979) found that college students when asked to rate resumes with an attached
picture favored the more attractive male for a masculine job. On the contrary, Saruwatari
suggested that attractive females were less likely to be hired for a job with masculine
characteristics due to their perceived increased in femininity levels. Yet, more attractive female
applicants were also perceived to be more qualified for jobs overall (Cash, Gillen, and Burns,
1977), which coincides with this studies hypothesis on attractive female applicants being more
likely to be hired by recruiters.
Race. Online dating preferences have been analyzed to see what type of attributes,
characteristics, or values draw individuals to connect with a potential “mate”. As seen in prior
research, similarities play a detrimental role in attractiveness levels (Hunt, 1995), but similarities
have influenced online dating preferences (Hitsch, Hortacsu, & Ariely, 2010). Although strategic
behavior has not been found to influence dating preferences, race and income have had a
significant influence (Hitsch et al., 2010). Similarities between education levels in online dating
has indicated that higher education levels for a woman result in the woman searching for a mate
with a similar education level (Kalmijn, 1998). This suggests that perceived intelligence is
related to attractiveness level (as in Dion et al., 1972), resulting in more attractive individuals
attracting highly educated “mates.” Across the United States, interracial relationships are rare,
potentially due to same race preferences (Kalmijn, 1998) and later research has discovered that
same-race preferences are significant for both males and females (Hitsch et al., 2010).
Race has also had a significant impact on hiring preferences for recruiters. Fix and Turner
(1998) found that minority auditors were less likely to be given a call back after an in person

Katz 25
interview, performed worse (potentially due to the social psychological stereotype threat), and if
they are called back, they are less likely to receive an offer. In addition, even before the
interview process takes place, the minority candidates were less likely to offered an interview
based upon their resume. Even with the black and white participants in their study being matched
in exact characteristics and resume content, the white participants were more likely to be hired.
An interesting finding about paper resumes that were utilized in Bertrand and Mullainathan
(2004) suggests that an additional year of work experience on a resume increases the chances of
a callback by 0.4%. When combining this statistic with the percent call back between a white and
minority candidate, an extra year of experience on a white named candidate actually equals over
eight years of additional experience compared with a minority candidate.
What Bertrand and Mullainathan (2004) lack in their study is the addition of the online
resume. The present study examined not only the “white” or “black” names on a recruiter’s racial
bias, but the addition of an online profile picture as seen on LinkedIn.com. Quality of paper
resumes have been found to be higher quality with characteristics of work experience, minimal
holes in employment history, work at school, some military experience (Bertrand &
Mullainathan, 2004). Even with equal resume quality, the chances of receiving a callback
between white-named applicants and black-named applicants was significant with white-named
candidates receiving a higher chance of a callback. White-named applicants with a higher quality
resume received over a 2% increase in callbacks compared with a 0.5% increase in callback for a
higher quality resume with a black-name. This finding is alarming. Could a black-name hinder a
recruiter from even looking down the resume at the potential candidate’s experience? This study
was all based on a written name and written information without an image.

Katz 26
Race and sex composition are vital for creating a healthy and positive work environment
if composed justly (Reskin et al. 1999). It is also very important to have a diverse employee
composition overall in order to interact with a wide range of different races and sexes that
encompass today’s society. According to Kalleberg, Knoke, Marsden, and Spaeth (1996), an
average company averages one-fifth of their workforce as racial/ethnic minorities. This number
becomes even more significant when over one-fourth of companies do not have a single minority
employed (Reskin et al. 1999). These findings coincide with this studies hypothesis on how
racial biases will effect hiring and dating trends. For sexual biases, the results of Yoder et al.
(1989) suggest that men were more likely to favor male applicants over female applicants in
nonacademic professions. With an overwhelming amount of males representing the work force,
especially in hiring positions, Yoder et al. (1989) findings may illustrate why minorities were
being underrepresented in United States companies during that time. Industries that have been
found to be more male dominated for a significant part of history have even started to even out
between men and women. For instance, the field of psychology was largely occupied by male
psychologists only, but by as far back as 1986, over fifty percent of doctoral degrees were
granted to women (Howard et al. 1986). At the turn of the 1980's to the 1990's, affirmative action
policies were pressuring organizations with low levels of minorities (women especially) to hire
qualified female candidates in order to even out their representation in the areas lacking diversity
(Yoders et al., 1989). The reduction of negative stereotypes against women and other minorities
assisted in progressing businesses to open their eyes to potential candidates outside of male
dominated industries.
Picture professionalism. According to a study conducted by eHarmony.com, profiles
with poor quality images, narrow portraits that may suggest that a prior mate was cut out of the

Katz 27
photo, and wide, far away images received the least amount of communication (eharmony.com).
In addition, bussinessinsider.com found that word choice greatly influences whether your online
dating profile is more attractive, while selfies for men and women differed greatly in
effectiveness (45% for women who were rated as “hot” and 13% for men). In the present study,
recruiters and relationship seekers will be studied by an eye-tracker to predict whether there are
similarities or differences between what they most often view on a LinkedIn.com and Match.com
profile page. In addition, the study will be analyzing whether professional attire or race
influences attractiveness by a recruiter or relationship seeker as well as the how an image on a
profile influences the time spent on an “about me” section of a profile. Lastly, this study measure
the similarities between a professional profile and a casual dating profile.
Current Study
As demonstrated, the world has shifted from paper to the internet and written resumes,
although still collected by a majority of employers, is not the first line of defense in hiring
screening. Facebook, Twitter, Instagram etc. are the online social place to be as a millennial and
LinkedIn.com is the new professional online resume where employers are searching through
millions of potential candidates. Similarly, online dating sites have become a commonplace way
for people to meet prospective partners. The present study measured how changes in arousal
levels caused by viewing different online dating and job search profiles effects visual tendencies.
The levels of arousal were measured through a survey which will elicit cognitive and behavioral
feelings toward the specific profile. The eye-tracker recorded visual tendencies during the
viewing of the profiles.
My hypotheses for LinkedIn are as follows:

Katz 28
1. It is predicted that the participants (recruiters) who view the professional LinkedIn
profiles with professional profile pictures will spend more time looking at the pictures and the
skills section compared to the LinkedIn profiles with unprofessional profile pictures. It is also
predicted that there will be gender group differences for how long the participants look at
professional versus unprofessional profiles.
2. It is predicted that the participants (recruiters) will spend more time looking at the
white profiles compared to the minority profiles. It is also predicted that there will be gender
group differences for how long the participants look at white versus male profiles.
3. It is predicted that there will not be a significant difference in how long the participants
(recruiters) will view the male versus female profiles. It is predicted that there will be gender
group differences for how long the participants look at male versus female profiles.
4. It is predicted that the participants (recruiters) who view the professional LinkedIn
profiles with professional profile pictures will be more likely to hire the professional profiles
compared to the LinkedIn profiles with unprofessional profile pictures. It is also predicted that
there will not be gender group differences for the participants on whether or not they would hire
professional versus unprofessional profiles.
My hypotheses for Match.com are as follows:
1. It is predicted that the participants (relationship seekers) who view the professional
Match.com profiles with professional profile pictures will spend more time looking at the
pictures compared to the Match.com profiles with unprofessional profile pictures. It is also
predicted that there will be not any gender or racial group differences for how long the
participants look at professional versus unprofessional profiles.

Katz 29
2. It is predicted that there will not be a significant different for the time spent viewing
the white and minority profiles by the participants (relationship seekers). It is predicted that there
will not be any differences with regard to the race or gender of the participants when viewing the
white vs. minority profiles.
3. It is predicted that participants (relationship seekers) will spend more time viewing
the female profiles compared to the male profiles. It is predicted that white participants will view
the white profile pictures longer than the minority profile pictures and the minority participants
will view the minority profile pictures longer than the white profile pictures. In addition, it is
predicted that the male participants will view the female profile pictures longer than the male
profile pictures.
4. Attractiveness hypotheses differing according to professionalism, race and gender of
target profile. It is predicted that participants (relationship seekers) will rate the professional
profiles higher compared to the unprofessional profiles. It is predicted that white participants will
rate the minority target profiles lower in comparison to white target profiles and that minority
participants will rate minority target profiles higher in comparison to white target profiles. It is
also predicted that the female target profiles will be rated as higher than male target profiles.
This study will also provide descriptive analyses of the comments participants made about both
LinkedIn and Match.com profiles. Specifically, the strengths and weaknesses of profiles
according to race, gender, and professionalism will be highlighted.

Katz 30
Method
Participants
Twelve recruiters (i.e. employers with hiring responsibilities) were recruited by email and
22 College of Wooster students were recruited through the research participation system at the
College of Wooster called SONA. Students received academic credit for participating. The
students were current first- or second-year undergraduates and the ages for the recruiters ranged
from 24-60 (M = 40). Two of the recruiters declined to give their age. There were a total of 3
male recruiters and 9 female recruiters. Students were recruited from a small, Midwestern, liberal
arts college in Northeastern Ohio and recruiters were employees who have hiring experience
within the career center at the College of Wooster. The recruiters’ racial backgrounds were all
Caucasian/white. From the student sample, there were 12 female participants and 10 male
participants. The student samples were divided according to white participants and minority
participants. Minority students had a large range of racial backgrounds: black, Indian, Asian,
African American, Asian American, Nigerian American, Hispanic, and bi-racial. The students
did not give their actual age, just the year of undergraduate study in which they were engaged.
Materials
In this study, relationship seekers (i.e. student participants) were asked to view 15
Match.com profiles. Recruiters were asked to view 10 LinkedIn.com profiles. Every relationship
seeker viewed the same 15 Match.com profiles and every recruiter viewed the same 10 LinkedIn
profiles. The professional profiles were profiles that had the individual in the profile wearing
professional-styled clothing. For men, that entailed a suit, jacket, and tie with dress pants if
visible in the image. For females, this entailed either a professional dress or suit in the image.
Unprofessional profiles had individuals in the profile picture wearing casual clothing. All

Katz 31
profiles were real profiles. The Match.com profiles were selected by creating fake Match.com
profiles and having them available to select as a potential match. The profiles were then captured
as an image via screen-shot and projected on a projector. The LinkedIn profiles were also real
profiles that were shown on a Google website via a URL link which connected the participant to
the target profiles. All LinkedIn profiles were clicked on beforehand so all the participant had to
do was click on the profiles listed across the top of the web page as they completed each target
profile.
M100 Smart Glasses were used to track what visual information the participants were
looking at most frequently. The Smart Glasses recorded what the relationship seekers viewed on
a projected screen and recorded what the recruiters viewed on a laptop. After the participants
viewed each of their respected profiles individually, they filled out a survey pertaining to the
individual profile for either the LinkedIn.com or Match.com. Survey questions can be found in
the appendix.
Procedure
The relationship seekers met the researcher in an academic classroom on campus, and the
recruiters met the researcher in an academic office or classroom at a specific time in order to
complete the study. The participants took a seat and were asked to place the eye-tracking device
on their head. After the eye-tracking device was fitted to their head, the instructions were read to
them by the researcher. Participants were told they would have approximately 20 minutes to look
through their respected profiles (LinkedIn.com for recruiters and Match.com for relationship
seekers) and respond to each profile with the individual survey pertaining to the specific profile.
The approximate time was told to the participants due to the battery life and storage capacity on
the Smart Glasses. The participants were not told to stop once the 20 minutes were completed.

Katz 32
Individual participants and their data were identified by a code number that was known only to
the experimenter. The participants identified themselves on the survey with a subject number that
the experimenter gave to them before the study began.

Katz 33
Results
LinkedIn
Professionalism of target profile. A 2 (gender of recruiter) x 2 (professionalism of
target profile) mixed model ANOVA was run to see if there were differences in time spent
viewing the profile pictures. There was a main effect for gender of the recruiter F(1,10) = 7.107,
p = .024, and there was a main effect for the professionalism of the target profiles F(1,10) =
44.957, p < .001. Participants looked longer at profiles with professional pictures, M = 23.611
seconds, compared to unprofessional pictures, M = 19.208 seconds. The interaction between the
participant’s gender and the target’s professionalism was significant, F(1,10) = 58.304, p < .001.
Female participants looked longer at unprofessional photos, M = 15.417 seconds, compared to
professional photos, M = 14.806. Male participants looked longer at professional photos, M =
32.417, compared to unprofessional photos, M = 23.000 seconds.
The amount of time viewing the profile pictures did differ significantly between the
target gender and target professionalism F(1,10) = 8.021, p = .018. Unprofessional male profile
pictures were viewed longer, M = 21.500 seconds, compared to professional male profile
pictures, M = 20.472 seconds. Professional female profile pictures were viewed longer, M =
26.750 seconds, compared to unprofessional female profile pictures, M = 16.917 seconds. The
interaction between the target race and professional profile picture with the gender of the
participant was significant, F(1,10) = 7.914, p = .018. Female participants looked longer at
unprofessional minority profile pictures, M = 14.833 seconds, compared to professional minority
profile pictures, M = 13.111 seconds. Female participants looked longer at professional white
profile pictures, M = 16.500 seconds, compared to unprofessional white profile pictures, M =
16.000 seconds. Male participants looked longer at professional minority profile pictures, M =

Katz 34
21.833 seconds, compared to unprofessional minority profile pictures, M = 20.667 seconds. Male
participants looked longer at professional white profile pictures, M = 43.000 seconds, compared
to unprofessional white profile pictures, M = 25.333 seconds. All other interactions did not have
a significant effect.
A 2 (gender of recruiter) x 2 (professionalism of target profile) mixed model ANOVA
was run to see if there were differences in time spent viewing the skills section. There was a
main effect for gender of the recruiter F(1,10) = 5.477, p = .041, but there was not a main effect
for the professionalism of the target profiles F(1,10) = .475, p > .05. Female participants viewed
the skill sections longer, M = 61.083 seconds, in comparison with the male participants, M =
46.500 seconds. The interaction between the target’s race and the target’s professionalism did
differ significantly, F(1,10) = 6.20, p = .032. Minority professional targets were viewed longer,
M = 52.972 seconds, compared to the minority unprofessional targets, M = 49.139 seconds.
White unprofessional targets were viewed longer, M = 60.583 seconds, compared to the white
professional targets, M = 52.472 seconds. All other interactions did not have a significant effect.
Race of target profile. A 2 (gender of recruiter) x 2 (race of target profile) mixed model
ANOVA was run to see if there were differences for time spent looking at the profile picture.
There was a main effect for gender of the recruiter F(1,10) = 7.107, p = .024, and there was a
main effect for the race of the target profiles F(1,10) = 8.389, p = .016. Participants looked
longer at white profiles, M = 25.208 seconds, compared to minority profiles, M = 17.611
seconds. The interaction between the target’s race and gender with the participant’s gender was
significant, F(1,10) = 6.922, p < .025. Female participants looked longer at white male targets, M
= 18.556 seconds, compared to minority male targets, M = 15.389 seconds. Female participants
looked longer at white female targets, M = 13.944 seconds, compared to minority female targets,

Katz 35
M = 12.556 seconds. Male participants looked longer at white male targets, M = 26.833 seconds,
compared to minority male targets, M = 23.167 seconds. Male participants looked longer at
white female participant’s M = 41.500 seconds, compared to minority female participants, M =
19.333 seconds.
Profile picture looking times did differ significantly between target race and target
professionalism, F(1,10) = 13.608, p = .004. Unprofessional minority profile pictures were
looked at longer, M = 17.750 seconds, compared to professional minority profile pictures, M =
17.472 seconds. Professional white profile pictures were looked at longer, M = 29.750 seconds,
compared to unprofessional white profile pictures, M = 20.667 seconds. All other interactions did
not have a significant effect.
A 2 (gender of recruiter) x 2 (race of target profile) mixed model ANOVA was run to see
if there were differences for time spent looking at the skills section. There was a main effect for
gender of the recruiter F(1,10) = 5.477, p = .016, but there was not a main effect for the race of
the target profiles F(1,10) = 3.092, p > .05. The interaction between the target’s race and the
target’s gender was significant, F(1,10) = 6.200, p = .032. Minority professional targets were
viewed longer, M = 52.972 seconds, compared to the minority unprofessional targets, M =
49.139 seconds. White unprofessional targets were viewed longer, M = 60.583 seconds,
compared to the white professional targets, M = 52.472 seconds. All other interactions did not
have a significant effect.
Gender of target profile. A 2 (gender of recruiter) x 2 (gender of target profile) mixed
model ANOVA was run to see if there were differences for time spent looking at the profile
picture. There were a main effect for gender of the recruiter F(1,10) = 7.107, p = .024, but there
was not a main effect for the gender of the target profile F(1,10) = .186, p > .05. Male

Katz 36
participants viewed the profiles pictures longer, M = 27.71 seconds, compared to female
participants, M = 15.12 seconds. The interaction between the participant’s gender and the gender
of the target picture profiles was significant F(1,10) = 5.420, p < .05. Female participants looked
longer at female pictures, M = 16.250 seconds, compared to male pictures, M = 13.250 seconds
and male participants looked longer at female pictures, M = 30.417 seconds, compared to male
pictures, M = 25.000 seconds. All other interactions did not have a significant effect.
Hiring of job seekers. Several chi-squared tests of independence were run to see whether
recruiters would hire the job-seekers based on their profiles. There were not gender differences
in willingness to hire a professional white female, and results revealed that there were no
differences χ2
(1, N = 12) = .364, p > .05. 3 cells had expected count less than 5. The minimum
expected count is .25. The relation between being hired for a professional white male and the
gender of the participant was tested. The relation between these variables was not significant, χ2
(2, N = 12) = 2.000, p > .05. 5 cells had expected count less than 5. The minimum expected
count is .25. The relation between being hired for an unprofessional minority female and the
gender of the participant was tested. The relation between these variables was significant, χ2
(2,
N = 12) = 12.000, p = .002. Female participants were more likely to hire the unprofessional
minority female, M = 9, compared to male participants, M = 0 (for Yes Hire). 5 cells had
expected count less than 5. The minimum expected count was .25.
The relation between being hired for an unprofessional white male and the gender of the
participant was tested. The relation between these variables was not significant, χ2
(1, N = 12) =
.000, p > .05. 3 cells had expected count less than 5. The minimum expected count was .25. The
relation between being hired for a professional minority male and the gender of the participant
was tested. The relation between these variables was not significant, χ2
(2, N = 12) = 1.333, p >

Katz 37
.05. 5 cells had expected count less than 5. The minimum expected count was .25. The relation
between being hired for an unprofessional white female and the gender of the participant was
tested. The relation between these variables was not significant, χ2
(2, N = 12) = 1.333, p > .05. 5
cells had expected count less than 5. The minimum expected count was .25. The relation between
being hired for an unprofessional minority female and the gender of the participant was tested.
The relation between these variables was not significant, χ2
(2, N = 12) = 1.333, p > .05. Five
cells had expected count less than 5. The minimum expected count was .25. The relation between
being hired for an unprofessional minority male and the gender of the participant was tested. The
relation between these variables was significant, χ2
(2, N = 12) = 8.000, p = .018. Female
participants were more likely to hire the unprofessional minority male target, M = 6 (for Yes
Hire), compared to male participants, M = 0 (for Yes Hire). 3 cells had expected count less than
5. The minimum expected count was .25.
Match.com
Professionalism of target profile. A 2 (gender of relationship seeker) x 2 (gender of
target profile) mixed model ANOVA was run to see if there were differences for time spent
looking at the profile picture. There was not a main effect for gender of the recruiter F(1,16) =
2.808, p > .05, and the main effect for profile picture looking times for the male target’s
professionalism was approaching a level of significant, F(1,16) = 3.292, p = .088, but was not
significant. Participants viewed the professional male targets longer, M = 10.913 seconds,
compared to the unprofessional male targets, M = 7.259 seconds. Profile picture looking times
for the minority target profiles did not differ significantly between the target’s professionalism,
F(1,16) = 2.761, p > .05. The interactions for the profile picture looking times between the
minority target’s professionalism, F(1,16) = .260, p > .05. The interaction for the profile picture

Katz 38
looking time for the minority targets between the minority target’s professionalism and the
participant’s race did not differ significantly, F(1,16) = 1.203, p > .05. All other interactions did
not have a significant effect.
A 2 (race of relationship seeker) x 2 (gender of target profile) mixed model ANOVA was
run to see if there were differences for time spent looking at the profile picture. There was not a
main effect for race of the recruiter F(1,20) = 8.098, p > .05, and the main effect for profile
picture looking times for the male target’s professionalism was approaching a level of
significant, F(1,16) = 3.292, p = .088, but was not significant. The interaction for the profile
picture looking times between the male target’s professionalism and the participant’s race was
not significant, F(1,16) = 1.489, p > .05. The interaction for the profile picture looking times
between the male target’s race and professionalism with the participant’s race was not
significant, F(1,16) = 1.709, p > .05. All other interactions did not have a significant effect.
A 2 (gender of relationship seeker) x 2 (professionalism of target profile) mixed model
ANOVA was run to see if there were differences in time spent viewing the demographic
sections. There was not a main effect for gender of the recruiter F(1,16) = 2.009, p > .05, but
there was a main effect for the professionalism of the male target profiles F(1,16) = 5.473, p =
.033. Professional male targets demographic looking times were looked at longer, M = 13.213
seconds, compared to the unprofessional male targets demographic looking times, M = 9.841
seconds. The interaction between the professionalism of the male targets and the gender of the
participant was not significant, F(1,16) = .719, p > .05. Demographic looking times for the
minority target’s professionalism was approaching levels of significance, F(1,16) = 3.752, p =
.071, but was not significant. Professional minority targets demographic looking times were
looked at longer, M = 15.306 seconds, compared to unprofessional minority demographic

Katz 39
looking times, M = 12.185 seconds. The interaction between professionalism of the minority
target’s demographic looking times and the gender of the participant did not differ statistically,
F(1,16) = .196, p > .05. All other interactions did not have significant effects.
A 2 (race of relationship seeker) x 2 (professionalism of target profile) mixed model
ANOVA was run to see if there were differences in time spent viewing the demographic
sections. There was a main effect for the race of the recruiter F(1,16) = 6.633, p = .020, but
there was a main effect for the professionalism of the male target profiles F(1,16) = 5.473, p =
.033. White participants viewed the demographic sections longer, M = 16.773 seconds, compared
to the minority participants, M = 9.839 seconds. The interaction for the time spent viewing the
demographic information between the male target’s professionalism and the participant’s race
was not significant, F(1,16) = 2.353, p > .05. The interaction for the time spent viewing the
demographic information between the male target’s race and professionalism with the
participant’s race was not significant, F(1,16) = .414, p > .05. The interaction for viewing the
demographic information for the minority profiles between the minority target’s professionalism
and the participant’s race was not significant, F(1,16) = 1.307, p > .05. The interaction for
viewing the demographic information for the minority profiles between the minority target’s
gender and professionalism with the participant’s race was not significant, F(1,16) = .213, p >
.05. All other interactions did not have significant effects.
ANOVA was run to see if there were differences in time spent viewing the about me sections.
There was not a main effect for the gender of the recruiter F(1,16) = 2.136, p > .05, but there
was a main effect for the professionalism of the minority target profiles F(1,16) = 2.136, p =
.033. The about me looking times for the professional minority targets were looked at longer, M

Katz 40
= 44.978 seconds, compared to the unprofessional minority targets, M = 30.711 seconds. The
interaction between the minority target’s professionalism and the gender of the participant did
differ significantly, F(1,16) = 5.672, p < .030. Female participants viewed the about me sections
of the professional minority targets longer, M = 33.656 seconds, compared to the unprofessional
minority targets, M = 29.240 seconds. Male participants viewed the about me sections of the
professional minority targets longer, M = 56.300 seconds, compared to the unprofessional
minority targets, M = 32.183 seconds. The about me looking times for the male targets did differ
significantly between the professionalism of the profiles, F(1,16) = 12.720, p = .003. The about
me sections for the professional male targets were looked at longer, M = 45.559 seconds,
compared to the unprofessional male targets, M = 30.513 seconds. The interaction for the
looking times for the about me sections between the professionalism of the target and gender of
the participant was approaching significance, F(1,16) = 3.334, p = .087, but was not significant.
Female participants looked longer at the about me sections of the professional targets, M =
35.469 seconds, compared to the about me sections of the unprofessional targets, M = 28.125
seconds. Male participants looked longer at the about me sections of the professional targets, M
= 55.650 seconds, compared to the about me sections of the unprofessional targets, M = 32.900
seconds. The interaction for the looking times for the about me sections between the race and the
professionalism of the targets with the gender of the participant was approaching significance,
F(1,16) = 3.284, p = .089, but was not significant. Female participants viewed the about me
sections of the professional minority male targets longer, M = 29.375 seconds, compared to the
about me sections of the unprofessional minority male targets, M = 22.313 seconds. Female
participants viewed the about me sections of the professional white male targets longer, M =
41.563 seconds, compared to the about me sections of the unprofessional white male targets, M =

Katz 41
33.938. Male participants viewed the about me sections of the professional minority male targets
longer, M = 61.100 seconds, compared to the about me sections of the unprofessional minority
male targets, M = 26.000 seconds. Male participants viewed the about me sections of the
professional white male targets longer, M = 50.200 seconds, compared to the about me sections
of the unprofessional white male targets, M = 39.800 seconds. All other interactions did not have
significant results.
ANOVA was run to see if there were differences in time spent viewing the about me sections.
There was not a main effect for the race of the recruiter F(1,14) = 1.387, p > .05, but there was a
main effect for the professionalism of the male target profiles F(1,16) = 11.325, p = .004. The
about me section for the professional minority target profiles were viewed longer, M = 45.011
seconds, compared to the unprofessional minority target profiles, M = 31.443 seconds. The
interaction for the viewing of the about me section of the male target’s between the male target’s
race and professionalism with the participant’s race was not significant, F(1,16) = 1.286, p > .05.
The interaction for the viewing of the about me section of the male target’s between the male
targets’ professionalism and the participant’s race was significant, F(1,16) = 5.191, p < .05. The
minority participants’ viewed the about me section of the professional male profiles longer, M =
39.536 seconds, compared to the about me section of the unprofessional male profiles, M =
35.071 seconds. The white participants’ viewed the about me section of the professional male
profiles longer, M = 51.227 seconds, compared to the about me section of the unprofessional
male profiles, M = 28.045 seconds. The interaction for the viewing of the About Me section for
the minority target profiles between minority target’s gender and professionalism with the

Katz 42
participant’s race was not significant, F(1,16) = 2.722, p > .05. All other interactions did not
have significant effects.
ANOVA was run to see if there were differences in attractiveness levels. There was not a main
effect for the race of the recruiter F(1,14) = .106, p > .05, but there was a main effect for the
professionalism of the male target profiles F(1,20) = 27.065, p = .000. The attractiveness levels
of the professional male target profiles were higher, M = 5.823, compared to the attractiveness
levels of the unprofessional male target profiles, M = 4.485. All other interactions did not have
significant effects.
ANOVA was run to see if there were differences in attractiveness levels. There was not a main
effect for the race of the recruiter F(1,20) = 2.181, p > .05, but there was a main effect for the
professionalism of the male target profiles F(1,20) = 30.907, p = 000. The professional male
target’s had higher attractiveness levels, M = 5.927, compared to the unprofessional male target
profiles, M = 4.525. The interaction for the attractiveness levels for the minority target profiles
between the minority target’s professionalism and the participant’s race did not differ
significantly, F(1,20) = .588, p > .05. The interaction for the attractiveness levels for the minority
target profiles between the minority target’s professionalism and gender with the participant’s
race did not differ significantly, F(1,20) = .067, p > .05. The interaction of the attractiveness
levels for the male targets between the male target’s professionalism and the participant’s race
were not significant, F(1,20) = 1.473, p > .05. The interaction of the attractiveness levels for the
male targets between the male target’s race and professionalism with the participant’s race was
not significant, F(1,20) = .520, p > .05. The attractiveness levels of the minority target’s did

Katz 43
differ significantly between professionalism, F(1,20) = 5.377, p = .031. The professional profiles
were rated higher, M = 5.827, compared to the unprofessional profiles, M = 5.306. All other
interactions did not have a significant effect.
Race of target profile. A 2 (gender of relationship seeker) x 2 (race of target profile)
mixed model ANOVA was run to see if there were differences in viewing times for the profile
pictures. There was a main effect for the gender of the relationship seeker F(1,14) = 18.909, p =
.001, but there was not a main effect for the race of the male target profiles F(1,16) = .645, p >
.05. The male participants viewed the profile pictures longer, M = 10.708 seconds, compared to
the female participants, M = 3.994 seconds. The interaction between the race of the male targets
and the gender of the participant was not significant, F(1,16) = .046, p > .05. The interaction
between the target gender and target race did not differ statistically for the profile picture looking
times, F(1,14) = .778, p > .05.
A 2 (race of relationship seeker) x 2 (race of target profile) mixed model ANOVA was
run to see if there were differences in viewing times for the profile pictures. There was not a
main effect for the gender of the relationship seeker F(1,14) = 1.735, p > .05, and there was not a
main effect for the race of the male target profiles F(1,14) = .003, p > .05. The interaction for the
profile picture looking times between the unprofessional target’s race and the participant’s race
was significant, F(1,14) = 5.559, p < .033. Minority participants viewed the unprofessional
minority profile pictures longer, M = 7.929 seconds, compared to the unprofessional white
profile pictures, M = 6.357 seconds. White participants viewed the unprofessional white profile
pictures longer, M = 9.083 seconds, compared to the unprofessional minority profile pictures, M
= 7.435 seconds. The interaction for the profile picture looking times between the male target’s

Katz 44
race and the participant’s race was not significant, F(1,16) = .006, p > .05. All other interactions
did not have significant effects.
A 2 (gender of relationship seeker) x 2 (race of target profile) mixed model ANOVA was
run to see if there were differences in viewing times for the demographic sections. There was a
main effect for the gender of the relationship seeker F(1,16) = 4.625, p = .047, and there was a
main effect for the race of the male target profiles F(1,16) = 9.566, p = .007. Minority male
target’s demographic looking times were looked at longer, M = 13.625 seconds, compared to the
white male targets, M = 9.428 seconds. The interaction between the race of the male targets and
the gender of the participant was not significant, F(1,16) = .395, p > .05. All other interactions
run to see if there were differences in viewing times for the demographic sections. There was a
main effect for the race of the relationship seeker F(1,16) = 7.572, p = .014, and there was a
main effect for the race of the male target profiles F(1,16) = 9.540, p = .007. White participants
viewed the demographic sections longer, M = 14.000 seconds, in comparison to the minority
demographic sections, M = 8.125 seconds. The minority target demographic sections were
viewed longer, M = 13.224 seconds, in comparison with the white target demographic sections,
M = 8.901 seconds. The interaction for viewing the demographic information on the
unprofessional profiles between the target’s race and gender with the participant’s race was not
significant, F(1,14) = .453, p > .05. All other interactions did not have significant effects.
run to see if there were differences in viewing times for the About Me sections. There was not a
main effect for the race of the relationship seeker F(1,16) = 2.136, p > .05, and there was not a

Katz 45
main effect for the race of the male target profiles F(1,16) = 2.245, p > .05. The interaction for
the looking times for the about me section between the race of the target and the gender of the
applicant did not differ significantly, F(1,16) = 1.381, p > .05. The interaction for the viewing of
the about me section for the minority target profiles between minority target’s professionalism
and the participant’s race was not significant, F(1,16) = 3.254, p > .05. The interaction for the
viewing of the unprofessional target profiles between the target gender and the target race was
significant, F(1,14) = 8.796, p = .010. Unprofessional white male About Me sections were
viewed longer, M = 33.798 seconds, compared to the unprofessional minority male about me
sections, M = 23.071 seconds. Unprofessional minority female about me sections were viewed
longer, M = 35.717 seconds, compared to the unprofessional white female About Me sections, M
= 34.187 seconds.
run to see if there were differences in viewing times for the about me sections. There was not a
main effect for the race of the relationship seeker F(1,14) = 1.387, p > .05, and there was not a
main effect for the race of the male target profiles F(1,14) = 3.217, p > .05. The interaction for
the viewing of the unprofessional target profiles between the target gender and the target race
was significant, F(1,14) = 8.626, p = .011. Unprofessional white male about me sections were
viewed longer, M = 35.171 seconds, compared to the unprofessional minority male about me
sections, M = 23.817 seconds. Unprofessional minority female about me sections were viewed
longer, M = 36.294 seconds, compared to the unprofessional white female about me sections, M
= 35.544 seconds. The interaction for the viewing of the about me section of the male target’s
between the male target’s race and the participant’s race was not significant, F(1,16) = .720, p >
.05. The interaction for the viewing of the about me section between the unprofessional target’s

Katz 46
race and the participant’s race was not significant, F(1,14) = .316, p > .05. The interaction for the
viewing of the about me section between the unprofessional target’s race and gender with the
participant’s race was not significant, F(1,14) = .081, p > .05. All other interactions did not have
run to see if there were differences in attractiveness levels. There was not a main effect for the
race of the relationship seeker F(1,20) = 4.036, p > .05, and there was not a main effect for the
race of the male target profiles F(1,20) = .106, p > .05. The attractiveness level of the male target
profiles was approaching significance between the race of the target profiles, F(1,20) = 4.036, p
= .058, but was not significant. The minority male target profiles had a higher attractiveness
level, M = 5.379 seconds, compared to the attractiveness levels of the white male target profiles,
M = 4.929 seconds. The interaction between the race of the male target profiles and the gender of
the participant was not significant, F(1,20) = .377, p > .05. All other interactions did not have
race of the relationship seeker F(1,20) = 2.181, p > .05, and there was not a main effect for the
race of the male target profiles F(1,20) = 3.386, p > .05. The interaction of the attractiveness
levels for the male targets between the male target’s race and the participant’s race was not
significant, F(1,20) = .192, p > .05. The interaction of the attractiveness levels of the
unprofessional targets between the target’s race and the participant’s race was not significant,
F(1,20) = .005, p > .05. The interaction of the attractiveness levels of the unprofessional targets

Katz 47
between the target’s gender and race with the participant’s race was not significant, F(1,20) =
.355, p > .05. All other interactions did not have significant effects.
Gender of target profile. A 2 (gender of relationship seeker) x 2 (gender of target
profile) mixed model ANOVA was run to see if there were differences in time spent viewing the
profile pictures. There was a main effect for the gender of the relationship seeker F(1,14) =
18.909, p = .001, but there was not a main effect for the gender of the male target profiles
F(1,14) = 2.926, p > .05. The male relationship seekers spent more time, M = 10.708 seconds
looking at the profile pictures than the female relationship seekers, M = 3.994 seconds. The
interaction between the profile picture looking time of the gender of the participant and the
gender of the target did not differ statistically, F(1,14) = .355, p > .05. The interaction between
the profile picture looking times of the target’s gender’s race and the gender of the participant
did not differ statistically, F(1,14) = .543, p > .05. Profile picture looking times for the minority
target profiles did not differ significantly between the target’s professionalism, F(1,16) = 2.761,
p > .05, or the target’s gender F(1,16) = .001, p > .05. The interactions for the profile picture
looking times between the minority target’s professionalism, F(1,16) = .260, p > .05, and the
minority target’s gender, F(1,16) = .668, p > .05, were both not significant. All other interactions
run to see if there were differences in time spent viewing the profile pictures. There was not a
main effect for the race of the relationship seeker F(1,14) = .226, p > .05, and there was not a
main effect for the gender of the male target profiles F(1,14) = 2.978, p > .05. The interaction for
the profile picture looking time for minority targets between the minority target’s gender and the
participant’s race did not differ significantly, F(1,16) = .182, p > .05. The interaction for the

Katz 48
profile picture looking time for minority targets between the minority target’s professionalism
and gender with the participant’s race did not differ significantly, F(1,16) = 1.815, p > .05. The
interaction for the profile picture looking times between the unprofessional target’s gender and
the participant’s race was not significant, F(1,14) = 2.978, p > .05.
A 2 (gender of relationship seeker) x 2 (gender of target profile) mixed model ANOVA
was run to see if there were differences in time spent viewing the demographic sections. There
was a main effect for the gender of the relationship seeker F(1,16) = 4.625, p = .047, but there
was not a main effect for the gender of the male target profiles F(1,16) = .027, p > .05. Male
relationship seekers viewed the demographic section longer, M = 16.721 seconds, compared to
the female relationship seekers, M = 10.771 seconds. The interaction between the gender of the
minority targets and the gender of the participant did not differ significantly, F(1,16) = 1.343, p
> .05. All other interactions did not have significant effects.
run to see if there were differences in time spent viewing the demographic sections. There was a
main effect for the race of the relationship seeker F(1,16) = 6.633, p = .020, but there was not a
main effect for the gender of the male target profiles F(1,16) = 4.358, p > .05. White relationship
seekers viewed the demographic sections longer, M = 16.773 seconds, compared to the minority
relationship seekers, M = 9.839 seconds. The interaction for viewing the demographic
information on the unprofessional profiles between the target’s gender and the participant’s race
was not significant, F(1,14) = .036, p > .05. The interaction for viewing the demographic
information for the minority profiles between the minority target’s gender and the participant’s
race was not significant, F(1,16) = .617, p > .05. All other interactions did not have significant
results.

Katz 49
was run to see if there were differences in time spent viewing the About Me sections. There was
not a main effect for the gender of the relationship seeker F(1,14) = .642, p > .05, but although
the main effect for the gender of the male target profiles was approaching significance, F(1,14) =
3.763, p = .073, it was not significant as well. The About Me sections of the unprofessional
female targets were viewed longer, M = 34.952 seconds, compared to the About Me sections of
the unprofessional male targets, M = 28.435 seconds. The interaction for the About Me looking
times between the target’s professionalism and the target’s gender was approaching significance,
F(1,16) = 3.991, p = .063, but was not significant. The About Me sections of the professional
minority male targets were viewed longer, M = 45.283 seconds, compared to the professional
minority female targets, M = 44.719 seconds. The About Me sections of the unprofessional
minority female profiles were viewed longer, M = 37.267 seconds, compared to the
unprofessional male profiles, M = 24.156 seconds. All other interactions did not have significant
effects.
run to see if there were differences in time spent viewing the About Me sections. There was not a
main effect for the race of the relationship seeker F(1,14) = 1.387, p > .05, there was not a main
effect for the gender of the target profile, F(1,14) = 3.700, p > .05. The interaction for the
viewing of the About Me section for the minority target profiles between minority target’s
gender and the participant’s race was not significant, F(1,16) = .899, p > .05. The interaction for
the viewing of the About Me section between the unprofessional target’s gender and the
participant’s gender was not significant, F(1,14) = .230, p > .05. All other interactions did not
have significant results.

Katz 50
was run to see if there were differences in attractiveness levels. There was not a main effect for
the gender of the relationship seeker F(1,20) = .106, p > .05, and there was not a main effect for
the gender of the target profile, F(1,20) = .377, p > .05. All other interactions did not have
race of the relationship seeker F(1,20) = 2.181, p > .05, but there was a main effect for the
gender of the target profile, F(1,20) =46.487, p = .000. The unprofessional female targets had
higher attractiveness levels, M = 6.099, compared to the unprofessional male targets, M = 4.525.
The interaction of the attractiveness level for the unprofessional target profiles between the
target’s race and the target’s gender was significant, F(1,20) = 26.183, p < .05. Unprofessional
minority female targets had higher attractiveness levels, M = 5.618, compared to the
unprofessional minority male targets, M = 4.994. Unprofessional white female targets had higher
attractiveness levels, M = 6.581, compared to the unprofessional white male’s targets, M = 4.057.
The interaction of the attractiveness levels of the unprofessional targets between the target’s
gender and the participant’s race was not significant, F(1,20) = .290, p > .05. The interaction for
the attractiveness levels for the minority target profiles between the minority target’s gender and
the participant’s race did not differ significantly, F(1,20) = .351, p > .05. All other interactions
Comments on profiles. The comments on the profiles came from the participant’s
survey responses. The relationship seekers would fill out individual surveys for all 15
Match.com profiles while the recruiters would fill out individual surveys for all 10 LinkedIn

Katz 51
profiles. The categories were chosen after the experimenter analyzed the common responses
within the strengths and weaknesses for both Match.com and LinkedIn. Each graph is
representing the strengths and weaknesses according to the gender, race, and professionalism of
the individual profiles for both Match.com and LinkedIn.
Some of the interesting differences within the comments from the survey are illustrated
when comparing between the strengths and weaknesses for relationship seekers. The majority of
the strengths listed for the Match.com profiles focused around the personality of the individual.
With at least 55% of all strength comments for Match.com focusing on personality, it was
important for the target profiles to portray a positive personality within their About Me sections.
Experience across the board for the LinkedIn profiles was a commonality between all of the
recruiters. A person’s hands-on experience or lack thereof would be either a benefit or major
detriment on all of the recruiters’ eyes. When comparing the strengths of the professional
Match.com and LinkedIn profiles, 7% of the strengths focused around the profile picture
LinkedIn, while 14% of the strengths for professional Match.com profiles focused around the
“looks” of the individual. Although not on the same scale as personality for Match.com or
experience for LinkedIn, the image of the individual on the screen did draw benefits for the
professional profiles. On the other hand, the weaknesses for the unprofessional profiles for
Match.com focused 9% of the weaknesses on the “looks” of the individual, while 28% of the
weakness comments for LinkedIn unprofessional profiles focused around the quality of the
picture. These findings show that profile pictures for both dating purposes and job search
applications have an impact on how people view the profiles.

Katz 52
Survey Responses for LinkedIn
Figure 1. Comments Made about White Profiles’ Strengths
Figure 2. Comments Made about White Profiles’ Weaknesses
33%
11%
12%
4%
13%
11%
11%
5%
1 2 3 4 5 6 7 8
4% 6%
13%
19%
38%
18%
0%2%
1 2 3 4 5 6 7 8
Legend
1. Experience
2. Recommendations
3. Summary
4. Quality of Picture
5. Descriptions/Titles
6. Advancement/Job
Hopping
7. Connections/Followers
8. Education
Legend
1. Experience
2. Recommendations
3. Summary
6. Advancement/Job
Hopping
8. Education

Katz 53
Figure 3. Comments Made about Minority Profiles’ Strengths
Figure 4. Comments made About Minority Profiles’ Weaknesses
40%
8%19%
5%
13%
2%
6% 7%
1 2 3 4 5 6 7 8
19%
7%
5%
9%
33%
24%
0%3%
1 2 3 4 5 6 7 8
Legend
1. Experience
2. Recommendations
3. Summary
6. Advancement/Job
Hopping
8. Education
Legend
1. Experience
2. Recommendations
3. Summary
6. Advancement/Job
Hopping
8. Education

Katz 54
Figure 5. Comments made About Male Profiles’ Strengths
Figure 6. Comments Made about Male Profiles’ Weaknesses
38%
10%14%
6%
10%
7%
8%
7%
1 2 3 4 5 6 7 8
14%
4%
7%
9%
38%
25%
0%3%
1 2 3 4 5 6 7 8
Legend
1. Experience
2. Recommendations
3. Summary
6. Advancement/Job
Hopping
8. Education
Legend
1. Experience
2. Recommendations
3. Summary
6. Advancement/Job
Hopping
8. Education

Katz 55
Figure 7. Comments Made about Female Profiles’ Strengths
Figure 8. Comments Made about Female Profile’s Weaknesses
35%
10%
18%
3%
17%
4%
9% 4%
1 2 3 4 5 6 7 8
7%
9%
12%
21%
33%
16% 0%2%
1 2 3 4 5 6 7 8
Legend
1. Experience
2. Recommendations
3. Summary
6. Advancement/Job
Hopping
8. Education
Legend
1. Experience
2. Recommendations
3. Summary
6. Advancement/Job
Hopping
8. Education

Katz 56
Figure 9. Comments Made about Professional Profiles’ Strengths
Figure 10. Comments Made about Professional Profiles’ Weaknesses
34%
7%
18%
7%
11%
7%
8%
8%
1 2 3 4 5 6 7 8
13%
9%
9%
2%
35%
30%
0%2%
1 2 3 4 5 6 7 8
Legend
1. Experience
2. Recommendations
3. Summary
6. Advancement/Job
Hopping
8. Education
Legend
1. Experience
2. Recommendations
3. Summary
6. Advancement/Job
Hopping
8. Education

Katz 57
Figure 11. Comments Made about Unprofessional Profiles’ Strengths
Figure 12. Comments Made about Unprofessional Profiles’ Weaknesses
40%
15%
11%
2%
18%
3%
8% 3%
1 2 3 4 5 6 7 8
8% 2%
12%
28%
33%
14% 0%3%
1 2 3 4 5 6 7 8
Legend
1. Experience
2. Recommendations
3. Summary
6. Advancement/Job
Hopping
8. Education
Legend
1. Experience
2. Recommendations
3. Summary
6. Advancement/Job
Hopping
8. Education

Katz 58
Survey Responses for Match.com
Figure 13. Comments Made about White Profiles’ Strengths
Figure 14. Comments Made about White Profiles’ Weaknesses
9% 1%
68%
2%2%2%
6%
4%2% 4%0%
1 2 3 4 5 6 7 8 9 10 11
13%
6%
26%
10%4%1%
18%
7%
5%
8% 2%
1 2 3 4 5 6 7 8 9 10 11
Legend
1. Looks
2. Age
3. Personality
4. Education
5. Political Views
6. Religions/Spirituality
7. Quality of Profile
8. Alcohol and Drugs
9. Past Marriage
Status/Kids
10. Financial/Occupation
11. Sexuality
Legend
1. Looks
2. Age
3. Personality
4. Education
5. Political Views
9. Past Marriage
Status/Kids
11. Sexuality

Katz 59
Figure 15. Comments Made about Minority Profiles’ Strengths
Figure 16. Comments Made about Minority Profiles’ Weaknesses
15%
1%
54%
5%
1%3%
3%
7%
4% 7% 0%
1 2 3 4 5 6 7 8 9 10 11
11%
4%
19%
9%
7%3%
21%
4%
14%
7% 1%
1 2 3 4 5 6 7 8 9 10 11
Legend
1. Looks
2. Age
3. Personality
4. Education
5. Political Views
9. Past Marriage Status/Kids
11. Sexuality
Legend
1. Looks
2. Age
3. Personality
4. Education
5. Political Views
9. Past Marriage
Status/Kids
11. Sexuality

Katz 60
Figure 17. Comments Made about Professional Profiles’ Strengths
Figure 18. Comments Made about Professional Profiles’ Weaknesses
14%
0%
60%
5%
2%2%3%
6% 4% 4%0%
1 2 3 4 5 6 7 8 9 10 11
8%
5%
25%
11%7%
3%
15%
3%
12%
9% 2%
1 2 3 4 5 6 7 8 9 10 11
Legend
1. Looks
2. Age
3. Personality
4. Education
5. Political Views
9. Past Marriage
Status/Kids
11. Sexuality
Legend
1. Looks
2. Age
3. Personality
4. Education
5. Political Views
9. Past Marriage
Status/Kids
11. Sexuality

Katz 61
Figure 19. Comments Made about Unprofessional Profiles’ Strengths
Figure 20. Comments Made about Unprofessional Profiles’ Weaknesses
11%
1%
61%
2%1%3%
6%
5% 3% 7% 0%
1 2 3 4 5 6 7 8 9 10 11
14%
5%
20%
9%4%1%
23%
6%
10%
6% 2%
1 2 3 4 5 6 7 8 9 10 11
Legend
1. Looks
2. Age
3. Personality
4. Education
5. Political Views
9. Past Marriage
Status/Kids
11. Sexuality
Legend
1. Looks
2. Age
3. Personality
4. Education
5. Political Views
9. Past Marriage
Status/Kids
11. Sexuality

Katz 62
Figure 21. Comments Made about Male Profiles’ Strengths
Figure 22. Comments Made about Male Profiles’ Weaknesses
12%
0%
61%
5%
1%2%
4%
5% 3% 7% 0%
1 2 3 4 5 6 7 8 9 10 11
14%
5%
21%
8%2%2%
20%
7%
11%
8% 2%
1 2 3 4 5 6 7 8 9 10 11
Legend
1. Looks
2. Age
3. Personality
4. Education
5. Political Views
9. Past Marriage
Status/Kids
11. Sexuality
Legend
1. Looks
2. Age
3. Personality
4. Education
5. Political Views
9. Past Marriage
Status/Kids
11. Sexuality

Katz 63
Figure 23. Comments Made about Female Profiles’ Strengths
Figure 24. Comments Made about Female Profiles’ Weaknesses
13%
1%
60%
2%2%3%
5%
7% 3% 4%0%
1 2 3 4 5 6 7 8 9 10 11
8%
5%
23%
12%10%
2%
21%
2%
9%
6% 2%
1 2 3 4 5 6 7 8 9 10 11
Legend
1. Looks
2. Age
3. Personality
4. Education
5. Political Views
9. Past Marriage
Status/Kids
11. Sexuality
Legend
1. Looks
2. Age
3. Personality
4. Education
5. Political Views
9. Past Marriage
Status/Kids
11. Sexuality

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Discussion
With today’s online platforms offering users endless amounts of options for uses, the job
market and dating scene have made their way to the top of user’s opportunities. With over 40
million Americans using online dating services (Broussard, 2011) and over 414 million people
utilizing LinkedIn worldwide, what people are viewing on these sites becomes an intriguing
question (Number of LinkedIn members 2009-2015, 2016). Capturing the attention of a recruiter
searching through LinkedIn profiles and a potential relationship seeker skimming through
Match.com profiles is crucial for people in the job or dating market. Brasel and Gips’ (2008)
research findings on how brand recognition is weakened when commercials are fast-forwarded
through may simulate findings on how recruiters and relationship seekers view job or dating
websites respectfully. Recruiters sifting through LinkedIn profiles will be “actively” sifting
through the profiles and although this skimming may be considered “fast-forwarding”, they are
still closely paying attention to the digital content.
It is important to understand what people are looking at when viewing online profiles, but
in addition, being able to see if there are certain racial or gender biases that may influence what
people view. Secondly, professionalism is a trait that is discussed at length in regards to people’s
occupation. What has not been analyzed is the impact that a professional or even more
importantly, unprofessional attire has on people being hired or being sought after in a romantic
way (e.g. online dating websites). This study’s goal is to analyze whether race, gender, or
professionalism causes the viewers of the profiles to either positively or negatively react to the
profiles through the behavioral responses to a survey pertaining to each individual profile. In
addition, race, gender, and professionalism were analyzed to see if these attributes ultimately
effected whether or not an applicant would be hired based upon their LinkedIn profile or the
attractiveness level of Match.com profiles.

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LinkedIn
In this study, it was predicted that the LinkedIn profiles with professional pictures will be
viewed for a longer amount of time than the LinkedIn profiles with unprofessional pictures. This
study concluded that although female participants viewed the unprofessional profiles longer
compared to the professional profiles, male participants looked longer at the professional profiles
compared to the unprofessional profiles. This could suggest that male recruiters, but not female
recruiters, favor professionalism over unprofessionalism. In contrast, when taking gender of the
participant out of the equation and strictly focusing on the total time spent on profile, the total
time spent on unprofessional male targets was longer compared to professional male targets and
the total time spent on professional female targets was longer compared to unprofessional female
targets. This finding may suggest that the gender of the applicant on the LinkedIn profile has a
greater impact on professionalism compared to the gender of the recruiter. This study’s findings
coincide with Hirsh-Pasek and Golinkoff’s (1996) conclusions on eye movements and arousal,
while also suggesting that arousal levels may be influenced by selective attention (Portas et al.,
1998; Das, Naglieri, Kirby, 1994).
In addition, it was predicted that the LinkedIn profiles with the professional photos would
more likely be hired compared to the LinkedIn profiles with the unprofessional photos. After
running chi-squared tests for the “hire/no hire” dependent variable in relation to the gender of the
applicant, there was not a significant relationship between any of the professional profiles and
the gender of the participant. When analyzing the survey responses, for the unprofessional
profiles, over 25% of the weaknesses stated by the recruiters focused around the quality of the
profile picture. This is contrasted with 7% of the recruiter’s statements on the surveys about the
LinkedIn profiles with professional profile pictures focusing on the quality of the profile picture

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as a strength. Although there was not a significant finding for the chi-squared tests, 11 out of the
12 recruiters (91.666%), regardless of the recruiter’s gender stated that they would hire the
professional white female. In addition, 2/3 of the recruiters, regardless of the recruiter’s gender,
stated that they would hire the professional white male, 75% of the recruiters would hire the
professional minority male, and 2/3 of the recruiters stated that they would hire the professional
minority female. Altogether, 75% of the recruiters stated that they would hire an individual
regardless of the target profile’s race or gender (36 out of 48 total responses). This can be
compared with the fact that only 16.667% of the recruiters, regardless of the recruiter’s gender,
stated that they would hire the unprofessional white female. All of these findings illustrate that
white females in the job market will be more likely to be given a second interview or even hired
in comparison to minority females. Yet the important take away from these results show that by
dressing professionally in a LinkedIn profile picture, you have a better chance at being
considered for employment in contrast to wearing unprofessional clothing.
There were two significant relations between the two variables. The relation between
being hired for unprofessional minority females and unprofessional minority males with the
gender of the participant was found to be significant. This finding contrasted the prediction that
the white profiles would be hired more than the minority profiles. There was not a significant
result for any of the white profiles in relation to the gender of the recruiter. For the
unprofessional minority females, nine female participants stated they would hire the
unprofessional minority female compared to none of the male participants. This finding in
regards to the male participants in this study correlates with past research findings stating that
minority applicants are less likely to be given an interview compared with white applicants (Fix
& Turner, 1998). In contrast, the female participants differed with past research findings and all

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nine of the female participants stated that they would hire the unprofessional minority female.
This may be caused by gender biases male participants in hiring positions are more likely to hire
male applicants. The same has been found for female participants in hiring positions being more
likely to hire female applicants (Yoder et al., 1989). A major limitation of the study is the lack of
male participants, which may be an additional reason for the lack of “hiring” by the male
participants for the unprofessional minority female applicants. In addition, the lack of
professionalism in the unprofessional minority female profiles may have also factored into the
male participant’s choosing to not hire the target. None of the male participants chose to hire the
unprofessional male minority in comparison to the female participants. These findings could
open the conversation to whether male recruiters vs. female recruiters differ in how they view the
importance of professionalism in hiring decisions.
Professionalism has been linked to increased attractiveness levels (McElroy, Morrow, &
Eroglu, 1990). In short, McElroy, Morrow, and Eroglu (1990) argued that attractiveness of the
salesman may arouse the customer causing them to elicit positive or negative emotional labeling
of the salesperson. This in turn may impact the customer’s reactions to the salesperson or product
they are selling. These past research findings by McElroy, Morrow, and Eroglu (1990),
specifically the significant interaction between professionalism and distance resulting in
increased donations are consistent. It was also predicted that male viewers would spend longer
amounts of time viewing the LinkedIn profiles. The interaction between the gender of the
participant and the gender of the target was not significant, but male participants viewed female
targets longer, compared to male targets. In addition, the interaction between the participant’s
gender and the gender of the target picture profiles was significant. Male participants looked
longer at female pictures, compared to male pictures. With the increased time spent on the

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female pictures, this may influence whether or not the male recruiters higher female applicants
more than male applicants. It may also be suggested that the longer a recruiter spends on an
individual profile, the higher the likelihood the recruiter will be interested in the applicant.
Furthermore, if a recruiter is spending more time on one particular profile, this will be taking
away from potential time spent on other profiles. An intriguing finding pertaining to race was
that looking times for the skills/experience sections did differ significantly between the race of
the target. White target’s skills section was looked at longer, compared to minority target’s skills
section. Having only white recruiters participate in the study (potentially due to a lack of
diversity in higher education and one of the major limitations of the study) may have produced
the racial biases that have been discussed at length in hiring discrimination research (Stoll et al.,
2004). Hiring discrimination occurs at all levels in the hiring process. From recruiting to
interview selection, hiring biases based on race often lead to discrimination in the hiring process.
For a hiring manager, their first outlet when beginning the hiring process is to reach out to their
personal network and coworkers for recommendations. The best hires for a company usually
come from referred candidates from people who work at the firm hiring as they are up-to-date on
the company's culture and work environment. An individual’s social network will be influenced
by their race (Stoll et al., 2004). They will prefer to hire within their social network, emphasizing
the “it’s not what you know, it’s who you know” statement, and black candidates for
employment are more likely to stay in touch with their personal social network. This will allow
for more black candidates to feel as a priority in the hiring process and be more likely to apply
toward firms with black hiring managers (Holzer, 2000). The societal and professional pressures
to hire candidates that match with the business owner’s ideals for the hiring managers is
constant, causing the likelihood of a black-owned business to hire black candidates over white

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candidates. With black candidates more likely to apply to black-owned businesses (Holzer,
2000), these findings deter from the notion that reverse discrimination is a potential effect of
these hiring practices.
Prior research suggests that black hiring managers are more likely to higher black job
candidates over white job candidates (Stoll, Raphael, and Holzer, 2004). Even into the early
2000’s there continues to be a significant difference between the amount of African-American
workers compared with white workers, especially in large firms (Stoll, Raphael, & Holzer,
2004). This disparity is nullified when a black hiring manager is in charge of the hiring process,
whether it be in suburban or central firms (Raphael, 1998). Stoll et al. (2004) studied the
reasoning behind the greater likelihood of black hiring managers hiring black candidates. Their
results suggested that more black candidates applied to firms with black hiring managers and
when hiring between a black candidate and a white candidate, the black hiring manager is more
likely to hire the black candidate (Stoll et al., 2004). Other research suggested that blacks are
disproportionately sorted into firms, companies, and organizations (Carrington and Troske,
1998) as well as the percentage of blacks hired in minority and non-minority areas that are black-
owned firms (Bates, 1993).
When analyzing the results between the Match.com and LinkedIn groups, there are some
interesting findings to discuss. For instance, although only the professional LinkedIn profiles had
a significant difference in looking times in comparison to the unprofessional LinkedIn profiles,
the Match.com profiles was approaching significance in regards to professionalism. The missing
data cell in Match.com (white professional female) negatively impacted the ability to truly
analyze group differences between professional vs. unprofessional profiles for Match.com. With
the missing data cell added into the data, it is fair to assume that a significant difference between

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looking times for professional profile pictures vs. unprofessional profile pictures for Match.com
may occur.
Match.com
When analyzing the Match.com data, it was predicted that the looking times for the
Match.com profiles with professional profile pictures in the profile would be looked at longer
compared to the profiles with unprofessional profile pictures. Unfortunately, due to the missing
data cell (professional white females), this analysis was not able to be tested. For the male targets
specifically, the profile picture looking times did not differ significantly between the race of the
male targets, but the profile picture looking times for the male target’s professionalism were
approaching a level of significant, but were not significant. Participants viewed the professional
male targets longer, compared to the unprofessional male targets. These statistics illustrate two
interesting concepts. First, no matter what the race of the target was, the race was not a
significant factor in either drawing a participant away from the profile picture or drawing a
participant toward the profile picture. Racial biases in dating, as seen in studies such as Robnett
& Feliciano (2011), state that people are less likely to date outside of their race. Although
viewing a profile picture does not directly insinuate dating preferences, the lack of significant
data could open the argument that millennials have adopted different outlooks on dating that are
not impacted by race. On the other hand, college students tend to have a more open-minded
outlook on life compared to older aged people, which may be the cause for this sense of
openness in the relationship seekers. When looking at the survey responses, even with having 3
additional unprofessional profiles, the unprofessional profiles only outgained the professional
profiles by 88 total strength comments (unprofessional = 420 total strength comments,
professional = 332). One could make the argument that if the two missing professional white

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female profiles were added and the additional unprofessional minority female profile was taken
away, there could have been a significant result between professionalism and profile picture
looking time.
In this study, it was also predicted that professional profiles would be rated as more
attractive than the unprofessional profiles. Although the missing data cell hindered the analysis
of all professional vs. unprofessional profiles, the attractiveness level of the male target profiles
did differ statistically between the professionalism of the target profiles. The attractiveness levels
of the professional male target profiles were higher, compared to the attractiveness levels of the
unprofessional male target profiles. This suggests that professional male targets on average were
rated almost one and a half points higher than the unprofessional male targets. This finding backs
research conducted by Ronkainen and Reingen (1979) and more recent work completed by
Caballero and Solomon (1984) which concluded that physical attractiveness has an effect on
personal selling; however, the present study view this past research from a different angle. The
professionalism of the male profile target increased the attractiveness level of the targets. In
addition, the unprofessionalism of the male profiles had a negative impact on the overall
attractiveness of the unprofessional male targets. Comments such as, “he looks like a want to be
soulja boy” which was directed at one of the unprofessional minority male profiles or “not very
good looking” which was directed at an unprofessional white male profile are just two of the
assortment of poor comments made about the unprofessional pictures. Altogether, 14% of the
weaknesses for the unprofessional profile focused around the people’s looks.
Race has been found to have a major influence on who people decide to date as well as
who they find attractive (Robnett & Feliciano, 2011; Gordon, 1964; Kalmijn, 1998). Survey
responses such as “African descent” listed as a weakness on many of the minority profiles

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occurred in this study. In addition, minority profiles (393 weakness comments) received over
140 more “Weaknesses” comments in comparison to the white profiles (249 weakness
comments). Unfortunately, the missing data cell will have an impact on the total number of
weaknesses for the white profiles (2 missing professional white female profiles) as well as the
additional unprofessional minority female profile, but the amount of negative comments about
the minority profiles in a study where 13 out of the 22 participants are white could suggest some
racial bias occurring in the present study. This study predicted that the white participants would
rate the white target profiles as more attractive when compared with the minority profiles. The
interaction of the attractiveness levels for the male targets between the male target’s race and the
participant’s race was not significant. As stated earlier, the missing data cell restricted this
analysis, but racial biases between the male targets was not found in this study. This study’s
findings contradict findings of Robnett and Feliciano (2011), Gordon (1964), and Kalmijn
(1998), but the missing cell may have had a significant impact on the data. Judging solely from
the survey responses, it is possible to make the argument that with the missing cells, racial biases
may have been concluded in this study.
Regardless of race, gender, or professionalism, and interesting finding of this study is that
the majority of strengths across the all profiles focused around the personality of the individual’s
profile. For white target profiles, 68% of the survey responses revolved around the personality of
the individual. The lowest percentage for the personality percentages was the minority target
profiles which still had over 50% of the comments focused around the personality of the
individual. Could this be another reflection of what millennial relationship seeker’s value when
searching for a relationship? Do people look past race or potentially even gender when assessing

Katz 73
an individual’s attractiveness level? All of these questions are important factors when assessing
the future of dating websites.
Limitations and Future Research
Sample. The sample size of this study greatly impacts the results. Due to time restraints
and unforeseen technological setbacks, this current studies sample size is too small. With a total
of 12 recruiters, 3 males and 9 females, the sample size directly impacted the data for the
LinkedIn profiles. In addition, the MH100 Smart Glasses’ storage capacity and battery were both
weaker than expected when purchased. Instead of a 20 minute, in-and-out experiment, one
individual who took the full 20 minutes to complete the 10 LinkedIn profiles or the 15
Match.com profiles would require an additional 15-20 minutes to download the recorded video
off of the glasses onto another hard drive in order to complete another participant. With the
deadline for completing the study fast approaching and fitting into recruiter’s schedules, it was
difficult to obtain a large sample size of recruiters. A problem that may also spark future research
into higher education system is the lack of diversity within the career center of a private liberal
arts school. All of the recruiters tested in this study were white and 9 out of the 12 participants
were white women. This could pose the argument for greater diversity within career centers at
higher education universities.
For this study, the relationship seekers sample size was also very small, 22 total
participants (13 white and 9 minority participants). Even with this small sample size, there was a
greater amount of diversity in this sample size. For the first-year and sophomores who
participated in this survey, there were a range of minority students from bi-racial to Native
American. Stemming back to the lack of diversity within the career center’s staff, the question
can be asked about how are these minority student’s needs being met with the absence of a

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career advisor that grew up in a similar background as them. The technological issues that
hindered the LinkedIn studies also impacted the Match.com studies with the relationship seekers.
The battery would often die out after 25 minutes of use, which would result in lost video
recording data. In two incidents, the battery died out or the storage became too full with only 2
megabytes of storage available and two participants video recordings were lost completely. This
unforeseen technological issues affected the amount of participants that could complete the
experiment within a realistic timeframe.
Materials. As stated earlier, even though the MH100 Smart Glasses recorded the
necessary footage needed to collect data, there were multiple issues with the glasses. Often
times, the participants were wearing glasses and a limitation that was not foreseen personally
was the effect the glasses would have on an individual who could not see without their
prescription glasses. This required the participant to fit the Smart Glasses over their prescription
glasses which was uncomfortable and awkward for the participant. On the other hand, the glasses
would either slide up or down the glasses and resulted in the video footage being difficult to
analyze. Lastly, the battery life and storage capabilities of the glasses were below average
resulting in unforeseen loss of time between participants and lost video footage.
The recruiters’ viewed the LinkedIn profiles on a PC. Many of the recruiters were either
accustomed to a Mac computer or were not accustomed to using a PC. This required additional
technical difficulties in viewing the LinkedIn profiles while responding to the survey questions
for each profile. The uncomfortableness of the Smart Glasses (heating up, sliding up or down, or
becoming heavy) also resulted in the participant discomfort during the experiment. These
technological issues were completely unforeseen and had to be dealt with during the entirety of
data collection.

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Missing Data Cell. Due to personal lack in awareness, the data cell for the white
professional females was missing from the Match.com profiles. Until the data collection ended
and analysis of the data began, the knowledge of the missing data cell was unknown to the
researcher. Due to the time restraints of the research project, any chance of redoing the data
collection was not possible and analysis of the data continued with the professional white female
profile missing. Unfortunately, this missing data cell negatively impacted the ability of the
researcher to fully analyze the professional or the gender data for Match.com.
Future Research. The magnitude of the data collected from this experiment leads to an
assortment of potential future research. The full analysis of the nested variables within the data
would require a multi-level modeling analysis, something that was hindered by the time
restraints and experimenter expertise levels with this type of high level analysis. Having the
ability to analyze each individual participant without collapsing the data may show even greater
impacts of racial, gender, or professional biases for the profiles. Time restraints and the necessity
for data analysis required the profiles for Match.com to be collapsed. The data collapsing did
deter any type of individual profile biases that may have increased or decreased the time spent on
the profiles different features or the attractiveness level for one subject group (e.g.
unprofessional female minority).
With a more diverse sample size for the recruiters, racial differences in hiring could also
be analyzed. Having only white recruiters viewing the LinkedIn profiles limited the type of
analysis that could be used to assess the potential racial biases that are occurring when viewing
online profiles. Earlier research suggested that “task competence” should be the sole factor when
choosing between candidates (Bayles, 1973). Task competence pertains to an individual's ability
to perform the job responsibilities (tasks) that are relevant to the job description. Some may

Katz 76
argue that reverse discrimination plays a part in the hiring process when skin color, race,
ethnicity, or religious affiliation are weighed into the hiring decision (Bayles, 1973). There are
two premises that Bayles (1973) discusses which pertain to the situations that may involve
reverse discrimination. First and foremost, the equality of opportunity requires that every
individual is given the same opportunity to receive rewards (benefits) or penalties (burdens). In
addition, the integration process states that the workforce should replicate the total population in
society without favoring one part of society over another in regards to sex, race, religion, ethnic
background, or national alliance. Names have also had a significant influence in the hiring
process. Bertrand and Mullainathan (2004) concluded that white-sounding names were 50%
more likely to be given a callback for an interview compared to African-American sounding
names. Is this caused by racial similarities or racial biases? The Council of Economic Advisers
(1998) concluded that in addition to later findings on unequal interview callbacks, when African
Americans did secure employment, they were paid 25% less than their white counterparts. All of
this literature suggests that as the hiring process moves to a more digital culture (e.g. Skype
interview, Go-To-Meeting applications, and LinkedIn “resumes”), it will be very interesting to
research how past racial biases may be seen in today’s hiring process.
Lastly, properly analyzing how sexual orientation or age is an area where future research
can improve upon this current study’s findings. Age discrimination is a major issue occurring in
today’s workforce. This experiment has the age of all of the participant’s but due to time
restraints, this data was not fully analyzed. There were an additional two profiles that were
viewed for LinkedIn that were not discussed in the results. One of the profile pictures featured a
cartoon and the other profile picture did not have a picture. Further research could look into the
effect that “no picture” or “cartoon pictures” could have on a person’s chances of being hired.

Katz 77
Depending on a person’s industry or even facial abnormalities, there is a possibility of having
one of the two “alternative” pictures and it would be interesting to see the impact. In addition, as
sexual orientation and sexuality in general continues to become a headline topic in society,
understanding how sexual bias impacts hiring decisions would be very intriguing.
Conclusion
In spite of these limitations, this study looks at some important issues pertaining to
racial/gender biases in dating and hiring. In addition, by adding in the concept of professionalism
into both the dating and job search industry, it is pertinent for individuals to understand the
impact that appearing professional vs. unprofessional has on their love life or professional career.
Within a flash of a second, an individual could have connected with the love of their life or the
next stepping stone in their career. Dressing professionally and understanding the potential
racial/gender biases may also assist people searching for relationships or exploring new career
opportunities on LinkedIn. It is fair to say that all is fair in love and work.

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Appendix A
Demographic Information Form
Demographic Information
Age:
Gender: Male FemaleTrans-Male Transgender-Female Other ____________
Sexual Orientation: Male Female Bisexual Other________ Prefer not to disclose
Race:

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Appendix B
Recruiter Survey
LinkedIn.com – Adam
1. What are the strengths of this individual?
2. What are the weaknesses of this individual?
3. Would you hire this individual? (Yes or No)

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Appendix C
Relationship Seeker Survey
Match.com – Lance
1. What are the strengths of this individual?
2. What are the weaknesses of this individual?
3. How would you rate this individual’s attractiveness level on a scale of 1-10
(“1” being unattractive and “10” being attractive)

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Appendix D
Example of Match.com Profile

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Appendix E
Example of LinkedIn Profile

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