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DS Skills Lab 10

Unhinged: Dan’s Dating DisastRs

Dr Danielle Evans

28 Nov 2024

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"You'll never get married. You're unlovable."

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"You'll never get married. You're unlovable."

My Mum

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Overview

  • Factorial Designs: gone (cat)fishing

  • Repeated Measures: 50 first dates

  • Logistic Regression: the unfriendly ghost

  • KahootR

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VoteRs

  • We're going to vote on a few things today...

For anonymous voting 😎:

For non-anonymous voting 😁:

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Dan's Dating Dilemmas

  • R is obviously the love of my life, but everyone laughs at me when I say that, so I'm searching for something else..

  • On my mission to find real life love, I've encountered 3 dilemmas I need your help with:

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Dan's Dating Dilemmas

  • R is obviously the love of my life, but everyone laughs at me when I say that, so I'm searching for something else..

  • On my mission to find real life love, I've encountered 3 dilemmas I need your help with:

    1. What's the best way to get dating app matches?
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Dan's Dating Dilemmas

  • R is obviously the love of my life, but everyone laughs at me when I say that, so I'm searching for something else..

  • On my mission to find real life love, I've encountered 3 dilemmas I need your help with:

    1. What's the best way to get dating app matches?

    2. What's the best date to go on?

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Dan's Dating Dilemmas

  • R is obviously the love of my life, but everyone laughs at me when I say that, so I'm searching for something else..

  • On my mission to find real life love, I've encountered 3 dilemmas I need your help with:

    1. What's the best way to get dating app matches?

    2. What's the best date to go on?

    3. Why do I keep getting ghosted? [everybody say 'aww']

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Dan's Dating Dilemmas

  • R is obviously the love of my life, but everyone laughs at me when I say that, so I'm searching for something else..

  • On my mission to find real life love, I've encountered 3 dilemmas I need your help with:

    1. What's the best way to get dating app matches?

    2. What's the best date to go on?

    3. Why do I keep getting ghosted? [everybody say 'aww']




So let's do some research!!😁

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The Matchmaker

  • Dating apps have gained immense popularity in recent years, as they offer a convenient and accessible way for people to explore potential romantic relationships

  • The apps provide users with a platform to create a profile, often including information about themselves and photos

  • Users can then browse through other profiles, and based on their preferences, they can "match" with other users they find attractive or interesting

  • So my first super important life-changing research question is: how can I increase matches on my profile?!

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Gone (cat)fishing

  • From my observations of the world, I've noticed that profiles with a higher level of attractiveness tend to receive more matches...

  • So I conducted an experiment to investigate the two key components of a dating app profile: user information, and user photos

  • I asked 500 heterosexual men to view a dating app profile and provide a 'match rating' based on their level of interest in matching with the individual (scored from 1-10, higher score = higher interest)

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Gone (cat)fishing

  • From my observations of the world, I've noticed that profiles with a higher level of attractiveness tend to receive more matches...

  • So I conducted an experiment to investigate the two key components of a dating app profile: user information, and user photos

  • I asked 500 heterosexual men to view a dating app profile and provide a 'match rating' based on their level of interest in matching with the individual (scored from 1-10, higher score = higher interest)

Now for the important part:

  • Half of the participants saw an accurate user photo, the other half saw a photo that's been embellished to be more appealing 🐱🐟 - the photo condition

  • In each group of the photo condition, half of the participants saw an accurate user profile, and the other half saw a profile that's been enhanced to be more attractive to others 🐱🐟 - the profile condition

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Beauty is in the Eye of the Beholder

  • So, half our participants saw one of the following photos:
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A Day in the Life

⏰ 7:30 AM: The alarm clock blares, signalling the start of another unremarkable day.
🚿 7:45 AM: Dragging myself out of bed, I have a quick shower to wake myself up, followed by the painting of my face to look more human.
☕️ 8:30 AM: Once ready, I run to the kitchen and grab a Poptart to eat on the go before I’m late to work.
🚗 8:30 AM: Joining the endless stream of traffic, I begin my mundane commute to work.
💼 9:00 AM - 5:00 PM: Work is a series of meetings, emails, teaching and routine tasks.
🥪 12:00 PM: Lunchtime arrives, and I’ve forgotten to make lunch in advance, so I hunt for the Oreos in my desk.
📚 5:30 PM: Finally, the workday is over, and I head home. No thrilling post-work adventures or glamorous social events, just the usual drive home.
🍔 6:30 PM: Dinner is a solo affair, with a microwaveable meal or something equally basic, just a humble dinner for one.
🛋 7:30 PM: The evening entertainment involves watching the same shows on Netflix over and over again. I also spend several hours mindlessly, and bitterly scrolling through social media.
💤 11:30 PM: As the night winds down, I go through my mundane bedtime routine. No luxurious pampering or elaborate rituals, just the usual washing, brushing and flossing.
🛌 12:00 PM: I crawl into bed, cry, and eventually fall asleep embracing the void.

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A Day in the Life

⏰ 6:00 AM: The day begins with the soft melody of a chime, and I awaken in the comfort of my opulent mansion. Greeted by a picturesque view of the sunrise, I start my morning with a refreshing swim in my private pool or a session of yoga to centre my mind and body.
☕️ 7:30 AM: Savouring a gourmet breakfast prepared by my personal chef, I indulge in the finest delicacies from around the world. Whether it's fresh fruits from exotic lands or a sumptuous array of pastries, each bite is a delight for the senses.
💼 9:00 AM: With a sharp mind and a heart full of ambition, I dive into the world of business. From my luxurious home office or in the bustling heart of the city, I make strategic decisions, connect with visionary partners, and lead my empire to even greater heights.
🏞 12:30 PM: Noon calls for an exquisite luncheon with friends, family, or esteemed colleagues. We gather in an exclusive dining venue or a Michelin-starred restaurant, where culinary delights and captivating conversations flow.
🚁 2:00 PM: Stepping onto the helipad of my high-rise building, I embark on an aerial adventure, soaring above the city's skyline. The breathtaking views remind me of the endless possibilities life has to offer.
🏛 4:00 PM: As the afternoon sun graces the city, I indulge in my passion for art and culture. Whether it's visiting an art gallery, attending a private exhibition, or supporting a philanthropic cause, I embrace the beauty that enriches the world.
🛍 6:00 PM: What better way to unwind than with a little retail therapy? I explore exclusive boutiques and flagship stores, surrounded by opulent choices that reflect my discerning taste.
🌅 8:00 PM: Evenings are for enchanting moments, whether hosting an extravagant dinner party at my mansion or indulging in a candlelit affair at a world-class restaurant. Every sip and every bite is an exquisite symphony of flavours.
🎭 10:00 PM: As the moon rises, the night comes alive with entertainment. I attend exclusive events, dazzling parties, or indulge in a private screening of a movie at my very own home theatre.
🌌 12:00 AM: As the clock strikes midnight, I retire to my luxurious sanctuary, reflecting on the day's grand experiences. Drifting off to sleep, I know that tomorrow holds even more captivating opportunities and a chance to make the world a more beautiful place.

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Place Your Predictions!

  • Type 's' to draw your predictions of the interaction on the graph!

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Well, Well, Well 👀

Model

Effect df MSE F ges p.value
photo 1, 496 0.54 4637.66 * .903 <.001
profile 1, 496 0.54 2020.82 * .803 <.001
photo:profile 1, 496 0.54 203.16 * .291 <.001

Estimated Marginal Means

photo profile emmean SE df lower.CL upper.CL
accurate accurate 1.59 0.07 496 1.46 1.72
catfish accurate 7.00 0.07 496 6.87 7.13
accurate catfish 5.48 0.07 496 5.35 5.61
catfish catfish 9.02 0.07 496 8.89 9.15
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Well, Well, Well 👀

Model

Effect df MSE F ges p.value
photo 1, 496 0.54 4637.66 * .903 <.001
profile 1, 496 0.54 2020.82 * .803 <.001
photo:profile 1, 496 0.54 203.16 * .291 <.001

Estimated Marginal Means

photo profile emmean SE df lower.CL upper.CL
accurate accurate 1.59 0.07 496 1.46 1.72
catfish accurate 7.00 0.07 496 6.87 7.13
accurate catfish 5.48 0.07 496 5.35 5.61
catfish catfish 9.02 0.07 496 8.89 9.15

So what do we think about these results?!

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Catfishing it is!

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50 first dates

  • OK so now that we've decided being a super catfish is the best way to get matches, let's think about what type of date would be the most successful in finding me love!

  • For this next experiment, I went on multiple dates (10) with 50 participants, and got them to rate their 'dating interest' in me after each date

    • Every participant did every date type, which was counterbalanced

    • Dating interested was measured on a scale from 1-10, a high score = more interest

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Place Your Predictions!

  • Type 's' to draw your predictions for each date type on the graph!

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Show Me The Means!

Model

Effect df MSE F ges p.value
date_type 6.64, 3311.39 0.93 5048.18 * .901 <.001

Estimated Marginal Means

date_type emmean SE df lower.CL upper.CL
drinks 2.99 0.05 499 2.88 3.09
cinema 2.02 0.03 499 1.96 2.08
bowling 2.95 0.03 499 2.89 3.01
rave 5.50 0.02 499 5.46 5.55
funeral 4.02 0.03 499 3.96 4.09
parents_dinner 1.49 0.02 499 1.44 1.53
R_conference 8.02 0.03 499 7.95 8.08
maldives_holiday 8.99 0.03 499 8.93 9.05
friends_wedding 3.93 0.06 499 3.82 4.04
skydiving 7.45 0.04 499 7.37 7.53
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So. Many. Posthocs.

term contrast null.value estimate std.error df conf.low conf.high statistic adj.p.value
date_type drinks - cinema 0 0.972 0.060 499 0.774 1.170 16.134 0.000
date_type drinks - bowling 0 0.034 0.062 499 -0.171 0.239 0.545 0.586
date_type drinks - rave 0 -2.514 0.057 499 -2.702 -2.326 -43.935 0.000
date_type drinks - funeral 0 -1.036 0.064 499 -1.247 -0.825 -16.114 0.000
date_type drinks - parents_dinner 0 1.500 0.059 499 1.305 1.695 25.214 0.000
date_type drinks - R_conference 0 -5.028 0.064 499 -5.238 -4.818 -78.576 0.000
date_type drinks - maldives_holiday 0 -6.002 0.061 499 -6.204 -5.800 -97.628 0.000
date_type drinks - friends_wedding 0 -0.940 0.074 499 -1.183 -0.697 -12.712 0.000
date_type drinks - skydiving 0 -4.462 0.067 499 -4.681 -4.243 -66.888 0.000
date_type cinema - bowling 0 -0.938 0.045 499 -1.086 -0.790 -20.848 0.000
date_type cinema - rave 0 -3.486 0.037 499 -3.607 -3.365 -94.586 0.000
date_type cinema - funeral 0 -2.008 0.043 499 -2.149 -1.867 -46.565 0.000
date_type cinema - parents_dinner 0 0.528 0.037 499 0.408 0.648 14.418 0.000
date_type cinema - R_conference 0 -6.000 0.044 499 -6.144 -5.856 -136.794 0.000
date_type cinema - maldives_holiday 0 -6.974 0.044 499 -7.118 -6.830 -158.890 0.000
date_type cinema - friends_wedding 0 -1.912 0.061 499 -2.114 -1.710 -31.115 0.000
date_type cinema - skydiving 0 -5.434 0.054 499 -5.610 -5.258 -101.238 0.000
date_type bowling - rave 0 -2.548 0.039 499 -2.675 -2.421 -66.001 0.000
date_type bowling - funeral 0 -1.070 0.043 499 -1.211 -0.929 -24.905 0.000
date_type bowling - parents_dinner 0 1.466 0.039 499 1.337 1.595 37.348 0.000
date_type bowling - R_conference 0 -5.062 0.045 499 -5.208 -4.916 -113.637 0.000
date_type bowling - maldives_holiday 0 -6.036 0.043 499 -6.177 -5.895 -140.673 0.000
date_type bowling - friends_wedding 0 -0.974 0.063 499 -1.180 -0.768 -15.473 0.000
date_type bowling - skydiving 0 -4.496 0.054 499 -4.673 -4.319 -83.406 0.000
date_type rave - funeral 0 1.478 0.040 499 1.347 1.609 36.970 0.000
date_type rave - parents_dinner 0 4.014 0.031 499 3.912 4.116 128.646 0.000
date_type rave - R_conference 0 -2.514 0.039 499 -2.641 -2.387 -64.855 0.000
date_type rave - maldives_holiday 0 -3.488 0.039 499 -3.617 -3.359 -88.572 0.000
date_type rave - friends_wedding 0 1.574 0.060 499 1.379 1.769 26.409 0.000
date_type rave - skydiving 0 -1.948 0.050 499 -2.111 -1.785 -39.247 0.000
date_type funeral - parents_dinner 0 2.536 0.039 499 2.408 2.664 64.782 0.000
date_type funeral - R_conference 0 -3.992 0.046 499 -4.143 -3.841 -86.780 0.000
date_type funeral - maldives_holiday 0 -4.966 0.045 499 -5.113 -4.819 -110.664 0.000
date_type funeral - friends_wedding 0 0.096 0.066 499 -0.120 0.312 1.460 0.290
date_type funeral - skydiving 0 -3.426 0.055 499 -3.605 -3.247 -62.644 0.000
date_type parents_dinner - R_conference 0 -6.528 0.038 499 -6.653 -6.403 -170.766 0.000
date_type parents_dinner - maldives_holiday 0 -7.502 0.038 499 -7.626 -7.378 -197.773 0.000
date_type parents_dinner - friends_wedding 0 -2.440 0.060 499 -2.635 -2.245 -40.964 0.000
date_type parents_dinner - skydiving 0 -5.962 0.048 499 -6.118 -5.806 -125.366 0.000
date_type R_conference - maldives_holiday 0 -0.974 0.047 499 -1.128 -0.820 -20.770 0.000
date_type R_conference - friends_wedding 0 4.088 0.064 499 3.879 4.297 64.248 0.000
date_type R_conference - skydiving 0 0.566 0.054 499 0.389 0.743 10.516 0.000
date_type maldives_holiday - friends_wedding 0 5.062 0.063 499 4.854 5.270 79.914 0.000
date_type maldives_holiday - skydiving 0 1.540 0.055 499 1.360 1.720 28.085 0.000
date_type friends_wedding - skydiving 0 -3.522 0.068 499 -3.746 -3.298 -51.459 0.000
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Maldives it is! ✈️

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The Unfriendly Ghost 👻

  • Ghosting is a phenomenon that often takes place within the realm of dating and has become increasingly common in the era of digital communication

  • Ghosting can have various negative effects on the ghostee, both emotionally and psychologically 😢

  • So, in the quest to become a ghostbuster, I'm researching predictors of being ghosted (or not) to save people (me) from an unwanted fright (more therapy)

    • Note. Ghosts are not to be confused with Zombies, so we need to think carefully about how we operationalise our outcome!
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Who You Gonna Call? 👻🔫

  • For our final experiment, I went on dates with 500 participants, and categorised the outcome of those dates into: ghosted vs not ghosted based on whether we spoke again after the date

  • I collected the participants' fear of commitment score (1-5, higher score = more fear), and whether I talked about R on the date (R talk or no R talk)

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I Ain't Afraid of No Ghost

term estimate std.error statistic p.value conf.low conf.high
(Intercept) 1.050 0.367 0.134 0.893 0.510 2.164
commitment_fear 0.945 0.115 -0.488 0.625 0.753 1.185
talk_typeR_talk 0.717 0.488 -0.681 0.496 0.275 1.868
commitment_fear:talk_typeR_talk 1.172 0.150 1.058 0.290 0.874 1.575
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I Ain't Afraid of No Ghost

term estimate std.error statistic p.value conf.low conf.high
(Intercept) 1.050 0.367 0.134 0.893 0.510 2.164
commitment_fear 0.945 0.115 -0.488 0.625 0.753 1.185
talk_typeR_talk 0.717 0.488 -0.681 0.496 0.275 1.868
commitment_fear:talk_typeR_talk 1.172 0.150 1.058 0.290 0.874 1.575





Welp, no luck there, back to the dRawing boaRd!

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In Conclusion*:

  • Catfishing is the way to go

    • The greatest number of matches were found for photo catfishing and profile catfishing combined
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In Conclusion*:

  • Catfishing is the way to go

    • The greatest number of matches were found for photo catfishing and profile catfishing combined
  • The best date you can go on is a holiday to the Maldives, closely followed by attending an R conference

    • It doesn't matter if you go to a funeral, or to a friend's wedding, both are pretty average...

    • But dinner with parents is the worst overall!

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In Conclusion*:

  • Catfishing is the way to go

    • The greatest number of matches were found for photo catfishing and profile catfishing combined
  • The best date you can go on is a holiday to the Maldives, closely followed by attending an R conference

    • It doesn't matter if you go to a funeral, or to a friend's wedding, both are pretty average...

    • But dinner with parents is the worst overall!

  • & we still don't know what increases the likelihood of being ghosted... dissertation project anyone?? 😀





*all data are fictional, dating success may vary!

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That's all - unhappy dating! 😒

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KahootR!

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"You'll never get married. You're unlovable."

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