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Phubbing: A relationship dealbreaker? In this day and age, it is…
Phubbing: A relationship dealbreaker?
In this day and age, it is rare to know of a person who does not own or use some form of digital technology. We use our digital devices at work and in our personal lives.
Our smartphones help to keep us connected with friends, parents, and partners who may be physically out of reach. And in the aftermath of isolation and lockdowns due to the COVID-19 pandemic, connection with loved ones through our devices has become the norm. But our smartphone use does have its downsides. For example, some studies report that over-reliance on digital technologies is linked with depression and anxiety. Addiction is another issue: previous studies have found that a person’s need for haptic, or touch, stimulation is a predictor of smartphone addiction. One area that has recently caught the interest of researchers is the use of smartphones in the context of romantic relationships. Specifically, phubbing – phone snubbing your partner. Phubbing is when you are engaged with your partner in an interaction, but they are also on their phones. This is a very common experience for people today, and prior studies tell us that phubbing is correlated with worse relationships. In other words, more frequent phubbing is related to poorer relationships. Not surprising, right? A 2022 study done by psychologists at the University of Surrey in the U.K. wanted to understand more about why phubbing is problematic in relationships. The researchers implemented a diary method in which the participants completed online questionnaires each day for a period of 7 days.
The 100 participants in this study were sampled from an online crowdsourcing market, Prolific. Eligibility for the study was based on being in a committed, co- habiting relationship with their partner for at least 2 years and if they did not have any children. Participants were, on average, 32 years old.
Each day, participants filled out a variety of questionnaires: how frequently their partner phubbed that day during interactions with them, perceived relationship quality and partner responsiveness, and whether they made moral judgments about their partner’s phubbing (“behaviour was justified”, “behaviour was inappropriate”).
Surprisingly, the data revealed phubbing frequency was not related to worse relationship quality! Although this finding contradicts previous research, it shows that phubbing your partner is not a relationship dealbreaker.
Rather, the researchers found that relationship quality was caused by low partner responsiveness and negative moral judgments of the partner during phubbing. In short, people who viewed their partners as unresponsive to their needs were more likely to report low relationship quality. And people who are more judgmental of their partners’ traits are also likely to experience their relationship as low quality.
As humans, we have an innate need for connection – it is no surprise that phubbing can be aggravating, if you are the phubbee. What seems to matter more is how we think about our partner’s phubbing. So, the key to maintaining a positive relationship, if your partner tends to snub you for their phone, is to re-frame our interpretations of why they are on their phones.
Rather than blaming our partners, maybe we should look at ourselves. Data from the current study suggest that being judgmental and emotionally needy lead to poorer relationship quality – so maybe your partner phubs as a way of disengaging from this kind of behaviour. Or worse yet – what if they are on their phones to get emotional support from someone else?
So, it seems that smartphones themselves are not problematic for intimate relationships – in fact, most of us have met our partners using digital technology. But it is clear that phubbing should not be ignored: it might be a sign that your thoughts and traits are creating this obnoxious behaviour in your partner.
THE END
Read the article and : analyze the claims being made according to 2 of the 6 principles of scientific thinking
– Select the most relevant principles but in both cases, find the principles that the research report fails to follow or violates. DO NOT choose issues that the report has done a good job at.
Principle 1: Ruling out alternative
Principle 2: Correlation vs. causation
Principle 3: Falsifiability
Principle 4: Replicability
Principle 6: Parsimony (a.k.a. Occam’s razor)
– define the general principle, then provide a detailed response showing how the research description specifically fails to follow the principle. Finally, try to say what would need to be done to make sure the research description follows the principles.
Principle 1: Ruling out alternative explanations – Usually the results of any single study are consistent with several different explanations (or hypotheses) and additional research is often needed to decide which explanation/hypothesis is best supported. When looking at a pattern of results that has been reported from a study, it is important to ask “are there any alternative hypotheses that could explain this pattern of data?” That is, we should consider whether there are any other reasons why the researchers might have found the particular results that they found in their study. Maybe there was a confounding variable in an experiment that could offer a different explanation for the results, other than the one that the researchers have given. The alternative explanations that are most important to acknowledge are those that could explain the specific pattern of results that has been found in the study. It is useful to consider how we could attempt to rule out these alternative hypotheses.
Principle 2: Correlation vs. causation – A correlation between two things (a statistical association) does not necessarily mean there is a cause-and-effect relationship between them. If a pattern of results was produced simply by measuring two different things and comparing them, we cannot say anything for sure about whether one of these things caused the other; all we can say is that the two things go together. When a causal claim (e.g., A causes B) is made from a correlation, it’s always important to ask whether the causal connection could be reversed (i.e., B causes A) or whether a third variable could explain the relationship (i.e., A and B do not cause each other; instead C causes A and B to go together). If there is more than one possible pattern of cause-and-effect that could result in a correlation, we cannot use that correlation as evidence that any one specific pattern is necessarily true.
Principle 3: Falsifiability – Scientific claims must be capable of being disproved. In other words, we should be able to think of a way to test whether or not a claim is true; there should be data we can collect that tell us if our hypothesis is likely to be true or false. If the claim is made in such a way that there’s no good way to test it, the claim is not really scientific. In science, we should always be open to the possibility that our ideas are wrong. If there are no data that could convince us that our ideas are wrong, then our ideas are not properly scientific.
Principle 4: Replicability – Scientific findings must be capable of being duplicated following the same methodology. In other words, in science, other people must be able to follow our methods and should get similar results. In addition, the most reliable claims are those that have converging evidence for them. We can only really be confident in a claim if it has been tested in multiple different ways and all of them point to the same effect.
Principle 5: Extraordinary claims – Science is, for the most part, a cumulative process, where new claims represent small advances over older ones. A claim that contradicts what we already know, or that seems to promise to completely explain or solve a complex problem in
a new way, must have a lot of evidence to back it up. The bigger the claim, the more evidence must be provided.
Principle 6: Parsimony (a.k.a. Occam’s razor) – If two hypotheses explain a phenomenon equally well, in science we generally prefer the simpler explanation. The simpler explanation is not necessarily correct, but we should start by using that explanation and only make a more complicated one when the simple explanation cannot account for our results. In other words, we shouldn’t make our explanations more complicated than necessary.
