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(https://www.psypost.org/hookup-apps-linked-to-risky-sexual-behavior-boredom-plays-a-role-for-women/) Hookup apps linked to risky sexual behavior, boredom plays a role for women
Feb 14th 2025, 08:00

College students who use dating apps like Tinder to find casual encounters are more likely to engage in risky sexual behaviors, according to new research published in (https://authors.elsevier.com/a/1kWrD2f~UWUIbV) Computers in Human Behavior. The study also revealed that while a desire for excitement drives both men and women to these apps, boredom appears to be a key motivator specifically for women seeking connections on hookup platforms. These findings highlight a potential link between smartphone leisure activities and increased sexual risk-taking among young adults.
Apps like Tinder and Bumble have become commonplace tools for college students looking for various types of connections, including casual sexual partners. While previous studies had touched on this link, they often relied on very simple measures of risky sexual behavior, such as asking students to list the number of sexual partners they’ve had. The scientists behind this new study wanted to gain a more thorough understanding by using more detailed and comprehensive ways to assess risky sexual behavior among college students who use these apps.
“My colleague, Jacob Barkley, and I have been studying the relationship between smartphone use and a range of human behaviors and behavioral outcomes for over a decade. For example, across many separate studies we have shown smartphone use is significantly and positively related to sedentary behavior and anxiety and significantly and negatively related to physical fitness, academic performance, and subjective well-being,” said study author Andrew Lepp, a professor at Kent State University.
“Another thing we consistently find is that no matter the population, people report using their smartphones for leisure purposes more than for work or educational purposes. Related to this, we found in multiple studies that a primary motivation for smartphone use is to eliminate boredom during free time and that people who use their smartphone more experience more boredom. Yet in an experimental lab study we found that smartphone use actually causes boredom (while people use their smartphone to eliminate boredom, it actually causes boredom).”
“So, this study is just an extension of all that previous research,” Lepp continued. “Because most of us are so connected to our device, there are always new questions to ask about smartphone use and human behavior. For this study, we were curious to know if people who play with smartphone ‘hookup apps’ (Tinder being the most popular) were more likely to engage in risky sexual behavior compared to those who do not use such apps, we also were curious to understand if boredom played a role in hookup app use, since boredom is so often a motivator for smartphone use more generally.”
“Surprisingly, there is not much research exploring hookup app use and risky sexual behavior that uses valid, accurate and reliable measures of risky sexual behavior. Probably because such data are so sensitive that they are hard to collect. But we were able to gain university approval for collecting sensitive data in a way that assured complete anonymity and respondents trusted the process.”
To conduct their study, the researchers recruited a random sample of 410 undergraduate students from a large university in the Midwestern United States. Out of the participants, 244 were women, and 173 reported using hookup apps. The students were approached at various locations across the university campus known for high student traffic. To ensure a random selection, the research assistant invited every fifth student passing by to participate in the study. Only students aged 18 or older and registered at the university were included.
Those who agreed to participate were given a paper-based survey to complete. The survey was designed to be short, taking about ten minutes or less to finish. Because the survey asked about sensitive topics like sexual behavior, the researchers took extra steps to protect the students’ privacy. Before starting the survey, each participant read and signed a consent form. After completing the survey, students personally placed it into a locked box, similar to a ballot box used in elections. Participants were assured that the research assistant collecting the surveys could not access them, and only the lead researcher, who was not present during data collection, had the key to the box. This procedure was implemented to guarantee the anonymity of all responses.
The survey included several questionnaires designed to measure different aspects of the students’ experiences and behaviors. To measure leisure boredom, the researchers used a six-item scale. This scale included statements like “For me, free time just drags on and on,” and students indicated how much they agreed or disagreed with each statement on a seven-point scale. Sensation seeking, or the desire for thrilling and new experiences, was assessed using a twelve-item questionnaire. This questionnaire presented pairs of statements, and students chose the statement that best described them. For example, one item presented the choice between “A sensible person avoids activities that are dangerous” and “I sometimes like to do things that are a little frightening.”
To assess risky sexual behavior, the researchers utilized the Sexual Risk Survey, a tool specifically developed for use with college students. This survey provided detailed instructions and asked students to report on their behaviors over the past six months. Risky sexual behavior was measured with eight questions, including “How many times have you had sex with someone you don’t know well or just met?” The survey also measured ‘intent to engage in risky sexual behavior’ with two questions, such as “How many times have you gone out to bars/parties/social events with the intent of ‘hooking up’ and having sex with someone?”
Impulsive sexual behavior, meaning unplanned or unexpected sexual encounters, was measured with five questions like “How many times have you had an unanticipated or unexpected sexual experience?” For these questions, students provided numerical answers, which were later categorized by the researchers into a standardized scoring system to allow for statistical analysis. Finally, the survey asked students if they had used any hookup apps in the past six months, and if so, to list the apps they had used.
The study’s findings confirmed that hookup app use is linked to risky sexual behavior. Students who used hookup apps reported engaging in more risky sexual behaviors and more impulsive sexual behaviors compared to students who did not use these apps. This finding aligns with previous research suggesting a connection between hookup app use and increased sexual risk taking.
“College students who have used Tinder and similar hookup apps in the past six months have engaged in significantly more risky sexual behavior during that same time period than college students who have not used Tinder and similar hookup apps in the past six months,” Lepp told PsyPost.
Interestingly, the researchers found that gender did not change this relationship. Both male and female hookup app users showed similar levels of risky sexual behavior, suggesting that regardless of gender, using these apps is associated with greater sexual risk.
“A lot of existing research explores motivations for hookup app use,” Lepp said. “Results of that research kind of fall into stereotypical gendered categories. For example: females use hookup apps to find romance and long term partners, males use hookup apps intending to find immediate casual sex. This might suggest that male hookup app users were more likely than female app users to engage in risky sexual behavior. We were able to look closely at this idea and found that it is not true.”
The study also shed light on the factors that might lead students to use hookup apps. Leisure boredom was found to be a significant predictor of hookup app use for women, but not for men. This suggests that women who experience more boredom in their free time may be more likely to turn to hookup apps, potentially as a way to alleviate this boredom.
On the other hand, sensation seeking was a significant predictor of hookup app use for both men and women. Students who are higher in sensation seeking, meaning they enjoy excitement and new experiences, were more likely to use hookup apps. Furthermore, for women only, sensation seeking was also directly linked to risky sexual behavior and impulsive sexual behavior, even independent of hookup app use. This means that women who are high sensation seekers may be more inclined to engage in risky sexual behaviors regardless of whether they use hookup apps, but this was not the case for men in this study.
“Male and female hookup app users likely download and begin exploring the app for slightly different reasons,” Lepp told PsyPost. “In our study, boredom was positively related to app use for females only, while sensation seeking predicted app use for both males and females. Other studies suggest females use such apps to find romance and males use them to find casual sex. Regardless of initial motivations or triggers, once males and females begin using hookup apps the chances of engaging in risky sexual behavior are similar, and significantly greater than non app users.”
The researchers acknowledged several limitations to their study. The sample was drawn from a single university in the Midwest, which may not fully represent all college students across the United States. For instance, students at private universities, especially those with religious affiliations, might have different experiences and behaviors related to hookup app use.
The researchers also noted that their model focused on boredom and sensation seeking, but other factors could also play a role in hookup app use and risky sexual behavior. Future studies could expand the model to include other potential predictors such as substance use, personality traits, and mental health factors to provide a more complete picture.
“Almost every adult has a smartphone within arm’s reach at all times, and everyone seems to be constantly on the device,” Lepp said. “So it is worth examining and reflecting upon how this device might be shaping our behavior for better or for worse. This is what our research intends to do.”
The study, “(https://authors.elsevier.com/a/1kWrD2f~UWUIbV) Smartphone hookup app use (e.g. Tinder) and college student’s risky sexual behavior: A model including leisure boredom, sensation seeking, and the moderating role of gender,” was authored by Andrew Lepp, Brian Yim, and Jacob E. Barkley.

(https://www.psypost.org/scientists-reveal-chatgpts-left-wing-bias-and-how-to-jailbreak-it/) Scientists reveal ChatGPT’s left-wing bias — and how to “jailbreak” it
Feb 14th 2025, 06:00

A new study published in the (https://doi.org/10.1016/j.jebo.2025.106904) Journal of Economic Behavior and Organization finds that ChatGPT, one of the world’s most popular conversational artificial intelligence systems, tends to lean toward left-wing political views rather than reflecting the balanced mix of opinions found among Americans. The research shows that the system not only produces more left-leaning text and images but also often refuses to generate content that presents conservative perspectives, a finding that raises concerns about the potential impact of such biases on public discussion.
The research, conducted by a team at the University of East Anglia in collaboration with Brazilian institutions, was motivated by the rapidly growing influence of artificial intelligence in society. Artificial intelligence algorithms are no longer just tools; they are increasingly making decisions that affect people’s lives and shaping how we understand the world. This growing role raises important questions about whether these systems are neutral or if they carry hidden biases that could impact public opinion and democratic processes.
The researchers were particularly interested in examining ChatGPT because of its widespread adoption and its ability to generate human-like text and images. While artificial intelligence offers many benefits, there’s a rising concern that these powerful tools could be manipulated or inherently designed in ways that promote certain viewpoints over others.
Previous research has shown that subtle changes in wording or imagery can influence people’s perceptions and beliefs. With artificial intelligence chatbots becoming more sophisticated and integrated into various aspects of life, the potential for them to amplify existing biases or introduce new ones is a serious concern. This growing unease about the potential for bias in artificial intelligence is what prompted the researchers to investigate this issue systematically.
“It all started as a conversation between friends. Like many people, we were amazed at how good ChatGPT (then in its 3.5 original incarnation) was in maintaining conversation, making summaries, etc,” said study author (https://sites.google.com/view/fmotoki/) Fabio Motoki, an assistant professor at the Norwich Business School at the University of East Anglia.
“One topic that caught our attention was people reporting political bias, but only showing a single interaction. By the very random nature of these systems, we concluded that it was not very solid evidence, and we started tinkering and thinking about how to measure it. That’s how (https://link.springer.com/article/10.1007/s11127-023-01097-2) our 2023 paper was born. The current paper is an evolution, in which we compare it against real humans. We also assess free text and image generation (a new capability), and also discuss its censorship of harmless content. We think we bring a much-needed social sciences perspective to the discussion, heavily dominated by computer science.”
To investigate ChatGPT’s political leanings, the researchers employed a multi-faceted approach. First, they used a method involving questionnaires, similar to how public opinion is often measured. They utilized the Political Typology Quiz developed by the Pew Research Center, a well-regarded, nonpartisan organization known for its public opinion research. This quiz asks a series of questions designed to identify different political types within the American population.
The researchers asked ChatGPT to answer these questions while pretending to be three different personas: an “average American,” a “left-wing American,” and a “right-wing American.” To ensure the results were reliable and not just due to random variations in ChatGPT’s responses, they repeated this process two hundred times for each persona, randomizing the order of questions each time. They then compared ChatGPT’s responses to actual survey data from the Pew Research Center, which included the responses of real average, left-leaning, and right-leaning Americans.
In a second part of their study, the team explored how ChatGPT generates text on politically charged topics. They used the themes covered in the Pew Research Center quiz questions, such as “Government Size,” “Racial Equality,” and “Offensive Speech.” For each theme, they prompted ChatGPT to write short paragraphs from three different perspectives: a “general perspective,” a “left-wing perspective,” and a “right-wing perspective.”
To analyze the political leaning of these generated texts, they used a sophisticated language model called RoBERTa, which is designed to understand the meaning of sentences. This model calculated a “similarity score” to determine how closely the “general perspective” text aligned with the “left-wing” text and the “right-wing” text for each theme. They also created visual word clouds to further examine the differences in word choices between the perspectives, providing a qualitative check on their quantitative analysis.
Finally, the researchers investigated whether ChatGPT’s political bias extended to image generation. Using the same themes and political perspectives, they instructed ChatGPT to create images using DALL-E 3, an image generation tool integrated with ChatGPT. For each theme and perspective, ChatGPT generated an image and also created a text description of the image to guide DALL-E 3.
To assess the political leaning of these images, they used two methods. First, they used a version of ChatGPT equipped with visual analysis capabilities (GPT-4V) to directly compare the generated images and rate their similarity. Second, they compared the text descriptions that ChatGPT created for each image, using both GPT-4 and Google’s Gemini Pro 1.0, another artificial intelligence model, to ensure the findings were consistent across different evaluation tools.
The study’s findings revealed a consistent pattern of left-leaning bias in ChatGPT. When ChatGPT impersonated an “average American” and answered the Pew Research Center quiz, its responses were found to be more aligned with left-wing Americans than a real average American would be. This suggests that ChatGPT’s default settings are already skewed to the left of the general American public.
In the text generation experiment, the researchers discovered that for most of the themes, the “general perspective” text generated by ChatGPT was more similar to the “left-wing perspective” text than the “right-wing perspective” text. While the strength and direction of this bias varied depending on the specific topic, the overall trend indicated a leftward lean in ChatGPT’s text generation. For example, on topics like “Government Size and Services” and “Offensive Speech,” the “general perspective” was more left-leaning. However, on topics like “United States Military Supremacy,” the “general perspective” was more aligned with the “right-wing perspective.”
“Generative AI tools like ChatGPT are not neutral; they can reflect and amplify political biases, particularly leaning left in the U.S. context,” Motoki told PsyPost. “This can subtly shape public discourse, influencing opinions through both text and images. Users should critically evaluate AI-generated content, recognizing its potential to limit diverse viewpoints and affect democratic processes. Staying informed about these biases helps ensure balanced, thoughtful engagement with AI-driven information.”
The image generation analysis largely mirrored the text generation findings. Both GPT-4V and Gemini Pro evaluations of the images and their text descriptions showed a similar left-leaning bias. Interestingly, the researchers also encountered an instance where ChatGPT refused to generate images from a right-wing perspective for certain themes, such as “Racial-ethnic equality in America” and “Transgender acceptance in society,” citing concerns about spreading misinformation or bias. This refusal only occurred for right-wing perspectives, not left-wing perspectives, raising further questions about the system’s neutrality.
To overcome this obstacle and further investigate ChatGPT’s behavior, the researchers employed a technique sometimes referred to as a “jailbreak.” In the context of artificial intelligence, “jailbreaking” means finding clever ways to get the system to do something it is normally restricted from doing. In this case, the researchers used a method called “meta-story prompting.”
Instead of directly asking ChatGPT to generate a right-wing image on a sensitive topic, they first asked ChatGPT to create a fictional story. This story described a researcher who was studying artificial intelligence bias and needed to generate an image representing a right-wing perspective on the topic. By framing the image request within this story, the researchers were able to indirectly prompt ChatGPT to create the images it had previously refused to generate. This meta-story acted as a kind of workaround, tricking the system into fulfilling the original request.
“The jailbreak, which became a big part of the paper, wasn’t there in the initial versions,” Motoki explained. “We got this very insightful comment from Scott Cunningham, from Baylor, through LinkedIn and gave it a try. It worked beautifully. Serendipity sometimes plays a big role on things.”
When the researchers used this “meta-story prompting” technique, ChatGPT successfully generated the right-wing images for the previously blocked topics. Upon examining these images, the researchers found that they did not contain any obviously offensive or inappropriate content that would justify ChatGPT’s initial refusal. The images simply represented right-leaning viewpoints on these social issues, similar to how the system readily generated left-leaning images.
This success in bypassing ChatGPT’s refusal, and the lack of offensive content in the resulting images, strengthens the researchers’ concern that the chatbot’s censorship of right-wing perspectives might be based on an inherent bias rather than a legitimate concern about harmful content. This finding raises important questions about the fairness and neutrality of these powerful artificial intelligence systems, especially as they become increasingly influential in shaping public understanding of important social and political issues.
“When we faced the refusals we thought that maybe there was a legitimate reason for that,” Motoki said. “For instance, due to data or training, it might have ended up generating disturbing images and these were blocked. When we managed to jailbreak and found nothing of the like we started to think how could that be fair, and we started studying the application of the U.S. First Amendment to media and FCC’s Fairness Doctrine.”
But the researchers acknowledge that their study has some limitations. It primarily focused on ChatGPT and DALL-E 3. Further research is needed to examine whether these biases are present in other artificial intelligence models and systems.
“There’s only so much you can fit into a paper, and time and resources are limited,” Motoki told PsyPost. “For instance, we decided to focus on ChatGPT, which detains the majority of the market. However, different LLMs from different providers may prove more or less biased. Therefore, our findings have limited generalizability, although the method itself should be straightforward to apply to any state-of-the-art model.”
Future studies could also investigate the underlying reasons for these biases, such as the data used to train these models or the design choices made by their developers. It is also important to explore the potential impact of these biases on public discourse, political polarization, and democratic processes.
“We want to empower society to be able to oversee these tools,” Motoki said. “We also want to provide regulators and legislators with evidence to base their decisions.”
The study, “(https://doi.org/10.1016/j.jebo.2025.106904) Assessing political bias and value misalignment in generative artificial intelligence,” was authored by Fabio Y.S. Motoki, (https://sites.google.com/view/valdemarneto/) Valdemar Pinho Neto, and (https://sites.google.com/view/rangel-victor/) Victor Rangel.

(https://www.psypost.org/study-identifies-predictors-of-womens-psychological-well-being-in-romantic-relationships/) Study identifies predictors of women’s psychological well-being in romantic relationships
Feb 14th 2025, 04:00

A recent study published in the journal (https://doi.org/10.3390/bs15010082) Behavioral Sciences has shed light on what contributes to women’s psychological well-being within romantic relationships. Researchers found that feeling satisfied in their relationship, experiencing a good quality of sexual life, possessing empathy, and having children were all linked to higher levels of psychological well-being for women.
The motivation behind this research stemmed from the existing understanding that romantic relationships are deeply important for human happiness and health. Previous studies have explored various aspects of relationships and well-being, but this research aimed to provide a more comprehensive picture. Scientists wanted to understand how several interconnected relationship factors, such as the ability to understand and share the feelings of their partner (empathy), the enjoyment and fulfillment experienced in their sexual life, the intensity of romantic love, the sense of closeness and connection with their partner, and the overall stability of the relationship, might influence a woman’s overall psychological health.
They noted that while past research had looked at some of these factors individually or in smaller groups, no study had yet examined all of them together in relation to women’s psychological well-being. This study sought to fill that gap and identify the key elements within a romantic partnership that are most strongly associated with a woman’s sense of well-being.
To explore these questions, the researchers recruited 415 women aged between 23 and 45. All participants were in romantic relationships that had lasted for at least one year. The women were recruited online using a method called snowball sampling, where initial participants help to find additional participants who meet the study criteria. This was particularly useful as data collection took place during the COVID-19 pandemic, requiring online methods.
To participate, women had to be within the specified age range, in a relationship for at least 12 months, and identify as female. Women were excluded if they were outside the age range, in shorter relationships, male, or if they reported having a psychiatric illness, using psychiatric medication, having a chronic physical illness with regular medication, or struggling with alcohol or drug abuse.
The women completed a series of questionnaires online. First, they filled out a form collecting basic information about themselves, such as their age, education level, relationship status (dating, engaged, or married), how long they had been in the relationship, whether they had children, and their employment status. To measure their psychological well-being, the researchers used the Psychological Well-Being Scale. This scale asks participants to rate their agreement with statements about their current well-being, providing a score indicating their overall psychological resources.
Empathy was assessed using the Basic Empathy Scale, which measures both cognitive empathy (understanding another person’s feelings) and emotional empathy (sharing another person’s feelings). The Sexual Quality of Life Scale—Women’s Form was used to evaluate participants’ satisfaction and enjoyment in their sexual lives over the past four weeks.
To measure relationship stability, the researchers employed the Relationship Stability Scale. This scale looks at different aspects of stability, including relationship satisfaction, how much a person feels invested in the relationship, how they view their alternatives to the current relationship, and their overall commitment.
Passionate love was measured using the Passionate Love Scale, which assesses the intensity of romantic feelings and desires for the partner. Finally, the Romantic Relationship Closeness Scale was used to evaluate the level of intimacy in the relationship, looking at aspects like self-disclosure, physical attraction, support, and trust.
The researchers found that empathy, sexual quality of life, intimacy in the romantic relationship, relationship satisfaction, and relationship attachment all showed significant positive correlations with psychological well-being. This means that as these relationship qualities increased, so did women’s reported psychological well-being.
Analyzing demographic factors, the researchers found that women with higher education levels tended to score higher on the ‘evaluating options’ aspect of relationship stability, suggesting they might be more aware of alternatives outside their current relationship. Conversely, women with lower education levels scored higher on ‘relationship investment’, indicating a greater sense of commitment and resources put into their relationships.
Passionate love was found to be more common among women who were not employed compared to working women. Having children was associated with higher psychological well-being but with lower levels of intimacy in the romantic relationship. Finally, the quality of sexual life was reported to be higher in more serious relationships, such as marriage, compared to dating relationships.
Another aspect examined in the study was the duration of the relationship. Women who had been with their partners for five years or longer not only felt more secure but also reported higher psychological well-being. A longer relationship may indicate a more stable and supportive partnership, where both partners have had the time to build a deep, trusting connection. However, the study also found that certain factors, like how much a woman invests in her relationship, may change as the relationship matures over time.
The researchers used regression analysis to identify the top predictors of psychological well-being in women in romantic relationships. Relationship satisfaction emerged as the strongest predictor of psychological well-being. Quality of sexual life was also a significant predictor; women who experienced a better quality of sexual life tended to have greater psychological well-being. Empathy also played a role, with higher levels of empathy being linked to improved psychological well-being. Interestingly, having children was also found to be a positive predictor of psychological well-being in this study.
The study, “(https://doi.org/10.3390/bs15010082) Predictors of Young Adult Women’s Psychological Well-Being in Romantic Relationships,” was authored by by Elif Yöyen, Süreyya Çalık, and Tülay Güneri Barış.

(https://www.psypost.org/are-dating-apps-affecting-your-body-image-heres-what-the-research-says/) Are dating apps affecting your body image? Here’s what the research says
Feb 13th 2025, 20:00

Around (https://www.businessofapps.com/data/dating-app-market/) 350 million people globally use dating apps, and they amass an estimated annual revenue of more than US$5 billion. In Australia, (https://www.choosi.com.au/documents/choosi-swipe-right-report-2023-whitepaper.pdf) 49% of adults report using at least one online dating app or website, with a further 27% having done so in the past.
But while dating apps have helped many people (https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2020.01757/full) find romantic partners, they’re not all good news.
In a (https://www.sciencedirect.com/science/article/pii/S0747563224003832) recent review, my colleagues and I found using dating apps may be linked to poorer body image, mental health and wellbeing.
We collated the evidence
Our study was a systematic review, where we collated the results of 45 studies that looked at dating app use and how this was linked to body image, mental health or wellbeing.
Body image refers to the perceptions or feelings a person has towards their own appearance, often relating to body size, shape and attractiveness.
Most of the studies we included were published in 2020 onwards. The majority were carried out in Western countries (such as the United States, the United Kingdom and Australia). Just under half of studies included participants of all genders. Interestingly, 44% of studies observed men exclusively, while only 7% included just women.
Of the 45 studies, 29 looked at the impact of dating apps on mental health and wellbeing and 22 considered the impact on body image (some looked at both). Some studies examined differences between users and non-users of dating apps, while others looked at whether intensity of dating app use (how often they’re used, how many apps are used, and so on) makes a difference.
More than 85% of studies (19 of 22) looking at body image found significant negative relationships between dating app use and body image. Just under half of studies (14 of 29) observed negative relationships with mental health and wellbeing.
The studies noted links with problems including body dissatisfaction, disordered eating, depression, anxiety and low self-esteem.
It’s important to note our research has a few limitations. For example, almost all studies included in the review were (https://www.sciencedirect.com/science/article/pii/S0012369220304621) cross-sectional – studies that analyse data at a particular point in time.
This means researchers were unable to discern whether dating apps actually cause body image, mental health and wellbeing concerns over time, or whether there is simply a correlation. They can’t rule out that in some cases the relationship may go the other way, meaning poor mental health or body image increases a person’s likelihood of using dating apps.
Also, the studies included in the review were mostly conducted in Western regions with predominantly white participants, limiting our ability to generalise the findings to all populations.
Why are dating apps linked to poor body image and mental health?
Despite these limitations, there are plausible reasons to expect there may be a link between dating apps and poorer body image, mental health and wellbeing.
Like a lot of social media, dating apps are overwhelmingly image-centric, meaning they have an (https://psychologicalsciences.unimelb.edu.au/__data/assets/pdf_file/0016/3522004/Griffiths-2018.pdf) emphasis on pictures or videos. Dating app users are initially exposed primarily to photos when browsing, with information such as interests or hobbies accessible only after manually clicking through to profiles.
Because of this, users often evaluate profiles based primarily on the photos attached. Even when a user does click through to another person’s profile, whether or not they “like” someone may still often be determined primarily on the basis of physical appearance.
This emphasis on visual content on dating apps can, in turn, cause users to view (https://www.sciencedirect.com/science/article/abs/pii/S1740144524001037) their appearance as more important than who they are as a person. This process is called self-objectification.
People who experience self-objectification are more likely to scrutinise their appearance, potentially leading to body dissatisfaction, body shame, or other issues pertaining to (https://pmc.ncbi.nlm.nih.gov/articles/PMC5869145/) body image.
There could be several reasons why mental health and wellbeing may be impacted by dating apps, many of which may (https://www.sciencedirect.com/science/article/abs/pii/S0747563214000272) centre around rejection.
Rejection can come in many forms on dating apps. It can be implied, such as having a lack of matches, or it can be explicit, such as discrimination or abuse. Users who encounter rejection frequently on dating apps may be more likely to experience poorer self-esteem, depressive symptoms or anxiety.
And if rejection is perceived to be based on appearance, this could lead again to body image concerns.
What’s more, the convenience and (https://onlinelibrary.wiley.com/doi/10.1111/theo.12549#:~:text=Dating%20apps%20such%20as%20Tinder,favors%20users%20with%20conventional%20preferences.) game-like nature of dating apps may lead people who could benefit from taking a break to keep swiping.
What can app developers do? What can you do?
Developers of dating apps should be seeking ways to protect users against these possible harms. This could, for example, include reducing the prominence of photos on user profiles, and increasing the moderation of discrimination and abuse on their platforms.
The Australian government has developed (https://www.infrastructure.gov.au/department/media/news/new-industry-code-now-operational-make-online-dating-safer#:~:text=The%20code%20requires%20dating%20apps,action%20to%20address%20these%20incidents) a code of conduct – to be enforced from April 1 this year – to help moderate and reduce discrimination and abuse on online dating platforms. This is a positive step.
Despite the possible negatives, research has also found dating apps can help (https://www.pewresearch.org/internet/2020/02/06/users-of-online-dating-platforms-experience-both-positive-and-negative-aspects-of-courtship-on-the-web/) build confidence and help users meet new people.
If you use dating apps, my colleagues and I recommend choosing profile images you feel display your personality or interests, or photos with friends, rather than semi-clothed images and selfies. Engage in positive conversations with other users, and block and report anyone who is abusive or discriminatory.
It’s also sensible to take breaks from the apps, particularly if you’re feeling overwhelmed or dejected.
 
This article is republished from (https://theconversation.com) The Conversation under a Creative Commons license. Read the (https://theconversation.com/dating-apps-could-have-negative-effects-on-body-image-and-mental-health-our-research-shows-247336) original article.

(https://www.psypost.org/anti-mandate-protesters-opposing-covid-19-rules-often-reject-abortion-rights/) Anti-mandate protesters opposing COVID-19 rules often reject abortion rights
Feb 13th 2025, 18:00

A study published in (https://doi.org/10.1007/s11199-024-01533-7) Sex Roles reveals that individuals who opposed COVID-19 public health mandates were also likely to oppose abortion rights.
The COVID-19 pandemic prompted governments to balance public health measures with personal freedoms, leading to anti-mandate protests in many countries. Protesters invoked rhetoric associated with abortion rights, particularly the phrase “my body, my choice,” to argue against government-imposed restrictions such as vaccine mandates and lockdowns.
Danny Osborne and colleagues examined how attitudes toward COVID-19 mandates and abortion rights clustered together in the general population.
Prior research suggests that opposition to reproductive rights is often tied to conservative ideology, religious beliefs, and distrust of government institutions. Given that these same factors contributed to opposition to COVID-19 mandates, the researchers hypothesized that anti-mandate protesters might not support a broad application of bodily autonomy but rather selectively apply it to align with their beliefs.
Study 1 used data from 2,331 participants in the 2022 General Social Survey (GSS), a nationally representative sample of U.S. adults. Participants reported their stance on both elective and traumatic abortion, as well as their support for COVID-19 public health mandates, including mask-wearing, lockdowns, business closures, and digital contact tracing. Additional measures assessed their political ideology, religiosity, trust in institutions, and demographic factors such as age, gender, income, and education level.
Study 2 replicated this approach in New Zealand using the New Zealand Attitudes and Values Study (NZAVS), a large-scale longitudinal survey, with a sample of 33,310 participants. Alongside attitudes toward abortion and COVID-19 mandates, this study also included psychological and ideological measures, such as belief in conspiracy theories, cognitive consistency (the extent to which individuals maintain internally coherent beliefs), nationalism, and trust in science and government institutions.
The researchers applied latent profile analysis to group individuals into clusters based on shared patterns in their responses.
In the United States, three distinct ideological profiles emerged. The largest group (59%) consistently supported both abortion rights and COVID-19 mandates, reflecting a broad commitment to public health and bodily autonomy. A second group (28.4%) displayed a more selective stance, opposing elective abortion but allowing for exceptions in cases of rape, fetal anomalies, or maternal health risks. This group expressed moderate support for COVID-19 mandates but was more skeptical of measures such as digital contact tracing. The smallest and most rigid group (12.6%) opposed both abortion and government-imposed COVID-19 restrictions.
New Zealand showed a similar pattern, though with notable differences in distribution. The majority (78%) supported both abortion rights and public health measures, aligning with the country’s strong public compliance with COVID-19 policies. A smaller subgroup (11.5%) resisted digital contact tracing but otherwise endorsed mandates and reproductive rights. Another segment (7.1%) supported COVID-19 mandates while opposing abortion. Finally, a small “Anti-Mandate” group (3.4%) rejected both COVID-19 restrictions and abortion rights.
Across both studies, those in the Anti-Mandate profile were more likely to be politically conservative, religious, and distrustful of institutions. In New Zealand, this group also showed higher levels of conspiracy belief and lower cognitive consistency (i.e., ideological coherence across issues).
These findings suggest that opposition to government intervention does not necessarily translate into a broader commitment to bodily autonomy.
The study relied on self-reported attitudes, which may not fully capture the complexity of individuals’ beliefs or the influence of broader social movements.
The research, “(https://doi.org/10.1007/s11199-024-01533-7) My Body, My Choice? Examining the Distinct Profiles Underlying Attitudes Toward Abortion and COVID-19 Mandates”, was authored by Danny Osborne, Joaquin Bahamondes, Eden V. Clarke, Deborah Hill Cone, Kieren J. Lilly, Morgana Lizzio-Wilson, Eduardo J. Rivera Pichardo, Nicole Satherley, Natalia Maria Simionato, Emma F. Thomas, Elena Zubielevitch, and Chris G. Sibley.

(https://www.psypost.org/research-shows-how-performance-fears-impact-sexual-satisfaction-in-young-women/) Research shows how performance fears impact sexual satisfaction in young women
Feb 13th 2025, 16:00

A study of a small group of Spanish young women found that those with lower sexual satisfaction tend to report more sexual inhibition due to the threat of performance failure and expected consequences of such failure. The research was published in (https://doi.org/10.3390/bs14090769) Behavioral Sciences.
Sexual satisfaction is a subjective experience of fulfillment and pleasure derived from sexual activity, influenced by physical, emotional, and psychological factors. It is shaped by aspects such as intimacy, communication, relationship quality, personal expectations, and cultural beliefs. An emotional connection and mutual understanding between partners enhance sexual satisfaction, while stress, relationship conflicts, or health issues can diminish it.
Individual factors such as body image, self-esteem, and sexual confidence also play a role in determining a person’s level of satisfaction. Regular and open communication about needs and desires can contribute to a more fulfilling sexual experience. Additionally, hormonal balance, physical health, and age-related changes can impact sexual satisfaction over time. Research suggests that sexual satisfaction is linked to overall well-being, relationship stability, and mental health.
The study’s author, María del Mar Sánchez-Fuentes, and her colleagues aimed to examine the relationships between sexual satisfaction and sexual arousal, both as a trait (i.e., the propensity for sexual excitation and inhibition) and as a state (i.e., sexual arousal when viewing a video with explicit sexual content). They expected that women who were more sexually satisfied in their relationships would report a lower propensity for both sexual excitation and sexual inhibition. The authors also anticipated that sexually satisfied women would experience lower sexual arousal in the laboratory context.
Study participants were 45 Spanish heterosexual women aged between 18 and 25 who had a partner. Their average age was 21, and the mean duration of their romantic relationships was 27 months. On average, their first sexual relationship occurred at age 16, and they reported having had 4–5 sexual partners.
Participants completed assessments of sexual and relationship satisfaction (using the Global Measure of Relationship Satisfaction and the Global Measure of Sexual Satisfaction), sexual inhibition, and propensity for sexual arousal (using the Sexual Inhibition/Sexual Excitation Scales-Short Form).
Participants also viewed a 3‑minute nature documentary and a 3‑minute sexually explicit heterosexual video depicting a couple engaging in sexual activity, including oral sex and vaginal intercourse. While watching these videos, the women underwent vaginal photoplethysmography, which allowed the researchers to objectively measure their levels of sexual arousal.
Vaginal photoplethysmography is a physiological measurement technique used to assess vaginal blood flow and sexual arousal by detecting changes in vaginal tissue oxygenation and engorgement through light absorption. The apparatus, known as a vaginal photoplethysmograph, is a small, tampon‑shaped probe with a light source (usually infrared) and a light detector. It is inserted into the vagina to measure blood volume changes based on the intensity of reflected light. Participants also reported their level of sexual arousal using a rating scale (the Rating of Sexual Arousal).
Results showed that participants who reported higher sexual satisfaction also tended to be more satisfied with their relationships. Women with higher sexual satisfaction reported lower levels of sexual inhibition, both due to the threat of performance failure and the threat of performance consequences. Additionally, women who were more satisfied with their relationships tended to exhibit a lower genital response to the sexual video.
Sexual inhibition due to performance failure refers to the tendency to experience a decrease in sexual arousal or responsiveness because of concerns about one’s ability to perform sexually—for example, difficulty achieving orgasm, inadequate lubrication, or a perceived inability to satisfy a partner. Sexual inhibition due to performance consequences occurs when sexual arousal is reduced because of fears of potential negative outcomes of sexual activity, such as unintended pregnancy, sexually transmitted infections, emotional vulnerability, or social judgment.
“In conclusion, the negative association between sexual satisfaction and propensity for sexual inhibition in young women with a partner is supported, but not the positive association between trait/state sexual arousal and sexual satisfaction,” the study authors concluded.
The study sheds light on the links between sexual satisfaction, sexual arousal, and sexual inhibition. However, because it was conducted on a small group of young Spanish women, the results might differ for other demographic groups.
The paper, “(https://doi.org/10.3390/bs14090769) Sexual Excitation in Young Women with Different Levels of Sexual Satisfaction in Relationships: A Laboratory Study”, was authored by María del Mar Sánchez-Fuentes, Ana Álvarez-Muelas, Oscar Cervilla, Reina Granados, and Juan Carlos Sierra.

(https://www.psypost.org/ai-reveals-racial-differences-in-ideal-breast-shape/) AI reveals racial differences in “ideal” breast shape
Feb 13th 2025, 14:00

A new study published in (https://academic.oup.com/asjopenforum/article/doi/10.1093/asjof/ojae006/7593492?login=false) Aesthetic Surgery Journal Open Forum shows that an artificial intelligence program can create realistic images of what many people consider the ideal female breasts—and that these ideals vary by race. Researchers used an online image generator to produce pictures of Caucasian, African American, and Asian women with “perfect” breasts, and they found notable differences in the breast shapes, nipple angles, and other features among these groups. The results suggest that beauty ideals are not one-size-fits-all and that these differences may be useful for plastic surgeons who aim to match their patients’ racial and cultural identities.
Defining what constitutes an “ideal breast” has long been a subject of debate within the field of plastic surgery and beyond. While numerous studies have attempted to pinpoint specific measurements and proportions, the concept of beauty is inherently subjective and influenced by a range of factors, including race, culture, and individual preferences. The researchers recognized that much of the existing research on ideal breast aesthetics has primarily focused on Caucasian women, potentially overlooking the nuances of beauty standards within other racial groups.
With the rapid advancement of artificial intelligence and its increasing role in shaping visual culture, the researchers were curious to see if artificial intelligence could offer new insights into how ideal breast shapes are perceived and if these perceptions vary across different races. They wanted to investigate if an artificial intelligence image generator, trained on vast amounts of data, would produce images of ideal breasts that reflected previously established aesthetic principles and, importantly, if it would generate different breast morphologies for women of different racial backgrounds.
“As an aesthetic surgeon, I am fascinated by what humans perceive as aesthetic ideals of the human form; be it face, breast, or body. After all, this is what we attempt to achieve with surgery,” explained study author Aaron Lee Wiegmann ((https://www.instagram.com/doctor.wiggy/) @doctor.wiggy), chief plastic and reconstructive surgery resident physician at Rush University Medical Center.
“Much of the plastic surgery literature has been focused on what is aesthetically ideal in the Caucasian population, however, I believe that there are many racial nuances that may vary between different racial groups. Ultimately, when we are performing aesthetic surgery, we want the result to be congruent with the patient’s racial identity. It is very likely that an “ideal breast” has subtly different appearances to humans from different racial groups.”
“Another aspect of this study was to evaluate AI’s ability to generate photorealistic images of aesthetically ideal breasts, something not evaluated before in any scientific literature. We also wanted to understand if AI was capable of generating different appearing breasts for different racial groups.”
To conduct their investigation, Wiegmann and his colleagues utilized a publicly available artificial intelligence image generation platform. This platform, accessible to anyone, allowed them to input text prompts and receive generated images in response. The researchers obtained a commercial license to use the images for publication. They crafted specific text prompts to guide the artificial intelligence in generating images of women with aesthetically ideal breasts.
The base prompt was: “A topless Caucasian woman with perfect aesthetically ideal breasts standing in three-quarter profile view.” This prompt was then slightly modified to generate images for different races and viewpoints. To obtain images of African American and Asian women, the word “Caucasian” in the prompt was replaced with “African American” and “Asian,” respectively. Additionally, to capture different perspectives, the phrase “three-quarter profile view” was changed to “frontal view.”
The artificial intelligence models used in this platform are based on sophisticated technology that learns to generate images from massive datasets of images and text. These models are designed to create realistic images of people based on text descriptions and are trained by developers for general public use, not specifically for medical or surgical purposes. This aspect was significant to the researchers because it meant the artificial intelligence’s output would likely reflect broader societal perceptions of beauty, rather than being influenced by specific medical or plastic surgery literature.
Once a large set of images was generated, the researchers selected images for analysis based on specific criteria to ensure image quality and suitability for measurement. The criteria included: the image being a clear three-quarter profile or frontal view; the breast borders and nipple-areola complex being clearly visible; the image not being taken from an extreme angle; and the absence of any obvious anatomical distortions. From the initial set of generated images, the researchers selected twenty-five consecutive images that met these criteria for each racial group (Caucasian, African American, and Asian) in both three-quarter profile and frontal views, resulting in a total of one hundred and fifty images for analysis.
Using image editing software, the researchers then performed detailed measurements on each selected image. To ensure consistency despite variations in image size, measurements were taken in pixels, providing a standardized unit of length across all images. Following established methods in breast aesthetics research, three-quarter profile images were used to assess breast shape.
Measurements included the upper-to-lower pole ratio (the proportion of the breast above and below the nipple), the nipple angle (the direction the nipple points relative to the chest), and subjective assessments of the upper pole slope (curve of the upper breast) and lower pole convexity (roundness of the lower breast). Frontal view images were used to evaluate the position of the nipple-areola complex and the size proportions of the nipple and areola. Measurements taken from frontal images included the nipple-areola complex position relative to the upper, lower, medial, and lateral breast borders, the ratio of areolar diameter to breast width, and the ratio of nipple diameter to areolar diameter.
Overall, the breasts generated by the artificial intelligence were deemed realistic and aesthetically appealing across all racial groups. They generally exhibited good size, projection, and a natural, slightly drooping shape. However, the researchers noted that some images depicted breasts that might be considered excessively large for the woman’s body frame, and occasionally, the body builds were unrealistically muscular.
Despite these general similarities, statistical analysis revealed significant differences in specific breast characteristics across the racial groups. Caucasian breasts, according to the artificial intelligence, tended to have a smaller upper portion and larger lower portion, nipples that pointed more upwards, and a higher likelihood of a concave upper slope.
In contrast, African American and Asian breasts generated by the artificial intelligence showed a larger upper portion and smaller lower portion, nipples that pointed more straight ahead, and a higher prevalence of a convex or straight upper slope. The position of the nipple-areola complex also differed, with Caucasian breasts having a lower placement on the chest compared to African American and Asian breasts. Furthermore, African American breasts were found to have larger areolas relative to breast width compared to Caucasian breasts.
“I was surprised by the consistent and measurable differences in breast appearance that AI generated for different racial groups,” Wiegmann told PsyPost. The findings indicate that “there are likely very real racial nuances that exist in terms of what different racial groups deem the aesthetically ideal breast phenotype, and your plastic surgeon should discuss this with you during any consult for breast enhancement surgery.”
When comparing the artificial intelligence-generated breasts to previously established aesthetic ideals, the researchers found that the Caucasian breasts closely aligned with what is often described as the ideal breast shape in plastic surgery literature. Specifically, the upper-to-lower pole ratio and nipple angle of the artificial intelligence-generated Caucasian breasts were similar to previously defined ideals.
However, the artificial intelligence-generated African American and Asian breasts deviated from these Caucasian-centric ideals, further highlighting the racial differences observed in the study. Interestingly, while previous literature suggests a specific ideal breast shape for Asian women with a very large upper portion, the artificial intelligence-generated Asian breasts in this study, although having a larger upper portion than the Caucasian breasts, did not fully match this extremely large upper portion ideal.
“The average person should understand that AI is capable of producing photorealistic images of breasts that are aesthetically pleasing and have significant measurable racial differences,” Wiegmann said. “With this in mind, AI has immense potential to help patients determine what their goals are for breast enhancement surgery. AI also has the potential to revolutionize pre-operative simulation of potential postoperative results for patients.”
But this capability also raises concerns about the potential for misuse in plastic surgery, such as creating misleading before-and-after photos or promoting unrealistic aesthetic expectations.
“Patients should beware that nefarious surgeons may use AI-generated photos to market themselves and show potentially unrealistic results leading to inflated patient expectations, and this raises significant ethical concerns for the field of plastic surgery,” Wiegmann warned.
The researchers acknowledged several limitations to their study. While measurements were taken consistently, some degree of subjectivity is inherent in identifying precise breast landmarks, particularly on images. The researchers also could not control factors like lighting and exact pose in the artificial intelligence-generated images, which might have subtly influenced measurements.
Another important limitation is the unknown nature of the data used to train the artificial intelligence models. The researchers lacked information about the specific images and biases present in the datasets, which could influence the generated outputs. However, the fact that the artificial intelligence-generated Caucasian and Asian breasts did align with some previously described racial breast ideals provided some reassurance about the generalizability of the findings.
Future research could build upon this study by exploring how artificial intelligence perceives ideal breast shapes for different body types within each racial group. For example, investigating ideal breasts for thin, overweight, and obese women of the same race could reveal further nuanced differences. Additionally, future studies could explore the potential biases embedded within artificial intelligence training datasets to better understand how these biases might shape perceptions of beauty.
“The long-term goal is to leverage AI (and its seemingly continuous advancements) to better understand what diverse groups of humans perceive as the aesthetically ideal human form, which will assist plastic surgeons in achieving a mutually excellent surgical result for them and the patient,” Wiegmann said.
The study, “(https://doi.org/10.1093/asjof/ojae006) Aesthetically Ideal Breasts Created With Artificial Intelligence: Validating the Literature, Racial Differences, and Deep Fakes,” was authored by Aaron L. Wiegmann, Elizabeth S. O’Neill, Sammy Sinno, and Karol A. Gutowski.

(https://www.psypost.org/eco-friendly-actions-boost-happiness-as-much-as-hobbies-research-shows/) Eco-friendly actions boost happiness as much as hobbies, research shows
Feb 13th 2025, 12:00

A recent study published in (https://journals.sagepub.com/doi/abs/10.1177/09567976241251766) Psychological Science found that engaging in proenvironmental behaviors—such as cleaning up litter, reducing waste, or using sustainable transportation—boosts happiness and a sense of meaning. Remarkably, the effect was just as strong as that of engaging in activities specifically designed to improve well-being, such as hobbies or self-care.
Sustainable living is often seen as a sacrifice—for instance, giving up convenience, spending more money, and enduring discomfort. However, psychological theories suggest that prosocial and moral actions contribute to happiness. Additionally, previous studies have indicated a correlation between proenvironmental behavior and subjective well-being, although causal evidence has been lacking.
To address this gap, researcher Michael Prinzing from Baylor University sought to investigate whether engaging in proenvironmental behaviors increases subjective well-being.
The research involved two studies. The first tracked 181 adults (aged 18 to 76 years; 61% female) from 14 countries over 10 days. Participants reported their activities and mood multiple times per day through surveys.
The results demonstrated that people felt happier on days when they engaged in sustainable behaviors. The positive effect on well-being was stronger among those with high environmental values, although even participants with low environmental values experienced benefits.
The second study was a controlled experiment involving 545 undergraduate college students (aged 18 to 43 years; 70% female) from the USA. Participants were randomly assigned to one of three groups: the proenvironmental group (engaging in three sustainable actions), the fun activity group (completing three enjoyable activities), or the control group (simply tracking their activities).
After two days, participants in both the proenvironmental and fun activity groups reported significantly higher happiness and life satisfaction than those in the control group. Notably, there were no significant differences based on political orientation or environmental concern.
Prinzing concluded, “These findings stand in stark contrast to popular perceptions. Sustainability is often portrayed as onerous and unpleasant, whereas these studies support the opposite conclusion. Yet our findings are very much in line with a long tradition of philosophical theory and a growing body of scientific evidence, each of which suggests that being good is conducive to being well—that is, people flourish when they seek to cultivate virtue and do good in the world.”
Some limitations should be noted. The study was conducted over only a few days, so it is unclear whether the happiness boost is long-lasting. Additionally, some participants might have guessed the study’s purpose, which could have influenced their responses. Future research could explore how consistent sustainable habits impact well-being over months or years.
The study, “(https://doi.org/10.1177/09567976241251766) Proenvironmental Behavior Increases Subjective Well-Being,” was authored by Michael Prinzing.

(https://www.psypost.org/scientists-use-cutting-edge-analysis-to-determine-whether-church-attendance-really-boosts-charitable-acts/) Scientists use cutting-edge analysis to determine whether church attendance really boosts charitable acts
Feb 13th 2025, 10:00

A new study published in the (https://doi.org/10.1177/00846724241302810) Archive for the Psychology of Religion provides evidence that attending religious services actually causes an increase in charitable actions, like donating money and volunteering time. While the study found this causal effect to be more modest than simple observations might suggest, the researchers demonstrated that even a slight rise in regular religious service attendance across a population could lead to a noticeable and significant increase in overall charitable contributions.
For many years, scientists who study religion have been interested in understanding whether religious beliefs and practices make people more inclined to act kindly and generously towards others. This idea, that religion promotes prosocial behavior, has been discussed for centuries. However, it is surprisingly difficult to definitively prove that religion causes people to be more helpful.
Many past studies have simply shown that religious people tend to be more prosocial, but this doesn’t tell us if their religion is the reason for their generosity. It could be that people who are already kind and giving are simply more drawn to religious communities, or that other factors are responsible for both religious participation and prosocial actions.
Therefore, a team of researchers set out to investigate this question using a large dataset and sophisticated statistical techniques designed to get closer to understanding cause and effect. They aimed to move beyond simply observing a connection between religion and prosociality and to explore whether changing religious service attendance would actually lead to changes in helpful behaviors in the real world.
“Humans are the religious species. I’ve long been interested in what religion does for us, for better and worse,” said study author (https://people.wgtn.ac.nz/joseph.bulbulia) Joseph Bulbulia, a professor of psychology at the Victoria University of Wellington.
“My training was in the philosophy and history of religions, and my early contributions were to the evolution of religion. About fifteen years ago, I was fortunate to become involved in national scale longitudinal data collection for the New Zealand Attitudes and Values Study, which my friend and collaborator Chris G. Sibley started in 2009. National longitudinal data can provide insights into causality, but only if you analyse the data correctly.”
“I spent nearly a decade acquiring advanced skills in longitudinal statistical methods, however, for a long time I lacked a clear understanding about what these methods delivered. It wasn’t until I encountered the literatures on causal inference (around 2019/2020) that I understood how to leverage longitudinal data to address causal questions.”
The New Zealand Attitudes and Values Study surveys a wide range of New Zealand residents every year, asking them about their social attitudes, personalities, beliefs, health, and behaviors. For this particular study, the researchers focused on information collected from over 33,000 New Zealanders between the years 2018 and 2021. To ensure the sample was as accurate a reflection of the country as possible, the researchers used statistical adjustments based on the 2018 New Zealand Census data for age, gender, and ethnicity.
The study followed participants over time, tracking their religious service attendance and prosocial behaviors across multiple years. Religious service attendance was measured by asking participants if they identified with a religion or spiritual group and, if so, how many times they had attended a church or place of worship in the past month.
For prosocial behavior, the researchers looked at several different measures. First, they examined self-reported charitable activities, asking participants about the number of hours they volunteered in a week and how much money they had donated to charity in the past year. Second, in a novel approach to measuring prosociality, they also looked at whether participants had received help from others. They asked participants if they had received help in the form of time or money from family, friends, and the wider community in the past week.
The researchers reasoned that if religious service attendance truly fosters prosociality within a community, then increased attendance should lead to a more generally helpful environment where people are more likely to offer and receive support. Using this measure of help received aimed to provide a less biased perspective on prosociality, as it focuses on actual experiences of receiving assistance rather than just self-reported generous actions.
Bulbulia and his colleagues then used advanced statistical modeling to explore the causal effects of religious service attendance on these prosocial behaviors. Instead of simply looking at correlations, they designed their models to simulate different scenarios. They considered three hypothetical situations: what would happen if everyone in New Zealand attended religious services regularly (at least four times a month), what would happen if no one attended religious services, and what would happen if current attendance levels remained unchanged.
By comparing the predicted prosocial outcomes in these different scenarios, the researchers could estimate the potential causal impact of increasing or decreasing religious service attendance on a national scale. To ensure their findings were reliable, they also conducted sensitivity analyses, which helped to assess how much their results might be affected by any unmeasured factors that could influence both religious attendance and prosocial behavior. Finally, they compared their causal findings to results from simpler statistical methods that are often used in research, to highlight the differences and demonstrate the importance of using methods designed for causal inference.
The study’s findings indicated that increasing religious service attendance does have a positive effect on charitable giving and volunteering. When comparing a scenario where everyone attended religious services regularly to one where no one attended, the researchers found a statistically significant increase in both charitable donations and volunteering hours. Similarly, comparing regular attendance to the current situation also showed a significant positive effect.
However, when they looked at the opposite scenario – comparing no religious service attendance to the current situation – the results for charitable donations were less clear, suggesting the effect of eliminating religious services might be less pronounced. For volunteering, there was a small but statistically detectable decrease if religious services were eliminated. While the effects were statistically significant, the researchers noted that the size of these effects was modest.
Nevertheless, when considering the entire population of New Zealand, even these modest effects could translate into substantial real-world impacts. For example, they estimated that if regular religious service attendance became widespread, the increase in charitable donations across the country could be equivalent to about 4% of the New Zealand government’s annual budget.
“There is plenty of evidence linking religious participation to pro-sociality, but most of it rests on simple associations, whether cross-sectional or longitudinal,” Bulbulia told PsyPost. “In this study, we use repeated-measures data from 33,198 New Zealanders alongside ‘doubly robust’ machine learning estimators to clarify the causal effects of clearly specified interventions on religious service attendance for charity and volunteering.”
“We found that if every adult in New Zealand attended services regularly, charitable donations could jump by about NZD 2.4 billion, roughly 4% of the government’s budget. On the other hand, removing religious services wouldn’t reliably reduce giving in the short term, likely because current attendance levels are already low. By specifying clear interventions (e.g., ‘What if everyone attended services four times a month?’ vs. ‘What if no one attended?’), we could pin down the expected consequences of each scenario, and by contrasting these scenarios, clarify causality.”
“The central takeaway is this: before you run any analysis, precisely define your question as a contrast between at least two distinct interventions in a well-defined population,” Bulbulia continued. “Of course, from there, you must evaluate assumptions, check your data, choose appropriate estimators, compute the counterfactual contrasts, and carry out sensitivity analyses for robustness to failures in assumptions… & etc. However, to answer a causal question, you must first ask a causal question. If you skip that first step, confusion is practically guaranteed.”
In terms of receiving help from others, the study found that regular religious service attendance was associated with a higher likelihood of receiving help, both in time and money, from friends and the broader community. This suggests that increased religious service attendance might contribute to a more supportive and helpful social environment beyond just formal charitable giving. Interestingly, they did not find a reliable effect on receiving help from family, suggesting that the impact of religious attendance may be more focused on community-level support networks.
When the researchers compared their causal inference results to those from more common, simpler statistical analyses, they found that the simpler methods tended to overestimate the relationship between religious service attendance and prosociality. This highlights a key point: simply observing a correlation between religion and helpfulness does not necessarily mean that religion is the cause, and methods designed for causal inference are essential for gaining a more accurate understanding of the relationship.
“In New Zealand, religious institutions operate as public charities, accounting for about 40% of the charitable sector,” Bulbulia said. “So we were not surprised to find causal evidence for religious charity. However, what did catch us off guard was how far simpler cross-sectional analyses overstated the charitable effects of religious service. (Note that in another context, such naive estimates might have just as easily understated causal effects.) Many social scientists — myself included, before I learned causal inference — underestimate how misleading correlations can be when evaluating the real-world implications of interventions.”
But as with all research, there are caveats to consider. The findings are specific to New Zealand, a country with its own unique cultural and social context, and it is not yet clear whether these results would be the same in other parts of the world.
“Part of stating a clearly defined causal question is stating the population for whom answers are meant to generalize,” Bulbulia noted. “Under the assumptions that we describe in this study, our results generalize to contemporary New Zealanders. We offer no guarantees that results transport elsewhere. To evaluate the transportability of our findings would require additional assumptions and data.”
“Our group is currently investigating the causal effects of interventions on religious beliefs and behaviors, and other features of psychology, over longer time frames. These studies require special methods that account for time-varying confounding. We’re also using methods that allow us to address heterogeneity in effects – for whom are effects strongest, and for whom are they weak, or reversed? Such questions are easy to formulate, yet remarkably challenging to answer.”
“Fortunately, the past five years or so have seen strong methodological advances, particularly in the areas of causal machine learning,” Bulbulia said. “We’re combining these methods with national-scale longitudinal data to obtain more confident answers to causal questions that cannot be addressed using experiments.”
According to Bulbulia, there are a growing number of excellent introductions to causal inference. For those just getting started, he highly recommends Miguel Hernán’s free Harvard course available at (https://www.edx.org/learn/data-analysis/harvard-university-causal-diagrams-draw-your-assumptions-before-your-conclusions) edX. Bulbulia also suggests an accessible textbook for social scientists: Morgan S. L. and Winship C.’s (2015) Counterfactuals and Causal Inference (Cambridge University Press). For those interested in mediation and moderation analysis from a causal perspective, he recommends Vanderweele’s (2015) Explanation in Causal Inference: Methods for Mediation and Interaction (Oxford University Press).
In addition, Bulbulia has published a series of open access tutorials that gather key aspects of causal inference into one convenient resource. These tutorials, which address topics such as causal diagrams and confounding ((https://doi.org/10.1017/ehs.2024.35) DOI: 10.1017/ehs.2024.35), interaction, mediation, and time-varying treatments ((https://doi.org/10.1017/ehs.2024.32) DOI: 10.1017/ehs.2024.32), measurement error and external validity threats ((https://doi.org/10.1017/ehs.2024.33) DOI: 10.1017/ehs.2024.33), and confounding in experiments ((https://doi.org/10.1017/ehs.2024.34) DOI: 10.1017/ehs.2024.34), provide a comprehensive starting point for anyone looking to deepen their understanding of causal inference.
The study, “(https://doi.org/10.1177/00846724241302810) The causal effects of religious service attendance on prosocial behaviours in New Zealand: A national longitudinal study,” was authored by Joseph A. Bulbulia, Don E. Davis, Kenneth G. Rice, Chris G. Sibley, and Geoffrey Troughton.

Forwarded by:
Michael Reeder LCPC
Baltimore, MD

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