As such, younger adults may be concerned with presenting themselves in terms of their careers and accomplishments. Self-presentations indicating status may not show age differences, however. Evolutionary theory suggests that adults are likely to highlight attributes indicative of higher mate value. For men especially, mate value is conceptualized in terms of status, reflecting an ability to provide for future offspring Buss, , Alterovitz and Mendelsohn used content analysis to examine dating profiles from people aged 20 to more than 75 years.
Older women emphasized desiring status in a partner, whereas older men offered more status-related information about themselves. Furthermore, research suggests that older women are particularly concerned with financial independence; therefore, older men may be more inclined to address money when seeking a partner in order to assuage concerns related to financial dependency Dickson et al. We also considered socioemotional motivations in dating profiles.
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Thus, older adults may be more likely to present themselves in positive terms than younger adults. This is not to say that we expected young adults to present themselves negatively, but rather, older adults may be more likely to focus on positive aspects of what they bring to a dating relationship, particularly positive emotions.
The current study involved a systematic analysis of the language used by adults of different ages in the text of online dating profiles. Data from online dating profiles offered an ecologically valid way to examine age differences in self-presentations. We also considered geographic location and ethnic differences. To examine a national sample of dating profiles, we drew samples from five major metropolitan areas encompassing urban, suburban, and rural outlying areas from across the United States.
Prior research has not addressed geographic or ethnic differences in dating motivations, but they were considered in the current study. In summary, we expected the following patterns regarding age differences in online profiles. Compared with younger adults, older adults will use a higher proportion of words in the following categories: Compared with younger adults, older adults will use a lower proportion of words in the following categories: We did not have strong age-by-gender predictions, but considered possible gender differences in each of these patterns.
The sample of dating profiles was drawn from two major dating websites. We identified these websites using search engines e. We also limited the study to dating websites that allow users to search for potential partners rather than assigning a limited array of partners; e. After exclusions, two popular websites remained. There was no charge for creating a profile on either website, but one of the websites charged to connect with a potential dating partner. Users completed an optional free response section i.
The instructions to create the free response section differed among the websites. We did not collect profiles that contained fewer than 30 words; potential profiles from a random sampling described in Participants were excluded due to responses with fewer than 30 words. The study included 4, profiles, 2, sampled from each of the online dating websites using random quota sampling without replacement. Within each website, we collected 1, profiles from heterosexual males and 1, profiles from heterosexual females.
Users search for profiles via geographic location, age, and gender filters. To ensure a geographic dispersion of profiles, we selected equal numbers of profiles from five major metropolitan areas including urban, suburban, and rural areas: We randomly selected zip codes from each of the five areas to search for profiles. Within each zip code, for each gender, we then randomly selected profiles among four age groups: We used these stratifications to assure a full age range of dating profiles in sampling.
Because the older adults group could incorporate up to 30 years, we treated age as a continuous variable rather than as a grouping variable in analyses.
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From each profile, we extracted: To assure anonymity of profile writers, we did not obtain additional demographic information e. The sampling method is illustrated in Supplementary Appendix A. The sample ranged in age from 18 to 95 years. We used the LIWC software to analyze the content of the profiles. This software calculates the frequency and proportions of specific categories of words within a text file.
The LIWC program compares each word of a text file with an internal dictionary of more than 4, words assigned to word categories. This study drew on 11 established LIWC categories: We also created a category of words for attractiveness not available in established LIWC categories.
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Then, we selected 25 words most representative of attractiveness based on appearance in thesauruses and participant responses e. The attractiveness category was almost entirely distinct from the sexual category, with only one overlapping word sexy. Examples for the attractiveness category are also found in Table 1 ; for the complete list of words in the attractiveness category, see Supplementary Table 1. We first examined descriptive statistics for the proportions of words people of different ages used in their dating profiles. We also generated illustrative figures portraying the most common words.
We then turned to hypothesis testing using ordinary least squares regression. The outcome variables in this study were the proportion of words fitting each of the 12 categories in the LIWC analyses. The LIWC categories were all positively skewed due to the number of zero values i. The pattern of findings was similar after applying the transformations. For ease of interpretation, findings are presented using the untransformed LIWC category data. The independent variable was age, treated as a continuous variable.
We also included gender. One significant interaction was found in the category of positive emotion, such that women had higher mean proportions of positive emotion words than men at all ages, with women showing a slightly steeper linear increase with age than men. For the two websites, six of the twelve t -tests were significant in the following categories: These differences likely reflect disparities in instructions provided by the websites and the fact that one website charges daters to contact a potential romantic partner and the other allows contacts for free.
Due to the number of significant t -tests, we adjusted for the effect of website in our analyses by treating it as a dummy coded covariate. Contrasts revealed significant differences between White and all other ethnic groups in four of the six significant ANOVAs. Of the 12 ANOVA tests related to geographic region, only two were significant family and positive emotion. Because the differences were not theoretically meaningful, we did not consider geographic region in subsequent analyses.
Frequency of word use is evident in descriptive statistics see Table 1 and via word-clouds. The word-cloud technique illustrates the most commonly used words across the entire sample and in each of the age groups. The word-cloud program automatically excludes certain words, including articles a, and, the and prepositions to, with, on. The remaining content words are scaled in size relative to their frequency, creating an intuitive portrait of the most prevalent content words across the sample Wordle, Figure 1 shows the 20 most common content words used in the entire sample. Thus, the most common words were similar across age groups.
Figure 2 shows the next 30 most common content words in the youngest and oldest age groups.
By removing the first 20 common content words across the sample, we illustrate heterogeneity in the dating profiles. Next 30 most common words in the youngest and oldest age groups after subtracting the 20 most common words from Figure 1. To test hypotheses, the percentage of words from the dating profile that fit each LIWC category served as the dependent variables in regressions.
We examined age and gender as independent variables as well as adjusting for website and ethnicity. Older age will be associated with a higher percentage of words in the following categories: Findings largely supported Hypothesis 1 see Table 2. Four of the five regressions revealed a significant main effect for age, such that as the age of the profile writer increased, the percentage of words in the category increased in the following categories: We found no significant age effect for the proportion of words in the family category. The two websites were dictomously coded as 1 and 0.
Younger age will be associated with a higher percentage of words in the following categories: We found mixed support for Hypothesis 2 see Table 3. Four of the seven regressions revealed a pattern consistent with hypotheses, such that as the age of the profile writer increased, the percentage of words in the category decreased.
Younger adults showed higher percentages of words in the first-person singular, work, achievement, and negative emotion categories. The model for the category of money showed a significant main effect of age in the opposite direction of predictions, such that as age increased, so did the percentage of words in the money category.
The models for attractiveness and sexuality categories did not show significant effects of age. Regressions also revealed significant gender differences in the proportion of words in relevant LIWC categories. For example, women had a higher percentage of words in the first-person singular category, whereas men had a higher percentage of words in the first-person plural category.
Men had higher proportions of words in the work category. Women had higher proportions of words in the categories of friends, family, health, sexuality, and positive emotion. No significant gender differences were found in the categories of achievement, money, attractiveness, or negative emotion. Older adults are increasingly involved in dating. Recent years have seen a proliferation of romantic relationships formed via the Internet, and older adults are increasingly turning online to find romantic partners. The ubiquity of dating websites as a means to find a relationship provides scholars with a unique opportunity to examine dating strategies and motivations in the context in which they actually occur.
This study is the largest examination of age differences in dating profiles to date; we collected profile text from 4, adults across the United States.
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This study revealed similarities in the most widely used words in the dating profiles, with a focus on affection and affiliation love, like and companionship. Nonetheless, the questions were open ended, and findings suggest that adults of all ages may share predilections in seeking affection in romantic partnerships. Though the current study revealed similarities in profile content across ages, these systematic analyses revealed age differences consistent with predictions across a variety of key content areas.
Self-presentations reflected goals in a dating context and more broadly reflected the motivations of individuals at different life stages. Profiles revealed differences in how younger and older adults approach finding a mate. Findings generally supported the hypotheses and were consistent with prior research on sociocultural motivations.
Younger adults are focused on establishing themselves and their identities in an adult world Arnett, , and those goals translated to self-focused self-presentations. With regard to a focus on self versus others, older adults may feel more connected to existing relationships and the needs of others Blieszner, Gender differences in evolutionary motivations across life stages also were evident, with older and younger men mentioning their work, whereas older and younger women focusing more on sexuality.
Findings regarding achievements and status only partially supported the hypotheses. Surprisingly, older adults were more likely to mention money in their profiles than younger adults.
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Some research suggests that older women are particularly concerned with the income of potential dating partners, to avoid entering a relationship that becomes a financial strain William et al. Older adults were also more likely to mention health, which likely reflects the greater relevance of health to the identities of older adults Levy, Finally, gender differences revealed that women are more likely to focus on the self as well as themes related to positive emotion, friends, family, health, and sex.
This type of visualization can help presenters to quickly collect data from their audience, highlight the most common answers and present the data in a way that everyone can understand. The Word Cloud question type can be a great way to test your students and keep them engaged throughout the lesson.
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This example is perfect to test vocabulary. Use a World Cloud questions to test the knowledge of your students. Kick off your presentation with a quick question visualised in a word cloud to get your audience engaged and focused. Learn more about your audience and let them learn more about each other with this example that can be used in many different occasions! Mentimeter is an easy-to-use tool that makes facilitators and presenters look like stars. No installations or downloads required - and it's free!
Create an interactive presentation with the questions that you want to ask your audience. When it's time to present, your audience goes to menti. Show your audience's answers in real-time with colorful data visualizations that will help you identify trends and prompt discussions. Mentimeter does not support the browser you are using.
Please switch to another one. Any of the following will do:. Sign up for Mentimeter. Get started with Mentimeter for free to start creating word clouds with your audience. Create Account Mentimeter is free for as long as you'd like. Go to your presentations. What is a Word Cloud? How to create Word Clouds with Mentimeter?