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Who meets online? Personality traits and sociodemographic characteristics associated with online partnering in Germany
Personality and Individual Differences 143 (2019) 139–144
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Who meets online? Personality traits and sociodemographic characteristics associated with online partnering in Germany
T
Mirkka Danielsbackaa,c, , Antti O. Tanskanena,c,d, Francesco C. Billarib ⁎
a
Department of Social Research, University of Turku, Assistentinkatu 7, 20014 University of Turku, Finland Department of Social and Political Sciences, Carlo F. Dondena Center for Research on Social Dynamics and Public Policy, Bocconi University, Via Roberto Sarfatti, 25, 20100 Milano, MI, Italy c Population Research Institute (Väestöliitto), P.O.Box 849, FI 00101 Helsinki, Finland d Faculty of Social Sciences, University of Helsinki, 00014 University of Helsinki, Finland b
ARTICLE INFO
ABSTRACT
Keywords: Big Five Germany Internet Online Partnership formation
The Internet has partly displaced traditional offline meeting venues for partners. Here, we study whether meeting online is selective, i.e., whether there are differences in Big Five personality traits and sociodemographic background characteristics between those who meet online and offline. Using eight waves of the German Family Panel (pairfam), with observations from 7192 respondents from three birth cohorts (1991–93, 1981–83, and 1971–73) between 2008 and 2016, we found that meeting online is more likely for female respondents than males; for respondents from the older birth cohorts compared to the youngest one; for respondents with primary education compared to those who are currently enrolled and have no degree; for respondents who have shorter relationship durations; for those who have a higher number of previous partners; and for those who have less extroverted personalities. As we split the data by cohorts, it appears that meeting a partner online is slightly more selective for the oldest birth cohort than for the youngest one. Moreover, extraversion was consistently associated with meeting online in every birth cohort, and the association was negative in every case. These findings are discussed with reference to compensation and rich-get-richer hypotheses.
1. Introduction During the era of digitalization and the rise of the Internet, an increasing number of people tend to meet their romantic partners online, either through dating sites or social media, meaning that Internet has partly displaced traditional offline meeting venues for partners (Bellou, 2015; Rosenfeld & Thomas, 2012). In the US, it is estimated that more than 20% of heterosexual people meet their partners online (Rosenfeld & Thomas, 2012), and more than one-third of marriages began via the Internet (Cacioppo, Cacioppo, Gonzaga, Ogburn, & VanderWeele, 2013). Despite the growing access and use of Internet in most sociodemographic groups, it appears that some groups might still be more likely than others to use social media and online dating sites (e.g., Blackhart, Fitzpatrick, & Williamson, 2014; Hall, Park, Song, & Cody, 2010; Nam, 2017; Sautter, Tippett, & Morgan, 2010) and thus have higher likelihood to meet a future spouse online (e.g., Cacioppo et al., 2013; Rosenfeld & Thomas, 2012). Using the Internet as a venue for partner search may be due to certain endogenous factors, such as personality traits, and/or it could be due to different sociodemographic
⁎
factors relating to an individual's life course phase (e.g., age) and social status (e.g., education). Here, we investigated whether meeting online is selective, i.e., whether differences exist in Big Five personality traits and sociodemographic factors between those who meet their partner online and offline. Furthermore, regarding different dispositional factors and personality traits associated with dating online, one can formulate two opposite hypotheses. First, according to social compensation hypothesis, individuals who have problems meeting a partner through more traditional venues, online dating allows them to compensate for deficits that they encounter offline (e.g., Bellou, 2015; Rosenfeld & Thomas, 2012). Second, based on the rich-get-richer hypothesis (or the Matthew effect), online dating may benefit extrovert individuals who already have strong dating skills and who use the Internet as an additional strategic tool to find a partner (Kraut et al., 2002; Valkenburg & Peter, 2007). Studies concerning sociodemographic factors and personality traits associated with the use of social network sites or online dating have provided more support for the rich-get-richer than the social compensation hypothesis. First, it has been found that those who use online
Corresponding author. E-mail addresses: [email protected] (M. Danielsbacka), [email protected] (A.O. Tanskanen), [email protected] (F.C. Billari).
https://doi.org/10.1016/j.paid.2019.02.024 Received 1 December 2018; Received in revised form 15 February 2019; Accepted 16 February 2019 0191-8869/ © 2019 Elsevier Ltd. All rights reserved.
Personality and Individual Differences 143 (2019) 139–144
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dating sites are typically more highly educated and have higher incomes than the overall population (Hitsch, Hortaçsu, & Ariely, 2010a, 2010b; Menkin, Robles, Wiley, & Gonzaga, 2015; Sautter et al., 2010). Second, some have detected that more extroverted people and those who are more open to experiences are more likely to look for a partner online (Correa, Hinsley, & de Zuniga, 2010; Ross et al., 2009; Zywica & Danowski, 2008), while others have detected opposite results (Orr et al., 2009), as well as negligible results (Blackhart et al., 2014). Although there are studies detecting whether personality is associated with the use of online dating or network sites, there is lack of studies testing whether personality traits are associated with the likelihood of meeting partner online. Searching for a partner via the Internet may not necessarily lead to union formation. Thus, those who search for partners via the Internet may be a different group than those who actually start a relationship with a partner they have met online. Prior studies have shown that meeting a partner online is typical for middleaged (Rosenfeld & Thomas, 2012) and (on average) higher educated people who work full-time (Cacioppo et al., 2013). The Internet may also promote weaker couple endogamy (measured as educational, ethnic and religious endogamy) compared to more traditional meeting venues (Potarca, 2017), although facilitated partner searches through online dating sites may also promote couple homogamy (Hitsch et al., 2010a). Although prior studies have detected whether different sociodemographic factors are associated with meeting partner online, they do not show whether personality is associated with meeting partner online. Although the previous online partnering studies have several strengths, they also have their limitations, which highlight the importance of additional investigations. First, to the best of our knowledge, previous studies have not examined personality differences between those who actually meet their partners online and offline, as discussed above. Second, most previous studies have been conducted with data from the US, and investigations from other countries are needed because dating cultures may vary significantly from one country to another. Third, there is lack of studies detecting cohort differences in online partnering among younger adults. All these issues are considered in the present study. Moreover, we study whether meeting a partner online has become more common in Germany in 2008–2016 and whether individuals with certain sociodemographic characteristics are more likely to meet their partners online than other individuals.
The dependent variable indicates whether a respondent has met a partner online. In the first wave of pairfam, those who reported having a partner was asked the meeting venue of this partner (“How did you meet?”), and one of the nine alternatives was “Through the Internet” (coded as 0 = offline, 1 = online). In subsequent waves, the question was asked only of those with a new partner. For the analytical sample, we included heterosexual individuals who have a partner or who met a partner during waves 2 to 8. As independent variables, we use sex, cohort, highest education, ethnicity, whether the respondent currently lives in East Germany, relationship duration, number of previous partners, number of children and personality, as measured with the Big Five personality test (extraversion, openness to experience, neuroticism, agreeableness and conscientiousness). Because personality was asked only in waves 2 and 6, we had to extract this information for the other waves, meaning that there is a drop in observations, particularly in the first wave. For sensitivity purposes, we have also rerun our full model, without any personality variables included, which did not considerably change the results of the other background characteristics. The analytical sample in the fully adjusted model is 7192. We used a multivariate between-person regression models. To help the readers to interpret the effect sizes, the statistically significant results are illustrated by calculating adjusted means or predicted probabilities (and 95% confidence intervals) from the fully adjusted regression models. 3. Results 3.1. Frequency In 2008–2016, among all cohorts, the cumulative proportion of those heterosexual individuals who have met their spouse via the Internet increased from 5% to 9% (Table 1). Among individuals who formed a new relationship between waves 2 to 8, the proportion of those who met online increased from 11% to 21% (Table 2). Among the oldest cohort (1971–1973), the proportion of those who had met their spouse online, before the first wave, was 3%, however, among those who formed a new relationship between waves 2 to 8, the proportion increased from 20% to 29% (Table 3). Among the middle cohort (1981–1983), the proportion of those who had met their spouse via the Internet in the first wave was 6%, and among new relationships, the rate increased during subsequent waves from 18% to 31% (Table 4). Among the youngest cohort (born 1991–1993) the proportion of online partnering was 9% in 2008–2009 but increased from 8% to 14% from 2010 to 2016 (Table 5).
2. Material and methods We used data from the Panel Analysis of Intimate Relationships and Family Dynamics (pairfam), which offers information on partnership, childbearing and several socio-ecological factors in Germany (Brüderl et al., 2017; Huinink et al., 2011). Pairfam provides longitudinal data on three birth cohorts born in 1971–1973, 1981–1983 and 1991–1993. The first pairfam wave was conducted in 2008–2009, when the cohort members were approximately 15–17, 25–27 and 35–37 years of age, respectively. Data included German-speaking persons (regardless of nationality) living in private households in Germany. Further data collections have been conducted annually, and the last data used here are from wave eight (conducted in 2015–2016). The achieved pairfam samples vary between 12,402 respondents in first wave to 5461 respondents in the eighth wave.
3.2. Who meets online? Next, we examined whether individuals who have met their spouses online have different characteristics compared to those who have met their spouses offline. We performed three stepwise regression models: the first one controls for the respondents' sex, cohort, ethnicity, highest education, whether the respondents currently live in East Germany and whether the respondents met their spouses before wave 1 or during waves 2 to 8; the second model controls for the personality traits of a respondent, in addition to the previously mentioned variables, and in the third model, we added family characteristics (i.e., the number of
Table 1 All heterosexual respondents who are in a relationship and have met their partners online between 2008 and 2016 (%).
Other Met online Total (n)
Wave 1
Wave 2
Wave 3
Wave 4
Wave 5
Wave 6
Wave 7
Wave 8
2008/09
2009/10
2010/11
2011/12
2012/13
2013/14
2014/15
2015/16
95 5 7158
94 6 5335
95 6 5752
94 6 5346
94 6 4989
93 7 4592
92 8 4216
140
91 9 3962
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Table 2 All heterosexual respondents who have met their partners online in 2008 and those with new partner between 2010 and 2016 (%).
Other Met online Total (n)
Wave 1
Wave 2
Wave 3
Wave 4
Wave 5
Wave 6
Wave 7
Wave 8
2008/09
2009/10
2010/11
2011/12
2012/13
2013/14
2014/15
2015/16
95 5 7158
89 11 1109
89 11 1038
89 11 875
88 12 722
85 15 570
83 18 470
79 21 426
compared to the older ones. To test this hypothesis, we split the data by birth cohorts and studied whether background characteristics are associated differently with meeting a partner online among the different birth cohorts. The results are shown in Appendix Table A1. In the youngest birth cohort, primary and lower secondary education, compared to being currently enrolled and currently living in East Germany, were positively associated with meeting online (predicted probability for primary or lower secondary 13.39, 95% CI = 0.11–0.16; for upper secondary education 10.10, 95% CI = 0.08–0.12; for currently enrolled 0.06, 95% CI = 0.05–0.08; for living in East Germany 0.13, 95% CI = 0.10–0.15; for living in West Germany 0.08, 95% CI = 0.06–0.09). In addition, those who met online were less extroverted than those met offline (adjusted mean for those who met online 14.40, 95% CI = 13.95–14.84; for those who met offline 14.98, 95% CI = 14.85–15.10). In the middle birth cohort, those who met online were more likely to have met during waves 2 to 8 than before wave 1 (predicted probability for those who met during waves 2 to 8 0.14, 95% CI = 0.11–0.16; for those who met before wave 1 0.09, 95% CI = 0.07–0.10), were less extroverted (adjusted mean for those who met online 13.80, 95% CI = 13.38–14.21; for those who met offline 14.31, 95% CI = 14.18–14.44) and had shorter relationship durations (adjusted mean for those who met online 28.78, 95% CI = 24.78–32.78; for those who met offline 38.72, 95% CI = 37.45–39.98) than those met offline. In the oldest birth cohort, sex (females more likely than males; predicted probabilities for female 0.08, 95% CI = 0.07–0.09; for male 0.06, 95% CI = 0.05–0.08), living currently in East Germany (negative association; predicted probability for East Germany 0.05, 95% CI = 0.03–0.07; for West Germany 0.08, 95% CI = 0.07–0.09), having met current partners during waves 2 to 8 (positive association; predicted probability for waves 2 to 8 0.16, 95% CI = 0.13–0.18; for wave 1 0.05, 95% CI = 0.04–0.06), extraversion (less; adjusted mean for those who met online 13.41, 95% CI = 12.91–13.91; for those who met offline 14.12, 95% CI = 14.0–14.24), openness (more; adjusted mean for those who met online 19.09, 95% CI = 18.54–19.64; for those who met offline 18.25, 95% CI = 18.12–18.39) and relationship duration (shorter; adjusted mean for those who met online 74.10, 95% CI = 64.77–83.44; for those who met offline 112.10, 95% CI = 109.80–114.40) were all associated with meeting online. Thus, it appears that most differences in background characteristics between those met online and offline are found among the oldest birth cohort; however, differences in personality traits (extraversion) are surprisingly robust in all birth cohorts.
children, relationship duration and number of previous partners). Table 6 shows three multivariate between-person models predicting the likelihood to meet online. In the first model, meeting online was significantly and positively associated with the middle cohort compared to the youngest cohort. The respondents with Turkish backgrounds were less likely than German natives to meet their spouses online. Those who met online were more likely to have a primary or lower secondary education when compared to the currently enrolled respondents (who did not have any degree). The respondents were more likely to have met their spouses online during waves 2 to 8 than before wave 1 (Table 6, Model 1). In the second model, we added personality traits (extraversion, neuroticism, agreeableness, conscientiousness and openness), which altered the model so that the respondents from the oldest cohort were more likely to meet their partners online than the respondents from the youngest cohort, and ethnicity was no longer associated with meeting online. Extraversion was the only personality trait that was associated with meeting online, and the association was negative, meaning that those respondents with less extraversion were more likely to meet their spouses online (Table 6, Model 2). In the last model, we added family characteristics (i.e., the number of children, relationship duration and number of previous partners), which also altered the previous coefficients. In the last and fully adjusted model (Table 6, Model 3), females were more likely to have met their spouses online than males (predicted probability for females 0.09, 95% CI = 0.07–0.09; for males 0.08, 95% CI = 0.09–0.10), and respondents from older birth cohorts were more likely to have met their partners online compared to the youngest cohort (predicted probability for 1971–73 cohort 0.14, 95% CI = 0.12–0.15; for 1981–83 cohort 0.10, 95% CI = 0.09–0.11; for 1991–93 cohort 0.05, 95% CI = 0.03–0.06). Those who met online were more likely to have primary or lower secondary or upper secondary education compared to the currently enrolled respondents (who did not have any degree) (predicted probability for primary or lower secondary or upper secondary education 0.12, 95% CI = 0.10–0.13; for currently enrolled 0.07, 95% CI = 0.05–0.09). In addition, extraversion was still negatively associated with meeting online (adjusted mean for those who met online 13.97, 95% CI = 13.71–14.23; for those who met offline 14.53, 95% CI = 14.46–14.61). Those who met online had also shorter relationship durations (adjusted mean for those who met online 26.43, 95% CI = 22.86–30.00; for those who met offline 46.39, 95% CI = 45.37–47.40) and higher numbers of previous partners than those met offline (adjusted mean for those who met online 1.50, 95% CI = 1.37–1.58; for those who met offline 1.35, 95% CI = 1.32–1.38) (predicted probabilities and adjusted means not shown in the tables). It could be that using the Internet could be more equal among younger cohorts than older cohorts, meaning that there should be less division in the background characteristics among younger birth cohorts
4. Discussion and conclusions In this article, we studied whether meeting a partner online has become more common in Germany among three birth cohorts in
Table 3 Heterosexual respondents in cohort 1971–1973 who have met their partners online in 2008 and those with a new partner between 2010 and 2016 (%).
Other Met online Total (n)
Wave 1
Wave 2
Wave 3
Wave 4
Wave 5
Wave 6
Wave 7
Wave 8
2008/09
2009/10
2010/11
2011/12
2012/13
2013/14
2014/15
2015/16
97 3 3315
81 20 113
81 19 108
78 22 119
74 26 77
75 25 73
74 26 69
141
71 29 68
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Table 4 Heterosexual respondents in cohort 1981–1983 who have met their partners online in 2008 and those with a new partner between 2010 and 2016 (%).
Other Met online Total (n)
Wave 1
Wave 2
Wave 3
Wave 4
Wave 5
Wave 6
Wave 7
Wave 8
2008/09
2009/10
2010/11
2011/12
2012/13
2013/14
2014/15
2015/16
94 6 2725
82 18 223
87 13 276
87 13 208
83 17 192
81 19 143
76 25 106
70 31 105
with mate value meet several women online, while the men with low mate value meet only few women or none. Among women, however, the division between individuals lower and higher with mate value may not be as crucial, meaning that women with lower mate value may still have opportunities to meet partners online. We call future studies to investigate whether this is the case. The present study has several strengths. To the best of our knowledge, we have first time compared personality traits between those who have met a romantic partner and started a relationship online and those who have met offline. In addition to personality, with the pairfam data it was possible to study whether individuals with certain sociodemographic characteristics are more likely to meet their partners online than others. Finally, we were able to compare differences among three birth cohorts and study whether meeting a partner online has become more common in Germany in 2008–2016. Obviously, the present study is not without limitations. Due to the panel characteristic of the data, we only have a limited number of people who met a new partner, as well as those who met a new partner online, during the panel. A larger sample size could provide additional data, for instance, how background characteristics of those who meet online have changed over time in different birth cohorts. We may conclude that meeting online is still somewhat selective, particularly among middle-aged adults, and that it may provide a meeting venue for those who have slightly less extroverted personalities (and who perhaps have limited partnership markets). It has been claimed that digitalization and the rise of the Internet may change partnership markets permanently (Bellou, 2015). In line with this prediction, our results indicate that during the recent years Internet has become more common venue to meeting a partner in Germany. In addition, it has been assumed that Internet as a meeting venue may create digital divide between those individuals who have access and skills to use Internet for mate search and those who do not have (Valkenburg & Peter, 2007). Interestingly, we found that in terms of sociodemographic characteristics meeting a partner online is not as selective among youngest age cohort compared to older ones, indicating decreasing importance of digital divide among younger people, which could be related to the fact that the Internet is more “normal” partner meeting venue for younger people. Moreover, the finding that in all birth cohorts less extrovert individuals were more likely to meet a partner online implies that meeting online could influence to further family related outcomes because personality plays an important role in several life outcomes (Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007). For instance, it has been detected that increased level of extraversion is associated with decreased childbearing (Jokela, Alvergne, Pollet, & Lummaa, 2011; Jokela, Kivimäki, Elovainio, & Keltikangas-Järvinen, 2009). Thus, future research should focus on possible differences in
2008–2016 and whether those who meet their partners online differ from those who meet partners offline. Our results show that overall, meeting a partner online has become somewhat more common in Germany between 2009 and 2016. If we look at those who met a new partner during the panel within a cohort, we see that meeting online is becoming relatively more common among older birth cohorts. However, if we look at those who overall meet a new partner, meeting online is most common in the youngest birth cohort. This result is partly because among the youngest birth cohort, more relationships are formed overall, that is to say, people from older birth cohorts already have partners. Our general finding is that meeting online is somewhat selective. Those who met their partners online were more likely to be female than male, from the youngest birth cohort compared to the oldest cohort, to have primary education compared to those who are currently enrolled and have no degree, have shorter relationship durations, a higher number of previous partners and a less extroverted personality. Interestingly, the only Big Five personality trait that was associated with meeting online was extraversion, and the association was negative, meaning that those who demonstrated less extraversion were more likely to meet online. As we split the data by cohorts, it showed that most of the differences in background characteristics were found among the oldest birth cohort, who, in general, were least likely to meet a partner online but among whom, a relatively large proportion of new relationships were formed via the Internet. However, the differences in extraversion were quite robust in all birth cohorts, meaning that among all cohorts, those with less extraversion were more likely to meet their partners online. In addition, and perhaps contradictory to the finding concerning extraversion, in the oldest birth cohort, those who met online also had higher scores in openness to experiences. Our results are mostly in contrast with previous studies that have found evidence for the rich-get-richer hypothesis, which posits that online dating may benefit extrovert individuals who already have strong dating skills and who use the Internet as an additional strategic tool to find a partner (Correa et al., 2010; Kraut et al., 2002; Ross et al., 2009; Valkenburg & Peter, 2007; Zywica & Danowski, 2008. However, see Orr et al., 2009). However, although previous evidence on those who use online dating sites and meet online indicates that online dating might be more common among people who already have good dating skills, actual online couple formation could be more typical for those who might not meet partners that easily in traditional settings. Our results align with this latter mentioned prediction, particularly if we look our results concerning the personality traits (less extraversion) associated with meeting online. In contrast, the finding that women meet partners online more often compared to men may provide support for the rich-get-richer hypothesis. One can speculate that the men high
Table 5 Heterosexual respondents in cohort 1991–1993 who have met their partners online in 2008 and those with a new partner between 2010 and 2016 (%).
Other Met online Total (n)
Wave 1
Wave 2
Wave 3
Wave 4
Wave 5
Wave 6
Wave 7
Wave 8
2008/09
2009/10
2010/11
2011/12
2012/13
2013/14
2014/15
2015/16
91 9 1118
92 8 773
91 9 654
93 8 548
93 7 453
89 11 354
87 13 295
142
86 14 253
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Table 6 Factors associated with meeting online. Stepwise between-person regression analyses. Model 1
Model 2 95% CI
Sex Cohort Ethnicity
Highest education
Currently living in East Germany Met current spouse Extraversion Neuroticism Agreeableness Conscientiousness Openness Number of children Relationship duration Number of previous partners n r2
Male (ref.) Female 1991–1993 (ref.) 1981–1983 1971–1973 German native (ref.) Ethnic-German immigrant Half-German Turkish background Other non-German background Currently enrolled (no degree) (ref.) Primary and lower secondary Upper secondary Post-secondary Tertiary No (ref.) Yes On wave 1 (ref.) During waves 2–8
Model 3 95% CI
95% CI
Coeff
Lower
Upper
Coeff
Lower
Upper
Coeff
Lower
Upper
0.0001
−0.01
0.01
0.004
−0.01
0.02
0.01⁎
0.001
0.03
0.04 0.02
0.02 −0.002
0.06 0.04
0.05 0.03⁎⁎
0.03 0.01
0.07 0.05
0.05 0.09⁎⁎⁎
0.03 0.06
0.07 0.11
−0.01 −0.01 −0.03⁎ −0.01
−0.03 −0.03 −0.06 −0.03
0.01 0.01 −0.01 0.005
−0.02 −0.01 −0.03 −0.01
−0.05 −0.04 −0.06 −0.03
0.003 0.01 0.002 0.01
−0.02 −0.01 −0.01 −0.01
−0.04 −0.04 −0.04 −0.03
0.01 0.01 0.02 0.01
0.03⁎ 0.02 0.001 0.01
0.005 −0.01 −0.02 −0.01
0.05 0.04 0.02 0.04
0.03⁎ 0.01 −0.01 0.01
0.01 −0.01 −0.03 −0.02
0.06 0.04 0.02 0.04
0.05⁎⁎ 0.03⁎ 0.005 0.01
0.02 0.0003 −0.02 −0.01
0.07 0.05 0.03 0.04
0.005
−0.01
0.02
0.005
−0.01
0.02
0.01
−0.01
0.02
0.09
0.08
0.10
0.09 −0.003⁎⁎ −0.0005 −0.001 −0.0005 0.001
0.08 −0.01 −0.002 −0.003 −0.003 0.000
0.11 −0.001 0.002 0.001 0.002 0.003
0.04 −0.004⁎⁎⁎ −0.001 0.0003 −0.0003 0.001 −0.01 −0.001⁎⁎⁎ 0.01⁎ 7192 0.0531
0.03 −0.01 −0.003 −0.002 −0.003 −0.001 −0.02 −0.001 0.001
0.06 −0.002 0.001 0.002 0.002 0.002 0.001 −0.001 0.01
⁎⁎⁎
⁎⁎⁎
9564 0.0247
⁎⁎⁎
⁎⁎⁎
7434 0.029
⁎⁎⁎
⁎⁎⁎
p < .05. p < .01. ⁎⁎⁎ p < .001. ⁎
⁎⁎
family related outcomes of those individuals who meet a partner online and offline.
on data from the first eight waves of the German Family Panel (pairfam), release 8.0 (Brüderl et al., 2017). A detailed description of the study can be found in Huinink et al. (2011). This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 694262), project DisCont Discontinuities in Household and Family Formation. In addition, the present study was supported by the Academy of Finland (grant number 316229 and 317808) and the Kone Foundation.
Acknowledgements This paper uses data from the German Family Panel pairfam, coordinated by Josef Brüderl, Sonja Drobnič, Karsten Hank, Bernhard Nauck, Franz Neyer, and Sabine Walper. pairfam is funded as long-term project by the German Research Foundation (DFG). Analyses are based Appendix A. Appendix Table A1
actors associated with meeting online. Between-person regression analyses for every cohort. COHORT 1991–93
COHORT 1981–83
95% CI
Sex Ethnicity
Highest education
Male (ref.) Female German native (ref.) Ethnic-German immigrant Half-German Turkish background Other non-German background Currently enrolled (no degree) (ref.) Primary and lower secondary Upper secondary Post-secondary Tertiary
COHORT 1971–73
95% CI
95% CI
Coeff
Lower
Upper
Coeff
Lower
Upper
Coeff
Lower
Upper
0.004
−0.02
0.03
0.02
−0.01
0.04
0.02⁎
0.0001
0.04
0.02 0.002 0.02 0.03
−0.03 −0.04 −0.04 −0.01
0.06 0.04 0.08 0.07
−0.04 −0.04 −0.06 −0.02
−0.09 −0.09 −0.13 −0.07
0.01 0.01 0.01 0.02
−0.02 −0.002 −0.001 −0.02
−0.07 −0.04 −0.05 −0.05
0.02 0.04 0.05 0.01
0.07⁎⁎⁎ 0.04⁎ −0.002 0.08
0.04 0.01 −0.04 −0.03
0.10 0.07 0.03 0.20
0.11 0.10 0.09 0.08
−0.10 −0.10 −0.11 −0.12
0.31 0.30 0.30 0.28
0.04 0.04 0.02 0.03
−0.18 −0.18 −0.20 −0.19
0.26 0.25 0.23 0.24
143
(continued on next page)
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Table A1 (continued) COHORT 1991–93
COHORT 1981–83
95% CI
Currently living in East Germany Met current spouse Extraversion Neuroticism Agreeableness Conscientiousness Openness Number of children Relationship duration Number of previous partners n r2
No (ref.) Yes On wave 1 (ref.) During waves 2–8
COHORT 1971–73
95% CI
95% CI
Coeff
Lower
Upper
Coeff
Lower
Upper
Coeff
Lower
Upper
0.05⁎⁎⁎
0.03
0.08
−0.01
−0.03
0.02
−0.04⁎⁎
−0.06
−0.01
−0.003 −0.004⁎ −0.001 −0.002 −0.001 −0.001 −0.08 0.001 0.01 2477 0.0251
−0.03 −0.01 −0.005 −0.01 −0.004 −0.004 −0.16 −0.001 −0.002
0.03 −0.001 0.002 0.002 0.003 0.002 0.01 0.002 0.02
0.05⁎⁎ −0.004⁎ 0.0002 0.002 −0.001 −0.001 −0.01 −0.001⁎⁎⁎ 0.01 2273 0.053
0.02 −0.01 −0.003 −0.002 −0.01 −0.005 −0.02 −0.001 −0.003
0.09 −0.001 0.004 0.01 0.003 0.002 0.01 −0.001 0.02
0.1⁎⁎⁎ −0.004⁎⁎ −0.001 0.001 0.001 0.004⁎⁎ −0.004 −0.001⁎⁎⁎ 0.0001 2442 0.1165
0.07 −0.01 −0.004 −0.003 −0.003 0.001 −0.01 −0.001 −0.01
0.14 −0.001 0.002 0.004 0.004 0.01 0.004 −0.0005 0.01
p < .05. p < .01. ⁎⁎⁎ p < .001 ⁎
⁎⁎
Psychology, 96(1), 218–230. Kraut, R., Kiesler, S., Boneva, B., Cummings, J., Helgeson, V., & Crawford, A. (2002). Internet paradox revisited. Journal of Social Issues, 58, 49–74. Menkin, J. A., Robles, T. F., Wiley, J. F., & Gonzaga, G. C. (2015). Online dating across the life span: Users' relationship goals. Psychology and Aging, 30(4), 987e993. Nam, T. (2017). Who is dating and gaming online? Categorizing, profiling, and predicting online daters and gamers. Computers in Human Behavior, 73, 152–160. https://doi. org/10.1016/j.chb.2017.03.044. Orr, E. S., Sisic, M., Ross, C., Simmering, M. C., Arseneault, J. M., & Orr, R. (2009). The influence of shyness on the use of Facebook in an undergraduate sample. CyberPsychology and Behavior, 12, 337–340. https://doi.org/10.1089/cpb.2008.0214. Potarca, G. (2017). Does the internet affect assortative mating? Evidence from the U.S. and Germany. Social Science Research, 61, 278–297. https://doi.org/10.1016/j. ssresearch.2016.06.019. Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science, 2(4), 313–345. Rosenfeld, M. J., & Thomas, R. J. (2012). Searching for a mate: The rise of the internet as a social intermediary. American Sociological Review, 77(4), 523–547. Ross, C., Orr, E. S., Sisic, M., Arseneault, J. M., Simmering, M. G., & Orr, R. R. (2009). Personality and motivation associated with Facebook use. Computers in Human Behavior, 25, 5578–5586. https://doi.org/10.1016/j.chb.2008.12.024. Sautter, J. M., Tippett, R. M., & Morgan, S. P. (2010). The social demography of internet dating in the United States. Social Science Quarterly, 91(2), 554e575. Valkenburg, P. M., & Peter, J. (2007). Who visits online dating sites? Exploring some characteristics of online daters. Cyberpsychology & Behavior, 10, 849–852. https://doi. org/10.1089/cpb.2007.9941. Zywica, J., & Danowski, J. (2008). The faces of Facebookers: Investigating social enhancement and social compensation hypotheses; predicting Facebook and offline popularity from sociability and self-esteem, and mapping the meanings of popularity with semantic networks. Journal of Computer-Mediated Communication, 14, 1–34. https://doi.org/10.1111/j.1083-6101.2008.01429.
References Bellou, A. (2015). The impact of internet diffusion on marriage rates: Evidence from the broadband market. Journal of Population Economics, 28, 265–297. https://doi.org/10. 1007/s00148-014-0527-7. Blackhart, G. C., Fitzpatrick, J., & Williamson, J. (2014). Dispositional factors predicting use of online dating sites and behaviors related to online dating. Computers in Human Behavior, 33, 113–118. https://doi.org/10.1016/j.chb.2014.01.022. Brüderl, J., Hank, K., Huinink, J., Nauck, B., Neyer, F. J., Walper, S., ... Wilhelm, B. (2017). The German Family Panel (pairfam). GESIS Data Archive, Cologne. ZA5678 Data file Version 8.0.0. https://doi.org/10.4232/pairfam.5678.8.0.0. Cacioppo, J. T., Cacioppo, S., Gonzaga, G. C., Ogburn, E. L., & VanderWeele, T. J. (2013). Marital satisfaction and break-ups differ across on-line and off-line meeting venues. Proceedings of the National Academy of Sciences, 110, 10135–10140. Correa, T., Hinsley, A. W., & de Zuniga, H. G. (2010). Who interacts on the web? The intersection of users' personality and social media use. Computers in Human Behavior, 26, 247–253. https://doi.org/10.1016/j.chb.2009.09.003. Hall, J. A., Park, N., Song, H., & Cody, M. J. (2010). Strategic misrepresentation in online dating: The effects of gender, self-monitoring, and personality traits. Journal of Social and Personal Relationships, 27, 117–135. https://doi.org/10.1177/ 0265407509349633. Hitsch, G. J., Hortaçsu, A., & Ariely, D. (2010a). Matching and sorting in online dating. American Economic Review, 100(1), 130–163. Hitsch, G. J., Hortaçsu, A., & Ariely, D. (2010b). What makes you click?—Mate preferences in online dating. Quantitative Marketing and Economics, 8, 393–427. https:// doi.org/10.1007/s11129-010-9088-6. Huinink, J., Brüderl, J., Nauck, B., Walper, S., Castiglioni, L., & Feldhaus, M. (2011). Panel analysis of intimate relationships and family dynamics (pairfam): Conceptual framework and design. Journal of Family Research, 23, 77e101. Jokela, M., Alvergne, A., Pollet, T. V., & Lummaa, V. (2011). Reproductive behavior and personality traits of the Five Factor Model. European Journal of Personality, 25(6), 487–500. Jokela, M., Kivimäki, M., Elovainio, M., & Keltikangas-Järvinen, L. (2009). Personality and having children: A two-way relationship. Journal of Personality and Social
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Who meets online? Personality traits and sociodemographic characteristics associated with online partnering in Germany
T
Mirkka Danielsbackaa,c, , Antti O. Tanskanena,c,d, Francesco C. Billarib ⁎
a
Department of Social Research, University of Turku, Assistentinkatu 7, 20014 University of Turku, Finland Department of Social and Political Sciences, Carlo F. Dondena Center for Research on Social Dynamics and Public Policy, Bocconi University, Via Roberto Sarfatti, 25, 20100 Milano, MI, Italy c Population Research Institute (Väestöliitto), P.O.Box 849, FI 00101 Helsinki, Finland d Faculty of Social Sciences, University of Helsinki, 00014 University of Helsinki, Finland b
ARTICLE INFO
ABSTRACT
Keywords: Big Five Germany Internet Online Partnership formation
The Internet has partly displaced traditional offline meeting venues for partners. Here, we study whether meeting online is selective, i.e., whether there are differences in Big Five personality traits and sociodemographic background characteristics between those who meet online and offline. Using eight waves of the German Family Panel (pairfam), with observations from 7192 respondents from three birth cohorts (1991–93, 1981–83, and 1971–73) between 2008 and 2016, we found that meeting online is more likely for female respondents than males; for respondents from the older birth cohorts compared to the youngest one; for respondents with primary education compared to those who are currently enrolled and have no degree; for respondents who have shorter relationship durations; for those who have a higher number of previous partners; and for those who have less extroverted personalities. As we split the data by cohorts, it appears that meeting a partner online is slightly more selective for the oldest birth cohort than for the youngest one. Moreover, extraversion was consistently associated with meeting online in every birth cohort, and the association was negative in every case. These findings are discussed with reference to compensation and rich-get-richer hypotheses.
1. Introduction During the era of digitalization and the rise of the Internet, an increasing number of people tend to meet their romantic partners online, either through dating sites or social media, meaning that Internet has partly displaced traditional offline meeting venues for partners (Bellou, 2015; Rosenfeld & Thomas, 2012). In the US, it is estimated that more than 20% of heterosexual people meet their partners online (Rosenfeld & Thomas, 2012), and more than one-third of marriages began via the Internet (Cacioppo, Cacioppo, Gonzaga, Ogburn, & VanderWeele, 2013). Despite the growing access and use of Internet in most sociodemographic groups, it appears that some groups might still be more likely than others to use social media and online dating sites (e.g., Blackhart, Fitzpatrick, & Williamson, 2014; Hall, Park, Song, & Cody, 2010; Nam, 2017; Sautter, Tippett, & Morgan, 2010) and thus have higher likelihood to meet a future spouse online (e.g., Cacioppo et al., 2013; Rosenfeld & Thomas, 2012). Using the Internet as a venue for partner search may be due to certain endogenous factors, such as personality traits, and/or it could be due to different sociodemographic
⁎
factors relating to an individual's life course phase (e.g., age) and social status (e.g., education). Here, we investigated whether meeting online is selective, i.e., whether differences exist in Big Five personality traits and sociodemographic factors between those who meet their partner online and offline. Furthermore, regarding different dispositional factors and personality traits associated with dating online, one can formulate two opposite hypotheses. First, according to social compensation hypothesis, individuals who have problems meeting a partner through more traditional venues, online dating allows them to compensate for deficits that they encounter offline (e.g., Bellou, 2015; Rosenfeld & Thomas, 2012). Second, based on the rich-get-richer hypothesis (or the Matthew effect), online dating may benefit extrovert individuals who already have strong dating skills and who use the Internet as an additional strategic tool to find a partner (Kraut et al., 2002; Valkenburg & Peter, 2007). Studies concerning sociodemographic factors and personality traits associated with the use of social network sites or online dating have provided more support for the rich-get-richer than the social compensation hypothesis. First, it has been found that those who use online
Corresponding author. E-mail addresses: [email protected] (M. Danielsbacka), [email protected] (A.O. Tanskanen), [email protected] (F.C. Billari).
https://doi.org/10.1016/j.paid.2019.02.024 Received 1 December 2018; Received in revised form 15 February 2019; Accepted 16 February 2019 0191-8869/ © 2019 Elsevier Ltd. All rights reserved.
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dating sites are typically more highly educated and have higher incomes than the overall population (Hitsch, Hortaçsu, & Ariely, 2010a, 2010b; Menkin, Robles, Wiley, & Gonzaga, 2015; Sautter et al., 2010). Second, some have detected that more extroverted people and those who are more open to experiences are more likely to look for a partner online (Correa, Hinsley, & de Zuniga, 2010; Ross et al., 2009; Zywica & Danowski, 2008), while others have detected opposite results (Orr et al., 2009), as well as negligible results (Blackhart et al., 2014). Although there are studies detecting whether personality is associated with the use of online dating or network sites, there is lack of studies testing whether personality traits are associated with the likelihood of meeting partner online. Searching for a partner via the Internet may not necessarily lead to union formation. Thus, those who search for partners via the Internet may be a different group than those who actually start a relationship with a partner they have met online. Prior studies have shown that meeting a partner online is typical for middleaged (Rosenfeld & Thomas, 2012) and (on average) higher educated people who work full-time (Cacioppo et al., 2013). The Internet may also promote weaker couple endogamy (measured as educational, ethnic and religious endogamy) compared to more traditional meeting venues (Potarca, 2017), although facilitated partner searches through online dating sites may also promote couple homogamy (Hitsch et al., 2010a). Although prior studies have detected whether different sociodemographic factors are associated with meeting partner online, they do not show whether personality is associated with meeting partner online. Although the previous online partnering studies have several strengths, they also have their limitations, which highlight the importance of additional investigations. First, to the best of our knowledge, previous studies have not examined personality differences between those who actually meet their partners online and offline, as discussed above. Second, most previous studies have been conducted with data from the US, and investigations from other countries are needed because dating cultures may vary significantly from one country to another. Third, there is lack of studies detecting cohort differences in online partnering among younger adults. All these issues are considered in the present study. Moreover, we study whether meeting a partner online has become more common in Germany in 2008–2016 and whether individuals with certain sociodemographic characteristics are more likely to meet their partners online than other individuals.
The dependent variable indicates whether a respondent has met a partner online. In the first wave of pairfam, those who reported having a partner was asked the meeting venue of this partner (“How did you meet?”), and one of the nine alternatives was “Through the Internet” (coded as 0 = offline, 1 = online). In subsequent waves, the question was asked only of those with a new partner. For the analytical sample, we included heterosexual individuals who have a partner or who met a partner during waves 2 to 8. As independent variables, we use sex, cohort, highest education, ethnicity, whether the respondent currently lives in East Germany, relationship duration, number of previous partners, number of children and personality, as measured with the Big Five personality test (extraversion, openness to experience, neuroticism, agreeableness and conscientiousness). Because personality was asked only in waves 2 and 6, we had to extract this information for the other waves, meaning that there is a drop in observations, particularly in the first wave. For sensitivity purposes, we have also rerun our full model, without any personality variables included, which did not considerably change the results of the other background characteristics. The analytical sample in the fully adjusted model is 7192. We used a multivariate between-person regression models. To help the readers to interpret the effect sizes, the statistically significant results are illustrated by calculating adjusted means or predicted probabilities (and 95% confidence intervals) from the fully adjusted regression models. 3. Results 3.1. Frequency In 2008–2016, among all cohorts, the cumulative proportion of those heterosexual individuals who have met their spouse via the Internet increased from 5% to 9% (Table 1). Among individuals who formed a new relationship between waves 2 to 8, the proportion of those who met online increased from 11% to 21% (Table 2). Among the oldest cohort (1971–1973), the proportion of those who had met their spouse online, before the first wave, was 3%, however, among those who formed a new relationship between waves 2 to 8, the proportion increased from 20% to 29% (Table 3). Among the middle cohort (1981–1983), the proportion of those who had met their spouse via the Internet in the first wave was 6%, and among new relationships, the rate increased during subsequent waves from 18% to 31% (Table 4). Among the youngest cohort (born 1991–1993) the proportion of online partnering was 9% in 2008–2009 but increased from 8% to 14% from 2010 to 2016 (Table 5).
2. Material and methods We used data from the Panel Analysis of Intimate Relationships and Family Dynamics (pairfam), which offers information on partnership, childbearing and several socio-ecological factors in Germany (Brüderl et al., 2017; Huinink et al., 2011). Pairfam provides longitudinal data on three birth cohorts born in 1971–1973, 1981–1983 and 1991–1993. The first pairfam wave was conducted in 2008–2009, when the cohort members were approximately 15–17, 25–27 and 35–37 years of age, respectively. Data included German-speaking persons (regardless of nationality) living in private households in Germany. Further data collections have been conducted annually, and the last data used here are from wave eight (conducted in 2015–2016). The achieved pairfam samples vary between 12,402 respondents in first wave to 5461 respondents in the eighth wave.
3.2. Who meets online? Next, we examined whether individuals who have met their spouses online have different characteristics compared to those who have met their spouses offline. We performed three stepwise regression models: the first one controls for the respondents' sex, cohort, ethnicity, highest education, whether the respondents currently live in East Germany and whether the respondents met their spouses before wave 1 or during waves 2 to 8; the second model controls for the personality traits of a respondent, in addition to the previously mentioned variables, and in the third model, we added family characteristics (i.e., the number of
Table 1 All heterosexual respondents who are in a relationship and have met their partners online between 2008 and 2016 (%).
Other Met online Total (n)
Wave 1
Wave 2
Wave 3
Wave 4
Wave 5
Wave 6
Wave 7
Wave 8
2008/09
2009/10
2010/11
2011/12
2012/13
2013/14
2014/15
2015/16
95 5 7158
94 6 5335
95 6 5752
94 6 5346
94 6 4989
93 7 4592
92 8 4216
140
91 9 3962
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Table 2 All heterosexual respondents who have met their partners online in 2008 and those with new partner between 2010 and 2016 (%).
Other Met online Total (n)
Wave 1
Wave 2
Wave 3
Wave 4
Wave 5
Wave 6
Wave 7
Wave 8
2008/09
2009/10
2010/11
2011/12
2012/13
2013/14
2014/15
2015/16
95 5 7158
89 11 1109
89 11 1038
89 11 875
88 12 722
85 15 570
83 18 470
79 21 426
compared to the older ones. To test this hypothesis, we split the data by birth cohorts and studied whether background characteristics are associated differently with meeting a partner online among the different birth cohorts. The results are shown in Appendix Table A1. In the youngest birth cohort, primary and lower secondary education, compared to being currently enrolled and currently living in East Germany, were positively associated with meeting online (predicted probability for primary or lower secondary 13.39, 95% CI = 0.11–0.16; for upper secondary education 10.10, 95% CI = 0.08–0.12; for currently enrolled 0.06, 95% CI = 0.05–0.08; for living in East Germany 0.13, 95% CI = 0.10–0.15; for living in West Germany 0.08, 95% CI = 0.06–0.09). In addition, those who met online were less extroverted than those met offline (adjusted mean for those who met online 14.40, 95% CI = 13.95–14.84; for those who met offline 14.98, 95% CI = 14.85–15.10). In the middle birth cohort, those who met online were more likely to have met during waves 2 to 8 than before wave 1 (predicted probability for those who met during waves 2 to 8 0.14, 95% CI = 0.11–0.16; for those who met before wave 1 0.09, 95% CI = 0.07–0.10), were less extroverted (adjusted mean for those who met online 13.80, 95% CI = 13.38–14.21; for those who met offline 14.31, 95% CI = 14.18–14.44) and had shorter relationship durations (adjusted mean for those who met online 28.78, 95% CI = 24.78–32.78; for those who met offline 38.72, 95% CI = 37.45–39.98) than those met offline. In the oldest birth cohort, sex (females more likely than males; predicted probabilities for female 0.08, 95% CI = 0.07–0.09; for male 0.06, 95% CI = 0.05–0.08), living currently in East Germany (negative association; predicted probability for East Germany 0.05, 95% CI = 0.03–0.07; for West Germany 0.08, 95% CI = 0.07–0.09), having met current partners during waves 2 to 8 (positive association; predicted probability for waves 2 to 8 0.16, 95% CI = 0.13–0.18; for wave 1 0.05, 95% CI = 0.04–0.06), extraversion (less; adjusted mean for those who met online 13.41, 95% CI = 12.91–13.91; for those who met offline 14.12, 95% CI = 14.0–14.24), openness (more; adjusted mean for those who met online 19.09, 95% CI = 18.54–19.64; for those who met offline 18.25, 95% CI = 18.12–18.39) and relationship duration (shorter; adjusted mean for those who met online 74.10, 95% CI = 64.77–83.44; for those who met offline 112.10, 95% CI = 109.80–114.40) were all associated with meeting online. Thus, it appears that most differences in background characteristics between those met online and offline are found among the oldest birth cohort; however, differences in personality traits (extraversion) are surprisingly robust in all birth cohorts.
children, relationship duration and number of previous partners). Table 6 shows three multivariate between-person models predicting the likelihood to meet online. In the first model, meeting online was significantly and positively associated with the middle cohort compared to the youngest cohort. The respondents with Turkish backgrounds were less likely than German natives to meet their spouses online. Those who met online were more likely to have a primary or lower secondary education when compared to the currently enrolled respondents (who did not have any degree). The respondents were more likely to have met their spouses online during waves 2 to 8 than before wave 1 (Table 6, Model 1). In the second model, we added personality traits (extraversion, neuroticism, agreeableness, conscientiousness and openness), which altered the model so that the respondents from the oldest cohort were more likely to meet their partners online than the respondents from the youngest cohort, and ethnicity was no longer associated with meeting online. Extraversion was the only personality trait that was associated with meeting online, and the association was negative, meaning that those respondents with less extraversion were more likely to meet their spouses online (Table 6, Model 2). In the last model, we added family characteristics (i.e., the number of children, relationship duration and number of previous partners), which also altered the previous coefficients. In the last and fully adjusted model (Table 6, Model 3), females were more likely to have met their spouses online than males (predicted probability for females 0.09, 95% CI = 0.07–0.09; for males 0.08, 95% CI = 0.09–0.10), and respondents from older birth cohorts were more likely to have met their partners online compared to the youngest cohort (predicted probability for 1971–73 cohort 0.14, 95% CI = 0.12–0.15; for 1981–83 cohort 0.10, 95% CI = 0.09–0.11; for 1991–93 cohort 0.05, 95% CI = 0.03–0.06). Those who met online were more likely to have primary or lower secondary or upper secondary education compared to the currently enrolled respondents (who did not have any degree) (predicted probability for primary or lower secondary or upper secondary education 0.12, 95% CI = 0.10–0.13; for currently enrolled 0.07, 95% CI = 0.05–0.09). In addition, extraversion was still negatively associated with meeting online (adjusted mean for those who met online 13.97, 95% CI = 13.71–14.23; for those who met offline 14.53, 95% CI = 14.46–14.61). Those who met online had also shorter relationship durations (adjusted mean for those who met online 26.43, 95% CI = 22.86–30.00; for those who met offline 46.39, 95% CI = 45.37–47.40) and higher numbers of previous partners than those met offline (adjusted mean for those who met online 1.50, 95% CI = 1.37–1.58; for those who met offline 1.35, 95% CI = 1.32–1.38) (predicted probabilities and adjusted means not shown in the tables). It could be that using the Internet could be more equal among younger cohorts than older cohorts, meaning that there should be less division in the background characteristics among younger birth cohorts
4. Discussion and conclusions In this article, we studied whether meeting a partner online has become more common in Germany among three birth cohorts in
Table 3 Heterosexual respondents in cohort 1971–1973 who have met their partners online in 2008 and those with a new partner between 2010 and 2016 (%).
Other Met online Total (n)
Wave 1
Wave 2
Wave 3
Wave 4
Wave 5
Wave 6
Wave 7
Wave 8
2008/09
2009/10
2010/11
2011/12
2012/13
2013/14
2014/15
2015/16
97 3 3315
81 20 113
81 19 108
78 22 119
74 26 77
75 25 73
74 26 69
141
71 29 68
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Table 4 Heterosexual respondents in cohort 1981–1983 who have met their partners online in 2008 and those with a new partner between 2010 and 2016 (%).
Other Met online Total (n)
Wave 1
Wave 2
Wave 3
Wave 4
Wave 5
Wave 6
Wave 7
Wave 8
2008/09
2009/10
2010/11
2011/12
2012/13
2013/14
2014/15
2015/16
94 6 2725
82 18 223
87 13 276
87 13 208
83 17 192
81 19 143
76 25 106
70 31 105
with mate value meet several women online, while the men with low mate value meet only few women or none. Among women, however, the division between individuals lower and higher with mate value may not be as crucial, meaning that women with lower mate value may still have opportunities to meet partners online. We call future studies to investigate whether this is the case. The present study has several strengths. To the best of our knowledge, we have first time compared personality traits between those who have met a romantic partner and started a relationship online and those who have met offline. In addition to personality, with the pairfam data it was possible to study whether individuals with certain sociodemographic characteristics are more likely to meet their partners online than others. Finally, we were able to compare differences among three birth cohorts and study whether meeting a partner online has become more common in Germany in 2008–2016. Obviously, the present study is not without limitations. Due to the panel characteristic of the data, we only have a limited number of people who met a new partner, as well as those who met a new partner online, during the panel. A larger sample size could provide additional data, for instance, how background characteristics of those who meet online have changed over time in different birth cohorts. We may conclude that meeting online is still somewhat selective, particularly among middle-aged adults, and that it may provide a meeting venue for those who have slightly less extroverted personalities (and who perhaps have limited partnership markets). It has been claimed that digitalization and the rise of the Internet may change partnership markets permanently (Bellou, 2015). In line with this prediction, our results indicate that during the recent years Internet has become more common venue to meeting a partner in Germany. In addition, it has been assumed that Internet as a meeting venue may create digital divide between those individuals who have access and skills to use Internet for mate search and those who do not have (Valkenburg & Peter, 2007). Interestingly, we found that in terms of sociodemographic characteristics meeting a partner online is not as selective among youngest age cohort compared to older ones, indicating decreasing importance of digital divide among younger people, which could be related to the fact that the Internet is more “normal” partner meeting venue for younger people. Moreover, the finding that in all birth cohorts less extrovert individuals were more likely to meet a partner online implies that meeting online could influence to further family related outcomes because personality plays an important role in several life outcomes (Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007). For instance, it has been detected that increased level of extraversion is associated with decreased childbearing (Jokela, Alvergne, Pollet, & Lummaa, 2011; Jokela, Kivimäki, Elovainio, & Keltikangas-Järvinen, 2009). Thus, future research should focus on possible differences in
2008–2016 and whether those who meet their partners online differ from those who meet partners offline. Our results show that overall, meeting a partner online has become somewhat more common in Germany between 2009 and 2016. If we look at those who met a new partner during the panel within a cohort, we see that meeting online is becoming relatively more common among older birth cohorts. However, if we look at those who overall meet a new partner, meeting online is most common in the youngest birth cohort. This result is partly because among the youngest birth cohort, more relationships are formed overall, that is to say, people from older birth cohorts already have partners. Our general finding is that meeting online is somewhat selective. Those who met their partners online were more likely to be female than male, from the youngest birth cohort compared to the oldest cohort, to have primary education compared to those who are currently enrolled and have no degree, have shorter relationship durations, a higher number of previous partners and a less extroverted personality. Interestingly, the only Big Five personality trait that was associated with meeting online was extraversion, and the association was negative, meaning that those who demonstrated less extraversion were more likely to meet online. As we split the data by cohorts, it showed that most of the differences in background characteristics were found among the oldest birth cohort, who, in general, were least likely to meet a partner online but among whom, a relatively large proportion of new relationships were formed via the Internet. However, the differences in extraversion were quite robust in all birth cohorts, meaning that among all cohorts, those with less extraversion were more likely to meet their partners online. In addition, and perhaps contradictory to the finding concerning extraversion, in the oldest birth cohort, those who met online also had higher scores in openness to experiences. Our results are mostly in contrast with previous studies that have found evidence for the rich-get-richer hypothesis, which posits that online dating may benefit extrovert individuals who already have strong dating skills and who use the Internet as an additional strategic tool to find a partner (Correa et al., 2010; Kraut et al., 2002; Ross et al., 2009; Valkenburg & Peter, 2007; Zywica & Danowski, 2008. However, see Orr et al., 2009). However, although previous evidence on those who use online dating sites and meet online indicates that online dating might be more common among people who already have good dating skills, actual online couple formation could be more typical for those who might not meet partners that easily in traditional settings. Our results align with this latter mentioned prediction, particularly if we look our results concerning the personality traits (less extraversion) associated with meeting online. In contrast, the finding that women meet partners online more often compared to men may provide support for the rich-get-richer hypothesis. One can speculate that the men high
Table 5 Heterosexual respondents in cohort 1991–1993 who have met their partners online in 2008 and those with a new partner between 2010 and 2016 (%).
Other Met online Total (n)
Wave 1
Wave 2
Wave 3
Wave 4
Wave 5
Wave 6
Wave 7
Wave 8
2008/09
2009/10
2010/11
2011/12
2012/13
2013/14
2014/15
2015/16
91 9 1118
92 8 773
91 9 654
93 8 548
93 7 453
89 11 354
87 13 295
142
86 14 253
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M. Danielsbacka, et al.
Table 6 Factors associated with meeting online. Stepwise between-person regression analyses. Model 1
Model 2 95% CI
Sex Cohort Ethnicity
Highest education
Currently living in East Germany Met current spouse Extraversion Neuroticism Agreeableness Conscientiousness Openness Number of children Relationship duration Number of previous partners n r2
Male (ref.) Female 1991–1993 (ref.) 1981–1983 1971–1973 German native (ref.) Ethnic-German immigrant Half-German Turkish background Other non-German background Currently enrolled (no degree) (ref.) Primary and lower secondary Upper secondary Post-secondary Tertiary No (ref.) Yes On wave 1 (ref.) During waves 2–8
Model 3 95% CI
95% CI
Coeff
Lower
Upper
Coeff
Lower
Upper
Coeff
Lower
Upper
0.0001
−0.01
0.01
0.004
−0.01
0.02
0.01⁎
0.001
0.03
0.04 0.02
0.02 −0.002
0.06 0.04
0.05 0.03⁎⁎
0.03 0.01
0.07 0.05
0.05 0.09⁎⁎⁎
0.03 0.06
0.07 0.11
−0.01 −0.01 −0.03⁎ −0.01
−0.03 −0.03 −0.06 −0.03
0.01 0.01 −0.01 0.005
−0.02 −0.01 −0.03 −0.01
−0.05 −0.04 −0.06 −0.03
0.003 0.01 0.002 0.01
−0.02 −0.01 −0.01 −0.01
−0.04 −0.04 −0.04 −0.03
0.01 0.01 0.02 0.01
0.03⁎ 0.02 0.001 0.01
0.005 −0.01 −0.02 −0.01
0.05 0.04 0.02 0.04
0.03⁎ 0.01 −0.01 0.01
0.01 −0.01 −0.03 −0.02
0.06 0.04 0.02 0.04
0.05⁎⁎ 0.03⁎ 0.005 0.01
0.02 0.0003 −0.02 −0.01
0.07 0.05 0.03 0.04
0.005
−0.01
0.02
0.005
−0.01
0.02
0.01
−0.01
0.02
0.09
0.08
0.10
0.09 −0.003⁎⁎ −0.0005 −0.001 −0.0005 0.001
0.08 −0.01 −0.002 −0.003 −0.003 0.000
0.11 −0.001 0.002 0.001 0.002 0.003
0.04 −0.004⁎⁎⁎ −0.001 0.0003 −0.0003 0.001 −0.01 −0.001⁎⁎⁎ 0.01⁎ 7192 0.0531
0.03 −0.01 −0.003 −0.002 −0.003 −0.001 −0.02 −0.001 0.001
0.06 −0.002 0.001 0.002 0.002 0.002 0.001 −0.001 0.01
⁎⁎⁎
⁎⁎⁎
9564 0.0247
⁎⁎⁎
⁎⁎⁎
7434 0.029
⁎⁎⁎
⁎⁎⁎
p < .05. p < .01. ⁎⁎⁎ p < .001. ⁎
⁎⁎
family related outcomes of those individuals who meet a partner online and offline.
on data from the first eight waves of the German Family Panel (pairfam), release 8.0 (Brüderl et al., 2017). A detailed description of the study can be found in Huinink et al. (2011). This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 694262), project DisCont Discontinuities in Household and Family Formation. In addition, the present study was supported by the Academy of Finland (grant number 316229 and 317808) and the Kone Foundation.
Acknowledgements This paper uses data from the German Family Panel pairfam, coordinated by Josef Brüderl, Sonja Drobnič, Karsten Hank, Bernhard Nauck, Franz Neyer, and Sabine Walper. pairfam is funded as long-term project by the German Research Foundation (DFG). Analyses are based Appendix A. Appendix Table A1
actors associated with meeting online. Between-person regression analyses for every cohort. COHORT 1991–93
COHORT 1981–83
95% CI
Sex Ethnicity
Highest education
Male (ref.) Female German native (ref.) Ethnic-German immigrant Half-German Turkish background Other non-German background Currently enrolled (no degree) (ref.) Primary and lower secondary Upper secondary Post-secondary Tertiary
COHORT 1971–73
95% CI
95% CI
Coeff
Lower
Upper
Coeff
Lower
Upper
Coeff
Lower
Upper
0.004
−0.02
0.03
0.02
−0.01
0.04
0.02⁎
0.0001
0.04
0.02 0.002 0.02 0.03
−0.03 −0.04 −0.04 −0.01
0.06 0.04 0.08 0.07
−0.04 −0.04 −0.06 −0.02
−0.09 −0.09 −0.13 −0.07
0.01 0.01 0.01 0.02
−0.02 −0.002 −0.001 −0.02
−0.07 −0.04 −0.05 −0.05
0.02 0.04 0.05 0.01
0.07⁎⁎⁎ 0.04⁎ −0.002 0.08
0.04 0.01 −0.04 −0.03
0.10 0.07 0.03 0.20
0.11 0.10 0.09 0.08
−0.10 −0.10 −0.11 −0.12
0.31 0.30 0.30 0.28
0.04 0.04 0.02 0.03
−0.18 −0.18 −0.20 −0.19
0.26 0.25 0.23 0.24
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Personality and Individual Differences 143 (2019) 139–144
M. Danielsbacka, et al.
Table A1 (continued) COHORT 1991–93
COHORT 1981–83
95% CI
Currently living in East Germany Met current spouse Extraversion Neuroticism Agreeableness Conscientiousness Openness Number of children Relationship duration Number of previous partners n r2
No (ref.) Yes On wave 1 (ref.) During waves 2–8
COHORT 1971–73
95% CI
95% CI
Coeff
Lower
Upper
Coeff
Lower
Upper
Coeff
Lower
Upper
0.05⁎⁎⁎
0.03
0.08
−0.01
−0.03
0.02
−0.04⁎⁎
−0.06
−0.01
−0.003 −0.004⁎ −0.001 −0.002 −0.001 −0.001 −0.08 0.001 0.01 2477 0.0251
−0.03 −0.01 −0.005 −0.01 −0.004 −0.004 −0.16 −0.001 −0.002
0.03 −0.001 0.002 0.002 0.003 0.002 0.01 0.002 0.02
0.05⁎⁎ −0.004⁎ 0.0002 0.002 −0.001 −0.001 −0.01 −0.001⁎⁎⁎ 0.01 2273 0.053
0.02 −0.01 −0.003 −0.002 −0.01 −0.005 −0.02 −0.001 −0.003
0.09 −0.001 0.004 0.01 0.003 0.002 0.01 −0.001 0.02
0.1⁎⁎⁎ −0.004⁎⁎ −0.001 0.001 0.001 0.004⁎⁎ −0.004 −0.001⁎⁎⁎ 0.0001 2442 0.1165
0.07 −0.01 −0.004 −0.003 −0.003 0.001 −0.01 −0.001 −0.01
0.14 −0.001 0.002 0.004 0.004 0.01 0.004 −0.0005 0.01
p < .05. p < .01. ⁎⁎⁎ p < .001 ⁎
⁎⁎
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