<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The fields of social network analysis and network science have recently undertaken efforts to address issues of gender diversity and representation. However, despite these important efforts, subtle threats to representation are likely to persist. I use a 2023 global survey of 260 network researchers to show that men rate themselves as having more expertise with networks than women. This is troubling because, to the extent that women believe they lack the expertise to be considered for awards or collaboration opportunities, it can impede other efforts to diversify the field. I conclude by discussing some possible explanations for this finding and some possible strategies for addressing its consequences.</p>
</abstract>

The fields of social network analysis and network science have recently undertaken efforts to address issues of gender diversity and representation. However, despite these important efforts, subtle threats to representation are likely to persist. I use a 2023 global survey of 260 network researchers to show that men rate themselves as having more expertise with networks than women. This is troubling because, to the extent that women believe they lack the expertise to be considered for awards or collaboration opportunities, it can impede other efforts to diversify the field. I conclude by discussing some possible explanations for this finding and some possible strategies for addressing its consequences.


<div xmlns="http://www.w3.org/1999/xhtml">
<sec id="j_connections-2022-0003_s_001"><div>Men Think they Know More about Networks</div>
<p>The fields of social network analysis and network science have recently undertaken efforts to address issues of gender diversity and representation. For example, in 2018, the Women in Network Science (WiNS) group was formed to “connect and promote network scientists who identify as underrepresented genders.” Similarly, in 2023, the International Network for Social Network Analysis (INSNA) adopted a diversity, equity, and inclusion (DEI) policy to promote diversity by gender and other characteristics across the association's activities. Despite these important efforts, subtle threats to representation are likely to persist.</p>
<p>One such threat is that men and women may judge their network expertise differently. In this paper, I use a 2023 global survey of 260 network researchers to show that men rate themselves as having more expertise with networks than women. This is troubling because, to the extent that women believe they lack the expertise to be considered for awards or collaboration opportunities, it can impede other efforts to diversify the field. I discuss some possible explanations for this finding and some possible strategies for addressing the consequences of a gender gap in self-rated expertise. The data and code necessary to reproduce the analyses reported here are available at <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://osf.io/zjwm7/">https://osf.io/zjwm7/</ext-link>.</p>
</sec>
<sec id="j_connections-2022-0003_s_002"><div>Methods</div>
<sec id="j_connections-2022-0003_s_002_s_001"><div>Data Collection</div>
<p>This analysis draws on data collected through two separate web-based surveys designed for a larger study of guidelines for reporting about network data: a probability sample of network journal and association board members, and a convenience sample of network researchers. Both surveys were approved as exempt by Michigan State University's Institutional Review Board (STUDY00008880).</p>
<p>The board survey sought to include all members of the boards of two network associations (INSNA and NetSci) and seven network journals (<italic>Applied Network Science, Connections, Journal of Complex Networks, Journal of Social Structure, Network Science, Social Networks, Social Network Analysis and Mining</italic>). An invitation to participate was sent to the 237 board members with valid email addresses on 15 March 2023. Non-respondents received weekly email reminders until the survey closed on 20 April 2023. A total of 95 board members provided responses; however, only 84 provided complete data on the variables used in this analysis, yielding an effective response rate of 35%.</p>
<p>The convenience sample sought to solicit responses from anyone with “the expertise necessary to evaluate research about networks where nodes represent something, and that are not solely mathematical abstractions.” Invitations to participate in the survey were circulated via social media (Mastodon, Twitter) and listservs (SOCNET, REDES (in Spanish), cna2023, siam-ns) in May and June 2023. A total of 211 unique non-anonymous individuals provided responses; however, only 176 provided complete data on the variables used in this analysis.</p>
</sec>
<sec id="j_connections-2022-0003_s_002_s_002"><div>Measures</div>
<p>The variable of interest—self-rated expertise in networks—was measured by asking: “On 10-point scale where 1 means ‘novice’ and 10 means ‘expert,’ please rate your level of expertise with network analysis or network science.”</p>
<p>Gender was measured by asking: “With which gender do you most closely identify.” Respondents were provided three response options: Man, Woman, and Non-binary/third gender. Seven respondents did not enter a response, but were personally known to the author; in these cases their gender was imputed. The use of imputed genders does not affect the results reported below. Four respondents reported identifying as non-binary. To preserve respondents’ anonymity, their responses to the gender question are suppressed in the public data and they are excluded from analysis. Grouping these respondents with women, as a minoritized gender, does not affect the results reported below.</p>
<p>Field was measured by asking: “What is the name of the discipline or field with which you most closely identify now?” Responses to this open-ended question were re-coded into three exhaustive and mutually exclusive categories: social, physical, and network. Social science disciplines (e.g., sociology, psychology, political science) were coded as “social,” and physical science disciplines (e.g., biology, neuroscience) were coded as “physical.” Responses that specifically named networks (e.g. network science), and disciplines that deal with network methods rather than substantive topics (e.g. mathematics, statistics), were coded as “network.”</p>
<p>Experience was measured by the logged number of years since the respondent earned their highest degree. A logarithmic transformation is used to account for the diminishing returns to expertise from additional experience.</p>
<p>Because the survey was completed by a global pool of respondents, data on race were not collected. Respondents were asked whether they “identify as a member of a minority racial or ethnic group”; however, many chose not to provide a response. Therefore, these analyses do not consider race, ethnicity, or racial/ethnic minority status.</p>
</sec>
</sec>
<sec id="j_connections-2022-0003_s_003"><div>Results</div>
<sec id="j_connections-2022-0003_s_003_s_001"><div>Sample</div>
<p><a ref-type="table" href="#j_connections-2022-0003_tab_001">Table 1</a> describes the board and web survey samples in terms of gender and field distribution, mean experience, and mean expertise. Although the web sample is a convenience sample, its gender and field distributions closely mirror those of the probability sample of board members, suggesting that it may nonetheless be representative of the population of network researchers. The two samples differ in expected ways by experience and expertise: members of the board sample have more experience (21.3 years vs. 11.7) and higher self-rated expertise (8.8 vs. 7.8). In the analyses reported below, the two samples are pooled to achieve a larger sample size and greater statistical power. The results remain the same if analyses are performed using separate non-pooled samples.</p>
<table-wrap id="j_connections-2022-0003_tab_001" position="float"><label>Table 1.</label>
<caption><p>Survey descriptive statistics.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="center" valign="top" rowspan="2"></th>
<th align="center" valign="top" colspan="2"><bold>Survey</bold></th>
</tr>
<tr>
<th align="left" valign="top"><bold>Board</bold></th>
<th align="left" valign="top"><bold>Web</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">N</td>
<td align="left" valign="top">84</td>
<td align="left" valign="top">176</td>
</tr>
<tr>
<td align="left" valign="top" colspan="3">Gender</td>
</tr>
<tr>
<td align="left" valign="top">  Man</td>
<td align="left" valign="top">62</td>
<td align="left" valign="top">131</td>
</tr>
<tr>
<td align="left" valign="top">  Woman</td>
<td align="left" valign="top">22</td>
<td align="left" valign="top">45</td>
</tr>
<tr>
<td align="left" valign="top" colspan="3">Field</td>
</tr>
<tr>
<td align="left" valign="top">  Network</td>
<td align="left" valign="top">41</td>
<td align="left" valign="top">85</td>
</tr>
<tr>
<td align="left" valign="top">  Social</td>
<td align="left" valign="top">38</td>
<td align="left" valign="top">81</td>
</tr>
<tr>
<td align="left" valign="top">  Physical</td>
<td align="left" valign="top">5</td>
<td align="left" valign="top">10</td>
</tr>
<tr>
<td align="left" valign="top">Experience</td>
<td align="left" valign="top">21.3 (11.9)</td>
<td align="left" valign="top">11.7 (11)</td>
</tr>
<tr>
<td align="left" valign="top">Expertise</td>
<td align="left" valign="top">8.8 (1.2)</td>
<td align="left" valign="top">7.8 (1.6)</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="j_connections-2022-0003_s_003_s_002"><div>Gender and Self-Rated Expertise</div>
<p><a ref-type="fig" href="#j_connections-2022-0003_fig_001">Figure 1</a> illustrates the distribution of network expertise self-rated by men (left) and women (right). Men's self-rated expertise is skewed toward higher values (<italic>M</italic> = 8.32, <italic>SD</italic> = 1.52), while women's self-rated expertise is more symmetrically distributed and is statistically significantly lower (<italic>M</italic> = 7.39, <italic>SD</italic> = 1.5; <italic>t</italic>[116.62] = 4.38, <italic>p</italic> &lt; 0.01).</p>
<figure id="j_connections-2022-0003_fig_001" position="float" fig-type="figure"><h2>Figure 1:</h2>
<figCaption><p>Distribution of self-rated network expertise by gender.</p></figCaption>
<img xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_connections-2022-0003_fig_001.jpg" src="https://sciendo-parsed.s3.eu-central-1.amazonaws.com/64715a1b2b88470fbea16d90/j_connections-2022-0003_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20240630T182043Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=18000&amp;X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20240630%2Feu-central-1%2Fs3%2Faws4_request&amp;X-Amz-Signature=081e37b66d6e4babf826e29df161a2211b55f43240aa561d929980372b3bc8a4" class="mw-100"/>
</figure>
<p>This apparent difference could be due to sampling error, if expert men were more likely to complete the survey than expert women. However, this difference persists in the board sample, which is composed solely of respondents who have a sufficiently high level of expertise to have been appointed or elected to journal or association board roles. Within the high-expertise board sample, men report having statistically significantly more expertise (<italic>M</italic> = 9.08, <italic>SD</italic> = 1.08) than women (<italic>M</italic> = 7.91, <italic>SD</italic> = 1.27; <italic>t</italic>[32.35] = 3.87, <italic>p</italic> &lt; 0.01).</p>
<p>The apparent difference could also be due to confounding variables. For example, a gender gap in expertise could appear if women entered the field of network analysis more recently and therefore have less experience on average than men. It could also appear if women are more likely to pursue network research in the social or physical science fields where their substantive expertise is primary, while men are more likely to pursue network research in the field of network science where their network expertise is primary. However, this difference persists when controlling for such potentially confounding variables.</p>
<p><a ref-type="table" href="#j_connections-2022-0003_tab_002">Table 2</a> reports the results of an OLS regression predicting self-rated network expertise as a function of logged experience, board membership, identifying with a network-focused field (as opposed to a substantive discipline), and identifying as a man. As expected, experience, identification with a network-focused field, and board membership are all positively associated with higher self-rated network expertise. But, after controlling for these factors, men still rate their network expertise as 0.75 points higher than women. Indeed, as the standardized estimates indicate, gender has a slightly stronger association with self-rated network expertise (b = 0.21) than does experience (b = 0.17).</p>
<table-wrap id="j_connections-2022-0003_tab_002" position="float"><label>Table 2.</label>
<caption><p>Regression predicting self-reported network expertise.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top"></th>
<th align="left" valign="top"><bold>B</bold></th>
<th align="left" valign="top"><bold>SE</bold></th>
<th align="left" valign="top"><bold>p</bold></th>
<th align="left" valign="top"><bold>β</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Intercept</td>
<td align="left" valign="top">6.32</td>
<td align="left" valign="top">0.28</td>
<td align="left" valign="top">&lt; 0.01</td>
<td align="left" valign="top">–</td>
</tr>
<tr>
<td align="left" valign="top">log(Experience)</td>
<td align="left" valign="top">0.65</td>
<td align="left" valign="top">0.23</td>
<td align="left" valign="top">0.01</td>
<td align="left" valign="top">0.17</td>
</tr>
<tr>
<td align="left" valign="top">Board</td>
<td align="left" valign="top">0.8</td>
<td align="left" valign="top">0.2</td>
<td align="left" valign="top">&lt; 0.01</td>
<td align="left" valign="top">0.24</td>
</tr>
<tr>
<td align="left" valign="top">Network</td>
<td align="left" valign="top">0.54</td>
<td align="left" valign="top">0.18</td>
<td align="left" valign="top">&lt; 0.01</td>
<td align="left" valign="top">0.17</td>
</tr>
<tr>
<td align="left" valign="top">Man</td>
<td align="left" valign="top">0.75</td>
<td align="left" valign="top">0.2</td>
<td align="left" valign="top">&lt; 0.01</td>
<td align="left" valign="top">0.21</td>
</tr>
<tr>
<td align="left" valign="top">R<sup>2</sup></td>
<td align="left" valign="top"></td>
<td align="left" valign="top"></td>
<td align="left" valign="top">0.21</td>
<td align="left" valign="top"></td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="j_connections-2022-0003_s_004"><div>Discussion</div>
<p>I find in a global sample of 260 network researchers that men rate themselves as having more expertise in networks than women. Even after controlling for years of experience, membership on a network journal or association board, and identification with a network-focused field, on average men rate their expertise as 0.75 points higher than women on a 10-point scale.</p>
<p>There are several possible explanations for this gender gap in self-rated network expertise. First, there could be true differences in expertise. Decades of research have consistently shown there are no innate differences in men and women's cognitive abilities (<a ref-type="bibr" href="#j_connections-2022-0003_ref_003">Hyde et al., 1990</a>; <a ref-type="bibr" href="#j_connections-2022-0003_ref_010">Spelke, 2005</a>). However, true differences in network expertise could still emerge if, for example, women experience more barriers to building expertise (e.g. bias, harassment) during their training (<a ref-type="bibr" href="#j_connections-2022-0003_ref_002">Hussénius, 2020</a>), or if women are not invited as co-authors in fields dominated by men (<a ref-type="bibr" href="#j_connections-2022-0003_ref_005">Kwiek &amp; Roszka, 2021</a>; <a ref-type="bibr" href="#j_connections-2022-0003_ref_011">Wang et al., 2023</a>). Second, men may overestimate their expertise. Overestimation of one's actual abilities is often described as the <italic>Dunning-Kruger Effect</italic>; however, there is limited evidence that such biases are stronger for men (<a ref-type="bibr" href="#j_connections-2022-0003_ref_004">Kruger &amp; Dunning, 1999</a>; <a ref-type="bibr" href="#j_connections-2022-0003_ref_008">Reilly et al., 2022</a>). Third, women may underestimate their expertise. Women's underestimation of their actual abilities is often described as <italic>Imposter Syndrome</italic>, whichcan lead them to incorrectly believe they are less skilled than others in a given setting, and therefore are undeserving of full membership in the setting (<a ref-type="bibr" href="#j_connections-2022-0003_ref_001">Clance &amp; Imes, 1978</a>; <a ref-type="bibr" href="#j_connections-2022-0003_ref_009">Silver, 2022</a>). In practice, the observed differences are likely some combination of institutionally generated true differences in expertise, overestimation of expertise by men, and underestimation of expertise by women.</p>
<p>Although these data cannot identify the causes of the observed gender gap in self-rated network expertise, the gap may have significant practical implications for individual network researchers and for the field. Because even the most rigorous and compelling research does not “speak for itself,” advancing one's research and career often requires significant self-promotion. The visibility of one's work, and subsequently its likelihood of being cited, depends in part on sharing work with colleagues (e.g., I thought you might like this paper) and disseminating it broadly via listservs and social media (e.g., check out my new article). Similarly, receiving support and recognition for one's research depends in part on a favorable self-presentation in grant proposals (e.g., I am the best person to do this work), award nominations (e.g., I am the most deserving of this award), and media interviews (e.g., I am confident in this finding). If women are even a little less likely to engage in these forms of self-promotion than men, there is the risk that their work will be less visible, supported, or recognized, even if they are numerically well-represented in the field.</p>
<p>Identifying solutions to the issue is challenging. Frequently proposed solutions including promoting diversity, reducing bias, and equalizing numerical representation may help, but it is unclear whether these specifically address issues associated with inequality in <italic>self-rated</italic> expertise. One challenge is that imposter syndrome is often framed as individually generated (e.g., a lack of confidence in one's own abilities), and therefore requiring a change in one's own behavior (e.g., having more confidence). However, <a ref-type="bibr" href="#j_connections-2022-0003_ref_006">Mullangi and Jagsi (2019)</a> caution that this may not be the right approach, and thus that simply encouraging women to “be confident” or “speak up” is not helpful. More promising strategies for addressing inequalities generated by imposter syndrome may involve focusing on the behaviors of high-confidence others (here, men) and on institutional processes. For example, men may consider supplementing self-promotion with other-promotion and practicing intellectual humility (<a ref-type="bibr" href="#j_connections-2022-0003_ref_007">Porter et al., 2022</a>), while we can all view self-promotions (particularly those from men) with a healthier dose of skepticism. Additionally, institutions may aim to normalize self-nomination as routine and expected, rather than as a form of self-promotion. These are, of course, just some ideas ... I’m no expert.</p>
</sec>
<sec id="j_connections-2022-0003_s_005"><div>Data Availability Statement</div>
<p>The data and code necessary to reproduce the analyses reported in this manuscript are available at <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://osf.io/zjwm7/">https://osf.io/zjwm7/</ext-link>.</p>
</sec>
</div>

Men Think they Know More about Networks

Connections

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Men Think they Know More about Networks"}

The fields of social network analysis and network science have recently undertaken efforts to address issues of gender diversity and representation. However,...

Men Think they Know More about Networks

Article Category: Article

Online veröffentlicht: 29. Aug. 2023

Seitenbereich: 50 - 54

DOI: https://doi.org/10.2478/connections-2022-0003

Schlüsselwörter
Expertise, Gender, Network, Survey

© 2023 Zachary P. Neal, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1:

Regression predicting self-reported network expertise.

Survey descriptive statistics.

	B	SE	p	β
Intercept	6.32	0.28	< 0.01	–
log(Experience)	0.65	0.23	0.01	0.17
Board	0.8	0.2	< 0.01	0.24
Network	0.54	0.18	< 0.01	0.17
Man	0.75	0.2	< 0.01	0.21
R²			0.21

	Survey
	Board	Web
N	84	176
Gender
Man	62	131
Woman	22	45
Field
Network	41	85
Social	38	81
Physical	5	10
Experience	21.3 (11.9)	11.7 (11)
Expertise	8.8 (1.2)	7.8 (1.6)

Men Think they Know More about Networks

Article Category: Article

Online veröffentlicht: 29. Aug. 2023

Seitenbereich: 50 - 54

DOI: https://doi.org/10.2478/connections-2022-0003

SchlüsselwörterExpertise, Gender, Network, Survey

© 2023 Zachary P. Neal, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1:

Regression predicting self-reported network expertise.

Survey descriptive statistics.

Schlüsselwörter
Expertise, Gender, Network, Survey