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implicit and explicit stereotypes, thus the Draw-a-Scientist Test, as well as the Implicit Association Test, have been used in recent decades as implicit measures to capture people’s unconscious beliefs about scientists.

3.2.2.3.1. Draw-a-scientist studies

The Draw-a-Scientist Test (DAST), in which no introspective verbal responses are required, only a drawing of a person is made and evaluated with reference to a selection of stereotypes, was developed by Chambers (1983) and used mostly to measure children’s perceptions of scientists. This instrument can be regarded as an implicit measurement given that test takers are unlikely to be aware of the purpose of the study. The DAST can be advantageous in developmental research because children may learn stereotypes before reporting them explicitly (Galdi, Cadinu, & Tomasetto, 2014).

that people often draw is an older or middle-aged man who wears a white coat and glasses doing experiments alone in a laboratory (Finson, 2002). Evidence from the DAST studies revealed that such stereotypical image of scientists can be formed very early in life, by the time children reach the second grade in elementary school (Losh, Wilke, & Pop, 2008; Newton & Newton, 1998). Moreover, Finson's (2002) meta- analysis of studies utilizing DAST reveals a remarkable consistency in the image of scientists drawn by people across nations, across age groups, across education levels. The latest meta-analysis of DAST studies conducted by Miller, Nolla, Eagly and Uttal (2018) found that children depicted female scientists more often in later decades, but they still associate science with men as they grow older.

Despite being successfully utilised in many studies for a long time, DAST has been criticized for its limited application to older age-groups and being prone to the experimenter bias in terms of coding (Finson, 2002). Moreover, the DAST also entails a prerequisite of drawing skills and boys’ drawings have been found more abstract than girls’, which often provided little information for researchers to extract important traits such as the drawing’s gender (Thomas et al., 2006).

3.2.2.3.2. Gender-Science IAT studies

Another popular implicit measurement that has been used to gather unconscious stereotypes about scientists is the Gender-Science Implicit Association Test (GS-IAT; Nosek, Banaji, & Greenwald, 2002b). The GS-IAT captures people’s automatic associations between men and science by assessing different reaction times to stimuli that are either paired in a stereotypical way (i.e., men and science) or not (i.e., women and science). Research using the GS-IAT has shown that people’s implicit association between men and science emerges early in childhood (Cvencek et al., 2011), is developmentally stable (Nosek et al., 2002b), ubiquitous across different cultures (Nosek et al., 2009), but also substantially variable across different individuals (Smyth

& Nosek, 2015).

Existing studies have found that the implicit male-science stereotype predicts judgment and behaviour that contribute to the gender gap in science-related activities and occupations. These studies have found significant relationships of implicit male-science stereotypes with participants’ math engagement (Nosek & Smyth, 2011), performance and achievement, intentions to choose scientific major subjects (Lane et al., 2012; Nosek et al., 2009; Zitelny, Shalom, & Bar-Anan, 2017), and careers (Cundiff, Vescio, Loken, & Lo, 2013). These relations were usually moderated by gender. Among women, stronger implicit male-science stereotypes were usually correlated with worse math performance and achievement, as well as weaker identification with math and science.

Nevertheless, among men, the implicit male-science stereotypes sometimes had no correlations with relevant outcomes, and on other studies, stronger implicit male- science stereotypes could, in turn, be correlated with better performance, achievements, and the stronger identification with math and science (Zitelny et al., 2017). In their meta-analysis examining seventeen studies using both implicit and explicit measures for gender-science stereotypes, Zitelny et al., (2017) reported that fifteen of those studies found that the implicit male-science stereotype had a stronger relationship with an outcome variable than the explicit stereotype. It was speculated that implicit stereotypes may “shape choices by subtly constraining preferences without the individual’s awareness or conscious exertion of choice” (Nosek et al., 2002b, p. 50) and “sincere and conscious beliefs that men and women are equally well suited for STEM fields do not preclude internalization of the beliefs at a less conscious level” (Lane et al., 2012, p. 222).

Moreover, Smyth and Nosek (2015) conducted an online survey with 176,000 college- educated participants examining relations between gender ratios and male-science stereotyping. They argued that gender-science stereotypes should change as conditions in local environments change, including gender ratios. Higher female proportion of a

STEM major should be correlated with weaker science-male stereotyping. Evidence from their study only supported this hypothesis for the explicit stereotype, but not the implicit stereotype. Participants from high-female disciplines including biological and health sciences showed weaker explicit male-science stereotype than those from low- female disciplines such as computer sciences, physics and engineering. However, implicit male-science stereotype did not correspond with disciplines’ gender ratios, but was correlated with scientific intensity, positively for men and negatively for women. That is to say, women who majored in subjects perceived as more science-intensive (e.g., physics) showed weaker implicit male-science stereotype than did men in the same disciplines. Furthermore, particularly among women, those who identified with more science-intensive subjects (e.g., physics) showed weaker male-science implicit stereotypes than women majoring in less science-intensive subjects (e.g., biology). Existing research using the Gender-Science IAT has found evidence for relations between implicit gender-science stereotypes and scientific academic achievement and career aspirations. On the basis of these findings, we propose to examine whether stereotypes of empathy in scientists also correlate with participants’ science career aspirations. Moreover, given that variations in implicit and explicit male-science stereotypes have shown different relationships with gender ratios of the discipline and scientific intensities, the present study will also discuss the individual differences in the stereotypes of empathy in scientists differently by whether they are measured implicitly or explicitly.

To sum up, previous studies looking at stereotypes of scientists have three main limitations. Firstly, most self-report questionnaires used in projects investigating attitudes toward science in the UK covers a broad range of topics (e.g., attitudes toward school science) and only a few items tapped into perceptions about scientists in particular. Secondly, research focusing on the image of scientists has mostly been conducted among children and adolescents in the US using DAST. The DAST could only provide a general picture of a scientist and was unable to systematically examine a specific trait in the scientist. It remains unknown how young adults in the UK perceive

scientists nowadays and a new instrument is in need to measure stereotypes of empathy in scientists in particular. Last but not least, most existing studies only used the IAT to measure implicit gender stereotype of scientists. Though we have found a robust link between men and science, further investigation is required to figure out which specific stereotypical trait of scientists prevents people from associating science with women. As discussed earlier in Section 3.1, women have been found to rate themselves high in empathy but scientists usually rate themselves low in empathy (Baron-Cohen, 2002; Manson & Winterbottom, 2012). This contradictory evaluations of empathy among women and among scientists may serve as a gatekeeper for women to pursue science. Research could be fruitful to examine whether stereotypes of empathy in scientists are related to gender, major subject selection as well as career aspirations in science. Given that there is a lack of reliable measurement to capture people’s perceptions of empathy in scientists, the first goal of the present study is thereby to develop a measurement that taps into stereotypes of empathy in scientists using the Implicit Association Test (IAT) paradigm. The following sections present how IATs differ from self-report questionnaires as well as existing studies about psychometric properties of the IAT, providing background information for further development and testing of the new IAT for stereotypes of empathy in scientists in the present study.