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22 of the 26 chapters are Open Access Published: 10 Jun 2022 The Handbook of Sociological Science offers a refreshing, integrated perspective on research programs and ongoing developments in sociological science. It highlights key shared theoretical and methodological features, thereby contributing to progress and cumulative growth of sociological knowledge. Reflecting ‘unity in diversity’, chapters explore a wide variety of research fields, ranging from cultural capital, migration, social networks, gender inequality, historical sociology and ethnography to the intersection of sociology and the life sciences. Examining basic methodological standards for theory construction and empirical research, the Handbook exemplifies commonalities between research programmes within these fields. The contributors also explore rigorous sociology related to theory construction, empirical research, and methods, including statistical modelling and the integration of theoretical and empirical research. Forward-thinking and original, the Handbook concludes by illustrating the common core of rigorous sociology, how it can contribute to understanding societal problems and to policy making, and how research into sociological science can continue to thrive in the future. Accessible and engaging, this Handbook will be invaluable for scholars and researchers of sociology and sociological theory, research methods in sociology and social policy, and comparative social policy. Exploring new developments and applications, it will also act as a useful reference guide for policy makers. The Handbook will likewise be an important resource for teaching advanced courses and training graduate students. INTRODUCTION 1. Rigorous sociology 2, Werner Raub, Nan Dirk de Graaf, and Klarita Gërxhani PART I RESEARCH PROGRAMS 2. Order from chaos: sociology as a population science 21, Michelle Jackson 3. Analytical sociology 38, Gianluca Manzo 4. Computational approaches in rigorous sociology: agent-based computational, modeling and computational social science 57, Andreas Flache, Michael Mäs, and Marijn A. Keijzer 5. Stochastic network modeling as generative social science 73, Christian E.G. Steglich and Tom A.B. Snijders 6. Rational choice sociology: heuristic potential, applications, and limitations 100, Andreas Diekmann PART II NEW AND ONGOING DEVELOPMENTS IN SELECTED FIELDS 7. Cultural capital and educational inequality: an assessment of the state of the art 121, Mads Meier Jæger 8. Integration in migration societies 135, Frank Kalter 9. Social networks: effects and formation 154, Vincent Buskens, Rense Corten, and Werner Raub 10. Gender inequality, households, and work 176, Eva Jaspers, Tanja van der Lippe, and Marie Evertsson 11. Validation strategies in historical sociology (and beyond) 196, Ivan Ermakoff 12. Rigorous ethnography 215, Federico Varese 13. Evolution, biology, and society 232, Rosemary L. Hopcroft, Joseph Dippong, Hexuan Liu, and Rachel Kail 14. Sociogenomics: theoretical and empirical challenges of integrating molecular genetics into sociological thinking 250, Melinda C. Mills PART III METHODS 15. Causal inference with observational data 272, Richard Breen 16. Longitudinal designs and models for causal inference 287, Markus Gangl 17. Experimental sociology 309, Klarita Gërxhani and Luis Miller PART IV RIGOROUS SOCIOLOGY IN ACTION: SHOWCASES 18. Explaining educational differentials: towards a formal rational action theory 325, Richard Breen and John H. Goldthorpe 19. ‘Explaining educational differentials’ revisited: an evaluation of rigorous theoretical foundations and empirical findings 356, Rolf Becker 20. Structural holes and good ideas 372, Ronald S. Burt 21. Network mechanisms in innovation: borrowing and sparking ideas around structural holes 423, Balazs Vedres 22. Experimental study of inequality and unpredictability in an artificial cultural market 443, Matthew J. Salganik, Peter Sheridan Dodds, and Duncan J. Watts 23. Self-correcting dynamics in social influence processes 446, Arnout van de Rijt PART V FURTHER PERSPECTIVES 24. The climate crisis: what sociology can contribute 475, Dingeman Wiertz and Nan Dirk de Graaf 25. Roots of sociology as a science: some history of ideas 493, Thomas Voss 26. How to increase reproducibility and credibility of sociological research 512, Katrin Auspurg and Josef Brüderl -- Index 528

In this study we test the effects of racial and gender appearances on behavior in competitive environments. Specifically, we measure the effects that different appearances have on participants’ matching preferences and on Willingness to Enter Competition. To test our hypotheses, we include an online experiment in which participants take part in three rounds of interactive games. Participants are represented by avatars that vary in racial and gender appearance.

In recent years several languages have experienced an increase in gender- inclusive language (GIL) in different forms. In grammatical gender languages (such as French, Italian, German, Spanish), a key aspect of gender-inclusive language is adapting person nouns, which are gender marked, to be inclusive towards all genders. This can occur in two versions: a softer, binary gender approach where women and men are addressed by the respective person noun, or an inclusive gender approach where all genders are addressed. It has repeatedly been found that using GIL (usually tested in a binary form) leads to a higher mental representation of women (Stahlberg et al., 2007). While there are many areas of application for GIL, one that has received a lot of attention and where the potential real-world impact is high is broadly speaking the labour market and occupational interest. Nearly a quarter of the gender pay gap can be attributed to women and men working in different occupations, whereby the occupations that men work in tend to have higher salaries (European Commission, n.d.). What if a part of this gap is due to women’s behaviour in applying for jobs, which may be mitigated by using GIL? Rather than looking at the demand side of the problem, e.g., discrimination against women, this study looks at the supply side, i.e., the role women’s behaviour plays in choosing certain jobs. We focus on gender- inclusive language (GIL) as a potential policy instrument for increasing the quantity as well as quality of female labour supply. Ample research indicates a positive effect of GIL on attitudes and preferences for women toward masculine jobs (Vervecken et al., 2013; Hentschel et al., 2018; Bem & Bem, 1973; Stout & Dasgupta, 2011). There is an underlying assumption that this has real world implications as these attitudes carry over into behaviour. However, the existing research on GIL and job applications does not test actual application behaviour. Looking at the relationship between attitudes and behaviour in other spheres, it is known that attitudes and behaviour correlate, but need not perfectly align. Indeed, the relationship between attitudes and behaviour is not straightforward (Ajzen & Fishbein 1977). Therefore, the link should not be taken for granted in the case of GIL and instead warrants empirical testing. We plan to test an application effect (RQ1) and a performance effect of the advertisement (RQ2) and a performance effect of the task description (RQ3): do more women apply for a male-stereotyped task when it is advertised in GIL? Second, do women perform better when a male-stereotyped task is advertised in GIL? Third, do women perform better when a male-stereotype task is described in GIL? The theory of stereotype threat predicts that, in certain situations, someone can think that other people think that others will be better at a specific task, so feel under pressure to perform well, and this performance pressure makes them perform worse. Specifically, there is a widespread stereotype that men are better at maths. Thus, when women are asked to do a maths task, this stereotype can be salient and trigger stereotype threat (Spencer, Steele and Quinn 1999). Therefore, when a woman is looking for tasks to do on a crowd working platform and sees a math-related task, advertised in masculine language, this triggers a stereotype threat. However, when gender-inclusive language is used, there is the potential that this stereotype threat is weakened, as gender-inclusive language has the potential to reduce the stereotypicality associated with a role (Gabriel et al. 2008a). We run the same experiment in two countries (Germany and Italy) and pool the analyses. Anica Waldendorf is the main contributor and thus first author. Arnout van de Rijt and Klarita Gërxhani are equal contributors, hence they appear in alphabetical order.

This paper tests the existence of status discrimination in the case of social class. We implement a novel experimental design and analytic strategy. This particular pre-registration concerns an extension to an original experiment, which has already been implemented.

In this study we test the effects of racial and gender appearances of bots on behavior in human-AI competitive environments. Specifically, we measure the effects that different appearances of bots have on participants’ matching preferences and on Willingness to Enter Competition. To test our hypotheses, we include an online experiment in which participants take part in three rounds of interactive games. Bots are represented by avatars that vary in racial and gender appearance.

This research mainly asks whether we can observe the gender status effect in virtual interaction through video conferencing as it does in face-to-face interaction. It aims not only to test how existing status theory can be applied in a new societal condition, digitalisation, but also to have a better understanding of whether the prevalent use of video conferencing today may contribute to gender inequality.

In Europe, women earned 14% less than men per hour in 2019. 24% of this gender pay gap can be attributed to women and men working in different occupations, whereby the occupations that men work in tend to have higher salaries (European Commission n.d.). For example, women are underrepresented in managerial positions (37% of managers, 28% of board members, 18% of senior executives (Eurostat 2020)). Rather than looking at the demand side of the problem, e.g., discrimination against women, this study looks at the supply side. Specifically, the role women’s behaviour plays in choosing certain jobs. Here, we focus on gender-inclusive language (GIL) as a potential policy instrument for increasing the quantity as well as quality of female labour supply. Previous research has indicated a positive effect of GIL on attitudes and preferences for women toward masculine jobs. For example, GIL increases schoolgirls’ interest and women’s expected success in masculine typed jobs (Vervecken, Hannover, and Wolter 2013). In a hypothetical hiring situation, more women would apply for a typically masculine job when GIL is used (Bem and Bem 1973). This is mirrored in the use of gender-exclusive language being associated with women feeling less motivated to apply for a job and feeling like they do not belong (Stout and Dasgupta 2011). The only study looking at behavioural outcomes reports that addressing women in feminine-only language rather than in masculine-only language can increase their math performance (Kricheli-Katz and Regev 2021). Overall, the evidence is (1) predominantly attitudinal; (2) unclear whether both women and men benefit from GIL, or whether it is especially women who profit; and (3) non-existent on whether use of gendered language impacts recruitment, that is, who applies for the job. The latter is particularly relevant, because it is the first channel through which GIL could have a gendered impact, before it may carry over to gender differences in performance. We plan to test a recruitment effect (RQ1) and a performance effect (RQ2): do more women apply for a male-stereotyped task when it is advertised in GIL? Second, do women perform better when a male-stereotyped task is advertised in GIL? The theory of stereotype threat predicts that, in certain situations, someone can think that other people think that others will be better at a specific task, so feel under pressure to perform well, and this performance pressure makes them perform worse. Specifically, there is a widespread stereotype that men are better at maths. Thus, when women are asked to do a maths task, this stereotype can be salient and trigger stereotype threat (Spencer, Steele and Quinn 1999). Therefore, when a woman is looking for tasks to do on a crowd working platform and sees a math-related task, advertised in masculine language, this triggers a stereotype threat. However, when gender-inclusive language is used, there is the potential that this stereotype threat is weakened, as gender-inclusive language has the potential to reduce the stereotypicality associated with a role (Gabriel et al. 2008a).

While there is a well-established literature on gender-based occupational segregation and hiring discrimination, on the one hand, and on the role of informal networks in the hiring process (i.e. through referrals), on the other, these strands of literature have not been combined so far. In this paper, we aim to look at employers’ hiring networks from a supply-side perspective, uncovering a hidden channel of discrimination through employers’ beliefs concerning the aptitude of referrers to assess and recommend job applicants. Our main research questions are: RQ1: From the employer’s perspective, does the gender of the referrers matter in general for hiring? RQ2: From the employer’s perspective, does the effect of referrer gender depend on the applicant’s gender? RQ3: From the employer’s perspective, why would referrer gender affect the hiring decision? We will run a three-stage experimental design, hiring participants representing the three crucial nodes of a hiring network: applicants, referrers, and employers.

This study aims to disentangle the effect of statistical discrimination and status mechanisms on discrimination in the cases of social class and gender. It does so by varying the level of information on performance of task-players seen by a sample of task-evaluators.

In this study, we examine whether there are gender/class differences in the performance of participants depending on whether they are informed beforehand that their performance score and demographic characteristics will be known to an evaluator who will compare them to the scores and characteristics of others, and use this information to decide which participant is better.

Driving us further apart? The impact of social media news exposure on attitudes toward Covid-19 policies

Social networks affect individual behavior as well as social phenomena resulting from behavior ('effects'). Also, when actors can choose with whom to interact, social networks themselves are shaped by behavior ('formation'). This chapter provides a selective overview of rigorous sociology on effects of social networks and their formation. We discuss two examples ('case studies'). One is on effects of exogenously given network characteristics, the other on endogenous network formation. Our examples highlight how micro-macro models account for effects and formation of social networks.

We discuss two computational approaches of particular significance for rigorous sociology, Agent-Based Computational Modeling (ABCM) and Computational Social Science (CSS). CSS exploits novel sources of large-scale data from, for example, the Internet, telephone-communication records, or population register data, studying digital traces of social interaction with great precision. ABCM uses computer simulation to study how social regularities can arise from complex interactions among interdependent individuals. CSS and ABCM share a strong focus on computational methods, but only a few contributions combine them. We briefly introduce both approaches and argue that they can strongly benefit from more interaction and integration. ABCM can address the generalizability and theoretical explanation of empirical patterns identified in CSS, whilst empirical CSS can help validate and calibrate otherwise abstract models from ABCM. We discuss examples of a successful combination of both approaches and address directions for more theoretical and methodological integration in the future.

Additional file 1. Appendix A. Methodological tools to address endogeneity of (co-ethnic) social networks. Appendix B. Allocation of refugees, ethnic Germans and Jewish immigrants in Germany. Appendix C. Information about the coding of variables. Appendix D. Comparison of model specifications. Appendix E. Models of robustness checks. Appendix F. Omitted confounders.

In the economic theory of tax evasion, individuals and corporations pay taxes only because they are forced to (i.e., because they believe that if they do not, they would be liable to prosecution by the state). If this were the case, it would be essential that the probability of being discovered for tax evading and the size of the penalty if caught and convicted are sufficiently large to deter evasion. One problem with this standard view is that for some taxes, such as self-reported income taxes, it is hard to believe that the probability of being caught for evasion is very large. In fact, all countries do encounter tax evasion, even those with the most sophisticated systems for gaining compliance. To illustrate, the United States Internal Revenue Service (IRS) estimates that the proportion of all individual tax returns that are audited was 0.5% in 2017 (down from 0.8% in 1990 and 4.75% in 1965). Civil penalties can add an additional 85% to the underpayment, depending on whether there is a specific misconduct such as negligence, substantial understatement, or substantial intentional wrongdoing. In very serious cases, criminal penalties may be applied. However, the penalties imposed are either small or infrequent (Alm 2019). Yet, the IRS estimates that, for the tax year 2015, 90.8% of income that should have been reported was in fact reported.

This paper studies experimentally how the existence of social information networks affects the ways in which firms recruit new personnel. Through such networks firms learn about prospective employees' performance in previous jobs. Assuming individualistic preferences social networks are predicted not to affect overall labor market behavior, while with social preferences the prediction is that when bilaterally negotiated: (i) wages will be higher and (ii) that workers in jobs with incomplete contracts will respond with higher effort. Our experimental results are consistent with the social preferences view, both for the case of excess demand and excess supply of labor. In particular, the presence of information networks leads to more efficient allocations.

Supplemental material, sj-docx-1-abs-10.1177_0002764221996749 for Anticipating Peer Ranking Causes Hormonal Adaptations That Benefit Cognitive Performance by Carsten K. W. De Dreu, Klarita Gërxhani and Arthur Schram in American Behavioral Scientist