The present qualitative study analyzes how a group of young people already involved in STEM fields perceive the prototypical person working in STEM. Gender differences between participants in technological and non-technological STEM fields were analyzed. A total of 27 young people (59.3% women) took part in the interviews (Mean Age = 25.48 years). Of them, 16 participants were working in STEM professions, and 11 were enrolled in the final courses of STEM degrees. The results of the content analysis were examined in light of social role theory and the multidimensional structure of gender stereotypes. Men in these fields were therefore attributed an unappealing and weird physical appearance. Some female participants linked STEM professionals’ intellectual abilities to the stereotype that men have higher abilities in these fields. Whereas females attributed effort and perseverance to STEM professionals’ intellectual aptitudes, males referred to the development of soft skills. Participants in technological STEM fields connected the stereotype of being a ‘weirdo’ to a boring job, whereas those in non-technological fields linked it to their unconventional character. Some participants were disappointed by a lack of correspondence between expectations and the actual job STEM professionals do. Moreover, females in technological STEM fields commented on the job’s low social impact, while males mentioned low attainment of technical qualifications. Most referents in STEM fields were masculine, some of whom were present in the mass media. The practical implications of the findings are discussed.
Sensor-based data are becoming increasingly widespread in social, behavioral, and organizational sciences. Far from providing a neutral window on “reality,” sensor-based big-data are highly complex, constructed data sources. Nevertheless, a more systematic approach to the validation of sensors as a method of data collection is lacking, as their use and conceptualization have been spread out across different strands of social-, behavioral-, and computer science literature. Further debunking the myth of raw data, the present article argues that, in order to validate sensor-based data, researchers need to take into account the mutual interdependence between types of sensors available on the market, the conceptual (construct) choices made in the research process, and the contextual cues. Sensor-based data in research are usually combined with additional quantitative and qualitative data sources. However, the incompatibility between the highly granular nature of sensor data and the static, a-temporal character of traditional quantitative and qualitative data has not been sufficiently emphasized as a key limiting factor of sensor-based research. It is likely that the failure to consider the basic quality criteria of social science measurement indicators more explicitly may lead to the production of insignificant results, despite the availability of high volume and high-resolution data. The paper concludes with recommendations for designing and conducting mixed methods studies using sensors.
The underrepresentation of young people and particularly young women in many STEM fields has inspired various intervention programmes and research intended to boost their interest in these areas. The purpose of this scoping review is to examine the characteristics and effectiveness of interventions designed to encourage interest in STEM among secondary school students, particularly female students, over the past 20 years. A systematic search of the literature in five databases and additional search strategies resulted in identifying 215 studies evaluating interventions in different disciplinary fields. Data extraction and synthesis of these studies were carried out, focusing on the methodologies and theoretical foundations used. Twenty-five exemplars were selected to illustrate best practices in designing and evaluating interventions that address the various facets of young people's lack of interest in STEM. These interventions attempt to modify and/or manipulate multiple environmental and school factors to impact students' personal factors associated with STEM interest, such as achievement, self-perception of ability, and self-efficacy. Implications for the design of future interventions and potential outcomes are then discussed.
IntroductionMixed methods research intervention studies integrate quantitative evaluation approaches, such as randomized controlled trials and quasi-experimental designs, with qualitative research to evaluate the effectiveness, efficacy, or other results of an intervention or program. These types of studies, which have attracted growing attention in recent years, enhance the scope and rigor of the evaluation. While various frameworks that summarize the justifications for carrying out these types of studies and provide implementation guidance have been published in the last few years in the health sciences, we do not know whether such frameworks have been properly implemented in the social and educational sciences. This review examined the methodological features and reporting practices of mixed methods intervention studies aimed at increasing young people’s interest in STEM.MethodsA systematic search was carried out in APA PsycNET, ERIC, ProQuest, Scopus, and Web of Science, and a hand search in 20 journals. We included peer-reviewed English-language articles that reported intervention studies with a quantitative component measuring outcomes specific to increasing secondary school students’ interest in STEM fields, a qualitative component conducted before, during, or after the quantitative component, and evidence of integration of both components. Qualitative content analysis and ideal-type analysis were used to synthesize the findings.ResultsWe found 34 studies; the majority published in the last ten years. Several patterns of mixed methods application were described in these studies, illustrating the unique insights that can be gained by employing this methodology. The reporting quality of the included studies was generally adequate, especially regarding the justification for using a mixed methods intervention design and the integration of the quantitative and qualitative components. Nonetheless, a few reporting issues were observed, such as a lack of detail in the presentation of the mixed methods design, an inadequate description of the qualitative sampling and analysis techniques, and the absence of joint displays for representing integration.DiscussionAuthors must pay attention to these issues to ensure that the insights obtained by the use of mixed methods research are effectively communicated.
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