Researcher:
Selçuk, Esra Çetinkaya

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PhD Student

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Esra Çetinkaya

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Selçuk

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Selçuk, Esra Çetinkaya

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2020 - 20213

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    Adapting the values affirmation intervention to a multi-stereotype threat framework for female students in STEM
    (Springer, 2020) Herrmann, Sarah D.; N/A; Department of Psychology; Selçuk, Esra Çetinkaya; Sakarya, Yasemin Kisbu; PhD Student; Faculty Member; Department of Psychology; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; 219275
    We examined if an adapted version of a brief social psychological intervention following a multi-threat framework can enhance the mental task performance of female college students under stereotype threat. In experiment 1, under self-as-target stereotype threat, as expected, students who were exposed to the self-affirmation intervention had the highest task performance. However, under group-as-target stereotype threat, we found similar performances of the students in both the self-affirmation and group-affirmation conditions compared to control condition. In experiment 2, we showed that the extent a female student is identified with her gender group moderates the effectiveness of the group-affirmation intervention. The current research encourages researchers to consider different understandings of self while instituting common stereotype threat interventions rather than taking a uniform approach.
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    Does learning to code influence cognitive skills of elementary school children? findings from a randomized experiment
    (Wiley, 2021) N/A; Department of Industrial Engineering; N/A; Department of Psychology; Department of Psychology; Özcan, Meryem Şeyda; Selçuk, Esra Çetinkaya; Göksun, Tilbe; Sakarya, Yasemin Kisbu; Teaching Faculty; PhD Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Department of Psychology; College of Engineering; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A; 47278; 219275
    Background Coding has been added to school curricula in several countries, being one of the necessary competencies of the 21st century. Although it has also been suggested to foster the development of several cognitive skills such as computational thinking and problem-solving, studies on the effects of coding are very limited, provide mixed results, and lack causal evidence. Aim This study aims to evaluate the impact of a learn-to-code programme on three cognitive skills in children: computational thinking, fluid intelligence, and spatial orientation, using a randomized trial. Sample One hundred seventy-four (n = 81 girls) 4th-grade children participated in the study. Methods Children were randomly assigned to one of the three 10-week learning programmes: learn-to-code (treatment of interest), mathematics (another STEM-related comparison treatment), and reading (control). Children responded to paper-pencil computational thinking, and spatial orientation measurements, and face-to-face matrix reasoning task at pre- and post-tests. Results Results showed that children's computational thinking scores increased significantly only in the learn-to-code condition. Fluid intelligence significantly increased in all conditions, possibly due to a practice effect. The spatial orientation did not improve in any of the conditions. Conclusion These findings suggested that learning to code can be selectively beneficial for the development of computational thinking skills while not effective for spatial reasoning and fluid intelligence.
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    PublicationOpen Access
    Causal mediation analysis in the presence of post-treatment confounding variables: a Monte Carlo simulation study
    (Frontiers, 2020) MacKinnon, David P.; Valente, Matthew J.; Department of Psychology; Sakarya, Yasemin Kisbu; Selçuk, Esra Çetinkaya; Faculty Member; Department of Psychology; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; 219275; N/A
    In many disciplines, mediating processes are usually investigated with randomized experiments and linear regression to determine if the treatment affects the outcome through a mediator. However, randomizing the treatment will not yield accurate causal direct and indirect estimates unless certain assumptions are satisfied since the mediator status is not randomized. This study describes methods to estimate causal direct and indirect effects and reports the results of a large Monte Carlo simulation study on the performance of the ordinary regression and modern causal mediation analysis methods, including a previously untested doubly robust sequential g-estimation method, when there are confounders of the mediator-to-outcome relation. Results show that failing to measure and incorporate potential post-treatment confounders in a mediation model leads to biased estimates, regardless of the analysis method used. Results emphasize the importance of measuring potential confounding variables and conducting sensitivity analysis.