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For this assignment, I would like you to focus on creating a cognitive remediation with two independent variables (IVs) and one dependent variable (DV) outcome measure (Factorial ANOVA) that is designed to reduce the social bias you were able to demonstrate.
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To receive full credit, you must explain your rationale for developing this intervention.
You may NOT use the examples provided here (gender bias or education) for your cognitive test. Please review the example below before you begin. Then, you must use the attached template worksheet file to confirm general format, prepare your One-sentence summary, and develop a relevant Global Significance paragraph.
You can use the example and replace the red writing of the file as your own.
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Here’s an example:
Rationale – I continue to experience attitudes that suggest I should fit the expected mold for a “girl.” I believe exposure to famous individuals who had non-traditional roles can help demonstrate that professional and educational achievements are not linked to gender status.
Hypothesis – A verbal memory test training that pairs popular historical figures (e.g., Florence Nightingale) with their gender based on non-traditional roles and educational background can help reduce significant gender and education bias observed in college students as measured by fewer incorrect gender-biased recall errors on categorical cued recall tests.
Testable prediction – Male, female, and non-binary historical figures are paired with their traditional and non-traditional professional roles (independent variable #1 – e.g., Nurse and Statistician) with expected and non-traditional educational background (independent variable #2 – Italian, mathematics) during a 10-minute visual and verbal memory test presented three days per week for 16 weeks. At the end of the intervention period, the subjects will complete the cognitive test proposed for hypothesis #2. It is expected that the primed exposure to non-traditional roles and educational paths by gender identity will reduce the incorrect recall of male-biased words (e.g., engineering, statistics, blue) when male historical figures (e.g., Thomas Jefferson, John Locke, Benjamin Franklin) are presented compared with female (e.g., Florence Nightingale, Barbara McClintock, Marie Curie) or gender-neutral (e.g., Chien-Shiung Wu, Nira Chamberlain) names.
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