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The course moves from data generating processes to causal inference, supported by dedicated computing sessions on data extraction, management, variable manipulation, and descriptive analysis in R. 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It is deliberately designed as complementary toa formal and theoretically-oriented introductory statistics course. This course introduces basic knowledge about quantitative data analysis from a social scientific perspective, from data generating processes to causal inference. Devoted computing sessions, a signature feature of the course, demonstrate hands-on techniques, such as data extraction, data management, variable manipulation, and descriptive analysis. Students will have the chance to implement ideas and methods from the lectures through in-class exercises, as well as a project, by applying what they learn to real-world data. Upon completion of the course, students should have acquired useful skills for social data analysis as well as a better understanding of quantitative social scientific research. Not to be taken by students admitted to BSc Quantitative Social Analysis from 2022-23 and onwards.  \nIntended Learning Outcomes (ILOs)  \nOn successful completion of the course, students will be able to:  \n1. Learn how to design and conduct a statistical analysis to explore a social issue by connecting between theory, survey, and data analysis  \n2. Develop more confidence and appreciation in using statistics to describe and clarify apparently unclear relationships among socioeconomic factors  \n3. Use R effectively for managing and analyzing socioeconomic data and presenting outcomesin a reader friendly way  \nAssessment and Grading  \nThis course will be assessed using criterion-referencing and grades will not be assigned using a curve. Details are provided below.  \nAssessments:  \n\n| Assessment Task | Contribution to Overall Course grade (%) | Date/due date |\n| --- | --- | --- |\n| In-class exam 1 | 20% | March 12 |\n| In-class exam 2 | 20% | April 16 |\n| In-class exam 3 (final exam) | 25% | May 19 |\n| Four take-home problem\u003Cbr>sets | 20% | To be announced via Canvas |\n| Participation | 10% |  |\n| Attendance | 5% |  |\n\nGrading Policy  \nYour final grade will be based on the following:  \n• Take-home problem sets (20%): Details about the four take-home problem sets will be announced during the class/via Canvas.  \n• In-class exam 1 (20%) & In-class exam 2 (20%) : The exams will be no longer than one hour and be a mix of multiple choice and short answers.  \n• Final exam (25%): The closed-book final exam will cover the content learned throughout the semester.  \n• Participation (10%): Students are expected to attend all classes and actively participate. At a minimum, this includes remaining attentive during class (e.g., refraining from using digital devices for non-course purposes), coming prepared to discuss assigned readings or materials, and engaging constructively in class activities and discussions. Participation is evaluated based on both the quality of engagement and the quantity of contributions.  \n• Attendance (5%)  \nSubmission of Problem Sets  \nAll problem sets must be submitted electronically through Canvas by the stated deadline (11:59pm) . Assignments submitted via email will not be accepted.  \nLate Work Policy  \nLate assignments will not be accepted. Exceptions will be granted only in cases of documented illness, verified by a note from a health care provider. Assignments submitted late ","cbCaitgbNDmNsaw2","https://ap.wps.com/l/cbCaitgbNDmNsaw2","pdf",141304,"English","# Course Description\n## Intended Learning Outcomes (ILOs)\n## Assessment and Grading\n## Submission of Problem Sets\n## Late Work Policy\n## Grade Review Policy\n## Mapping of Course ILOs to Assessment Tasks\n## Course AI Policy","[{\"question\":\"What is the main focus of SOSC 1100?\",\"answer\":\"The course focuses on practical aspects and implementation of social data analysis, teaching hands-on techniques for presenting, analyzing, and interpreting quantitative data.\"},{\"question\":\"What are the key intended learning outcomes?\",\"answer\":\"Students learn to design and conduct statistical analysis linking theory, survey, and data; they gain confidence in using statistics to clarify relationships among socioeconomic factors; and they use R to manage, analyze, and present outcomes.\"},{\"question\":\"How is student performance assessed and graded?\",\"answer\":\"Assessment includes three in-class exams (20%, 20%, and 25%), four take-home problem sets (20%), participation (10%), and attendance (5%), with late work generally not accepted except for documented illness.\"}]","SOSC 1100 - Quantitative Data Analysis for Social Research I - Course Syllabus | PDF"]