After taking this course, students should be able:
- to compute and interpret commonly used descriptive statistics such as the sample mean, the median, the mode, variance and standard deviation, and the correlation coefficient.
- to explain the essential aspects of null-hypothesis significance testing, including sampling distributions, Type I and Type II errors, one-tailed versus two-tailed testing, and statistical power.
- to test hypotheses about the mean difference between independent samples (t-test) and between means from repeated measures (paired sample t-test)
- to explain basic concepts in regression analysis, including: linear association, least-squares estimation, explained variance, Multiple R, multiple correlation, adjusted R-square, raw and standardized regression coefficients, model-comparison tests, predicted scores, residuals;
- to perform a linear multiple regression analyses (using SPSS) for models including continuous and categorical predictors; both for models with main effects only, and models including interactions between categorical and continuous variable, or two continuous variables.
- to gauge the reliability of measurements from questionnaires and identify problematic items.
- to explore the dimensionality of questionnaire data.
- to choose the appropriate analysis technique for answering a specific research problem from the range of techniques that are covered in the course.
- to clarify the statistical and/or methodological assumptions that apply to the techniques that are discussed in this course.
- to use the software package SPSS to perform several statistical data analyses and be able to correclty interpret and report the output to an informed audience (e.g., Liberal arts students, researchers from the social sciences).
- to draw valid conclusions from the results of empirical data analyses given specific research questions envisaged.
Throughout the course there are 12 lab sessions (schedule will be announced on BB). In the lab sessions, students will (mostly) work with the online platform Best Practices in Statistics to practice the techniques, including exercises with SPSS and Excel. The lab sessions are not-obligatory, but students are strongly encouraged to attend the lab sessions as well. Students who attend all practicals to the satisfaction of the teaching staff earn that maximally 2 wrong answers on the final exam are ignored. Students who miss only one of all the practicals earn that 1 wrong answer on the final exam is ignored.
Exam, Quizzes, and Final Grade
The final exam will be a multiple-choice exam consisting of about 40 multiple choice questions (#answer categeries range from two to five) on the obligatory readings (=book and lecture notes).
This course also includes four quizzes. The quize covers all the subjects hat have been discussed so far and specific parts of the book. These quizzes are not mandatory, but these quizzes can be used to earn a bonus on the final grade. In total, 2 points can be earned with the quizzes (0.5 point per sub-test). The final grade will be computed as: final_grade = #points quizzes + (10 - #points quizzes)/10 x exam_grade. The final grade will be rounded to the nearest half-valued grade, with an exception for 5.5. If the final grade is smaller than a 5.5, the final grade will be rounded to a 5.0; if the final grade is larger than or equal to a 5.5, the final grade will rounded to a 6.0. Students pass the course if they (a) pass the lab assignments; and (b) the final grade is 6.0 or higher.
PLEASE NOTE: THIS COURSE IS FOR LIBERAL ARTS AND SCIENCES STUDENTS IN THE MAJOR BUSINESS AND ECONOMICS ONLY!
This course is taught through co-teaching, which entails that more than a single professor is responsible for teaching the course and guiding the students that take it.