Course Details
Course Information Package
Course Unit Title | STATISTICS | ||||||||
Course Unit Code | ASST304 | ||||||||
Course Unit Details | |||||||||
Number of ECTS credits allocated | 5 | ||||||||
Learning Outcomes of the course unit | By the end of the course, the students should be able to:
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Mode of Delivery | Face-to-face | ||||||||
Prerequisites | NONE | Co-requisites | NONE | ||||||
Recommended optional program components | NONE | ||||||||
Course Contents | • The use of quantitative and qualitative methods. Strengths and limitations in social sciences. • Statistics in practice: Kinds of data (discrete and continuous, ordinal and nominal). Different kinds of variables. Frequency tables, cumulative distribution tables and graphs (histograms, bar charts, pie charts). Shape of various data distributions (skewed, and symmetric). Applications in the social sciences. • Summarizing quantitative data: Measures of location (mean, mode, and median) and measures of dispersion (variance, standard deviation, range) for group data and raw data. Difference between measures of location and measures of dispersion and their significance. Extreme values, outliers and their importance. Applications in the social sciences. • The idea of probability: Experiments, events, outcomes and sample space. Relative frequencies. Calculation of probabilities and basic relationships of probability (union of events, complement of event, intersection of events). •The chi square distribution and the two tests: Goodness of fit test and test of independence. The table of the chi-square distribution. • The comparison of means: t-test, anova, ancova • The exploratory factor analysis. Understanding the logic of the confirmatory factor analysis. • Validity and reliability test, Correlation analysis. • The IBM Statistical Package of the Social Sciences: Data entry, and analysis of data. | ||||||||
Recommended and/or required reading: | |||||||||
Textbooks |
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References |
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Planned learning activities and teaching methods | The course is structured around lectures, discussions, and tutorials. In addition students are encouraged for class work, and problem solving. Part of the course is given in the computer labs to ensure that students are becoming familiar data entry and data analysis using IBM SPSS. | ||||||||
Assessment methods and criteria |
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Language of instruction | Greek | ||||||||
Work placement(s) | NO |