Syllabus
[MACHINE TRANSLATION] 1 Introduction and motivation 2 Basic concepts of descriptive and exploratory analysis 3 Levels of measurement 4 Data distribution, measures of centrality, variability 5 Categorical data, frequency distribution 6 Classification of statistical units and description of relationships between variables 7 Graphical representation of data distribution and empirical relationships 8 Probability, conditional probability 9 Basics of inferential reasoning (population selection, point and interval estimation, goodness-of-fit tests) 10 Data transformation and standardisation 11 Direct data standardisation, contingency table weighting and interpretation 12 How to unravel relationships between traits and interpret results: interaction (moderation) and logic of elaboration
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Annotation
[machine translation]
This course is the first step for sociology students into statistics and quantitative data analysis. It pursues two main objectives. The first is to build and strengthen the foundation of data or statistical literacy in students so that they can be critical users of statistical outputs in the media and eventually in academic papers. The second goal is to familiarize them with the process of quantitative data analysis and to teach them how to practically use simple statistical methods of exploration and description. The basics of inferential statistics will also be introduced. The course graduate should be able to use basic descriptive statistical methods to answer simple research questions (classification of level 1 and 2 data) with a focus on categorical data (tables), test the validity of a simple hypothesis (confidence intervals and basic statistical tests of bivariate analysis, e.g. goodness-of-fit test), present results graphically and interpret them substantively. In this way, learners should embark on the path to becoming proficient producers of quantitative sociological knowledge themselves.
One repeated enrollment is possible.
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