May 8, 2018

10-20: Data analysis (descriptive)- micro lecture VARIANCE AND STANDARD DEVIATION

In this micro lecture the following items will be explained regarding this lecture: In this first module, we’ll introduce the basic concepts of descriptive statistics. We’ll talk about cases and variables, and we’ll explain how you can order them in a so-called data matrix. We’ll discuss various levels of measurement and we’ll show you how you can present your data by means of tables and graphs. We’ll also introduce measures of central tendency (like mode, median and mean) and dispersion (like range, interquartile range, variance and standard deviation). We’ll not only tell you how to interpret them; we’ll also explain how you can compute them. Finally, we’ll tell you more about z-scores. In this module we’ll only discuss situations in which we analyze one single variable. This is what we call univariate analysis. In the next module we will also introduce studies in which more variables are involved

February 18, 2022

Trueness and Precision

February 18, 2022

Repeatability and Reproducibility

May 8, 2018

10-25: Data analysis (descriptive)- ASSIGNMENT 10: SUMMARIZING RATIO VARIABLES

In this assignment the following items will be explained regarding this assignment: In this assignment you will learn how to omprehend the meaning and implications of basic summary measures for ratio variables. Mean, median, quartiles, interquartile range,

May 8, 2018

06-09: Scale index construction and assessment - micro lecture IDENTIFYING PROBLEMATIC SCALE ITEMS

In this micro lecture the following items will be explained regarding this lecture: Know the criteria for a good scale Know how to identify items that might be problematic Know possible reasons for problematic items Know how to improve problematic items

August 23, 2023

Tutorial: Understanding Data

Learner acquires demonstrable knowledge of data forms used in AI. Learner can talk about data using the right terminology (e.g., features, variables, observations). Learner knows the different data types and knows how to transform them (e.g., nominal, ordinal). Learner knows the most common measures for data description (e.g., mean, median, standard deviation) and knows how to calculate them. Learner acquires demonstrable knowledge of how data are described via measures and visualisation. Learner knows the most common graphs and knows how to pick a visualisation, matching the given data.

August 23, 2023

Lecture: Understanding data

Learner acquires demonstrable knowledge of data forms used in AI. Learner can talk about data using the right terminology (e.g., features, variables, observations). Learner knows the different data types and knows how to transform them (e.g., nominal, ordinal). Learner knows the most common measures for data description (e.g., mean, median, standard deviation) and knows how to calculate them. Learner acquires demonstrable knowledge of how data are described via measures and visualisation. Learner knows the most common graphs and knows how to pick a visualisation, matching the given data.

May 8, 2018

10-10: Data analysis (descriptive)- micro lecture IQR AND BOX PLOT

In this micro lecture the following items will be explained regarding this lecture: In this first module, we’ll introduce the basic concepts of descriptive statistics. We’ll talk about cases and variables, and we’ll explain how you can order them in a so-called data matrix. We’ll discuss various levels of measurement and we’ll show you how you can present your data by means of tables and graphs. We’ll also introduce measures of central tendency (like mode, median and mean) and dispersion (like range, interquartile range, variance and standard deviation). We’ll not only tell you how to interpret them; we’ll also explain how you can compute them. Finally, we’ll tell you more about z-scores. In this module we’ll only discuss situations in which we analyze one single variable. This is what we call univariate analysis. In the next module we will also introduce studies in which more variables are involved.

May 8, 2018

10-03: Data analysis (descriptive)- micro lecture CENTRAL TENDENCY: MODE, MEDIAN AND MEAN

In this micro lecture the following items will be explained regarding this lecture: Understanding various meanings of the words ‘causal explanation’. In this first module, we’ll introduce the basic concepts of descriptive statistics. We’ll talk about cases and variables, and we’ll explain how you can order them in a so-called data matrix. We’ll discuss various levels of measurement and we’ll show you how you can present your data by means of tables and graphs. We’ll also introduce measures of central tendency (like mode, median and mean) and dispersion (like range, interquartile range, variance and standard deviation). We’ll not only tell you how to interpret them; we’ll also explain how you can compute them. Finally, we’ll tell you more about z-scores. In this module we’ll only discuss situations in which we analyze one single variable. This is what we call univariate analysis. In the next module we will also introduce studies in which more variables are involved