Course Content
Orientation, introduction to the course
1. Human-Robot Interaction (HRI)
2. Research Methods in Human-Robot Interaction
3. Smart Cities & HRI
The demand for city living is already high, and it appears that this trend will continue. According to the United Nations World Cities Report, by 2050, more than 70% of the world's population will be living and working in cities — one of many reports predicting that cities will play an important role in our future (UN-Habitat, 2022). Thus, as cities are growing in size and scope, it is shaped into complex urban landscape where things, data, and people interact with each other. Everything and everyone has become so connected that Wifi too often fails to meet digital needs, online orders don't arrive fast enough, traffic jams still clog the roads and environmental pollution still weighs on cities. New technologies, technical intelligence, and robots can contribute to the direction of finding solutions to ever-increasing problems and assist the evolution of the growing urban space.
Human-Robot Interaction
About Lesson

Measures of Spread

Descriptive measures that indicate how far the data points deviate from the centre of the data set are important to determine how spread out the data set is. These measures include the following: 

  • Min and max: Used to indicate the lower and the higher values of the data set. 
  • Range: Used to measure the difference between the highest and lowest scores in the data set. 
  • Variance: The variance of a data set is the mean of the squared distances of all the scores from the mean of the data set. 
  • Standard deviation: The standard deviation is the square root of the variance. Like range, higher variances or standard deviations indicate that the data set is more distributed. 

A normal distribution, a bell-shaped distribution defined by the mean and standard deviation, is commonly used to describe the distribution of a data set. The normal distribution is important for data analysis since many attributes from various fields of study are distributed normally, such as population heights, student grades, and various performance measures. 

Testing a data set to determine if it is normally distributed is necessary when selecting the type of significance tests to conduct. Parametric tests assume that the data set is normally distributed or approximately normally distributed. If the data set is not normally distributed, data transformation may be required, or non-parametric tests adopted for analysis. 

You can use a normality test to examine if your data follow the normal distribution, as shown in this video: 

For further reading about normality tests and how to use normality test using SPSS you may read this:

A simple video about variance and standard deviation is: 


Lazar, J. , Feng, J. H., Hochheiser, H.  (2017), Research methods in human-computer interaction: Morgan Kaufmann, 2017. 

Rosenthal, R., Rosnow, R., 2008. Essentials of Behavioral Research: Methods and Data Analysis, third ed. McGraw Hill, Boston, MA. 

Dix, A. (2020). Statistics for HCI: Making Sense of Quantitative Data. Morgan & Claypool Publishers.
Robertson, J., & Kaptein, M. (2016). An introduction to modern statistical methods in HCI (pp. 1-14). Springer International Publishing.
Larson-Hall, J. (2015). A guide to doing statistics in second language research using SPSS and R. Routledge.
Aldrich, J. O. (2018). Using IBM SPSS statistics: An interactive hands-on approach. Sage Publications.