Quantitative Methods - Statistics Lecture

The course aims at giving the student an understanding of the meaning and importance of quantitative methods and statistics in the scientific activity in general, with a specific focus on social science research. While in the first lessons the general logic and the main terms of the scientific methods are introduced, the bulk of the course presents in a piece-wise fashion and starting with the most basic concepts all the elements necessary to understand what a statistical analysis consists on, up to a point where all the major statistical techniques are discussed and explained. 

The student should gradually learn, be able to use and understand the main tools researchers apply in order to reach valid and general conclusions from the collection of empirical data. Starting from the ability of summarizing and describing the quantitative data observed, to the link of data and scientific hypotheses researchers posit in their study, to the capacity of using probability theory in generating conclusion. The topics follow and build on each other in a gradually increasing complexity.  

After completing the course the student should know when and how to use the tools of descriptive statistics, and hence the variety of way data can be visualized and summarized in numbers; he or she should understand the use of probability in statistical inference and the logic of frequentist null hypothesis significance testing (NHST) applied in the large majority of statistical tests; finally he or she should know and be able to choose the appropriate statistical tool (i.e. test) given the collected data and the research plan adopted. The student should understand the way tests work, be able to do some computations autonomously and link the results obtained with the hypotheses posited at the beginning of the analysis.

In the course the student will solve single-choice and multiple-choice tasks. When solving each problem,
he or she will have two attempts to give the correct answer. Some of the tasks will be used only for practice (they will not be included in the final grade and the number of attempts in such cases will be unlimited).

PRELIMINARY REQUIREMENTS

The course is intended for everyone willing to understand and starting to use statistical methods in empirical research. It does only assume previous knowledge of basic, high school algebra. The materials of the course are usually offered to students during the first years of college.  

Although the examples given during the course draw heavily on research problems in social sciences and especially psychology, the applications and the statistical tools introduced are general and can be fruitfully learned and used by students with other backgrounds.  

Knowledge of standard English is required to understand the content of the course. Specific technical terms and acronyms are explained as necessary. 

COURSE OBJECTIVES

You will gain the necessary knowledge about the issues: 

• Basic concepts in research methods 
• Descriptive statistics 
• Probability theories and probability distributions 
• Inferential statistics – logic 
• Statistical tools in inferential statistics 

The course is a complete whole and covers in an incremental way all aspects of collecting and treating numerical data, deciding the best analytical strategy, applying and interpreting the results of the most appropriate statistical tool in a given situation. 

ISSUES DISCUSSED

MODULE 1
Introductory concepts

This module includes:

• Basics concepts
• Language of research

MODULE 2
Descriptive Statistics

This module includes:

• Tabular and graphical summary representation of data
• Numerical descriptive statistics
• Data transformations and z scores

MODULE 3
Probability

This module includes:

• Issues on probability
• Probability distributions
• Reading the standard normal distribution tables

MODULE 4
Inferential Statistics

This module includes:

• Population, samples and sampling
• Sampling distributions
• Estimating population parameters
• Hypothesis testing

MODULE 5
Statistical tools

This module includes:

• Categorical data analysis
• Inference on one sample
• Comparing means on two related samples
• Comparing means on two independent samples
• Checking assumptions
• Correlation
• Linear regression
• Topics in linear regression
• Other techniques to compute correlation
• Comparing two samples - non-parametric alternatives
• Comparing more than two means - One-way ANOVA
• Comparing more than two means - other topics
• Conclusion - New perpspectives in statistics

CONDITIONS TO BE FULFILLED TO RECEIVE A CERTIFICATE

To receive the certificate, you must complete the course. To pass, you need to obtain at least 60% correct answers.

At the end of each section a series of tasks or short exercises are presented to ground the knowledge acquired and train analytical skills into practical work. These are single or multiple choice closed tasks, sometimes "drag and drop". You can approach the correct answer twice.

COURSE STAFF

  dr Giuseppe Leonardi

Doctor of Psychology, specialized in methodology and statistics. A graduate of the University of Padua (Italy). During his doctorate in experimental psychology, which he completed in Trieste, Italy, he studied applications of Dynamical System Theory to psychology and spent two years at the Center for Complex Systems at Florida Atlantic University, USA. He specializes in dynamical analyses of naturalistic human interactions, with a focus in Recurrence Quantification Analysis. He lives in Poland since 1997 where he worked as a Senior Analyst at a leading strategic marketing company after which he was at the Universities of Toruń and Poznan (Poland) and at Paderborn University (Germany). In the course of the years he collaborated with other universities and scientific institutions like Helena Chodkowska University of Technology and Economics, SWPS University and the Educational Research Institute (IBE). He is now the dean of psychological studies at the University of Economics and Human Sciences in Warsaw, where he works since 2017.