Quantitative Methods - Statistic Tutorials

The course aims at giving You 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 is consisted of, up to a point where all the major statistical techniques are discussed and explained. 

You should gradually learn to 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 You should know when and how to use the tools of descriptive statistics, and hence a variety of ways data can be visualized and summarized in numbers; You 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 You should know and be able to choose the appropriate statistical tool (i.e. test) given the collected data and the research plan adopted. You 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 You will solve single-choice and multiple-choice tasks. When solving each problem, You 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

To begin the course, you must first pass the lecture part i.e. the Quantitative Methods Statistic Lecture. Access to the PS Imago (SPSS) program is required to complete the course.

COURSE OBJECTIVES

• You will be able to freely interpret and enter data into the SPSS
• You will know and you will be able to use the basic operations in SPSS
• You will know and You will be able to check the assumptions of parametric tests: correlation, difference tests and one-way ANOVA
• You will know and You will be able to calculate and interpret the results of correlation, difference tests and one-way ANOVA
• You will be able to graphically describe in the form of a table the calculation results: correlation, difference tests and one-way ANOVA

SUBJECT MATTERS

MODULE 1

• Introduction to SPSS: Getting Started
• Entering data into the SPSS
• Entering data into the SPSS in practice
• Recoding data in SPSS
• "Calculate values" function
• Calculating means using “Calculate values” function. Descriptive statistics
• Exploration function - compute descriptive statistics in subgroups
• Database splitting and Frequencies
• Preparing charts in excel. Cross tables
• Revision and summary - descriptive statistics

MODULE 2

• Revision and summary - frequencies, recoding and calculating values
• Assessment of the normality of the distribution: Skewness, Kurtosis and Histogram
• Normality tests. Correlations: Basic Assumptions
• Significance level. Correlations: Basic Assumptions
• Calculating Pearson's r. Non-parametric correlations
• Relationship between two nominal variables. Correlations – repetition
• Correlations – repetition
• Difference tests - basic assumptions
• Independent-Sample T-test - basic information

MODULE 3

• Calculating independent-Sample T-test and preparing tables
• Mann-Whitney test
• Chi2 – difference between two nominal variables. Repetition of independent groups tests
• Difference tests for dependent groups - introduction
• Wilcoxon test. Preparation of tables containing test results for dependent samples
• One-sample t-test
• Paired-Sample Difference Tests and Single Sample Tests: Repetition
• Testing assumptions: one-way ANOVA
• Calculating one-way ANOVA
• Non-parametric ANOVA. Post hoc tests

CONDITIONS FOR RECEIVING A CERTIFICATE

To receive the certificate, you must complete the course. To pass the course, it is necessary to provide a minimum of 80% correct answers in the final test (weight 60%) and a minimum of 80% correct answers to the control questions in all lessons of the course (weight 40%).

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". In the single choice tasks You can approach the correct answer once. In case of multiple choice tasks You can approach them twice.

COURSE CADRE

 Daniel Pankowski, M.Sc.

Daniel Pankowski, M.Sc., PhD student at the Faculty of Psychology, University of Warsaw. In his research work, he focuses on Health Psychology, in particular on cognitive determinants of the adaptation process and issues related to medical neuropsychology. He collaborates scientifically with numerous medical institutions, as well as conducts classes at the University of Economics and Human Sciences in Warsaw and the Faculty of Psychology of the University of Warsaw.