Introduction

The seminar "SPSS: Statistical applications and data analysis" is organized by the specialized high-tech company Digital Academy created by the Group of Lecturers of the NTUA Multimedia Technology Laboratory. This is an intensive seminar that is carried out through the Distance Learning method, giving the learner the flexibility to follow an integrated data analysis learning program using the widely used SPSS program from any place and at any time, via the Internet.

The seminar is organized in 10 modules, completed over a period of 4 weeks and corresponds to 140-160 hours of lessons.

Topics

The seminar is structured in 10 main thematic sections:

1. Familiarity with the working environment of the program.
   This section is essentially the first introduction to the program. That is, the guidelines for managing the program options are given and the basic concepts and functions of the program are explained.
2. Data management in the program interface.
   This section introduces the use of the program's toolbars in order to become familiar with database management.
3. Introduction to Statistics-Descriptive Statistics-Tables/Graphs.
   This section is an introduction to Statistics. Basic concepts of descriptive statistics are explained and the learner becomes familiar with presenting descriptive numerical measures using the program's functions.
4. Analytical Statistics-Hypothesis Testing-Distribution Testing.
   In this module, the hypothesis testing process is taught as the basic stage of analytical statistics.
5. Quantitative data analysis – Testing of two-sample mean values ​​by Student's t-test.
   The purpose of this section is to investigate the differences of quantitative variables in two groups.
6. Quantitative Data Analysis – Analysis of Variance (ANOVA).
   The purpose of this section is to investigate the differences of quantitative variables in more than two groups.
7. Non-parametric controls.
   In this section, statistical tests are taught when the appropriate conditions of their distributions are not met.
8. Correlation tests-X2, Pearson's r, Spearman's rho.
   This section describes the statistical tests for investigating associations.
9. Linear Regression.
   Creating models to investigate factors associated with the appearance of quantitative traits.
10. Logarithmic Regression.
    Building models to investigate factors associated with the emergence of binomial traits.

Methodology

According to our educational program, the material is divided into 10 educational units corresponding to 4 teaching weeks. Each unit is accompanied by the corresponding exercise in order to assimilate the teaching material. The program concludes with a final exam. The distance learning program is based on the latest version of the free software (open source) Moodle, which is implemented on the PHP & MySQL programming platform. The online courses are hosted on proprietary servers incorporating state-of-the-art technologies.

After the seminar

The significant development of research nowadays in every field (e.g., medicine, sociology, engineering, etc.) combined with the increased needs for the correct execution of data analysis strategies makes SPSS an easy-to-use and functional software in hands of any researcher of any specialty. Upon completion of the seminar, the trainee can be employed in positions related to data analysis and the extraction of results and participate in the writing of research papers (e.g., theses, scientific articles, conference papers), as well as use the knowledge provided in improving its scientific progress.

Purpose of the seminar

The purpose of the seminar is:

• understanding:
  • of learning data analysis
  • of the decision-making process with the application of statistical criteria
• learning and handling SPSS (Statistical Package for Social Science), the most widely used statistical software.

The basic aim is for the trainee to be able to carry out a statistical analysis, having a comprehensive knowledge and understanding of the program's capabilities. In this way, he can handle the available data in various ways and answer research questions according to his own requirements and needs.