Methods

Methods

A profound knowledge in analytical methods has become essential in today’s data-driven world. In academia, PhD programmes and scientific journals require rigorous research that is based on state-of-the-art methods. In industry, an increasing number of positions require practical experience and/or strategic knowledge of sophisticated data analysis.

The important thing is not to stop questioning. Curiosity has its own reason for existing.

~ Albert Einstein

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Methods

The important thing is not to stop questioning. Curiosity has its own reason for existing.

~ Albert Einstein

Machine learning

Teaching computers to learn from experience, more commonly known as machine learning, is already widely used across industries and represents the foundation of any “artificial intelligence” application.

Data visualization

Communicating data in an accurate, efficient, and appealing way is becoming increasingly important, with modern software packages offering powerful data visualizations tools that are easy to implement.

Regression analysis

Fundamentals about regression analysis are key for various statistical tests such as analysis of variance and non-parametric tests, and they build the basis for more advanced methods such as machine learning techniques.

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Instructors regularly teaching research methods for GSERM at different GSERM-destinations are portraying these methods, also explaining which use participants will gain by learning or deepening their knowledge in these methods.

Analyzing Panel Data

Professor Christopher Zorn (Pennsylvania State University) has taught several courses for GSERM since 2015. In this video he explains what participants can learn when attending his course Analyzing Panel Data.

Mediation, Moderation, and Conditional Process Analysis

The courses “Mediation, Moderation, and Conditional Process Analysis I and II” are taught at GSERM by Amanda K. Montoya from University of California, Los Angeles. Mediation analysis is used to test hypotheses about various intervening mechanisms by which causal effects operate. Moderation analysis is used to examine and explore questions about the contingencies or conditions of an effect, also called “interaction”. Increasingly, moderation and mediation are being integrated analytically in the form of what has become known as “conditional process analysis,” used when the goal is to understand the contingencies or conditions under which mechanisms operate.

Research & Information

We regularly post helpful information and materials related to empirical methods based on our experienced lecturers and beyond. This includes state-of-the-art tools, newly published research, and current trends in qualitative and quantitative methods.

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