This site is built to support part of a second-year module in Psychology, focusing on ‘Research Methods in Practice’ (PSYC519 and PSYC719). These are the materials for academic year 2021/2. For previous years see the archive.

This module is about understanding the relationships between the things that matter to people and the psychological constructs or variables that help us to understand them.

In the initial strand of the module we introduced approaches to qualitative research. In your group project you identified a research question, and have begun the process of interviewing participants and coding transcripts for common themes in that spoken material.

In this second strand of the module we introduce a quantitative approach to the same research question: Building on the same substantive subject you will develop a focused research question and create a numerical self-report measure of a relevant outcome variable.
In the final sessions you will collect and analyse data using this measure, and interpret your findings.

Each week you will work in groups to solve problems and develop a practical presentation of your work at the end of the semester.

Aims and overview

Qualitative techniques, introduced in the first strand of the module, allow us to examine how people talk about and feel about their experiences. In this second strand we introduce techniques and approaches commonly adopted by quantitative researchers in psychology and the social sciences, who may be researching similar phenomena to those using quantitative methods.

Most scientists aim to develop theories that explain, predict, and control the behaviour and phenomena we observe. Quantitative researchers tend to place more emphasis on prediction and control than qualitative researchers, although the distinction is not always clear-cut.

Scientists in the quantitative tradition are also empiricists and tend to believe that our knowledge comes from sensory experience (rather than divine inspiration, personal intuition, or even logic alone). They (we?) view the scientific method as important because it provides a systematic way to extend our experience and establish truths about the world.

There are many important critiques of this approach, often made forcefully by researchers who use qualitative methods. One important question is whether we can discover scientific “truths” when science itself — the selection and organisation of data — is a flawed and politicised human activity? In fact, the charge can be not merely that science is misused or misrepresented by those in power, but the more serious one that scientific theories are social constructs, and that claims to objective reality are simply an expression of our culture (and that our culture is just one of many).

A common response of practising scientists is to simply ignore these criticisms, and not engage with them directly (although some do). Researchers are pragmatists; they often don’t judge scientific theories by how ‘true’ they are or worry about these deeper philosophical questions. Instead, they judge theories by by how productive they are. How much of the data can their theory account for? How useful is the theory in guiding further research, or making predictions in the real world? Thomas Kuhn’s book, The Structure of Scientific Revolutions (Kuhn 2012) provides an historical perspective on the development of this pragmatic science, the benefits it can bring, and the potential pitfalls as we do what Kuhn terms ‘ordinary science’.

Aims

Consider your research topic from the qualitative strand It might have been something like:

“Social media use, anxiety, and students’ academic performance”.

So far you’ve used qualitative techniques to examine how people talked about and felt about their experiences.

Quantitative researchers hope to add to this perspective by:

  1. Guessing at the network of causes and effects which link the phenomena: this produces a theoretical or causal model of how the world works.

  2. Using this causal model to generate testable hypotheses.

  3. Making measurements of the key variables (collecting data).

  4. Using graphs and statistics to check how well the model predicted reality. That is, are the hypotheses compatible with the data?

  5. Updating the model and starting all over again!

This module emphasizes learning by doing. So, we first need to think about the network of causes and effects which link the psychological or behavioural phenomena you explored in your qualitative research, which is the topic of the first session here.

Semester 2

Documentation to support the second research methods module, PSYC520/720, running in semester 2 and covering experimental methods and analyses, can be found here:

https://ajwills72.github.io/rmip/

All content on this site distributed under a Creative Commons licence. CC-BY-SA 4.0.

Kuhn, Thomas S. 2012. The Structure of Scientific Revolutions. University of Chicago press.