We use research methods to explore the contextual implications for the design and development of our software applications.
The goal is to build an application that adds efficiency and utility to a given workflow, therefore we must define what efficiency and utility mean.
We may want to consider similar products and services that exist in the market in order to design a product or service that adds value to our customers.
Research is a systematic investigation into something unknown. During research, we aim to build knowledge or insights that relate to our area of application.
Research explores our understanding of a given space in relation to a number of factors. We tend to focus on things we can measure to determine their effects on other things.
We want to isolating variables as best we can in order to avoid confounding factors that may impact our findings.
We have to ensure that our research is robust and meaningful to our research agenda.
Think about a business you use regularly. Consider:
Research also has validity. This can tell us whether or not we are measuring what we say we are measuring.
Let’s consider this case. I sample 10 people. I ask each of them to run 100 meters on different tracks. They run on different days, under different weather conditions and with access to different types of equipment, for instance, shoes, physical equipment that allows them to perform better. I look at my results and see that the two fastest runners were over six feet tall, the rest of the runners were under that threshold. I draw the conclusion that being taller makes you run faster. Is my premise faulty?
Another important topic is that of reliability. We want to ensure our results have meaning from the context of experimentation.
Let’s go back to our example of a 100m race, Olympic events are a good example of reliable metrics. We can contextualize our results in a given way.
Through careful measurements, we’re able to regulate extraneous variables to some degree. Of course, we don’t have complete control over the universe, and there are going to be strange situations that occur.
Now in any given race, the conditions are broadly the same for each runner. That we probably cannot generalize across races due to variances in humidity, precipitation, and such. If it’s raining, it’s highly likely that the runners aren’t going to run as fast, and so each of these things in the environment are going to impact our results.
5.02 The Hawthorne Effect
The Hawthorne effect refers to a tendency in some individuals to alter their behaviour in response to their awareness of being observed.
This phenomenon implies that when people become aware that they are subjects in an experiment, the attention they receive from the experimenters may cause them to change their conduct.
5.03 Quantitative and qualitative research
There are two main types of research:
Quantitative explores measures, often relying on mathematical models and tools such as statistics.
It tends to focus on what is happening, who is involved, where it’s happening, when it’s happening and so on.
Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.
Qualitative focuses on dynamic realities such as emergent themes and behaviours. It often focuses on how and why things are happening.
Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.
Both can be valid and reliable. One is not better than the other – you might want to consider which one works best in your approach.
5.04 Sources of information