4/19 Quantitative research issues
There are four main issues in quantitative data analysis, these are:
- Hypotheses
- Causality
- Generalisability
- Reliability
When we conduct quantitative research we will often be concerned with finding evidence to either support or contradict, an idea or hypothesis you might have. To recap, a hypothesis is where a predicted answer to a research question is proposed, for example, you might propose that if you give a student training in how to use a search engine it will improve their success in finding information on the Internet.
You could then go on to explain why a particular answer is expected - you put forward a theory.
1. Hypothesis
In hypothesis testing we generally have two hypotheses: 1) a null hypothesis (which usually indicates no change or no effect) and 2) an alternative hypothesis (which is usually our experimental hypothesis). The evidence from the sample is taken to support either the null or the alternative hypothesis.
When a researcher is interested in hypothesis testing they will conduct an experiment to gather their data. So, we could take one sample from our population of students, give them some training in how to search and then ask them to find some specific information. We ask another sample of students to search for the same specific information but don't give them training - and we see which group did better through a variety of different measures, some subjective and some objective. So, does the data we gather contain evidence that agrees with the alternative (experimental) hypothesis or the null hypothesis?
In testing a hypothesis we never actually prove or disprove a hypothesis, all we ever get is evidence from a sample that either 1) supports a hypothesis or 2) contradicts a hypothesis.
The Hypothesis contains concepts which need to be measured.
To do this we need to:
- translate concepts into measurable factors
- take these measurable factors and treat them as variables
- identify measurement scales to quantify variables
We'll look at variables in more detail later.
