What is quantitative research?
Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.
What is quantitative example?
Quantitative is an adjective that simply means something that can be measured. For example, we can count the number of sheep on a farm or measure the gallons of milk produced by a cow.
What are the strengths of quantitative research design?
Quantitative method
| Strengths | Limitations |
|---|---|
| Relatively easy to analyse | Difficult to understand context of a phenomenon |
| Data can be very consistent, precise and reliable | Data may not be robust enough to explain complex issues |
What is the advantage of quantitative research?
Quantitative Research Pros: Larger sample: A broader study can be made, involving more subjects and enabling more generalization of results. Objectivity and accuracy: Few variables are involved as data relates to close-ended information.
Is quantitative research less expensive?
Qualitative research allows one to explore topics in more depth and detail than quantitative research. Also, qualitative research is often less expensive than quantitative research, because you don’t need to recruit as many participants or use extensive methods.
Is objective a strength of quantitative research?
Instead of conducting in-depth interviews, a researcher may use analysis and questionnaires to test a hypothesis. An advantage of using quantitative research is that the researcher remains more objective while proving or disproving a hypothesis.
What are the weaknesses of quantitative research design?
Quantitative study requires extensive statistical analysis, which can be difficult to perform for researchers from non- statistical backgrounds. Statistical analysis is based on scientific discipline and hence difficult for non-mathematicians to perform.
How do I analyze quantitative data?
Steps to conduct Quantitative Data Analysis
- Relate measurement scales with variables: Associate measurement scales such as Nominal, Ordinal, Interval and Ratio with the variables.
- Connect descriptive statistics with data: Link descriptive statistics to encapsulate available data.