Quantitative research methods attaches more importance to the use of numeric values when collecting, measuring, analyzing, and interpreting quantitative data. Researching with quantitative methods can help find patterns and averages, test causal relationships , forecast future patterns/trends, and generalize sample results to larger populations. It relies heavily on the use of statistical methods and principles.
Data used in quantitative research can be primary or secondary in nature. Primary data are those generated by the researcher through the use of valid and reliable procedures and instruments such as questionnaire surveys, interviews, and others. In contrast, secondary data are data that the researcher obtains from external sources, e.g., government agencies, company records, etc.
Whatever type of he or she uses, the researcher is obligated to carefully explain all the methods used in the collection and analysis of such data. Software products like SPSS, STATA, and Eviews are designed to work with quantitative data.
Below are some of the main characteristics of quantitative research methods.
Quantitative research characteristics
- Data is usually collected through the use of structured research instruments
- To ensure validity, the sample size should be large enough to represent the population of interest
- To ensure reliability, the research should be such that can be easily replicated or repeated
- The research should have clearly worded research question(s)
- All aspects of the study should be carefully planned before data is collected
- Data are in numeric form and can be arranged in several ways, e.g., in tables, graphs, charts, figures, or other non-textual forms
- A vast array of instruments, techniques, or tools [e.g., questionnaires, secondary data sources, computer software, etc] are used to gather and analyze data
- Empirical findings can be used to generalize concepts more widely, predict future results, and determine causal relationships, among others
There are different kinds of quantitative research. Some of them are discussed below.
Experiments & Experimental research
Experimentation is a popular method of quantitative research in the natural sciences. In an experiment, the researcher manipulates an independent variable (or more) and measures its impact on one or more dependent variables. This is usually based on a test of hypothesis.
The subjects for experiments are usually divided into two groups – the experimental or treatment group and the control group. The experimental group receives the treatment while the control group does not receive any treatment. Observations from both groups are then recorded, analyzed, and utilized.
An example is a pharmaceutical company trialing a new vaccine on an experimental group while using the control group to make comparisons based on the treatment received.
This is a non-experimental research design where the relationship between two (or more) variables is measured. Unlike experiments, there is no need to control any variable or group. The main objective of correlational research is to find and clarify relationships among variables with the use of correlation coefficients. There are three possible outcomes in correlational research – Positive correlation, negative correlation, and zero correlation. Positive correlation occurs when variable A changes in the same direction as variable B. In a negative correlation, both variables A and B change inversely. Lastly, zero correlation implies that no relationship exists between variables A and B.
Note that correlation is not causation. That a relationship exists between A and B does not necessarily mean that A causes B or vice-versa. There are different techniques to measure causality (such as the Granger causality test in economics).
In this type of research, the quantitative data to be analyzed is collected at a specific point in time. Cross-sectional research can be used to describe the characteristics of sample members but cannot measure cause and effect relationships. It can also be used to determine possible correlations as well as to collect preliminary quantitative data for further research. This form of research is frequently used in education, especially in the natural and social sciences. In economics, the variables are measured through the use of cross-sectional regression. It is important to note that although a certain research method is more common in one dicipline, it does not necessarily mean that its exclusive to that dicipline. For example, you may find that both quantitative and qualitative methods are used in the social sciences.
A longitudinal or panel study is a form of research where the same subjects or variables are repeatedly observed by the researcher over a short or long period. It is a longer process than cross-sectional studies. Also, unlike cross-sectional research, longitudinal studies can be used to establish causal relationships. However, there are arguments that observational longitudinal studies may be less effective in determining causal relationships than experiments. Cohort studies are a popular form of longitudinal study.
Surveys are a popular and flexible non-experimental data collection instrument all over the world. They can be applied in both cross-sectional and longitudinal studies. In survey research, data are collected through the use of psychometric tools and procedures such as questionnaires, rating scales, scorecards, tests, interviews, checklists, and others. It can be a good method when you intend to study a large sample. A census is a type of survey that studies an entire target population.
Though surveys are old instruments, their use for research (survey research) was introduced by 20th-century researchers in the field of sociology. It has grown in usage since then to cover several fields of education. For example, in areas like psychology, statistics, anthropology, economics, marketing political science, etc.
Some quantitative validity and reliability methods
- Internal validity methods
- External validity methods
- Replication of experiments to see if results are the same or otherwise
- Construct validity
- Content validity
- Face validity
- Criterion validity
- Test-Retest method [ measure of stability]
- Parallel, alternate or equivalent form[s] method [measure of equivalence]
- Split-halves method [measure of internal consistency]