Quantitative research is a type of investigation that collects numerical data and analyses it using mathematically based methodologies (in particular statistics).
QUANTITATIVE APPROACHES TO RESEARCH
Quantitative research can be experimental as well as non experimental.
EXPERIMENTAL RESEARCH
The goal of experimental design is to see if a particular treatment has an effect on a particular outcome. The researcher determines this by giving one group (experimental group) a certain therapy while withholding it from another group (control group), and then comparing how both groups performed on an outcome.
Note: In a commonly conducted experimental study, a control group is a group that does not receive the treatment or experimental manipulation; the treatment group, on the other hand receives it.
MOST COMMON TYPES OF EXPERIMENTAL RESEARCH DESIGN
- Pre-experimental design
- Quasi experiments
- True experiments
In pre-experimental designs, a researcher studies a single group; there is no control group to compare to the experimental group In quasi-experiments, the researcher utilises control and experimental groups but does not allocate participants to groups at random In a true experiment, the investigator randomly assigns the participants to treatment groups.
NON EXPERIMENTAL RESEARCH
Non-experimental researcher relies on correlations, surveys or case studies; such a research does not demonstrate a true cause-and-effect relationship. Mentioned below are some of the types of non-experimental designs:
- Causal-comparative research
- Correlational research
- Survey research
CAUSAL COMPARATIVE RESEARCH
Ex-post facto (Latin for "after the fact") study is a term used to describe causal-comparative research. In causal comparative research, the researcher tries to figure out what is causing or explaining pre-existing disparities in groups of people. In other words, the researcher notices that groups differ on some characteristics and tries to figure out what the major factors are that caused the difference.
EXAMPLE
For example, a researcher might anticipate that participation in pre-school education is the most important factor influencing disparities in first-graders' social adjustment. To test this hypothesis, the researcher will choose a sample of first graders who have had preschool education and a sample of first graders who have not, and compare their social adjustment. The researcher's hypothesis would be supported if the group that did participate in preschool education had a greater level of social adjustment. As a result, basic causal comparative research begins by examining an effect and, as a result, examining possible causes.
CORRELATIONAL RESEARCH
Because correlational research describes an existing condition, it is frequently referred to as a type of descriptive research. The state it represents, on the other hand, is not the same as what is normally documented in survey studies. Correlational research entails gathering data in order to establish whether and to what extent two or more quantifiable variables are related. A correlation coefficient (Statistical measure of the linear relationship (correlation) between a dependent-variable and an independent variable) expresses the degree of relationship. When two variables have a relationship, it means that scores within a specific range on one variable are related to scores within a certain range on the other.
EXAMPLE:
There is a link between intellect and academic accomplishment, for example; people who score well on intelligence exams have high grade point averages, while people who score poorly on intelligence tests have low grade point averages. (Correlational studies are used to determine correlations between variables or to generate predictions based on these relationships.) Correlational studies calculate the degree of relationship between two variables numerically. Correlational statistics are used by the researcher to characterise and quantify the degree of relationship or association between two or more variables. The stronger the correlation, the more related the two variables are, and the more accurate the predictions based on the relationships are.
SURVEY RESEARCH
By investigating a sample of a population, survey research provides a quantitative or numeric depiction of trends, attitudes, or opinions in that population. It encompasses cross-sectional and longitudinal studies that collect data using questionnaires or structured interviews with the goal of extrapolating from a sample to a population. Surveys can be used to collect descriptive information about a target population (for example, to measure literacy levels in a region) or to investigate relationships between various factors (for example, to explain differences in mathematical competency among students based on their age, gender, exposure to the mathematics curriculum, and time spent in class learning mathematics).
Cross-sectional surveys are used to collect data on current conditions in a population at a single point in time or over a relatively short period of time. Then, across the variables of interest, comparisons are made. Rather than establishing causal patterns, the goal is to characterise occurrences and estimate frequencies.
In longitudinal surveys, data is collected at multiple points and the researcher is interested in making comparisons over time. The information can come from one or more groups. The longitudinal method's fundamental feature is repeated observation for at least two points in time, which allows (e.g.) the educational researcher to study the processes and patterns of change and stability in the educational sector. The reason for longitudinal studies is that educational research is concerned with the process of change, and the study of change necessitates at least two points in temporal observations. Because older events frequently influence later occurrences, time is an important factor in causal influence.
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