A basic understanding of Quantitative Research

Quantitative research design is the process by which researchers collect and analyze quantitative data. The main components of a quantitative study are: collection of data, analysis of data, and reporting of findings.

Each component has a specific purpose. Collecting data requires that researchers identify the type and amount of data they will need in order to answer their research question. Answering the research question requires that researchers find a way to measure, quantify, or quantify variables to determine if there is a relationship between them. Reporting findings requires that researchers organize and present all of their findings in an accessible manner. This includes writing appropriate and clear explanations for each finding as well as clearly identifying any limitations, such as biases and assumptions.

Quantitative research design can be broken down into different categories such as descriptive research design and experimental research design. 

• Descriptive research design involves collecting quantitative data through observation, self-report scales, or other qualitative methods. A common type of descriptive research is survey research, where researchers ask participants questions about their experiences to measure variables such as frequency or duration of experience.
• Experimental studies involve manipulating one or more variables in order to test different hypotheses about causality. For example, one might compare groups of people who are given different interventions in order to test the hypothesis that those who are treated with medication will have lower blood pressure than those who receive no treatment at all.

Correlational Quantitative Research Design

Correlational research design is the most common type of research design in social science. In a correlational research design, two or more variables are measured at the same time. The relationship between these variables is then measured and analyzed.

There are several potential advantages to using this type of research design.

• First, it is relatively easy to measure and analyze.
• Second, it is relatively cheap and easy to set up.
• Third, it can be done quickly, which makes it useful for many kinds of studies.

When new researchers mix up correlation with causation, they often get confused about the distinction. After all, it seems to be causative in nature that a waiter who frequently drops trays would receive lower tips. Unfortunately, correlational studies do not provide conclusive proof that one variable precedes the other. Correlational research and descriptive research both lack a strategy to influence the variables, so they're comparable. Both variables and the investigator monitoring them are measured or evaluated in a correlational study. The variables are linked in a correlational study in an effort to comprehend their relationship.

Correlational studies can also determine whether the variables move in the same direction or in opposite directions. In a positive correlation, both variables rise, whereas in a negative correlation, they fall. For example, “As a person lifts more weights, they grow bigger muscle mass.” In the opposite scenario, a waiter’s tips decrease as they drop more trays.

Correlations found in a correlational study can also be zero. It's common for new researchers to confuse correlational research, which cannot establish causality, with causal research, which can be resolved with a proper understanding of the research design. After all, it would appear to be causative in nature that a waiter who regularly drops trays would receive smaller tips. Nonetheless, correlational studies do not provide conclusive proof that one variable leads to the other.

Analyzing Correlational Data

• As a statistical technique, correlation analysis gauges the strength of a linear relationship between two variables and quantifies their association. In essence, it gauges the degree to which one variable changes in response to a change in the other. A high correlation indicates a strong relationship between the variables, whereas a low correlation indicates weak ties.
• Regression analysis is a quantitative research method that is used to analyze and model multiple variables in a study, where the relationship involves a dependent variable and one or more independent variables. A regression equation describes the line on a graph of one variable based on the other variable. You can use this equation to predict the value of one variable based on the value(s) of another variable. To put it more simply, regression analysis is a quantitative method used to investigate the relationship between a dependent variable and one or more independent variables.