There are varied ways of doing quantitative research. In this lesson, we focus on the five types of quantitative research.
Kinds of Quantitative Research
Quantitative research encompasses a wide range of methodologies, often classified into non-experimental and experimental designs. In non-experimental research, variables are examined without manipulation, while in experimental research, variables are manipulated to establish cause-and-effect relationships.
Non-Experimental Designs
Descriptive Design. Descriptive research, as defined by Anastas (1999, as cited in Salkind & Rasmussen, 2007), answers questions of "who, what, when, where, and how" related to a research problem but does not address the "why." This design aims to objectively describe a phenomenon as it naturally occurs, without manipulation, gathering extensive data to provide a comprehensive overview of the current status. Unlike quantitative research, it does not test hypotheses or seek definitive answers, relying instead on observational methods and measurement instrumentation. This rich descriptive data can inform decision-making and improve practices, often serving as a valuable precursor to more quantitative studies by identifying variables worthy of further investigation.
Correlational Design. Correlational research examines the relationships between variables, identifying patterns and associations without establishing cause-and-effect. Data is collected through observation, focusing on the degree of correlation between paired variables to determine the strength of their relationship (Black, 1999). This approach allows researchers to explore potential interrelations, assess the usefulness of future causality research, and even make predictions based on high correlations (e.g., predicting weight from height). However, a high correlation does not definitively establish a causal link between the variables; further research is needed to determine if such a relationship exists.
Causal-Comparative Design. Causal-comparative research, also known as ex post facto research ("after the fact"), investigates the relationship between past events and current conditions to explore potential causes of an already existing phenomenon (Gray, 1996). While sharing similarities with descriptive and correlational research in its lack of experimental manipulation and observation of naturally occurring variables, causal-comparative research distinguishes itself by attempting to establish cause-and-effect relationships—a goal not pursued by purely descriptive or correlational studies. It examines relationships among variables, similar to correlational research, but explicitly aims to identify potential causal links.
Experimental Designs
Quasi-Experimental Design. This design seeks to establish cause-and-effect relationships between variables, but it is limited in terms of validity due to the absence of random selection and assignment of subjects. The independent variable is identified but not manipulated. The researcher compares a group exposed to treatment (experimental group) with a group not exposed to treatment (control group).
True Experiment (Experimental) Design. Similar to quasi-experimental design, this design aims to establish cause-and-effect relationships. However, it provides more conclusive results due to the random assignment of subjects and experimental manipulations. This design offers a higher level of control and allows for stronger inferences about causality.
Understanding these different quantitative research designs is crucial for selecting the most appropriate methodology for your research question and objectives.