Quantitative study designs are approaches used in research to collect and analyze numerical data. While various specific designs exist, they are broadly categorized based on their research purpose and methodology. Based on common classifications in research methodology, quantitative study designs are broadly classified into two main types: Non-Experimental and Experimental (or Causal). These two categories encompass the four main types listed in the provided reference: Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research.
Understanding the Two Broad Categories
The distinction between non-experimental and experimental designs primarily lies in the researcher's ability to manipulate variables and establish cause-and-effect relationships.
1. Non-Experimental Quantitative Designs
This broad category includes research designs where the researcher observes phenomena as they occur naturally without manipulating any variables. The goal is typically to describe characteristics, measure relationships between variables, or explore existing correlations.
Two of the main types listed in the reference fall under this non-experimental umbrella:
- Descriptive Research: Focuses on describing the characteristics of a population, situation, or phenomenon accurately and systematically. It answers questions like "what is?" or "how often?"
- Correlational Research: Aims to determine the extent to which two or more variables are related or associated. It explores relationships but does not imply that one variable causes the other.
Non-experimental designs are valuable for generating hypotheses, describing trends, and identifying potential relationships for future, more controlled studies.
2. Experimental and Causal Quantitative Designs
This broad category includes designs specifically constructed to investigate cause-and-effect relationships. Researchers actively intervene or manipulate one or more variables (independent variables) to observe their effect on an outcome variable (dependent variable).
The provided reference highlights designs within this category, stating that Causal-Comparative/Quasi-Experimental, and Experimental Research attempts to establish cause- effect relationships among the variables. The reference also notes that these types of design are very similar to true experiments, but with some key differences.
The two main types within this category listed in the reference are:
- Causal-Comparative/Quasi-Experimental Research: These designs examine cause-and-effect relationships but lack some of the control elements of a true experiment, such as random assignment to groups. They often study the effects of pre-existing conditions or interventions.
- Experimental Research: Considered the gold standard for establishing causality, true experimental designs involve manipulating an independent variable, using random assignment of participants to groups (like a control group and an experimental group), and controlling extraneous variables to isolate the effect of the independent variable on the dependent variable.
Experimental and causal designs provide stronger evidence for causality than non-experimental designs due to the control and manipulation involved.
How the Four Main Types Fit
The four main types of Quantitative research listed in the reference fit into these two broader categories based on their primary objective:
| Broad Category | Specific Design Types from Reference | Key Characteristic (Based on Reference & Common Classification) |
|---|---|---|
| Non-Experimental | Descriptive, Correlational | Describes variables or measures relationships without manipulation |
| Experimental / Causal | Causal-Comparative/Quasi-Experimental, Experimental Research | Attempts to establish cause-effect relationships (as noted in ref) |
Understanding these broad categories and the specific designs within them is crucial for designing effective quantitative studies and interpreting their findings accurately.
