Qualities and Types of Quantitative Research

Quantitative research deals with numerical values and how they can describe a phenomenon or infer a relationship.
It serves as the go-to approach for scientific inquiry primarily because of its ability to test hypotheses.
Variables are the basis of formulating and testing hypotheses. These are traits that numerically describe or give meaning to an object, phenomenon, or group of people.
The process involves identifying, examining, describing, or correlating variables to answer a scientific inquiry.

Variables represent the characteristics or attributes being studied.
Independent Variable: This is the variable that is manipulated by the researcher, particularly in experimental designs.
Dependent Variable: This variable depends on or is influenced by the independent variable.

Controllability: The research should take place in an environment where all variables are identified and can be controlled to ensure that the results are due to the manipulated factors.
Generalizability: Results are derived from larger sample sizes that serve as a representative of the population. This allows the findings to be applied to the broader group from which the sample was drawn.
Objectivity: Data results are both observable and measurable. This is achieved through the use of structured instruments to eliminate researcher bias.
Replicability: The study design must allow other teams of researchers to replicate the research, eventually arriving at similar outcomes to verify the findings.

Strengths:
- Data analysis is assisted with statistical methods, providing mathematical rigor.
- It allows for large-scale research projects to be undertaken.
- Data can be effectively presented in graphical or tabular forms for clarity.
Weaknesses:
- Managing a large sample size requires a significant investment of time and effort.
- The statistical analysis of data requires an expert with specialized knowledge to perform correctly.
- Reducing complex observations to purely numerical values can make the research too simplistic, potentially missing qualitative nuances.

Quantitative research is broadly divided into experimental and nonexperimental types, depending on the topic and the nature of the problem being investigated.
Experimental Research:
- True Experimental Research: The primary objective is to identify a cause-effect relationship between variables. This design requires the use of randomized samples.
- Quasi-experimental Research: This mirrors experimental research to determine causal relationships using dependent and independent variables but does not use random samples. Instead, it utilizes assigned samples.
Nonexperimental Research:
- Descriptive Research.
- Comparative Research.
- Correlational Research.

True Experimental Research Example: Investigating if sunlight affects plant growth.
- Setup A: A seed is grown with an ample amount of sunlight.
- Setup B: A seed is grown in a location with limited sunlight.
- Setup C: A seed is grown completely in the dark.
- Controlled Variables: All seeds are planted in the same type of soil and provided equal amounts of water.
- Outcome: Results are recorded after several days of observation to determine the cause-effect relationship.
Quasi-experimental Research Example: Investigating the effect of height on milk brand preference.
- Reasoning: Because height is an innate characteristic, respondents cannot be randomly assigned to groups (a requirement for true experiments).
- Methodology: Respondents are assigned to groups based on their existing heights rather than random assignment.
- Comparison: A true experiment would have randomly assigned respondents to taste tests regardless of height to determine brand preference.

Descriptive Research: Focuses on describing factors, variables, or phenomena occurring in nature.
- Example: Identifying factors contributing to food spoilage via a survey.
- Statistical Application: Researchers use descriptive statistics such as the mean, median, or mode. If the frequency for temperature as a factor is the highest in the survey results, temperature is identified as the primary contributor to food spoilage.
Comparative Research: Also known as causal-comparative research. The objective is to compare two variables to identify if a causative relationship exists.
- Requirements: This involves two or more groups and at least one independent variable.
- Example: Determining if attendance in a summer program increases class participation. Researchers identify two groups (those who enrolled in the program and those who did not) and analyze collected data on their participation levels to conclude if the program had an effect.
Correlational Research: The primary objective is to compare two variables and identify the relationship between them.
- Example: Testing the relationship between length of sleep and student productivity.
- Data Points: Sleep length is measured by bedtime and waking time; productivity is measured by the number of activities completed daily.
- Findings: Data analysis may show that as sleep duration approaches $8$ hours, the number of activities completed increases. This indicates a direct relationship between the variables.

Specific terminology in research titles or objectives can indicate the research type:
- The word "effect" suggests the study aims to understand a cause-and-effect relationship, which is typical of comparative, experimental, or quasi-experimental research.
- The words "relationship" or "correlation" serve as clues that the study is either comparative or correlational in nature.