Quantitative Research: Characteristics, Strengths, Weaknesses, & Kinds

According to International Market Research (2018), quantitative research is a structured way of collecting and analyzing data from different sources.
It uses computational, statistical, and mathematical tools to derive results.
It aims to quantify problems and understand their prevalence by projecting results to a larger population.
Data collection tools include surveys and experiments.
Experiments can reveal cause-and-effect relationships between independent and interdependent variables related to a problem.

Data is gathered using structured research instruments.
Results are based on larger sample sizes representative of the population.
The research study can be replicated, given its high reliability.
Researchers have clearly defined research questions seeking objective answers.
All aspects of the study are carefully designed before data collection.
Data is in the form of numbers and statistics, often in tables, charts, figures, or other non-textual forms.
Results can generalize concepts, predict future outcomes, or investigate causal relationships.
Researchers use tools like questionnaires or computer software to collect numerical data (Babbie, 2011).

Feature	Qualitative	Quantitative
Process	Starts with a problem, ends with a new problem.	Tests a hypothesis or theory.
Data Presentation	Uses inductive & deductive methods.	Uses numbers, scales, hypotheses, calculations, & statistical tools.
Theory	Develops a new theory based on data.	Standardized/structured.
Data Format	Uses pictures, words, sentences, paragraphs, narrations, short stories.
Sample Size	Small sample size, uses judgment & sampling.	Large sample representative sample.
Structure	Structured or semi-structured (flexible process).	Closed-ended questions.
Questions	Open-ended questions.	Objective.
Perspective	Subjective.	Endorses development & its high output replicability.
Goal	Cultivates understanding with high validity.
Data Analysis	Use textual forms in analyzing and interpreting data

Tests and validates already constructed theories about how and why phenomena occur.
Formulates hypotheses before data collection.
Can generalize research findings based on random samples with sufficient size.
Can be replicated on many populations and subpopulations.
Useful for obtaining data to test quantitative predictions.
Can eliminate confounding influences to establish cause-and-effect relationships.
Data collection can be quick (e.g., telephone, chats, Google Forms).
Provides precise, numerical data.
Data analysis is relatively time-consuming, using statistical software.
Research results are relatively independent of the researcher (e.g., statistical significance).
May have higher credibility with people in power (e.g., administrators, politicians).
Useful for studying large numbers of people.

Researcher's categories might not reflect local constituencies' understandings.
Theories used might not reflect local constituencies’ understandings.
May miss phenomena due to focus on theory or hypothesis testing (confirmation bias).
Knowledge produced might be too abstract and general for direct application to specific local situations, contexts, and individuals.

Experimental
- True Experimental
  - Sample groups must be assigned randomly.
  - Must have a viable control group.
  - Only one variable can be manipulated and tested at a time.
  - Tested subjects must be randomly assigned to either control or experimental groups.
- Quasi-Experimental
  - Uses constructions that already exist in the real world.
  - Categories may fall short of true experimental criteria.
  - Has control and experimental groups, but not necessarily randomly selected.
- Pre-Experimental
  - Employs a single group that receives the treatment, without a control group.
  - Includes pilot studies, one-shot case studies, and research using only one group.
Non-Experimental
- Descriptive
  - Systematic gathering of information from respondents to understand and predict population behavior.
  - Concerned with sampling, questionnaire design, administration, and data analysis.
- Correlational
  - Examines two or more quantitative variables from the same group.
  - Determines if there is a relationship (correlation) between the variables.
- Causal-Comparative
  - Also known as "ex post facto" research.
  - Starts with an effect and seeks possible causes.
- Comparative
  - Examines patterns of similarities and differences across a moderate number of cases (handful to fifty or more).
- Evaluative
  - Uses standard social research methods for evaluative purposes.
  - Specific research methodology assessing social programs.