Quantitative Research Overview

Definition: A systematic investigation that primarily focuses on quantifying relationships, behaviors, or phenomena.
Characteristics:
- Uses statistical, mathematical, or computational techniques.
- Data is collected through instruments like surveys, tests, or questionnaires.
- Emphasizes objectivity and replicability.
Types:
- Descriptive: Describes characteristics of a population or phenomenon.
- Correlational: Examines the relationship between two or more variables.
- Experimental: Tests hypotheses through controlled experiments; involves manipulation of variables.
- Quasi-Experimental: Similar to experimental but lacks random assignment to groups.
Data Analysis:
- Involves use of statistical software.
- Commonly employs descriptive statistics (mean, median, mode) and inferential statistics (t-tests, ANOVA).
Strengths:
- Allows for generalization of results to larger populations.
- Can identify patterns and predict outcomes.
Limitations:
- May overlook context and depth of understanding.
- Requires large sample sizes for reliability.

Definition: A research approach that combines qualitative and quantitative methods to provide a more comprehensive understanding of a research problem.
Characteristics:
- Integrates both numerical data and verbal data.
- Aims to leverage the strengths of both methodologies.
Types:
- Convergent Parallel Design: Collects qualitative and quantitative data simultaneously and integrates results.
- Explanatory Sequential Design: Starts with quantitative data collection followed by qualitative data to explain findings.
- Exploratory Sequential Design: Begins with qualitative data collection to explore an issue, then quantifies the findings.
Data Collection:
- Uses surveys, interviews, observations, and document analysis.
Data Analysis:
- Requires integration of both quantitative and qualitative analysis techniques.
Strengths:
- Provides a fuller, more nuanced understanding of research questions.
- Can validate findings across different methods.
Limitations:
- More complex to design and analyze.
- Requires expertise in both qualitative and quantitative methods.

Systematic investigation aimed at quantifying relationships, behaviors, or phenomena.
Employs statistical, mathematical, or computational techniques for data analysis.
Data collection methods include surveys, tests, and questionnaires.
Prioritizes objectivity and the ability to replicate results.

Descriptive: Captures and describes characteristics of a population or phenomenon.
Correlational: Investigates relationships between two or more variables.
Experimental: Tests hypotheses under controlled conditions with variable manipulation.
Quasi-Experimental: Similar to experimental research but does not randomly assign subjects to groups.

Utilizes statistical software for data processing and analysis.
Incorporates descriptive statistics (e.g., mean, median, mode) and inferential statistics (e.g., t-tests, ANOVA).

Strengths:
- Results can be generalized to larger populations.
- Capable of identifying patterns and predicting outcomes.
Limitations:
- May ignore the context and depth of understanding.
- Requires large sample sizes to ensure reliability.

Research approach that merges qualitative and quantitative methods for a comprehensive view of the research problem.
Integrates both numerical and verbal data, leveraging strengths of each methodology.

Convergent Parallel Design: Simultaneous collection of qualitative and quantitative data, followed by integration of results.
Explanatory Sequential Design: Begins with quantitative data, followed by qualitative data to clarify findings.
Exploratory Sequential Design: Starts with qualitative data collection to explore issues, then quantifies findings.

Employs diverse methods including surveys, interviews, observations, and document analysis.
Requires the integration of both quantitative and qualitative analysis techniques.

Strengths:
- Offers a richer and nuanced understanding of research questions.
- Validates findings through multiple methods.
Limitations:
- More complex to design and analyze than single-method studies.
- Demands expertise in both qualitative and quantitative methodologies.