Empirical Study Design and Literature Review

Definition: A structured approach to research that collects data through observation or experimentation.
Components:
- Research Questions: Clear, specific questions to guide the study.
- Sampling: Selecting a representative group from the population; can be random, stratified, or convenience sampling.
- Variables: Identify independent (manipulated) and dependent (measured) variables.
- Control Groups: Use of control groups to compare against experimental groups.
Types of Designs:
- Cross-sectional: Data collected at a single point in time.
- Longitudinal: Data collected over an extended period to observe changes.
- Experimental: Involves manipulation of variables and control over extraneous factors.

Purpose: To summarize and synthesize existing research relevant to the study.
Process:
- Identify Sources: Use academic journals, books, and credible online resources.
- Analyze Trends: Look for patterns, gaps, and inconsistencies in previous research.
- Establish Context: Clarify how the current study fits into the existing body of knowledge.
Writing: Organize findings thematically or chronologically; critically evaluate sources.

Definition: A systematic plan for conducting research, detailing how data will be collected and analyzed.
Types:
- Qualitative: Focuses on understanding concepts, experiences, and social contexts; methods include interviews, focus groups, and observations.
- Quantitative: Emphasizes numerical data and statistical analysis; methods include surveys and experiments.
- Mixed Methods: Combines qualitative and quantitative approaches for a comprehensive understanding.
Framework: Outline the steps from problem definition to conclusion and implications.

Purpose: Provides a lens through which to view and interpret research findings.
Components:
- Concepts: Key ideas and terms relevant to the research.
- Propositions: Statements that suggest relationships between concepts.
- Models: Visual representations of theoretical concepts and their interconnections.
Selection: Choose frameworks that align with the research question and methodology; common frameworks include social constructivism, systems theory, and behaviorism.

Qualitative Analysis:
- Thematic Analysis: Identifying patterns and themes in qualitative data.
- Content Analysis: Examining texts and media to understand communication patterns.
- Grounded Theory: Developing theories based on data collected through qualitative methods.
Quantitative Analysis:
- Descriptive Statistics: Summarizing data through means, medians, modes, and standard deviations.
- Inferential Statistics: Drawing conclusions about populations based on sample data; includes t-tests, ANOVA, and regression analysis.
- Software Tools: Use statistical software (e.g., SPSS, R, or Python) for complex analyses.
Mixed Methods Analysis: Integrating both qualitative and quantitative data to provide a fuller understanding of the research problem.

Structured research method for data collection via observation or experimentation.
Research questions must be clear and specific, driving the study's focus.
Sampling techniques include random, stratified, and convenience sampling to ensure representativeness.
Variables should be identified as independent (manipulated) and dependent (measured).
Control groups are essential to evaluate the impact of experimental interventions.
Types of study designs:
- Cross-sectional: Data captured at a single moment, suitable for snapshot analyses.
- Longitudinal: Data gathered over time to track changes and developments.
- Experimental: Involves variable manipulation and control of extraneous factors to establish causation.

Aims to summarize and synthesize relevant existing research to provide context.
Identify credible sources, including academic journals, books, and reputable online resources.
Analyze trends in prior research for patterns, gaps, or inconsistencies in findings.
Establish the relationship of the current study to the existing body of knowledge.
Organize the literature thematically or chronologically while critically evaluating the quality of sources.

A comprehensive plan detailing the approach to data collection and analysis.
Types of methodologies include:
- Qualitative: Focuses on exploring concepts and experiences through interviews, focus groups, and observations.
- Quantitative: Prioritizes numerical data and statistical analysis, using methods such as surveys and experiments.
- Mixed Methods: Integrates both qualitative and quantitative methods for a richer analysis.
The framework should describe each step from defining the research problem to drawing conclusions and implications.

Serve as lenses for interpreting and contextualizing research findings.
Key components include:
- Concepts: Fundamental ideas and terms pertinent to the study.
- Propositions: Statements indicating potential relationships between identified concepts.
- Models: Visual tools to illustrate interconnections between theoretical concepts.
Framework selection should align with the research question and methodology; common frameworks utilized include social constructivism, systems theory, and behaviorism.

Qualitative Analysis methods include:
- Thematic Analysis: Identifying recurring themes in qualitative data.
- Content Analysis: Analyzing texts and media for communication patterns.
- Grounded Theory: Deriving theories directly from qualitative data collected.
Quantitative Analysis techniques involve:
- Descriptive Statistics: Summarizing data characteristics using means, medians, modes, and standard deviations.
- Inferential Statistics: Making inferences about populations based on sample data, incorporating t-tests, ANOVA, and regression analysis.
Utilize statistical software tools (e.g., SPSS, R, Python) for comprehensive data analysis.
Mixed Methods Analysis combines qualitative and quantitative data to enhance understanding of the research problem.