research-methodology

name

research-methodology

description

A specialized skill providing a detailed framework for academic research methodology design. Used by the methodology-expert agent when designing quantitative, qualitative, or mixed-methods research and selecting samples, instruments, and analysis methods. Automatically applied in contexts such as 'research methodology', 'research design', 'sample design', 'measurement instruments', 'validity and reliability', 'statistical analysis methods'. However, running statistical software (SPSS, R) directly and filing IRB applications are outside the scope of this skill.

Research Methodology — Research Methodology Design Framework

A specialized skill that enhances the methodology-expert agent's research design capabilities.

Target Agent

• methodology-expert — Research design, sample, instrument, and analysis method selection

Research Design Type Selection Matrix

Research Question -> Design Type Mapping

Research Question Type	Suitable Design	Data Type
"What is ~?" (exploratory)	Qualitative (grounded theory, phenomenology)	Interviews, observation
"What is the relationship between ~?" (correlational)	Quantitative (survey, secondary data)	Numerical data
"Does ~ affect ~?" (causal)	Experimental / quasi-experimental	Experimental data
"How is ~ experienced?" (phenomenological)	Phenomenological research	In-depth interviews
"How has ~ developed?" (process)	Grounded theory, case study	Multiple sources
"For an integrated understanding of ~?"	Mixed methods	Quantitative + qualitative

Quantitative Research Designs

Design Type	Characteristics	Internal Validity	Application
True experiment (RCT)	Random assignment + control group	High	Medicine, educational interventions
Quasi-experiment	Non-random + control/comparison group	Medium	Field education research
Non-experimental (survey)	Observational, correlational	Low	Exploratory, descriptive
Longitudinal	Tracks changes over time	Medium	Developmental, panel studies
Cross-sectional	Single-point measurement	Low	Status assessment

Qualitative Research Designs

Design Type	Purpose	Data Collection	Participant Count
Phenomenology	Explore the essence of experience	In-depth interviews	5-25
Grounded theory	Generate theory	Interviews, observation	20-30
Case study	Understand phenomena in context	Multiple sources	1-10 cases
Ethnography	Understand cultural context	Participant observation	Extended period
Narrative	Personal experience narrative	Life history interviews	1-5

Sample Design

Quantitative Sample Size Calculation

n = (Z^2 x p x (1-p)) / e^2

Z = Confidence level (95% -> 1.96, 99% -> 2.576)
p = Expected proportion (if unknown, use 0.5)
e = Margin of error (typically +/-5% = 0.05)

Population	95% conf, +/-5%	95% conf, +/-3%
100	80	92
500	217	341
1,000	278	516
10,000	370	964
100,000+	384	1,067

Minimum Sample for Regression Analysis

N >= 50 + 8 x (number of independent variables)  (Tabachnick & Fidell)
or
N >= 15 x (number of independent variables)  (Stevens)

Measurement Instrument Validity and Reliability

Validity

Type	Verification Method	Criterion
Content validity	Expert panel review (CVI)	CVI >= 0.80
Construct validity	Factor analysis (EFA/CFA)	Factor loading >= 0.40
Convergent validity	AVE	AVE >= 0.50
Discriminant validity	sqrt(AVE) > correlation	Fornell-Larcker criterion
Criterion validity	Correlation with external criterion	r >= 0.40

Reliability

Type	Verification Method	Criterion
Internal consistency	Cronbach's alpha	alpha >= 0.70
Composite reliability	CR (SEM)	CR >= 0.70
Test-retest	Repeated measurement with same instrument	r >= 0.70
Inter-rater	Cohen's Kappa	kappa >= 0.60

Analysis Method Selection Guide

Research Purpose -> Analysis Method

Research Purpose	Independent Variable	Dependent Variable	Analysis Method
Difference test (2 groups)	Categorical	Continuous	t-test
Difference test (3+ groups)	Categorical	Continuous	ANOVA
Relationship analysis	Continuous	Continuous	Correlation analysis
Impact analysis	Continuous/Categorical	Continuous	Regression analysis
Structural relationships	Latent variables	Latent variables	SEM
Group classification	Continuous/Categorical	Categorical	Logistic regression
Categorical relationship	Categorical	Categorical	Chi-square test

Alternatives When Assumptions Are Violated

Assumption	Alternative
Normality	Mann-Whitney, Kruskal-Wallis
Equal variance	Welch's t-test, Games-Howell
Independence	Repeated measures ANOVA, paired t-test
Linearity	Nonlinear regression, variable transformation