Research Methodology Cheatsheet

Quick reference guide covering key research methodology concepts, formulas, and decision frameworks

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This cheatsheet provides a rapid-access reference for key research methodology concepts, decision rules, formulas, and frameworks. Use it for exam revision, thesis writing, or quick lookups during your research process.

Research Paradigms at a Glance

Paradigm	Ontology	Epistemology	Methods	Goal
Positivism	Objective reality	Observable, measurable	Quantitative	Explain, predict
Interpretivism	Constructed reality	Subjective meaning	Qualitative	Understand, interpret
Pragmatism	Whatever works	Problem-driven	Mixed	Solve problems
Critical Theory	Shaped by power	Value-laden	Either	Emancipate, transform

Research Design Decision Tree

Is your goal to establish cause-and-effect?

Yes → Experimental/Quasi-experimental design
No → Continue below

Are you measuring and testing relationships between variables?

Yes → Quantitative (Survey, Correlational)
No → Continue below

Are you exploring experiences, meanings, or processes?

Yes → Qualitative (Interviews, Ethnography, Case Study)

Do you need both breadth AND depth?

Yes → Mixed Methods

Sampling Quick Reference

Method	Type	When to Use
Simple Random	Probability	Complete list available, homogeneous population
Stratified	Probability	Need representation from subgroups
Cluster	Probability	Population geographically dispersed, no individual list
Systematic	Probability	Ordered list available, no periodicity
Convenience	Non-probability	Pilot studies, exploratory, no access constraints
Purposive	Non-probability	Specific expertise or characteristics needed
Snowball	Non-probability	Hidden/hard-to-reach populations

Sample Size Quick Guide

Survey research (estimating proportions):

±5% margin, 95% CI: n = 384
±3% margin, 95% CI: n = 1,067
±5% margin, 99% CI: n = 663

Comparing two groups (t-test, power=0.80):

Small effect (d=0.2): 394 per group
Medium effect (d=0.5): 64 per group
Large effect (d=0.8): 26 per group

Multiple regression: n ≥ 50 + 8k (k = number of predictors)

Qualitative: 5-25 (phenomenology), 20-60 (grounded theory), 15-30 (thematic analysis)

Statistical Test Selection

IV Type	DV Type	Groups/Variables	Test
Categorical (2 groups)	Continuous	Independent	Independent t-test
Categorical (2 groups)	Continuous	Related/paired	Paired t-test
Categorical (3+ groups)	Continuous	Independent	One-way ANOVA
Continuous	Continuous	2 variables	Pearson correlation
Multiple continuous	Continuous	Multiple IVs → 1 DV	Multiple regression
Categorical	Categorical	2 variables	Chi-square
Categorical (2 groups)	Ordinal/Non-normal	Independent	Mann-Whitney U
Categorical (3+ groups)	Ordinal/Non-normal	Independent	Kruskal-Wallis

Effect Size Interpretation

Measure	Small	Medium	Large
Cohen's d	0.2	0.5	0.8
Pearson's r	0.10	0.30	0.50
Eta-squared (η²)	0.01	0.06	0.14
Cohen's f² (regression)	0.02	0.15	0.35
Cramér's V	0.10	0.30	0.50
Odds Ratio	1.5	2.5	4.0

Reliability Standards

Cronbach's α	Interpretation
≥ 0.90	Excellent (possible redundancy if > 0.95)
0.80 – 0.89	Good
0.70 – 0.79	Acceptable
0.60 – 0.69	Questionable
< 0.60	Unacceptable

Assumption Checks

Normality: Shapiro-Wilk test (p > .05 = normal), skewness ±2, kurtosis ±7 Homogeneity of variance: Levene's test (p > .05 = equal variances) Multicollinearity: VIF < 5 (conservative: < 3), Tolerance > 0.2 Linearity: Scatter plots, residual plots Independence: Durbin-Watson (1.5-2.5 acceptable)

Key Formulas

Standard Error: SE = SD / √n Confidence Interval: CI = x̄ ± Z × SE t-statistic: t = (x̄₁ - x̄₂) / √(s₁²/n₁ + s₂²/n₂) Chi-square: χ² = Σ[(O-E)²/E] Cohen's d: d = (M₁ - M₂) / SD_pooled Pearson's r: r = Σ[(x-x̄)(y-ȳ)] / √[Σ(x-x̄)² × Σ(y-ȳ)²] R-squared: R² = 1 - (SS_residual / SS_total) Cronbach's α: α = (k/(k-1)) × [1 - (Σsᵢ²/s²_total)]

APA Reporting Templates

t-test: "t(df) = X.XX, p = .XXX, d = X.XX, 95% CI [X.XX, X.XX]" ANOVA: "F(df_between, df_within) = X.XX, p = .XXX, η² = .XXX" Correlation: "r(df) = .XX, p = .XXX" Regression: "β = .XX, t = X.XX, p = .XXX, R² = .XX" Chi-square: "χ²(df, N = XXX) = X.XX, p = .XXX, V = .XX"

Validity Types Summary

Type	Question	Assessment Method
Content	Does it cover the construct fully?	Expert judgment, CVI
Construct	Does it measure the right thing?	Factor analysis, convergent/discriminant
Criterion	Does it predict relevant outcomes?	Correlation with gold standard
Internal	Can I trust the causal inference?	Control of confounds
External	Can I generalize?	Representative sampling

Common Mistakes to Avoid

Confusing correlation with causation
Reporting p-values without effect sizes
Using parametric tests on ordinal data
Not justifying sample size
Running multiple t-tests instead of ANOVA
Ignoring non-significant results
Over-interpreting in small samples
Not checking assumptions before analysis
Claiming generalizability from convenience samples
Writing "the results prove" (research supports, not proves)

Conclusion

Keep this cheatsheet accessible during your research journey. While understanding the concepts deeply is essential, having quick-reference decision tools helps you make consistent, defensible methodological choices throughout your study.

Research Paradigms at a Glance

Paradigm	Ontology	Epistemology	Methods	Goal
Positivism	Objective reality	Observable, measurable	Quantitative	Explain, predict
Interpretivism	Constructed reality	Subjective meaning	Qualitative	Understand, interpret
Pragmatism	Whatever works	Problem-driven	Mixed	Solve problems
Critical Theory	Shaped by power	Value-laden	Either	Emancipate, transform

Research Design Decision Tree

Is your goal to establish cause-and-effect?

Yes → Experimental/Quasi-experimental design
No → Continue below

Are you measuring and testing relationships between variables?

Yes → Quantitative (Survey, Correlational)
No → Continue below

Are you exploring experiences, meanings, or processes?

Yes → Qualitative (Interviews, Ethnography, Case Study)

Do you need both breadth AND depth?

Yes → Mixed Methods

Sampling Quick Reference

Method	Type	When to Use
Simple Random	Probability	Complete list available, homogeneous population
Stratified	Probability	Need representation from subgroups
Cluster	Probability	Population geographically dispersed, no individual list
Systematic	Probability	Ordered list available, no periodicity
Convenience	Non-probability	Pilot studies, exploratory, no access constraints
Purposive	Non-probability	Specific expertise or characteristics needed
Snowball	Non-probability	Hidden/hard-to-reach populations

Sample Size Quick Guide

Survey research (estimating proportions):

±5% margin, 95% CI: n = 384
±3% margin, 95% CI: n = 1,067
±5% margin, 99% CI: n = 663

Comparing two groups (t-test, power=0.80):

Small effect (d=0.2): 394 per group
Medium effect (d=0.5): 64 per group
Large effect (d=0.8): 26 per group

Multiple regression: n ≥ 50 + 8k (k = number of predictors)

Qualitative: 5-25 (phenomenology), 20-60 (grounded theory), 15-30 (thematic analysis)

Statistical Test Selection

IV Type	DV Type	Groups/Variables	Test
Categorical (2 groups)	Continuous	Independent	Independent t-test
Categorical (2 groups)	Continuous	Related/paired	Paired t-test
Categorical (3+ groups)	Continuous	Independent	One-way ANOVA
Continuous	Continuous	2 variables	Pearson correlation
Multiple continuous	Continuous	Multiple IVs → 1 DV	Multiple regression
Categorical	Categorical	2 variables	Chi-square
Categorical (2 groups)	Ordinal/Non-normal	Independent	Mann-Whitney U
Categorical (3+ groups)	Ordinal/Non-normal	Independent	Kruskal-Wallis

Effect Size Interpretation

Measure	Small	Medium	Large
Cohen's d	0.2	0.5	0.8
Pearson's r	0.10	0.30	0.50
Eta-squared (η²)	0.01	0.06	0.14
Cohen's f² (regression)	0.02	0.15	0.35
Cramér's V	0.10	0.30	0.50
Odds Ratio	1.5	2.5	4.0

Reliability Standards

Cronbach's α	Interpretation
≥ 0.90	Excellent (possible redundancy if > 0.95)
0.80 – 0.89	Good
0.70 – 0.79	Acceptable
0.60 – 0.69	Questionable
< 0.60	Unacceptable

Assumption Checks

Key Formulas

APA Reporting Templates

Validity Types Summary

Type	Question	Assessment Method
Content	Does it cover the construct fully?	Expert judgment, CVI
Construct	Does it measure the right thing?	Factor analysis, convergent/discriminant
Criterion	Does it predict relevant outcomes?	Correlation with gold standard
Internal	Can I trust the causal inference?	Control of confounds
External	Can I generalize?	Representative sampling

Common Mistakes to Avoid

Confusing correlation with causation
Reporting p-values without effect sizes
Using parametric tests on ordinal data
Not justifying sample size
Running multiple t-tests instead of ANOVA
Ignoring non-significant results
Over-interpreting in small samples
Not checking assumptions before analysis
Claiming generalizability from convenience samples
Writing "the results prove" (research supports, not proves)

Research Methodology Cheatsheet

Research Paradigms at a Glance

Research Design Decision Tree

Sampling Quick Reference

Sample Size Quick Guide

Statistical Test Selection

Effect Size Interpretation

Reliability Standards

Assumption Checks

Key Formulas

APA Reporting Templates

Validity Types Summary

Common Mistakes to Avoid

Conclusion

Continue learning this concept

Research Methodology Cheatsheet

Research Paradigms at a Glance

Research Design Decision Tree

Sampling Quick Reference

Sample Size Quick Guide

Statistical Test Selection

Effect Size Interpretation

Reliability Standards

Assumption Checks

Key Formulas

APA Reporting Templates

Validity Types Summary

Common Mistakes to Avoid

Conclusion

Continue learning this concept