How to Determine Sample Size for a Quantitative Doctoral Dissertation

How to Determine Sample Size for a Quantitative Doctoral Dissertation

Determining the sample size for a quantitative doctoral dissertation is a critical step in the research design process because it directly affects the statistical validity, reliability, and generalizability of your findings. Unlike qualitative research, quantitative studies rely on statistical power and mathematical justification to determine how many participants are needed.

In simple terms, quantitative sample size answers: “How many participants are needed to detect a real effect in the data with confidence?”

What Is Sample Size in Quantitative Research?

The sample size refers to the number of participants included in a study used for statistical analysis.

In quantitative research, sample size is important because it influences:

• Statistical power

• Accuracy of results

• Ability to detect significant effects

• Generalizability to a population

A sample that is too small may fail to detect real effects, while a sample that is too large may waste resources.

Why Sample Size Matters in a Doctoral Dissertation

A properly justified sample size ensures:

• Valid statistical conclusions

• Reduced risk of Type I and Type II errors

• Stronger dissertation defense

• Publication-quality research

• Ethical use of participant data

Dissertation committees expect a clear, defensible rationale for sample size selection.

Key Methods for Determining Sample Size

There are several accepted methods used in quantitative dissertation research.

1. Power Analysis (Gold Standard Method)

The most widely accepted method is statistical power analysis.

Power analysis helps determine the minimum sample size needed to detect an effect if one truly exists.

It is based on four key elements:

Key components:

• Effect size (strength of the relationship or difference)

• Alpha level (α) (usually .05)

• Statistical power (1 - β) (usually .80 or higher)

• Number of variables or groups

Common tools:

• G*Power (free software)

• SPSS power functions

• R statistical packages

Example:

A power analysis indicated that a minimum sample of 120 participants was required to detect a medium effect size with 80% power at a .05 significance level.

2. Effect Size Estimation

Effect size refers to the expected magnitude of a relationship or difference.

Common benchmarks:

• Small effect (0.2)

• Medium effect (0.5)

• Large effect (0.8)

Smaller expected effects require larger sample sizes.

3. Rule-of-Thumb Guidelines (Use with Caution)

While not as rigorous, some researchers use general guidelines:

• Correlation studies: 100–200 participants

• Regression analysis: 10–20 participants per predictor

• ANOVA: 30+ participants per group

These should support, not replace, power analysis.

4. Population Size Considerations

When the population is small:

• Census sampling (entire population) may be used

• Adjustments can be made using finite population correction

For large populations:

• Sample size depends more on statistical requirements than population size

5. Sampling Method Influence

Different sampling methods may affect sample size:

• Random sampling → supports generalization

• Stratified sampling → ensures subgroup representation

• Convenience sampling → may require larger samples to reduce bias

Step-by-Step: How to Determine Sample Size in a Quantitative Dissertation

Step 1: Define Your Research Design

Your design determines sample size requirements.

Examples:

• Correlational design

• Experimental or quasi-experimental design

• Survey research

• Regression analysis

Each design has different statistical needs.

Step 2: Identify Variables and Statistical Tests

Ask:

• What variables are being tested?

• What statistical analysis will be used?

Examples:

• t-test → two groups

• ANOVA → multiple groups

• Regression → multiple predictors

• Correlation → relationships between variables

Step 3: Estimate Effect Size

Use:

• Prior research literature

• Pilot studies

• Theoretical expectations

If unknown:

• Use a medium effect size as a conservative estimate

Step 4: Set Statistical Parameters

Typically:

• Alpha (α) = 0.05

• Power = 0.80 or 0.90

• Confidence level = 95%

These values ensure acceptable error rates.

Step 5: Run a Power Analysis

Use tools such as:

• G*Power

• SPSS

• R

Input:

• Effect size

• Alpha level

• Power level

• Number of groups or predictors

Step 6: Adjust for Real-World Considerations

Increase sample size to account for:

• Non-response rates

• Missing data

• Attrition (especially in longitudinal studies)

Example:

A 20% increase was added to account for potential incomplete survey responses.

Step 7: Justify Sample Size in Your Dissertation

Your methodology chapter should clearly explain:

• The method used (power analysis preferred)

• Input values (effect size, alpha, power)

• Software used

• Final sample size decision

Example Justification Statement:

A priori power analysis using G*Power indicated that a minimum sample of 180 participants was required to detect a medium effect size (f² = 0.15) with 80% power at a 0.05 significance level.

Common Sample Size Ranges in Quantitative Dissertation Research

Design Type	Typical Sample Size
Correlational	100–300+
Survey research	150–500+
Experimental (two groups)	30–100 per group
Regression analysis	100–200+
Large-scale educational studies	300–1000+

Common Mistakes When Determining Sample Size

Avoid:

• Guessing sample size without justification

• Relying only on rules of thumb

• Ignoring statistical power analysis

• Failing to account for attrition

• Not aligning sample size with analysis type

Strengths of Proper Sample Size Determination

• Increases statistical validity

• Improves dissertation credibility

• Ensures sufficient power to detect effects

• Supports publication-quality research

• Strengthens committee approval

Final Thoughts on How to Determine Sample Size for a Quantitative Doctoral Dissertation

Determining sample size for a quantitative doctoral dissertation is a statistical and methodological process, not a guess. The most rigorous approach is power analysis, supported by effect size estimation, research design considerations, and practical constraints.

A strong dissertation clearly justifies sample size using statistical reasoning, ensuring that the study is both scientifically valid and defensible at the doctoral level.

If you need help selecting a methodology, consider qualitative dissertation editing! If you need help with your Chapter 3, please visit our website.