Determining Sample Size for a Dissertation

Quantitative and Qualitative Approaches

Determining Sample Size for a Dissertation. Choosing the right sample size is a critical part of dissertation research, as it directly affects the validity, reliability, and applicability of the findings. The number of participants needed depends on the research approach—quantitative or qualitative. This document outlines key factors to consider when determining sample size for both methods, along with strategies and justifications to support your decision. 

Sample Size in Quantitative Research 

Quantitative research relies on numerical data to test hypotheses, measure variables, and establish statistical relationships. The sample size in quantitative research must be sufficiently large to ensure statistical significance and generalizability. 

Key Considerations for Quantitative Sample Size 

1. Statistical Power 

• Statistical power is the probability of detecting an effect when one exists. A commonly accepted power level is 0.80, meaning there is an 80% chance of correctly identifying a true effect. 

• Increasing sample size enhances statistical power, reducing the risk of Type II errors (failing to detect a true effect). 

2. Effect Size 

• Effect size measures the strength of differences or relationships within data. A smaller effect size requires a larger sample to achieve statistical significance, while a larger effect size allows for a smaller sample. 

• Standardized effect sizes (e.g., Cohen’s d for mean differences, Pearson’s r for correlations) are used to estimate the appropriate sample size. 

3. Confidence Level and Margin of Error 

• The confidence level (typically 95%) represents how certain researchers can be that the sample represents the population. A smaller margin of error requires a larger sample size. 

4. Population Size 

• When researching a finite population, the required sample size varies based on the total number of individuals. Researchers often use statistical formulas, such as Krejcie and Morgan’s or Cochran’s formula, to determine an appropriate sample size. 

5. Sampling Technique 

• As suggested by Ahmed (2024),  selecting the right sampling technique is crucial for ensuring representativeness. Probability sampling techniques, such as simple random sampling, stratified sampling, and cluster sampling, require statistical methods to determine sample size. 

• Non-probability sampling techniques, such as convenience sampling and snowball sampling, are more flexible but require a strong justification for the sample size selection. 

Sample Size Calculation in Quantitative Research 

Many researchers use statistical tools such as G*Power, SPSS, or online sample size calculators. A formula that is commonly used for simple random sampling is: 

n=Z2 ×p ×(1−p)n=Z2 ×p ×(1−p)

/

E2E2

• n = required sample size 

• Z = Z-score (e.g., 1.96 for a 95% confidence level) 

• p = estimated population proportion (typically 0.5 if unknown) 

• E = margin of error (e.g., 0.05 for 5%) (Ahmed, 2024) 

For example, for a 95% confidence level with a 5% margin of error in a large population, the required sample size is approximately 385 respondents. 

Sample Size in Qualitative Research 

Unlike quantitative research, qualitative research focuses on in-depth understanding, meaning that sample sizes are typically smaller. The appropriate sample size is determined by data saturation, rather than statistical formulas. 

Key Considerations for Qualitative Sample Size 

1. Data Saturation 

• Saturation occurs when no new themes, patterns, or insights emerge from additional data collection. 

• The required sample size depends on the complexity of the research question and the richness of participant responses. 

2. Research Methodology 

• Different qualitative approaches have varying sample size expectations: 

• Phenomenology: 5-25 participants 

• Grounded Theory: 20-30 participants 

• Ethnography: 30-50 participants or prolonged field engagement 

• Case Study: 1-10 cases, depending on depth of analysis 

3. Sampling Technique 

• Hennink and Kaiser (2022) emphasize the importance of selecting an appropriate non-probability sampling technique in qualitative studies. 

• Common techniques include purposive sampling, snowball sampling, and theoretical sampling, which ensure the selection of participants who can provide in-depth insights. 

4. Theoretical Framework 

• The research framework guides sample size decisions. Studies grounded in well-established theories may require fewer participants, whereas exploratory studies may require larger samples. 

Justification for Sample Size in Qualitative Research 

Since qualitative research lacks a universal formula for determining sample size, researchers justify their choices based on: 

• Evidence of Saturation: Demonstrating that additional participants do not contribute new information. 

• Methodological Precedents: Citing similar studies to justify chosen sample size. 

• Research Scope: Larger sample sizes for broader research questions, smaller samples for detailed, in-depth inquiries. 

Comparing Quantitative and Qualitative Sample Sizes 

Factor	Quantitative Research	Qualitative Research
Purpose	Tests hypotheses, establishes relationships, and generalizes findings	Explores experiences, meanings, and perspectives
Sample Size	Large (often 100+)	Small (often 10-50)
Selection Method	Probability sampling (random, stratified, cluster)	Non-probability sampling (purposive, snowball, theoretical)
Justification	Statistical power, effect size, confidence level	Data saturation, theoretical framework
Calculation	Uses statistical formulas and software	Determined by the depth of insight and the emergence of themes

Conclusion 

Determining the appropriate sample size for a dissertation depends on the research approach. Quantitative research requires a sufficiently large sample to ensure statistical validity, guided by effect size, confidence levels, and statistical power. In contrast, qualitative research prioritizes data saturation and depth of insight, often relying on smaller, purposefully selected samples. As discussed in How to Choose a Sampling Technique and Determine Sample Size for Research, researchers must carefully choose an appropriate sampling technique that aligns with their study’s objectives and provides sufficient justification for their sample size decisions. Regardless of methodology, a well-supported sample size selection strengthens the credibility and impact of the research findings. 

References 

Ahmed, S. K. (2024). How to choose a sampling technique and determine sample size for     research: A Simplified Guide for Researchers. Oral Oncology Reports, 12, 100662. https://doi.org/10.1016/j.oor.2024.100662. 

Hennink, M., & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social Science & Medicine, 292, 114523. https://doi.org/10.1016/j.socscimed.2021.114523.