What is a good sample size for a dissertation survey?

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The ideal sample size for a dissertation survey depends on several factors, including the population size, required confidence level and margin of error, types of analysis being conducted, and resource constraints. Common sample sizes for dissertation surveys range from 100 to 1000 respondents. A minimum sample of 30 respondents per subgroup is recommended for statistical analysis. The most important considerations are obtaining a representative sample, minimizing sampling error, and collecting enough data to test hypotheses and draw conclusions.

Determining an appropriate sample size is a key step when designing a survey for a dissertation. The sample size has important implications for the quality of the results and conclusions that can be drawn. Too small of a sample risks the survey providing inaccurate or inconclusive data, while an overly large sample wastes resources. Striking the right balance requires taking into account statistical principles, the specific research aims, and pragmatic considerations. This article provides an overview of the factors to consider when determining sample size for a dissertation survey. It aims to help students calculate an optimal sample that aligns with academic requirements and is feasible within time and budget constraints.

Key Factors in Determining Sample Size

When embarking on determining sample size for a dissertation survey, there are some key factors and criteria to consider:

Population Size

The size of the overall population being studied is the foremost consideration. The population refers to the full group that the survey aims to represent and generalize findings to. For example, if researching stress levels among nurses in a particular city hospital, the target population would be all nurses working at that hospital. The larger the total population, the more respondents needed for an adequate sample.

Confidence Level and Margin of Error

The confidence level indicates how sure the researcher can be that responses reflect the wider population within a certain margin of error. A 95% confidence level is standard for most academic research. The margin of error is the amount of potential difference between the survey results and real population parameters. A typical margin of error is 5%. Together, the confidence level and margin of error determine the sample size needed to achieve sufficient statistical power and precision.

Types of Analysis

The types of quantitative analysis being applied to the survey data impact ideal sample size. More complex statistical tests require larger sample sizes to produce valid results. For instance, regression analysis typically needs a bare minimum of 50 respondents, while factor analysis calls for at least 100 respondents and preferably closer to 250.

Number of Subgroups

If the research aims to compare or analyze differences between subgroups in the population, each subgroup needs an adequate number of respondents. As a general rule, each subgroup should contain at least 30 respondents for the comparisons to be statistically valid and reliable.

Resource Constraints

Practical considerations like timeline, budget, and access to respondents are also weighed when determining a target sample size. While a probabilistic sample of 1000 respondents may be ideal, it may not be feasible within the limitations of a dissertation study. Constraints force a balance between scientific precision and pragmatism.

Common Sample Sizes for Dissertation Surveys

Taking the key factors into consideration, sample sizes in the range of 100 to 1000 respondents are typical for dissertation surveys:

– 100 respondents – Minimum sample providing usable data for simple analysis. Allows for basic descriptive statistics and frequency distributions.

– 150-200 respondents – Recommended minimum for correlations, cross tabulations, and basic statistical testing. Enables more sophisticated analysis than a sample of 100.

– 250-500 respondents – Ideal range for factor analysis, multivariate regressions, and subgroup comparisons. Provides statistical power while remaining manageable for thorough analysis.

– 500-1000 respondents – Maximum needed except for highly complex research. Allows extensive analysis but requires extensive time and resources to collect and analyze.

– Over 1000 respondents – Only justified for studies aiming to generalize results to an extremely large population or that employ advanced statistical techniques requiring immense sample sizes.

The majority of dissertation surveys fall in the 200-500 range as this balances robust analysis with feasibility. Consultation with academic supervisors can help determine an appropriate range.

How to Calculate Minimum Sample Size

For dissertation surveys, formal sample size calculations are used to determine the minimum number of responses needed. The required sample size depends primarily on the margin of error, confidence level, and population size. The steps are:

Step 1: Define the Confidence Level

The typical confidence level for academic research is 95%, which means there is a 95% probability that the sample reflects the actual population within the margin of error.

Step 2: Define the Margin of Error

The allowable margin of error is usually set at 5% for a dissertation survey. This means survey results could differ from true population values by +/- 5%.

Step 3: Identify the Population Size

The total population being studied needs to be estimated or known. Census data, organizational records, or published statistics can provide this.

Step 4: Plug Values into Sample Size Formula

The standard formula used is:

Sample Size = (Z-score)2 * StdDev*(1-StdDev) / (margin of error)2


Z-score = 1.96 for 95% confidence level

StdDev = Standard Deviation (use 0.5 for maximum variability)

Margin of Error = 0.05 for 5% margin

Plugging in the values:

Sample Size = (1.96)2 * (0.5)(1-0.5) / (0.05)2

Sample Size = 384

This provides the sample size for an infinite population.

Step 5: Adjust for Finite Population

If the target population is finite, the sample size needs to be adjusted downwards using this equation:

Adjusted Sample Size = Sample Size / [1 + (Sample Size – 1) / Population]

For example, for a population of 5000, the adjusted sample size with a 384 baseline would be:

Adjusted Sample Size = 384 / [1 + (384 – 1) / 5000] = 357

The adjusted figure provides the minimum sample size needed.

How to Determine Optimal Sample Size

While the calculations provide a useful guideline, the optimum sample size for a particular dissertation survey entails combining the statistical requirements with pragmatic considerations. Ways to determine an optimal, feasible target include:

  • Consult academic supervisors on requirements and recommendations based on similar past projects.
  • Review sample sizes used in published studies with comparable research questions and methods.
  • Consider resources available and estimate realistic response rates when recruiting participants.
  • Increase minimum sample size to exceed subgroups needed for the analysis (e.g. by gender, age, etc).
  • Collect pilot data from a small initial sample and determine variability to better estimate sample size.
  • Account for contingencies like unusable or incomplete survey responses.

Factors When Sample Size Exceeds Minimum

For dissertation surveys, it can be advantageous to exceed the minimum sample size required. Collecting data from a larger sample:

  • Increases the statistical power of analysis.
  • Allows for more subgroup comparisons.
  • Enables the use of more sophisticated analytical techniques.
  • Yields more generalizable and publishable results.
  • Provides greater reassurance that the data is representative.
  • Offers insurance against discarded or missing data.

However, resources required to collect, process, and analyze a much larger sample must also be considered. The benefits of a larger sample need to be weighed against feasibility constraints.

Factors When Sample Size is Below Minimum

While not ideal, dissertation surveys may end up with sample sizes below the minimum calculated. This can occur due to:

  • Difficulty accessing the target population.
  • Lower than expected response rates to recruitment efforts.
  • Limited timeline and resources for data collection.
  • High rate of incomplete or unusable responses.

If this happens, steps can be taken to strengthen validity:

  • Report limitations imposed by the smaller sample size.
  • Restrict breadth of research questions and analysis.
  • Supplement with qualitative data for richer insights.
  • Increase subsample sizes to minimums needed for key tests.
  • Adjust confidence level downward from 95% to 90% or 80%.

Providing justification for the sample size and acknowledging limitations can still allow usable insights to be derived.

Guidelines for Subsample Sizes

In addition to total sample size, subgroup samples sizes are an important consideration when designing dissertation surveys. Subsamples by categories such as gender, age groups, and education levels allow comparative analysis. Some key guidelines include:

  • Minimum subsample size of 30 to provide sufficient cases for basic statistical analysis.
  • 50 or more per subgroup for bivariate analysis between subgroups.
  • 100 or more to conduct multivariate analysis controlling for other factors.
  • Proportional allocation to reflect composition of wider population.
  • Equal allocation across subgroups if comparative testing is a priority.

When resources allow, having at least 50 to 100 respondents per key subgroup enables robust findings from comparative analysis.

Obtaining a Representative Sample

While hitting target sample sizes is important, having a sample that is representative of the populations is arguably even more critical for dissertation survey research. Some tips for achieving a representative sample:

  • Use random sampling to give all population members an equal chance of selection.
  • Stratify the sample across relevant subgroups in proportion to their representation.
  • Adopt robust procedures to recruit respondents and minimize self-selection bias.
  • Apply demographic quotas to mirror the wider population.
  • Use weighting techniques to correct imbalances from convenience sampling.
  • Report sample characteristics to demonstrate representativeness.

Representativeness enables generalizing findings more confidently from the sample to the target population.

Optimizing Sample Size Using Power Analysis

Power analysis offers a more rigorous approach to determine optimal sample size. The key steps are:

  1. Specify expected differences or relationships to detect (effect size).
  2. Define other parameters like significance level and number of variables.
  3. Determine desired statistical power.
  4. Use power analysis tables or software to calculate minimum sample size.
  5. Adjust upwards for anticipated data losses and subgroup sizes.

Power analysis customizes sample size to the specific hypotheses and analysis. It requires clear research design and expected outcomes. If used, power analysis should be reported in the dissertation methodology.

Role of Qualitative Research

For mixed methods dissertation research, integrating a qualitative component complements the sample size needs for quantitative survey data. Ways qualitative data can assist with sampling include:

  • Providing contextual background to refine research hypotheses and instrument design prior to the survey.
  • Informing strategies to access hard-to-reach populations for recruitment.
  • Helping explain outlier responses that differ from main survey findings.
  • Strengthening conclusions by validating quantitative results.

Qualitative techniques like interviews and focus groups require far smaller samples. Including open-ended survey questions also generates useful textual data for analysis.

Ethical Considerations

When determining sample sizes for dissertation surveys, ethical obligations must be considered:

  • Avoid excessively large samples that waste resources or the time of volunteers.
  • Protect vulnerable groups from over-research and fatigue effects.
  • Adhere to data protection standards for collecting and storing sensitive information.
  • Allow participants to withdraw at any time regardless of impact on sample size.
  • Do not create risks by disclosing personally identifiable data from small subsample groups.

Research integrity, voluntary participation, and avoiding participant harm are imperative regardless of benefits from larger samples.


Determining sample size for a dissertation survey involves balancing multiple factors. While a minimum sample size can be calculated based on statistical principles, the optimum number of responses considers both scientific requirements and pragmatic constraints. Typical samples range from 100 to 1000, with 200-500 enabling robust analysis for most dissertation research. Equally or more important than the total size is ensuring the sample accurately reflects the characteristics of the target population. Combined with qualitative data, an appropriately sized survey sample allows graduate students to derive valid conclusions and contribute original insights.

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