The Complete Guide to Survey Sampling + Sample Size Calculator

The Complete Guide to Survey Sampling + Sample Size Calculator

Introduction

“Think bigger by focusing smaller.” That’s the unexpected mantra in today’s market research, a legacy left by George Gallup, who demonstrated the strength of sampling with his groundbreaking prediction in the 1936 U.S. Presidential election. Using a methodical sample of 50,000, Gallup’s approach outshone magazine polls with millions of responses, proving that quality trumps quantity. In this guide, we embrace this principle, cutting through the noise to show you how the right sample size can offer deeper market insights. Armed with a Sample Size Calculator, you’re set for smarter, not harder, market research.

Basics of sampling

Sampling in market research, at its heart, is about representation—a concept that Gallup masterfully showcased. It’s the art of selecting a part to capture the essence of the whole, a cost-effective and practical strategy that remains central to extracting valuable market insights. Where extensive data collection is neither feasible nor efficient, well-designed sampling methods step in, allowing for the efficient gathering of insights that reflect the broader market. This technique, founded on the principles of representativeness and accuracy, continues to be the cornerstone of market research.

Sampling methods

There are several sampling methods. By understanding these, you can tailor your approach to align with your research objectives, budget, and time constraints:

  • Random Sampling: Offers an unbiased selection, with each individual having an equal chance of being chosen. Ideal for general market research.
    • Pros: Eliminates selection bias.
    • Cons: May not represent specific segments.
  • Stratified Sampling: Segregates the population into subgroups, sampling from each for diverse representation. Best for studies requiring detailed segment analysis.
    • Pros: Enhances representation across groups.
    • Cons: Requires detailed population knowledge.
  • Systematic Sampling: This method selects individuals at regular intervals, beginning at a random point. It’s effective for evenly distributing samples across a population.
    • Pros: Ensures a uniform sample spread, reducing bias.
    • Cons: If the population is widely dispersed, it may require more time and resources.

Sample sizes

Determining the right sample size is crucial. Too small, and your data might not be credible. Too large, and you might be investing unnecessary resources. The magic lies in finding that balance.

For example, a smaller population might need a larger sample percentage to be representative. On the other hand, with larger populations, you can achieve the same confidence level with a smaller sample percentage.

  • Small Samples (around 30-100 respondents):Suitable for niche or highly targeted populations. Offers quick insights but with a higher margin of error.
  • Medium Samples (about 100-300 respondents): Commonly used for small to medium-sized market research. Balances the need for diverse insights with resource constraints.
  • Large Samples (300+ respondents): Ideal for representing a large population or for results that require high precision. More resources and time are needed, but the results are typically more reliable and have a lower margin of error.

How to draw a survey sample

Community Extension in Survalyzer acts as a bridge to your ideal respondents. It’s like forming an exclusive club tailored to your research needs, comprising individuals selected based on age, location, interests, or other key criteria. This setup is more than gathering people; it’s about building a continuously engaged panel that provides ongoing valuable insights.

Once your panel is established, the next critical step is determining the size of your sample. This is where the precision our new research tools comes into play.

Defining Sample Size

Transitioning from panel creation to sample size determination, the Sample Size Calculator in Survalyzer emerges as a vital tool. The calculator’s role is pivotal in ensuring your sample size accurately reflects and respects the characteristics of your target population while remaining practical and manageable. It simplifies a complex process: you input your total population size, required confidence level, and margin of error:

  • The margin of error helps determine how closely your survey results may reflect the overall population’s views.
  • When using the Sample Size Calculator in Survalyzer, you’ll need to input your target group’s total size (population size). If you’re unsure of the exact number, it’s okay to leave this blank. The calculator can work with estimates or even without a population size, adjusting the sample size accordingly. Generally, a larger population size leads to a sample size closer to what you’d expect with an infinite population. Also, you’ll set a confidence level, typically at 95%, which can be adjusted. This indicates the likelihood that the real value lies within your survey’s errorrange.
  • But what’s a confidence level? As explained in Survalyzer’s guide on significance testing, it’s about how sure you want to be about your results. A 95% confidence level, often used, means there’s only a 5% chance your findings are due to random chance. This calculator is your shortcut to ensuring your sample is neither too big (wasteful) nor too small (unreliable). It guides you in making informed decisions about sample size, based on your research goals and the characteristics of your target population.
The first page of Survalyzer's survey sample size calculator

How it works

The Sample Size Calculator in Survalyzer offers four initial options, each catering to different aspects of survey design:

  • Minimum Sample Size for Proportions: Ideal for surveys with categorical data, like yes/no or multiple-choice questions. This option helps you find the smallest sample size needed to estimate what percentageof your population falls into each category.
  • Margin of Error for Proportions from Previous Surveys: Use this if you have previously conducted a similar survey. It calculates how much the estimated percentage might vary from the true population percentage, based on your past survey’s data.
  • Minimum Sample Size for Means: Best for surveys with continuous data, such as ratings on a scale. This helps determine the smallest number of responses needed to accurately find the average(or ‘mean’) response.
  • Margin of Error for Means from Previous Surveys: Calculates the possible variation in the average response, using data from a survey you’ve done before.

Each option allows you to refine your sample size or margin of error, depending on whether you’re dealing with categorical (proportions) or continuous (means) data, and whether you have data from a previous survey. By selecting the appropriate option, you can move to the main calculator page, where you’ll input your total population size, desired confidence level, and acceptable margin of error.

Interface of Survalyzer's calculator for calculating survey sample size

Entering Sample Size

Once you’ve got your magic number, it’s time to plug it into your survey tool. This step is more than just a number entry; it’s about mapping your survey’s journey across your audience. With options like those described in Survalyzer’s guide on additional distribution options for panels, you can fine-tune who receives your survey and how.

This is where you decide the ‘who’ and ‘how’ of reaching your audience. Maybe you want to zero in on a specific age group, or ensure that every part of your demographic gets a voice. Survalyzer’s flexibility allows you to tailor your approach, ensuring that your sample truly reflects the group you’re studying.

Alternative Sampling Approach

Moving from the structured environment of Survalyzer’s Community Extension, let’s explore an alternative approach for those who might not have access to this feature. This method involves a bit of DIY and makes use of tools you likely already have at your disposal.

Export Client Data to Spreadsheet

If you’re working without the Community Extension, start by exporting your client data from your CRM system to a Google Spreadsheet or MS Excel. This step transforms your database into a manageable list, laying the groundwork for random sampling.

Utilize RAND Function

In your spreadsheet:

  1. Apply RAND Function: In a new column next to your email list, use the =RAND() formula. This function generates a random number between 0 and 1 for each row. It’s a simple way to introduce randomness to your list.

  2. Sort Your Data: Sort the entire list by the column containing the random numbers. This effectively shuffles your email addresses, randomizing the order.

  3. Select Your Sample: After sorting, select the top portion for your sample (e.g., the first 500 rows for a sample of 500).

  4. Copy and Prepare for Import: Copy your selected emails to the new excel file. These are the emails you’ll import into Survalyzer for your survey.

To keep track of the selected emails in the future, it is advised to mark them in an additional column with a note like “sample.” RAND() function changes numbers with every edit and it may require more manual work in the future to cross-reference files without that additional marker.

Import Filtered List to Survalyzer

Once you have your randomly selected sample, the final step is to import this filtered list back into Survalyzer. This way, you can conduct your survey with a sample that, while manually curated, still adheres to the principles of randomness and representativeness.

Conclusion

Sampling in market research is more than a technique; it’s a gateway to understanding your audience. Through this guide, we’ve delved into various methods and Survalyzer’s Community Extension, offering insights to enrich your research. Keen on harnessing these tools for sharper, more effective data analysis? A conversation with our sales team can lead you to more insights and tailored solutions. Reach out, and let’s transform how you gather and interpret market data.

Christian Hyka

Managing Partner

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