Your guide to mastering survey data analysis- Part 1

Data is potent in driving big change and transformation, but only when it is meaningful. Turning your survey data into clear, meaningful, compelling analysis isn’t always a straightforward task. But first things first, what is survey data? Survey data refers to information collected through a systematic process of asking questions to individuals or groups of people in order to gather insights, opinions, attitudes, behaviors, or characteristics on a particular topic of interest.

 

Surveys are a common research method used in various fields such as social sciences, market research, psychology, public health, and more. The data collected from surveys can provide valuable insights into trends, preferences, and patterns among a specific population. Because of this, survey data becomes a crucial component of any organization’s strategy.

 

We’ve collected our experience into survey analysis to put together some good-to-know for starters about survey data components and steps to analyze the results.

Components of survey analytics

1. Sampling

Sampling is like taking a snapshot of a vast landscape. In survey research, data is collected from a subset of the larger population, known as the sample. The art of sampling lies in selecting this subset in a way that reflects the diverse characteristics of the entire population. The choice of the sample, whether random, stratified, or convenience-based, impacts how well the findings can be generalized to the larger population. It’s a critical step in ensuring that the survey’s insights hold weight beyond the sample itself.

 

Weighting in survey data analysis is a critical step that follows the sampling process. Once a sample is collected, it may not perfectly represent the entire population, and some groups or segments might be underrepresented or overrepresented. Weighting is a statistical technique used to address these imbalances and ensure that the survey results accurately reflect the population being studied.

 

Example: Imagine you want to conduct a political survey to understand public opinion. The entire population consists of all eligible voters in a country. Instead of surveying every single voter (which would be impractical), you select a sample of 1,000 voters using random sampling. This sample should reflect the country’s diversity in terms of age, gender, geographical regions, and political affiliations, ensuring your findings can be applied to the entire voter population.

2. Questionnaire

The questionnaire is the bridge between researchers and respondents. It’s a carefully constructed tool, comprising a series of questions designed to extract specific information from those participating in the survey. These questions can take on various forms, from the structured nature of multiple-choice to the open-ended canvas of open questions. Likert scales are employed to gauge attitudes, providing a spectrum of responses. The questionnaire’s construction is an art, as the quality of questions directly impacts the quality of the data gathered.

 

Example: In a customer satisfaction survey for a restaurant, the questionnaire includes questions like “On a scale of 1 to 5, how satisfied are you with the food quality?” (Likert scale), “What dish did you order?” (open-ended), and “Would you recommend us to a friend?” (multiple-choice). These questions are carefully designed to gather specific feedback from diners.

3. Data collection techniques

Just as artists select the right canvas, survey researchers choose the appropriate data collection method. Surveys can be administered through face-to-face interviews, telephone conversations, online interfaces, paper-based questionnaires, or even mobile apps. The choice of method depends on a multitude of factors, including the characteristics of the target population, the nature of the questions, available resources, and the desired depth of insights. It’s here that the survey’s practical journey begins.

 

Example: If you’re conducting a survey about smartphone usage among teenagers, you might choose to use an online survey via a mobile app. This method is more likely to reach and engage your target audience. In contrast, a face-to-face interview might be suitable for collecting in-depth responses about healthcare experiences from a group of elderly patients.

4. Data analysis

Data analysis for survey data is a pivotal phase in the survey research process, as it transforms collected information into meaningful insights. Survey data often contain a wealth of responses from participants, and the goal of data analysis is to extract, interpret, and communicate the patterns, trends, and insights hidden within the data.
One of the key distinctions in survey data analysis is the differentiation between quantitative and qualitative data. Quantitative data involves numerical values and structured responses, making it amenable to statistical analysis. Researchers can compute averages, percentages, correlations, and perform hypothesis testing to uncover statistically significant relationships.

 

Example: If your survey examines the impact of a new educational program, quantitative analysis can reveal that student scores increased by an average of 15%. Qualitative analysis of open-ended comments might highlight that students particularly appreciated the interactive learning materials.

5. Reporting and presentation

Reporting and presentation are the final and critical steps in the journey of survey data analysis. These steps transform raw data and analysis into a format that can be easily understood and used by stakeholders, whether they are organizational leaders, policymakers, or the general public.
In addition to visual aids, it is vital to include context and interpretation in reports and presentations. Simply presenting numbers and graphs is insufficient; the data must be accompanied by explanations, analysis, and recommendations. Providing context helps to explain the significance of the findings and the implications for decision-making. Interpretation bridges the gap between data and actionable insights, while recommendations guide the next steps.

 

Example: After a survey on workplace satisfaction, you prepare a report with clear charts and graphs that show the overall satisfaction score, broken down by department. The presentation highlights the key findings, making it easy for company leaders to understand the results and take action.

Steps to analyze survey data

Analyzing survey data holds paramount importance as it provides a lens through which to understand and decipher the thoughts, behaviors, and opinions of respondents. This analytical process unveils hidden trends, underlying patterns, and shifts in attitudes, contributing to informed decision-making. The insights derived from survey data lend credibility to hypotheses, validating assumptions and enriching research.

 

By decoding responses, organizations can identify growth prospects, address challenges, and tailor strategies to suit evolving needs. This analytical journey also fosters personalized engagement, cultivates customer satisfaction, and offers a platform for evaluating the efficacy of interventions. Overall, survey data analysis is the gateway to insights that lead to business growth, innovation, and effective communication.

 

Here are some steps to undergo while analyzing survey data:

1. Data preparation and cleaning

2. Exploratory Data Analysis (EDA)

3. Data transformation

4. Hypothesis testing and statistical analysis

5. Segmentation and group comparisons

6. Qualitative analysis (if applicable):

7. Visualization

8. Interpretation and conclusion

9. Results and report

Conclusion

Survey data might be an underrated tool of gaining consumer understanding, but the insights it can provide are invaluable. Survey data is a highly reliable way of knowing not only what your consumers are doing, but why they’re doing it. Survey data allows you to dig deeper than other sources allow, helping you understand far more than what’s on the surface. This enables organizations to design data-driven buyer personas and consumer journey maps that build a foundation for truly cutting-edge personalized marketing.

 

Stay tuned for the next part of this blog, we will be covering some best practices to derive insights from the collected data.

About the author

Jayant Singh is a Senior Data Scientist at Sigmoid. He has a strong expertise in AI/ML algorithms. In his current role, he leverages the data science technology spectrum to solve complex business problems and design customized cutting-edge solutions. His innovative solutions have consistently contributed to success for CPG, Retail and Fintech companies globally.