The use of online surveying is increasing, and research shows that the online survey market is expected to reach almost $14 billion by 2026. Having the right respondents is crucial for online surveys. However, data without a proper analysis may not tell a lot, is hard to interpret, and can lead you in the wrong direction. That’s why analyzing your data is as important as getting it.
Even though the quality of your data analysis depends mainly on your dataset and question types, there are some essential steps in conducting a proper analysis to get the best from your survey data. Here we focus specifically on online surveys and provide guidelines for getting best insights from your data through online survey analytics.
What are the types of data to be analyzed?
There are different data types, each with unique analysis methods. For instance, if you ask people where they are from, you cannot just rank them because no country has priority over others. Thus, you need to find a different way of interpreting these data types.
In general, there are two different types of data: qualitative and quantitative.
You can collect qualitative data to understand “why” or “how” a phenomenon occurs. For instance, when you want to understand why some customers do not prefer talking to chatbots, you can understand it by asking questions that require descriptions or explanations. Qualitative data is also called descriptive data and is usually represented by nouns, names, written content, and audio or video recordings.
Quantitative data refers to numbers, something that can be quantified and measured. Through quantitative data, you can understand “how much,” “how often,” “how likely,” or “when” a phenomenon occurs. Thus, it is an essential data type to understand the patterns of customers, the market, or the public in general.
What are the essential steps in conducting survey analysis?
1. Understand types of data you collected and the levels of measurement
As mentioned above, each data type requires a different analysis method. Thus, first, you need to identify your questions based on their levels of measurement.
Figure 1. The levels of measurement
2. Start with the less complicated data type
Qualitative data contains language and is harder to categorize correctly, so you may not get useful insights unless you conduct deep analysis. Thus, starting with quantitative data is better, as it is already structured and faster to analyze.
3. Determine a framework for qualitative data
As you deal with a bunch of words, you need a solid framework to analyze your data. The framework usually creates a coding scheme so that you can label your data and better understand the results.
4. (Optional but highly recommended): Implement AI techniques to analyze qualitative data
As manually labeling and analyzing the data requires great human effort, you can also implement AI-powered solutions. Natural Language Processing (NLP), for instance, is a method that enables machines to categorize texts or speeches and conduct an analysis much faster than humans.
You can learn more about data labeling with NLP by reading our comprehensive article.
5. Visualize your data
Although numbers are powerful in showing the relationship between different constructs, it may be hard to understand them at first glance. Thus, visualizing your data analysis can help you comprehend your findings better.
Figure 2. Data visualization techniques suitable for different measurement levels
To learn more, read our guide on data visualization.
How crowdsourcing can help analyzing surveys?
Crowdsourcing platforms offer sophisticated solutions for survey analysis:
- Diverse Perspectives: They host a vast array of individuals from varied backgrounds and using different languages, ensuring a multifaceted analysis of open-ended survey responses.
- Operational Scalability: For extensive surveys, these platforms facilitate rapid processing, negating the need to burden internal teams.
- Cost Efficiency: Instead of engaging a team for survey projects, one can strategically allocate resources by utilizing these platforms as required.
- Data Standardization: The crowd can be asked to pre-process the survey data, ensuring the data is primed for rigorous analysis.
- Qualitative Analysis Enhancement: Open-ended responses can be systematically categorized, coded, and assessed to derive insights.
In short, crowdsourcing platforms increase efficiency, offering a sophisticated and cost-effective methodology for comprehensive survey analysis. Here is our article on crowdsourcing sentiment analysis, if interested.
For those interested, here is also our benchmarking on the available survey participant recruitment services in the market.
You can also check out our data-driven list of survey participant recruitment services and survey tools.
Reach us if you have any questions about conducting research surveys:
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