Survey Research and Tools

Explore software options available at GW for building, distributing, and analyzing surveys. 

Understanding survey research can be broken down into a few simple ideas:

• Organizing Data: Survey research collects structured responses from many people to measure opinions, behaviors, or characteristics. Each question produces a variable, and each respondent contributes a record, creating a dataset that can be analyzed quantitatively, qualitatively, or both.
• Designing Questions: Good survey design starts with clear goals. Questions should be worded precisely, avoid bias, and fit the type of data you want, whether that’s a rating scale, multiple choice, or open-ended response. The order of questions and response options can shape how participants interpret them.
• Data Layout: Survey data is typically stored in a table: rows for participants, columns for questions, and cells for answers. Closed-ended questions yield numeric or categorical data, while open-ended responses provide rich text suitable for qualitative coding.
• Preparing Data: Before analysis, survey data must be cleaned: checking for missing values, removing duplicates, verifying response logic, and ensuring that variable names and labels are clear. Open-ended responses may be categorized or coded to identify common ideas.
• Analyzing Data: Analysis depends on question type. Quantitative items are summarized with descriptive or inferential statistics to find trends or group differences. Qualitative items are analyzed for themes or illustrative quotes. Together, these results help answer your core research questions.

Survey research transforms individual responses into a collective picture of what people think, do, or experience. The process begins by defining what you want to learn and who you want to reach. You then design your questions and sampling strategy to capture data that’s both meaningful and reliable.

After data collection, you clean, analyze, and interpret the results, often iteratively, checking whether findings align with your original goals or raise new questions. Reflecting on response quality, sample coverage, and potential bias helps ensure your conclusions are trustworthy.

While digital platforms and analytics tools simplify much of this process, thoughtful design and interpretation remain essential. The insight of the researcher -- how they frame questions, understand context, and interpret results -- is what turns survey data into the foundation of theory.