Survey statistics: methods, examples, and best practices

Survey data analysis is the natural next step after data collection. With the right information and tools, you can use statistical methods to analyze your survey data without being an expert.

You can assess if observed trends are significant, compare your data, identify the most influential factor on your company, guide your next research efforts, and use survey insights to drive meaningful change.

This guide will help you analyze survey data using statistical methods.

Data analysis is the systematic approach to examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.

Data analysis simplifies the process of studying data trends, patterns, and relationships by employing various statistical techniques. It enables researchers and organizations to identify actionable insights that can inform strategies and solutions.

By analyzing survey statistics, you can:

There are several types of statistical analysis for surveys, and the right one will depend on your survey goals, the type of data, the data collection method, resources, and the sophistication of your data analysis software.

When you're ready to analyze your survey data, you'll want to choose a method that best suits your data and research goals. 

CHAPTER 1: CLEANING YOUR DATA

Before you can begin your analysis, you need to clear your survey data.

Cleaning survey data includes identifying and removing any answers from respondents who don't match your target market or didn't answer questions thoughtfully.

If you skip this step, you reduce your study's credibility and the ability to capture valuable insights.

CHAPTER 2: CHOOSING YOUR ANALYSIS METHOD

Selecting one of several survey statistical analysis methods will depend on the level of measurement and the number of variables involved. 

Three levels of measurement dictate how to measure survey questions and which statistical analysis method to use.

Nominal data classifies data that doesn't have quantitative value. Any numeric scores assigned to response categories are arbitrary.

For example, “Choose your preferred toothpaste brand from the list below.” From this data, you can only track how many respondents chose each option and which was selected most.

Ordinal scales classify data with a quantitative value to show its ranking order.

For example, ordinal scales that place data in ranks could include support-oppose, agree-disagree, or excellent-poor rating scales.

You can determine the median and mode. You can also analyze ordinal scale data through cross-tabulation.

Interval scales show both the order and difference between values. It's a quantitative measurement scale that shows order, a meaningful and equal difference between variables, and the presence of zero is arbitrary.

Examples of survey interval scales would be age in years or monthly spending in dollars. There is also a quantitative value; you can analyze the median, mode, and mean. 

As a reminder, the mean is what most of us refer to as the average of a set of numbers, the median is the middle number in a set of values, the mode is the most common number in a dataset, and the range is the difference between the largest and smallest number in the dataset.

Including the number of variables, you should consider the type of variable.

A dependent variable is a variable that is being tested and measured. An independent variable is a research component that the researcher can manipulate or change. This independent variable is assumed to directly affect the dependent variable. 

As you'll see, the number and type of variables and level of measurement factor heavily into your decision when choosing a survey statistical analysis method. 

CHAPTER 3: SELECT AN ANALYSIS METHOD

A frequency distribution is a representation of a survey dataset within a table. It is used to organize and summarize data. It is a list of values a variable takes in a dataset and the number of times each value occurs.

This statistical test compares the mean of two groups or the difference between one group's mean and a standard value or benchmark. This is generally used when the datasets come from the same population and may have unknown variances.

In this case, the population can be described as the full set of individuals who could potentially participate in your research, and variance measures the range of the responses. A T-test is used as a hypothesis-testing tool to understand whether the differences in groups are statistically significant. 

Because of this, it allows the following assumptions of the data:

Tip: While T-tests can tell you if something is significantly different, you will have to determine whether the identified difference is meaningful to your study.

There are two types of ANOVA tests:

One-way ANOVA compares the means of one independent variable with two or more groups to determine whether there is evidence that their population means are different. If there is a statistically significant result, the two populations are unequal (different).

Two-way ANOVA extends the one-way ANOVA to examine the influence of two different independent variables on one continuous dependent variable.

This type of analysis uses data tables to display the results of each respondent. It enables you to examine relationships that may not be overtly apparent when examining survey responses. Crosstabs are used for categorical data—values that are mutually exclusive to each other.

In regression analysis, a set of statistical methods is used to estimate the relationships between a dependent variable and one or more independent variables. Regression analysis identifies the precise impact of a change in the independent variable.

Cluster analysis groups data so that a particular set of data elements are more similar to each other than those in other groups. There is no dependent variable when clustering, so this method often indicates hidden patterns in the data. This can also provide additional context to the dataset.

This method, also called dimension reduction, is a way to reduce the complexity of your findings by trading a large number of initial variables for a smaller number of underlying variables. With factor analysis, you'll uncover hidden factors that explain variances in your findings. Factor analysis can be used as a pre-step in segmentation.

The clarity and specificity of your research questions heavily influence the quality and relevance of the data and insights you obtain. Well-defined research questions guide your investigative process, ensuring the information gathered directly addresses the core issues.

Consequently, the more thoughtful and precise your questions are, the higher the likelihood of acquiring valuable insights that can inform decision-making and contribute to meaningful conclusions.

Related reading: Sample survey questions and examples

When selecting a sampling method, it is crucial to choose one that precisely aligns with the specific objectives of your research study.

Consider factors such as the target population, the nature of the data needed, and the overall goals of your research to ensure that the sampling method effectively captures the essential characteristics of the group you intend to analyze.

Additionally, reflect on methods such as random sampling, stratified sampling, or convenience sampling, among others, to determine which will best facilitate the integrity and validity of your findings.

Evaluate and compare your data against established standards or benchmarks to identify performance levels, trends, and areas for improvement.

SurveyMonkey Benchmarks is a simple way to compare your survey results with thousands of other organizations. Benchmarking uses weighting to adjust variables that might affect overall results. This information provides you with a “standard” reference to help you identify variances in your data.

Statistical analysis of your survey data can seem daunting, but it's well worth it. You'll uncover information that can't be seen in a basic review of your survey results.

Several methods can help you glean the most relevant insights from your surveys. If you need help analyzing your data, check out these five best integrations to use with SurveyMonkey.

When you're ready to make the most of your survey data, start with SurveyMonkey.