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How to analyze survey data: best practices for actionable insights from survey analysis

Just started using a new survey tool ? Collected all of your survey data? Great. Confused about what to do next and how to achieve the optimal survey analysis? Don’t be.

If you’ve ever stared at an Excel sheet filled with thousands of rows of survey data and not known what to do, you’re not alone. Use this post as a guide to lead the way to execute best practice survey analysis.

Customer surveys can have a huge impact on your organization. Whether that impact is positive or negative depends on how good your survey is (no pressure). Has your survey been designed soundly ? Does your survey analysis deliver clear, actionable insights? And do you present your results to the right decision makers? If the answer to all those questions is yes, only then new opportunities and innovative strategies can be created.

What is survey analysis?

Survey analysis refers to the process of analyzing your results from customer (and other) surveys. This can, for example, be Net Promoter Score surveys that you send a few times a year to your customers.

Why do you need for best in class survey analysis?

Data on its own means nothing without proper analysis. Thus, you need to make sure your survey analysis produces meaningful results that help make decisions that ultimately improve your business.

There are multiple ways of doing this, both manual and through software, which we’ll get to later.

Types of survey data

Data exists as numerical and text data, but for the purpose of this post, we will focus on text responses here.

Close-ended questions

Closed-ended questions can be answered by a simple one-word answer, such as “yes” or “no”. They often consist of pre-populated answers for the respondent to choose from; while an open-ended question asks the respondent to provide feedback in their own words.

Closed-ended questions come in many forms such as multiple choice, drop down and ranking questions.

In this case, they don’t allow the respondent to provide original or spontaneous answers but only choose from a list of pre-selected options. Closed-ended questions are the equivalent of being offered milk or orange juice to drink instead of being asked: “What would you like to drink?”

These types of questions are designed to create data that are easily quantifiable, and easy to code, so they’re final in their nature. They also allow researchers to categorize respondents into groups based on the options they have selected.

Open-ended questions

An open-ended question is the opposite of a closed-ended question. It’s designed to produce a meaningful answer and create rich, qualitative data using the subject’s own knowledge and feelings.

Open-ended questions often begin with words such as “Why” and “How”, or sentences such as “Tell me about…”. Open-ended questions also tend to be more objective and less leading than closed-ended questions.

How to analyze survey data

How do you find meaningful answers and insights in survey responses?

To improve your survey analysis, use the following 5 steps:

• Start with the end in mind – what are your top research questions?
• Filter results by cross-tabulating subgroups
• Interrogate the data
• Analyze your results
• Draw conclusions

1. Check off your top research questions

Go back to your main research questions which you outlined before you started your survey. Don’t have any? You should have set some out when you set a goal for your survey. (More on survey planning below).

A top research question for a business conference could be: “How did the attendees rate the conference overall?”.

The percentages in this example show how many respondents answered a particular way, or rather, how many people gave each answer as a proportion of the number of people who answered the question.

Thus, 60% or your respondents (1098 of those surveyed) are planning to return. This is the majority of people, even though almost a third are not planning to come back. Maybe there’s something you can do to convince the 11% who are not sure yet!

2. Filter results by cross-tabulating subgroups

At the start of your survey, you will have set up goals for what you wanted to achieve and exactly which subgroups you wanted to analyze and compare against each other.

This is the time to go back to those and check how they (for example the subgroups; enterprises, small businesses, self-employed) answered, with regards to attending again next year.

For this, you can cross-tabulate, and show the answers per question for each subgroup.

Here, you can see that most of the enterprises and the self-employed must have liked the conference as they’re wanting to come back, but you might have missed the mark with the small businesses.

By looking at other questions and interrogating the data further, you can hopefully figure out why and address this, so you have more of the small businesses coming back next year.

You can also filter your results based on specific types of respondents, or subgroups. So just look at how one subgroup (women, men) answered the question without comparing.

Then you apply the cross tab to look at different attendees to look at female enterprise attendees, female self-employed attendees etc. Just remember that your sample size will be smaller every time you slice the data this way, so check that you still have a valid enough sample size.

3. Interrogate the data

Look at your survey questions and really interrogate them. The following are some questions we use for this:

• What are the most common responses to questions X?
• Which responses are affecting/impacting us the most?
• What’s different about this month/this year?
• What did respondents in group Y say?
• Which group of respondents are most affected by issue Z?
• Have customers noticed our efforts in solving issue Z?
• What do people say about Z?

For example, look at question 1 and 2. The difference between the two is that the first one returns the volume, whereas in the second one we can look at the volume relating to a particular satisfaction score. If something is very common, it may not affect the score. But if, for example, your Detractors in an NPS survey mention something a lot, that particular theme will be affecting the score in a negative way. These two questions are important to take hand in hand.

You can also compare different slices of the data, such as two different time periods, or two groups of respondents. Or, look at a particular issue or a theme, and ask questions such as “have customers noticed our efforts in solving a particular issue?”, if you’re conducting a continuous survey over multiple months or years.

For tips on how to analyze results, see below. This is a whole topic in itself, and here are our best tips. For best practice on how to draw conclusions you can find in our post  How to get meaningful, actionable insights from customer feedback .

4 best practices for analyzing survey data

Make sure you incorporate these tips in your analysis, to ensure your survey results are successful.

1. Ensure sample size is sufficient

To always make sure you have a sufficient sample size, consider how many people you need to survey in order to get an accurate result.

You most often will not be able to, and shouldn’t for practicality reasons, collect data from all of the people you want to speak to. So you’d take a sample (or subset) of the people of interest and learn what we can from that sample.

Clearly, if you are working with a larger sample size, your results will be more reliable as they will often be more precise. A larger sample size does often equate to needing a bigger budget though.

The way to get around this issue is to perform a sample size calculation before starting a survey. Then, you can have a large enough sample size to draw meaningful conclusions, without wasting time and money on sampling more than you really need.

Consider how much margin of error you’re comfortable working with first, as your sample size is always an estimate of how the overall population think and behave.

2. Statistical significance – and why it matters

How do you know you can “trust” your survey analysis ie. that you can use the answers with confidence as a basis for your decision making? In this regard, the “significant” in statistical significance refers to how accurate your data is. Or rather, that your results are not based on pure chance, but that they are in fact, representative of a sample. If your data has statistical significance, it means that to a large extent, the survey results are meaningful.

It also shows that your respondents “look like” the total population of people about whom you want to draw conclusions.

3. Focus on your insights, not the data

When presenting to your stakeholders, it’s imperative to highlight the insights derived from your data, rather than the data itself.

You’ll do yourself a disservice. Don’t even present the information from the data. Don’t wait for your team to create insights out of the data, you’ll get a better response and better feedback if you are the one that demonstrates the insights to begin with, as it goes beyond just sharing percentages and data breakouts.

4. Complement with other types of data

Don’t stop at the survey data alone. When presenting your insights, to your stakeholders or board, it’s always helpful to use different data points and which might include even personal experiences. If you have personal experience with the topic, use it! If you have qualitative research that supports the data, use it!

So, if you can overlap qualitative research findings with your quantitative data, do so.

Just be sure to let your audience know when you are showing them findings from statistically significant research and when it comes from a different source.

3 ways to code open-ended responses

When you analyze open-ended responses, you need to code them. Coding open-ended questions have 3 approaches, here’s a taster:

• Manual coding by someone internally.   If you receive 100-200 responses per month, this is absolutely doable. The big disadvantage here is that there is a high likelihood that whoever codes your text will apply their own biases and simply not notice particular themes, because they subconsciously don’t think it’s important to monitor.
• Outsource to an agency.  You can email the results and they would simply send back coded responses.
• Automating the coding.  You use an algorithm to simulate the work of a professional human coder.

Whichever way you code text, you want to determine which category a comment falls under. In the below example, any comment about friends and family both fall into the second category. Then, you can easily visualize it as a bar chart.

Code frames can also be combined with a sentiment.

Below, we’re inserting the positive and the negative layer under customer service theme.

So, next, you apply this code frame. Below are snippets from a manual coding job commissioned to an agency.

In the first snippet, there’s a code frame. Under code 1, they code “Applied courses”, and under code “2 Degree in English”. In the second snippet, you can see the actual coded data, where each comment has up to 5 codes from the above code frame. You can imagine that it’s actually quite difficult to analyze data presented in this way in Excel, but it’s much easier to do it using software.

The best survey analysis software tools

Traditional survey analysis is highly manual, error-prone, and subject to human bias. You may think of this as the most economical solution, but in the long run, it often ends up costing you more (due to time it takes to set up and analyze, human resource, and any errors or bias which result in inaccurate data analysis, leading to faulty interpretation of the data.  So, the question is:

Do you need software?

When you’re dealing with large amounts of data, it is impossible to manage it all properly manually. Either because there’s simply too much of it or if you’re looking to avoid any bias, or if it’s a long-term study, for example. Then, there is no other option but to use software”

On a large scale, software is ideal for analyzing survey results as you can automate the process by analyzing large amounts of data simultaneously. Plus, software has the added benefit of additional tools that add value.

Below we give just a few examples of types of software you could use to analyze survey data. Of course, these are just a few examples to illustrate the types of functions you could employ.

1. Thematic software

As an example, with Thematic’s software solution you can identify trends in sentiment and particular themes. Bias is also avoided as it is a software tool, and it doesn’t over-emphasize or ignore specific comments to come to unquantified conclusions.

Below is an example we’ve taken from the tool, to visualize some of Thematic’s features.

Our visualizations tools show far more detail than word clouds, which are more typically used.

You can see two different slices of data. The blue bars are United Airlines 1 and 2-star reviews, and the orange bars are the 4 and 5-star reviews. It’s a fantastic airline, but you can identify the biggest issue as mentioned most frequently by 1-2 stars reviews, which is their flight delays. But the 4 and 5-star reviews have frequent praise for the friendliness of the airline.

You can find more features, such as Thematic’s Impact tool, Comparison, Dashboard and Themes Editor  here.

If you’re a DIY analyzer, there’s quite a bit you can do in Excel. Clearly, you do not have the sophisticated features of an online software tool, but for simple tasks, it does the trick. You can count different types of feedback (responses) in the survey, calculate percentages of the different responses survey and generate a survey report with the calculated results. For a technical overview, see  this article.

You can also build your own text analytics solution, and rather fast.

How to build a Text Analytics solution in 10 minutes

The following is an excerpt from a blog written by Alyona Medelyan, PhD in Natural Language Processing & Machine Learning.

As she mentions, you can type in a formula, like this one, in Excel to categorize comments into “Billing”, “Pricing” and “Ease of use”:

It can take less than 10 minutes to create this, and the result is so encouraging! But wait…

Everyone loves simplicity. But in this case, simplicity sucks

Various issues can easily crop up with this approach, see the image below:

Out of 7 comments, here only 3 were categorized correctly. “Billing” is actually about “Price”, and three other comments missed additional themes. Would you bet your customer insights on something that’s at best 50 accurate?

Developed by QRS International,  Nvivo  is a tool where you can store, organize, categorize and analyze your data and also create visualisations. Nvivo lets you store and sort data within the platform, automatically sort sentiment, themes and attribute, and exchange data with SPSS for further statistical analysis. There’s a transcription tool for quick transcription of voice data.

It’s a no-frills online tool, great for academics and researchers.

4.  Interpris

Interpris is another tool from QRS International, where you can import and store free text data directly from platforms such as Survey Monkey and store all your data in one place. It has numerous features, for example automatically detecting and categorizing themes.

Favoured by government agencies and communities, it’s good for employee engagement, public opinion and community engagement surveys.

Other tools worth mentioning (for survey analysis but not open-ended questions) are SurveyMonkey, Tableau and DataCracker.

There are numerous tools on the market, and they all have different features and benefits. Choosing a tool that is right for you will depend on your needs, the amount of data and the time you have for your project and, of course,  budget. The important part to get right is to choose a tool that is reliable and provides you with quick and easy analysis, and flexible enough to adapt to your needs.

An idea is to check the list of existing clients of the product, which is often listed on their website. Crucially, you’ll want to test the tool, or at the least, get a demo from the sales team, ideally using your own data so that you can use the time to gather new insights.

A few tips on survey design

Good surveys start with smart survey design. Firstly, you need to plan for survey design success. Here are a few tips:

Our 9 top tips for survey design planning

1. keep it short.

Only include questions that you are actually going to use. You might think there are lots of questions that seem useful, but they can actually negatively affect your survey results. Another reason is that often we ask redundant questions that don’t contribute to the main problem we want to solve. The survey can be as short as three questions.

2. Use open-ended questions first

To avoid enforcing your own assumptions, use open-ended questions first. Often, we start with a few checkboxes or lists, which can be intimidating for survey respondents. An open-ended question feels more inviting and warmer – it makes people feel like you want to hear what they want to say and actually start a conversation. Open-ended questions give you more insightful answers, however, closed questions are easier to respond to, easier to analyze,  but they  do not create rich insights.

The best approach is to use a mix of both types of questions, as It’s more compelling to answer different types of questions for respondents.

3. Use surveys as a way to present solutions

Your surveys will reveal what areas in your business need extra support or what creates bottlenecks in your service. Use your surveys as a way of presenting solutions to your audience and getting direct  feedback  on those solutions in a more consultative way.

4. Consider your timing

It’s important to think about the timing of your survey. Take into account when your audience is most likely to respond to your survey and give them the opportunity to do it at their leisure, at the time that suits them.

5. Challenge your assumptions

It’s crucial to challenge your assumptions, as it’s very tempting to make assumptions about why things are the way they are. There is usually more than meets the eye about a person’s preferences and background which can affect the scenario.

6. Have multiple survey-writers

To have multiple survey writer can be helpful, as having people read each other’s work and test the questions helps address the fact that most questions can be interpreted in more than one way.

7. Choose your survey questions carefully

When you’re choosing your survey questions, make it really count. Only use those that can make a difference to your end outcomes.

8. Be prepared to report back results and take action

As a respondent you want to know your responses count, are reviewed and are making a difference. As an incentive, you can share the results with the participants, in the form of a benchmark, or a measurement that you then report to the participants.

9. What’s in it for them?

Always think about what customers (or survey respondents) want and what’s in it for them. Many businesses don’t actually think about this when they send out their surveys.

If you can nail the “what’s in it for me”, you automatically solve many of the possible issues for the survey, such as whether the respondents have enough incentive or not, or if the survey is consistent enough.

For a good survey design, always ask:

•      What insight am I hoping to get from this question?
•      Is it likely to provide useful answers?

For more pointers on how to design your survey for success, check out our blog on  4 Steps to Customer Survey Design – Everything You Need to Know .

Agi loves writing! She enjoys breaking down complex topics into clear messages that help others. She speaks four languages fluently and has lived in six different countries.

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How to Analysis of Survey Data: Methods & Examples

Analysis of Survey Data transforms raw data into meaningful insights. By adhering to best practices, you can leverage survey findings to enhance business strategies or inform research outcomes.

Analysis of Survey Data : As a researcher, marketer, or student, have you ever struggled to make sense of all the responses from a survey you administered? You’re not alone – understanding large amounts of survey data can be an overwhelming task. Data, data everywhere – but are you making sense of it all? However, raw survey results don’t always tell the full story – real understanding comes from carefully analyzing your data.

Remember to break down your data, use visual aids, and look for patterns in the responses. These strategies will help you make informed decisions and guide your next steps. But don’t stop here! Keep learning and honing your skills by enrolling in a data analytics course like the one offered by Physics Wallah.

With our experienced instructors and practical approach, you’ll be equipped with the tools and techniques needed to master survey analysis. And as a token of appreciation for being a dedicated reader, use “ READER ” as a coupon code to receive a discounted price for the course.

Table of Contents

Survey Data Analysis Examples

Let’s consider a hypothetical survey about customer satisfaction with a new mobile application. The survey was distributed to 500 users, and we collected both quantitative and qualitative data. Here’s a simplified example of how you might analyze the survey data:

Quantitative Data Analysis:

• Descriptive Statistics : Begin by calculating basic statistics like mean, median, mode, and standard deviation for questions that had numerical responses, such as “On a scale of 1-10, how satisfied are you with the app?”
• Cross-Tabulation : Create cross-tabulation tables to analyze relationships between different variables. For instance, you could cross-tabulate satisfaction levels with the frequency of app usage.
• Regression Analysis : Determine if there’s a correlation between user demographics (like age, location, or occupation) and satisfaction levels. A regression model might help predict satisfaction based on these variables.

Qualitative Data Analysis:

• Thematic Analysis : Manually review open-ended responses to identify recurring themes or sentiments. For instance, common themes might include “ease of use,” “features lacking,” or “customer support.”
• Sentiment Analysis : Use text analytics tools or software to perform sentiment analysis on qualitative responses. This will help categorize feedback as positive, negative, or neutral, providing an overall sentiment score.
• Word Clouds : Generate word clouds to visualize frequently mentioned words or phrases in the qualitative feedback. This gives a quick snapshot of what users are talking about most frequently.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

How do you Analyze Survey Data? (Effective Ways)

Analyzing survey data is a crucial step in extracting meaningful insights that can drive informed decision-making and strategic planning. While gathering data is essential, interpreting it correctly is equally vital. Here’s a comprehensive guide on how to analyze survey data effectively, utilizing various techniques and best practices.

1) Comprehend the Measurement Scales

Understanding the types of measurement scales—nominal, ordinal, interval, and ratio—is foundational. Each scale serves a unique purpose and requires distinct analytical approaches:

• Nominal Scales : Utilized for qualitative data, nominal scales categorize responses without imposing any order.
• Ordinal Scales : These scales rank responses based on preferences or orders, allowing for comparative analysis.
• Interval Scales : Ideal for capturing responses within a predefined range, facilitating more nuanced analysis.
• Ratio Scales : Similar to interval scales but starting at zero, these scales provide a comprehensive quantitative assessment.

2) Prioritize Quantitative Insights

Initiate your analysis by focusing on quantitative questions that yield numerical data. Metrics like the Net Promoter Score (NPS) can offer insights into customer sentiments and brand loyalty, enabling you to identify brand advocates and areas for improvement.

3) Harness Qualitative Feedback

While quantitative data provides numerical insights, qualitative feedback offers depth and context. Analyze open-ended responses by:

• Creating visual representations to identify common themes or keywords.
• Individualizing responses to understand unique customer perspectives and expectations.

4) Implement Cross-Tabulation Analysis

Cross-tabulation facilitates a deeper understanding of the relationship between variables, especially when targeting specific demographics or segments. By segmenting data based on relevant criteria, such as age or location, you can derive more targeted insights relevant to your objectives.

5) Distinguish Between Correlation and Causation

Avoid conflating correlation with causation, as it can lead to misleading interpretations. Scrutinize data meticulously, considering external factors and variables, to draw accurate conclusions and avoid erroneous assumptions.

6) Benchmark Against Historical Data

Comparing current survey results with past data sets enables you to assess progress, identify trends, and evaluate the effectiveness of implemented strategies. By tracking key metrics over time, you can measure improvements and refine your approach continually.

7) Utilize Industry Benchmarks

Benchmarking against industry standards provides context and perspective, allowing you to gauge your performance relative to competitors and market leaders. Aligning your survey results with industry benchmarks ensures realistic goals and actionable insights.

8) Mitigate Inaccurate or Incomplete Responses

Addressing incomplete or inaccurate survey responses is crucial for maintaining data integrity. Identify and categorize inattentive respondents, such as speeders, straight-liners, slackers, and imposters, to filter out unreliable data and enhance the validity of your analysis.

Analyzing survey data is a multifaceted process that necessitates a structured approach, incorporating both quantitative and qualitative methods. By understanding measurement scales, prioritizing actionable insights, leveraging analytical techniques like cross-tabulation, and benchmarking against relevant benchmarks, organizations can derive meaningful insights to inform decision-making, optimize strategies, and drive continuous improvement.

Statistical Analysis of Survey Data

Statistical analysis of survey data involves employing various statistical techniques to analyze and interpret the collected survey responses. This analytical process aims to uncover patterns, trends, relationships, and insights from the data, enabling organizations to make informed decisions, optimize strategies, and address specific research objectives. Here’s an overview of the statistical analysis techniques commonly used in survey data analysis:

1) Descriptive Statistics:

Descriptive statistics provide a summary of the main aspects of the survey data, including measures of central tendency (mean, median, mode), variability (standard deviation, variance, range), and distribution (skewness, kurtosis). These statistics offer an initial understanding of the data’s characteristics, such as the average response, variability among responses, and distribution patterns.

2) Inferential Statistics:

Inferential statistics enable researchers to generalize findings from a sample to a larger population, assess relationships between variables, and test hypotheses. Common inferential statistical tests include:

• T-tests: Used to compare the means of two groups or assess differences between two sets of data.
• ANOVA (Analysis of Variance): Employed to compare means across multiple groups simultaneously.
• Chi-Square Test: Applied to examine the association between categorical variables and determine if observed frequencies differ significantly from expected frequencies.
• Regression Analysis: Used to identify and quantify relationships between a dependent variable and one or more independent variables, predicting the outcome based on predictor variables.

3) Correlation Analysis:

Correlation analysis assesses the strength and direction of the relationship between two continuous variables. The Pearson correlation coefficient measures the linear relationship between variables, ranging from -1 (negative correlation) to 1 (positive correlation), with 0 indicating no correlation.

4) Factor Analysis:

Factor analysis is a multivariate statistical technique used to identify underlying relationships between observed variables, uncover latent variables or factors, and reduce data dimensionality. By grouping related variables into distinct factors, researchers can simplify complex data structures and identify underlying patterns or constructs.

5) Cluster Analysis:

Cluster analysis categorizes survey respondents or variables into distinct groups or clusters based on similarities within groups and differences between groups. This technique helps segment the target population, identify distinct respondent profiles, or group similar survey items, facilitating more targeted and personalized strategies.

6) Regression Modeling:

Regression modeling encompasses various regression techniques, including linear regression, logistic regression, and multiple regression, to predict or explain the relationship between dependent and independent variables. By evaluating the impact of predictor variables on the outcome variable, organizations can identify key drivers, assess relationships, and develop predictive models.

Also Read: Analysis vs. Analytics: How Are They Different?

Analysis of Survey Data in Research

Analysis of survey data in research is a critical component that involves examining, interpreting, and making sense of the collected survey responses. It enables researchers to derive meaningful insights, identify patterns, trends, and relationships, and draw valid conclusions to address research objectives or hypotheses effectively. Here’s a comprehensive overview of the analysis of survey data in research:

1) Data Preparation:

Before conducting any analysis, researchers must prepare the survey data by cleaning, organizing, and coding the responses. This involves:

• Data Cleaning: Identifying and addressing missing, incomplete, or erroneous responses to ensure data accuracy and reliability.
• Data Transformation: Converting raw survey data into a format suitable for analysis, such as numerical coding, categorization, or scaling.
• Variable Identification: Defining variables, distinguishing between independent and dependent variables, and categorizing variables based on their type (e.g., nominal, ordinal, interval, ratio).

2) Descriptive Analysis:

Descriptive analysis involves summarizing and describing the main features of the survey data using:

• Measures of Central Tendency: Calculating mean, median, and mode to determine the average or typical response.
• Measures of Dispersion: Assessing variability using standard deviation, variance, and range to understand the spread or dispersion of responses.
• Frequency Distributions: Creating frequency tables, histograms, or bar charts to display the distribution of categorical or continuous variables.

3) Inferential Analysis:

Inferential analysis focuses on making predictions, generalizing findings, or testing hypotheses based on the survey sample data. Common inferential techniques include:

• Hypothesis Testing: Using statistical tests such as t-tests, ANOVA, chi-square tests, or regression analysis to test research hypotheses, assess differences between groups, or determine associations between variables.
• Confidence Intervals: Estimating the range within which population parameters (e.g., means, proportions) are likely to fall based on sample data.

4) Correlation and Regression Analysis:

Correlation and regression analysis help researchers understand relationships between variables, predict outcomes, and identify key predictors:

• Correlation Analysis: Using correlation coefficients to assess the strength and direction of relationships between two or more continuous variables.
• Regression Analysis: Developing predictive models to explain the relationship between dependent and independent variables, identify significant predictors, and predict outcomes based on predictor variables.

5) Factor and Cluster Analysis:

Factor and cluster analysis are advanced techniques used to identify underlying patterns, group variables or respondents, and reduce data complexity:

• Factor Analysis: Identifying latent variables or underlying constructs, reducing data dimensionality, and uncovering patterns or relationships between observed variables.
• Cluster Analysis: Segmenting respondents or variables into distinct groups based on similarities, facilitating targeted analysis, and understanding respondent segments or patterns.

Survey Data Analysis Methods

Survey data analysis serves as a critical step in understanding the collected information, drawing meaningful insights, and making informed decisions. By employing specific methods tailored to the type and structure of the survey data, researchers and analysts can effectively interpret and leverage the information gathered. Here’s a detailed exploration of various survey data analysis methods:

1) Statistical Analysis:

Statistical analysis stands as a cornerstone in survey data analysis, offering rigorous methods to examine relationships, differences, and patterns within the data. Key statistical techniques include:

• Regression Analysis: Assessing the relationship between dependent and independent variables to predict outcomes or understand associations.
• T-Test: Comparing means between two groups to determine if there are significant differences.
• Analysis of Variance (ANOVA): Evaluating differences in means across multiple groups or categories.
• Cluster Analysis: Identifying distinct groups or clusters within the data based on similarities.
• Factor Analysis: Uncovering underlying relationships between observed variables by identifying latent factors or constructs.
• Conjoint Analysis: Analyzing respondent preferences and trade-offs among different attributes or features.

2) Measurement Scales Understanding:

Recognizing the measurement scales of survey questions forms a foundational aspect of data analysis . Different scales, including nominal, ordinal, interval, and ratio scales, dictate the type of statistical tests and analyses appropriate for the data, ensuring accurate and meaningful interpretation.

3) Quantitative Questions Analysis:

Initiating the analysis with quantitative questions facilitates establishing numerical trends, patterns, and relationships within the data. By prioritizing quantitative analysis, researchers can quantify responses, calculate descriptive statistics, and derive statistical inferences to address research objectives effectively.

4) Visualization Tools:

Visual representation of survey data plays a pivotal role in conveying insights, identifying trends, and communicating findings to stakeholders. Utilizing visualization tools such as pie charts, Venn diagrams, line graphs, scatter plots, histograms, and pictograms enhances data interpretation, fosters comprehension, and facilitates decision-making processes.

5) Popular Methods Utilization:

Embracing popular methods specific to survey data analysis ensures comprehensive insights extraction. By leveraging the nine most recognized methods for survey data analysis, researchers can navigate the complexities of data interpretation, uncover hidden patterns, validate research hypotheses, and inform strategic decisions effectively.

Also Read: Learning Path to Become a Data Analyst in 2024

How to Present Survey Data

Presenting survey data in a coherent, compelling, and easily digestible manner is crucial for conveying insights, fostering understanding, and driving informed decision-making among stakeholders. By employing various methods tailored to the nature and complexity of the survey data, you can effectively communicate findings and facilitate meaningful discussions. Here’s an in-depth exploration of how to present survey data:

1) Graphical Representation:

Graphs stand as a cornerstone in presenting survey data due to their ability to simplify complex information and facilitate visual interpretation. Depending on the nature of your data, consider utilizing the following graphical representations:

• Pie Charts: Ideal for illustrating proportions and percentages, pie charts offer a clear visualization of categorical data distribution.
• Venn Diagrams: Useful for showcasing overlaps or intersections between different data sets or categories.
• Scatter Plots: Effective for displaying relationships and correlations between two variables, facilitating trend identification.
• Histograms: Perfect for representing frequency distributions and identifying data distribution patterns.
• Pictograms: Employ visuals or icons to represent data quantities, making data more relatable and engaging.

Ensure selecting the most appropriate graph type that aligns with your data characteristics and resonates with your target audience’s preferences and comprehension levels.

2) Data Tables:

Data tables serve as a structured and systematic approach to presenting numerical survey data. By leveraging tools like Excel, you can organize, categorize, and display quantitative data in a tabular format, enhancing clarity, and facilitating comparative analysis. Ensure incorporating relevant headers, footnotes, and annotations to provide context and facilitate interpretation.

3) Interactive Presentations:

Crafting interactive presentations enables you to amalgamate textual and graphical data, fostering engagement and facilitating comprehensive understanding. Begin by outlining the research objectives, methodology, and hypothesis, followed by systematically presenting survey findings, insights, and implications. Utilize visuals, animations, and infographics to enhance engagement, convey key messages, and facilitate interactive discussions.

4) Infographics:

Infographics emerge as a potent tool for presenting survey data in a visually appealing, concise, and easily consumable format. By transforming survey results into compelling visuals, statistics, and narratives, infographics enhance information retention, facilitate comprehension, and augment the aesthetic appeal of your presentations. Consider incorporating color coding, icons, and concise text to convey key findings, trends, and insights succinctly.

5) Comprehensive Reports:

For investor meetings, shareholder discussions, or detailed presentations, comprehensive reports serve as an invaluable tool for presenting survey data. While incorporating graphs, tables, and infographics, reports provide an in-depth analysis, interpretation, and contextualization of survey findings.

Ensure structuring your report systematically, including an executive summary, methodology, findings, discussions, conclusions, and recommendations. Facilitate accessibility by incorporating a table of contents, appendices, and references, ensuring stakeholders can delve deeper into specific sections or data points as required.

Common Mistakes in Analysis of Survey Data and How to Avoid Them

Analyzing survey data is a pivotal step in extracting valuable insights that can drive informed decisions, shape strategies, and inform future research endeavors. However, several common pitfalls can compromise the accuracy, reliability, and validity of your findings. Recognizing these challenges and implementing strategies to mitigate them is crucial for ensuring robust and actionable survey data analysis. Here’s a comprehensive exploration of these common mistakes and how to navigate them effectively:

1) Premature Interpretation of Results:

Common Mistake: Succumbing to confirmation bias by hastily interpreting survey results that align with preconceived notions or expectations without ensuring statistical significance.

Mitigation Strategy: Prioritize a rigorous statistical analysis approach to ascertain the validity, reliability, and significance of your findings. Emphasize the importance of a sufficiently large sample size to minimize the likelihood of skewed or coincidental results. Adopt a systematic and unbiased approach to data interpretation, emphasizing objectivity, and evidence-based conclusions.

2) Misinterpreting Correlation as Causation:

Common Mistake: Conflating correlation with causation, attributing causative relationships between variables solely based on observed correlations without considering potential confounding variables or underlying mechanisms.

Mitigation Strategy: Exercise caution and critical thinking when interpreting relationships between variables. Emphasize the importance of exploring underlying factors, mechanisms, and variables that may influence observed correlations. Encourage a comprehensive and nuanced analysis that considers potential confounders, alternative explanations, and causal pathways, ensuring accurate and informed interpretations.

3) Overlooking Nuances in Qualitative Natural Language Data:

Common Mistake: Oversimplifying the analysis of qualitative survey data, such as speech or text responses, by relying solely on superficial categorizations or failing to capture the richness, context, and intricacies of human language.

Mitigation Strategy: Leverage advanced AI solutions and machine learning algorithms capable of sophisticated sentiment analysis, contextual understanding, and nuanced interpretation of qualitative data. Prioritize tools that emulate human-like comprehension, considering context, emotion, intent, and conversational dynamics. Foster a multidimensional approach to qualitative data analysis, emphasizing depth, richness, and comprehensive understanding to extract meaningful insights effectively.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Tools for Analysis of Survey Data

Analyzing survey data requires specialized tools that can efficiently process, visualize, and interpret the collected information. Here are some commonly used tools for the analysis of survey data:

If you still feel overwhelmed or want to enhance your skills further, we highly recommend enrolling in the Data Analytics course offered by Physics Wallah . Their comprehensive syllabus covers all aspects of survey data analysis and is taught by experienced professionals who are passionate about imparting their knowledge. And as a token of appreciation for being a reader of this blog post, use the “READER” coupon code to avail yourself of a special discount on the course fee.

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Analysis of Survey Data FAQs

What is the survey method of data analysis.

The survey method of data analysis involves collecting structured information from respondents through questionnaires or interviews. Once gathered, this data undergoes systematic examination to extract insights, trends, or patterns that can inform decision-making or research objectives.

What is the best tool to analyze survey data?

Several tools can effectively analyze survey data based on specific needs, such as SPSS, Qualtrics, SurveyMonkey, and Microsoft Excel. The "best" tool often depends on the complexity of the survey, required analytical techniques, user expertise, and desired output formats.

What is the purpose of survey analysis?

The purpose of survey analysis is to interpret collected data to understand respondent opinions, behaviors, preferences, or attitudes. By analyzing survey results, organizations or researchers can derive insights, make informed decisions, assess trends, identify patterns, and address research objectives or business challenges effectively.

What is the primary objective of analyzing survey data?

The primary objective of analyzing survey data is to extract valuable insights, patterns, and trends from the collected responses. This analysis aids in understanding respondent behaviors, preferences, opinions, and perceptions, enabling organizations to make informed decisions, shape strategies, and inform future initiatives effectively.

What are the key steps involved in analyzing survey data?

The key steps involved in analyzing survey data encompass data cleaning and preparation, defining objectives and research questions, selecting appropriate analytical techniques, conducting statistical analyses (e.g., regression analysis, t-tests, ANOVA), interpreting findings, and communicating results effectively.

Data Mining Architecture: Components, Types & Techniques

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• Doing Survey Research | A Step-by-Step Guide & Examples

Doing Survey Research | A Step-by-Step Guide & Examples

Published on 6 May 2022 by Shona McCombes . Revised on 10 October 2022.

Survey research means collecting information about a group of people by asking them questions and analysing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyse the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyse the survey results, step 6: write up the survey results, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research: Investigating the experiences and characteristics of different social groups
• Market research: Finding out what customers think about products, services, and companies
• Health research: Collecting data from patients about symptoms and treatments
• Politics: Measuring public opinion about parties and policies
• Psychology: Researching personality traits, preferences, and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and longitudinal studies , where you survey the same sample several times over an extended period.

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Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• University students in the UK
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18 to 24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalised to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every university student in the UK. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalise to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions.

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by post, online, or in person, and respondents fill it out themselves
• An interview , where the researcher asks a set of questions by phone or in person and records the responses

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by post is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g., residents of a specific region).
• The response rate is often low.

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyse.
• The anonymity and accessibility of online surveys mean you have less control over who responds.

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping centre or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g., the opinions of a shop’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations.

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data : the researcher records each response as a category or rating and statistically analyses the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analysed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g., yes/no or agree/disagree )
• A scale (e.g., a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g., age categories)
• A list of options with multiple answers possible (e.g., leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analysed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an ‘other’ field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic.

Use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no bias towards one answer or another.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by post, online, or in person.

There are many methods of analysing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also cleanse the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organising them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analysing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analysed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyse it. In the results section, you summarise the key results from your analysis.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection  methods, and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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How to analyze survey data

Last updated

30 March 2023

Reviewed by

Once you complete the survey and have raw data in your hands, you must understand how to analyze and contextualize it. Turning survey results into a clear analysis is key to getting the insights and information needed to make better decisions.

This article guides you on how to analyze survey data.

• What is survey data analysis?

Survey analysis involves converting raw data into answers, information, and insights that can help improve things in a business.

The process aims to turn numbers into objectives that can help your team improve customer experience , customer support, products, and the business as a whole.

• Quantitative data vs. qualitative data

Quantitative data is any information you can measure or count and provide in numerical value. It tells you how often , how much , and how many .

On the other hand, qualitative data is descriptive information that uses language instead of numerical values. It answers the how and why questions.

Qualitative data is unique and subjective, while quantitative data is universal and fixed.

Analyzing qualitative data involves grouping the data into themes and categories, while analyzing quantitative data involves analyzing statistical data.

• Types of survey data

Survey questions give data in different forms. These can be divided into the categories below. However, note that the categories frequently overlap, and survey data usually belongs to more than one category.

Closed-ended questions

Close-ended questions ask respondents to choose from a distinct set of limited, predefined responses. Here are some examples:

Yes or no questions

Multiple choice questions

Drop-down menu

Rating scale

The data resulting from close-ended questions is easy to sort and quantify. However, this simplicity means that some of the finer details respondents could have given get lost.

Open-ended questions

Open-ended questions provide insight into how the respondent feels.

They might start with describe , why , and how , which encourages the respondent to give as much information as possible. The respondents give personalized responses that are not based on the researcher’s assumptions.

Due to the volume and complexity of the unstructured data obtained from open-ended questions, you need advanced tools to obtain full value from the responses.

Categorical (nominal) data

Categorical data exists in categories of equal hierarchical status. Examples include genders (male vs. female) and primary colors (blue vs. red). The data often comes from multiple choice questions.

Ordinal data

Ordinal data has an intrinsic ranking where variables follow a natural rank order. Ranking scales and Likert scales usually provide this kind of data.

Categorizing economic status (wealthy, middle income, low-income) or customer satisfaction levels (very satisfied, somewhat satisfied, somewhat dissatisfied, very dissatisfied) are examples.

Typically, there is no clearly defined interval between the categories of ordinal data.

Scalar data

Scalar data deals with qualitative and quantitative data on a relative basis, like ordinal data. However, scalar data is unique because it uses an established scale, such as test scores (out of 100) or age (expressed as a number).

• Steps to analyze your survey data

Data cannot help you if you don’t analyze it to reveal the bigger picture. 

Here are the steps to analyzing survey data:

1. Understand the measurement scales

Since survey data can be quantitative or qualitative, understanding the measurement scales you need is crucial for survey analysis. For instance, if you asked quantitative questions in your survey, you need numerical scales.

Examples of measurement scales include the following:

Interval scales

The interval scale is helpful when participants need to give a response that falls along a pre-determined range. For instance, “How long would it take to complete the assignment?”

Nominal scales

These scales help classify qualitative data. They are similar to labels and don’t have numerical values.

Nominal scales help keep track of the number of respondents who selected a similar option. An example of a question requiring a nominal scale is: “Which model is your car?”

Ratio scales

Ratio and interval scales are similar in purpose and function. However, ratio scales begin at zero. For instance, “How often do you consume espresso coffee in a day?”

Ordinal scales

Ordinal scales or 'ordered' scales can be used to understand respondent attitudes on a subject using a set of ordered responses.  For instance, "How satisfied are you with your recent product purchase?"  In this instance, the ordered response options would be something like:  'very satisfied', 'satisfied', 'dissatisfied', and 'very dissatisfied'.

2. Start with the quantitative questions

Qualitative data can provide interesting information about a topic, but the insight is subjective and more complex to analyze.

Survey analysis should start with quantitative questions. This is because a response to this type of question would depend on numbers and statistics that you can quickly analyze. Close-ended questions provide data that you can convert into a numerical value (quantify), making it much easier to compare results and identify trends.

Starting with quantitative data makes it easier to understand qualitative data. For instance, when 75% of your clients say they are unhappy with your service, you can decide to focus on the negative reviews. The qualitative data from the reviews can help you identify problems in the customer journey and rectify any pain points behind your customer churn.

3. Pay attention to qualitative responses

A question like “What makes you love our products?” provides qualitative data. If you ask this question to 10 people, you could get a different answer from each participant.

You can analyze qualitative data through the following:

Making a visual representation

Pay attention to each response and establish common keywords, such as quality , value , or style . Note down the keywords and communicate them to the relevant departments.

Focusing on individual responses

A question such as, “Which changes would you want us to implement?” will lead to answers that help you understand customer expectations at an individual level and how you can meet these expectations to improve customer experience.

4. Consider using cross-tabulation analysis

Survey answers often have responses from people outside your target audience, meaning the results could be generally irrelevant or skewed toward a certain opinion. Cross-tabulation helps you understand the relationship between independent variables.

For instance, let’s say you plan to start a sports bike rental business and you place a survey on your site asking people how often they go cycling. You may be looking for responses from people aged 18–35 who live in your state.

However, since your site and survey are visible to everyone, you may get responses from people outside of your primary target audience. Fortunately, cross-tabulation analysis can help you filter data.

5. Understand correlation vs. causation

The human brain is very good at establishing patterns between events. It often links two independent events together as it believes they are mutually inclusive, even when they exist independently of each other. This can cause bias.

For example, let’s say you observe rising cases of stolen iPhones and increased car sales in a given period. The data may suggest a link between the variables, but there is probably no relationship. 

A correlation doesn’t necessarily mean one variable causes the other. Typically, these cases have a third variable that influences the two, and considering the correlation alone will lead to an incorrect or insufficient conclusion. Therefore, you must analyze all the data and identify all the influencing factors before drawing conclusions.

6. Compare survey data against past results

The current data will keep you updated, but you should compare it to data collected in the past.

If you are analyzing data for the first time, keep the reports and use them as a benchmark for the upcoming analysis. If you have done surveys in the past, gotten results, and written reports, compare the current results to the past records and track changes over your preferred interval.

7. Use industry benchmarks

Many businesses have hundreds of competitors selling similar items and targeting the same audience. Understanding industry benchmarks and comparing these against your survey results helps you interpret them in a meaningful way.

During the data analysis process, interpreting the results as good or bad can be complex. While you might have improved since the last survey, you may still be behind the industry standards. Benchmarking provides a clear picture and helps establish the real success of your efforts.

8. Avoid inaccurate or incomplete responses

People may leave a survey halfway through, perhaps because they feel the questions are uninteresting or too personal. Others might skip some of the questions, especially in surveys with multiple questions.

Incorrect and out-of-context responses can lead to inaccurate analysis.

You might need to decide if partial completion is helpful or if you want to throw out all of an individual participant’s data.  If many participants are skipping certain questions or not completing the survey, consider editing your survey.

• Benchmarking your survey data

Repeating surveys helps you uncover insights from the results and strengthen them over time.

Provided you use consistent data types and methods, the initial results can become your benchmark for future research. You can answer questions on yearly data changes when you analyze your data consistently. If your data collection is consistent enough to reveal the development of patterns and processes, you can use them to predict future events and outcomes.

• Common mistakes in survey data analysis and how to avoid them

Below are some common mistakes made in survey data analysis and tips for how you can avoid them.

Failing to differentiate correlation from causation

Treating a correlation as a causal relationship is a common pitfall in research. If the result correlates with the cause, it doesn’t automatically mean they directly affect each other.

Interpreting survey results prematurely

Sometimes the data can appear to confirm the hypothesis you started with or show the results you expected. However, you must use statistics to ensure the survey report is statistically significant based on reality—not on coincidence. You are also more likely to get a coincidental or skewed result if you have a smaller sample size .

Missing the nuances in responses from open-ended questions

Analyzing survey results in the form of speech or text is complex, unlike mapping vocabulary elements as negative or positive codes. Artificial intelligence (AI) solutions can now go further in uncovering meaning, intent, and emotion within human language.

However, trusting AI to interpret your rich qualitative data means depending on the software to read and understand language like a human and consider factors such as context and conversational dynamics. You must ensure the software has sentiment analysis that uses ML to understand the survey responses.

• Tools for survey analysis

Conventional survey analysis can lead to faulty data interpretation. It’s prone to errors and is subject to human bias. It’s also impossible to handle large amounts of data properly.

If you plan to analyze survey data and extract valuable insights and information from your results, tools can help make the process easy and efficient.

Here’s a list of survey data analysis tools you can consider:

This feedback analysis tool uses natural language processing to help sort through data. Faster, more efficient, and with good visualization capabilities, Thematic is ideal for organizations intending to automate their customer feedback analysis .

However, you must train it to recognize your keywords, meaning the setup phase may take time.

Excel is a popular spreadsheet tool that’s great for simple tasks. Once you know how to analyze survey data in Excel, you can count different response types, establish percentages of the different responses, and develop a survey report with the calculated results.

This tool is great for people who want to conduct quantitative analysis or have a small amount of qualitative data to analyze. However, its ability to perform analysis is limited and it may become slower as you add more data.

This software allows you to analyze qualitative and quantitative data from different sources. You can use it to store, organize, classify, and analyze your survey data and also create visualizations. 

The user interface is user-friendly and familiar with a Microsoft-like feel. Although it provides some automation, you may need to do some manual analysis. For this reason, you may need to involve research consultants or more team members if you are analyzing large amounts of data.

MarketSight

MarketSight comes with various features to help analyze survey data. It has data visualization capability, advanced analytics, crosstabs, and more.

It also provides application programming interfaces (APIs), allowing you to integrate insights into your products and systems.

However, its user interface is not that easy to use or intuitive. Using it could be a steep learning curve for people who are unfamiliar with qualitative research.

Dovetail is an excellent tool for survey data analysis that helps uncover insights with accuracy, speed, and flexibility.

You can use this software to analyze everything from product feedback to customer feedback. The software also allows you to import data, uncover patterns, tag key themes, and share impact insights. You can translate your results into well-written reports that inspire action with your stakeholders and team.

• How to present survey results

Survey data should be accessible and interesting. Here’s how to present survey results:

Make it visual

Presenting data in a visual form, such as a graph or chart, makes it appealing. The patterns and colors are easy on the eyes and the data is also easier to understand.

You could also consider a graphic format that presents the results in a relevant way. You can choose bar graphs, word clouds, Venn diagrams, pie charts, or linear graphs.

Use plain language

Try to express your findings in plain language. You can ask respondents for consent to add their direct quotes, enabling you to incorporate immediacy and illustrate your points.

Narrate your research as a story

Storytelling is another great approach to expressing data. You can use a situation-crisis-resolution or a beginning-middle-end style to show the approach used to overcome challenges or how trends have emerged. This helps people understand the context of your research.

A good data analysis story aims to weave insights together so that they can build on each other. Some data serves as the story’s foundation, and all the points in the presentation tie back to the foundation. Lastly, the key findings act as the roof by supporting the conclusion of your research.

A story will help communicate data to stakeholders of different analytical abilities.

Include your insights

Insights are usually more striking and easier to grasp than data. They come after applying ideas and knowledge to the data in the survey. Insights can examine a connection between two different data points or form a recommended action.

You can present data in terms of proportions and numbers, but providing the insights produced is also important.

Create an infographic

Infographics are great for sharing data that’s easy to read and quick to consume. They break down complex ideas into simple messages that people find more appealing to read than blocks of text.

• How to write a survey report

Once you have conducted a survey and analyzed the results, you need to write a report that you can present to your team and stakeholders.

Here’s how to write a report:

Craft the outcome of your survey

Writing the introduction to a survey report may seem like the best first step. However, to keep the report focused on the specific outcome you want the reader to take away, start with a detailed explanation of that outcome. Although you will include a section that focuses on the outcome in the middle, this is a great way to get the bearings when writing a report.

Let’s say you have conducted a market survey. Some possible outcomes that you might want to discuss in your report include venturing into a new, promising market, adjusting a product’s price to increase revenue, or introducing a new product due to customer feedback. However, you should ensure that the data you gathered fully backs up the outcome. Avoid discussing ideas that you can’t substantiate with other information in the report.

Write your research summary

A research summary contains notable results and any insights that correlate with other studies you may have conducted previously. People will concentrate on this part, since it’s a condensed version of the information you have discussed throughout the report.

The research summary should be no longer than a page. Ensure there are headers above paragraphs to guide the reader through the summary.

Create an outline for the report

Once you have the outcome and research summary, it’s time to create the outline. Survey reports are usually 8–10 pages long, so you need to use a concise outline that has all the relevant details that stakeholders want to know.

Here’s a sample outline:

Introduction —sets the stage for the survey report

Summary —summarizes the key elements and findings of the research

Methodology —explains how you conducted the research

Results —describes what the research revealed

Outcome —analyzes the results in the context of the organization’s goals

Limitations —reveals any research oversights or areas where more research is needed

Conclusion —summarizes the research analysis

Choose a layout

The outline gives you an idea of the space you need for each section. You can publish your survey report in a landscape or portrait layout, depending on your preference.

A landscape layout is best for graphs and charts where the rows exceed the columns. However, you should not dedicate entire pages to infographics or images as they take up space that could be used for crucial text-based content. A landscape layout is ideal for design-heavy survey reports. It works well for high-quality imagery, charts, and graphs, since there is more space to place text-based content to the side.

On the other hand, a portrait landscape is ideal when you are writing a text-heavy report.

Describe your survey’s methodology

This section of the report should explain how you conducted the survey, who participated, and the types of tests you used for data analysis. Charts or graphs can help convey this data.

Mention the number of participants, the method used to select them, and any demographic data gathered about them. Also, include the process for choosing the survey questions and why you preferred certain tests for the data analysis.

Providing lots of detail in this section helps the reader reassures the reader that the survey provides valid outcomes.

Describe any limitations in your research

Even if you prepared for your survey well, you may still find some information in the results that could have been more conclusive had you used a different variable.

You can continue your research in the future. The limitations section sets the stage for yourself or future researchers to start where you left off or rectify any mistakes you made in the current survey.

Add appendices if necessary

You may be fortunate enough to have all your data fit perfectly into a chart with the survey report, meaning you won’t need an appendix. However, if the charts and graphs you had on the pages are condensed version of large data sets offering context, you should put them at the end of the report in their raw forms.

Reference the appendix throughout the survey report to ensure the reader can review it for an in-depth understanding of the content.

• Analyze your data with the right tools

Surveys are a great way to collect feedback from a target audience. A survey will give you results, but the data won’t help you in its raw form. You need to analyze it to get insights that you can use to make crucial decisions.

Survey data analysis is a critical step in the survey process. You can perform the analysis manually, but you might end up with an inaccurate conclusion due to errors and human bias. Manual methods are also time-consuming.

Investing in the right survey data analysis tool makes the process fast and efficient and ensures you get valuable insights.

What is the best way to ensure survey analysis is easy and effective?

Biased questions and asking questions that are too complex or confusing can hinder your analysis. Make sure you have the right tools and know-how for an easy and effective survey analysis. You should also understand the difference between close-ended and open-ended questions and when to use them.

What are the different ways to analyze data?

The common data analysis methods are predictive analysis, statistical analysis, text analysis, diagnostic analysis, and prescriptive analysis.

How do you analyze data with multiple responses?

Multiple response analysis involves analyzing data with two or more responses per participant. Here’s how to analyze data with multiple responses:

Start by defining a set of two or more responses

Work out the response frequency for this set of responses

Use a data analysis tool to create a graph of frequencies or percentages

Get started today

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The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

• Handling qualitative data
• Transcripts
• Field notes
• Introduction

Understanding qualitative research in surveys

The nature of data in surveys, transferring survey data into records, understanding survey responses, managing and storing survey data.

• Visual and audio data
• Data organization
• Data coding
• Coding frame
• Auto and smart coding
• Organizing codes
• Qualitative data analysis
• Content analysis
• Thematic analysis
• Thematic analysis vs. content analysis
• Narrative research
• Phenomenological research
• Discourse analysis
• Grounded theory
• Deductive reasoning
• Inductive reasoning
• Inductive vs. deductive reasoning
• Qualitative data interpretation
• Qualitative analysis software

Survey data and responses

To analyze survey data, it is first important to take into consideration the process of organizing your data into a form that facilitates analysis . The analysis method most appropriate for your research will depend on the research inquiry you are looking to address.

You also need to look at how responses are structured before you can start coding or statistical analysis. Keeping all of these in mind will ensure the success of your survey research project.

When we discuss or collect data for survey research , it's essential to distinguish between two main methodologies: qualitative and quantitative research . Both approaches offer unique strengths and can often complement each other in a mixed methods study .

However, each approach requires distinct strategies for data collection , analysis , and interpretation .

Qualitative vs. quantitative approach in surveys

In a nutshell, quantitative research involves numerical data and statistical analysis. It is typically used when researchers want to count frequency, categorize data, or measure things in a way that allows for generalizable, statistical analysis.

Quantitative survey analysis often finds insights in the statistical significance of numerical values, where differences in values between two items (e.g., the popularity of one genre of music over another) are significant enough to be confident in assertions about the survey population.

Qualitative research , on the other hand, is non-numerical and often exploratory. It aims to delve deeper into complex issues, exploring meaning, experiences, or descriptions. Qualitative survey questions often come in the form of open-ended questions, which allow survey respondents to provide unique and individual responses. This kind of data can provide a richness of context, emotion, and depth that is not typically found in numerical data.

Ideally, survey analysis that adopts both quantitative and qualitative methods can prove useful in getting a more holistic view of the target audience and the research inquiry you are looking to address.

The value of qualitative data in surveys

The value of qualitative responses in surveys is in their depth, detail, and ability to provide a nuanced understanding of complex issues. It offers insights into participants' attitudes, behaviors, and experiences in their own words. These insights can be particularly useful in identifying patterns or themes that might not be evident from purely quantitative data.

For example, a quantitative survey might identify that a large number of employees in a company are not satisfied with their work. Still, it is the qualitative data that provides the reasons behind this dissatisfaction - perhaps there are issues with management, workload, or lack of career progression opportunities.

This is not to say that qualitative research is "better" than quantitative - they each have their own strengths and can be incredibly powerful when used together. The important thing, when it comes time to analyze survey data, is to choose the right approach for the research questions you are seeking to answer.

Definition of key terms

In order to best understand and engage with the content of this chapter and prepare for survey analysis, it's necessary to define a few key terms.

These definitions will provide a conceptual framework for our discussions on survey data collection and analysis.

What is survey data?

Survey data refers to the information or responses collected from individuals through a survey . This data can be both qualitative and quantitative.

Data from qualitative responses typically include open-ended responses, descriptions, and narratives. In contrast, quantitative data consists of numerical responses or information that can be categorized or ranked before analyzing survey data.

Survey data is a valuable resource for researchers, businesses, and policymakers, offering insights into the behaviors, attitudes, preferences, or characteristics of a sample group or population.

What is survey response analysis?

Analysis of survey responses is the process of examining, interpreting, and reporting the data collected from a survey.

This process involves a variety of techniques and approaches depending on the type of data in order to draw meaningful conclusions about how respondents answer.

Survey response analysis of customer feedback, for example, looks for customer insights directly embedded in survey results as well as how answers are framed in order to identify useful data points about market trends and consumer preferences.

For qualitative data, analysis often involves processes such as coding , thematic analysis , and narrative interpretation to understand the themes and patterns within the responses.

For quantitative data, statistical analysis methods are often used to summarize, describe, and compare the data.

What is survey data analysis?

Survey data analysis is a term that is often used interchangeably with survey response analysis. It refers to the survey analysis methods and techniques used to process, interpret, and draw conclusions from the data collected in a survey.

The type of analysis sought depends in part on whether the inquiry is qualitative or quantitative in nature. A qualitative survey analysis looks to uncover themes and patterns among survey results, while a quantitative analysis seeks out statistically significant differences among responses from different groups of survey respondents.

The goal of survey data analysis is to transform raw data into meaningful information that can be used to make informed decisions, develop strategies, or contribute to academic knowledge. Depending on the research questions and the nature of the data, different methods of analysis can be applied.

One of the key elements in survey research is the type of data being collected . The data collected from a survey can greatly vary depending on the survey's purpose, target population, and research questions .

Understanding the different ways to collect survey data is fundamental in designing effective surveys and efficiently analyzing the responses .

What kind of data is collected in a survey?

In general, survey data can be categorized into four main types: demographic, behavioral, attitudinal, and relational.

Demographic data

Demographic data provide information about the respondent's characteristics, such as age, gender, race, income, education level, and employment status. This type of data is often used to analyze and compare responses across different demographic groups.

Behavioral data

Behavioral data involves information about the respondent's actions and behaviors. This could include their purchasing habits, use of services, or lifestyle behaviors. Behavioral data can offer valuable insights into what respondents do, helping researchers understand patterns and tendencies in certain populations.

Attitudinal data

Attitudinal data refers to information about a respondent's attitudes, beliefs, and opinions. This data can provide insights into how respondents think or feel about specific issues, brands, policies, or services.

Attitudinal data is often collected through Likert-scale questions or open-ended questions in a survey.

Relational data

Relational data provides information about the relationships between respondents and other entities or individuals. This might include their relationship with their employer, their engagement with brands, or their interactions with public services.

Each of these types of data contributes a piece to the puzzle, helping researchers gain a more comprehensive understanding of their target population.

Understanding the diversity of survey data

While the four categories mentioned above provide a simplified overview of the types of data collected in a survey , it's important to acknowledge the diversity within this data.

For instance, within attitudinal data, researchers could be exploring a wide range of attitudes, from political opinions to consumer preferences. Similarly, behavioral data could span from online browsing habits to physical exercise routines. Each survey is unique and will collect a specific mix of data depending on its individual objectives and research questions.

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Once data has been collected from a survey , the next crucial step is to organize this data into a format that can be easily analyzed . This process involves turning survey data into records, an important process for managing and manipulating the data effectively.

The importance of turning survey data into records

Creating records from survey data allows researchers to systematically organize , categorize, and store responses. This process enables easier access to data and facilitates its analysis. When data is appropriately recorded and organized, researchers can more effectively track patterns, identify trends, and derive meaningful insights.

How to structure survey data for recording

The structure for recording survey data largely depends on the type of data collected. Quantitative data, being numerical, is often recorded into structured formats like spreadsheets or databases, where each respondent's answers are stored in a separate row and each question in a separate column.

For qualitative data , such as responses to open-ended questions, the recording process can be a bit more complex. Responses are typically transcribed verbatim and then organized into a format that allows for text analysis, such as spreadsheets or text documents.

These formats could include coding systems or qualitative data analysis software . It's also important to note any non-verbal cues or observations if the survey was conducted face-to-face.

Categorizing qualitative responses

A significant aspect of recording qualitative data involves categorizing responses. Researchers may begin with broad categories based on the survey questions and then develop more specific categories or themes as they familiarize themselves with the data. This process, known as coding, is a crucial step in preparing data for analysis.

Data transcription and its significance

Transcription refers to the process of converting spoken language into written text or transforming written responses into a digital format. For qualitative surveys conducted in person or over the telephone, this often means typing up responses to open-ended questions, focus group discussions , or interview responses .

For surveys that are conducted in a digital format, there is also the great advantage that participants’ responses are already typed out and thus do not require transcription. Data transcription is an essential part of data preparation as it ensures all information is in a format that can be easily analyzed. Depending on the size and scope of the survey, transcription can be a time-consuming process. However, the benefits of having all data in a consistent, analyzable format make it a crucial step in the survey research process.

Once survey data has been properly recorded, the next step is to understand the responses. This process involves closely examining the responses and identifying meaningful patterns, trends, and insights.

Ultimately, a critical examination of the survey results before fully analyzing data will help inform the findings in the survey report.

What are good responses?

Good responses are those that provide valuable and insightful information in relation to the survey's research objectives. While the exact characteristics of a "good" response can vary depending on the survey's purpose, there are a few common features that typically indicate a high-quality response:

Relevance : The response directly addresses the survey question and stays on topic. Completeness : The respondent provides a full and thorough answer to the question. Clarity : The respondent's answer is clear and easy to understand. Detail : The response provides enough detail to give a nuanced understanding of the respondent's perspective.

Characteristics of useful responses

Beyond the qualities mentioned above, useful responses often contain insights that illuminate the respondent's perspectives, experiences, or behaviors. These might include explanations for their attitudes or behaviors, personal experiences that illustrate their point of view, or suggestions for improvements or changes.

Handling incomplete or vague responses

Inevitably, you'll encounter incomplete or vague responses in your survey data. These responses can be challenging to interpret and analyze , but they're a common part of the data collection process .

When dealing with incomplete responses, it's important to handle these in a way that maintains the integrity of your data. If a response is incomplete, it may be best to exclude it from certain analyses where it could skew the results.

For vague responses, you might have to infer the respondent's intended meaning based on the context of their other responses or categorize these responses separately during your analysis.

Validating survey responses

One of the critical aspects of managing responses is ensuring their validity . This process, known as data validation, checks that the responses are accurate, reliable, and fit for their intended use.

What is data validation?

Data validation is a process of checking the quality and accuracy of data before it's used for analysis or decision-making. In the context of surveys, validation involves ensuring that the responses are consistent, complete, and reliable.

This process may involve checking for any discrepancies or errors in the data, ensuring responses are consistent across similar questions, and verifying that the data adheres to the required format.

Why is validating responses important?

Validating responses is crucial for several reasons. Firstly, it ensures the integrity of your data, providing confidence that your findings and conclusions are based on accurate and reliable information.

Secondly, it helps identify any errors or inconsistencies in the data early in the process, preventing potential issues during analysis. This is particularly important for larger surveys, where errors can significantly impact the results.

Moreover, it facilitates the use of survey data analysis methods. Responses should, as best as possible, be properly formatted and organized into a structure allowing for easy and efficient survey analysis later.

Finally, validation can also provide insights into the quality of your survey design. If many respondents are skipping certain questions or providing inconsistent responses, it may suggest that these questions are confusing or poorly designed.

How to validate responses?

There are several strategies you can employ to validate your responses:

• Consistency checks: Compare responses to similar or related questions to check for consistency. If a respondent provides conflicting answers, it could indicate a misunderstanding or error.
• Range checks: If your survey includes numerical responses, check that these fall within a reasonable or expected range. Any outliers may require further investigation.
• Completeness checks: Review your data for any missing or incomplete responses. Depending on the nature of the missing data, you may decide to exclude these responses from your analysis or use statistical methods to impute the missing values.
• Coding checks: If you've coded your responses (particularly for open-ended questions), review a sample of these to ensure the coding is accurate and consistent.

Remember that while data validation is a crucial step, it's not foolproof. It's always important to interpret your survey results with an understanding of the potential limitations and sources of error in your data.

After data collection and validation, a critical step is the proper management and storage of survey data. Adequate data management ensures that data remains accessible, secure, and reliable throughout the research process.

Importance of data management in survey research

Data management involves a host of activities, including data entry, storage, backup, and security. Good data management practices are essential to maintain the integrity of your research data and ensure its availability for current and future use.

Effective data management can enhance the efficiency of your research process, reduce the risk of data loss, and protect your data from unauthorized access. Additionally, proper data management can also make data sharing and collaboration easier if needed.

How to organize your survey data for easy retrieval?

When managing survey data, organization is key. Good data organization makes it easier to navigate your data, identify specific subsets of data, and streamline the data analysis process.

Here are a few strategies for organizing your survey data:

• File naming conventions: Use consistent and descriptive file names to help you identify what each file contains at a glance.
• Folder structures: Use a logical folder structure to organize your data files. This could be based on the survey round, data type, or any other system that suits your project.
• Metadata: Keep a record of metadata - information about your data. This could include details about when and how the data was collected, who collected it, what each variable represents, and any coding or transformation that has been applied to the data.

Data security and privacy considerations in survey research

Given the sensitive nature of some survey data, it's crucial to ensure your data is stored securely and that respondent privacy is maintained. This involves protecting your data from both physical and digital threats.

Digital data should be encrypted and protected by strong, unique passwords. Physical data, such as printed surveys or interview transcripts, should be stored securely, and access should be restricted to authorized individuals.

In addition, it's important to adhere to relevant data privacy laws and regulations and to anonymize your data where appropriate to protect respondent confidentiality.

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Home » Survey Research – Types, Methods, Examples

Survey Research – Types, Methods, Examples

Table of Contents

Survey Research

Definition:

Survey Research is a quantitative research method that involves collecting standardized data from a sample of individuals or groups through the use of structured questionnaires or interviews. The data collected is then analyzed statistically to identify patterns and relationships between variables, and to draw conclusions about the population being studied.

Survey research can be used to answer a variety of questions, including:

• What are people’s opinions about a certain topic?
• What are people’s experiences with a certain product or service?
• What are people’s beliefs about a certain issue?

Survey Research Methods

Survey Research Methods are as follows:

• Telephone surveys: A survey research method where questions are administered to respondents over the phone, often used in market research or political polling.
• Face-to-face surveys: A survey research method where questions are administered to respondents in person, often used in social or health research.
• Mail surveys: A survey research method where questionnaires are sent to respondents through mail, often used in customer satisfaction or opinion surveys.
• Online surveys: A survey research method where questions are administered to respondents through online platforms, often used in market research or customer feedback.
• Email surveys: A survey research method where questionnaires are sent to respondents through email, often used in customer satisfaction or opinion surveys.
• Mixed-mode surveys: A survey research method that combines two or more survey modes, often used to increase response rates or reach diverse populations.
• Computer-assisted surveys: A survey research method that uses computer technology to administer or collect survey data, often used in large-scale surveys or data collection.
• Interactive voice response surveys: A survey research method where respondents answer questions through a touch-tone telephone system, often used in automated customer satisfaction or opinion surveys.
• Mobile surveys: A survey research method where questions are administered to respondents through mobile devices, often used in market research or customer feedback.
• Group-administered surveys: A survey research method where questions are administered to a group of respondents simultaneously, often used in education or training evaluation.
• Web-intercept surveys: A survey research method where questions are administered to website visitors, often used in website or user experience research.
• In-app surveys: A survey research method where questions are administered to users of a mobile application, often used in mobile app or user experience research.
• Social media surveys: A survey research method where questions are administered to respondents through social media platforms, often used in social media or brand awareness research.
• SMS surveys: A survey research method where questions are administered to respondents through text messaging, often used in customer feedback or opinion surveys.
• IVR surveys: A survey research method where questions are administered to respondents through an interactive voice response system, often used in automated customer feedback or opinion surveys.
• Mixed-method surveys: A survey research method that combines both qualitative and quantitative data collection methods, often used in exploratory or mixed-method research.
• Drop-off surveys: A survey research method where respondents are provided with a survey questionnaire and asked to return it at a later time or through a designated drop-off location.
• Intercept surveys: A survey research method where respondents are approached in public places and asked to participate in a survey, often used in market research or customer feedback.
• Hybrid surveys: A survey research method that combines two or more survey modes, data sources, or research methods, often used in complex or multi-dimensional research questions.

Types of Survey Research

There are several types of survey research that can be used to collect data from a sample of individuals or groups. following are Types of Survey Research:

• Cross-sectional survey: A type of survey research that gathers data from a sample of individuals at a specific point in time, providing a snapshot of the population being studied.
• Longitudinal survey: A type of survey research that gathers data from the same sample of individuals over an extended period of time, allowing researchers to track changes or trends in the population being studied.
• Panel survey: A type of longitudinal survey research that tracks the same sample of individuals over time, typically collecting data at multiple points in time.
• Epidemiological survey: A type of survey research that studies the distribution and determinants of health and disease in a population, often used to identify risk factors and inform public health interventions.
• Observational survey: A type of survey research that collects data through direct observation of individuals or groups, often used in behavioral or social research.
• Correlational survey: A type of survey research that measures the degree of association or relationship between two or more variables, often used to identify patterns or trends in data.
• Experimental survey: A type of survey research that involves manipulating one or more variables to observe the effect on an outcome, often used to test causal hypotheses.
• Descriptive survey: A type of survey research that describes the characteristics or attributes of a population or phenomenon, often used in exploratory research or to summarize existing data.
• Diagnostic survey: A type of survey research that assesses the current state or condition of an individual or system, often used in health or organizational research.
• Explanatory survey: A type of survey research that seeks to explain or understand the causes or mechanisms behind a phenomenon, often used in social or psychological research.
• Process evaluation survey: A type of survey research that measures the implementation and outcomes of a program or intervention, often used in program evaluation or quality improvement.
• Impact evaluation survey: A type of survey research that assesses the effectiveness or impact of a program or intervention, often used to inform policy or decision-making.
• Customer satisfaction survey: A type of survey research that measures the satisfaction or dissatisfaction of customers with a product, service, or experience, often used in marketing or customer service research.
• Market research survey: A type of survey research that collects data on consumer preferences, behaviors, or attitudes, often used in market research or product development.
• Public opinion survey: A type of survey research that measures the attitudes, beliefs, or opinions of a population on a specific issue or topic, often used in political or social research.
• Behavioral survey: A type of survey research that measures actual behavior or actions of individuals, often used in health or social research.
• Attitude survey: A type of survey research that measures the attitudes, beliefs, or opinions of individuals, often used in social or psychological research.
• Opinion poll: A type of survey research that measures the opinions or preferences of a population on a specific issue or topic, often used in political or media research.
• Ad hoc survey: A type of survey research that is conducted for a specific purpose or research question, often used in exploratory research or to answer a specific research question.

Types Based on Methodology

Based on Methodology Survey are divided into two Types:

Quantitative Survey Research

Qualitative survey research.

Quantitative survey research is a method of collecting numerical data from a sample of participants through the use of standardized surveys or questionnaires. The purpose of quantitative survey research is to gather empirical evidence that can be analyzed statistically to draw conclusions about a particular population or phenomenon.

In quantitative survey research, the questions are structured and pre-determined, often utilizing closed-ended questions, where participants are given a limited set of response options to choose from. This approach allows for efficient data collection and analysis, as well as the ability to generalize the findings to a larger population.

Quantitative survey research is often used in market research, social sciences, public health, and other fields where numerical data is needed to make informed decisions and recommendations.

Qualitative survey research is a method of collecting non-numerical data from a sample of participants through the use of open-ended questions or semi-structured interviews. The purpose of qualitative survey research is to gain a deeper understanding of the experiences, perceptions, and attitudes of participants towards a particular phenomenon or topic.

In qualitative survey research, the questions are open-ended, allowing participants to share their thoughts and experiences in their own words. This approach allows for a rich and nuanced understanding of the topic being studied, and can provide insights that are difficult to capture through quantitative methods alone.

Qualitative survey research is often used in social sciences, education, psychology, and other fields where a deeper understanding of human experiences and perceptions is needed to inform policy, practice, or theory.

Data Analysis Methods

There are several Survey Research Data Analysis Methods that researchers may use, including:

• Descriptive statistics: This method is used to summarize and describe the basic features of the survey data, such as the mean, median, mode, and standard deviation. These statistics can help researchers understand the distribution of responses and identify any trends or patterns.
• Inferential statistics: This method is used to make inferences about the larger population based on the data collected in the survey. Common inferential statistical methods include hypothesis testing, regression analysis, and correlation analysis.
• Factor analysis: This method is used to identify underlying factors or dimensions in the survey data. This can help researchers simplify the data and identify patterns and relationships that may not be immediately apparent.
• Cluster analysis: This method is used to group similar respondents together based on their survey responses. This can help researchers identify subgroups within the larger population and understand how different groups may differ in their attitudes, behaviors, or preferences.
• Structural equation modeling: This method is used to test complex relationships between variables in the survey data. It can help researchers understand how different variables may be related to one another and how they may influence one another.
• Content analysis: This method is used to analyze open-ended responses in the survey data. Researchers may use software to identify themes or categories in the responses, or they may manually review and code the responses.
• Text mining: This method is used to analyze text-based survey data, such as responses to open-ended questions. Researchers may use software to identify patterns and themes in the text, or they may manually review and code the text.

Applications of Survey Research

Here are some common applications of survey research:

• Market Research: Companies use survey research to gather insights about customer needs, preferences, and behavior. These insights are used to create marketing strategies and develop new products.
• Public Opinion Research: Governments and political parties use survey research to understand public opinion on various issues. This information is used to develop policies and make decisions.
• Social Research: Survey research is used in social research to study social trends, attitudes, and behavior. Researchers use survey data to explore topics such as education, health, and social inequality.
• Academic Research: Survey research is used in academic research to study various phenomena. Researchers use survey data to test theories, explore relationships between variables, and draw conclusions.
• Customer Satisfaction Research: Companies use survey research to gather information about customer satisfaction with their products and services. This information is used to improve customer experience and retention.
• Employee Surveys: Employers use survey research to gather feedback from employees about their job satisfaction, working conditions, and organizational culture. This information is used to improve employee retention and productivity.
• Health Research: Survey research is used in health research to study topics such as disease prevalence, health behaviors, and healthcare access. Researchers use survey data to develop interventions and improve healthcare outcomes.

Examples of Survey Research

Here are some real-time examples of survey research:

• COVID-19 Pandemic Surveys: Since the outbreak of the COVID-19 pandemic, surveys have been conducted to gather information about public attitudes, behaviors, and perceptions related to the pandemic. Governments and healthcare organizations have used this data to develop public health strategies and messaging.
• Political Polls During Elections: During election seasons, surveys are used to measure public opinion on political candidates, policies, and issues in real-time. This information is used by political parties to develop campaign strategies and make decisions.
• Customer Feedback Surveys: Companies often use real-time customer feedback surveys to gather insights about customer experience and satisfaction. This information is used to improve products and services quickly.
• Event Surveys: Organizers of events such as conferences and trade shows often use surveys to gather feedback from attendees in real-time. This information can be used to improve future events and make adjustments during the current event.
• Website and App Surveys: Website and app owners use surveys to gather real-time feedback from users about the functionality, user experience, and overall satisfaction with their platforms. This feedback can be used to improve the user experience and retain customers.
• Employee Pulse Surveys: Employers use real-time pulse surveys to gather feedback from employees about their work experience and overall job satisfaction. This feedback is used to make changes in real-time to improve employee retention and productivity.

Survey Sample

Purpose of survey research.

The purpose of survey research is to gather data and insights from a representative sample of individuals. Survey research allows researchers to collect data quickly and efficiently from a large number of people, making it a valuable tool for understanding attitudes, behaviors, and preferences.

Here are some common purposes of survey research:

• Descriptive Research: Survey research is often used to describe characteristics of a population or a phenomenon. For example, a survey could be used to describe the characteristics of a particular demographic group, such as age, gender, or income.
• Exploratory Research: Survey research can be used to explore new topics or areas of research. Exploratory surveys are often used to generate hypotheses or identify potential relationships between variables.
• Explanatory Research: Survey research can be used to explain relationships between variables. For example, a survey could be used to determine whether there is a relationship between educational attainment and income.
• Evaluation Research: Survey research can be used to evaluate the effectiveness of a program or intervention. For example, a survey could be used to evaluate the impact of a health education program on behavior change.
• Monitoring Research: Survey research can be used to monitor trends or changes over time. For example, a survey could be used to monitor changes in attitudes towards climate change or political candidates over time.

When to use Survey Research

there are certain circumstances where survey research is particularly appropriate. Here are some situations where survey research may be useful:

• When the research question involves attitudes, beliefs, or opinions: Survey research is particularly useful for understanding attitudes, beliefs, and opinions on a particular topic. For example, a survey could be used to understand public opinion on a political issue.
• When the research question involves behaviors or experiences: Survey research can also be useful for understanding behaviors and experiences. For example, a survey could be used to understand the prevalence of a particular health behavior.
• When a large sample size is needed: Survey research allows researchers to collect data from a large number of people quickly and efficiently. This makes it a useful method when a large sample size is needed to ensure statistical validity.
• When the research question is time-sensitive: Survey research can be conducted quickly, which makes it a useful method when the research question is time-sensitive. For example, a survey could be used to understand public opinion on a breaking news story.
• When the research question involves a geographically dispersed population: Survey research can be conducted online, which makes it a useful method when the population of interest is geographically dispersed.

How to Conduct Survey Research

Conducting survey research involves several steps that need to be carefully planned and executed. Here is a general overview of the process:

• Define the research question: The first step in conducting survey research is to clearly define the research question. The research question should be specific, measurable, and relevant to the population of interest.
• Develop a survey instrument : The next step is to develop a survey instrument. This can be done using various methods, such as online survey tools or paper surveys. The survey instrument should be designed to elicit the information needed to answer the research question, and should be pre-tested with a small sample of individuals.
• Select a sample : The sample is the group of individuals who will be invited to participate in the survey. The sample should be representative of the population of interest, and the size of the sample should be sufficient to ensure statistical validity.
• Administer the survey: The survey can be administered in various ways, such as online, by mail, or in person. The method of administration should be chosen based on the population of interest and the research question.
• Analyze the data: Once the survey data is collected, it needs to be analyzed. This involves summarizing the data using statistical methods, such as frequency distributions or regression analysis.
• Draw conclusions: The final step is to draw conclusions based on the data analysis. This involves interpreting the results and answering the research question.

Advantages of Survey Research

There are several advantages to using survey research, including:

• Efficient data collection: Survey research allows researchers to collect data quickly and efficiently from a large number of people. This makes it a useful method for gathering information on a wide range of topics.
• Standardized data collection: Surveys are typically standardized, which means that all participants receive the same questions in the same order. This ensures that the data collected is consistent and reliable.
• Cost-effective: Surveys can be conducted online, by mail, or in person, which makes them a cost-effective method of data collection.
• Anonymity: Participants can remain anonymous when responding to a survey. This can encourage participants to be more honest and open in their responses.
• Easy comparison: Surveys allow for easy comparison of data between different groups or over time. This makes it possible to identify trends and patterns in the data.
• Versatility: Surveys can be used to collect data on a wide range of topics, including attitudes, beliefs, behaviors, and preferences.

Limitations of Survey Research

Here are some of the main limitations of survey research:

• Limited depth: Surveys are typically designed to collect quantitative data, which means that they do not provide much depth or detail about people’s experiences or opinions. This can limit the insights that can be gained from the data.
• Potential for bias: Surveys can be affected by various biases, including selection bias, response bias, and social desirability bias. These biases can distort the results and make them less accurate.
• L imited validity: Surveys are only as valid as the questions they ask. If the questions are poorly designed or ambiguous, the results may not accurately reflect the respondents’ attitudes or behaviors.
• Limited generalizability : Survey results are only generalizable to the population from which the sample was drawn. If the sample is not representative of the population, the results may not be generalizable to the larger population.
• Limited ability to capture context: Surveys typically do not capture the context in which attitudes or behaviors occur. This can make it difficult to understand the reasons behind the responses.
• Limited ability to capture complex phenomena: Surveys are not well-suited to capture complex phenomena, such as emotions or the dynamics of interpersonal relationships.

Following is an example of a Survey Sample:

Welcome to our Survey Research Page! We value your opinions and appreciate your participation in this survey. Please answer the questions below as honestly and thoroughly as possible.

1. What is your age?

• A) Under 18
• G) 65 or older

2. What is your highest level of education completed?

• A) Less than high school
• B) High school or equivalent
• C) Some college or technical school
• D) Bachelor’s degree
• E) Graduate or professional degree

3. What is your current employment status?

• A) Employed full-time
• B) Employed part-time
• C) Self-employed
• D) Unemployed

4. How often do you use the internet per day?

•  A) Less than 1 hour
• B) 1-3 hours
• C) 3-5 hours
• D) 5-7 hours
• E) More than 7 hours

5. How often do you engage in social media per day?

6. Have you ever participated in a survey research study before?

7. If you have participated in a survey research study before, how was your experience?

• A) Excellent
• E) Very poor

8. What are some of the topics that you would be interested in participating in a survey research study about?

……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

9. How often would you be willing to participate in survey research studies?

• A) Once a week
• B) Once a month
• C) Once every 6 months
• D) Once a year

10. Any additional comments or suggestions?

Thank you for taking the time to complete this survey. Your feedback is important to us and will help us improve our survey research efforts.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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12.1 What is survey research, and when should you use it?

Learning objectives.

Learners will be able to…

• Distinguish between survey as a research design and questionnaires used to measure concepts
• Identify the strengths and weaknesses of surveys
• Evaluate whether survey design fits with their research question

Pre-awareness check (Knowledge)

Have you ever been selected as a participant to complete a survey? How were you contacted? Would you incorporate the researchers’ methods into your research design?

Researchers quickly learn that there is more to constructing a good survey than meets the eye. Survey design takes a great deal of thoughtful planning and often many rounds of revision, but it is worth the effort. As we’ll learn in this section, there are many benefits to choosing survey research as your data collection method. We’ll discuss what a survey is, its potential benefits and drawbacks, and what research projects are the best fit for survey design.

Is survey research right for your project?

Questionnaires are completed by individual people, so the unit of observation is almost always individuals, rather than groups or organizations. Generally speaking, individuals provide the most informed data about their own lives and experiences, so surveys often also use individuals as the unit of analysis . Surveys are also helpful in analyzing dyads, families, groups, organizations, and communities, but regardless of the unit of analysis, the unit of observation for surveys is usually individuals.

In some cases, getting the most-informed person to complete the questionnaire may not be feasible . As we discussed in Chapter 2 and Chapter 6 , ethical duties to protect clients and vulnerable community members is important. The ethical supervision needed via the IRB to complete projects that pose significant risks to participants takes time and effort. Sometimes researchers rely on key informants and gatekeepers like clinicians, teachers, and administrators who are less likely to be harmed by the survey. Key informants are people who are especially knowledgeable about the topic. If your study is about nursing, you would probably consider nurses as your key informants. These considerations are more thoroughly addressed in Chapter 10 . Sometimes, participants complete surveys on behalf of people in your target population who are infeasible to survey for some reason. Some examples of key informants include a head of household completing a survey about family finances or an administrator completing a survey about staff morale on behalf of their employees. In this case, the survey respondent is a proxy , providing their best informed guess about the responses other people might have chosen if they were able to complete the survey independently. You are relying on an individual unit of observation (one person filling out a self-report questionnaire) and group or organization unit of analysis (the family or organization the researcher wants to make conclusions about). Proxies are commonly used when the target population is not capable of providing consent or appropriate answers, as in young children.

Proxies are relying on their best judgment of another person’s experiences, and while that is valuable information, it may introduce bias and error into the research process. For instance, If you are planning to conduct a survey of people with second-hand knowledge of your topic, consider reworking your research question to be about something they have more direct knowledge about and can answer easily.

Remember, every project has limitations. Social work researchers look for the most favorable choices in design and methodology, as there are no perfect projects. A missed opportunity is when researchers who want to understand client outcomes (unit of analysis) by surveying practitioners (unit of observation). If a practitioner has a caseload of 30 clients, it’s not really possible to answer a question like “how much progress have your clients made?” on a survey. Would they just average all 30 clients together? Instead, design a survey that asks them about their education, professional experience, and other things they know about first-hand. By making your unit of analysis and unit of observation the same, you can ensure the people completing your survey are able to provide informed answers.

Researchers may introduce measurement error if the person completing the questionnaire does not have adequate knowledge or has a biased opinion about the phenomenon of interest. [ INSERT SOME DISCUSSION HERE, FOR EXAMPLE GALLUP OPINION POLLS, ELECTION POLLING ]

In summary, survey design tends to be used in quantitative research and best fits with research projects that have the following attributes:

• Researchers plan to collect their own raw data, rather than secondary analysis of existing data.
• Researchers have access to the most knowledgeable people (that you can feasibly and ethically sample) to complete the questionnaire.
• Individuals are the unit of observation, and in many cases, the unit of analysis.
• Researchers will try to observe things objectively and try not to influence participants to respond differently.
• Research questions asks about indirect observables—things participants can self-report on a questionnaire.
• There are valid, reliable, and commonly used scales (or other self-report measures) for the variables in the research question.

Strengths of survey methods

Researchers employing survey research as a research design enjoy a number of benefits. First, surveys are an excellent way to gather lots of information from many people and is cost-effective due to its potential for generalizability. Related to the benefit of cost-effectiveness is a survey’s potential for generalizability. Because surveys allow researchers to collect data from very large samples for a relatively low cost, survey methods lend themselves to probability sampling techniques, which we discussed in Chapter 10 . When used with probability sampling approaches, survey research is the best method to use when one hopes to gain a representative picture of the attitudes and characteristics of a large group.

Survey research is particularly adept at investigating indirect observables or constructs . Indirect observables (e.g., income, place of birth, or smoking behavior) are things we have to ask someone to self-report because we cannot observe them directly.  Constructs such as people’s preferences (e.g., political orientation), traits (e.g., self-esteem), attitudes (e.g., toward immigrants), or beliefs (e.g., about a new law) are also often best collected through multi-item instruments such as scales. Unlike qualitative studies in which these beliefs and attitudes would be detailed in unstructured conversations, survey design seeks to systematize answers so researchers can make apples-to-apples comparisons across participants. Questionnaires used in survey design are flexible because you can ask about anything, and the variety of questions allows you to expand social science knowledge beyond what is naturally observable.

Survey research also tends to use reliable instruments within their method of inquiry, many scales in survey questionnaires are standardized instruments. Other methods, such as qualitative interviewing, which we’ll learn about in Chapter 18 , do not offer the same consistency that a quantitative survey offers. This is not to say that all surveys are always reliable. A poorly phrased question can cause respondents to interpret its meaning differently, which can reduce that question’s reliability. Assuming well-constructed questions and survey design, one strength of this methodology is its potential to produce reliable results.

The versatility of survey research is also an asset. Surveys are used by all kinds of people in all kinds of professions. They can measure anything that people can self-report. Surveys are also appropriate for exploratory, descriptive, and explanatory research questions (though exploratory projects may benefit more from qualitative methods). Moreover, they can be delivered in a number of flexible ways, including via email, mail, text, and phone. We will describe the many ways to implement a survey later on in this chapter.

In sum, the following are benefits of survey research:

• Cost-effectiveness
• Generalizability
• Reliability
• Versatility

Weaknesses of survey methods

As with all methods of data collection, survey research also comes with a few drawbacks. First, while one might argue that surveys are flexible in the sense that we can ask any kind of question about any topic we want, once the survey is given to the first participant, there is nothing you can do to change the survey without biasing your results. Because surveys want to minimize the amount of influence that a researcher has on the participants, everyone gets the same questionnaire. Let’s say you mail a questionnaire out to 1,000 people and then discover, as responses start coming in, that your phrasing on a particular question seems to be confusing a number of respondents. At this stage, it’s too late for a do-over or to change the question for the respondents who haven’t yet returned their questionnaires. When conducting qualitative interviews or focus groups, on the other hand, a researcher can provide respondents further explanation if they’re confused by a question and can tweak their questions as they learn more about how respondents seem to understand them. Survey researchers often ask colleagues, students, and others to pilot test their questionnaire and catch any errors prior to sending it to participants; however, once researchers distribute the survey to participants, there is little they can do to change anything.

Depth can also be a problem with surveys. Survey questions are standardized; thus, it can be difficult to ask anything other than very general questions that a broad range of people will understand. Because of this, survey results may not provide as detailed of an understanding as results obtained using methods of data collection that allow a researcher to more comprehensively examine whatever topic is being studied. Let’s say, for example, that you want to learn something about voters’ willingness to elect an African American president. General Social Survey respondents were asked, “If your party nominated an African American for president, would you vote for him if he were qualified for the job?” (Smith, 2009). [2] Respondents were then asked to respond either yes or no to the question. But what if someone’s opinion was more complex than could be answered with a simple yes or no? What if, for example, a person was willing to vote for an African American man, but only if that person was a conservative, moderate, anti-abortion, antiwar, etc. Then we would miss out on that additional detail when the participant responded “yes,” to our question. Of course, you could add a question to your survey about moderate vs. radical candidates, but could you do that for all of the relevant attributes of candidates for all people? Moreover, how do you know that moderate or antiwar means the same thing to everyone who participates in your survey? Without having a conversation with someone and asking them follow up questions, survey research can lack enough detail to understand how people truly think.

In sum, potential drawbacks to survey research include the following:

• Inflexibility
• Lack of depth
• Problems specific to cross-sectional surveys, which we will address in the next section.

Secondary analysis of survey data

This chapter is designed to help you conduct your own survey, but that is not the only option for social work researchers. Look back to Chapter 2 and recall our discussion of secondary data analysis . As we talked about previously, using data collected by another researcher can have a number of benefits. Well-funded researchers have the resources to recruit a large representative sample and ensure their measures are valid and reliable prior to sending them to participants. Before you get too far into designing your own data collection, make sure there are no existing data sets out there that you can use to answer your question. We refer you to Chapter 2 for all full discussion of the strengths and challenges of using secondary analysis of survey data.

Key Takeaways

• Strengths of survey research include its cost effectiveness, generalizability, variety, reliability, and versatility.
• Weaknesses of survey research include inflexibility and lack of potential depth. There are also weaknesses specific to cross-sectional surveys, the most common type of survey.

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

If you are using quantitative methods in a student project, it is very likely that you are going to use survey design to collect your data.

• Check to make sure that your research question and study fit best with survey design using the criteria in this section
• Remind yourself of any limitations to generalizability based on your sampling frame.
• Refresh your memory on the operational definitions you will use for your dependent and independent variables.

TRACK 2 (IF YOU  AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

You are interested in understanding more about the needs of unhoused individuals in rural communities, including how these needs vary based on demographic characteristics and personal identities.

• Develop a working research question for this topic.
• Using the criteria for survey design described in this section, do you think a survey would be appropriate to answer your research question? Why or why not?
• What are the potential limitations to generalizability if you select survey design to answer this research question?
• Unless researchers change the order of questions as part of their methodology and ensuring accurate responses to questions ↵
• Smith, T. W. (2009). Trends in willingness to vote for a Black and woman for president, 1972–2008.  GSS Social Change Report No. 55 . Chicago, IL: National Opinion Research Center ↵

The use of questionnaires to gather data from multiple participants.

the group of people you successfully recruit from your sampling frame to participate in your study

A research instrument consisting of a set of questions (items) intended to capture responses from participants in a standardized manner

a participant answers questions about themselves

the entities that a researcher actually observes, measures, or collects in the course of trying to learn something about her unit of analysis (individuals, groups, or organizations)

entity that a researcher wants to say something about at the end of her study (individual, group, or organization)

whether you can practically and ethically complete the research project you propose

Someone who is especially knowledgeable about a topic being studied.

a person who completes a survey on behalf of another person

things that require subtle and complex observations to measure, perhaps we must use existing knowledge and intuition to define.

Conditions that are not directly observable and represent states of being, experiences, and ideas.

The degree to which an instrument reflects the true score rather than error.  In statistical terms, reliability is the portion of observed variability in the sample that is accounted for by the true variability, not by error. Note : Reliability is necessary, but not sufficient, for measurement validity.

analyzing data that has been collected by another person or research group

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Understanding and Evaluating Survey Research

A variety of methodologic approaches exist for individuals interested in conducting research. Selection of a research approach depends on a number of factors, including the purpose of the research, the type of research questions to be answered, and the availability of resources. The purpose of this article is to describe survey research as one approach to the conduct of research so that the reader can critically evaluate the appropriateness of the conclusions from studies employing survey research.

SURVEY RESEARCH

Survey research is defined as "the collection of information from a sample of individuals through their responses to questions" ( Check & Schutt, 2012, p. 160 ). This type of research allows for a variety of methods to recruit participants, collect data, and utilize various methods of instrumentation. Survey research can use quantitative research strategies (e.g., using questionnaires with numerically rated items), qualitative research strategies (e.g., using open-ended questions), or both strategies (i.e., mixed methods). As it is often used to describe and explore human behavior, surveys are therefore frequently used in social and psychological research ( Singleton & Straits, 2009 ).

Information has been obtained from individuals and groups through the use of survey research for decades. It can range from asking a few targeted questions of individuals on a street corner to obtain information related to behaviors and preferences, to a more rigorous study using multiple valid and reliable instruments. Common examples of less rigorous surveys include marketing or political surveys of consumer patterns and public opinion polls.

Survey research has historically included large population-based data collection. The primary purpose of this type of survey research was to obtain information describing characteristics of a large sample of individuals of interest relatively quickly. Large census surveys obtaining information reflecting demographic and personal characteristics and consumer feedback surveys are prime examples. These surveys were often provided through the mail and were intended to describe demographic characteristics of individuals or obtain opinions on which to base programs or products for a population or group.

More recently, survey research has developed into a rigorous approach to research, with scientifically tested strategies detailing who to include (representative sample), what and how to distribute (survey method), and when to initiate the survey and follow up with nonresponders (reducing nonresponse error), in order to ensure a high-quality research process and outcome. Currently, the term "survey" can reflect a range of research aims, sampling and recruitment strategies, data collection instruments, and methods of survey administration.

Given this range of options in the conduct of survey research, it is imperative for the consumer/reader of survey research to understand the potential for bias in survey research as well as the tested techniques for reducing bias, in order to draw appropriate conclusions about the information reported in this manner. Common types of error in research, along with the sources of error and strategies for reducing error as described throughout this article, are summarized in the Table .

Sources of Error in Survey Research and Strategies to Reduce Error

The goal of sampling strategies in survey research is to obtain a sufficient sample that is representative of the population of interest. It is often not feasible to collect data from an entire population of interest (e.g., all individuals with lung cancer); therefore, a subset of the population or sample is used to estimate the population responses (e.g., individuals with lung cancer currently receiving treatment). A large random sample increases the likelihood that the responses from the sample will accurately reflect the entire population. In order to accurately draw conclusions about the population, the sample must include individuals with characteristics similar to the population.

It is therefore necessary to correctly identify the population of interest (e.g., individuals with lung cancer currently receiving treatment vs. all individuals with lung cancer). The sample will ideally include individuals who reflect the intended population in terms of all characteristics of the population (e.g., sex, socioeconomic characteristics, symptom experience) and contain a similar distribution of individuals with those characteristics. As discussed by Mady Stovall beginning on page 162, Fujimori et al. ( 2014 ), for example, were interested in the population of oncologists. The authors obtained a sample of oncologists from two hospitals in Japan. These participants may or may not have similar characteristics to all oncologists in Japan.

Participant recruitment strategies can affect the adequacy and representativeness of the sample obtained. Using diverse recruitment strategies can help improve the size of the sample and help ensure adequate coverage of the intended population. For example, if a survey researcher intends to obtain a sample of individuals with breast cancer representative of all individuals with breast cancer in the United States, the researcher would want to use recruitment strategies that would recruit both women and men, individuals from rural and urban settings, individuals receiving and not receiving active treatment, and so on. Because of the difficulty in obtaining samples representative of a large population, researchers may focus the population of interest to a subset of individuals (e.g., women with stage III or IV breast cancer). Large census surveys require extremely large samples to adequately represent the characteristics of the population because they are intended to represent the entire population.

DATA COLLECTION METHODS

Survey research may use a variety of data collection methods with the most common being questionnaires and interviews. Questionnaires may be self-administered or administered by a professional, may be administered individually or in a group, and typically include a series of items reflecting the research aims. Questionnaires may include demographic questions in addition to valid and reliable research instruments ( Costanzo, Stawski, Ryff, Coe, & Almeida, 2012 ; DuBenske et al., 2014 ; Ponto, Ellington, Mellon, & Beck, 2010 ). It is helpful to the reader when authors describe the contents of the survey questionnaire so that the reader can interpret and evaluate the potential for errors of validity (e.g., items or instruments that do not measure what they are intended to measure) and reliability (e.g., items or instruments that do not measure a construct consistently). Helpful examples of articles that describe the survey instruments exist in the literature ( Buerhaus et al., 2012 ).

Questionnaires may be in paper form and mailed to participants, delivered in an electronic format via email or an Internet-based program such as SurveyMonkey, or a combination of both, giving the participant the option to choose which method is preferred ( Ponto et al., 2010 ). Using a combination of methods of survey administration can help to ensure better sample coverage (i.e., all individuals in the population having a chance of inclusion in the sample) therefore reducing coverage error ( Dillman, Smyth, & Christian, 2014 ; Singleton & Straits, 2009 ). For example, if a researcher were to only use an Internet-delivered questionnaire, individuals without access to a computer would be excluded from participation. Self-administered mailed, group, or Internet-based questionnaires are relatively low cost and practical for a large sample ( Check & Schutt, 2012 ).

Dillman et al. ( 2014 ) have described and tested a tailored design method for survey research. Improving the visual appeal and graphics of surveys by using a font size appropriate for the respondents, ordering items logically without creating unintended response bias, and arranging items clearly on each page can increase the response rate to electronic questionnaires. Attending to these and other issues in electronic questionnaires can help reduce measurement error (i.e., lack of validity or reliability) and help ensure a better response rate.

Conducting interviews is another approach to data collection used in survey research. Interviews may be conducted by phone, computer, or in person and have the benefit of visually identifying the nonverbal response(s) of the interviewee and subsequently being able to clarify the intended question. An interviewer can use probing comments to obtain more information about a question or topic and can request clarification of an unclear response ( Singleton & Straits, 2009 ). Interviews can be costly and time intensive, and therefore are relatively impractical for large samples.

Some authors advocate for using mixed methods for survey research when no one method is adequate to address the planned research aims, to reduce the potential for measurement and non-response error, and to better tailor the study methods to the intended sample ( Dillman et al., 2014 ; Singleton & Straits, 2009 ). For example, a mixed methods survey research approach may begin with distributing a questionnaire and following up with telephone interviews to clarify unclear survey responses ( Singleton & Straits, 2009 ). Mixed methods might also be used when visual or auditory deficits preclude an individual from completing a questionnaire or participating in an interview.

FUJIMORI ET AL.: SURVEY RESEARCH

Fujimori et al. ( 2014 ) described the use of survey research in a study of the effect of communication skills training for oncologists on oncologist and patient outcomes (e.g., oncologist’s performance and confidence and patient’s distress, satisfaction, and trust). A sample of 30 oncologists from two hospitals was obtained and though the authors provided a power analysis concluding an adequate number of oncologist participants to detect differences between baseline and follow-up scores, the conclusions of the study may not be generalizable to a broader population of oncologists. Oncologists were randomized to either an intervention group (i.e., communication skills training) or a control group (i.e., no training).

Fujimori et al. ( 2014 ) chose a quantitative approach to collect data from oncologist and patient participants regarding the study outcome variables. Self-report numeric ratings were used to measure oncologist confidence and patient distress, satisfaction, and trust. Oncologist confidence was measured using two instruments each using 10-point Likert rating scales. The Hospital Anxiety and Depression Scale (HADS) was used to measure patient distress and has demonstrated validity and reliability in a number of populations including individuals with cancer ( Bjelland, Dahl, Haug, & Neckelmann, 2002 ). Patient satisfaction and trust were measured using 0 to 10 numeric rating scales. Numeric observer ratings were used to measure oncologist performance of communication skills based on a videotaped interaction with a standardized patient. Participants completed the same questionnaires at baseline and follow-up.

The authors clearly describe what data were collected from all participants. Providing additional information about the manner in which questionnaires were distributed (i.e., electronic, mail), the setting in which data were collected (e.g., home, clinic), and the design of the survey instruments (e.g., visual appeal, format, content, arrangement of items) would assist the reader in drawing conclusions about the potential for measurement and nonresponse error. The authors describe conducting a follow-up phone call or mail inquiry for nonresponders, using the Dillman et al. ( 2014 ) tailored design for survey research follow-up may have reduced nonresponse error.

CONCLUSIONS

Survey research is a useful and legitimate approach to research that has clear benefits in helping to describe and explore variables and constructs of interest. Survey research, like all research, has the potential for a variety of sources of error, but several strategies exist to reduce the potential for error. Advanced practitioners aware of the potential sources of error and strategies to improve survey research can better determine how and whether the conclusions from a survey research study apply to practice.

The author has no potential conflicts of interest to disclose.

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• Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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March 20, 2024

The Battleground 2024: Georgia

Marist Georgia Poll

Trump Edges Biden by 4 Percentage Points in Georgia

In Georgia, where President Joe Biden defeated former President Donald Trump by about two-tenths of a percentage point in 2020, Trump now edges Biden by four points among Georgia registered voters in this year’s presidential re-match. Among those who plan to definitely vote, three points separate Trump and Biden. Since 2020, Trump has gained ground among younger voters in the state. While Biden retains a wide lead over Trump among Black voters, Trump has made significant inroads among these voters.

Trump (51%) edges Biden (47%) by four percentage points among Georgia registered voters. Among those who plan to definitely vote, three percentage points separate Trump (51%) and Biden (48%).

Independents divide, 49% for Biden to 48% for Trump.

Compared with the Exit Polls of the 2020 General Election results, Trump has gained support among younger Americans. He is now +5 percentage points over Biden among those 18 to 29. Biden carried this age group in 2020 by 13 percentage points. Among those 30 to 44, Biden is +2 percentage points against Trump. Biden carried these voters by 10 percentage points in 2020.

While Biden (75%) has a significant lead against Trump (24%) among Black voters in Georgia, Biden carried this group by 77 percentage points in 2020.

In a three-candidate field, including Robert F. Kennedy Jr., Trump (45%) has a five-percentage point edge against Biden (40%). Kennedy receives 14% of the Georgia electorate. Kennedy has yet to qualify to be on the ballot in Georgia.

“Georgia has been ground zero for Trump’s legal problems dating back to the 2020 election. Although the overwhelming majority of Democrats and a plurality of independents think Trump broke the law, only a handful of Republicans think so,” says Lee M. Miringoff, Director of the Marist Institute for Public Opinion. “Despite all the attention focused on the state’s vote count in 2020, more than six in ten Georgia voters across party lines remain confident in the integrity of state and local elections.”

Biden’s Approval Rating Upside Down in Georgia

52% of Georgia residents disapprove of the job Biden is doing in office. 41% approve. In November 2022, 56% disapproved of how Biden was doing his job, and 38% approved.

By nearly two to one, Georgia adults strongly disapprove (42%) of Biden’s job performance rather than strongly approve (22%).

Trump Perceived More Favorably Than Biden… RFK Jr. Lesser Known

Georgia residents divide about their impressions of Trump. 47% perceive the former president favorably while 48% view him unfavorably. While 40% of Georgia residents have a positive view of Biden, 52% have a negative impression of the president. Kennedy suffers from low name recognition in the state. 33% have a favorable opinion of him, 28% have an unfavorable impression of him, and 39% have either never heard of Kennedy or are unsure how to rate him.

Biden’s Mental Fitness is More of a Concern Than Trump’s

Nearly six in ten adults statewide (59%), including 28% of Democrats, say Biden’s mental fitness is a real concern when it comes to his ability to carry out his duties in a second term. 38% think the attention to the president’s mental acumen is a campaign strategy used by Biden’s opponents. While opinions fall along party lines, 59% of independents express concern about Biden’s mental competency if re-elected.

In contrast, 51% of Georgia residents think the focus placed on Trump’s mental fitness is a political tactic, while 46% say Trump’s mental ability to be president is a genuine concern. 71% of Democrats and 51% of independents think Trump’s mental fitness is a real issue. 77% of Republicans say it is a campaign strategy being used against Trump.

State of the Union Had Little Impact on Georgians’ Confidence in Biden

President Biden’s State of the Union Address earlier this month made more than six in ten Democrats (62%) more confident in Biden’s ability but had less of an effect, overall, in Georgia. 30% of Georgia residents say they have more confidence in Biden. 32% say the speech made them less confident, and 37% said it made no difference in their level of confidence in the president. Among Republicans, 61% said the speech made them less confident in Biden while 48% of independents said it did not change their impression of him.

More Than Seven in Ten Georgians Perceive Wrongdoing in Trump’s Actions

71% of Georgians say Trump has either done something illegal (44%) or unethical but not illegal (27%). Driven by a majority of Republicans (53%), 27% of Georgians believe Trump did nothing wrong. Most Democrats (80%) and a plurality of independents (49%) think Trump broke the law. An additional 32% of independents believe Trump engaged in unethical behavior.

Nearly six in ten Georgians (59%), including 84% of Democrats and 70% of independents, do not think Trump should receive immunity from criminal prosecution for actions he took while president. 39% of residents statewide believe he should. This includes 72% of Republicans.

A majority of Georgia residents (53%) think the investigations into the former president are fair and are intended to find out if he broke the law. 47% say the investigations are unfair and a means to get in the way of Trump’s 2024 presidential campaign.

Preserving Democracy, Immigration, and Inflation Top 2024 Voting Issues

One in four Georgia adults (25%) say preserving democracy is top of mind when thinking about voting in November’s elections. 24% mention immigration, 24% cite inflation, and 10% highlight health care. Eight percent of Georgia residents say abortion is their top voting issue while 7% mention crime.

Preserving democracy is the priority for 40% of Georgia Democrats and 32% of independents. A plurality of Republicans (46%) say immigration is top of mind when thinking about November’s elections.

Trump Strongest on Economy and Immigration… Biden Viewed Better on Abortion

When thinking about the issues, majorities of Georgia residents perceive Trump to be the candidate who would better handle the economy (57%) and immigration (56%). However, a majority of Georgians (51%) say Biden would better handle the issue of abortion. Residents statewide divide about who would better handle preserving democracy. 50% think Biden is the stronger candidate while 49% believe Trump is better equipped to handle the issue.

Republicans with Slight Edge in Congressional Elections

Half of Georgia registered voters (50%) say they are more likely to vote for the Republican congressional candidate in their district rather than the Democrat (47%) on the ballot. Independents break, 48% for the Republican candidate to 43% for the Democratic candidate.

Nearly Seven in Ten Georgians Confident in the Integrity of Elections

69% of Georgia residents are either very confident or confident in their state or local government to carry out a fair and accurate election, including 34% who say they are very confident in the state’s electoral process. Democrats (81%) are more likely than independents (70%) and Republicans (62%) to be very confident or confident in Georgia’s ability to hold fair elections this year.

Majority of Georgia Residents Want the U.S. to Focus on the Homefront

53% of Georgians say, when thinking about the role of the United States in the world, the U.S. should focus on its own problems and play less of a leadership role around the world. 46% think it is crucial for the U.S. to play a major role in world events. A majority of Democrats (56%) prefer a strong global presence while a majority of Republicans (62%) prefer to focus on domestic issues. Independents divide. 48% think the U.S. should play a major international role while 51% think the nation should focus on its own matters.  

• Survey Data

Read our research on: TikTok | Podcasts | Election 2024

Regions & Countries

5 facts about religion and americans’ views of donald trump.

For most of the last decade, observers have been trying to understand why so many highly religious Americans have a favorable view of Donald Trump, asking how values voters can support a candidate who has been divorced twice, married three times and found liable for sexual abuse . Is Trump viewed most positively by those who might be described as “Christians in name only” – people who identify as Christians but aren’t actually religious?

The latest Pew Research Center survey sheds light on these and related questions. Here are five facts about religion and views of Trump, based on our survey of 12,693 U.S. adults conducted Feb. 13-25.

Pew Research Center conducted this analysis to explore the connection between religion and views of Donald Trump.

For this analysis, we surveyed 12,693 respondents from Feb. 13 to 25, 2024. Most of the respondents (10,642) are members of the Center’s American Trends Panel, an online survey panel recruited through national random sampling of residential addresses, which gives nearly all U.S. adults a chance of selection.

The remaining respondents (2,051) are members of three other panels, the Ipsos KnowledgePanel, the NORC AmeriSpeak panel and the SSRS opinion panel. All three are national survey panels recruited through random sampling (not “opt-in” polls). We used these additional panels to ensure that the survey would have enough Jewish and Muslim respondents to be able to report on their views.

The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education, religious affiliation and other categories.

For more, refer to the ATP’s methodology and the methodology for this survey .

Among religious groups, White evangelical Protestants continue to have the most positive opinion of Trump. Overall, two-thirds of White evangelical Protestants say they have a favorable view of the former president, including 30% who have a very favorable opinion of him.

Roughly half of White Catholics (51%) express positive views of Trump, as do 47% of White nonevangelical Protestants and 45% of Hispanic Protestants.

But in every other U.S. religious group large enough to be analyzed in this survey, large majorities have unfavorable opinions of Trump, including:  

• 88% of atheists
• 82% of agnostics
• 80% of Black Protestants
• 79% of Jewish Americans

These religious patterns largely reflect partisan differences . Most White evangelicals tend to vote for Republicans, as do smaller majorities of White Catholics and White nonevangelical Protestants. By contrast, most atheists, agnostics, Black Protestants and Jews tend to vote for Democrats.

Trump’s favorability rating is similar among Christians who attend church regularly and those who don’t. Some observers have pointed out that Trump’s political base consists largely of people who call themselves Christians but don’t go to church. However, our survey shows that Christians who regularly go to church express equally favorable views of Trump as those who don’t often attend religious services.

Among Christians as a whole, 47% of those who attend church at least monthly say they have a favorable view of the former president. That’s on par with the 46% of non-church-attending Christians who say the same.

Among White evangelical Protestants, 68% of regular churchgoers have a positive view of Trump – similar to the 64% among White evangelicals who don’t attend church regularly.

The only exception to this pattern is among White Protestants who do not identify as born-again or evangelical. In this group, Trump is viewed more favorably by those who don’t attend church regularly than by those who do (52% vs. 32%).

Many of the people who view Trump favorably don’t go to religious services regularly – but very few are nonreligious. Overall, 64% of respondents who have a favorable view of Trump say they attend religious services a few times a year or less often, while 35% say they go to services at least once or twice a month. (Among all respondents, 69% say they attend religious services a few times a year or less, while 30% go at least monthly.)

Religious attendance is just one way of looking at religious commitment. Another common way we measure it is to combine survey questions about attendance at religious services, how often people pray and how important religion is to them.

U.S. adults who attend religious services at least weekly, pray daily and say religion is very important in their lives are categorized as highly religious. Those who seldom or never attend services, seldom or never pray and say religion is not too important or not at all important in their lives are counted as having low religious commitment. Everyone else is counted as having medium religious commitment.

Looked at this way, 23% of U.S. adults with a favorable view of Trump are highly religious, including 11% who are highly religious White evangelical Protestants.

Another 62% of Americans with a favorable view of Trump have medium levels of religious commitment, including 13% who are White evangelicals.

Just 15% of people with a favorable view of Trump have low levels of religious commitment. By far the biggest subgroup within this category is religious “nones” – people who describe their religious identity as atheist, agnostic or “nothing in particular.” Overall, 18% of people with a positive view of Trump are religious “nones,” including 10% who are “nones” with low levels of religiousness.

Very few of the people who have a positive view of Trump are White evangelical Protestants with a low level of religiousness. Indeed, self-described White evangelical Protestants who are not religiously observant account for less than 1% of the overall U.S. population. Even if a candidate wanted to form a coalition rooted in support from nonreligious evangelicals, there just aren’t enough of them to be a national political base.

Most people who view Trump positively don’t think he is especially religious himself. But many think he stands up for people with religious beliefs like theirs. Just 8% of people who have a positive view of Trump think he is very religious, while 51% think he is somewhat religious and 38% say he is not too or not at all religious .

But 51% of those with a favorable view of Trump think he stands up for people with religious beliefs like their own, including 24% who think he does this a great deal and 27% who say he does this quite a bit.

Among White evangelical Protestants with a favorable view of Trump, just 9% view him as very religious. But roughly two-thirds think he does a great deal (32%) or quite a bit (35%) to stand up for people with religious beliefs like theirs.

Religious “nones” who are culturally Christian view Trump a bit more positively than religious “nones” who aren’t.

One way to measure for differences between “cultural” and “practicing” Christians is to compare Christians who do and don’t do go to church regularly, as we did above. Another is to look at religiously unaffiliated respondents, or “nones” – people who describe themselves, religiously, as atheist, agnostic, or “nothing in particular.” In our new survey, we asked these Americans whether they think of themselves as Christians “aside from religion … for example ethnically, culturally or because of your family’s background.”

Religious “nones” who identify as culturally Christian have a modestly more favorable opinion of Trump than “nones” who do not identify as Christian in any way. Still, large majorities in both groups express negative views of the former president.

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Two-thirds of Republicans want Trump to retain major political role; 44% want him to run again in 2024

A partisan chasm in views of trump’s legacy, how america changed during donald trump’s presidency, trump’s approval ratings so far are unusually stable – and deeply partisan, most americans don’t see trump as religious; fewer than half say they think he’s christian, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

Research Analysis Specialist - Survey Research Associate

Job posting for research analysis specialist - survey research associate at state of minnesota, job details.

Working Title: Survey Research Associate Job Class: Research Analysis Specialist Agency: Normandale Community College

• Who May Apply : Open to all qualified job seekers
• Date Posted : 03/13/2024
• Closing Date : 03/26/2024
• Hiring Agency/Seniority Unit : MN St Colleges & Universities / Normandale CC-MAPE
• Division/Unit : MnSCU Normandale CC / Institutional Research
• Appointment Type : Unlimited, Full-time
• Work Shift/Work Hours : Day Shift
• Days of Work : Monday - Friday
• Travel Required : No
• Salary Range: $26.64 - $39.06 / hourly; $55,624 - $81,557 / annually
• Classified Status : Classified
• Bargaining Unit/Union : 214 - MN Assoc of Professional Empl/MAPE
• Location : Bloomington
• FLSA Status : Nonexempt
• Telework Eligible : Hybrid following an initial probationary period
• Designated in Connect 700 Program for Applicants with Disabilities: Yes

Make a difference in the lives of Minnesotans.

The work you’ll do is more than just a job. Join the talented, engaged and inclusive workforce dedicated to creating a better Minnesota.

Job Summary

We are a proud equal opportunity and affirmative action employer, and we seek applicants with deep connections to the cultural communities to which our students belong. We actively seek and encourage applications from women, people of color, persons with disabilities, and individuals with protected veteran status.

The Position

This position conducts educational and institutional research to facilitate data-informed decision-making and support student success and institutional effectiveness at the college. This position will be responsible for administering the institution’s ongoing surveys, designing and implementing new surveys, analyzing survey data and disseminating the findings of survey research. This position will also provide technical advice and assistance to professional co-workers related to the design, administration, analysis, reporting, and communication of survey research.

This position will actively advance the college’s strategic goals, including to achieve equity in educational outcomes and to support a culturally responsive and service-oriented culture, through individual and departmental efforts.

Normandale currently has another similar opening in our Institutional Research department. Please refer to post ID 74819 for the additional opening and ensure that you are applying for the position that best matches your skills and experience.

Qualifications

Minimum qualifications.

• Bachelor’s degree in a related field
• Two years of current experience in survey research including development and administration of surveys as well as analysis and reporting of survey data
• Demonstrated experience programming and administering online surveys in Qualtrics or another web-based survey platform
• Demonstrated experience analyzing survey data in R or other statistical analysis package (e.g., Stata, SAS), including the writing of script/code to execute analytic commands
• Demonstrated experience visualizing and sharing survey data in Power BI or other data visualization software (e.g., Tableau)
• Knowledge of quantitative research methods, including descriptive and inferential statistical methods and basic predictive models sufficient to select and use procedures appropriate to problems and to interpret results

Preferred Qualifications

• Masters in statistics, applied research, or related field
• Three years of current experience statistically analyzing educational data
• Familiarity with higher education policies, procedures and standards
• Familiarity with Minnesota State data systems
• Experience implementing qualitative research methods
• Strong oral and written communication skills, including the ability to prepare clear and concise reports and presentations
• Strong problem-solving skills, attention to detail, and ability to manage competing priorities
• Demonstrated ability to build collaborative, cross-cultural working relationships

Additional Requirements

The College regrets that it is unable to offer H-1B sponsorship at this time. The successful candidate, under U.S. Citizenship and Immigration Services regulations, must be able to accept work in the U.S. by the day employment begins.

An offer for this position may be contingent upon the completion of a background check.

In accordance with the Minnesota State Vehicle Fleet Safety Program, employees driving on college/university business who use a rental or state vehicle shall be required to conform to Minnesota State's vehicle use criteria and consent to a Motor Vehicle Records check.

Application Details

How to apply.

Select “Apply for Job” at the top of this page. If you have questions about applying for jobs, contact the job information line at 651-259-3637 or email [email protected]. For additional information about the application process, go to http://www.mn.gov/careers.

If you have questions about the position, contact John Norman at [email protected].

To receive consideration as a Connect 700 Program applicant, apply online, email the Job ID#, the Working Title and your valid Proof of Eligibility Certificate by the closing date to John Norman at [email protected].

Why Normandale Community College

Normandale is the largest community college in the Minnesota State Colleges and Universities system, annually serving almost 15,000 students from a diverse set of backgrounds, 42% of whom are students of color and nearly 25% are first-generation. We offer opportunities for individuals to grow, learn, and advance their careers while working for an organization that is committed to providing an inclusive and equitable space for students and employees to learn. We are passionately committed to achieving racial equity in student outcomes and in advancing cultural competency in the classroom and services provided.

Diverse Workforce

We are committed to continually developing a workforce that reflects the diversity of our state and the populations we serve. The varied experiences and perspectives of employees strengthen the work we do together and our ability to best serve the people of Minnesota.

A recent engagement survey of State of Minnesota employees found:

• 95% of employees understand how their work helps achieve their agency’s mission
• 91% of employees feel trusted to do their jobs
• 88% of employees feel equipped to look at situations from other cultural perspectives when doing their job
• 87% of employees report flexibility in their work schedule

Comprehensive Benefits

Our benefits aim to balance four key elements that make life and work meaningful: health and wellness, financial well-being, professional development, and work/life harmony. As an employee, your benefits may include:

• Public pension plan
• Training and professional development
• Paid vacation and sick leave
• 11 paid holidays each year
• Paid parental leave
• Low-cost medical and dental coverage
• Prescription drug coverage
• Vision coverage
• Wellness programs and resources
• Employer paid life insurance
• Short-term and long-term disability
• Health care spending and savings accounts
• Dependent care spending account
• Tax-deferred compensation
• Employee Assistance Program (EAP)
• Tuition reimbursement
• Federal Public Service Student Loan Forgiveness Program

Programs, resources and benefits eligibility varies based on type of employment, agency, funding availability, union/collective bargaining agreement, location, and length of service with the State of Minnesota.

Other Information

Employment information for this position can be found in its collective bargaining agreement or its plan document at http://mn.gov/mmb/employee-relations/labor-relations/Labor/.

AN EQUAL OPPORTUNITY EMPLOYER

Minnesota State is an equal opportunity employer/educator committed to the principles of diversity. We prohibit discrimination against qualified individuals based on their race, sex, color, creed, religion, age, national origin, disability, protected veteran status, marital status, status with regard to public assistance, sexual orientation, gender identity, gender expression, or membership in a local commission as defined by law. As an affirmative action employer, we actively seek and encourage applications from women, minorities, persons with disabilities, and individuals with protected veteran status.

Reasonable accommodations will be made to all qualified applicants with disabilities. If you are an individual with a disability who needs assistance or cannot access the online job application system, please contact the job information line at 651-259-3637 or email [email protected]. Please indicate what assistance is needed.

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What is survey data analysis?

Types of survey data, steps to analyse your survey data, benchmarking your survey data, how to present survey results, common mistakes in analysing data and how to avoid them, tools for survey analysis, tips from the team at qualtrics, how to analyse survey data, try qualtrics for free, survey data analysis: best practices, helpful tips, and our favorite tools.

20 min read Data can do beautiful things, but turning your survey results into clear, compelling analysis isn’t always a straightforward task. We’ve collected our tips for survey analysis along with a beginner’s guide to survey data and analysis tools.

Survey analysis is the process of turning the raw material of your survey data into insights and answers you can use to improve things for your business. It’s an essential part of doing  survey-based research .

There are a huge number of survey data analysis methods available, from simple  cross-tabulation , where data from your survey responses is arranged into rows and columns that make it easier to understand, to  statistical methods for survey data analysis  which tell you things you could never work out on your own, such as whether the results you’re seeing have statistical significance.

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Different kinds of  survey questions  yield data in different forms. Here’s a quick guide to a few of them. Often, survey data will belong to more than one of these categories as they frequently overlap.

Quantitative data vs. qualitative data

What’s the difference between qualitative data and quantitative data?

• Quantitative data, aka numerical data, involves numerical values and quantities. An example of quantitative data would be the number of times a customer has visited a location, the temperature of a city or the scores achieved in an  NPS survey .
• Qualitative data is information that isn’t numerical. It may be verbal or visual, or consist of spoken audio or video. It’s more likely to be descriptive or subjective, although it doesn’t have to be. Qualitative data highlights the “why” behind the what.

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Closed-ended questions

These are questions with a limited range of responses. They could be a ‘yes’ or ‘no’ question such as ‘do you live in Portland, OR?’. Closed-ended questions can also take the form of multiple-choice, ranking, or drop-down menu items. Respondents can’t qualify their choice between the options or explain why they chose which one they did.

This type of question produces  structured data  that is easy to sort, code and quantify since the responses will fit into a limited number of ‘buckets’. However, its simplicity means you lose out on some of the finer details that respondents could have provided.

Natural language data (open-ended questions)

Answers written in the respondent’s own words are also a form of survey data. This type of response is usually given in open field (text box) question formats. Questions might begin with ‘how,’ ‘why,’ ‘describe…’ or other conversational phrases that encourage the respondent to open up.

This type of data, known as  unstructured data , is rich in information. It typically requires advanced tools such as Natural Language Processing and sentiment analysis to extract the full value from how the respondents answered, because of its complexity and volume.

Categorical (nominal) data

This kind of data exists in categories that have  no hierarchical relationship  to each other. No item is treated as being more or less, better or worse, than the others. Examples would be primary colors (red v. blue), genders (male v female) or  brand names  (Chrysler v Mitsubishi).

Multiple choice questions often produce this kind of data (though not always).

Ordinal data

Unlike categorical data, ordinal data  has an intrinsic rank  that relates to quantity or quality, such as degrees of preference, or how strongly someone agrees or disagrees with a statement.

Likert scales and ranking scales  often serve up this kind of data.

Scalar data

Like ordinal data, scalar data deals with quantity and quality on a relative basis, with some items ranking above others. What makes it different is that it uses an established scale, such as age (expressed as a number), test scores (out of 100), or time (in days, hours, minutes etc.)

You might get this kind of data from a drop-down or sliding scale question format, among others.

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The type of data you receive affects the kind of survey results analysis you’ll be doing, so it’s very important to consider the type of survey data you will end up with when you’re  writing your survey questions  and  designing survey flows .

Here’s an overview of how you can analyse survey data, identify trends and hopefully draw meaningful conclusions from your research.

1.   Review your research questions

Research questions  are the underlying questions your survey seeks to answer. Research questions are not the same as the questions in your questionnaire, although they may cover similar ground.

It’s important to review your research questions before you analyse your survey data to determine if it aligns with what you want to accomplish and find out from your data.

2.   Cross-tabulate your data

Cross-tabulation  is a valuable step in sifting through your data and uncovering its meaning. When you cross-tabulate, you’re breaking out your data according to the sub-groups within your research population or your sample, and comparing the relationship between one variable and another. The table you produce will give you an overall picture of how responses vary among your subgroups.

Target the survey questions that best address your research question. For example, if you want to know how many people would be interested in buying from you in the future, cross-tabulating the data will help you see whether some groups were more likely than others to want to return. This gives you an idea of where to focus your efforts when improving your product design or your  customer experience .

Cross-tabulation works best for categorical data and other types of structured data. You can cross-tabulate your data in multiple ways across different questions and sub-groups using  survey analysis software . Be aware, though, that slicing and dicing your data very finely will give you a smaller sample size, which then affects the reliability of your results.

1.   Review and investigate your results

Put your results in context – how have things changed since the last time you researched these kinds of questions? Do your findings tie in to changes in your market or other research done within your company?

Look at how different demographics within your sample or research population have answered, and compare your findings to other data on these groups. For example, does your survey analysis tell you something about why a certain group is purchasing less, or more? Does the data tell you anything about how well your company is meeting strategic goals, such as changing brand perceptions or appealing to a younger market?

Look at quantitative measures too. Which questions were answered the most? Which ones produced the most polarized responses? Were there any questions with very skewed data? This could be a clue to issues with  survey design .

2.   Use statistical analysis to check your findings

Statistics give you certainty (or as close to it as you can get) about the results of your survey.  Statistical tools  like T-test, regression and  ANOVA  help you make sure that the results you’re seeing have statistical significance and aren’t just there by chance.

Statistical tools can also help you determine which aspects of your data are most important, and what kinds of relationships – if any – they have with one another.

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One of the most powerful aspects of survey data analysis is its ability to build on itself. By repeating market research surveys at different points in time, you can not only use it to uncover insights from your results, but to strengthen those insights over time.

Using consistent types of data and methods of analysis means you can  use your initial results as a benchmark for future research . What’s changed year-on-year? Has your survey data followed a steady rise, performed a sudden leap or fallen incrementally? Over time, all these questions become answerable when you listen regularly and analyze your data consistently.

Maintaining your question and data types and your data analysis methods means you achieve a like-for-like measurement of results over time. And if you collect data consistently enough to see patterns and processes emerging, you can use these to  make predictions about future events  and outcomes.

Another benefit of data analysis over time is that you can compare your results with other people’s, provided you are using the same measurements and metrics. A classic example is  NPS (Net Promoter Score) , which has become a standard measurement of  customer experience  that companies typically track over time.

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Most data isn’t very friendly to the human eye or brain in its raw form. Survey data analysis helps you turn your data into something that’s accessible, intuitive, and even interesting to a wide range of people.

1.   Make it visual

You can present data in a visual form, such as a chart or graph, or put it into a tabular form so it’s easy for people to see the relationships between variables in your crosstab analysis. Choose a graphic format that best suits your data type and clearly shows the results to the untrained eye. There are plenty of options, including linear graphs, bar graphs, Venn diagrams, word clouds and pie charts. If time and budget allows, you can create an infographic or animation.

2.   Keep language human

You can express discoveries in plain language, for example, in phrases like “customers in the USA consistently preferred potato chips to corn chips.” Adding direct quotes from your natural language data (provided respondents have consented to this) can add immediacy and illustrate your points.

3.   Tell the story of your research

Another approach is to express data using the power of storytelling, using a beginning-middle-end or situation-crisis-resolution structure to talk about how trends have emerged or challenges have been overcome. This helps people understand the context of your research and why you did it the way you did.

4.   Include your insights

As well as presenting your data in terms of numbers and proportions, always be sure to share the insights it has produced too. Insights come when you apply knowledge and ideas to the data in the survey, which means they’re often more striking and easier to grasp than the data by itself. Insights may take the form of a  recommended action , or examine how two different data points are connected.

Related Reading: Maximize your research ROI with our eBook

1.   Being too quick to interpret survey results

It’s easy to get carried away when the data seems to show the results you were expecting or confirms a hypothesis you started with. This is why it’s so important to use statistics to make sure your survey report is statistically significant, i.e. based on reality, not a coincidence. Remember that a skewed or coincidental result becomes more likely with a smaller sample size.

2.   Treating correlation like causation

You may have heard the phrase “correlation is not causation” before. It’s well-known for a reason: mistaking a link between two independent variables as a causal relationship between them is a common pitfall in research. Results can correlate without one having a direct effect on the other.

An example is when there is another common variable involved that isn’t measured and acts as a kind of missing link between the correlated variables. Sales of sunscreen might go up in line with the number of ice-creams sold at the beach, but it’s not because there’s something about ice-cream that makes people more vulnerable to getting sunburned. It’s because a third variable – sunshine – affects both sunscreen use and ice-cream sales.

3.   Missing the nuances in qualitative natural language data

Human language is complex, and analysing survey data in the form of speech or text isn’t as straightforward as mapping vocabulary items to positive or negative codes. The latest AI solutions go further, uncovering meaning, emotion and intent within human language.

Trusting your rich qualitative data to an AI’s interpretation means relying on the software’s ability to understand language in the way a human would, taking into account things like context and conversational dynamics. If you’re investing in  software to analyse natural language data  in your surveys, make sure it’s capable of  sentiment analysis  that uses machine learning to get a deeper understanding of what survey respondents are trying to tell you.

If you’re planning to run an ongoing data insights program (and we recommend that you do), it’s important to have tools on hand that make it easy and efficient to perform your research and extract valuable insights from the results.

It’s even better if those tools help you to share your findings with the right people, at the right time, in a format that works for them. Here are a few attributes to look for in a survey analysis software platform.

• Easy to use (for non-experts) Look for software that demands minimal training or expertise, and you’ll save time and effort while maximising the number of people who can pitch in on your experience management program . User-friendly drag-and-drop interfaces, straightforward menus, and automated data analysis are all worth looking out for.
• Works on any platform Don’t restrict your team to a single place where software is located on a few terminals. Instead, choose a cloud-based platform that’s optimised for mobile, desktop, tablet and more.
• Integrates with your existing setup Stand-alone analysis tools create additional work you shouldn’t have to do. Why export, convert, paste and print out when you can use a software tool that plugs straight into your existing systems via API?
• Incorporates statistical analysis Choose a system that gives you the tools to not just process and present your data, but refine your survey results  using statistical tools  that generate deep insights and future predictions with just a few clicks.
• Comes with first-class support The best survey data tool is one that scales with you and adapts to your goals and growth. A large part of that is having an expert team on call to answer questions, propose bespoke solutions, and help you get the most out of the service you’ve paid for.

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We’ve run more than a few survey research programs in our time, and we have some tips to share that you may not find in the average survey data analysis guide. Here are some innovative ways to help make sure your survey analysis hits the mark, grabs attention, and provokes change.

Write the headlines

The #1 way to make your research hit the mark is to start with the end in mind. Before you even write your survey questions, make sample headlines of what the survey will discover. Sample headlines are the main data takeaways from your research. Some sample headlines might be:

• The #1 concern that travelers have with staying at our hotel is X
• X% of visitors to our showroom want to be approached by a salesperson within the first 10 minutes
• Diners are X% more likely to choose our new lunch menu than our old one

You may even want to sketch out mock charts that show how the data will look in your results. If you “write” the results first, those results become a guide to help you design questions that ensure you get the data you want.

Gut Data Gut

We live in a data-driven society. Marketing is a data-driven business function. But don’t be afraid to overlap  qualitative research  findings onto your quantitative data. Don’t be hesitant to apply what you know in your gut with what you know from the data.

This is called “Gut Data Gut”. Check your gut, check your data, and check your gut. If you have personal experience with the research topic, use it! If you have qualitative research that supports the data, use it!

Your survey is one star in a constellation of information that combines to tell a story. Use every atom of information at your disposal. Just be sure to let your audience know when you are showing them findings from statistically significant research and when it comes from a different source.

Write a mock press release to encourage taking action

One of the biggest challenges of research is  acting on it . This is sometimes called the “Knowing / Doing Gap” where an organisation has a difficult time implementing truths they know.

One way you can ignite change with your research is to write a press release dated six months into the future that proudly announces all the changes as a result of your research. Maybe it touts the three  new features  that were added to your product. Perhaps it introduces your new approach to technical support. Maybe it outlines the improvements to your website.

After six months, gather your team and read the press release together to see how well you executed change based on the research.

Focus your research findings

Everyone consumes information differently. Some people want to fly over your findings at 30,000 feet and others want to slog through the weeds in their rubber boots. You should package your research for these different research consumer types.

Package your survey results analysis findings in 5 ways:

• A 1-page executive summary with key insights
• A 1-page stat sheet that ticks off the top supporting stats
• A shareable slide deck with data visuals that can be understood as a stand-alone or by being presented in person
• Live dashboards with all the survey data that allow team members to filter the data and dig in as deeply as they want on a DIY basis
• The Mock Press Release (mentioned above)

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Reporting on survey results will prove the value of your work. Learn more about  statistical analysis types  or jump into an analysis type below to see our favorite tools of the trade:

• Conjoint Analysis
• CrossTab Analysis
• Cluster Analysis
• Factor Analysis
• Analysis of Variance (ANOVA)

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Related resources

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Text analysis 44 min read, sentiment analysis 21 min read, behavioural analytics 12 min read, descriptive statistics 15 min read, statistical significance calculator 18 min read, zero-party data 12 min read, request demo.

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• Financial services describes the various offerings within the finance industry, including money management and digital banking technology.
• And below we’ve outlined major terms, topics, and trends to provide a high-level financial services industry overview.
• Do you work in the Financial Services industry? Get business insights on the latest tech innovations, market trends, and your competitors with data-driven research .

The financial services sector is accelerating its adoption of digital technology. Paying with cash, participating in in-personal meetings with financial consultants, and even using an ATM are all fading facets of financial services.

To help you navigate the evolving industry, we’ve outlined major terms, topics, and trends to provide a high-level financial sector overview.

What is the financial services industry?

Financial services is a broad term used to describe the various offerings within the finance industry–encompassing everything from insurance and money management to payments and digital banking technology. 

There are a multitude of stakeholders and moving parts within financial services, from credit card issuers and processors, to legacy banks and emerging challengers. And with financial activity becoming increasingly digitized, especially as consumers are choosing to manage their finances from home amid the ongoing coronavirus pandemic, financial institutions and startups are sharpening their technology and expanding remote services.

Financial Services Industry Overview

There are three general types of financial services: personal, consumer, and corporate. These three categories encompass the major players and influencers for companies and organizations trying to climb the ladder of the industry. 

Personal Finance

Why is personal finance management (PFM) important? Personal finance is an individual’s budgeting, saving, and spending of monetary resources, like income, over time–while taking into consideration various monthly payments or future life events. It sets consumers up for all stages and major events in life, from buying their first car to retirement planning.

When choosing a bank or other financial institution, consumers typically look for businesses that offer personal finance services, such as financial advisors. As money management activities increasingly migrate online, consumers are looking to banks that allow them to manage personal accounts remotely and take control of their own financial health via online platforms and mobile apps. 

Financial institutions that offer personal finance management (PFM) tools are particularly attractive to younger, tech-savvy consumers. Some of the top players in the personal finance market include:

• Chime: This US neobank provides fee-free financial services through its mobile app. It recently launched a new Visa credit card, designed to help customers build a credit history. And during the coronavirus pandemic, Chime built customer loyalty by rolling out $200 stimulus check advances to 100,000 customers.
• N26: This German-headquartered neobank has no branch network, meaning it reaches consumers completely virtually. N26 products include a free checking account, personal loans, and a suite of PFM tools. 
• Personal Capital: This US-headquartered direct-to-consumer (D2C) digital wealth manager offers savings and retirement planning services.
• Varo: In 2020, Varo became the first neobank to receive FDIC approval and to receive a national bank charter. According to Insider Intelligence, Varo plans to use the approval to add credit products such as short-term loans, credit cards, and home financing.
• Cleo: You may recognize this service from Facebook Messenger. This AI-powered money management chatbot is now offered as an app that pulls in customers’ bank data to analyze spending in real time and generate personalized financial insights.

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Consumer Finance

From investing in real estate to paying for college, consumer finance helps people afford products and services by paying in installments over a fixed period of time. The consumer financial services market is made up of key players including credit card services, mortgage lenders, and personal and student loan services.

Some popular consumer finance services include:

• American Express: Amex is a popular payment firm, known for its charge and credit card services accompanied by various rewards programs. Recently, Amex partnered with Marriott Bonvoy to offer rewards for spending at gas stations and restaurants to a travel-focused credit card, in an effort to adjust perks based on the effects of the pandemic.
• Ally Financial: This digital-only bank went public in 2014 and is currently used by over 8.5 million people. It provides financial services ranging from vehicle financing  and insurance to mortgages and personal loans.
• LendingTree: This is the largest online lending marketplace in the US. LendingTree connects borrowers with various lenders to help them  find the best deals on loans–including car, home, and personal–credit cards, deposit accounts, and insurance.

Corporate Finance

Corporate financing is an all-encompassing term to describe the financial activities of a business, such as sources of funding, capital structure, actions to increase the company value, and tools to allocate resources. 

Jobs in the corporate finance sector include accountants, analysts, treasurers, and investor relation experts that all work to maximize the value of a company. 

Three key sources of funding in corporate finance include:

• Private equity: This is the value of company shares not publicly listed. High-net-worth investors buy shares of private companies or established mature companies that are failing. They are essentially in complete control of the companies they invest in. 
• Venture capital: Venture capital (VC) is financing provided to startups that firms believe are poised for long-term growth. Due to the risk associated with investing in young businesses, venture capitalists typically invest in less than 50% of the equity of the companies.
• Angel investors: These are independently wealthy individuals looking for small businesses and startups to invest in. Angel investors are essentially purchasing a portion of the company, which forces founders to relinquish some control.

Financial Services Industry Regulations

Regulatory bodies are interconnected with various industries, and financial services is no exception. Independent agencies are designated to oversee different financial institutions’ operations, uphold transparency, and ensure their clients are treated fairly.

Two key regulatory agencies within financial services include: The Financial Industry Regulatory Authority (FINRA) and the Office of the Comptroller of the Currency (OCC). 

• FINRA: This is the largest independent US regulator that oversees brokerage firms and exchange markets. In 2019 the FINRA launched the Office of Financial Innovation to aid communication between regulators, investors, and financial service providers. Essentially, it was set up to assist in understanding and regulating the technological advancements in the finance industry. 
• OCC: This is an independent bureau within the US Department of the Treasury designed to regulate all national banks. Most recently, the OCC announced that banks cannot use the coronavirus pandemic as a means for accelerating branch closures. According to Insider Intelligence, the OCC is standing by existing rules that govern bank closures.

Financial Services Industry Trends & Statistics

From personal finance to commercial banks, digital advancement and increased financial technology is rapidly transforming the financial sector. And two trends in particular that are driving this digital evolution are: tapping into a huge gig worker opportunity and the growing influence of big tech companies.

Gig Economy Workers

According to Insider Intelligence, gig workers have been massively underserved by financial services because they represent a high-risk demographic. 

But thanks to technological advancement in the financial sector, institutions can conduct more thorough risk assessments, which could make serving gig workers worthwhile. Half of the US population is expected to do gig work by 2028, and financial institutions that cater to this demographic could capture a major monetization opportunity.

Digital gig work generated $204 billion in customer volume in 2018 and is expected to grow to $455 billion by 2023, according to a recent Mastercard study.

Big Tech Companies

Big tech companies, like Apple and Amazon, could grab up to 40% of the $1.35 trillion in US financial services revenue from incumbent banks, according to an Insider Intelligence report.

Apple’s launch of the Apple Card could open doors to additional financial tools such as debit cards or PFM applications. And Amazon could bring Amazon Pay in-store–which could attract merchants by saving them interchange costs, cutting into a $90 billion annual source of revenue for issuers and networks. 

And with 54% of respondents to a Bain study indicating that they trust at least one tech company more than their own bank, consumer trust is making big tech players a huge threat in the finance industry.

Financial Services Industry Analysis

The influence of tech-savvy consumers, looming threat of big tech companies, and shifting attitudes of regulators toward new tech, are all impacting the financial services industry. 

Financial growth can be achieved with a touch of a button. And whether you’re an individual exploring wealth management options, or a CEO trying to increase the value of your company to shareholders, advanced tech will guide you to success within the finance sector.

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