Survey Data Analysis

You designed the questionnaire, got ethics approval, and chased colleagues for responses. Now you have a spreadsheet full of numbers and comments, and no clear idea what to do with it. Survey data analysis is the stage where many nursing students stall, not because they lack ability, but because few nursing programs teach statistics in a hands-on way.

This guide walks you through survey data analysis from start to finish. You will learn how to clean and code your responses, match each question type to the right statistical test, analyze Likert scale data correctly, check your instrument’s reliability, and report results the way your committee expects. A worked nursing example ties it all together.

What Is Survey Data Analysis?

Survey data analysis is the process of turning raw questionnaire responses into answers to your research questions. It covers preparing the data, summarizing it, testing relationships or differences, and interpreting what the results mean for nursing practice.

Most nursing surveys produce two kinds of data:

• Quantitative data comes from closed questions such as yes/no items, multiple choice, rating scales, and Likert items. You analyze it with descriptive and inferential statistics.
• Qualitative data comes from open-ended questions such as “Describe a barrier you face when reporting medication errors.” You analyze it with content or thematic analysis.

Many capstone and DNP projects use both, which makes the survey a simple mixed-methods design. Your analysis plan should state how each part is handled.

Before You Analyze: Preparing Your Survey Data

Good survey data analysis starts long before you run a single test. Errors in preparation carry straight into your results, and committees often spot them.

Clean the data

Export responses from your survey platform (Qualtrics, REDCap, Google Forms, or SurveyMonkey) into Excel or SPSS. Then screen every case, following these data cleaning steps for nursing research:

• Remove test submissions and duplicate responses.
• Check that every value falls within its possible range. A 7 on a 1–5 Likert item is a data entry error.
• Flag “straight-lining,” where a respondent picks the same answer for every item, and decide in advance whether to exclude those cases.
• Confirm that participants met your inclusion criteria, such as working as a registered nurse for at least six months.

Record every decision in a data cleaning log. Your methods chapter should report how many responses you received, how many you excluded, and why.

Code your responses

Statistical software needs numbers. Assign a numeric code to each answer option (for example, Strongly disagree = 1 to Strongly agree = 5) and create a codebook that lists every variable, its label, and its codes. Our guides to SPSS data entry and SPSS coding walk through this step by step.

Watch for reverse-coded items. If most items measure burnout positively but one reads “I feel energized at the end of my shift,” that item must be recoded (1 becomes 5, 2 becomes 4) before you calculate a total score.

Handle missing data

Almost every survey has gaps. First, describe how much data is missing and where. Then choose a strategy:

• Listwise deletion removes any case with a missing value. It is simple but shrinks your sample.
• Pairwise deletion uses all available data for each analysis, so sample sizes vary across tests.
• Multiple imputation estimates missing values and is preferred when more than about 5% of data is missing and the pattern is random.

State your approach and justify it. Committees rarely object to a reasonable method; they object to an unexplained one.

Check your response rate and sample size

Calculate your response rate (completed surveys divided by surveys distributed). Then compare your final sample with the size your G*Power analysis required. If you fell short, acknowledge it as a limitation rather than hiding it.

Types of Survey Questions and How to Analyze Each

The level of measurement of each question decides which analysis is appropriate. Use this table as a quick reference.

Likert items vs Likert scales

This distinction causes more committee feedback than almost any other in survey data analysis. A single Likert-type item is ordinal: the gap between “agree” and “strongly agree” is not guaranteed to equal the gap between “neutral” and “agree.” Report medians and frequencies for individual items and use nonparametric tests, as covered in our Likert scale analysis guide.

A Likert scale combines several items that measure one construct. Summed or averaged scale scores are widely treated as interval data, so means, standard deviations, and parametric tests are generally accepted, provided the scale is reliable and the test assumptions are met. Sullivan and Artino (2013) give a clear, open-access overview of this debate.

Descriptive Analysis of Survey Data

Descriptive statistics summarize who responded and what they said. Every survey results chapter starts here, and our guide to descriptive data analysis in nursing research covers it in more depth.

• Sample characteristics: Report frequencies and percentages for categorical demographics (gender, unit, education) and means with standard deviations for continuous ones (age, years of experience). A demographics table is standard.
• Item responses: Show the percentage of respondents choosing each option. Collapsing “agree” and “strongly agree” into a single “agreement” percentage often makes results easier to read.
• Scale scores: Report the mean, standard deviation, and range for each total or subscale score.
• Central tendency choice: Use the mean for normally distributed continuous data and the median when data is skewed or ordinal.

Descriptive results answer “what” questions, such as “What is the level of compassion fatigue among ICU nurses?” Many quality improvement and DNP projects need little more than this.

Inferential Analysis: Testing Relationships and Differences

Inferential statistics answer “is there a difference” and “is there a relationship” questions. Choose the test from your research question and the data level of your variables. Keep our statistical tests cheat sheet open alongside the table below.

Check the assumptions first

Parametric tests assume that the outcome is roughly normally distributed, that groups have similar variances, and that observations are independent. Test normality with the Shapiro-Wilk test or by inspecting histograms and Q-Q plots. Test equal variances with Levene’s test, explained in our guide to the homogeneity of variance assumption.

If assumptions are violated, switch to the nonparametric alternative. Our guide to parametric vs nonparametric tests explains how to make and justify that choice.

Report more than the p-value

A p-value below .05 suggests your result would be unlikely if there were truly no effect. It does not tell you whether the result matters clinically, as our article on p-values in nursing research explains. Report effect sizes (such as Cohen’s d, eta squared, Cramér’s V, or r) and confidence intervals alongside every test, as APA 7 requires.

Feeling stuck at this stage? Choosing tests, checking assumptions, and interpreting SPSS output is where most nursing students lose weeks. Our statisticians can run and explain your analysis so you can defend every result. Get inferential statistics help for your nursing research.

Reliability and Validity of Your Survey Instrument

Before you trust a scale score, show that the scale measured its construct consistently in your sample. The standard check is Cronbach’s alpha, which measures internal consistency.

Report alpha for every scale and subscale in your own sample, even if the original developers published a value; Tavakol and Dennick (2011) explain why published estimates should not be reused. If alpha is low, the “alpha if item deleted” column in SPSS shows which items weaken the scale. Our SPSS reliability analysis guide shows how to run and read it.

Validity asks whether the instrument measures what it claims to. If you used an established tool such as the Maslach Burnout Inventory, cite its published validity evidence. If you built your own questionnaire, describe how you established content validity, for example through expert panel review, a content validity index (CVI), and a pilot test. Polit and Beck (2006) explain how nurse researchers should calculate and report the CVI.

Analyzing Open-Ended Survey Responses

Open-ended answers often explain the “why” behind your numbers. Analyze them systematically rather than quoting a few striking comments.

1. Familiarize: Read all responses at least twice.
2. Code: Label meaningful segments with short codes, such as “short staffing” or “no feedback after reporting.”
3. Group: Cluster related codes into candidate themes.
4. Review: Check that each theme is supported across many responses and is distinct from the others.
5. Define and report: Name each theme, describe it, and support it with a few brief, anonymized quotes.

This follows Braun and Clarke’s (2006) widely used approach to thematic analysis. For short survey comments, counting how often each code appears (content analysis) is also acceptable and pairs well with your quantitative results. Our guide to types of data analysis in qualitative research compares both approaches.

Software Options for Survey Data Analysis

Check whether your program requires specific software before you start. Many universities provide free SPSS licenses to students. If you prefer spreadsheets, see using Excel for data analysis; for coding open-ended responses, see our NVivo data analysis guide.

A Worked Nursing Example

Imagine a DNP student surveys 180 medical-surgical nurses across three hospitals about burnout and intent to leave. The questionnaire includes demographics, a 10-item burnout scale (1–5 Likert), a yes/no item on intent to leave within a year, and one open-ended question about workplace stressors.

A results statement in APA 7 style might read:

Burnout scores differed significantly across the three hospitals, F(2, 177) = 5.84, p = .004, η² = .06. Post hoc Tukey tests showed that nurses at Hospital C reported higher burnout (M = 3.62, SD = 0.71) than nurses at Hospital A (M = 3.18, SD = 0.66), p = .003.

These figures are illustrative only. Notice how the statement gives the test, the statistic with degrees of freedom, the exact p-value, the effect size, and the group means in one or two sentences. For step-by-step instructions, see our guides to ANOVA in SPSS and logistic regression in SPSS.

Common Mistakes Nursing Students Make in Survey Data Analysis

1. Treating single Likert items as interval data. Reporting a mean of 3.7 for one item invites committee questions. Use medians and frequencies instead.
2. Skipping assumption checks. Running a t-test on heavily skewed data from 25 respondents produces results you cannot defend.
3. Forgetting to reverse-code items. One unrecoded item can drag Cronbach’s alpha down and distort every total score.
4. Running dozens of tests without a plan. Testing every variable against every other inflates false positives. Tie each test to a research question.
5. Reporting p-values alone. Without effect sizes, readers cannot judge clinical significance.
6. Ignoring missing data. Silently dropping incomplete cases hides potential bias.
7. Overstating findings. A cross-sectional survey shows associations, not causes. Write “was associated with,” not “caused.”
8. Pasting raw SPSS output. Committees expect APA-formatted tables and a written interpretation, not screenshots.

Frequently Asked Questions

How do you analyze Likert scale survey data?

Analyze individual Likert items as ordinal data using frequencies, percentages, medians, and nonparametric tests such as Mann-Whitney U. Analyze summed or averaged scale scores as interval data using means, standard deviations, and parametric tests such as t-tests or ANOVA, after confirming reliability and assumptions.

What statistical test should I use for survey data?

It depends on your research question and data level. Use chi-square for two categorical variables, t-tests or ANOVA to compare group means, correlation for relationships between continuous variables, and regression to predict outcomes. Use nonparametric alternatives when assumptions are not met.

Can I use SPSS for survey analysis?

Yes. SPSS is the most common choice for nursing survey research. It handles data cleaning, reliability analysis, descriptive statistics, and nearly every inferential test a dissertation or DNP project needs.

What is a good Cronbach’s alpha for a nursing survey?

An alpha of 0.70 or higher is generally considered acceptable, and 0.80 or higher is good. Values above 0.95 may indicate redundant items.

How many responses do I need for survey data analysis?

The number depends on your planned tests, expected effect size, and desired power. Run a power analysis (for example, in G*Power) before data collection. Many nursing studies aim for 80% power at an alpha of .05.

How do I analyze open-ended survey questions?

Use thematic or content analysis. Read the responses, assign codes, group the codes into themes, and report each theme with brief supporting quotes.

Conclusion

Survey data analysis becomes manageable once you break it into stages: clean and code the data, describe your sample and responses, match each research question to the right test, check reliability and assumptions, and report results in APA style with effect sizes. Handle Likert items carefully, document every decision, and link each finding back to nursing practice.

If you are short on time, unsure which test fits your data, or facing revisions on your results chapter, you do not have to work through it alone. The team at nursingdissertationhelp.com supports nursing students at every stage, from cleaning raw survey data to running SPSS analysis and writing up results your committee will accept. Get expert help with your survey data analysis today, or explore our qualitative data analysis help for open-ended responses.

Further reading

• Sullivan, G. M., & Artino, A. R. (2013). Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education.
• Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55.
• Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what’s being reported? Research in Nursing & Health, 29(5), 489–497.
• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.