Every nursing committee eventually asks the same question about your analysis plan: “Why this method?” Choosing the right survey data analysis methods matters more than running many tests. One well-matched method, clearly justified, is stronger than ten tests run because the software offered them.
This guide is a menu of the most common survey data analysis methods in nursing research. For each method, you will see what it does, when to use it, a nursing example, and what to report. If you need the full process from cleaning raw responses to writing up results, start with our step-by-step survey data analysis guide, then return here to choose your methods.
How to Choose a Survey Data Analysis Method
Three factors decide which survey data analysis methods fit your study.
- Your research question. Questions that ask “what is the level of…” need descriptive methods. Questions that ask “is there a difference…” or “is there a relationship…” need inferential methods. Questions that ask “why” or “how” from open-ended answers need qualitative methods.
- The level of measurement. Nominal data (yes/no, unit type), ordinal data (single Likert items, education level), and interval or ratio data (scale totals, years of experience) each allow different tests.
- Your sample size and design. Small samples limit regression and factor analysis. Repeated surveys of the same nurses call for paired or longitudinal methods, not independent-group tests.
Write your research questions first, then pick methods. Our guide on when to use statistical tests shows how to match each question type to a test.
Quantitative Survey Data Analysis Methods
Quantitative survey data analysis methods work with closed-ended questions: yes/no items, multiple choice, rating scales, and Likert items. They fall into two families. Descriptive methods summarize your sample, and inferential methods test differences and relationships. Most nursing surveys use both.
1. Descriptive analysis
What it does: Summarizes who responded and how they answered, using frequencies, percentages, means, medians, and standard deviations.
When to use it: In every survey study. Descriptive results form your demographics table and answer “what is the level of” questions.
Nursing example: Reporting that 62% of ICU nurses scored in the high range for compassion fatigue.
What to report: n and % for categorical variables, and M and SD (or median and IQR for skewed data) for continuous ones. See our guide to descriptive data analysis in nursing research.
2. Cross-tabulation and chi-square test
What it does: Shows how two categorical variables relate in a contingency table, and the chi-square test checks whether the association is statistically significant.
When to use it: Both variables are nominal or ordinal categories, such as shift type and intent to leave.
Nursing example: Testing whether night-shift nurses are more likely than day-shift nurses to report missed care.
What to report: χ² value, degrees of freedom, p-value, and Cramér’s V as the effect size. Use Fisher’s exact test when expected cell counts fall below 5.
3. Group comparison methods
What they do: Compare average scores between groups.
When to use them: You have a grouping variable and a continuous outcome, such as burnout score by hospital unit.
| Design | Parametric test | Nonparametric alternative |
| Two independent groups | Independent samples t-test | Mann-Whitney U |
| Three or more groups | One-way ANOVA | Kruskal-Wallis H |
| Same participants measured twice | Paired samples t-test | Wilcoxon signed-rank |
Nursing example: Comparing job satisfaction scores among medical-surgical, ICU, and emergency nurses.
What to report: The test statistic, degrees of freedom, p-value, effect size (Cohen’s d or η²), and group means or medians. Our guide to parametric vs nonparametric tests explains when to switch.
4. Correlation analysis
What it does: Measures the strength and direction of a relationship between two variables, from −1 to +1.
When to use it: Both variables are continuous or ordinal. Use Pearson’s r for normally distributed interval data and Spearman’s rho for ordinal or skewed data.
Nursing example: Examining whether years of experience relate to self-reported confidence in sepsis recognition.
What to report: r or ρ, n, and p-value. Remember that correlation does not show causation. Read more in correlation analysis in nursing research.
5. Regression analysis
What it does: Tests how well one or more predictors explain an outcome, while controlling for other variables.
When to use it: Your research question asks what predicts an outcome. Use multiple linear regression for a continuous outcome and binary logistic regression for a yes/no outcome.
Nursing example: Testing whether burnout, staffing ratio, and manager support predict intent to leave.
What to report: For linear regression, R², F, and each predictor’s B, β, and p. For logistic regression, odds ratios with 95% confidence intervals. See our logistic regression in SPSS guide.
6. Factor analysis
What it does: Groups many survey items into a smaller number of underlying factors, revealing the structure of an instrument.
When to use it: You developed a new questionnaire or adapted one for a new population. Exploratory factor analysis (EFA) needs a reasonably large sample; many guidelines suggest at least 5–10 respondents per item.
Nursing example: Checking whether a new 20-item patient safety culture survey measures the four domains it was designed to capture.
What to report: KMO and Bartlett’s test, extraction and rotation method, number of factors retained, and factor loadings. Our SPSS factor analysis guide walks through the output.
7. Reliability analysis
What it does: Checks whether items in a scale consistently measure the same construct, most often with Cronbach’s alpha.
When to use it: Any time you calculate a total or subscale score from multiple items.
Nursing example: Confirming that a 10-item burnout scale has an alpha of .70 or higher in your sample before comparing scores.
What to report: Cronbach’s α for each scale and subscale in your own sample. See our SPSS reliability analysis guide and Tavakol and Dennick (2011) for interpretation.
8. Trend and longitudinal analysis
What it does: Tracks how responses change over time, either across repeated surveys or before and after an intervention.
When to use it: Pre/post quality improvement projects, annual staff surveys, or cohort studies following the same nurses.
Nursing example: Measuring hand hygiene knowledge before and three months after an education program.
What to report: Paired tests for two time points, repeated measures ANOVA or mixed models for three or more. Our guide to applied longitudinal data analysis covers the options.
A note on Likert data
Likert data cuts across several methods above. Single Likert items are ordinal and suit frequencies, medians, and nonparametric tests. Summed Likert scales are usually treated as interval data and suit means and parametric tests. Our Likert scale analysis guide and Sullivan and Artino (2013) explain the distinction.
Not sure which method fits your research questions? Our statisticians can review your survey and analysis plan and recommend methods your committee will accept. Get inferential statistics help for nursing research.
Qualitative Survey Data Analysis Methods
Open-ended survey questions produce text, not numbers. Two qualitative survey data analysis methods dominate nursing research: thematic analysis and content analysis.
Thematic analysis identifies patterns of meaning across responses. You code the data, group codes into themes, then define and name each theme. Braun and Clarke’s (2006) six-phase approach is the most widely cited framework.
Content analysis organizes responses into categories and often counts how frequently each category appears. Elo and Kyngäs (2008) describe its inductive and deductive forms for nursing research.
| Feature | Thematic analysis | Content analysis |
| Main goal | Interpret patterns of meaning | Describe and categorize content |
| Output | Themes with supporting quotes | Categories, often with counts or percentages |
| Best for | Rich, detailed open-ended answers | Short comments and large numbers of responses |
| Nursing example | “What does feeling supported at work mean to you?” | “List barriers to reporting near misses” |
For short survey comments, content analysis is often the more practical choice. Our guide to types of data analysis in qualitative research compares these and other approaches. If coding hundreds of comments feels overwhelming, our qualitative data analysis help service can support you.
Mixed Methods Survey Analysis
Many nursing surveys combine closed and open-ended questions. Mixed methods survey analysis integrates both types of results rather than reporting them side by side and leaving readers to connect them.
The most common approach for single surveys is a convergent design. You analyze quantitative and qualitative data separately, then compare them to see where they agree, expand on each other, or conflict. For example, low scores on a manager support scale might be explained by open-ended comments about rarely receiving feedback.
Fetters, Curry, and Creswell (2013) describe practical ways to integrate findings, including joint displays that place numbers and themes in one table. Comparing results from different data sources this way is also a form of data triangulation in nursing research. For more detail, see our guide to mixed methods data analysis in nursing research.
Survey Data Analysis Methods Compared
Use this table to match your research question to a method. Keep our statistical tests cheat sheet nearby for the specific test names.
| Method | Research question it answers | Data needed | Nursing example |
| Descriptive analysis | What is the level or distribution? | Any | Prevalence of burnout among ICU nurses |
| Cross-tabulation / chi-square | Are two categories associated? | Two categorical variables | Shift type and missed care |
| Group comparison (t-test, ANOVA) | Do groups differ on an outcome? | Group variable + continuous score | Job satisfaction by unit |
| Correlation | Are two variables related? | Two continuous or ordinal variables | Experience and sepsis confidence |
| Regression | What predicts an outcome? | Several predictors + one outcome | Predictors of intent to leave |
| Factor analysis | What structure does my instrument have? | Many related items, large sample | Validating a new safety culture survey |
| Reliability analysis | Is my scale internally consistent? | Multi-item scale | Cronbach’s alpha for a burnout scale |
| Trend / longitudinal | Did responses change over time? | Repeated measurements | Knowledge before and after training |
| Thematic analysis | What patterns of meaning appear? | Rich open-ended text | Meaning of workplace support |
| Content analysis | What categories appear, and how often? | Short open-ended text | Barriers to incident reporting |
| Mixed methods integration | How do numbers and narratives fit together? | Closed and open-ended data | Explaining low support scores |
Common Mistakes When Choosing Survey Data Analysis Methods
- Choosing a test before writing research questions. The question drives the method, never the reverse.
- Running regression or factor analysis on tiny samples. These methods need enough respondents to produce stable results. Check your sample size before committing.
- Treating single Likert items as interval data. Report medians and frequencies for individual items; reserve means for summed scales.
- Skipping reliability analysis. Scale scores mean little if the scale is not internally consistent in your sample.
- Using independent-group tests on repeated measures. Pre/post data from the same nurses needs paired or repeated measures methods.
- Reporting open-ended comments as a few quotes. Without a systematic method such as thematic or content analysis, qualitative findings look anecdotal.
- Presenting quantitative and qualitative results separately. In mixed methods studies, integrate the findings so each strand explains the other.
Frequently Asked Questions
What are the methods of survey data analysis?
The main survey data analysis methods are descriptive analysis, cross-tabulation with chi-square, group comparisons (t-tests and ANOVA), correlation, regression, factor analysis, reliability analysis, and trend analysis for quantitative data. For open-ended responses, thematic analysis and content analysis are most common.
Which method is best for analyzing Likert scale data?
For single Likert items, use frequencies, medians, and nonparametric tests such as Mann-Whitney U. For summed Likert scales, use means, standard deviations, and parametric tests such as t-tests or ANOVA, after checking reliability and assumptions.
What is the difference between descriptive and inferential survey analysis?
Descriptive analysis summarizes your sample, such as the percentage of nurses reporting burnout. Inferential analysis tests whether differences or relationships in your sample are likely to exist in the wider population, such as whether burnout differs significantly between units.
How do you analyze open-ended survey questions?
Use thematic analysis to identify patterns of meaning or content analysis to categorize and count responses. Read all responses, assign codes, group codes into themes or categories, and report each with brief supporting quotes.
Can I use more than one survey data analysis method?
Yes. Most nursing surveys combine descriptive statistics with at least one inferential method, and many add qualitative analysis of open-ended questions. Each method should answer a specific research question.
What software is used for survey data analysis?
SPSS is the most common choice in nursing research. R, JASP, and Excel are alternatives for quantitative analysis, and NVivo is widely used for coding open-ended responses.
Conclusion
The best survey data analysis methods are the ones that directly answer your research questions and fit your data. Use descriptive analysis to summarize, inferential methods to test differences and relationships, factor and reliability analysis to support your instrument, and thematic or content analysis for open-ended answers. When you combine them, integrate the results so they tell one coherent story.
Choosing and justifying methods is where many nursing students get stuck. If you want expert help selecting, running, and defending your survey data analysis methods, our team is ready to support you. Get dissertation data analysis help today, or review the full process in our survey data analysis guide.
Further reading
- Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
- Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115.
- Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs: Principles and practices. Health Services Research, 48(6 Pt 2), 2134–2156.
- Sullivan, G. M., & Artino, A. R. (2013). Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education, 5(4), 541–542.
- Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55.
