How to run an independent-samples t-test in SPSS for nursing research is essential when a dissertation compares the mean of one continuous outcome between two separate groups. For example, the analysis may examine whether medication-safety knowledge differs between nurses who completed simulation training and nurses who did not.
A valid analysis requires more than selecting a menu option. You must confirm that the groups are genuinely independent, verify the outcome and coding, examine assumptions, select the correct output row, interpret the mean difference and confidence interval, and report effect size without confusing statistical significance with clinical importance.
This guide covers that complete process using nursing examples. All numerical results are illustrative and must be replaced with statistics from your verified dataset.
Key Takeaways
- Use this test only for one continuous outcome compared across two unrelated groups.
- Check coding, independence, outliers, approximate normality and variance assumptions before interpreting output.
- Report group descriptives, the correct SPSS row, mean difference, 95% confidence interval, p-value and effect size.
- An independent-samples t-test in SPSS for nursing research does not establish causation or clinical importance by itself.
What Is an Independent-Samples t-Test in SPSS?
An independent-samples t-test compares the means of a continuous outcome between two unrelated groups. “Independent” means that each participant contributes data to only one group and observations in one group are not paired or matched with observations in the other.
Typical nursing research questions include:
- Do nurses who completed simulation training have a different mean sepsis-recognition score from those who did not?
- Does mean discharge-readiness differ between patients receiving standard education and those receiving a nurse-led programme?
- Do day-shift and night-shift nurses report different mean safety-climate scores?
- Does average pain score differ between two independent treatment groups?
The test evaluates evidence about a difference between population means. It does not establish why a difference occurred. A cross-sectional comparison cannot support a causal claim merely because its p-value is small.
Independent-Samples Versus Paired-Samples t-Test
| Feature | Independent-samples t-test | Paired-samples t-test |
|---|---|---|
| Participants | Different people in two groups | The same people measured twice, or defensibly matched pairs |
| Example | Trained nurses versus untrained nurses | Nurses’ scores before versus after training |
| Normality focus | Outcome within each group or model residuals | Within-person difference scores |
| Main estimate | Difference between group means | Mean of the paired differences |
| Common mistake | Treating repeated measurements as independent | Treating separate groups as paired |
Choosing the wrong design changes the standard error and can invalidate inference. If each participant has a pre-intervention and post-intervention value, use the paired-samples procedure rather than the independent test.
When Is an Independent-Samples t-Test Appropriate?
Use the test when the nursing research question asks whether the mean of a suitable continuous outcome differs between exactly two independent groups.
Before opening SPSS, identify:
- Outcome variable: a continuous measure or defensibly treated scale score.
- Grouping variable: a categorical variable containing exactly two relevant groups.
- Independence: participants appear in one group only and observations are not paired.
- Research purpose: comparison of means rather than proportions, ranks or repeated measurements.
A single Likert item is ordinal and should not automatically be analysed as a continuous normal outcome. A multi-item score may be suitable when its construction, measurement properties and distribution justify that treatment. Consult the nursing Likert-scale SPSS guide before treating questionnaire codes as scale data.
When Should You Not Use This t-Test?
Do not use an independent-samples t-test merely because the dataset contains two columns of numbers. Another method is needed when:
- the outcome is nominal or binary;
- the groups contain the same participants at two time points;
- there are three or more independent groups;
- observations are clustered by ward, hospital or clinical team;
- the outcome is poorly represented by a mean-based model;
- the question concerns association, prediction or time-to-event outcomes; or
- participants were matched but the matching is ignored.
The broader statistical-test selection guide for nursing dissertations explains how design, variable type and question determine the analysis.
Prepare Nursing Data Before Running the Test
Preserve the original dataset
Keep an untouched copy of the original file. Perform corrections and recoding in a working copy, preferably with an auditable record or SPSS syntax. This protects the analysis from undocumented changes.
Check group coding
Confirm which value represents each group. For example, 0 may mean standard education and 1 may mean nurse-led education. Do not infer labels from table order. Use Variable View to add clear value labels.
Check the outcome range
Run frequencies or descriptives and compare minimum and maximum values with the instrument or clinical measurement rules. A knowledge score ranging from 0 to 100 cannot legitimately contain 999 unless 999 is correctly defined as missing.
Review missing data
Establish how many observations contribute to each group and why values are missing. Do not silently replace missing values with the mean. See the SPSS missing-data guide for nursing research for transparent handling.
Verify independence
Independence is mainly established from the design, not from an SPSS significance test. Repeated observations from the same person, twins, matched participants or nurses nested within a small number of wards may require another analytical structure.
Assumptions of the Independent-Samples t-Test
A defensible analysis examines assumptions relevant to the estimate and standard error. Avoid presenting them as a mechanical pass-or-fail checklist.
One suitable continuous outcome
The dependent variable should be continuous or a scale score that is defensibly analysed as approximately continuous. The SPSS “Scale” label alone does not prove measurement suitability.
Two independent groups
The grouping variable must identify two distinct groups. If one participant contributes to both categories, the observations are not independent.
No materially influential errors or outliers
Use boxplots and source records to investigate unusual values. An SPSS outlier marker is not permission to delete a genuine participant. Consider influence, measurement validity and prespecified rules. Sensitivity analysis can show whether a conclusion depends on an unusual observation.
Approximate normality within groups
Examine histograms, Q–Q plots, unusual observations and Shapiro–Wilk results in context. Large samples may produce significant normality tests for minor departures, while small samples can miss meaningful problems. Follow the SPSS normality-testing guide for nursing research.
Equality of variances for the pooled test
The traditional pooled-variance t-test assumes equal population variances. SPSS presents Levene’s test and normally reports both “Equal variances assumed” and “Equal variances not assumed” results.
Levene’s p-value does not test whether the outcome is normally distributed. When variances or group sizes differ, the unequal-variances result—often described as Welch’s t-test—is generally safer. Consider the design, descriptive statistics and analysis plan rather than following one threshold without judgement.
How to Run an Independent-Samples t-Test in SPSS for Nursing Research
- Select Analyze.
- Choose Compare Means and Proportions or Compare Means.
- Select Independent-Samples T Test.
- Move the continuous outcome into Test Variable(s).
- Move the two-category variable into Grouping Variable.
- Select Define Groups.
- Enter the exact codes for Group 1 and Group 2.
- Select Continue.
- Open Options and retain a 95% confidence interval unless the analysis plan specifies otherwise.
- Select OK.
Before interpreting significance, check that Group Statistics contains the intended labels and plausible sample sizes. A missing group often means the codes entered under Define Groups do not match the data.
SPSS Syntax for a Reproducible Independent t-Test
Syntax creates a transparent analysis record. For an outcome named knowledge_score and a group named training_group coded 0 and 1:
T-TEST GROUPS=training_group(0 1)
/VARIABLES=knowledge_score
/CRITERIA=CI(.95)
/MISSING=ANALYSIS.Variable names and codes must match the dataset. Save syntax with the working files. Do not copy example syntax without checking names, coding and missing-value rules.
Read the Group Statistics Table First
The Group Statistics table shows sample size, mean, standard deviation and standard error for each group. Begin here because it confirms what was analysed and shows the substantive direction.
- Are the group labels correct?
- Are sample sizes consistent with exclusions and missingness?
- Which group has the higher mean?
- Are standard deviations and values plausible?
Standard deviation describes variability among observations. Standard error describes uncertainty in an estimated mean. Do not interchange them in a dissertation table.
Interpret Levene’s Test and Select the Correct Row
The Independent Samples Test table begins with Levene’s test for equality of variances. It then shows the t statistic, degrees of freedom, two-sided p-value, mean difference, standard error and confidence interval.
A common starting convention is:
- if Levene’s p > .05, read “Equal variances assumed”;
- if Levene’s p ≤ .05, read “Equal variances not assumed”.
This should not replace statistical judgement. The unequal-variances row adjusts the standard error and degrees of freedom and is appropriate where variance equality is doubtful. Record which row was used and apply it consistently.
Never take the t statistic from one row and the p-value from the other.
Interpret t, Degrees of Freedom and the p-Value
The sign of t reflects group order and subtraction direction. Reversing Group 1 and Group 2 reverses the sign but does not change the two-sided evidence.
Under unequal variances, SPSS may display non-integer degrees of freedom. This is expected. Do not force it into an integer merely because the pooled test uses one.
The p-value is not the probability that the null hypothesis is true, the size of the difference, proof of clinical importance or proof of causation. Interpret it with the mean difference, confidence interval, effect size, design and nursing relevance.
Interpret the Mean Difference and Confidence Interval
The mean difference is normally Group 1 minus Group 2. A positive value means Group 1 has the higher mean; a negative value means Group 1 has the lower mean.
The 95% confidence interval communicates precision and the range of effect sizes reasonably compatible with the analysis. A narrow interval suggests greater precision than a wide interval. If it includes zero, a corresponding two-sided test is generally non-significant.
Do not describe the interval as containing 95% of individual participants or proving that the true value lies inside it.
Calculate and Interpret Effect Size
A p-value does not show how large the difference is. Cohen’s d expresses the mean difference in standard-deviation units. Depending on the SPSS version and options, effect-size output may be available; otherwise calculate the agreed measure transparently.
Common descriptive reference points for absolute Cohen’s d are about 0.20, 0.50 and 0.80 for small, medium and large effects. Lakens (2013) cautions that effect sizes require context. These labels are not clinical thresholds.
Ask whether the outcome has a minimally important difference, whether the estimate could affect nursing practice or safety, whether the interval is sufficiently precise, and whether the design permits an intervention-effect interpretation. See the effect-size guide for nursing research.
Worked Nursing Example: Training and Knowledge
Suppose a dissertation compares medication-safety knowledge between 42 nurses who completed simulation training and 39 nurses who received standard teaching. Scores range from 0 to 100.
| Group | n | Mean | SD |
|---|---|---|---|
| Simulation training | 42 | 78.4 | 8.6 |
| Standard teaching | 39 | 72.1 | 9.4 |
The illustrative difference is 6.3 points. Assume the relevant SPSS row reports t(76.4) = 3.14, p = .002, 95% CI [2.30, 10.30], Cohen’s d = 0.70.
Nurses in the simulation-training group had higher medication-safety knowledge scores than nurses receiving standard teaching. The estimated difference was 6.3 points, 95% CI [2.30, 10.30], t(76.4) = 3.14, p = .002, Cohen’s d = 0.70.
The pattern indicates a moderately sized difference with an interval excluding zero. However, if groups were observational rather than randomised, do not claim simulation caused higher scores. Every value is illustrative and must not be copied as a real result.
How to Report an Independent-Samples t-Test in SPSS for Nursing Research
A results paragraph should include groups and outcome, group sizes, means and standard deviations, mean difference and confidence interval, t statistic, degrees of freedom, p-value, effect size and a proportionate conclusion.
The intervention group (M = XX.XX, SD = XX.XX) recorded a higher mean outcome score than the comparison group (M = XX.XX, SD = XX.XX), mean difference = XX.XX, 95% CI [LL, UL], t(df) = X.XX, p = .XXX, Cohen’s d = X.XX.
Use verified values and follow university decimal rules. The nursing SPSS results-chapter guide explains placement within Chapter 4.
How to Report a Non-Significant Result
A non-significant result still answers the question. Do not say that the test failed or that the groups are proven identical.
Mean confidence scores were slightly higher in Group 1 than Group 2, but the estimate was imprecise and the 95% confidence interval included zero, t(df) = X.XX, p = .XXX. The study therefore did not provide clear evidence of a mean difference.
A wide interval may mean the study is inconclusive rather than demonstrating equivalence. Formal equivalence requires justified equivalence bounds and a dedicated analysis.
What to Do When Assumptions Are Questionable
Correct verified errors
Check unusual records against the source and document corrections. Never change genuine measurements to make assumptions pass.
Use the unequal-variances result appropriately
Welch’s approach addresses unequal variances more reliably than forcing the pooled result. It does not fix dependence or an inappropriate outcome.
Consider robust or alternative methods
A transformation, robust method or non-parametric comparison may suit the question and distribution. Mann–Whitney U does not automatically test a difference in means and is not a universal replacement.
Conduct sensitivity analysis
Compare reasonable methods when conclusions may depend on an assumption or influential value. Do not choose whichever produces the smallest p-value.
Common Independent-Samples t-Test Errors
- Using the test for repeated scores from the same participants.
- Entering incorrect group codes.
- Ignoring missing cases and changing sample sizes.
- Reading the wrong variance row.
- Combining statistics from different rows.
- Reporting p-values without group means or confidence intervals.
- Calling statistical significance clinically important without evidence.
- Deleting genuine outliers without justification.
- Treating non-significance as proof of equality.
- Claiming causation from an observational comparison.
Independent-Samples t-Test Checklist
- The question compares a suitable continuous outcome between two independent groups.
- Group codes and labels are correct.
- The original dataset is preserved.
- Missing values and group sizes are reported.
- Unusual values were investigated.
- Normality was interpreted in context.
- Variance differences and group imbalance were considered.
- The correct SPSS row was used consistently.
- Means, SDs, mean difference and confidence interval are reported.
- Test statistic, degrees of freedom and p-value match the output.
- Effect size and nursing relevance are considered.
- The conclusion remains within the design.
Frequently Asked Questions
Can I use this test for pre-test and post-test scores?
Not when the same participants provide both scores. That is a paired design. The independent test applies to unrelated participants.
Which row should I use when Levene’s test is significant?
The “Equal variances not assumed” row is normally the appropriate starting point. Consider the analysis plan, variance pattern and group sizes.
Does the outcome have to be perfectly normal?
No. Examine the extent and consequences of departure using graphs, unusual observations, sample sizes and robustness.
Does SPSS calculate Cohen’s d?
Some versions and options provide effect-size output. Otherwise calculate the agreed measure transparently from verified statistics or appropriate software.
What if my p-value exceeds .05?
Report the result with the estimated difference and confidence interval. Do not claim that groups are identical or change methods to obtain significance.
Conclusion
Knowing how to run an independent-samples t-test in SPSS for nursing research requires a clear design, accurate coding, appropriate assumption checks and careful interpretation. Confirm that groups are independent, read descriptives first, select the correct variance row and report the mean difference, confidence interval, t statistic, degrees of freedom, p-value and effect size together. Keep the conclusion proportional to the design and nursing context.
References
- Davis, S.L., Johnson, A.H., Lynch, T., Gray, L., Pryor, E.R., Azuero, A., Soistmann, H.C., Phillips, S.R. and Rice, M. (2021) ‘Inclusion of effect size measures and clinical relevance in research papers’, Nursing Research, 70(3), pp. 222–230. https://doi.org/10.1097/NNR.0000000000000494
- IBM (n.d.) ‘Independent-samples t test’, IBM SPSS Statistics Documentation. Available at: IBM independent-samples t-test documentation (Accessed: 26 August 2026).
- Kim, T.K. (2015) ‘T test as a parametric statistic’, Korean Journal of Anesthesiology, 68(6), pp. 540–546. https://doi.org/10.4097/kjae.2015.68.6.540
- Lakens, D. (2013) ‘Calculating and reporting effect sizes to facilitate cumulative science’, Frontiers in Psychology, 4, 863. https://doi.org/10.3389/fpsyg.2013.00863
- Wasserstein, R.L., Schirm, A.L. and Lazar, N.A. (2019) ‘Moving to a world beyond p < 0.05’, The American Statistician, 73(sup1), pp. 1–19. https://doi.org/10.1080/00031305.2019.1583913
Need Help With Your Nursing SPSS Analysis?
If you are unsure whether your groups are independent, which output row to report or how to connect the result to your nursing hypothesis, our nursing dissertation data analysis and SPSS support can review the verified dataset, assumptions, output and written interpretation.
