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Many nursing students know that SPSS can run ANOVA, but they struggle with the part that matters most: choosing the correct ANOVA test, checking assumptions, interpreting SPSS output, and writing APA 7th edition results for Chapter 4. This is why SPSS ANOVA often becomes stressful during dissertations, theses, DNP capstones, MSN projects, and healthcare research assignments.
The problem is not simply clicking the right SPSS menu. SPSS can produce ANOVA output quickly, but students still need to know whether one-way ANOVA, two-way ANOVA, repeated measures ANOVA, or mixed ANOVA fits the research question. They also need to understand Levene’s test, Mauchly’s test, post hoc comparisons, effect sizes, and the meaning of “Sig.” in SPSS output.
This guide explains how nursing and healthcare research students can choose, run, interpret, and report ANOVA in SPSS. It focuses on practical SPSS workflow, output interpretation, APA 7 reporting, and dissertation-ready Chapter 4 writing.
What Is SPSS ANOVA?
ANOVA means analysis of variance. It is an inferential statistical test used to compare mean scores across groups or repeated conditions. In nursing research, ANOVA helps determine whether differences between group means are statistically significant.
In simple terms, ANOVA asks:
Are the mean scores different enough to suggest that at least one group differs from another?
ANOVA does not prove that one group is clinically better. It also does not prove causation by itself. It tests whether the observed mean differences are unlikely to have occurred by random sampling variation alone.
For example, a nursing student may use ANOVA to compare patient satisfaction scores across three nurse communication training groups. If the ANOVA result is significant, it means at least one group mean differs from another. It does not automatically show which groups differ. Post hoc tests are usually needed to identify the specific differences.
IBM explains that one-way ANOVA is used when a quantitative dependent variable is compared across groups defined by a factor variable (IBM Corp., 2023). This makes ANOVA useful for nursing outcomes such as satisfaction scores, burnout scores, pain scores, knowledge scores, adherence scores, and quality-of-life scores.
Key SPSS ANOVA terms include:
- Mean: Average score for each group.
- Variance: Spread of scores within and between groups.
- F statistic: Main ANOVA test statistic.
- df: Degrees of freedom.
- Sig.: SPSS label for the p value.
- Effect size: Strength or practical size of the difference.
ANOVA belongs to inferential statistics because it uses sample data to test a hypothesis about a wider population. For more context, see Inferential Statistics Help for Nursing Research and Inferential Data Analysis in Nursing.
When Should Nursing Students Use ANOVA in SPSS?
Nursing students should use ANOVA in SPSS when the dependent variable is continuous or approximately continuous and the independent variable defines groups or repeated conditions.
Common nursing examples include:
- Patient satisfaction scores across three hospital units
- Medication adherence scores across three education methods
- Nurse burnout scores across experience levels
- Pain scores across treatment groups
- Knowledge scores across teaching strategies
- Anxiety scores at baseline, post-test, and follow-up
- Quality-of-life scores across models of care
For example, this research question fits one-way ANOVA:
Is there a statistically significant difference in medication adherence scores among patients receiving standard education, video-based education, and nurse-led counseling?
This question has one continuous dependent variable and one categorical independent variable with three groups.
ANOVA may not be suitable when the outcome is categorical, such as readmitted versus not readmitted, infected versus not infected, or passed versus failed. Those outcomes may require chi-square, logistic regression, or another method.
ANOVA may also be unnecessary when there are only two independent groups. In that case, an independent-samples t test may be enough. However, when there are three or more groups, multiple factors, or repeated measurements, ANOVA often becomes appropriate.
Before choosing ANOVA, students should understand how it fits into the larger analysis plan. See Types of Data Analysis in Research for a broader overview.
SPSS ANOVA vs t-Test, Chi-Square, and Regression
Students often select the wrong test because they focus on the SPSS menu instead of the research question.
A t-test compares two means. For example, it can compare mean anxiety scores between an intervention group and a control group.
ANOVA compares three or more means or tests factor effects. For example, it can compare patient satisfaction scores across standard education, video education, and nurse-led counseling.
A chi-square test examines categorical variables. For example, it can test whether readmission status differs by discharge education type.
Regression predicts an outcome or examines relationships among variables. For example, regression can examine whether age, staffing level, and communication scores predict patient satisfaction. ANOVA focuses on group mean differences, while regression focuses on prediction or relationships. For prediction-focused studies, see Regression Analysis Help.
Repeated measures ANOVA compares repeated scores from the same participants. For example, it can compare nurse stress scores before training, after training, and at follow-up.
Types of ANOVA in SPSS
One-Way ANOVA in SPSS
One-way ANOVA compares one continuous dependent variable across one categorical independent variable with three or more independent groups.
Example:
Is there a statistically significant difference in patient satisfaction scores across three nurse communication training groups?
- Dependent variable: patient satisfaction score
- Independent variable: communication training group
- Groups: standard education, video education, nurse-led counseling
SPSS menu path:
Analyze > Compare Means > One-Way ANOVA
Important output tables include:
- Descriptives
- Test of Homogeneity of Variances
- ANOVA table
- Robust Tests of Equality of Means, if selected
- Post hoc comparisons, if selected
Two-Way ANOVA in SPSS
Two-way ANOVA tests two categorical independent variables and one continuous dependent variable. It examines main effects and interaction effects.
Example:
Do patient satisfaction scores differ by communication training group and hospital unit type?
- Dependent variable: patient satisfaction score
- Factor 1: communication training group
- Factor 2: hospital unit type
SPSS menu path:
Analyze > General Linear Model > Univariate
Two-way ANOVA answers three questions:
- Does satisfaction differ by training group?
- Does satisfaction differ by unit type?
- Does the effect of training group depend on unit type?
The third question is the interaction effect. In nursing research, this may be clinically important. For example, a communication training program may work better in medical units than in emergency units.
Repeated Measures ANOVA in SPSS
Repeated measures ANOVA is used when the same participants are measured more than once.
Example:
Do nurse stress scores change from baseline to post-training to six-week follow-up?
- Same participants are measured repeatedly.
- The dependent variable is stress score.
- The within-subjects factor is time.
SPSS menu path:
Analyze > General Linear Model > Repeated Measures
Repeated measures ANOVA output may include Mauchly’s Test of Sphericity. If sphericity is violated, students may need Greenhouse-Geisser or Huynh-Feldt corrected results. Laerd Statistics explains that sphericity concerns equality of variances of the differences between repeated conditions (Laerd Statistics, n.d.).
Mixed ANOVA in SPSS
Mixed ANOVA includes one between-subjects factor and one within-subjects factor.
Example:
Do anxiety scores change over time differently for patients in an intervention group compared with a control group?
- Between-subjects factor: group
- Within-subjects factor: time
- Dependent variable: anxiety score
Mixed ANOVA is useful when students want to compare groups over time. The key result is often the group-by-time interaction.
SPSS ANOVA Assumptions
ANOVA results are strongest when the data reasonably meet the assumptions of the test. Students should not interpret assumptions mechanically, but they should show that they checked them responsibly.
1. Continuous or Approximately Continuous Dependent Variable
The outcome should be a numerical score, such as a pain score, knowledge score, satisfaction score, burnout score, or validated scale score.
2. Meaningful Group Variable
The independent variable should create meaningful categories, such as treatment group, hospital unit, education method, experience level, or intervention condition.
3. Independent Observations
For between-group ANOVA, each participant should belong to only one group. Repeated measures designs are different because the same participants are intentionally measured more than once.
4. Approximate Normality
The dependent variable should be approximately normally distributed within each group. SPSS can help students check this using Explore, histograms, Q-Q plots, boxplots, Shapiro-Wilk tests, skewness, and kurtosis.
5. Homogeneity of Variance
Groups should have reasonably similar variances. SPSS checks this using Levene’s test. If Levene’s test is significant, students may need Welch ANOVA or Games-Howell post hoc tests.
6. Sphericity for Repeated Measures ANOVA
Sphericity applies when the same participants are measured across three or more time points or conditions. SPSS checks this using Mauchly’s test. If violated, corrected results are usually reported.
7. No Extreme Outliers
Extreme outliers can distort means, variances, and F statistics. SPSS boxplots help students identify possible outliers. Students should not delete outliers automatically unless there is a clear data or methodological reason.
How to Run One-Way ANOVA in SPSS
Use this workflow for a standard one-way ANOVA:
- Open the dataset in SPSS.
- Confirm that each row represents one participant or case.
- Confirm that the dependent variable is numeric.
- Confirm that the group variable is coded correctly.
- Add value labels for group codes.
- Go to Analyze > Compare Means > One-Way ANOVA.
- Move the continuous outcome into Dependent List.
- Move the group variable into Factor.
- Click Options.
- Select Descriptive and Homogeneity of variance test.
- Select Means plot if useful.
- Click Post Hoc if pairwise comparisons are needed.
- Choose Tukey, Bonferroni, or Games-Howell based on assumptions.
- Click OK.
- Review descriptives, Levene’s test, ANOVA table, post hoc tests, and effect size.
Before writing results, ask:
- Are the group labels correct?
- Are group sizes reasonable?
- Are means and standard deviations sensible?
- Is Levene’s test significant?
- Is the ANOVA significant?
- Are post hoc tests needed?
- What is the effect size?
- Does the result answer the research question?
For help with SPSS output, statistical testing, and Chapter 4 writing, see Dissertation Data Analysis Help.
How to Run Two-Way ANOVA in SPSS
Two-way ANOVA is usually run through the General Linear Model menu:
- Go to Analyze > General Linear Model > Univariate.
- Move the continuous outcome into Dependent Variable.
- Move both categorical variables into Fixed Factor(s).
- Click Model and keep the full factorial model unless your supervisor requires another model.
- Click Options.
- Select Descriptive statistics, Homogeneity tests, and Estimates of effect size.
- Request estimated marginal means if needed.
- Click Plots if an interaction plot is useful.
- Click OK.
Important output includes Descriptive Statistics, Levene’s Test, Tests of Between-Subjects Effects, Estimated Marginal Means, and interaction plots.
Interpret the interaction first. If the interaction is significant, the effect of one factor depends on the level of the other factor.
How to Run Repeated Measures ANOVA in SPSS
Repeated measures ANOVA usually requires wide-format data. Each participant should have one row, with each time point in a separate column.
Use this workflow:
- Go to Analyze > General Linear Model > Repeated Measures.
- Name the within-subjects factor, such as Time.
- Enter the number of levels, such as 3 for baseline, post-test, and follow-up.
- Click Add.
- Click Define.
- Move the repeated variables into the correct order.
- Click Options.
- Select Descriptive statistics and Estimates of effect size.
- Request pairwise comparisons if needed.
- Click OK.
Important output includes Descriptive Statistics, Mauchly’s Test of Sphericity, Tests of Within-Subjects Effects, corrected results, pairwise comparisons, and effect sizes.
UCLA OARC provides SPSS guidance for repeated measures analysis and explains how within-subjects and between-subjects effects are interpreted in repeated designs (UCLA Office of Advanced Research Computing, n.d.).
How to Interpret SPSS ANOVA Output
Running SPSS ANOVA is easy. Interpreting the output correctly is what makes Chapter 4 strong.
Descriptives Table
The Descriptives table should be reviewed first. It shows the pattern of the data before significance testing.
Important columns include:
- N: Number of cases per group.
- Mean: Average score per group.
- Standard deviation: Spread of scores.
- Standard error: Precision of the mean estimate.
- 95% confidence interval: Estimated range for the group mean.
- Minimum and maximum: Lowest and highest scores.
Example interpretation:
The nurse-led counseling group had the highest mean satisfaction score, followed by video-based education and standard education. This suggests a possible group difference, but ANOVA is needed to test whether the difference is statistically significant.
A dissertation should report descriptive statistics before ANOVA results. Means and standard deviations help readers understand the direction and size of differences. For help with this, see Descriptive Data Analysis in Nursing.
Levene’s Test
Levene’s test checks whether group variances are similar.
Example:
| Levene Statistic | df1 | df2 | Sig. |
|---|---|---|---|
| 1.84 | 2 | 87 | .165 |
Interpretation:
Levene’s test was not significant, p = .165, suggesting that the homogeneity of variance assumption was met.
If Levene’s test is significant, such as p = .004, the assumption may be violated. The student may need Welch ANOVA, Games-Howell post hoc tests, or supervisor guidance.
ANOVA Table
The ANOVA table gives the main result.
Example:
| Source | Sum of Squares | df | Mean Square | F | Sig. |
|---|---|---|---|---|---|
| Between Groups | 1472.60 | 2 | 736.30 | 11.42 | < .001 |
| Within Groups | 5609.40 | 87 | 64.48 | ||
| Total | 7082.00 | 89 |
Interpretation:
A one-way ANOVA showed a statistically significant difference in patient satisfaction scores across the three communication training groups, F(2, 87) = 11.42, p < .001.
In SPSS, Sig. means the p value. APA Style recommends reporting exact p values when possible, except when p is less than .001, which should be reported as p < .001 (American Psychological Association, 2024). For more support, see P-Values in Nursing Research.
A significant ANOVA means at least one group differs. It does not identify which groups differ. That is why post hoc tests matter.
Post Hoc Tests
Post hoc tests identify where the differences are.
Common options include:
- Tukey HSD: Common when variances are equal.
- Bonferroni: Conservative and adjusts for multiple comparisons.
- Games-Howell: Useful when variances are unequal or group sizes differ.
Example:
| Comparison | Mean Difference | p |
|---|---|---|
| Nurse-led counseling vs Standard education | 9.90 | < .001 |
| Nurse-led counseling vs Video education | 6.20 | .018 |
| Video education vs Standard education | 3.70 | .084 |
Interpretation:
Tukey post hoc comparisons showed that nurse-led counseling produced significantly higher satisfaction scores than standard education, p < .001, and video-based education, p = .018. The difference between video education and standard education was not statistically significant, p = .084.
Effect Size
Effect size explains how large the difference is. This is important because statistical significance is not the same as clinical importance.
For one-way ANOVA, eta squared can be calculated as:
Eta squared = Between-groups sum of squares ÷ Total sum of squares
Using the mock output:
η² = 1472.60 ÷ 7082.00 = .21
Interpretation:
The effect size was η² = .21, suggesting that approximately 21% of the variance in patient satisfaction scores was associated with communication training group.
A result can be statistically significant but clinically small. Nursing students should explain whether the difference matters for patient care, education, quality improvement, or nursing practice.
Need help interpreting SPSS ANOVA output? Get support with test selection, assumption checks, post hoc comparisons, APA 7 reporting, and Chapter 4 results writing through Dissertation Data Analysis Help.
Worked Nursing Example: One-Way ANOVA in SPSS
Research Question
Is there a statistically significant difference in patient satisfaction scores across three nurse communication training groups?
Variables
| Role | Variable | Measurement |
|---|---|---|
| Dependent variable | Patient satisfaction score | Continuous scale score |
| Independent variable | Communication training group | Three-category group variable |
Hypotheses
Null hypothesis: There is no statistically significant difference in patient satisfaction scores across the three nurse communication training groups.
Alternative hypothesis: There is a statistically significant difference in patient satisfaction scores across at least two communication training groups.
Mock Descriptive Results
| Training Group | N | Mean | SD |
|---|---|---|---|
| Standard education | 30 | 72.40 | 8.20 |
| Video-based education | 30 | 76.10 | 7.50 |
| Nurse-led counseling | 30 | 82.30 | 6.90 |
| Total | 90 | 76.93 | 8.92 |
Mock ANOVA Result
| Source | Sum of Squares | df | Mean Square | F | Sig. |
|---|---|---|---|---|---|
| Between Groups | 1472.60 | 2 | 736.30 | 11.42 | < .001 |
| Within Groups | 5609.40 | 87 | 64.48 | ||
| Total | 7082.00 | 89 |
Dissertation-Ready Interpretation
A one-way ANOVA was conducted to determine whether patient satisfaction scores differed across three nurse communication training groups. Descriptive statistics showed that the nurse-led counseling group had the highest mean satisfaction score, followed by video-based education and standard education. Levene’s test was not significant, p = .165, suggesting that the homogeneity of variance assumption was met. The ANOVA result was statistically significant, F(2, 87) = 11.42, p < .001, η² = .21. Tukey post hoc comparisons showed that nurse-led counseling produced significantly higher satisfaction scores than standard education, p < .001, and video-based education, p = .018. The difference between video-based education and standard education was not statistically significant, p = .084. These findings suggest that nurse-led communication counseling may be associated with higher patient satisfaction.
How to Report SPSS ANOVA Results in APA 7th Edition
Students should not paste raw SPSS output into Chapter 4. They should convert output into clear APA-style results.
One-Way ANOVA: Significant Result
A one-way ANOVA was conducted to examine whether patient satisfaction scores differed across three nurse communication training groups. The results showed a statistically significant difference among groups, F(2, 87) = 11.42, p < .001, η² = .21. Tukey post hoc comparisons indicated that patients in the nurse-led counseling group reported significantly higher satisfaction scores than those in the standard education group, p < .001, and video-based education group, p = .018.
One-Way ANOVA: Non-Significant Result
A one-way ANOVA was conducted to determine whether medication adherence scores differed across three patient education methods. The results showed no statistically significant difference among groups, F(2, 84) = 1.26, p = .289, η² = .03. Therefore, the null hypothesis was retained. The findings suggest that medication adherence scores did not differ significantly across the three education methods in this sample.
Two-Way ANOVA Result
A two-way ANOVA was conducted to examine the effects of communication training group and hospital unit type on patient satisfaction scores. There was a statistically significant main effect of training group, F(2, 114) = 8.37, p < .001, partial η² = .13. There was no statistically significant main effect of unit type, F(1, 114) = 2.11, p = .149, partial η² = .02. The interaction between training group and unit type was statistically significant, F(2, 114) = 4.62, p = .012, partial η² = .08. This suggests that the effect of communication training differed by unit type.
Repeated Measures ANOVA: Sphericity Met
A repeated measures ANOVA was conducted to examine changes in nurse stress scores across baseline, post-training, and six-week follow-up. Mauchly’s Test of Sphericity was not significant, p = .217, indicating that the sphericity assumption was met. The results showed a statistically significant effect of time, F(2, 78) = 9.84, p < .001, partial η² = .20. Pairwise comparisons indicated that stress scores decreased significantly from baseline to post-training, p = .006, and from baseline to follow-up, p < .001.
Repeated Measures ANOVA: Sphericity Violated
A repeated measures ANOVA was conducted to examine changes in anxiety scores across baseline, post-intervention, and follow-up. Mauchly’s Test of Sphericity was significant, p = .031, indicating that sphericity was violated. Therefore, Greenhouse-Geisser corrected results were reported. The corrected results showed a statistically significant effect of time, F(1.61, 62.79) = 7.42, p = .003, partial η² = .16.
SPSS ANOVA for Likert Scale Data
Likert-scale data require careful handling in nursing dissertations. The main issue is whether the student is analyzing a single Likert item or a composite score.
A single Likert item is one ordinal question, such as “I am satisfied with discharge education,” rated from strongly disagree to strongly agree.
A summed scale combines several related Likert items into one total score.
An averaged scale calculates the mean of several related items.
A validated instrument score follows scoring rules from a published tool.
Many nursing dissertations treat validated composite scale scores as approximately continuous, especially when the scale has multiple items and acceptable reliability. However, students should not claim that ANOVA is always appropriate for Likert data.
Norman argued that parametric methods can be robust in many Likert-scale contexts, especially when scale-level data are used rather than isolated single items (Norman, 2010). Sullivan and Artino emphasized that researchers should distinguish Likert-type items from Likert scales when choosing statistical methods (Sullivan & Artino, 2013).
A strong dissertation explanation would say:
The patient satisfaction instrument produced a total scale score based on multiple items. Because the total score was treated as approximately continuous and assumption checks were acceptable, ANOVA was used to compare satisfaction scores across groups.
A weak explanation would say:
Likert data were analyzed with ANOVA because SPSS allows it.
The first statement explains the decision. The second does not.
Common Mistakes Nursing Students Make with SPSS ANOVA
Choosing ANOVA for the Wrong Outcome
ANOVA is not appropriate when the dependent variable is categorical. Categorical outcomes may require chi-square or logistic regression.
Using One-Way ANOVA for Repeated Measures Data
If the same participants are measured over time, repeated measures ANOVA may be needed.
Ignoring Assumption Tests
Levene’s test and Mauchly’s test can affect which results should be reported.
Reporting Only p-Values
Students should report descriptive statistics, F statistics, degrees of freedom, p values, effect sizes, and interpretation.
Forgetting Clinical Meaning
A statistically significant result may not be clinically meaningful. Chapter 5 should explain the nursing relevance.
Copying Raw SPSS Output
Raw SPSS output should be summarized, interpreted, and formatted for the dissertation.
Choosing the Wrong Post Hoc Test
Tukey, Bonferroni, and Games-Howell are not interchangeable. The choice depends on assumptions and the analysis plan.
Misreading Non-Significant Results
A non-significant ANOVA does not prove that groups are identical. It means the study did not find statistically significant evidence of a difference.
Where SPSS ANOVA Fits in a Nursing Dissertation
Chapter 3: Methodology
Chapter 3 should explain the planned analysis before results are known. It should include:
- Research question
- Hypotheses
- Dependent variable
- Independent variable
- ANOVA type
- Assumption checks
- Alpha level
- Post hoc test plan
- Effect size
- SPSS software
Example:
A one-way ANOVA will be used to determine whether patient satisfaction scores differ significantly across three nurse communication training groups. Assumptions of normality, homogeneity of variance, and outliers will be assessed before interpretation. SPSS will be used for statistical analysis, with alpha set at .05.
Chapter 4: Results
This Chapter should report:
- Descriptive statistics
- Assumption checks
- ANOVA result
- Post hoc tests, if applicable
- Effect size
- Answer to the research question
Chapter 5: Discussion
In Chapter 5, it should explain the clinical meaning of the findings. Students should connect the ANOVA result to nursing practice, literature, limitations, and recommendations.
For support with SPSS results and dissertation chapters, see Dissertation Data Analysis Help.
SPSS ANOVA and Excel: Which Should Nursing Students Use?
Excel can help with data entry, cleaning, and simple descriptive statistics. SPSS is stronger for dissertation-level ANOVA because it provides assumption tests, post hoc tests, repeated measures procedures, General Linear Model options, and effect size output.
Excel may help students organize data before analysis. SPSS is better for formal inferential testing and Chapter 4 reporting. For spreadsheet-based preparation, see Using Excel for Data Analysis in Nursing.
When to Get SPSS ANOVA Help
Students may need SPSS ANOVA help when the analysis becomes more complex than expected. This is common when there are multiple groups, repeated measurements, assumption problems, unequal group sizes, Likert-scale scores, or supervisor feedback.
You may need help if:
- You are unsure which ANOVA type fits your research question
- You cannot interpret Levene’s test or Mauchly’s test
- You are unsure which post hoc test to use
- Your SPSS output does not match your research question
- You need APA 7th edition reporting
- You need effect size interpretation
- Your Chapter 4 results need revision
Get expert SPSS ANOVA help with test selection, assumption checking, SPSS output interpretation, post hoc comparisons, APA 7 reporting, effect size explanation, and Chapter 4 results writing.
For full dissertation support, visit Nursing Dissertation Help.
Conclusion
SPSS ANOVA helps nursing students compare group means and answer quantitative research questions in dissertations, theses, DNP capstones, MSN projects, and healthcare studies. However, accurate results require more than running the test. Students must choose the right ANOVA type, check assumptions, interpret SPSS output, report post hoc results, explain effect sizes, and write results in APA 7th edition format.
A strong SPSS ANOVA section should clearly show what was tested, why ANOVA was appropriate, whether assumptions were met, what the output means, which groups differed, and how the findings answer the nursing research question.
If you are struggling with SPSS ANOVA, dissertation data analysis, SPSS output interpretation, APA 7 reporting, or Chapter 4 results writing, request expert help before submitting unclear or incomplete results.
FAQs
What is SPSS ANOVA?
SPSS ANOVA is the use of IBM SPSS Statistics to run analysis of variance tests. It compares mean scores across groups or repeated conditions.
When should I use ANOVA in nursing research?
Use ANOVA when your dependent variable is continuous or approximately continuous and your independent variable defines groups or repeated conditions.
What is the difference between one-way and two-way ANOVA in SPSS?
One-way ANOVA tests one factor. Two-way ANOVA tests two factors and their interaction.
What is repeated measures ANOVA in SPSS?
Repeated measures ANOVA compares scores from the same participants across multiple time points or conditions.
Can I use SPSS ANOVA for Likert scale data?
It depends. A single Likert item is ordinal, but a validated multi-item scale score may often be treated as approximately continuous if assumptions and scoring rules support that decision.
What does Sig. mean in SPSS ANOVA?
Sig. is the SPSS label for the p value. It shows whether the result is statistically significant.
Do I need post hoc tests after ANOVA?
Usually, yes, if a one-way ANOVA with three or more groups is significant. Post hoc tests show which groups differ.
How do I report ANOVA results in APA 7th edition?
Report the ANOVA type, variables, F statistic, degrees of freedom, p value, effect size, post hoc results if needed, and a clear interpretation.
References
American Psychological Association. (2024). Number and statistics guide: APA Style 7th edition. https://apastyle.apa.org/instructional-aids/numbers-statistics-guide.pdf
IBM Corp. (2023). IBM SPSS Statistics Base 29. https://www.ibm.com/docs/SSLVMB_29.0.0/pdf/IBM_SPSS_Statistics_Base.pdf
Kim, T. K. (2017). Understanding one-way ANOVA using conceptual figures. Korean Journal of Anesthesiology, 70(1), 22–26. https://pmc.ncbi.nlm.nih.gov/articles/PMC5296382/
Laerd Statistics. (n.d.). An introduction to, testing for, and interpreting sphericity. https://statistics.laerd.com/statistical-guides/sphericity-statistical-guide.php
Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15, 625–632. https://pubmed.ncbi.nlm.nih.gov/20146096/
Sullivan, G. M., & Artino, A. R., Jr. (2013). Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education, 5(4), 541–542. https://pmc.ncbi.nlm.nih.gov/articles/PMC3886444/
UCLA Office of Advanced Research Computing. (n.d.). Repeated measures analysis with SPSS. https://stats.oarc.ucla.edu/spss/seminars/repeated-measures/
UCLA Office of Advanced Research Computing. (n.d.). What statistical analysis should I use? Statistical analyses using SPSS. https://stats.oarc.ucla.edu/spss/whatstat/what-statistical-analysis-should-i-usestatistical-analyses-using-spss/
