Guide

From SPSS Output to APA Results

SPSS answers every question you ask — and a dozen you didn't. The skill your thesis actually needs is knowing which of the numbers on screen belong in your paragraph, which row is the right row, and how the sentence is formatted. This guide walks the five most common analyses: for each one, the menu path, a mock of the output with the reportable numbers highlighted, the classic misreading, and the exact APA 7 sentence. Three of the five use our practice datasets, so you can reproduce every number yourself.

Three rules before any output

Sig. means p, and it is never .000. SPSS truncates at three decimals, so .000 means p < .0005. Write p < .001, never p = .000. (Double-clicking a value in the output viewer reveals its full precision.)
Typography carries meaning. Latin statistical symbols are italic (t, F, p, r, d, M, SD, N); Greek ones are not (χ², η², β as a population parameter, though sample standardized β is commonly italicized). Statistics that cannot exceed 1 (p, r, η², Cramér's V) get no leading zero: p = .047, not 0.047.
A result is four things, not one: the descriptives, the test statistic with its df, the p-value, and an effect size. If your sentence has only a p, it's half a result — Effect Size & Power explains why. The full ruleset lives in Reporting Statistics in APA Style, and the APA results formatter builds any of the sentences below from your own numbers, and warns you if your p doesn't match your statistic.

1 · Independent-samples t-test

Menu: Analyze → Compare Means → Independent-Samples T Test. Data: sleep-experiment.csv from the practice datasets, 40 sleep-restricted vs 40 normally rested people, counting attention lapses (the same example our JASP guide runs, so you can compare the two programs side by side).

Group Statistics

condition	N	Mean	Std. Deviation	Std. Error Mean
restricted_sleep	40	22.77	6.02	.952
normal_sleep	40	20.07	5.92	.937

Independent Samples Test

	Levene's F	Sig.	t	df	Sig. (2-tailed)	Mean Diff.	Std. Error Diff.	95% CI Lower	95% CI Upper
Equal variances assumed	.095	.758	2.022	78	.047	2.700	1.335	.042	5.358
Equal variances not assumed			2.022	77.980	.047	2.700	1.335

Read Levene's columns first, and only to choose your row: Levene's p = .758 is above .05, so equal variances are fine and the top row applies. Had Levene's been significant, you'd read the bottom (Welch) row, which many statisticians simply always do.

The trap: that first Sig. column belongs to Levene's test, not your t-test. Reporting it as your p-value is the single most common SPSS misreading — your p lives under Sig. (2-tailed). The lesson explains what each piece means; note SPSS also gives Cohen's d if you tick Estimate effect sizes (here d = 0.45).

Sleep-restricted participants made more attention lapses (M = 22.77, SD = 6.02) than rested participants (M = 20.07, SD = 5.92), t(78) = 2.02, p = .047, d = 0.45, 95% CI of the difference [0.04, 5.36].

2 · One-way ANOVA

Menu: Analyze → Compare Means → One-Way ANOVA; under Options tick Homogeneity of variance test and Welch. Data: study-methods.csv, 105 students, three revision techniques, one exam score.

Test of Homogeneity of Variances

	Levene Statistic	df1	df2	Sig.
exam_score	3.615	2	102	.030

Levene's p = .030 < .05 — the equal-variances assumption fails here, on purpose (this dataset was built for exactly this practice). So the plain ANOVA row below is not the one you report.

ANOVA

exam_score	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	2200.30	2	1100.15	11.566	.000
Within Groups	9701.83	102	95.12
Total	11902.13	104

Robust Tests of Equality of Means

	Statistic	df1	df2	Sig.
Welch	13.104	2	66.43	.000

Because Levene's failed, report the Welch row (note its fractional df₂). And Sig. .000 becomes p < .001, always.

Effect size: recent SPSS versions offer η² in the options; from the table it's just SS_between / SS_total = 2200.30 / 11902.13 = .185. Follow-ups: a significant omnibus F says "the means aren't all equal," not which ones differ. Naming the pairs that differ is the post-hoc table's job (with unequal variances, use Games–Howell rather than Tukey; Post-Hoc Tests explains the multiple-comparisons problem it's solving). The one-way ANOVA lesson covers the logic end to end.

Exam scores differed across revision techniques, Welch's F(2, 66.43) = 13.10, p < .001, η² = .19. Because Levene's test indicated unequal variances (p = .030), Welch's robust test is reported.

3 · Pearson correlation

Menu: Analyze → Correlate → Bivariate. Data: screen-time.csv, 60 people's daily screen hours and sleep quality.

Correlations

		screen_hours	sleep_quality
screen_hours	Pearson Correlation	1	−.205
	Sig. (2-tailed)		.115
	N	60	60

The matrix mirrors itself: each pair appears twice, plus a useless diagonal of 1s. You need three numbers: r, its p, and N.

Two traps. First, the df in your sentence is N − 2 = 58, not N; SPSS never prints it, examiners always check it. Second, and bigger: this particular r is a lie. One influential participant (11.5 hours of screen time, excellent sleep) is dragging a real relationship toward zero — drop that point and r goes from −.21 to −.40. A correlation without a scatterplot is a rumor: plot it (Graphs → Chart Builder, or paste both columns into our correlation calculator, which computes the leave-one-out range automatically), then decide what to do and report both versions. Non-significant is also perfectly reportable, honestly, with the CI, not as "a trend."

Screen time and sleep quality were not significantly correlated, r(58) = −.21, p = .115. One influential observation attenuated the association; excluding it, r(57) = −.40, p = .002.

4 · Chi-square test of independence

Menu: Analyze → Descriptive Statistics → Crosstabs; under Statistics tick Chi-square and Phi and Cramér's V; under Cells tick Expected. Example: 120 students. Did those who attended a revision workshop (38 of 60) pass a methods exam more often than those who didn't (24 of 60)?

Chi-Square Tests

	Value	df	Asymptotic Sig. (2-sided)
Pearson Chi-Square	6.541	1	.011
Continuity Correction	5.640	1	.018
Likelihood Ratio	6.602	1	.010
N of Valid Cases	120

Report the Pearson Chi-Square row. The footnote SPSS prints under this table matters: if any cell's expected count falls below 5, the approximation wobbles; here the smallest expected count is 29, so all is well.

The trap: three plausible-looking rows. Pearson is the standard report; the continuity correction is a conservative historical fix for 2×2 tables that most modern advice skips; the likelihood ratio is a different statistic entirely. Pick Pearson, say what the cells showed in plain language, and add Cramér's V from the Symmetric Measures table as the effect size; Chi-Square Tests walks the whole logic.

Workshop attendees passed more often (63%) than non-attendees (40%), χ²(1, N = 120) = 6.54, p = .011, Cramér's V = .23.

5 · Simple linear regression

Menu: Analyze → Regression → Linear. Example: 85 first-year students; weekly exercise hours predicting wellbeing (1–7 scale). Regression output arrives as three tables, and your sentence needs pieces of each:

Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.436	.190	.180	.988

ANOVA

Model	Sum of Squares	df	Mean Square	F	Sig.
Regression	19.000	1	19.000	19.469	.000
Residual	81.000	83	.976
Total	100.000	84

Coefficients

Model	B	Std. Error	Beta	t	Sig.
(Constant)	1.860	.421		4.418	.000
exercise_hours	.353	.080	.436	4.412	.000

Model Summary → how much variance the model explains (R²). ANOVA → whether the model as a whole beats nothing (F, with df 1 and 83). Coefficients → the slope's size (B, in real units), its standardized version (Beta), and its own test (t). The (Constant) row is the intercept — you rarely interpret it, and its p-value is almost never worth reporting.

The trap: both .000s here become p < .001 — this is the table where students most often copy the forbidden p = .000 into a thesis. And don't confuse B (unstandardized: +0.35 wellbeing points per extra hour) with Beta (in SD units, comparable across predictors); Simple Linear Regression unpacks the difference, and Multiple Regression takes over when you add predictors. Before trusting any of it, glance at the residual plots.

Exercise significantly predicted wellbeing, b = 0.35, SE = 0.08, β = .44, t(83) = 4.41, p < .001; the model explained 19% of the variance, R² = .19, F(1, 83) = 19.47, p < .001.

The classic misreadings, in one list

Reporting Levene's Sig. as your p. Levene's columns only choose your row (t-test) or your table (ANOVA → Robust Tests).
Copying Sig. = .000. Write p < .001.
Wrong df. A correlation's df is N − 2; a t-test's df must match your group sizes. Examiners recompute these.
A p-value with no effect size. Add d, η², r itself, Cramér's V, or R². The effect-size converter translates between them.
Trusting a coefficient you never plotted. One influential point can hide (or invent) a relationship.
Interpreting in the Results section. The sentence states what was found; what it means waits for the Discussion (From Output to Results Section).

Check yourself in seconds: paste any statistic + df into the APA results formatter, and it rebuilds the sentence with correct typography and recomputes the p-value from the statistic, statcheck-style, so a typo in your numbers gets caught before your examiner finds it.

Keep going

Reporting Statistics in APA Style: the full formatting ruleset, with an error-hunt game.
From Output to Results Section: build results sentences from any output.
Analyze Your Thesis Data in JASP: the same journey in free software.
Practice Datasets: reproduce this guide's t-test, ANOVA, and correlation yourself.
Which test should I use? if you're not yet sure which of the five you need.
The Final Checklist: the numbers-match-everywhere pass before you submit.
One Study, Start to Finish: where a results paragraph comes from, traced back to the research question.