APA Reporting Quick Reference

One-line templates for reporting statistical results in APA 7th edition format. Replace bracketed placeholders with your values. All statistical symbols should be italicized in your manuscript.

APA Formatting Reminders

  • Italicize statistical symbols: t, F, p, r, R², d, M, SD, n, df, N
  • Use p < .001 (not p = .000). Never write p = 0.
  • Round to two decimal places; report p-values to three.
  • Report exact p-values (e.g., p = .034) except when p < .001.
  • Always include effect sizes and 95% confidence intervals when possible.
  • No zero before the decimal for values that cannot exceed 1 (p, r, R², η²).

Independent-Samples t-Test

An independent-samples t-test indicated that [DV] was significantly [higher/lower] for [Group 1] (M = [value], SD = [value]) than [Group 2] (M = [value], SD = [value]), t([df]) = [value], p = [value], d = [value], 95% CI [lower, upper].

Non-significant: replace "significantly higher/lower" with "not significantly different" and confirm p > .05.

Paired-Samples t-Test

A paired-samples t-test revealed a significant [increase/decrease] from pre-test (M = [value], SD = [value]) to post-test (M = [value], SD = [value]), t([df]) = [value], p = [value], d = [value].

Report df = n − 1 for paired tests.

One-Way ANOVA

A one-way ANOVA revealed a significant effect of [IV] on [DV], F([dfbetween], [dfwithin]) = [value], p = [value], η² = [value].

Follow up with post hoc tests (e.g., Tukey HSD) and report pairwise comparisons.

Post Hoc Pairwise Comparison

Tukey HSD post hoc comparisons indicated that [Group A] (M = [value], SD = [value]) scored significantly [higher/lower] than [Group B] (M = [value], SD = [value]), p = [value], d = [value].

Report all pairwise comparisons or only those relevant to your hypotheses.

Chi-Square Test of Independence

A chi-square test of independence indicated a significant association between [Variable 1] and [Variable 2], χ²([df], N = [value]) = [value], p = [value], Cramér's V = [value].

Report observed frequencies in a table. Use Fisher's exact test if any expected cell count < 5.

Pearson Correlation

There was a significant [positive/negative] correlation between [Variable 1] and [Variable 2], r([df]) = [value], p = [value], 95% CI [lower, upper].

df = N − 2 for Pearson correlation.

Simple Linear Regression

A simple linear regression indicated that [IV] significantly predicted [DV], F(1, [dfresidual]) = [value], p = [value], R² = [value]. For every one-unit increase in [IV], [DV] [increased/decreased] by [b] units (β = [value], t = [value], p = [value]).

Report b (unstandardized), β (standardized), SE, and R² for the overall model.

Multiple Regression

The overall model was significant, F([dfmodel], [dfresidual]) = [value], p = [value], R² = [value], adjusted R² = [value]. [IV name] was a significant predictor, b = [value], SE = [value], β = [value], t = [value], p = [value].

Present a coefficient table for multiple predictors. Report VIF to confirm no multicollinearity.

Logistic Regression

A logistic regression was performed to assess the effect of [IV] on [DV]. The model was significant, χ²([df]) = [value], p = [value]. [IV] was a significant predictor, b = [value], SE = [value], Wald = [value], p = [value], OR = [value], 95% CI [lower, upper].

Report Nagelkerke R² for model fit and classification accuracy when relevant.

Effect Size Reporting

Test Effect Size How to Report
t-test Cohen's d d = [value]
ANOVA Eta-squared / partial η² η² = [value] or ηp² = [value]
Correlation r (is its own effect size) r = [value]
Regression R² R² = [value]
Chi-square Cramér's V V = [value]
Logistic regression Odds Ratio (OR) OR = [value], 95% CI [lower, upper]