How to Report a Multiple Regression in APA 7 (With Example)

A multiple regression predicts one continuous outcome from two or more predictors. The output is larger than a simple regression, and APA 7 wants both the overall model and a coefficient row for every predictor, usually as a table. This guide gives you the exact format, a worked example you can copy, and the mistakes reviewers catch.

What you need before you write a single word

An APA 7 write-up of a multiple regression needs two layers.

The model fit:

1. R² and, because there are several predictors, adjusted R².
2. The F statistic with its two degrees of freedom (regression df = number of predictors, residual df = N minus predictors minus 1).
3. The p value for the overall model.

For each predictor:

1. The unstandardized coefficient (b) and its standard error (SE).
2. The standardized coefficient (beta).
3. The t value and p value.
4. Optionally a 95% confidence interval for b.

The APA 7 format template

Report the model in text, then give the coefficients in a table:

A multiple regression predicting [outcome] from [predictor 1], [predictor 2], and [predictor 3] was significant, F(df1, df2) = X.XX, p = .XXX, adjusted R² = .XX. [Predictor 1] was a significant positive predictor, b = X.XX, β = .XX, p = .XXX, while [predictor 2] was not, b = X.XX, β = .XX, p = .XXX.

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Formatting rules reviewers actually check:

• No leading zero on p, R², adjusted R², or beta. Keep the leading zero on b and the CI.
• Report both degrees of freedom for F, regression first: F(3, 96).
• Say which predictors are significant and in which direction; do not just report the omnibus F.
• Round to two decimals. If p is below .001, write p < .001.

A worked example

Suppose you predicted job performance from conscientiousness, cognitive ability, and years of experience in 100 employees.

• Model: F(3, 96) = 14.20, p < .001, R² = .31, adjusted R² = .29

Written up in APA 7, that becomes:

A multiple regression predicting job performance from conscientiousness, cognitive ability, and years of experience was significant, F(3, 96) = 14.20, p < .001, adjusted R² = .29. Conscientiousness (b = 0.38, β = .34, p < .001) and cognitive ability (b = 0.29, β = .27, p = .003) were significant positive predictors; years of experience was not (b = 0.05, β = .06, p = .48). Together the three predictors explained 29 percent of the variance in performance.

The APA 7 regression table

For multiple regression a coefficient table is expected. APA tables use horizontal rules only:

Note. N = 100. R² = .31, adjusted R² = .29, F(3, 96) = 14.20, p < .001.

Reporting a non-significant result

Use the same statistics; change the wording, and do not call it a trend:

A multiple regression with conscientiousness, cognitive ability and years of experience did not explain a significant share of the variance in job performance, F(3, 96) = 1.42, p = .242, R² = .04.

Where to find these numbers in SPSS

• Model Summary: R Square and Adjusted R Square.
• ANOVA: Regression df (number of predictors), Residual df, F and Sig.
• Coefficients: B, Std. Error, Beta, t, Sig. and the 95% CI for B for each predictor; tick Collinearity diagnostics to get VIF.

Mistakes reviewers catch

• Reporting R² but not adjusted R². With several predictors, report adjusted R², which penalizes adding predictors.
• Interpreting non-significant predictors as effects. If a predictor's coefficient is not significant, do not describe it as influencing the outcome.
• Confusing b and beta. b is in original units; beta is standardized and lets you compare predictors' relative strength.
• Ignoring multicollinearity. If predictors are highly correlated, coefficients become unstable. Check the variance inflation factor (VIF) and mention it if it is a concern.
• A leading zero on p, R², or beta. APA drops it.
• Writing p = .000. Report p < .001.

Before you report: did the model's assumptions hold?

Multiple regression assumes a linear relationship, independent and normally distributed residuals, homoscedasticity, and low multicollinearity among predictors. Check residual plots and VIFs before trusting the model. For a single predictor, see our guide on reporting a simple linear regression.

Let KyroStat do the write-up for you

Formatting a multiple regression, with adjusted R², a full coefficient table, and multicollinearity checks, is where errors creep in. KyroStat fits the model, checks the assumptions and VIFs, and hands you the finished APA 7 sentences, a publication-ready coefficient table, and the underlying Python or R code. Upload your spreadsheet, and the report is done in seconds.

Run a multiple regression yourself, in the browser

mtcars ships with R: 32 cars, and here fuel economy is predicted from two variables at once. Runnable in your browser, nothing to install.

# Multiple regression: fuel economy from weight and horsepower
model <- lm(mpg ~ wt + hp, data = mtcars)
summary(model)

Running that gives:

Call:
lm(formula = mpg ~ wt + hp, data = mtcars)

Residuals:
   Min     1Q Median     3Q    Max 
-3.941 -1.600 -0.182  1.050  5.854 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 37.22727    1.59879  23.285  < 2e-16 ***
wt          -3.87783    0.63273  -6.129 1.12e-06 ***
hp          -0.03177    0.00903  -3.519  0.00145 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.593 on 29 degrees of freedom
Multiple R-squared:  0.8268,	Adjusted R-squared:  0.8148 
F-statistic: 69.21 on 2 and 29 DF,  p-value: 9.109e-12

Every number in the write-up above comes from output like this. If you want the mechanics rather than the reporting, the free R lesson on this walks through it step by step, and the playground is there whenever you want to try something.

Frequently asked questions

Should I report R² or adjusted R²? Both are informative, but with multiple predictors adjusted R² is the headline value because it accounts for the number of predictors.

How do I report the F test degrees of freedom? Number of predictors first, then residual df: F(3, 96) for three predictors and 100 cases.

What is beta and why report it? Beta is the standardized coefficient. Because it is in standard-deviation units, it lets you compare the relative strength of predictors measured on different scales.

My p value shows as .000. What do I write? Report p < .001. A p value is never exactly zero.

What if my outcome is yes/no rather than a score? Then run a binary logistic regression, which reports odds ratios instead of b weights. If you are comparing groups rather than predicting from continuous variables, a one-way ANOVA or an ANCOVA is usually the clearer model. Before reporting, check the regression assumptions, and put the variables' intercorrelations in an APA correlation table.

Which related guides might I need? Often these: hierarchical regression, moderation analysis and mediation analysis.