ci.condslope: Confidence intervals for conditional (simple) slopes in a...

In statpsych: Statistical Methods for Psychologists

Confidence intervals for conditional (simple) slopes in a linear model

Description

Computes confidence intervals and test statistics for population conditional slopes (simple slopes) in a general linear model that includes a predictor variable (x1), a moderator variable (x2), and a product predictor variable (x1*x2). Conditional slopes are computed at specified low and high values of the moderator variable.

For more details, see Section 2.13 of Bonett (2021, Volume 2)

Usage

ci.condslope(alpha, b1, b2, se1, se2, cov, lo, hi, dfe)

Arguments

alpha	alpha level for 1-alpha confidence
b1	estimated slope coefficient for predictor variable
b2	estimated slope coefficient for product variable
se1	standard error for predictor coefficient
se2	standard error for product coefficient
cov	estimated covariance between predictor and product coefficients
lo	low value of moderator variable
hi	high value of moderator variable
dfe	error degrees of freedom

Value

Returns a 2-row matrix. The columns are:

• Estimate - estimated conditional slope

• t - t test statistic

• p - two-sided p-value

• LL - lower limit of the confidence interval

• UL - upper limit of the confidence interval

References

\insertRef

Bonett2021statpsych

Examples

ci.condslope(.05, .132, .154, .031, .021, .015, 5.2, 10.6, 122)

# Should return:
#                   Estimate        SE        t  df           p 
# At low moderator    0.9328 0.4109570 2.269824 122 0.024973618 
# At high moderator   1.7644 0.6070517 2.906507 122 0.004342076 
#                           LL       UL
# At low moderator   0.1192696 1.746330
# At high moderator  0.5626805 2.966119
 

statpsych documentation built on Jan. 13, 2026, 1:07 a.m.