Computes a confidence interval for a population standardized
regression coefficient, by the standard t-based approach or
the noncentral t approach. A thin convenience wrapper around
ci_reg_coef, which is the general engine; for the
coefficient in its raw metric use ci_rc.
ci_src(
beta_j = NULL,
SE_beta_j = NULL,
N = NULL,
p = NULL,
R2_Y_X = NULL,
R2_j_X_without_j = NULL,
conf_level = 0.95,
R2_Y_X_without_j = NULL,
t_value = NULL,
b_j = NULL,
SE_b_j = NULL,
s_Y = NULL,
s_X = NULL,
alpha_lower = NULL,
alpha_upper = NULL,
...
)A 3-row data.frame with columns term, value,
prob_less, and prob_greater. The term values are
"lower_limit", "src" (the standardized regression
coefficient point estimate), and "upper_limit", so the estimate
sits between its confidence limits. The prob_less and
prob_greater columns report the achieved tail probabilities at
each limit when the noncentral t method is used (NA for the
estimate row).
The standardized regression coefficient
The standard error of the standardized regression coefficient
Sample size
The number of predictors
The squared multiple correlation coefficient predicting Y from the p predictor variables
The squared multiple correlation coefficient predicting the jth predictor variable (i.e., the predictor of interest) from the remaining p-1 predictor variables
Desired level of confidence for the computed interval (i.e., 1 - the Type I error rate)
The squared multiple correlation coefficient predicting Y from the p-1 predictor variable with the jth predictor of interest excluded
The t-value evaluating the null hypothesis that the population regression coefficient for the jth predictor equals zero
The unstandardized regression coefficient
The standard error of the unstandardized regression coefficient
Standard deviation of Y, the dependent variable
Standard deviation of X, the predictor variable of interest
The Type I error rate for the lower confidence interval limit
The Type I error rate for the upper confidence interval limit
Optional additional specifications for nested functions
Ken Kelley [email protected]
For standardized variables, do not specify the standard deviation of the variables and input the
standardized regression coefficient for b_j.
Kelley, K. (2007). Confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20(8), 1--24. tools:::Rd_expr_doi("10.18637/jss.v020.i08")
Kelley, K., & Maxwell, S. E. (2003). Sample size for multiple regression: Obtaining regression coefficients that are accurate, not simply significant. Psychological Methods, 8(3), 305--321. tools:::Rd_expr_doi("10.1037/1082-989X.8.3.305")
Kelley, K., & Maxwell, S. E. (2008). Sample size planning with applications to multiple regression: Power and accuracy for omnibus and targeted effects. In P. Alasuutari, L. Bickman, & J. Brannen (Eds.), The Sage handbook of social research methods (pp. 166--192). Sage.
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). Designing experiments and analyzing data: A model comparison perspective (4th ed.). Routledge. (See Chapter 4 on individual comparisons of means and Chapter 6 on trend analysis.)
Smithson, M. (2003). Confidence intervals. Thousand Oaks, CA: Sage Publications.
Steiger, J. H. (2004). Beyond the F Test: Effect size confidence intervals and tests of close fit in the Analysis of Variance and Contrast Analysis. Psychological Methods, 9(2), 164--182. tools:::Rd_expr_doi("10.1037/1082-989X.9.2.164")
ss_aipe_reg_coef, ci_nc_t, ci_reg_coef, ci_rc
Other confidence intervals for effect sizes:
ci_R2(),
ci_c(),
ci_c_ancova(),
ci_c_ancova_bp(),
ci_correlation,
ci_cv(),
ci_eta_squared(),
ci_eta_squared_generalized(),
ci_eta_squared_partial(),
ci_mahalanobis(),
ci_omega_squared(),
ci_pvaf(),
ci_rc(),
ci_reg_coef(),
ci_rmsea(),
ci_sc(),
ci_sc_ancova(),
ci_sm(),
ci_smd(),
ci_smd_c(),
ci_snr(),
ci_srsnr(),
contrast_adjusted(),
plot_smd()
ci_src(beta_j = .6707, .1761, N = 30, p = 6, conf_level = .95)
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