A modern R package for design, measurement, and analysis, with special strength in effect sizes, confidence intervals, size planning, reliability and agreement, mediation analysis, equivalence testing, meta-analysis, experimental and quasi-experimental designs, repeated measures, and model comparison-based inference. DMAR (pronounced “Dee-Mar”) is heavily methodological in nature, drawing on the psychometric and statistical traditions, and is aligned with the methodological and applied research program and interests of the author. Many aspects of the package traces to the author's methodological work and collaborations, including sample size planning via accuracy in parameter estimation (AIPE; Kelley & Maxwell, 2003; Kelley & Rausch, 2006; Maxwell, Kelley, & Rausch, 2008), the definition and communication of effect sizes (Kelley & Preacher, 2012; Preacher & Kelley, 2011), and the model comparison perspective of Maxwell, Delaney, and Kelley (2027). It aims to be methodologically sound and particularly well-suited to research in which the independent or dependent variables involve the person, across psychology, sociology, education, behavioral economics, management, marketing, and information systems.
Details
The package makes accessible to researchers a variety of methods
that are easy to use, including from sample estimates or results
reported in published articles: effect size estimation, confidence
intervals for effect sizes, sample size planning, multivariate
methods, factor analysis, and certain latent variable models.
Particular strengths include sample size planning under several
complementary frameworks: accuracy in parameter estimation (AIPE),
power analysis, minimum risk, and equivalence. Most exported
functions return a tidy data.frame with a term
column and a numeric value column (some carry additional
typed columns per term), and ggplot2 is used for the plotting
functions. A few functions return the shape their task calls for
instead: model fits such as mlmr return a richer list-like
object with coef / vcov / confint methods,
descriptives returns a list of summary tables, and a small
number of scalar utilities such as skewness and
kurtosis return a bare numeric. The interface is consistent,
modern, and opinionated, and is designed for clarity and
reproducibility.
DMAR builds heavily on the MBESS package (Kelley, 2007a, 2007b), which has been on CRAN for more than two decades. MBESS was originally framed for the behavioral, educational, and social sciences, but its use has grown well beyond that scope; DMAR is quite general, though especially well-aligned with human-centered research.
Function families. A user-facing tour:
- Effect sizes
smd,smd_c,smd_trimmed,eta_squared,eta_squared_partial,eta_squared_generalized,omega_squared,omega_squared_partial,cohen_f,cles,cliff_delta,vargha_delaney_A,proportion_of_superiority,probability_of_superiority_paired,lin_ccc.- Confidence intervals on effect sizes
ci_smd,ci_smd_c,ci_R2,ci_R,ci_rc,ci_src,ci_eta_squared(and partial / generalized variants),ci_omega_squared,ci_pvaf,ci_snr,ci_srsnr,ci_mahalanobis,ci_eigenvalue,ci_cv,ci_sm,ci_reg_coef,ci_r,ci_rmsea.- Maximum likelihood regression
mlmr(univariate full information maximum likelihood (FIML), lm-like),mlmr_mv(multivariate FIML).- ANOVA and ANCOVA
ancova,anova_within_two_way,mixed_anova,manova_split_plot,simple_effects_AB,contrast_test,pairwise_within,mauchly_test,obrien_test.- Reliability and agreement
reliability,reliability_alpha,reliability_omega(with a model implied or observed total-variance denominator, and areliability_omega_categoricalfor ordered items),reliability_kr20,reliability_H,cohen_kappa,fleiss_kappa,krippendorff_alpha,gwet_ac,limits_of_agreement.- Mediation
mediate(the simple mediation model with bootstrap, Monte Carlo, and Sobel intervals),mediation_mbco(likelihood ratio tests of arbitrary mediation effects by model-based constrained optimization, with multiple groups and moderated mediation probing), andplot_mediation_mbco(conditional effect curves with confidence bands).- Confirmatory factor and SEM tools
cfa_1,cov_sem,covmat_from_cfa,compare_cov_structures.- Sample size planning (AIPE)
ss_aipe_smd,ss_aipe_R2,ss_aipe_reg_coef,ss_aipe_partial_r,ss_aipe_omega_squared,ss_aipe_icc,ss_aipe_cv,ss_aipe_pcm,ss_aipe_rmsea, the cluster-randomized plannersss_aipe_crd_*, plus their Monte Carlo sensitivity companionsss_aipe_*_sensitivity.- Sample size planning (power)
ss_power_smd,ss_power_R2,ss_power_r,ss_power_reg_coef,ss_power_sem,ss_power_c,ss_power_c_ancova,ss_power_contrast,ss_power_factorial_anova,ss_power_split_plot_anova,ss_power_mixed_effects,ss_power_one_way_anova,ss_power_pcm,ss_power_rm_anova,ss_power_sc.- Critical values and tests
cv_t,cv_z,cv_smm,cv_scheffe,cv_tukey_hsd,cv_dunnett,ci_dunnett,ci_tukey_kramer,ci_scheffe,welch_t,summary_t_test,correlations_test,power_fisher_exact,randomization_test_paired,equivalence_smd,equivalence_r,power_equivalence_md.- Parameterization conversions
convert_R2_f/convert_f_R2,convert_R2_lambda/convert_lambda_R2,convert_delta_lambda/convert_lambda_delta,convert_r_Z/convert_Z_r,convert_cor_cov.- Visualization
plot_smd,plot_ci,plot_R2,plot_trajectories,plot_trajectories_fitted.- Multilevel and clustering
icc,icc_lmer,variance_components_mls,design_effect(Kish design effect),ss_aipe_crd_*.- Data sets
bessel_errors(Bessel's 1818 grouped distribution of Bradley's astronomical observation errors),diagnosis_agreement(Cohen's 1968 weighted kappa illustration),drinks_trial(Smith, Meyers, and Delaney's 1998 Community Reinforcement Approach drinking trial),holzinger_swineford(the 1939 factor analysis study),prime_time_achievement(the Indiana Prime Time third grade achievement evaluation),pygmalion(Rosenthal and Jacobson's 1968 teacher-expectancy data),teacher_expectancy(Raudenbush's 1984 meta-analysis of 18 teacher-expectancy experiments), andtest_market(Bryant and Bruvold's 1980 controlled test-market experiment for ANCOVA with a random covariate).
Feedback. Bug reports, feature requests, and suggestions for new methods are welcomed by email to Ken Kelley kkelley@nd.edu (please put “DMAR” in the subject line). See https://kenkelley.org for Ken Kelley's web site, https://kenkelley.org/publications/ for related publications, and https://github.com/yelleKneK/DMAR for the project's GitHub page.
References
Kelley, K. (2007a). Confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20(8), 1–24. doi:10.18637/jss.v020.i08
Kelley, K. (2007b). Methods for the behavioral, educational, and social sciences: An R package. Behavior Research Methods, 39(4), 979–984. doi:10.3758/BF03192993
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. doi:10.1037/1082-989X.8.3.305
Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17(2), 137–152. doi:10.1037/a0028086
Kelley, K., & Rausch, J. R. (2006). Sample size planning for the standardized mean difference: Accuracy in parameter estimation via narrow confidence intervals. Psychological Methods, 11(4), 363–385. doi:10.1037/1082-989X.11.4.363
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). Designing experiments and analyzing data: A model comparison perspective (4th ed.). Routledge.
Maxwell, S. E., Kelley, K., & Rausch, J. R. (2008). Sample size planning for statistical power and accuracy in parameter estimation. Annual Review of Psychology, 59, 537–563. doi:10.1146/annurev.psych.59.103006.093735
Preacher, K. J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16(2), 93–115. doi:10.1037/a0022658
Author
Ken Kelley kkelley@nd.edu