{ "_id": "6ac24bc6d743750d376d7e7e", "Package": "DMAR", "Type": "Package", "Title": "Design, Measurement, and Analysis in R (DMAR)", "Version": "1.0.0", "Date": "2026-09-07", "Authors@R": "c(person(\"Ken\", \"Kelley\", role = c(\"aut\", \"cre\"),\nemail = \"kkelley@nd.edu\",\ncomment = c(ORCID = \"0000-0002-4756-8360\")))", "Description": "Methods for design, measurement, and analysis, with the\naim of being user friendly yet methodologically sound. 'DMAR'\n(pronounced \"Dee-Mar\") implements many advanced and nonstandard\nmethods and makes them available for straightforward use, with\ninterfaces, defaults, and documentation that are consistent\nacross the package and grounded in the methodological\nliterature, in support of sound and reproducible results. The\npackage emphasizes effect size estimation with confidence\nintervals; sample size planning through accuracy in parameter\nestimation (AIPE) and power analysis (including composite power\nfor designs whose conclusions require several results to hold\nat once), with minimum risk, sequential, and equivalence\nframeworks; reliability, agreement, and measurement more\nbroadly, from coefficient omega with confidence intervals to\nmeasurement invariance; factor analysis and structural equation\nmodeling, in which constructs, latent variables measured by\nmultiple indicators, are modeled directly, with confirmatory\nfactor analysis, convergent and discriminant validity, and\nsample size planning for structural equation models; mediation\nanalysis, from the simple mediation model with bootstrap\nintervals to likelihood ratio tests of arbitrary indirect\neffects by model-based constrained optimization (MBCO), with\nmultiple groups and the probing of moderated mediation;\nequivalence and noninferiority testing; meta-analysis; repeated\nmeasures, multivariate, ANOVA, and ANCOVA designs; and\ninference grounded in model comparison throughout. Measurement\nis approached from a psychometric perspective, and although\nmany of the methods grew up in human-centered research, they\napply broadly across the empirical sciences. Much of what is\nimplemented traces to the author's methodological work,\ninterests, and collaborations. 'DMAR' is a more modern, more\ngeneral, and greatly expanded reimagining of the 'MBESS'\npackage (Kelley, 2007a, ; 2007b,\n), which has been on CRAN for more than\ntwo decades and remains available there in stable form. Most\nfunctions accept either raw data or the summary statistics\ntypically reported in published articles, so an analysis can be\nreproduced from a paper without the original data, which is\nuseful both for extending a published analysis and for\nmeta-analytic work. The estimation, inference, and planning\nfunctions return one consistently formatted data frame per\nfunction that composes with the broader R ecosystem, and\nconfidence intervals are reported alongside effect sizes\nthroughout, as best practice recommends. Researchers who have\ndata and a question but who are not R experts will find the\npackage approachable, while methodologists gain access to\nadvanced and nonstandard methods, including tables of critical\nvalues not available elsewhere.", "URL": "https://kenkelley.org, https://yelleknek.github.io/DMAR/,\nhttps://github.com/yelleKneK/DMAR", "BugReports": "https://github.com/yelleKneK/DMAR/issues", "License": "GPL (>= 3)", "Encoding": "UTF-8", "Language": "en-US", "LazyData": "true", "LazyDataCompression": "xz", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Config/testthat/parallel": "true", "Config/testthat/start-first": "ss_aipe_crd_es,\nreliability_omega_categorical, cv_bryant_paulson, ci_dunnett,\nreliability_omega, ss_power_composite_sem,\nss_aipe_composite_sem, mediation_mbco,\nss_aipe_new_sensitivities", "Config/roxygen2/version": "8.1.0", "Repository": "https://yelleknek.r-universe.dev", "Date/Publication": "2026-09-25 13:45:24 UTC", "RemoteUrl": 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Models to One or Many Trajectories", "topics": [ "analysis_of_change" ] }, { "page": "ancova", "title": "Analysis of Covariance (ANCOVA)", "concept": [ "hypothesis tests" ], "topics": [ "ancova" ] }, { "page": "anova_within", "title": "One Way Within-Subjects ANOVA With Sphericity Diagnostics and Corrections", "concept": [ "hypothesis tests", "within-subjects analysis" ], "topics": [ "anova_within" ] }, { "page": "anova_within_two_way", "title": "Two-Factor Within-Subjects ANOVA With Sphericity Adjustments", "concept": [ "within-subjects analysis" ], "topics": [ "anova_within_two_way" ] }, { "page": "anova.mlmr", "title": "Likelihood Ratio Test for Nested Mlmr Fits", "topics": [ "anova.mlmr" ] }, { "page": "anova.mlmr_mv", "title": "Compare Nested Multivariate FIML Regression Fits", "topics": [ "anova.mlmr_mv" ] }, { "page": "average_variance_extracted", "title": "Average Variance Extracted (AVE)", "concept": [ "multivariate and latent variable methods" ], "topics": [ "average_variance_extracted" ] }, { "page": "bayes_independent_t", "title": "Bayesian Independent-Samples _t_ Analysis", "concept": [ "Bayesian t analyses" ], "topics": [ "bayes_independent_t" ] }, { "page": "bayes_one_sample_t", "title": "Bayesian One-Sample _t_ Analysis", "concept": [ "Bayesian t analyses" ], "topics": [ "bayes_one_sample_t" ] }, { "page": "bayes_paired_t", "title": "Bayesian Paired-Samples _t_ Analysis", "concept": [ "Bayesian t analyses" ], "topics": [ "bayes_paired_t" ] }, { "page": "bessel_errors", "title": "Bessel's (1818) Grouped Frequency Distribution of Bradley's Astronomical Observation Errors", "topics": [ "bessel_errors" ] }, { "page": "bifactor_indices", "title": "Bifactor Model Dimensionality and Reliability Indices", "concept": [ "multivariate and latent variable methods" ], "topics": [ "bifactor_indices" ] }, { "page": "bryant_paulson", "title": "The Bryant-Paulson Generalized Studentized Range Distribution", "topics": [ "bryant_paulson", "dbryant_paulson", "pbryant_paulson", "qbryant_paulson" ] }, { "page": "cfa_1", "title": "One Factor Confirmatory Factor Analysis Model", "concept": [ "multivariate and latent variable methods" ], "topics": [ "cfa_1" ] }, { "page": "cfa_2", "title": "Two Factor Confirmatory Factor Analysis Model", "concept": [ "multivariate and latent variable methods" ], "topics": [ "cfa_2" ] }, { "page": "cfa_k", "title": "Multiple-Factor Confirmatory Factor Analysis Model", "concept": [ "multivariate and latent variable methods" ], "topics": [ "cfa_k" ] }, { "page": "ci_c", "title": "Confidence Interval for a Contrast in a Fixed Effects ANOVA", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_c" ] }, { "page": "ci_c_ancova", "title": "Confidence Interval for an (Unstandardized) Contrast in ANCOVA With One Covariate", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_c_ancova" ] }, { "page": "ci_c_ancova_bp", "title": "Bryant-Paulson Simultaneous Confidence Intervals for Contrasts of Adjusted Means in ANCOVA", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_c_ancova_bp" ] }, { "page": "ci_correlation", "title": "Confidence Intervals for the Population Correlation and Multiple Correlation", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_correlation", "ci_R", "ci_r" ] }, { "page": "ci_cv", "title": "Confidence Interval for the Coefficient of Variation", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_cv" ] }, { "page": "ci_dunnett", "title": "Dunnett's Simultaneous Confidence Intervals Against a Control", "concept": [ "hypothesis tests" ], "topics": [ "ci_dunnett" ] }, { "page": "ci_eigenvalue", "title": "Confidence Interval on the Largest Eigenvalue of a Sample Covariance Matrix", "concept": [ "multivariate and latent variable methods" ], "topics": [ "ci_eigenvalue" ] }, { "page": "ci_eta_squared", "title": "Confidence Interval for Eta Squared (Effect Size for ANOVA)", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_eta_squared" ] }, { "page": "ci_eta_squared_generalized", "title": "Confidence Interval for Generalized Eta Squared (Approximate)", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_eta_squared_generalized" ] }, { "page": "ci_eta_squared_partial", "title": "Confidence Interval for Partial Eta Squared (Effect Size for ANOVA)", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_eta_squared_partial" ] }, { "page": "ci_games_howell", "title": "Provides Games-Howell Simultaneous Confidence Intervals for All Pairwise Comparisons Without Assuming Homogeneity of Variance", "topics": [ "ci_games_howell" ] }, { "page": "ci_mahalanobis", "title": "Confidence Interval for the Squared Mahalanobis Distance", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_mahalanobis" ] }, { "page": "ci_nc_chisq", "title": "Confidence Interval for the Noncentrality Parameter of a Noncentral Chi Square Distribution", "concept": [ "noncentral distribution confidence intervals" ], "topics": [ "ci_nc_chisq" ] }, { "page": "ci_nc_F", "title": "Confidence Interval for the Noncentrality Parameter of a Noncentral _F_ Distribution", "concept": [ "noncentral distribution confidence intervals" ], "topics": [ "ci_nc_F" ] }, { "page": "ci_nc_t", "title": "Confidence Interval for the Noncentrality Parameter of a Noncentral _t_ Distribution", "concept": [ "noncentral distribution confidence intervals" ], "topics": [ "ci_nc_t" ] }, { "page": "ci_omega_squared", "title": "Confidence Interval for Omega Squared (Effect Size for ANOVA)", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_omega_squared" ] }, { "page": "ci_proportion", "title": "Confidence Interval for a Single Proportion", "concept": [ "confidence intervals" ], "topics": [ "ci_proportion" ] }, { "page": "ci_pvaf", "title": "Confidence Interval for the Proportion of Variance Accounted for (in the Dependent Variable by Knowing the Levels of the Factor)", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_pvaf" ] }, { "page": "ci_R2", "title": "Confidence Interval for the Population Squared Multiple Correlation Coefficient", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_R2" ] }, { "page": "ci_rc", "title": "Confidence Interval for an Unstandardized Regression Coefficient", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_rc" ] }, { "page": "ci_reg_coef", "title": "Confidence Interval for a Regression Coefficient, Raw or Standardized", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_reg_coef" ] }, { "page": "ci_rmsea", "title": "Confidence Interval for the Population Root Mean Square Error of Approximation", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_rmsea" ] }, { "page": "ci_sc", "title": "Confidence Interval for a Standardized Contrast in a Fixed Effects ANOVA", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_sc" ] }, { "page": "ci_sc_ancova", "title": "Confidence Interval for a Standardized Contrast in ANCOVA With One Covariate", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_sc_ancova" ] }, { "page": "ci_scheffe", "title": "Scheffe-Adjusted Simultaneous Confidence Intervals for Contrasts", "concept": [ "hypothesis tests" ], "topics": [ "ci_scheffe" ] }, { "page": "ci_sm", "title": "Confidence Interval for the Standardized Mean", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_sm" ] }, { "page": "ci_smd", "title": "Confidence Interval for the Standardized Mean Difference (Two Independent Groups)", "concept": [ "Cohen's d", "Hedges' g", "confidence intervals for effect sizes" ], "topics": [ "ci_smd" ] }, { "page": "ci_smd_c", "title": "Confidence Limits for the Standardized Mean Difference Using the Control Group Standard Deviation as the Divisor", "concept": [ "Glass's delta", "confidence intervals for effect sizes" ], "topics": [ "ci_smd_c" ] }, { "page": "ci_snr", "title": "Confidence Interval for the Signal-to-Noise Ratio", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_snr" ] }, { "page": "ci_src", "title": "Confidence Interval for a Standardized Regression Coefficient", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_src" ] }, { "page": "ci_srsnr", "title": "Confidence Interval for the Square Root of the Signal-to-Noise Ratio", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "ci_srsnr" ] }, { "page": "ci_tukey_kramer", "title": "Tukey-Kramer Simultaneous Confidence Intervals for Pairwise Contrasts", "concept": [ "hypothesis tests" ], "topics": [ "ci_tukey_kramer" ] }, { "page": "cles", "title": "Common-Language Effect Size (McGraw & Wong, 1992)", "concept": [ "effect size estimates", "probability of superiority" ], "topics": [ "cles" ] }, { "page": "cliff_delta", "title": "Cliff's delta Ordinal Effect Size", "concept": [ "effect size estimates" ], "topics": [ "cliff_delta" ] }, { "page": "cohen_f", "title": "Cohen's _f_ Effect Size", "topics": [ "cohen_f" ] }, { "page": "cohen_h", "title": "Cohen's _h_ Effect Size for a Difference Between Two Proportions", "topics": [ "cohen_h" ] }, { "page": "cohen_kappa", "title": "Cohen's Kappa Coefficient of Inter-Rater Agreement", "concept": [ "reliability" ], "topics": [ "cohen_kappa" ] }, { "page": "combine_p", "title": "Combine Independent P-Values Across Studies", "concept": [ "meta-analysis" ], "topics": [ "combine_p" ] }, { "page": "common_method_marker", "title": "Marker-Variable Adjustment for Common Method Variance", "concept": [ "multivariate and latent variable methods" ], "topics": [ "common_method_marker" ] }, { "page": "common_method_single_factor", "title": "Single-Common-Factor Screen for Common Method Variance", "concept": [ "multivariate and latent variable methods" ], "topics": [ "common_method_single_factor" ] }, { "page": "compare_cov_structures", "title": "Likelihood-Ratio Comparison of Covariance Structures", "concept": [ "hypothesis tests" ], "topics": [ "compare_cov_structures" ] }, { "page": "content_validity_index", "title": "Content Validity Index From Expert Ratings", "concept": [ "agreement and measurement" ], "topics": [ "content_validity_index" ] }, { "page": "contrast_adjusted", "title": "Confidence Interval for a Contrast of Covariate-Adjusted Cell Means in a Factorial ANCOVA", "concept": [ "confidence intervals for effect sizes" ], "topics": [ "contrast_adjusted" ] }, { "page": "contrast_test", "title": "Tests One or More Contrasts of Group Means in a One-Way Design", "concept": [ "hypothesis tests" ], "topics": [ "contrast_test" ] }, { "page": "convert_cor_cov", "title": "Correlation Matrix to Covariance Matrix Conversion", "concept": [ "parameterization conversions" ], "topics": [ "convert_cor_cov" ] }, { "page": "convert_d_or", "title": "Convert Between the Standardized Mean Difference and the Odds Ratio", "concept": [ "parameterization conversions" ], "topics": [ "convert_d_or", "convert_or_d" ] }, { "page": "convert_d_r", "title": "Convert Between the Standardized Mean Difference and the Correlation", "concept": [ "parameterization conversions" ], "topics": [ "convert_d_r", "convert_r_d" ] }, { "page": "convert_F_chisq", "title": "Convert Between an _F_ Value and a Chi Square Value", "concept": [ "parameterization conversions" ], "topics": [ "convert_chisq_F", "convert_F_chisq" ] }, { "page": "convert_r_Z", "title": "Convert a Correlation Coefficient (_r_) Into the Scale of Fisher's _Z_", "concept": [ "parameterization conversions" ], "topics": [ "convert_r_Z" ] }, { "page": "convert_R2", "title": "Convert Between _F_, R^2, and Their Noncentral Parameters", "concept": [ "parameterization conversions" ], "topics": [ "convert_f_R2", "convert_lambda_R2", "convert_R2", "convert_R2_f", "convert_R2_lambda" ] }, { "page": "convert_t_smd", "title": "Conversion Functions for Noncentral _t_-distribution", "concept": [ "parameterization conversions" ], "topics": [ "convert_delta_lambda", "convert_lambda_delta", "convert_t_smd" ] }, { "page": "convert_z_normal", "title": "Convert a Standard Normal _z_ Value to the Corresponding Value on a Normal Distribution", "concept": [ "parameterization conversions" ], "topics": [ "convert_z_normal" ] }, { "page": "convert_Z_r", "title": "Convert Fisher's _Z_ Into the Scale of a Correlation Coefficient (_r_)", "concept": [ "parameterization conversions" ], "topics": [ "convert_Z_r" ] }, { "page": "correction_for_attenuation", "title": "Correct a Correlation for Attenuation Due to Measurement Error", "concept": [ "effect size estimates" ], "topics": [ "correction_for_attenuation" ] }, { "page": "correlations_test", "title": "Formatted Correlation Matrix With _p_-values and Confidence Intervals", "concept": [ "hypothesis tests" ], "topics": [ "correlations_test" ] }, { "page": "cov_sem", "title": "Model Implied Covariance Matrix From a Lavaan-Specified SEM", "topics": [ "cov_sem" ] }, { "page": "covmat_from_cfa", "title": "Generate a Population Covariance Matrix From a One-Factor Confirmatory Factor Model", "topics": [ "covmat_from_cfa" ] }, { "page": "cv", "title": "Coefficient of Variation (Biased or Unbiased Estimator)", "topics": [ "cv" ] }, { "page": "cv_bonferroni_f", "title": "Provides the Bonferroni-Adjusted Critical Value for an _F_ Test of One of Several Contrasts", "concept": [ "critical values" ], "topics": [ "cv_bonferroni_f" ] }, { "page": "cv_bryant_paulson", "title": "Provides the Critical Value for the Bryant-Paulson ANCOVA Multiple-Comparison Procedure", "concept": [ "critical values" ], "topics": [ "cv_bryant_paulson" ] }, { "page": "cv_chisq", "title": "Provides the Critical Value(s) for a Chi Square Distribution", "concept": [ "critical values" ], "topics": [ "cv_chisq" ] }, { "page": "cv_dunnett", "title": "Provides the Critical Value for Dunnett's Many-to-One Comparisons Procedure", "concept": [ "critical values" ], "topics": [ "cv_dunnett" ] }, { "page": "cv_f", "title": "Provides the Critical Value(s) for an _F_ Distribution", "concept": [ "critical values" ], "topics": [ "cv_f" ] }, { "page": "cv_scheffe", "title": "Provides the Critical Value for the Scheffé Procedure", "concept": [ "critical values" ], "topics": [ "cv_scheffe" ] }, { "page": "cv_smm", "title": "Provides the Critical Value of the Studentized Maximum Modulus Distribution", "concept": [ "critical values" ], "topics": [ "cv_smm" ] }, { "page": "cv_t", "title": "Provides the Critical Value(s) for a _t_-distribution", "concept": [ "critical values" ], "topics": [ "cv_t" ] }, { "page": "cv_tukey_hsd", "title": "Provides the Critical Value for the Tukey Honestly Significant Difference (HSD) Test", "concept": [ "critical values" ], "topics": [ "cv_tukey_hsd" ] }, { "page": "cv_z", "title": "Provides the Critical Value(s) for the Standard Normal Distribution (the _z_-distribution, With Mean 0 and Variance 1)", "concept": [ "critical values" ], "topics": [ "cv_z" ] }, { "page": "depression_bdi", "title": "Depression Treatment Study With a Pretest Covariate", "topics": [ "depression_bdi" ] }, { "page": "descriptives", "title": "Descriptive Statistics for One or More Variables", "concept": [ "descriptive statistics" ], "topics": [ "descriptives" ] }, { "page": "design_consequences", "title": "Consequences of a Design: Power, Sign and Magnitude Errors, and Expected Precision", "concept": [ "design utilities" ], "topics": [ "design_consequences" ] }, { "page": "design_effect", "title": "Kish's Design Effect (DEFF), DEFT, and the Effective Sample Size", "concept": [ "design utilities" ], "topics": [ "design_effect" ] }, { "page": "diagnosis_agreement", "title": "Cohen's (1968) Psychiatric Diagnosis Agreement Table", "concept": [ "reliability" ], "topics": [ "diagnosis_agreement" ] }, { "page": "dmacs", "title": "The dMACS Effect Size of Measurement Noninvariance", "concept": [ "multivariate and latent variable methods" ], "topics": [ "dmacs" ] }, { "page": "dmar_output_helpers", "title": "Publication-Ready Display of DMAR Result Tables", "topics": [ "as_kable", "as_kable.dmar_tbl", "dmar_output_helpers", "knit_print.dmar_tbl", "results_sentence" ] }, { "page": "drinks_trial", "title": "Community Reinforcement Approach Drinking Trial With Homeless Alcohol-Dependent Individuals (Smith, Meyers, & Delaney, 1998)", "topics": [ "drinks_trial" ] }, { "page": "dunn_test", "title": "Provides Dunn's Rank-Sum Test of All Pairwise Differences Following a Kruskal-Wallis Test", "topics": [ "dunn_test" ] }, { "page": "ecvi", "title": "Expected Cross-Validation Index (ECVI) for a Covariance-Structure Model", "concept": [ "multivariate and latent variable methods" ], "topics": [ "ecvi" ] }, { "page": "effects_coding", "title": "Effects-Coding Contrast Matrix for a Factor", "concept": [ "design utilities" ], "topics": [ "effects_coding" ] }, { "page": "epsilon_corrections", "title": "Greenhouse-Geisser, Huynh-Feldt, and Lower-Bound Epsilon Corrections", "concept": [ "within-subjects analysis" ], "topics": [ "epsilon_corrections" ] }, { "page": "equivalence_c", "title": "Equivalence and Noninferiority Tests for a Linear Contrast via Two One-Sided Tests (TOST)", "concept": [ "equivalence testing" ], "topics": [ "equivalence_c" ] }, { "page": "equivalence_r", "title": "Equivalence Test for the Pearson Correlation via Two One-Sided Tests (TOST)", "concept": [ "equivalence testing", "hypothesis tests" ], "topics": [ "equivalence_r" ] }, { "page": "equivalence_smd", "title": "Equivalence Test for the Standardized Mean Difference via Two One-Sided Tests (TOST)", "concept": [ "equivalence testing", "hypothesis tests" ], "topics": [ "equivalence_smd" ] }, { "page": "eta_squared", "title": "Eta Squared (Effect Size for ANOVA)", "concept": [ "effect size estimates" ], "topics": [ "eta_squared" ] }, { "page": "eta_squared_generalized", "title": "Generalized Eta Squared (Effect Size for ANOVA, Comparable Across Designs)", "concept": [ "effect size estimates" ], "topics": [ "eta_squared_generalized" ] }, { "page": "eta_squared_partial", "title": "Partial Eta Squared (Effect Size for ANOVA)", "concept": [ "effect size estimates" ], "topics": [ "eta_squared_partial" ] }, { "page": "expected_partial_r", "title": "Exact Expected Value of the Sample Partial Correlation", "concept": [ "effect size estimates" ], "topics": [ "expected_partial_r" ] }, { "page": "expected_r", "title": "Exact Expected Value of the Sample Pearson Correlation Given rho and n", "concept": [ "effect size estimates" ], "topics": [ "expected_r" ] }, { "page": "expected_R2", "title": "Expected Value of the Squared Multiple Correlation Coefficient", "topics": [ "expected_R2" ] }, { "page": "expected_smd", "title": "Exact Expected Value of Cohen's _d_ (and Hedges' _g_ Bias Correction)", "concept": [ "effect size estimates" ], "topics": [ "expected_smd" ] }, { "page": "factorial_anova", "title": "Between-Subjects Factorial ANOVA for Unbalanced Designs", "concept": [ "hypothesis tests" ], "topics": [ "factorial_anova" ] }, { "page": "fleiss_kappa", "title": "Fleiss's Kappa for Inter-Rater Agreement Among Multiple Raters", "concept": [ "reliability" ], "topics": [ "fleiss_kappa" ] }, { "page": "format_p", "title": "Format _p_-values for Display the DMAR Way", "topics": [ "format_p" ] }, { "page": "glance.dmar_cfa_k", "title": "Glance at a Multiple-Factor CFA Fit", "topics": [ "glance.dmar_cfa_k" ] }, { "page": "glance.dmar_mediation_mbco", "title": "Glance at an MBCO Mediation Fit", "topics": [ "glance.dmar_mediation_mbco" ] }, { "page": "glance.dmar_reliability", "title": "Glance at a Reliability Coefficient Estimate", "topics": [ "glance.dmar_reliability" ] }, { "page": "glance.mlmr", "title": "Glance at an Mlmr Fit", "topics": [ "glance.mlmr" ] }, { "page": "gwet_ac", "title": "Gwet's AC1 and AC2 Chance-Corrected Agreement Coefficients", "concept": [ "agreement and measurement" ], "topics": [ "gwet_ac" ] }, { "page": "helmert_coding", "title": "Helmert-Coding Contrast Matrix for a Factor", "concept": [ "design utilities" ], "topics": [ "helmert_coding" ] }, { "page": "holzinger_swineford", "title": "Holzinger and Swineford (1939) Factor Analysis Study", "topics": [ "holzinger_swineford" ] }, { "page": "htmt", "title": "Heterotrait-Monotrait Ratio of Correlations (HTMT)", "concept": [ "multivariate and latent variable methods" ], "topics": [ "htmt" ] }, { "page": "icc", "title": "Intraclass Correlation Coefficients With Confidence Intervals", "concept": [ "reliability" ], "topics": [ "icc" ] }, { "page": "icc_lmer", "title": "Intraclass Correlation From a Fitted 'lme4' Mixed-Effects Model", "concept": [ "agreement and measurement", "mixed models" ], "topics": [ "icc_lmer" ] }, { "page": "irt_grm", "title": "Graded Response Model for Ordered Categorical Items", "concept": [ "multivariate and latent variable methods" ], "topics": [ "irt_grm" ] }, { "page": "irt_information", "title": "Item and Test Information for the Graded Response Model", "concept": [ "multivariate and latent variable methods" ], "topics": [ "irt_information" ] }, { "page": "is_orthogonal_set", "title": "Check Whether a Set of Contrasts Is Mutually Orthogonal", "concept": [ "design utilities" ], "topics": [ "is_orthogonal_set" ] }, { "page": "krippendorff_alpha", "title": "Krippendorff's alpha Inter-Rater Agreement", "concept": [ "agreement and measurement" ], "topics": [ "krippendorff_alpha" ] }, { "page": "kurtosis", "title": "Bias-Corrected Sample Excess Kurtosis", "concept": [ "descriptive statistics" ], "topics": [ "kurtosis" ] }, { "page": "limits_of_agreement", "title": "Limits of Agreement (Bland-Altman) With Confidence Intervals on the Limits", "concept": [ "agreement and measurement" ], "topics": [ "limits_of_agreement", "loa" ] }, { "page": "lin_ccc", "title": "Lin's Concordance Correlation Coefficient", "concept": [ "agreement and measurement" ], "topics": [ "lin_ccc" ] }, { "page": "manova_split_plot", "title": "Mixed-Design Multivariate ANOVA With All Four Test Statistics", "concept": [ "hypothesis tests", "mixed models" ], "topics": [ "manova_split_plot" ] }, { "page": "mauchly_test", "title": "Mauchly's Test of Sphericity for a One-Way Within-Subjects Design", "concept": [ "hypothesis tests", "within-subjects analysis" ], "topics": [ "mauchly_test" ] }, { "page": "measurement_alignment", "title": "Approximate Measurement Invariance by Factor Alignment", "concept": [ "multivariate and latent variable methods" ], "topics": [ "measurement_alignment" ] }, { "page": "measurement_invariance", "title": "Measurement Invariance Across Groups", "concept": [ "multivariate and latent variable methods" ], "topics": [ "measurement_invariance" ] }, { "page": "mediate", "title": "Mediation Analysis With Bootstrap Confidence Intervals", "concept": [ "mediation" ], "topics": [ "mediate" ] }, { "page": "mediation_mbco", "title": "Mediation Analysis via Model-Based Constrained Optimization", "concept": [ "mediation" ], "topics": [ "mediation_mbco" ] }, { "page": "meta_contrast", "title": "Contrast Among Study Effect Sizes (Rosenthal-Rubin)", "concept": [ "meta-analysis" ], "topics": [ "meta_contrast" ] }, { "page": "meta_es", "title": "Random Effects Meta-Analysis of Generic Effect Sizes", "concept": [ "meta-analysis" ], "topics": [ "meta_es" ] }, { "page": "meta_r", "title": "Random Effects Meta-Analysis of Correlations", "concept": [ "meta-analysis" ], "topics": [ "meta_r" ] }, { "page": "meta_smd", "title": "Random Effects Meta-Analysis of Standardized Mean Differences", "concept": [ "meta-analysis" ], "topics": [ "meta_smd" ] }, { "page": "mixed_anova", "title": "Mixed-Model ANOVA F-Ratios for One- and Two-Way Designs", "concept": [ "hypothesis tests", "mixed models" ], "topics": [ "mixed_anova" ] }, { "page": "mlmr", "title": "Maximum Likelihood Multiple Regression", "topics": [ "mlmr" ] }, { "page": "mlmr_mv", "title": "Multivariate Maximum Likelihood Regression With Full Information Missing Data Handling", "topics": [ "mlmr_mv" ] }, { "page": "moments_nc_chisq", "title": "Moments of the Noncentral Chi Square Distribution", "concept": [ "noncentral distribution moments" ], "topics": [ "moments_nc_chisq" ] }, { "page": "moments_nc_F", "title": "Moments of the Noncentral _F_ Distribution", "concept": [ "noncentral distribution moments" ], "topics": [ "moments_nc_F" ] }, { "page": "moments_nc_t", "title": "Moments of the Noncentral _t_ Distribution", "concept": [ "noncentral distribution moments" ], "topics": [ "moments_nc_t" ] }, { "page": "mr_cv", "title": "Minimum Risk Point Estimation of the Population Coefficient of Variation", "topics": [ "mr_cv" ] }, { "page": "mr_smd", "title": "Minimum Risk Point Estimation of the Population Standardized Mean Difference", "topics": [ "mr_smd" ] }, { "page": "nnt_from_smd", "title": "Number Needed to Treat (NNT) From Cohen's _d_", "concept": [ "effect size estimates" ], "topics": [ "nnt_from_smd" ] }, { "page": "now", "title": "A Friendly Date / Time Stamp and a Simple Stopwatch", "topics": [ "now" ] }, { "page": "obrien_test", "title": "O'Brien's Test for Homogeneity of Variance", "concept": [ "hypothesis tests" ], "topics": [ "obrien_test" ] }, { "page": "omega_squared", "title": "Omega Squared (Effect Size for ANOVA)", "concept": [ "effect size estimates" ], "topics": [ "omega_squared" ] }, { "page": "omega_squared_partial", "title": "Partial Omega Squared (Effect Size for ANOVA)", "concept": [ "effect size estimates" ], "topics": [ "omega_squared_partial" ] }, { "page": "orthogonal_polynomial", "title": "Orthogonal-Polynomial (Trend) Contrast Coefficients", "concept": [ "design utilities" ], "topics": [ "orthogonal_polynomial" ] }, { "page": "pairwise_within", "title": "Paired Pairwise Comparisons With Multiple-Comparison Adjustment", "concept": [ "hypothesis tests", "within-subjects analysis" ], "topics": [ "pairwise_within" ] }, { "page": "plot_cfa_k", "title": "Plot the Estimates of a Multiple-Factor CFA", "concept": [ "plotting" ], "topics": [ "plot_cfa_k" ] }, { "page": "plot_ci", "title": "Forest-Plot-Style Confidence Interval Display", "concept": [ "plotting" ], "topics": [ "plot_ci" ] }, { "page": "plot_equivalence", "title": "Plot Contrasts Against an Equivalence Region", "concept": [ "equivalence testing", "plotting" ], "topics": [ "plot_equivalence" ] }, { "page": "plot_forest", "title": "Forest Plot of Study Effect Sizes With the Pooled Estimate", "concept": [ "meta-analysis", "plotting" ], "topics": [ "plot_forest" ] }, { "page": "plot_irt_information", "title": "Plot an Item Response Theory Information Curve", "concept": [ "plotting" ], "topics": [ "plot_irt_information" ] }, { "page": "plot_mediation_mbco", "title": "Plot Conditional Effects From a Moderated Mediation Analysis", "concept": [ "plotting" ], "topics": [ "plot_mediation_mbco" ] }, { "page": "plot_R2", "title": "Visualize the Proportion of Variance Explained (R^2)", "concept": [ "plotting" ], "topics": [ "plot_R2" ] }, { "page": "plot_randomization_test", "title": "Plot the Randomization Distribution Behind a Randomization Test", "concept": [ "plotting" ], "topics": [ "plot_randomization_test" ] }, { "page": "plot_regions_of_significance", "title": "Plot Regions of Significance for a Covariate by Group Interaction", "concept": [ "plotting" ], "topics": [ "plot_regions_of_significance" ] }, { "page": "plot_smd", "title": "Visualize a Standardized Mean Difference With Overlapping Distributions", "concept": [ "confidence intervals for effect sizes", "plotting" ], "topics": [ "plot_smd" ] }, { "page": "plot_trajectories", "title": "Visualize Observed Individual Trajectories in a Longitudinal Data Set", "concept": [ "plotting" ], "topics": [ "plot_trajectories" ] }, { "page": "plot_trajectories_fitted", "title": "Plot Observed and Fitted Individual Trajectories From a Multilevel Model", "concept": [ "plotting", "within-subjects analysis" ], "topics": [ "plot_trajectories_fitted" ] }, { "page": "power_density_equivalence_md", "title": "Density Underlying the TOST Power Calculation", "concept": [ "equivalence testing" ], "topics": [ "power_density_equivalence_md" ] }, { "page": "power_equivalence_c", "title": "Power of the TOST or Noninferiority Test for a Linear Contrast", "concept": [ "equivalence testing" ], "topics": [ "power_equivalence_c" ] }, { "page": "power_equivalence_md", "title": "Power of the Two One-Sided Tests Procedure (TOST) for Equivalence", "concept": [ "equivalence testing" ], "topics": [ "power_equivalence_md" ] }, { "page": "power_equivalence_md_plot", "title": "Plot TOST Equivalence-Test Power Curves Over a Range of True Differences", "concept": [ "equivalence testing", "plotting" ], "topics": [ "power_equivalence_md_plot" ] }, { "page": "power_fisher_exact", "title": "Power of Fisher's Exact Test (Noncentral Hypergeometric)", "concept": [ "sample size for power" ], "topics": [ "power_fisher_exact" ] }, { "page": "prime_time_achievement", "title": "Indiana Prime Time Third Grade Achievement Evaluation Data", "topics": [ "prime_time_achievement" ] }, { "page": "print_anova", "title": "Print a Model Comparison or ANOVA Table With DMAR _p_-value Formatting", "topics": [ "print_anova" ] }, { "page": "print_summary", "title": "Print a Model Summary With DMAR _p_-value Formatting", "topics": [ "print_summary" ] }, { "page": "probability_of_superiority_paired", "title": "Probability of Superiority for a Paired-Samples Design", "concept": [ "effect size estimates" ], "topics": [ "probability_of_superiority_paired" ] }, { "page": "procrustes_phi", "title": "Tucker's Congruence Coefficient phi (Factor Similarity)", "concept": [ "multivariate and latent variable methods" ], "topics": [ "procrustes_phi" ] }, { "page": "proportion_of_superiority", "title": "Proportion of Superiority (Sometimes Called Cohen's U_3)", "concept": [ "Cohen's U3", "effect size estimates" ], "topics": [ "proportion_of_superiority" ] }, { "page": "pygmalion", "title": "Pygmalion in the Classroom Teacher-Expectancy Data", "topics": [ "pygmalion" ] }, { "page": "R2_mixed_effects", "title": "Marginal and Conditional R^2 for a Mixed-Effects Model", "concept": [ "agreement and measurement", "mixed models" ], "topics": [ "R2_mixed_effects" ] }, { "page": "R2_mixed_effects_decomposition", "title": "R-Squared Measures for Mixed-Effects Models", "concept": [ "mixed models", "mixed-effects R-squared", "multilevel R-squared" ], "topics": [ "R2_mixed_effects_decomposition" ] }, { "page": "randomization_test", "title": "Randomization (Permutation) Test for Two Independent Groups", "concept": [ "hypothesis tests" ], "topics": [ "randomization_test" ] }, { "page": "randomization_test_paired", "title": "Paired-Samples Randomization (Sign-Flip) Test", "concept": [ "hypothesis tests" ], "topics": [ "randomization_test_paired" ] }, { "page": "regions_of_significance", "title": "Regions of Significance for a Covariate by Group Interaction", "concept": [ "hypothesis tests" ], "topics": [ "regions_of_significance" ] }, { "page": "reliability", "title": "Reliability Coefficient With a Confidence Interval (General Dispatch)", "concept": [ "reliability" ], "topics": [ "reliability" ] }, { "page": "reliability_alpha", "title": "Coefficient Alpha With a Confidence Interval", "concept": [ "Cronbach's alpha", "reliability" ], "topics": [ "reliability_alpha" ] }, { "page": "reliability_H", "title": "Maximal Reliability Coefficient _H_ (Hancock & Mueller, 2001)", "concept": [ "reliability" ], "topics": [ "reliability_H" ] }, { "page": "reliability_kr20", "title": "Kuder-Richardson Formula 20 (KR-20) With a Confidence Interval", "concept": [ "reliability" ], "topics": [ "reliability_kr20" ] }, { "page": "reliability_omega", "title": "Coefficient Omega (McDonald) With a Confidence Interval", "concept": [ "reliability" ], "topics": [ "reliability_omega" ] }, { "page": "reliability_omega_categorical", "title": "Categorical Omega for Ordered-Categorical Items, With a Confidence Interval", "concept": [ "reliability" ], "topics": [ "reliability_omega_categorical" ] }, { "page": "responder_analysis", "title": "Responder Analysis: Who Cleared the Threshold, by Group", "concept": [ "effect size estimates" ], "topics": [ "responder_analysis" ] }, { "page": "sd_unbiased", "title": "Unbiased Estimate of the Population Standard Deviation Under Normality", "topics": [ "sd_unbiased" ] }, { "page": "signal_to_noise_R2", "title": "Signal to Noise Estimators for the Squared Multiple Correlation Coefficient", "topics": [ "signal_to_noise_R2" ] }, { "page": "simple_effects_AB", "title": "Simple Effect F Tests for a Two-Factor Between-Subjects Design", "concept": [ "hypothesis tests" ], "topics": [ "simple_effects_AB" ] }, { "page": "simple_structure", "title": "Quantify Simple Structure in a Factor Loading Matrix", "concept": [ "multivariate and latent variable methods" ], "topics": [ "simple_structure" ] }, { "page": "simulate_ancova_data", "title": "Simulate Data From a One-Covariate ANCOVA Model", "concept": [ "data simulators" ], "topics": [ "simulate_ancova_data" ] }, { "page": "simulate_ancova_factorial_data", "title": "Simulate Data From a Factorial ANCOVA Design (up to Four Factors, Any Number of Covariates)", "concept": [ "data simulators" ], "topics": [ "simulate_ancova_factorial_data" ] }, { "page": "simulate_anova_data", "title": "Simulate Data From a One-Way Fixed-Effects ANOVA Model", "concept": [ "data simulators" ], "topics": [ "simulate_anova_data" ] }, { "page": "simulate_longitudinal_gompertz", "title": "Simulate Data From a Gompertz Change (Growth) Model", "concept": [ "data simulators" ], "topics": [ "simulate_longitudinal_gompertz" ] }, { "page": "simulate_longitudinal_logistic", "title": "Simulate Data From a Logistic Change (Growth) Model", "concept": [ "data simulators" ], "topics": [ "simulate_longitudinal_logistic" ] }, { "page": "simulate_longitudinal_negative_exponential", "title": "Simulate Data From a Negative Exponential (Asymptotic Regression) Change Model", "concept": [ "data simulators" ], "topics": [ "simulate_longitudinal_negative_exponential" ] }, { "page": "simulate_longitudinal_polynomial", "title": "Simulate Data From a Polynomial Change (Growth) Model", "concept": [ "data simulators" ], "topics": [ "simulate_longitudinal_polynomial" ] }, { "page": "simulate_longitudinal_richards", "title": "Simulate Data From a Richards Change (Growth) Model", "concept": [ "data simulators" ], "topics": [ "simulate_longitudinal_richards" ] }, { "page": "simulate_regression_data", "title": "Simulate Data From a Multivariate Normal Multiple-Regression Model", "concept": [ "data simulators" ], "topics": [ "simulate_regression_data" ] }, { "page": "skewness", "title": "Bias-Corrected Sample Skewness", "concept": [ "descriptive statistics" ], "topics": [ "skewness" ] }, { "page": "smd", "title": "Standardized Mean Difference", "concept": [ "Cohen's d", "Hedges' g" ], "topics": [ "smd" ] }, { "page": "smd_c", "title": "Standardized Mean Difference Using the Control Group as the Basis of Standardization", "topics": [ "smd_c" ] }, { "page": "smd_trimmed", "title": "Robust Standardized Mean Difference (Algina-Keselman-Penfield)", "concept": [ "effect size estimates" ], "topics": [ "smd_trimmed" ] }, { "page": "ss_aipe_c", "title": "Sample Size Planning for an ANOVA Contrast From the Accuracy in Parameter Estimation (AIPE) Perspective", "topics": [ "ss_aipe_c" ] }, { "page": "ss_aipe_c_ancova", "title": "Sample Size Planning for a Contrast in Randomized ANCOVA From the Accuracy in Parameter Estimation (AIPE) Perspective", "topics": [ "ss_aipe_c_ancova" ] }, { "page": "ss_aipe_c_ancova_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning for the (Unstandardized) Contrast in Randomized ANCOVA From the Accuracy in Parameter Estimation (AIPE) Perspective", "topics": [ "ss_aipe_c_ancova_sensitivity" ] }, { "page": "ss_aipe_c_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for an Unstandardized Contrast", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_c_sensitivity" ] }, { "page": "ss_aipe_cliff_delta", "title": "Sample Size for AIPE on Cliff's delta", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_cliff_delta" ] }, { "page": "ss_aipe_cliff_delta_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for Cliff's Delta", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_cliff_delta_sensitivity" ] }, { "page": "ss_aipe_composite_sem", "title": "Sample Size for Accurate Estimation of a Set of SEM Parameters", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_composite_sem" ] }, { "page": "ss_aipe_crd", "title": "Find Target Sample Sizes for the Accuracy in Unstandardized Conditions Means Estimation in CRD", "topics": [ "ss_aipe_crd", "ss_aipe_crd_both_fixed_budget", "ss_aipe_crd_both_fixed_width", "ss_aipe_crd_n_clusters_fixed_budget", "ss_aipe_crd_n_clusters_fixed_width", "ss_aipe_crd_n_individuals_fixed_budget", "ss_aipe_crd_n_individuals_fixed_width" ] }, { "page": "ss_aipe_crd_es", "title": "Find Target Sample Sizes for the Accuracy in Standardized Conditions Means Estimation in CRD", "topics": [ "ss_aipe_crd_es", "ss_aipe_crd_es_both_fixed_budget", "ss_aipe_crd_es_both_fixed_width", "ss_aipe_crd_es_n_clusters_fixed_budget", "ss_aipe_crd_es_n_clusters_fixed_width", "ss_aipe_crd_es_n_individuals_fixed_budget", "ss_aipe_crd_es_n_individuals_fixed_width" ] }, { "page": "ss_aipe_cv", "title": "Sample Size Planning for the Coefficient of Variation Given the Goal of Accuracy in Parameter Estimation Approach to Sample Size Planning", "topics": [ "ss_aipe_cv" ] }, { "page": "ss_aipe_cv_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the Accuracy in Parameter Estimation Perspective for the Coefficient of Variation", "topics": [ "ss_aipe_cv_sensitivity" ] }, { "page": "ss_aipe_equivalence_r", "title": "AIPE Sample Size Planning for an Equivalence Test on the Pearson Correlation", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_equivalence_r" ] }, { "page": "ss_aipe_equivalence_r_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for an Equivalence-Test Correlation", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_equivalence_r_sensitivity" ] }, { "page": "ss_aipe_equivalence_smd", "title": "AIPE Sample Size Planning for an Equivalence Test on the Standardized Mean Difference", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_equivalence_smd" ] }, { "page": "ss_aipe_equivalence_smd_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for an Equivalence-Test SMD", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_equivalence_smd_sensitivity" ] }, { "page": "ss_aipe_icc", "title": "Sample Size for AIPE on an Intraclass Correlation Coefficient", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_icc" ] }, { "page": "ss_aipe_icc_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the Accuracy in Parameter Estimation Perspective for an Intraclass Correlation Coefficient", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_icc_sensitivity" ] }, { "page": "ss_aipe_indirect_effect", "title": "Sample Size for AIPE on a Mediated (Indirect) Effect ab", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_indirect_effect" ] }, { "page": "ss_aipe_indirect_effect_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for an Indirect Effect", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_indirect_effect_sensitivity" ] }, { "page": "ss_aipe_mixed_effects", "title": "AIPE Sample Size Planning for a Fixed Effect in a Two-Level Mixed-Effects Model", "concept": [ "mixed models", "sample size for power" ], "topics": [ "ss_aipe_mixed_effects" ] }, { "page": "ss_aipe_mixed_effects_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for a Mixed-Effects Fixed Effect", "concept": [ "AIPE sample size planning", "mixed models" ], "topics": [ "ss_aipe_mixed_effects_sensitivity" ] }, { "page": "ss_aipe_omega_squared", "title": "Sample Size for AIPE on Omega Squared (ANOVA Effect Size)", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_omega_squared" ] }, { "page": "ss_aipe_omega_squared_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for Omega Squared", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_omega_squared_sensitivity" ] }, { "page": "ss_aipe_partial_r", "title": "Sample Size for AIPE on a Partial Correlation", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_partial_r" ] }, { "page": "ss_aipe_partial_r_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for a Partial Correlation", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_partial_r_sensitivity" ] }, { "page": "ss_aipe_pcm", "title": "Sample Size Planning for Polynomial Change Models in Longitudinal Study", "topics": [ "ss_aipe_pcm" ] }, { "page": "ss_aipe_pcm_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for a Polynomial Change Parameter", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_pcm_sensitivity" ] }, { "page": "ss_aipe_r", "title": "Sample Size for AIPE on a Pearson Correlation", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_r" ] }, { "page": "ss_aipe_r_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for a Pearson Correlation", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_r_sensitivity" ] }, { "page": "ss_aipe_R2", "title": "Sample Size Planning for Accuracy in Parameter Estimation for the Multiple Correlation Coefficient", "topics": [ "ss_aipe_R2" ] }, { "page": "ss_aipe_R2_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning With the Goal of Accuracy in Parameter Estimation (I.e., a Narrow Observed Confidence Interval)", "topics": [ "ss_aipe_R2_sensitivity" ] }, { "page": "ss_aipe_rc", "title": "Sample Size Necessary for the Accuracy in Parameter Estimation Approach for an Unstandardized Regression Coefficient of Interest", "topics": [ "ss_aipe_rc" ] }, { "page": "ss_aipe_rc_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the Accuracy in Parameter Estimation Perspective for the Unstandardized Regression Coefficient", "topics": [ "ss_aipe_rc_sensitivity" ] }, { "page": "ss_aipe_reg_coef", "title": "Sample Size Planning for a Single Regression Coefficient (AIPE)", "topics": [ "ss_aipe_reg_coef" ] }, { "page": "ss_aipe_reg_coef_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the Accuracy in Parameter Estimation Perspective for the (Standardized and Unstandardized) Regression Coefficient", "topics": [ "ss_aipe_reg_coef_sensitivity" ] }, { "page": "ss_aipe_reliability", "title": "Sample Size Planning for Accuracy in Parameter Estimation for Reliability Coefficients", "topics": [ "ss_aipe_reliability" ] }, { "page": "ss_aipe_reliability_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for a Reliability Coefficient", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_reliability_sensitivity" ] }, { "page": "ss_aipe_rmsea", "title": "Sample Size Planning for RMSEA in SEM", "topics": [ "ss_aipe_rmsea" ] }, { "page": "ss_aipe_rmsea_sensitivity", "title": "A Priori Monte Carlo Simulation for Sample Size Planning for RMSEA in SEM", "topics": [ "ss_aipe_rmsea_sensitivity" ] }, { "page": "ss_aipe_sc", "title": "Sample Size Planning for Accuracy in Parameter Estimation (AIPE) of the Standardized Contrast in ANOVA", "topics": [ "ss_aipe_sc" ] }, { "page": "ss_aipe_sc_ancova", "title": "Sample Size Planning From the AIPE Perspective for Standardized ANCOVA Contrasts", "topics": [ "ss_aipe_sc_ancova" ] }, { "page": "ss_aipe_sc_ancova_sensitivity", "title": "Sensitivity Analysis for the Sample Size Planning Method for Standardized ANCOVA Contrast", "topics": [ "ss_aipe_sc_ancova_sensitivity" ] }, { "page": "ss_aipe_sc_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning for the Standardized ANOVA Contrast From the Accuracy in Parameter Estimation (AIPE) Perspective", "topics": [ "ss_aipe_sc_sensitivity" ] }, { "page": "ss_aipe_sem_path", "title": "Sample Size Planning for SEM Targeted Effects", "topics": [ "ss_aipe_sem_path" ] }, { "page": "ss_aipe_sem_path_sensitivity", "title": "A Priori Monte Carlo Simulation for Sample Size Planning for SEM Targeted Effects", "topics": [ "ss_aipe_sem_path_sensitivity" ] }, { "page": "ss_aipe_semipartial_r", "title": "Sample Size for AIPE on a Semipartial (Part) Correlation", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_semipartial_r" ] }, { "page": "ss_aipe_semipartial_r_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for a Semipartial Correlation", "concept": [ "AIPE sample size planning" ], "topics": [ "ss_aipe_semipartial_r_sensitivity" ] }, { "page": "ss_aipe_sm", "title": "Sample Size Planning for Accuracy in Parameter Estimation (AIPE) of the Standardized Mean", "topics": [ "ss_aipe_sm" ] }, { "page": "ss_aipe_sm_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning for the Standardized Mean From the Accuracy in Parameter Estimation (AIPE) Perspective", "topics": [ "ss_aipe_sm_sensitivity" ] }, { "page": "ss_aipe_smd", "title": "Sample Size Planning for the Standardized Mean Difference (AIPE)", "topics": [ "ss_aipe_smd" ] }, { "page": "ss_aipe_smd_sensitivity", "title": "Sensitivity Analysis for Sample Size Given the Accuracy in Parameter Estimation Approach for the Standardized Mean Difference", "topics": [ "ss_aipe_smd_sensitivity" ] }, { "page": "ss_aipe_src", "title": "Sample Size Necessary for the Accuracy in Parameter Estimation Approach for a Standardized Regression Coefficient of Interest", "topics": [ "ss_aipe_src" ] }, { "page": "ss_aipe_src_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning From the Accuracy in Parameter Estimation Perspective for the Standardized Regression Coefficient", "topics": [ "ss_aipe_src_sensitivity" ] }, { "page": "ss_power_c", "title": "Sample Size or Power for an Unstandardized Contrast in a One-Way Between-Subjects ANOVA", "concept": [ "sample size for power" ], "topics": [ "ss_power_c" ] }, { "page": "ss_power_c_ancova", "title": "Sample Size or Power for an Unstandardized Contrast in a One-Way ANCOVA", "concept": [ "sample size for power" ], "topics": [ "ss_power_c_ancova" ] }, { "page": "ss_power_composite_ancova", "title": "Sample Size or Composite Power for a One-Way or Factorial ANCOVA", "concept": [ "composite power", "sample size for power" ], "topics": [ "ss_power_composite_ancova" ] }, { "page": "ss_power_composite_ancova_2group", "title": "Sample Size or Composite Power for a Two-Group ANCOVA With a Covariate", "concept": [ "composite power", "sample size for power" ], "topics": [ "plot.dmar_composite_power", "ss_power_composite_ancova_2group" ] }, { "page": "ss_power_composite_anova", "title": "Sample Size or Composite Power for a One-Way or Factorial ANOVA", "concept": [ "composite power", "sample size for power" ], "topics": [ "ss_power_composite_anova" ] }, { "page": "ss_power_composite_factorial_ancova", "title": "Sample Size or Composite Power for a Factorial ANCOVA", "concept": [ "composite power", "sample size for power" ], "topics": [ "plot.dmar_composite_power_factorial", "ss_power_composite_factorial_ancova" ] }, { "page": "ss_power_composite_factorial_ancova_het", "title": "Composite Power for a Factorial ANCOVA With Heterogeneous Slopes", "concept": [ "composite power", "sample size for power" ], "topics": [ "plot.dmar_composite_power_factorial_het", "ss_power_composite_factorial_ancova_het" ] }, { "page": "ss_power_composite_factorial_anova", "title": "Sample Size or Composite Power for a Factorial ANOVA", "concept": [ "composite power", "sample size for power" ], "topics": [ "ss_power_composite_factorial_anova" ] }, { "page": "ss_power_composite_sem", "title": "Sample Size or Composite Power for a Set of SEM Parameters", "concept": [ "composite power", "sample size for power" ], "topics": [ "ss_power_composite_sem" ] }, { "page": "ss_power_contrast", "title": "Sample Size and Statistical Power for a Contrast in a Fixed-Effects ANOVA", "concept": [ "sample size for power" ], "topics": [ "ss_power_contrast" ] }, { "page": "ss_power_equivalence_c", "title": "Sample Size for Equivalence or Noninferiority of a Linear Contrast", "concept": [ "equivalence testing", "sample size for power" ], "topics": [ "ss_power_equivalence_c" ] }, { "page": "ss_power_factorial_ancova", "title": "Sample Size Planning for Power in Factorial ANCOVA", "concept": [ "sample size for power" ], "topics": [ "ss_power_factorial_ancova" ] }, { "page": "ss_power_factorial_anova", "title": "Sample Size or Power for a Factorial Between-Subjects ANOVA Effect", "concept": [ "sample size for power" ], "topics": [ "ss_power_factorial_anova" ] }, { "page": "ss_power_indirect_effect", "title": "Sample Size Planning for Power for the Indirect (Mediation) Effect", "concept": [ "mediation", "sample size for power" ], "topics": [ "ss_power_indirect_effect" ] }, { "page": "ss_power_mixed_effects", "title": "Sample Size or Power for a Treatment Effect in a Two-Level Mixed-Effects Model", "concept": [ "mixed models", "sample size for power" ], "topics": [ "ss_power_mixed_effects" ] }, { "page": "ss_power_one_way_anova", "title": "Sample Size or Power for a One-Way Between-Subjects ANOVA Omnibus _F_ Test", "concept": [ "sample size for power" ], "topics": [ "ss_power_one_way_anova" ] }, { "page": "ss_power_pcm", "title": "Sample Size Planning for Power for Polynomial Change Models", "concept": [ "sample size for power" ], "topics": [ "ss_power_pcm" ] }, { "page": "ss_power_r", "title": "Sample Size or Power for a Pearson Correlation Coefficient (Fisher Z Transformation)", "concept": [ "sample size for power" ], "topics": [ "ss_power_r" ] }, { "page": "ss_power_R2", "title": "Plan Sample Size to Make the Test of the Squared Multiple Correlation Coefficient Sufficiently Powerful", "concept": [ "sample size for power" ], "topics": [ "ss_power_R2" ] }, { "page": "ss_power_R2_sensitivity", "title": "Sensitivity Analysis for Sample Size Planning to Make the Omnibus Test of R^2 Sufficiently Powerful", "concept": [ "sample size for power" ], "topics": [ "ss_power_R2_sensitivity" ] }, { "page": "ss_power_rc", "title": "Sample Size for a Targeted Regression Coefficient", "concept": [ "sample size for power" ], "topics": [ "ss_power_rc" ] }, { "page": "ss_power_reg_coef", "title": "Sample Size for a Targeted Regression Coefficient", "concept": [ "sample size for power" ], "topics": [ "ss_power_reg_coef" ] }, { "page": "ss_power_reg_coef_sensitivity", "title": "Sensitivity Analysis for the Power of a Targeted Regression Coefficient", "concept": [ "sample size for power" ], "topics": [ "ss_power_reg_coef_sensitivity" ] }, { "page": "ss_power_rm_anova", "title": "Sample Size or Power for a One-Way Repeated Measures ANOVA Omnibus _F_ Test", "concept": [ "sample size for power" ], "topics": [ "ss_power_rm_anova" ] }, { "page": "ss_power_sc", "title": "Sample Size or Power for a Standardized Contrast in a One-Way Between-Subjects ANOVA", "concept": [ "sample size for power" ], "topics": [ "ss_power_sc" ] }, { "page": "ss_power_sem", "title": "Sample Size Planning for Structural Equation Modeling From the Power Analysis Perspective", "concept": [ "sample size for power" ], "topics": [ "ss_power_sem" ] }, { "page": "ss_power_smd", "title": "Sample Size or Power for a Standardized Mean Difference (Two Independent Groups)", "concept": [ "sample size for power" ], "topics": [ "ss_power_smd" ] }, { "page": "ss_power_split_plot_anova", "title": "Sample Size or Power for a Mixed-Effects ANOVA (Between X Within Design)", "concept": [ "mixed models", "sample size for power" ], "topics": [ "ss_power_split_plot_anova" ] }, { "page": "ss_seq_c", "title": "Sequential Sample Size for a Fixed-Width Contrast Interval", "concept": [ "sequential estimation" ], "topics": [ "ss_seq_c" ] }, { "page": "ss_seq_c_sensitivity", "title": "Monte Carlo Sensitivity of the Sequential Fixed-Width Procedure", "concept": [ "sequential estimation" ], "topics": [ "ss_seq_c_sensitivity" ] }, { "page": "summary_t_test", "title": "Two-Sample _t_ Test From Summary Statistics", "concept": [ "hypothesis tests" ], "topics": [ "summary_t_test" ] }, { "page": "summary_t_test_broom", "title": "Broom-Style Tidy / Glance Methods for 'summary_t_test()'", "topics": [ "glance.dmar_summary_t_test", "summary_t_test_broom", "tidy.dmar_summary_t_test" ] }, { "page": "teacher_expectancy", "title": "Teacher Expectancy Meta-Analysis Data (Raudenbush, 1984)", "topics": [ "teacher_expectancy" ] }, { "page": "test_market", "title": "Controlled Test-Market Experiment (Bryant & Bruvold, 1980)", "topics": [ "test_market" ] }, { "page": "tidy.dmar_cfa_k", "title": "Tidy a Multiple-Factor CFA Fit", "topics": [ "tidy.dmar_cfa_k" ] }, { "page": "tidy.dmar_ci_R2", "title": "Tidy / Glance Methods for ci_R2 Output", "topics": [ "glance.dmar_ci_R2", "tidy.dmar_ci_R2" ] }, { "page": "tidy.dmar_ci_smd", "title": "Tidy / Glance Methods for ci_smd Output", "topics": [ "glance.dmar_ci_smd", "tidy.dmar_ci_smd" ] }, { "page": "dmar_tidiers", "title": "Tidy and Glance Methods for DMAR Result Tables", "topics": [ "dmar_tidiers", "glance.dmar_ci_anova", "glance.dmar_ci_long", "glance.dmar_content_validity", "glance.dmar_contrast_test", "glance.dmar_dmacs", "glance.dmar_measurement_alignment", "glance.dmar_measurement_invariance", "glance.dmar_post_hoc_ci", "glance.dmar_ss_aipe", "glance.dmar_ss_power", "glance.dmar_ss_power_sensitivity", "glance.dmar_tbl", "tidy.dmar_ci_anova", "tidy.dmar_ci_long", "tidy.dmar_content_validity", "tidy.dmar_contrast_test", "tidy.dmar_dmacs", "tidy.dmar_measurement_alignment", "tidy.dmar_measurement_invariance", "tidy.dmar_post_hoc_ci", "tidy.dmar_ss_aipe", "tidy.dmar_ss_power", "tidy.dmar_ss_power_sensitivity", "tidy.dmar_tbl" ] }, { "page": "tidy.dmar_mediation_mbco", "title": "Tidy an MBCO Mediation Table", "topics": [ "tidy.dmar_mediation_mbco" ] }, { "page": "tidy.dmar_R2_mixed_effects", "title": "Tidy / Glance Methods for R2_mixed_effects Output", "topics": [ "glance.dmar_R2_mixed_effects", "tidy.dmar_R2_mixed_effects" ] }, { "page": "tidy.dmar_reliability", "title": "A Reliability Coefficient Estimate", "topics": [ "tidy.dmar_reliability" ] }, { "page": "tidy.mlmr", "title": "An Mlmr Fit", "topics": [ "tidy.mlmr" ] }, { "page": "tidy.mlmr_mv", "title": "A Multivariate FIML Regression Fit", "topics": [ "glance.mlmr_mv", "tidy.mlmr_mv" ] }, { "page": "unbiased_R2", "title": "Unbiased and Adjusted Estimators of the Population Squared Multiple Correlation", "topics": [ "unbiased_R2" ] }, { "page": "var_alpha", "title": "Asymptotic Variance of Coefficient Alpha (Cronbach, Guttman)", "concept": [ "variance utilities" ], "topics": [ "var_alpha" ] }, { "page": "var_cv", "title": "Asymptotic Variance of the Coefficient of Variation", "concept": [ "variance utilities" ], "topics": [ "var_cv" ] }, { "page": "var_ete", "title": "Variance of the Estimated Treatment Effect in Two-Group ANCOVA With Heterogeneous Slopes", "concept": [ "variance utilities" ], "topics": [ "var_ete" ] }, { "page": "var_icc", "title": "Asymptotic Variance of the Intraclass Correlation Coefficient", "topics": [ "var_icc" ] }, { "page": "var_indirect_effect", "title": "Variance of the Mediated (Indirect) Effect ab", "concept": [ "variance utilities" ], "topics": [ "var_indirect_effect" ] }, { "page": "var_omega_squared", "title": "Asymptotic Variance of Omega Squared (ANOVA Effect Size)", "concept": [ "variance utilities" ], "topics": [ "var_omega_squared" ] }, { "page": "var_partial_r", "title": "Asymptotic Variance of the Partial Correlation Coefficient", "topics": [ "var_partial_r" ] }, { "page": "var_r", "title": "Asymptotic Variance of the Pearson Correlation Coefficient", "concept": [ "variance utilities" ], "topics": [ "var_r" ] }, { "page": "var_R2", "title": "Variance of the Squared Multiple Correlation Coefficient", "topics": [ "var_R2" ] }, { "page": "var_semipartial_r", "title": "Asymptotic Variance of the Semipartial (Part) Correlation Coefficient", "topics": [ "var_semipartial_r" ] }, { "page": "var_smd", "title": "Variance of Cohen's _d_ and Hedges' _g_", "concept": [ "variance utilities" ], "topics": [ "var_smd" ] }, { "page": "var_smd_trimmed", "title": "Asymptotic Variance of the Robust Trimmed SMD", "concept": [ "variance utilities" ], "topics": [ "var_smd_trimmed" ] }, { "page": "vargha_delaney_A", "title": "Vargha and Delaney's _A_ (Stochastic-Superiority Effect Size)", "concept": [ "Vargha-Delaney A", "probability of superiority" ], "topics": [ "vargha_delaney_A" ] }, { "page": "variance_components_mls", "title": "Modified-Large-Sample Confidence Intervals on Variance Components", "concept": [ "agreement and measurement" ], "topics": [ "variance_components_mls" ] }, { "page": "welch_t", "title": "Welch's Separate-Variance _t_ Test", "concept": [ "hypothesis tests" ], "topics": [ "welch_t" ] }, { "page": "welch_t_broom", "title": "Broom-Style Tidy / Glance Methods for 'welch_t()'", "topics": [ "glance.dmar_welch_t", "tidy.dmar_welch_t", "welch_t_broom" ] } ], "_pkglogo": "https://github.com/yelleknek/dmar/raw/HEAD/man/figures/logo.svg", "_readme": "https://github.com/yelleknek/dmar/raw/HEAD/README.md", "_rundeps": [ "generics", "MASS", "withr" ], "_vignettes": [ { "source": "composite_power_ancova.Rmd", "filename": "composite_power_ancova.html", "title": "Composite Power for Factorial ANCOVA: Planning Simple Effects, an Interaction, and a Moderator", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "The Design and the Population", "The Cell Means in a Two-by-Two Layout", "Viewing the Population", "The Effects Worth Planning", "Simple Effects of Both Factors", "Contrast Weights", "Analytic Power for a Single Contrast", "The Arithmetic", "A Picture of One Power Calculation", "The Shared Design", "Composite Power: Detecting Every Effect in One Study", "Sizing the Study for Composite Power", "Analytic Versus Monte Carlo Power", "The Moderator as a Covariate", "The Moderator as a Three-Way Interaction", "Visualizing the Three-Way Interaction", "Effect Sizes for the Moderated Model", "Confirming the Plan by Simulation", "Summary", "References" ], "created": "2026-08-17 12:43:14", "modified": "2026-09-07 23:04:59", "commits": 2 }, { "source": "composite_sem_planning.Rmd", "filename": "composite_sem_planning.html", "title": "Composite Sample Size Planning for SEM: A Simple Model and a Latent Growth Curve", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "The Workflow", "A Simple Model: Mediation Among Observed Variables", "The Population", "The Analysis Model and the Parameters of Interest", "Composite Power at a Candidate Sample Size", "The Necessary Sample Size", "The Weakest Parameter Governs the Design", "Accuracy for the Set: AIPE", "A Latent Growth Curve", "The Population Growth Process", "The Analysis Model", "Composite Power for the Growth Questions", "Accuracy for the Growth Parameters", "Practical Notes", "References" ], "created": "2026-08-17 12:43:14", "modified": "2026-08-17 12:43:14", "commits": 1 }, { "source": "pygmalion.Rmd", "filename": "pygmalion.html", "title": "Heterogeneity of Regression in the Pygmalion Data", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "The Data", "The Homogeneity-of-Regression Assumption", "A Tidy ANCOVA With ancova()", "The Treatment Effect Depends on the Covariate", "One Study and Its Literature", "References" ], "created": "2026-08-17 12:43:14", "modified": "2026-09-07 23:04:59", "commits": 2 }, { "source": "function_interactions.Rmd", "filename": "function_interactions.html", "title": "How DMAR's Functions Fit Together: A Deep Dive", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "Why This Vignette Exists", "The Four Building Blocks", "1. Variance Utility → AIPE Planner", "Worked Example: Pearson r", "2. Expected-Value Function → Design-Stage Substitution", "Worked Example: The Standardized Mean Difference", "3. Effect Size Transforms: smd ↔ cles ↔ proportion_of_superiority ↔ nnt", "Walked Example at Three Reference d Values", "4. Ordinal Alternatives: cliff_delta, vargha_delaney_A, probability_of_superiority_paired", "5. Reliability Composition", "6. The Mediation Pipeline", "7. Multivariate and Agreement Utilities", "Argument-Name Conventions", "Two Consistent Return Shapes", "Recommended Workflows", "A. Reporting a Standardized Mean Difference", "B. Planning a Partial Correlation Study", "C. Planning an Omega Squared CI", "D. Reporting an Indirect (Mediated) Effect", "E. Two-Method Agreement Study", "Summary" ], "created": "2026-08-17 12:43:14", "modified": "2026-09-07 23:04:59", "commits": 3 }, { "source": "DMAR.Rmd", "filename": "DMAR.html", "title": "Introduction to DMAR", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "What Is DMAR?", "Why Effect Size Confidence Intervals?", "Two Philosophies of Sample Size Planning", "When Do You Reach for DMAR?", "1. I Just Ran an ANOVA, t-Test, or Regression. What's the Effect Size and a CI for It?", "2. I'm Planning a Study. How Many Subjects Do I Need?", "3. I Want to Test a Specific Contrast or Set of Pairwise Comparisons.", "4. I Have Within-Subjects (Repeated Measures) Data.", "5. I Want Descriptive Statistics, a Correlation Table, or Distribution-Shape Diagnostics.", "What DMAR Is Not", "A Note on Argument Naming", "Where to Next", "References" ], "created": "2026-08-17 12:43:14", "modified": "2026-09-07 23:04:59", "commits": 2 }, { "source": "mlmr.Rmd", "filename": "mlmr.html", "title": "Maximum Likelihood Multiple Regression and Multivariate Regression", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "Purpose", "Rubin's Missingness Taxonomy", "Scenario 1: Complete Data (the Baseline)", "Scenario 2: Missing Outcome Under MCAR", "Scenario 3: Missing Predictor Under MAR (the Bias Case)", "Scenario 4: Differential Missingness in a Treatment-Versus-Control Design", "When Only the Outcome Is Missing", "When a Baseline Covariate Is Missing", "When Dropout Tracks an Auxiliary Variable", "Reading the Three Cases Together", "When FIML Cannot Help: MNAR", "Multivariate FIML: When Outcomes Are Correlated", "Summary", "See Also", "References" ], "created": "2026-08-17 12:43:14", "modified": "2026-08-17 12:43:14", "commits": 1 }, { "source": "mbess_to_dmar.Rmd", "filename": "mbess_to_dmar.html", "title": "Moving From MBESS to DMAR: A Migration Guide and a Tour of the New Methods", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "Why a Reimplementation and Expansion", "Migration Table: The Renames That Matter", "What DMAR Adds Beyond the MBESS Scope", "1. Maximum Likelihood Multiple Regression With FIML (mlmr() / mlmr_mv())", "2. broom-Style Integration (generics::tidy() and generics::glance())", "3. AIPE Sensitivity Analysis", "4. ANOVA and ANCOVA Wrappers", "5. Reliability With Proper CIs", "Other Additions", "A Short Worked Migration", "See Also", "References" ], "created": "2026-08-17 12:43:14", "modified": "2026-09-07 23:04:59", "commits": 5 }, { "source": "nonlinear_growth.Rmd", "filename": "nonlinear_growth.html", "title": "Nonlinear Growth Curves and the Meaning of Their Parameters", "engine": "knitr::rmarkdown", "headings": [ "The Negative Exponential: Change That Only Decelerates", "The Logistic: Symmetric S-Shaped Change", "The Gompertz: S-Shaped Change That Inflects Early", "The Richards Family: The Inflection Becomes a Parameter", "Individual Differences, One Parameter at a Time", "Nonlinear Versus Polynomial: Why the Parameters Matter", "From Curves to Data and Back", "References" ], "created": "2026-08-17 12:43:14", "modified": "2026-09-07 23:04:59", "commits": 2 }, { "source": "one_way_anova_power.Rmd", "filename": "one_way_anova_power.html", "title": "Power and Precision for the One-Way ANOVA: A Model Comparison Perspective", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "Two Models, One Question", "The Four-Group Running Example", "The Effect Size as a Proportional Reduction in Error", "Power for the Omnibus Test", "Planning the Sample Size", "The Model Comparison, Made Literal", "Estimating the Effect Size With a Confidence Interval", "Carrying Uncertainty Into the Next Study", "A Second Example: Three Groups", "A Planned Contrast as a Model Comparison", "Beyond the Reject or Retain Decision", "Planning a Contrast for Precision", "Why Not base R's power.anova.test()", "References" ], "created": "2026-08-17 12:43:14", "modified": "2026-09-07 23:04:59", "commits": 4 }, { "source": "ancova_2x4x3_power.Rmd", "filename": "ancova_2x4x3_power.html", "title": "Power for Factorial ANCOVA: A 2 x 4 x 3 Design Worked in Full", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "1. The Design and the Population", "2. Power, Three Ways: ANOVA, and ANCOVA at Two Covariate Strengths", "3. Hypothetical Data", "4. Type III Analysis", "5. Plotting the Interactions Well", "6. Focused Follow-up Comparisons", "7. Reading List" ], "created": "2026-08-17 12:43:14", "modified": "2026-09-07 23:04:59", "commits": 2 }, { "source": "dmar_output.Rmd", "filename": "dmar_output.html", "title": "Reading DMAR Result Tables", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "One Kind of Result, Everywhere", "What You See Is Rounded; What Is Stored Is Exact", "Getting a Single Number Out", "The \"Wide\" View and the One-Row Summary", "Wide Tables Read the Same Way", "p-Values and Information Criteria", "In One Paragraph" ], "created": "2026-08-17 12:43:14", "modified": "2026-09-07 23:04:59", "commits": 2 }, { "source": "reliability.Rmd", "filename": "reliability.html", "title": "Reliability in DMAR: Choosing the Coefficient, Choosing the Interval", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "A Running Example", "Alpha Assumes Equal Loadings", "The Denominator Choice in Omega", "Categorical Omega for Ordered Items", "Coefficient H: A Different Composite", "Choosing the Interval", "Missing Data: FIML and Auxiliary Variables", "One Entry Point and the Broom Verbs", "Planning the Study", "What Reliability Coefficients Cannot Do", "See Also", "References" ], "created": "2026-08-17 12:43:14", "modified": "2026-08-17 12:43:14", "commits": 1 }, { "source": "critical_values.Rmd", "filename": "critical_values.html", "title": "Reproducing the Textbook Critical-Value Tables", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "The t Table (A.1)", "The Studentized Range (A.4) and Maximum Modulus (A.5)", "The Dunnett Tables (A.6, A.7)", "Bryant-Paulson (A.8): Where Recomputation Caught an Error", "References" ], "created": "2026-08-17 12:43:14", "modified": "2026-09-04 14:26:01", "commits": 2 }, { "source": "bryant_paulson_ancova.Rmd", "filename": "bryant_paulson_ancova.html", "title": "Simultaneous Comparison of Adjusted Means in ANCOVA With DMAR", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "1. The Worked Example From Bryant & Bruvold (1980)", "Fit the ANCOVA", "Adjusted Panel Means", "Simultaneous Bryant-Paulson Intervals", "2. Simultaneous vs. Per-Comparison Intervals", "3. A One-Way ANCOVA With ancova()", "4. Critical Values on Their Own", "References" ], "created": "2026-08-17 12:43:14", "modified": "2026-09-07 23:04:59", "commits": 3 }, { "source": "sums_of_squares.Rmd", "filename": "sums_of_squares.html", "title": "Sums of Squares in Nonorthogonal Designs", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "Why the Question Arises", "A Short History in Psychology", "A Sum of Squares Is a Model Comparison", "Type I, Type II, and Type III", "A Worked Unbalanced Factorial", "How factorial_anova() Computes the Types", "Balanced Data: The Types Coincide", "ANCOVA Is Nonorthogonal by Construction", "The Multivariate Case: MANOVA and MANCOVA", "Recommendations", "References" ], "created": "2026-08-17 12:43:14", "modified": "2026-09-07 23:04:59", "commits": 2 }, { "source": "teacher_expectancy.Rmd", "filename": "teacher_expectancy.html", "title": "The Teacher Expectancy Meta-Analysis: Reproducing Raudenbush (1984)", "author": "Ken Kelley", "engine": "knitr::rmarkdown", "headings": [ "The Data", "Are the Studies Significant in Combination? (Table 2)", "Why Do the Studies Disagree? 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