meta_smd

Pools two-group standardized mean differences across independent studies.
Each study contributes its standardized mean difference and per-group
sample sizes; the function computes the within-study sampling variances,
applies the Hedges small-sample bias correction by default (the same
\(J\) factor as <code>expected_smd</code> and <code>smd</code>), and
fits the random effects model of <code>meta_es</code>, returning the
pooled effect with its confidence interval, tau and tau-squared with
intervals, I-squared, Cochran's Q, and a prediction interval for the
effect in a new study.

models

Methods for design, measurement, and analysis, with the
aim of being user friendly yet methodologically sound. 'DMAR' (pronounced "Dee-Mar") implements
many advanced and nonstandard methods and makes them available for
straightforward use, with interfaces, defaults, and documentation
that are consistent across the package and grounded in the
methodological literature, in support of sound and reproducible
results. The package
emphasizes effect size estimation with confidence intervals;
sample size planning through accuracy in parameter estimation
(AIPE) and power analysis (including composite power for designs
whose conclusions require several results to hold at once), with
minimum risk, sequential, and equivalence frameworks; reliability,
agreement, and measurement more broadly, from coefficient omega
with confidence intervals to measurement invariance; factor
analysis and structural equation modeling, in which constructs,
latent variables measured by multiple indicators, are modeled
directly, with confirmatory factor analysis, convergent and
discriminant validity, and sample size planning for structural
equation models; mediation analysis, from the
simple mediation model with bootstrap intervals to likelihood
ratio tests of arbitrary indirect effects by model-based
constrained optimization (MBCO), with multiple groups and the probing of
moderated mediation; equivalence and noninferiority testing;
meta-analysis; repeated measures, multivariate, ANOVA, and ANCOVA
designs; and inference grounded in model comparison throughout.
Measurement is approached from a psychometric perspective, and
although many of the methods grew up in human-centered research,
they apply broadly across the empirical sciences.
Much of what is implemented traces to the author's methodological
work, interests, and collaborations. 'DMAR' is a more modern, more general,
and greatly expanded reimagining of the 'MBESS' package (Kelley,
2007a, <doi:10.18637/jss.v020.i08>; 2007b,
<doi:10.3758/BF03192993>), which has been on CRAN for more than
two decades and remains available there in stable form. Most functions accept either
raw data or the summary statistics typically reported in published
articles, so an analysis can be reproduced from a paper without
the original data, which is useful both for extending a published
analysis and for meta-analytic work. The estimation, inference,
and planning functions return one consistently formatted data
frame per function that composes with the broader R ecosystem, and
confidence intervals are reported alongside effect sizes
throughout, as best practice recommends. Researchers who have data
and a question but who are not R experts will find the package
approachable, while methodologists gain access to advanced and
nonstandard methods, including tables of critical values not
available elsewhere.

Ken Kelley

DMAR

Design, Measurement, and Analysis in R (DMAR)

meta_smd function

<dl><dt>smd</dt>
<dd>Numeric vector of standardized mean differences (Cohen's
d), one per study, positive in the direction of the common
hypothesis.</dd>
<dt>n_1, n_2</dt>
<dd>Per-group sample sizes for each study.</dd>
<dt>unbiased</dt>
<dd>Logical: convert each d to Hedges g (the
small-sample unbiased estimator) before pooling? Default <code>TRUE</code>.
Set <code>FALSE</code> to pool the raw d values, for example when
reproducing a historical analysis such as Raudenbush (1984) that
predates routine use of the correction.</dd>
<dt>method, hartung_knapp, conf_level</dt>
<dd>Passed to <code>meta_es</code>:
the tau-squared estimator (<code>"reml"</code> default), the Hartung-Knapp
small-sample adjustment (default <code>TRUE</code>), and the confidence
level.</dd></dl>

Arguments

Ken Kelley <a href="/link/kkelley%40nd.edu?package=DMAR&version=1.0.0" data-mini-rdoc="DMAR::kkelley@nd.edu">kkelley@nd.edu</a>

Author

Random Effects Meta-Analysis of Standardized Mean Differences — meta_smd

<dl>

<dt>smd</dt>
<dd>Numeric vector of standardized mean differences (Cohen's
d), one per study, positive in the direction of the common
hypothesis.</dd>


<dt>n_1, n_2</dt>
<dd>Per-group sample sizes for each study.</dd>


<dt>unbiased</dt>
<dd>Logical: convert each d to Hedges g (the
small-sample unbiased estimator) before pooling? Default <code>TRUE</code>.
Set <code>FALSE</code> to pool the raw d values, for example when
reproducing a historical analysis such as Raudenbush (1984) that
predates routine use of the correction.</dd>


<dt>method, hartung_knapp, conf_level</dt>
<dd>Passed to <code>meta_es</code>:
the tau-squared estimator (<code>"reml"</code> default), the Hartung-Knapp
small-sample adjustment (default <code>TRUE</code>), and the confidence
level.</dd>

</dl>

Ken Kelley <a href='mailto:kkelley@nd.edu'>kkelley@nd.edu</a>

meta_smd: Random Effects Meta-Analysis of Standardized Mean Differences

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples