demands32

data

The 'CoTiMA' package performs meta-analyses of correlation matrices of repeatedly measured variables taken from
studies that used different time intervals. Different time intervals between measurement occasions impose problems for
meta-analyses because the effects (e.g. cross-lagged effects) cannot be simply aggregated, for example, by means of common
fixed or random effects analysis. However, continuous time math, which is applied in 'CoTiMA', can be used to extrapolate or
intrapolate the results from all studies to any desired time lag. By this, effects obtained in studies that used different
time intervals can be meta-analyzed. 'CoTiMA' fits models to empirical data using the structural equation model (SEM) package
'ctsem', the effects specified in a SEM are related to parameters that are not directly included in the model (i.e.,
continuous time parameters; together, they represent the continuous time structural equation model, CTSEM). Statistical
model comparisons and significance tests are then performed on the continuous time parameter estimates. 'CoTiMA' also allows
analysis of publication bias (Egger's test, PET-PEESE estimates, zcurve analysis etc.) and analysis of statistical power
(post hoc power, required sample sizes). See Dormann, C., Guthier, C., & Cortina, J. M. (2019) <doi:10.1177/1094428119847277>.
and Guthier, C., Dormann, C., & Voelkle, M. C. (2020) <doi:10.1037/bul0000304>.

Markus Homberg

CoTiMA

Continuous Time Meta-Analysis ('CoTiMA')

Christian Dormann

Christina Guthier

Manuel Voelkle

demands32 function

An object of class <code>character</code> of length 1.

Format

C. Dormann &amp; M. Homburg <a href="/link/CoTiMA%40uni-mainz.org?package=CoTiMA&version=0.7.0" data-mini-rdoc="CoTiMA::CoTiMA@uni-mainz.org">CoTiMA@uni-mainz.org</a>

Author

demands32 example vector — demands32

C. Dormann &amp; M. Homburg <a href='mailto:CoTiMA@uni-mainz.org'>CoTiMA@uni-mainz.org</a>

demands32: demands32 example vector

Description

Usage

Arguments

Format

Author