finalized_stroopdata


A data frame containing dependent measures <code>prep</code> for each <code>id</code>
 calculated according to grouping variables: block and target_type.
 <code>prep()</code> aggregates the columns for the dependent measures by first
 dividing them to the levels of the first independent variable in
 <code>wthin vars</code>, and then within each level <code>prep()</code> divides the columns
 according to the next variable in <code>within_vars</code> and so forth.
 Thus, for each dependent measure in this example there are four columns
 according to the order they where entered in <code>within_vars</code> argument in
 <code>prep</code>. For this data frame this argument was
 <code>within_vars = c("block", "target_type")</code>.


datasets

Prepares data for statistical analysis (e.g., analysis of variance
;ANOVA) by enabling the user to easily and quickly merge (using the
file_merge() function) raw data files into one merged table and then
aggregate the merged table (using the prep() function) into a finalized
table while keeping track and summarizing every step of the preparation.
The finalized table contains several possibilities for dependent measures of
the dependent variable. Most suitable when measuring variables in an
interval or ratio scale (e.g., reaction-times) and/or discrete values such
as accuracy. Main functions included are file_merge() and prep(). The
file_merge() function vertically merges individual data files (in a long
format) in which each line is a single observation to one single dataset.
The prep() function aggregates the single dataset according to any
combination of grouping variables (i.e., between-subjects and
within-subjects independent variables, respectively), and returns a data
frame with a number of dependent measures for further analysis for each cell
according to the combination of provided grouping variables. Dependent
measures for each cell include among others means before and after rejecting
all values according to a flexible standard deviation criteria, number of
rejected values according to the flexible standard deviation criteria,
proportions of rejected values according to the flexible standard deviation
criteria, number of values before rejection, means after rejecting values
according to procedures described in Van Selst & Jolicoeur (1994; suitable
when measuring reaction-times), standard deviations, medians, means according
to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data
frame prep() returns can also be exported as a txt file to be used for
statistical analysis in other statistical programs.

Ayala S Allon

prepdat

Preparing Experimental Data for Statistical Analysis

finalized_stroopdata function

A data frame with 15 rows and 98 columns.

finalized_stroopdata: Finalized Table `prepdat::prep()` returns for `stroopdata` According to the Example in `prepdat::prep()`.

Description

Usage

Arguments

Format

Details

Examples