ezStats: Function to obtain descriptive statistics from a factorial experiment

Description

This function provides easy computation of descriptive statistics (between-Ss means, between-Ss SD, Fisher's Least Significant Difference) for data from factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs.

Usage

ezStats(
    data
    , dv
    , wid
    , within = NULL
    , between = NULL
    , between_full = NULL
    , diff = NULL
    , reverse_diff = FALSE
	, type = 2
)

Arguments

data

Data frame containing the data to be analyzed.

.() object specifying the column in data that contains the dependent variable. Values in this column should be of the numeric class.

wid

.() object specifying the column in data that contains the variable specifying the case/Ss identifier. Values in this column will be converted to factor class if necessary.

within

Optional .() object specifying the column(s) in data that contains predictor variables that are manipulated within-Ss. Values in this column will be converted to factor class if necessary.

between

Optional .() object specifying the column(s) in data that contains predictor variables that are manipulated between-Ss. Values in this column will be converted to factor class if necessary.

between_full

Same as between, but must specify the full set of between-Ss variables if between specifies only a subset of the design.

diff

Optional .() object specifying a 2-level within-Ss varbiable to collapse to a difference score.

reverse_diff

Logical. If TRUE, triggers reversal of the difference collapse requested by diff.

type

Numeric value (either 1, 2 or 3) specifying the Sums of Squares "type" to employ when data are unbalanced (eg. when group sizes differ). See ezANOVA for details.

Value

A data frame containing the descriptive statistics for the requested effect. N = number of Ss per cell. Mean = between-Ss mean. SD = between-Ss SD. FLSD = Fisher's Least Significant Difference.

Warning

The descriptives include Fisher's Least Significant Difference for the requested effect. In the context of purely within-Ss or purely between-Ss this value may be used for post-hoc multiple comparisons. Note however that in the context of mixed within-and-between-Ss designs, this value can only be used for within-Ss comparisons.

Details

While within and between are both optional, at least one column of data must be provided to either within or between. Any numeric or character variables in data that are specified as either wid, within or between will be converted to a factor with a warning. Fisher's Least Significant Difference is computed as sqrt(2)*qt(.975,DFd)*sqrt(MSd/N), where N is taken as the mean N per group in cases of unbalanced designs.

Examples

Run this code

#Read in the ANT data (see ?ANT).
data(ANT)
head(ANT)
ezPrecis(ANT)


#Run an ANOVA on the mean correct RT data.
mean_rt_anova = ezANOVA(
    data = ANT[ANT$error==0,]
    , dv = .(rt)
    , wid = .(subnum)
    , within = .(cue,flank)
    , between = .(group)
)

#Show the ANOVA & assumption tests.
print(mean_rt_anova)

#Compute descriptives for the main effect of group.
group_descriptives = ezStats(
    data = ANT[ANT$error==0,]
    , dv = .(rt)
    , wid = .(subnum)
    , between = .(group)
)

#Show the descriptives.
print(group_descriptives)

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