ezANOVA: Function to perform a factorial ANOVA

Description

This function provides easy analysis of 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, yielding ANOVA results and assumption checks.

Usage

ezANOVA(
	data
	, dv
	, wid
	, within = NULL
	, between = NULL
	, observed = NULL
	, diff = NULL
	, reverse_diff = FALSE
	, type = 2
	, white.adjust = FALSE
	, detailed = FALSE
	, return_aov = FALSE
)

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 must be numeric.

wid

.() object specifying the column in data that contains the variable specifying the case/Ss identifier.

within

Optional .() object specifying one or more columns in data that contain predictor variables that are manipulated (or observed) within-Ss.

between

Optional .() object specifying one or more columns in data that contain predictor variables that are manipulated (or observed) between-Ss.

observed

Optional .() object specifying one or more columns in data that are already specified in either within or between that contain predictor variables that are observed variables (i.e. not manipulated). The presence of o

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). type = 2 is the default because this will yield identical ANOVA re

white.adjust

Only affects behaviour if the design contains only between-Ss predictor variables. If not FALSE, the value is passed as the white.adjust argument to Anova, which provides heteroscedasticity correction. See

detailed

Logical. If TRUE, returns extra information (sums of squares, raw likelihood ratios, AIC & BIC values used in computing corrected likelihood ratios, etc.).

return_aov

Logical. If TRUE, computes and returns an aov object corresponding to the requested ANOVA (useful for computing post-hoc contrasts).

Value

A list containing one or more of the following components:
ANOVAA data frame containing the ANOVA results.
Mauchly's Test for SphericityIf any within-Ss variables with >2 levels are present, a data frame containing the results of Mauchly's test for Sphericity. Only reported for effects >2 levels because sphericity necessarily holds for effects with only 2 levels.
Sphericity CorrectionsIf any within-Ss variables are present, a data frame containing the Greenhouse-Geisser & Huynh-Feldt epsilon values, and corresponding corrected p-values.
Levene's Test for HomgeneityIf the design is purely between-Ss, a data frame containing the results of Levene's test for Homgeneity of variance. Note that Huynh-Feldt corrected p-values where the Huynh-Feldt epsilon >1 will use 1 as the correction epsilon.
aovAn aov object corresponding to the requested ANOVA.

Details

While within and between are both optional, at least one column of data must be provided to either within or between. Prior to running, dv is collapsed to a mean for each cell defined by the combination of wid and any variables supplied to within and/or between and/or diff.

References

Bakeman, R. (2005). Recommended effect size statistics for repeated measures designs. Behavior Research Methods, 37 (3), 379-384.
Glover S, Dixon P. (2004) Likelihood ratios: a simple and flexible statistic for empirical psychologists. Psychonomic bulletin & review, 11 (5), 791-806.

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.
rt_anova = ezANOVA(
    data = ANT[ANT$error==0,]
    , dv = .(rt)
    , wid = .(subnum)
    , within = .(cue,flank)
    , between = .(group)
)

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

#Run an ANOVA on the mean_rt data, ignoring group.
rt_anova2 = ezANOVA(
    data = ANT[ANT$error==0,]
    , dv = .(rt)
    , wid = .(subnum)
    , within = .(cue,flank)
)

#Show the ANOVA & assumption tests.
print(rt_anova2)

#Run a purely between-Ss ANOVA on the mean_rt data.
rt_anova3 = ezANOVA(
    data = ANT[ANT$error==0,]
    , dv = .(rt)
    , wid = .(subnum)
    , between = .(group)
)

#Show the ANOVA & assumption tests.
print(rt_anova3)

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