ind.oneway: A one-way design with independent samples using individual...

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

ind.oneway conducts a one-way design with independent samples, namely one-way randomized-group analysis of variance, using individual data.

Usage

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ind.oneway(formula, data, 
            contr = NULL, sig.level = 0.05, digits = 3)

Arguments

formula

two-sided formula; the left-hand-side of which gives one dependent variable containing a numeric variable, and the right-hand-side of one independent variable containing a factor with two or more levels

data

a data frame contains the variables in the fomrmula

contr

a matrix or vector contains the contrast weights

sig.level

a numeric contains the significance level (default 0.05)

digits

the specified number of decimal places (default 3)

Details

This function conducts a one-way design with independent samples, namely one-way randomized-group analysis of variance, using individual data. If you do not specify contr, all possible pairwise contrasts will be calculated. Statistical power is calculated using the following specifications:

(a) small (η^2 = 0.01), medium (η^2 = 0.06), and large (η^2 = 0.14) population effect sizes, according to the interpretive guideline for effect sizes by Cohen (1992)

(b) sample size specified by formula and data

(c) significance level specified by sig.level

Value

The returned object of ind.oneway contains the following components:

anova.table

returns a ANOVA table containing sums of squares, degrees of freedom, mean squares, F values

omnibus.es

returns a omnibus effect size which is a η^2, and its' confidence interval

raw.contrasts

returns raw mean differences, their confidence intervals, and standard errors

standardized.contrasts

returns standardized mean differences for the contrasts (Hedges's g), their approximate confidence intervals for population standardized mean differences, and standard errors

power

returns statistical power for detecting small (η^2 = 0.01), medium (η^2 = 0.06), and large (η^2 = 0.14) population effect sizes

Author(s)

Yasuyuki Okumura
Department of Social Psychiatry,
National Institute of Mental Health,
National Center of Neurology and Psychiatry
yokumura@blue.zero.jp

References

Cohen J (1992) A power primer. Psychological Bulletin, 112, 155-159.

Kline RB (2004) Beyond significance testing: Reforming data analysis methods in behavioral research. Washington: American Psychological Association.

See Also

ind.oneway.second, samplesize.etasq

Examples

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##Kline (2004) Table 6.3
dat <- data.frame(y = c(9,12,13,15,16,
                       8,12,11,10,14,
                       10,11,13,11,15),
                  x =  rep(factor(c("a","b","c")), each=5)
                  )                 
ind.oneway(formula = y~x, data=dat, sig.level=.05, digits=3)


##contrast 1: a - c, contrast 2: 1/2(a + c) - b
my.cont <- matrix(c(1,0,-1,1/2,-1,1/2), ncol=3, nrow=2, byrow=TRUE)
ind.oneway(formula = y~x, data=dat, contr=my.cont, sig.level=.05, digits=3)

rpsychi documentation built on May 1, 2019, 10:10 p.m.