rpsychi (version 0.3)

ind.t.test: A t-test with independent samples using individual data

Description

ind.t.test conducts a t-test with independent samples using individual data.

Usage

ind.t.test(formula, data, 
                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 levels

data

a data frame contains the variables in the fomrmula

sig.level

a numeric contains the significance level (default 0.05)

digits

the specified number of decimal places (default 3)

Value

• The returned object of ind.t.test contains the following components:
• samp.stat
  returns the means, standard deviations, and sample sizes
• raw.difference
  returns a raw mean difference, its' confidence interval, and standard error
• standardized.difference
  returns a standardized mean difference (Hedges's $g$), its' approximate confidence interval for population standardized mean difference, and standard error
• power
  returns statistical power for detecting small ($d = 0.20$), medium ($d = 0.50$), and large ($d = 0.80$) population effect sizes

encoding

UTF-8

Details

This function conducts a t-test with independent samples using individual data. Statistical power is calculated using the following specifications: (a) small ($d = 0.20$), medium ($d = 0.50$), and large ($d = 0.80$) 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

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.t.test.second, samplesize.d

Examples

##Kline (2004) Table 4.4
dat <- data.frame(y = c(9,12,13,15,16,8,12,11,10,14),
                  x =  rep(factor(c("a","b")), each=5)
                  )
ind.t.test(y~x, data=dat)