`dep.t.test`

conducts a t-test with dependent samples using individual data.

1 2 | ```
dep.t.test(formula, data, block,
sig.level=.05, digits=3)
``` |

`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 |

`block` |
a character string specify the blocking variable |

`sig.level` |
a numeric contains the significance level (default 0.05) |

`digits` |
the specified number of decimal places (default 3) |

This function conducts a t-test with dependent samples using individual data.

The returned object of `dep.t.test.second`

contains the following components:

`samp.stat` |
returns the means, standard deviations, sample size, and correlation |

`raw.difference` |
returns a raw mean difference, its' confidence interval, and standard error |

`standardized.difference` |
returns a standardized mean difference (Hedges's |

Yasuyuki Okumura

Department of Social Psychiatry,

National Institute of Mental Health,

National Center of Neurology and Psychiatry

yokumura@blue.zero.jp

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

1 2 3 4 5 6 7 | ```
##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),
subj = rep(paste("s", 1:5, sep=""), times=2)
)
dep.t.test(y~x, block="subj", data=dat)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.