d.single.t.t: d for Single t from t

In MOTE: Effect Size and Confidence Interval Calculator

Description

This function displays d and non-central confidence interval for single t estimated from the t-statistic.

Usage

d.single.t.t(t, n, a = 0.05)

Arguments

t	t-test value
n	sample size
a	significance level

Details

To calculate d, the t-statistic is divided by the square root of the sample size.

d = t / sqrt(n)

Learn more on our example page.

Value

The effect size (Cohen's d) with associated confidence intervals and relevant statistics.

d	effect size
dlow	lower level confidence interval d value
dhigh	upper level confidence interval d value
n	sample size
df	degrees of freedom (sample size - 1)
t	sig stats
p	p-value
estimate	the d statistic and confidence interval in APA style for markdown printing
statistic	the t-statistic in APA style for markdown printing

Examples

#A school has a gifted/honors program that they claim is
#significantly better than others in the country. The gifted/honors
#students in this school scored an average of 1370 on the SAT,
#with a standard deviation of 112.7, while the national average
#for gifted programs is a SAT score of 1080.

    gift = t.test(singt_data, mu = 1080, alternative = "two.sided")

#According to a single-sample t-test, the scores of the students
#from the program were significantly higher than the national
#average, t(14) = 9.97, p < .001.

#You can type in the numbers directly as shown below, or refer
#to your dataset within the function.

    d.single.t.t(t = 9.968, n = 15, a = .05)

    d.single.t.t(9.968, 15, .05)

    d.single.t.t(gift$statistic, length(singt_data$SATscore), .05)

MOTE documentation built on May 2, 2019, 5:51 a.m.