Convenience function that runs a one sample t-test. This is a wrapper function intended to be used for pedagogical purposes only.
oneSampleTTest(x, mu, one.sided = FALSE, conf.level = 0.95)The variable to be tested (required).
The value against which the mean should be tested (required).
One sided or two sided hypothesis test (default = FALSE)
The confidence level for the confidence interval (default = .95).
An object of class 'TTest'. When printed, the output is organised into five short sections. The first section lists the name of the test and the variables included. The second provides means and standard deviations. The third states explicitly what the null and alternative hypotheses were. The fourth contains the test results: t-statistic, degrees of freedom and p-value. The final section includes the relevant confidence interval and an estimate of the effect size (i.e., Cohen's d).
The oneSampleTTest function runs a one-sample t-test on
the data in x, and prints the results in a format that is easier
for novices to handle than the output of t.test. All the actual
calculations are done by the t.test and cohensD functions.
As with the t.test function, the default test is two sided,
corresponding to a default value of one.sided = FALSE. To specify
a one sided test in which the alternative hypothesis is that x is
larger than mu, the input must be one.sided = "greater".
Similarly, if one.sided="less", then the alternative hypothesis
is that the mean of x is smaller than mu.
likert <- c(3,1,4,1,4,6,7,2,6,6,7)
oneSampleTTest( x = likert, mu = 4 )
#>
#> One sample t-test
#>
#> Data variable: likert
#>
#> Descriptive statistics:
#> likert
#> mean 4.273
#> std dev. 2.284
#>
#> Hypotheses:
#> null: population mean equals 4
#> alternative: population mean not equal to 4
#>
#> Test results:
#> t-statistic: 0.396
#> degrees of freedom: 10
#> p-value: 0.7
#>
#> Other information:
#> two-sided 95% confidence interval: [2.738, 5.807]
#> estimated effect size (Cohen's d): 0.119
#>
oneSampleTTest( x = likert, mu = 4, one.sided = "greater" )
#>
#> One sample t-test
#>
#> Data variable: likert
#>
#> Descriptive statistics:
#> likert
#> mean 4.273
#> std dev. 2.284
#>
#> Hypotheses:
#> null: population mean less than or equal to 4
#> alternative: population mean greater than 4
#>
#> Test results:
#> t-statistic: 0.396
#> degrees of freedom: 10
#> p-value: 0.35
#>
#> Other information:
#> one-sided 95% confidence interval: [3.024, Inf]
#> estimated effect size (Cohen's d): 0.119
#>
oneSampleTTest( x = likert, mu = 4, conf.level=.99 )
#>
#> One sample t-test
#>
#> Data variable: likert
#>
#> Descriptive statistics:
#> likert
#> mean 4.273
#> std dev. 2.284
#>
#> Hypotheses:
#> null: population mean equals 4
#> alternative: population mean not equal to 4
#>
#> Test results:
#> t-statistic: 0.396
#> degrees of freedom: 10
#> p-value: 0.7
#>
#> Other information:
#> two-sided 99% confidence interval: [2.738, 5.807]
#> estimated effect size (Cohen's d): 0.119
#>
likert <- c(3,NA,4,NA,4,6,7,NA,6,6,7)
oneSampleTTest( x = likert, mu = 4 )
#> Warning: 3 case(s) removed due to missingness
#>
#> One sample t-test
#>
#> Data variable: likert
#>
#> Descriptive statistics:
#> likert
#> mean 5.375
#> std dev. 1.506
#>
#> Hypotheses:
#> null: population mean equals 4
#> alternative: population mean not equal to 4
#>
#> Test results:
#> t-statistic: 2.582
#> degrees of freedom: 7
#> p-value: 0.036
#>
#> Other information:
#> two-sided 95% confidence interval: [4.116, 6.634]
#> estimated effect size (Cohen's d): 0.913
#>