Compute t-tests — t_test • tidycomm

Computes t-tests for one group variable and specified test variables. If no variables are specified, all numeric (integer or double) variables are used.

t_test(
  data,
  group_var,
  ...,
  var.equal = TRUE,
  paired = FALSE,
  pooled_sd = TRUE,
  levels = NULL,
  case_var = NULL
)

Arguments

data	a tibble
group_var	group variable (column name)
...	test variables (column names). Leave empty to compute t-tests for all numeric variables in data.
var.equal	a logical variable indicating whether to treat the two variances as being equal. If `TRUE` then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used. Defaults to `TRUE`.
paired	a logical indicating whether you want a paired t-test. Defaults to `FALSE`.
pooled_sd	a logical indicating whether to use the pooled standard deviation in the calculation of Cohen's d. Defaults to `TRUE`.
levels	optional: a vector of length two specifying the two levels of the group variable.
case_var	optional: case-identifying variable (column name). If you set `paired = TRUE`, specifying a case variable will ensure that data are properly sorted for a dependent t-test.

Value

a tibble

Examples

WoJ %>% t_test(temp_contract, autonomy_selection, autonomy_emphasis)
#> # A tibble: 2 x 10
#>   Variable M_Permanent SD_Permanent M_Temporary SD_Temporary Delta_M     t    df
#>   <chr>          <dbl>        <dbl>       <dbl>        <dbl>   <dbl> <dbl> <dbl>
#> 1 autonom~        3.91        0.755        3.70        0.932   0.212  1.96   998
#> 2 autonom~        4.12        0.768        3.89        0.870   0.237  2.17   995
#> # ... with 2 more variables: p <dbl>, d <dbl>
WoJ %>% t_test(temp_contract)
#> # A tibble: 11 x 10
#>    Variable     M_Permanent SD_Permanent M_Temporary SD_Temporary Delta_M      t
#>    <chr>              <dbl>        <dbl>       <dbl>        <dbl>   <dbl>  <dbl>
#>  1 autonomy_se~        3.91        0.755        3.70        0.932  0.212   1.96 
#>  2 autonomy_em~        4.12        0.768        3.89        0.870  0.237   2.17 
#>  3 ethics_1            1.57        0.850        1.98        0.990 -0.414  -3.41 
#>  4 ethics_2            3.24        1.26         3.51        1.23  -0.269  -1.51 
#>  5 ethics_3            2.37        1.12         2.28        0.928  0.0862  0.549
#>  6 ethics_4            2.53        1.24         2.57        1.22  -0.0323 -0.185
#>  7 work_experi~       17.7        10.5         11.3        11.8    6.42    4.29 
#>  8 trust_parli~        3.07        0.797        3.02        0.772  0.0539  0.480
#>  9 trust_gover~        2.87        0.847        2.64        0.811  0.229   1.92 
#> 10 trust_parti~        2.43        0.724        2.36        0.736  0.0719  0.703
#> 11 trust_polit~        2.53        0.707        2.40        0.689  0.136   1.37 
#> # ... with 3 more variables: df <dbl>, p <dbl>, d <dbl>
WoJ %>% t_test(employment, autonomy_selection, autonomy_emphasis,
  levels = c("Full-time", "Freelancer"))
#> # A tibble: 2 x 10
#>   Variable `M_Full-time` `SD_Full-time` M_Freelancer SD_Freelancer Delta_M     t
#>   <chr>            <dbl>          <dbl>        <dbl>         <dbl>   <dbl> <dbl>
#> 1 autonom~          3.90          0.782         3.76         0.993   0.139  2.03
#> 2 autonom~          4.12          0.781         3.90         0.852   0.217  3.29
#> # ... with 3 more variables: df <dbl>, p <dbl>, d <dbl>