Computes t-tests for one group variable and specified test variables. If no variables are specified, all numeric (integer or double) variables are used. A Levene's test will automatically determine whether the pooled variance is used to estimate the variance. Otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used.
t_test(
data,
group_var,
...,
var.equal = TRUE,
paired = FALSE,
pooled_sd = TRUE,
levels = NULL,
case_var = NULL,
mu = NULL
)group variable (column name) to specify where to split two samples (two-sample t-test) or which variable to compare a one-sample t-test on
test variables (column names). Leave empty to compute t-tests for all numeric variables in data. Also leave empty for one-sample t-tests.
this parameter is deprecated (previously: 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).
a logical indicating whether you want a paired t-test. Defaults
to FALSE.
a logical indicating whether to use the pooled standard
deviation in the calculation of Cohen's d. Defaults to TRUE.
optional: a vector of length two specifying the two levels of the group variable.
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.
optional: a number indicating the true value of the mean in the
general population (\(\mu\)). If set, a one-sample t-test (i.e., a
location test) is being calculated. Leave to NULL to calculate
two-sample t-test(s).
a tdcmm model
WoJ %>% t_test(temp_contract, autonomy_selection, autonomy_emphasis)
#> # A tibble: 2 × 12
#> Variable M_Permanent SD_Permanent M_Temporary SD_Temporary Delta_M t df
#> * <chr> <num:.3!> <num:.3!> <num:.3!> <num:.3!> <num:.> <num> <dbl>
#> 1 autonom… 3.910 0.755 3.698 0.932 0.212 1.627 56
#> 2 autonom… 4.124 0.768 3.887 0.870 0.237 2.171 995
#> # ℹ 4 more variables: p <num:.3!>, d <num:.3!>, Levene_p <dbl>, var_equal <chr>
WoJ %>% t_test(temp_contract)
#> # A tibble: 11 × 12
#> Variable M_Permanent SD_Permanent M_Temporary SD_Temporary Delta_M t
#> * <chr> <num:.3!> <num:.3!> <num:.3!> <num:.3!> <num:.> <num:>
#> 1 autonomy_se… 3.910 0.755 3.698 0.932 0.212 1.627
#> 2 autonomy_em… 4.124 0.768 3.887 0.870 0.237 2.171
#> 3 ethics_1 1.568 0.850 1.981 0.990 -0.414 -3.415
#> 4 ethics_2 3.241 1.263 3.509 1.234 -0.269 -1.510
#> 5 ethics_3 2.369 1.121 2.283 0.928 0.086 0.549
#> 6 ethics_4 2.534 1.239 2.566 1.217 -0.032 -0.185
#> 7 work_experi… 17.707 10.540 11.283 11.821 6.424 4.288
#> 8 trust_parli… 3.073 0.797 3.019 0.772 0.054 0.480
#> 9 trust_gover… 2.870 0.847 2.642 0.811 0.229 1.918
#> 10 trust_parti… 2.430 0.724 2.358 0.736 0.072 0.703
#> 11 trust_polit… 2.533 0.707 2.396 0.689 0.136 1.369
#> # ℹ 5 more variables: df <dbl>, p <num:.3!>, d <num:.3!>, Levene_p <dbl>,
#> # var_equal <chr>
WoJ %>% t_test(employment, autonomy_selection, autonomy_emphasis,
levels = c("Full-time", "Freelancer"))
#> # A tibble: 2 × 12
#> Variable `M_Full-time` `SD_Full-time` M_Freelancer SD_Freelancer Delta_M t
#> * <chr> <num:.3!> <num:.3!> <num:.3!> <num:.3!> <num:.> <num>
#> 1 autonom… 3.903 0.782 3.765 0.993 0.139 1.724
#> 2 autonom… 4.118 0.781 3.901 0.852 0.217 3.287
#> # ℹ 5 more variables: df <dbl>, p <num:.3!>, d <num:.3!>, Levene_p <dbl>,
#> # var_equal <chr>
WoJ %>% t_test(autonomy_selection, mu = 3.62)
#> # A tibble: 1 × 9
#> Variable M SD CI_95_LL CI_95_UL Mu t df p
#> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 autonomy_selection 3.88 0.803 3.83 3.92 3.62 11.0 1196 6.10e-27