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.
Usage
t_test(
data,
group_var,
...,
var.equal = TRUE,
paired = FALSE,
pooled_sd = TRUE,
levels = NULL,
case_var = NULL,
mu = NULL
)Arguments
- data
- group_var
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.
- var.equal
this parameter is deprecated (previously: a logical variable indicating whether to treat the two variances as being equal. If
TRUEthen the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used. Defaults toTRUE).- 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.- mu
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
NULLto calculate two-sample t-test(s).
Value
a tdcmm model
Examples
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