Independent samples or paired samples t-test

Runs a t-test and prints formatted group descriptives and test results. By default, runs the traditional Student's independent samples t-test assuming equal variances. Optional parameters provide Welch's correction, paired samples, effect size (Cohen's d), Levene's test, and confidence interval for the mean difference. Handles haven-labelled, numeric, and factor grouping variables. For haven-labelled variables, numeric codes are displayed alongside labels in the group descriptives table.

Usage

jt(
  formula,
  data,
  paired = FALSE,
  welch = FALSE,
  effect.size = NULL,
  levene = NULL,
  ci = NULL,
  subset = NULL,
  variable.id = NULL,
  value.id = NULL,
  case.processing.detail = NULL,
  full = FALSE,
  digits = NULL
)

Arguments

formula: A formula of the form DV ~ Group.
data: A data frame containing variables referenced in formula.
paired: Logical. If TRUE, runs a paired samples t-test. The two groups must have equal sample sizes. Default is FALSE.
welch: Logical. If FALSE (default), runs Student's t-test (equal variances assumed). If TRUE, runs Welch's t-test. Ignored when paired = TRUE.
effect.size: Logical or NULL. If TRUE, prints Cohen's d. If NULL (default), defers to joutput() session setting.
levene: Logical or NULL. If TRUE, prints Levene's test for homogeneity of variance. Ignored when paired = TRUE. If NULL (default), defers to joutput().
ci: Logical or NULL. If TRUE, adds 95% confidence interval for the mean difference. If NULL (default), defers to joutput().
subset: An optional unquoted logical expression (e.g. Group == 1) to subset cases for this call only. Applied after jcomplete and jsubset. Does not affect other function calls.
variable.id: Character or NULL. Variable label display mode: one of "both", "names", "labels", "legend", or "legend.bottom". "names" shows variable names only; "both" shows "name: label"; "labels" shows the DV and grouping-variable labels in the table captions (group levels follow the value.id mode) – best for short labels; "legend"/"legend.bottom" keep names and print a label legend after the output. NULL (default) defers to joutput()'s variable.id setting. Not a logical.
value.id: Character or NULL. Value-label display mode for the group descriptives rows: "both" ("code: label"), "values" (bare code), or "labels" (the label, degrading to the bare code where a code has none). "legend" and "legend.bottom" keep the bare code in the table and print a value-label legend after it ("legend" per-table, "legend.bottom" consolidated where multiple tables are produced). A no-op for grouping variables with no value labels. NULL (default) defers to joutput()'s value.id setting. Not a logical.
case.processing.detail: Per-call override of the Case Processing Summary detail tier: one of "none", "totals", or "per_code". NULL (default) uses the active joutput() level default.
full: Logical. If TRUE, turns on effect.size, levene, and ci all at once. Does not override explicit FALSE values.
digits: Integer or NULL. Number of decimal places for continuous statistics in the output tables (range 0-7; digits = 0 prints whole numbers with no trailing decimal point). Does not affect p-values, percentages, or integer quantities (counts, N, degrees of freedom), which keep their own fixed conventions. NULL (default) defers to joutput()'s digits setting (default 3).

Value

Invisibly returns a list of class jst_ttest containing: model (the t.test result), model_frame (the analysis data frame used for plotting), test_type, formula, descriptives, t, df, p, mean_difference, ci (95% CI), cohens_d, d_label, n, and sample_info (pipeline and missing data counts).

Details

A red title identifying the test type is printed first, followed by variable labels (if present), then the results tables.

Examples

# With explicit data frame
jt(WellbeingScore ~ Volunteer, data = community)
#> Independent Samples T-Test
#> Group Descriptives: WellbeingScore by Volunteer
#> Group    N    Mean      SD
#> ------  --  ------  ------
#> 0: No   58  46.431  10.318
#> 1: Yes  42  56.357  10.385
#> 
#> Independent Samples T-Test Results (equal variances assumed)
#>      t  df  p      Mean Difference  95% CI Lower  95% CI Upper
#> ------  --  -----  ---------------  ------------  ------------
#> -4.735  98  <.001           -9.926       -14.086        -5.766
#> 
#> Cohen's d: -0.959
#> 
jt(WellbeingScore ~ Volunteer, data = community, welch = TRUE)
#> Welch's Independent Samples T-Test
#> Group Descriptives: WellbeingScore by Volunteer
#> Group    N    Mean      SD
#> ------  --  ------  ------
#> 0: No   58  46.431  10.318
#> 1: Yes  42  56.357  10.385
#> 
#> Welch's T-Test Results (equal variances not assumed)
#>     t    df  p      Mean Difference  95% CI Lower  95% CI Upper
#> -----  ----  -----  ---------------  ------------  ------------
#> -4.73  88.2  <.001           -9.926       -14.096        -5.756
#> 
#> Cohen's d: -0.959
#> 
jt(WellbeingScore ~ Volunteer, data = community, full = TRUE)
#> Independent Samples T-Test
#> Levene's Test for Homogeneity of Variance
#> F  df1  df2  p   
#> -  ---  ---  ----
#> 0    1   98  .996
#> 
#> Group Descriptives: WellbeingScore by Volunteer
#> Group    N    Mean      SD
#> ------  --  ------  ------
#> 0: No   58  46.431  10.318
#> 1: Yes  42  56.357  10.385
#> 
#> Independent Samples T-Test Results (equal variances assumed)
#>      t  df  p      Mean Difference  95% CI Lower  95% CI Upper
#> ------  --  -----  ---------------  ------------  ------------
#> -4.735  98  <.001           -9.926       -14.086        -5.766
#> 
#> Cohen's d: -0.959
#> 

# Using juse() default
juse(community)
#> Default data frame set to: community
jt(WellbeingScore ~ Volunteer)
#> Independent Samples T-Test
#> Using default data frame: community
#> Group Descriptives: WellbeingScore by Volunteer
#> Group    N    Mean      SD
#> ------  --  ------  ------
#> 0: No   58  46.431  10.318
#> 1: Yes  42  56.357  10.385
#> 
#> Independent Samples T-Test Results (equal variances assumed)
#>      t  df  p      Mean Difference  95% CI Lower  95% CI Upper
#> ------  --  -----  ---------------  ------------  ------------
#> -4.735  98  <.001           -9.926       -14.086        -5.766
#> 
#> Cohen's d: -0.959
#> 
jt(WellbeingScore ~ Volunteer, full = TRUE)
#> Independent Samples T-Test
#> Using default data frame: community
#> Levene's Test for Homogeneity of Variance
#> F  df1  df2  p   
#> -  ---  ---  ----
#> 0    1   98  .996
#> 
#> Group Descriptives: WellbeingScore by Volunteer
#> Group    N    Mean      SD
#> ------  --  ------  ------
#> 0: No   58  46.431  10.318
#> 1: Yes  42  56.357  10.385
#> 
#> Independent Samples T-Test Results (equal variances assumed)
#>      t  df  p      Mean Difference  95% CI Lower  95% CI Upper
#> ------  --  -----  ---------------  ------------  ------------
#> -4.735  98  <.001           -9.926       -14.086        -5.766
#> 
#> Cohen's d: -0.959
#>

Independent samples or paired samples t-test

Usage

Arguments

Value

Details

See also

Examples