Code
estimate
Output
Analysis of raw data:
Data frame = data
Outcome variable(s) = transcription
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 transcription 8.811765 7.154642 10.46889 8.6 6.5 11.1
sd min max q1 q3 n missing df mean_SE median_SE
1 4.749339 1 20.1 5.2 11.275 34 0 33 0.8145049 1.021201
-- es_mean --
outcome_variable_name effect effect_size LL UL SE
1 transcription transcription 8.811765 7.154642 10.46889 0.8145049
df ta_LL ta_UL
1 33 7.746606 9.876923
-- es_median --
outcome_variable_name effect effect_size LL UL SE df ta_LL
1 transcription transcription 8.6 6.5 11.1 1.021201 33 7.1
ta_UL
1 10.8
-- es_mean_difference --
outcome_variable_name effect effect_size LL
1 transcription transcription 8.811765 7.154642
2 transcription Reference value 10.000000 NA
3 transcription transcription ‒ Reference value -1.188235 -2.845358
UL SE df ta_LL ta_UL type
1 10.4688874 0.8145049 33 7.746606 9.876923 Comparison
2 NA NA NA 7.746606 9.876923 Reference
3 0.4688874 0.8145049 33 -2.253394 -0.123077 Difference
-- es_median_difference --
outcome_variable_name effect effect_size LL UL
1 transcription transcription 8.6 6.5 11.1
2 transcription Reference value 10.0 NA NA
3 transcription transcription ‒ Reference value -1.4 -3.5 1.1
SE df ta_LL ta_UL type
1 1.021201 33 7.1 10.8 Comparison
2 NA NA NA NA Reference
3 1.021201 33 -2.9 0.8 Difference
-- es_smd --
outcome_variable_name effect effect_size LL
1 transcription transcription ‒ Reference value -0.2444527 -0.5838873
UL numerator denominator SE df d_biased
1 0.09856549 -1.188235 4.749339 0.1740983 33 -0.2501896
This standardized mean difference is called d_1 because the standardizer used was s. d_1 has been corrected for bias. Correction for bias can be important when df < 50. See the rightmost column for the biased value.
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Code
estimate
Output
Analysis of raw data:
Outcome variable(s) = My outcome variable
---Overview---
outcome_variable_name mean mean_LL mean_UL sd n df mean_SE
1 My outcome variable 12.09 11.01117 13.16883 5.52 103 102 0.5439018
-- es_mean --
outcome_variable_name effect effect_size LL UL
1 My outcome variable My outcome variable 12.09 11.01117 13.16883
SE df ta_LL ta_UL
1 0.5439018 102 11.38842 12.79158
-- es_mean_difference --
outcome_variable_name effect effect_size
1 My outcome variable My outcome variable 12.09
2 My outcome variable Reference value 0.00
3 My outcome variable My outcome variable ‒ Reference value 12.09
LL UL SE df ta_LL ta_UL type
1 11.01117 13.16883 0.5439018 102 11.38842 12.79158 Comparison
2 NA NA NA NA 11.38842 12.79158 Reference
3 11.01117 13.16883 0.5439018 102 11.38842 12.79158 Difference
-- es_smd --
outcome_variable_name effect effect_size
1 My outcome variable My outcome variable ‒ Reference value 2.174067
LL UL numerator denominator SE df d_biased
1 1.817286 2.527352 12.09 5.52 0.181166 102 2.190217
This standardized mean difference is called d_1 because the standardizer used was s. d_1 has been corrected for bias. Correction for bias can be important when df < 50. See the rightmost column for the biased value.
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.