Code
estimate
Output
Analysis of summary data:
-- es_r --
x_variable_name y_variable_name effect effect_size
1 My x variable My y variable My x variable and My y variable 0.4
LL UL SE n df ta_LL ta_UL
1 0.03953651 0.6606395 0.1559841 30 28 0.09986615 0.6250829
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Code
estimate
Output
Analysis of raw data:
Data frame = data
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 ls_pre 11.58333 9.476776 13.68989 12.0 9 14
2 ls_post 13.25000 11.410019 15.08998 13.5 10 16
sd min max q1 q3 n missing df mean_SE median_SE
1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488
2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186
-- es_r --
x_variable_name y_variable_name effect effect_size LL
1 ls_pre ls_post ls_pre and ls_post 0.8923908 0.62894
UL SE n df ta_LL ta_UL
1 0.967157 0.06139936 12 10 0.6882891 0.9596343
-- regression --
component values LL UL
1 Intercept (a) 4.2212267 0.8856544 7.556799
2 Slope (b) 0.7794624 0.5017391 1.057186
[1] "Ŷ = 4.221 + 0.7795*X"
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Code
estimate
Output
Analysis of summary data:
-- es_r --
x_variable_name y_variable_name effect effect_size
1 My x variable My y variable My x variable and My y variable 0.91
LL UL SE n df ta_LL ta_UL
1 0.853283 0.9445006 0.01685618 105 103 0.8598598 0.9417717
Note: LL and UL are lower and upper boundaries of confidence intervals with 99% expected coverage.
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