http://www.socscistatistics.com/effectsize/Default3.aspx


http://www.socscistatistics.com/effectsize/Default3.aspx Algorithm: SD=Standard error * sqrt(N);
Reference: http://handbook.cochrane.org/chapter_7/7_7_3_2_obtaining_standard_deviations_from_standard_errors_and.htm
QC: I checked the algorithm using SAS. The result was consistent with the algorithm (i.e., SD=standard error*sqrt(N)). proc means data=sashelp.class mean std stderr n; var height; run; ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Mean 62.3368421 SD 5.1270752 Stadard Error 1.1762317 N 19 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
http://stats.stackexchange.com/questions/156778/percentilevsquantilevsquartile This SAS code adjusts weights (e.g., sample weights) such that the sum of weights equals the sample size. Weight1 is the original weight and weight 2 is the result. proc sql; create table comp2 as select *, psweight * (count(weight1)/Sum(psweight1)) as weight2 from comp; run; http://www.tandfonline.com/doi/pdf/10.1080/00031305.2016.1154108 What works Clearinghouse considers an effect size of .25 as “substantively important” and interpreted as “qualified positive” even when the effect size is not statistically significant. See page 23. https://ies.ed.gov/ncee/wwc/Docs/referenceresources/wwc_procedures_v3_0_standards_handbook.pdf Mathematically, you only need the coefficient for the predictor to derive an odds ratio (you don't need the intercept value). oddsratio=exp(coefficient_size)
https://www.researchgate.net/post/Why_in_regression_analysis_does_the_inclusion_of_a_new_variable_makes_other_variables_that_previously_were_not_statistically_significant When describing statistical models and results in writing, the following are tricky issues and require decisions and standardized way of description (and they must be brief, intuitive, full of meaning): How do we choose omitted category/reference group? Why is there no level1 error term in logistic regression? Why use HLM? Why use logistic regression model? […] The UCLA site explains Cronbach's alpha as the average internal correlation among survey items. It also says that it is not a measure of unidimensionality. Rather, it is a measurement of internal consistency (though just intuitively I feel what is coherent tends to be also unidimensional… I think the point is that the measure is […] 

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