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KonFound-it! ©: BASICS
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unstandardized User enters values in yellowOptional: Only for Multivariate Impact Threshold (unconditional correlations)
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estimated effect
standard errorsample size# of covariatesα (significance level)null hypothesisstd(y)std(x)R2
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-9.010.6876392210.050000
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For a 1-tailed test, double the size of α in cell E4
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Calculated Valuesname of predictor of interest:
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dfdefault t critical (2-tailed)t criticalx
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7417-1.960-1.960
To override cell C8, type in your own value
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The default sign of t critical is the same as the sign of the estimated effect.
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Publishable statements
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Replacement of CasesCorrelation Based (linear models only)
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To invalidate the inference 85%
The minimum impact to invalidate an inference for a null hypothesis of 0 effect is -.132 and is based on correlations
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of the estimated effect would have to be due to bias
of -.364 with x and of .364 with the outcome, conditional on covariates.
(signs are interchangeable)
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To invalidate the inference 85% (6509) of the cases would have
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to be replaced with cases for which there is an effect of zero.
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citationcitation
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Frank, K.A., Maroulis, S., Duong, M., and Kelcey, B. 2013.  
Frank, K. 2000. "Impact of a Confounding Variable on the Inference of a Regression Coefficient." Sociological Methods and Research, 29(2), 147-194
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What would it take to Change an Inference?: 
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Using Rubin’s Causal Model to Interpret the Robustness of Causal Inferences.   
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Education, Evaluation and Policy Analysis.  Vol 35: 437-460.
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See details of your sensitivity analysis on other tabs
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 ↓ ↓ ↓ ↓ ↓ ↓
spreadsheet for calculating indices [KonFound-it!]
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powerpoint with examples and calculations
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cite this spreadsheet: Frank, K.A. (2014). KonFound-it!. Available at https://www.msu.edu/~kenfrank/research.htm#causal
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