The impact of ignoring random features of predictor and moderator variables on sample size for precise interval estimation of interaction effects |
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Authors: | Gwowen Shieh |
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Institution: | Department of Management Science, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan, 30050, Republic of China. gwshieh@mail.nctu.edu.tw |
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Abstract: | The influence of the joint distribution of predictor and moderator variables on the identification of interactions has been
well described, but the impact on sample size determinations has received rather limited attention within the framework of
moderated multiple regression (MMR). This article investigates the deficiency in sample size determinations for precise interval
estimation of interaction effects that can result from ignoring the stochastic nature of continuous predictor and moderator
variables in MMR. The primary finding of our examinations is that failure to accommodate the distributional properties of
regressors can lead to underestimation of the necessary sample size and distortion of the desired interval precision. In order
to take account of the randomness of regressor variables, two general and effective procedures for computing sample size estimates
are presented. Moreover, corresponding programs are provided to facilitate use of the suggested approaches. This exposition
helps to correct drawbacks in the existing techniques and to advance the practice of reporting confidence intervals in MMR
analyses. |
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