Some Practical Guidelines for Effective Sample Size Determination

By Russell V. Lenth. 2001

1,177 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bab3ae1bbfa438a6a31b67a239541b95ca2c425f

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In Statistics and Probability

Citations in the corpus

Un sistema de clasificación de los diseños de investigación en psicología (2013, background): “La justificación del tamaño muestral óptimo se ha tratado usualmente dentro del análisis de potencia y de la tradición del contraste de hipótesis (Bono y Arnau, 1995, Kraemer & Thieman, 1987, Lenth, 2001).”
Interpreting effect sizes when controlling for stability effects in longitudinal autoregressive models: Implications for psychological science (2015, background): “…that control for stability effects and cross-sectional effect size coefficients that control for confounds, but do not control for stability effects (see also Baguley, 2004, 2009, and Lenth, 2001, for additional arguments for avoiding the use of “canned” effect sizes when interpreting effects).”
Fertilization in a suite of coastal marine invertebrates from SE Australia is robust to near-future ocean warming and acidification (2010, background): “Although retrospective (post hoc) power analysis has been evoked to have confidence in such non-significant results (Havenhand and Schlegel 2009), this approach is statistically fraught (Underwood 1999; Hoenig and Heisey 2001; Lenth 2001 ).”
Information Processing, Knowledge Development, and Strategic Supply Chain Performance (2004, neutral): “One predictor variable had a power of .30 ( .05) in one equation; all other predictors had power above .80 in all equations (cf. Lenth, 2001 ).”
Use of the extreme groups approach: a critical reexamination and new recommendations. (2005, background): “For example, Lenth (2001) pointed out that it is all too easy for researchers to fall into the trap of inappropriately using a particular value of a standardized effect size measure (e.g., Cohen’s d 0.5) as a target in a priori power analysis or sample size determination.”
Sample Size Justification (2021, positive): “A common reason for the number of observations in a study is that resource constraints limit the amount of data that can be collected at a reasonable cost (Lenth, 2001).”
Selecting a sample size for studies with repeated measures (2013, background): “edu/~rlenth/Power/) provide power estimations for certain linear models such as t-tests, ANOVA, and linear regression studies [9].”
Sample size estimation for correlations with pre-specified confidence interval (2014, neutral): “Alternative sample size estimations based on statistical power analyses have been described by Descoteaux (2007), Lachin (1981) and Lenth (2001).”