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Single versus multiple sets for strength: a meta-analysis to address the controversy.

Article Excerpt
Key words: resistance training, weight training

Weight training can be used as a stressor to overload the neuromuscular system and develop strength. A number of weight training variables can be manipulated to overload this system. The main variables include volume, intensity, and the amount of rest allowed between sets or workouts. Of these variables, volume has received much interest among researchers and professionals, and considerable debate has arisen (Byrd, 1999) regarding an increased strength benefit of multiple-set programs.

The debate over the amount of volume needed to elicit maximal strength gains has continued in recent years with several narrative reviews (Carpinelli & Otto, 1999; Feigenbaum & Pollock, 1999) of research literature comparing single and multiple sets of training. These reviews have determined that single-set training programs elicit similar strength increases or health benefits (especially in untrained individuals) compared to multiple-set programs due to the inability of most of these studies to identify a statistical difference at the .05 level.

The reliance on probability values (p) places considerable importance on statistical power. If statistical power is low, the possibility of committing a Type II error (failing to reject the Null hypothesis despite a true difference existing) is increased. With this in mind, a power analysis (Cohen, 1988) was performed on a random sample of 10 studies from the literature comparing single and multiple sets. The mean power was calculated to be 0.56. Cohen (1988) reported that a power below 0.80 would incur too great a risk of a Type II error. Statistical significance is also heavily affected by large variance and small sample sizes. This can result in large differences between groups being deemed nonsignificant or in small differences reaching statistical significance solely based on sample size (and statistical power).

The body of research comparing single and multiple sets for maximal strength gains contains many studies performed with small sample sizes and low power; therefore, reliance solely on p values may be misleading. Completing a meta-analysis could be a valuable asset in this situation. This procedure, popularized by Glass (1982), combines the results of independent studies, sums sample sizes across studies, and increases statistical power. It involves calculating an effect size (ES), which represents the magnitude, in standard deviation...

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