ABSTRACT Although some research in confirmatory factor analysis has suggested that more indicators per factor are generally better, studies have also documented that sample size requirements increase as model size increases. The present study used Monte Carlo simulation to investigate the effect of indicators per factor on sample size requirements. Results demonstrated a nonlinear association between the number of indicators per factor and the minimum required sample size while avoiding six important consequences for the analysis, such as bias in the model chi-square statistic. There is an upper limit for the desirable number of indicators per factor, and this upper limit depends on the number of factors and factor determinacy. The results showed clear patterns for the specific consequences that were most likely with too few or too many indicators per factor and inadequate sample size. Implications for further research are discussed.

中文翻译：

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确认性因素分析中的每个因素指标：更多并不总是更好

摘要 尽管验证性因子分析中的一些研究表明每个因子的指标越多通常越好，但研究也证明样本量要求随着模型大小的增加而增加。本研究使用蒙特卡罗模拟来研究每个因素的指标对样本量要求的影响。结果表明，每个因素的指标数量与所需的最小样本量之间存在非线性关联，同时避免了分析的六个重要后果，例如模型卡方统计量中的偏差。每个因素的理想指标数量有一个上限，这个上限取决于因素的数量和因素的确定性。结果显示了特定后果的清晰模式，这些后果最有可能是每个因素的指标太少或太多以及样本量不足。讨论了对进一步研究的影响。