概化理论预算限制下最佳样本量估计

Estimating the Best Sample Size under Budget Constraints for Generalizability Theory

概化理论广泛应用于各种心理测评实践中。当有预算限制时,概化理论需要考虑如何设计一个测量可靠性相对较高且可行性也相对较强的测量程序,这就要求通过某些途径估计最佳样本量。拉格朗日乘法是概化理论预算限制下最佳样本量估计较为成熟的方法。探讨了概化理论预算限制下最佳样本量估计的一些影响因素,如受总预算舍入的影响等,也提出了一些后续改善的建议,如推导出拉格朗日乘法的统一公式等。

Generalization theory is widely used in various psychological evaluation practices.Budget and cost are the problems that cannot be neglected in the research of measurement.When there is a budget constraint,the generalization theory needs to consider how to design a measurement program with relatively high reliability and feasibility,which requires the optimal sample size to be estimated by some means.Lagrange multiplication is a more mature method for estimating the optimal sample size under the budget constraints in generalization theory.Some influencing factors of optimal sample size estimation under the budget constraints in generalization theory,such as the influence of total budget rounding,are discussed,and some follow-up solutions are also proposed,such as deducing the unified formula of Lagrange multiplication.