387
个编辑
更改
跳到导航
跳到搜索
第148行：
第148行： + + + + + + + + + + + + + + + +
→Advantage
# Stratified randomization can accurately reflect the outcomes of the general population since influential factors are applied to stratify the entire samples and balance the samples' vital characteristics among treatment groups. For instance, applying stratified randomization to make a sample of 100 from the population can guarantee the balance of males and females in each treatment group, while using simple randomization might result in only 20 males in one group and 80 males in another group.<ref name=":0" />
# Stratified randomization can accurately reflect the outcomes of the general population since influential factors are applied to stratify the entire samples and balance the samples' vital characteristics among treatment groups. For instance, applying stratified randomization to make a sample of 100 from the population can guarantee the balance of males and females in each treatment group, while using simple randomization might result in only 20 males in one group and 80 males in another group.<ref name=":0" />
# Stratified randomization makes a smaller error than other sampling methods such as [[cluster sampling]], simple random sampling, and [[systematic sampling]] or [http://dissertation.laerd.com/non-probability-sampling.php non-probability methods] since measurements within strata could be made to have a lower [[standard deviation]]. Randomizing divided strata are more manageable and cheaper in some cases than simply randomizing general samples.<ref name=":1" />
# Stratified randomization makes a smaller error than other sampling methods such as [[cluster sampling]], simple random sampling, and [[systematic sampling]] or [http://dissertation.laerd.com/non-probability-sampling.php non-probability methods] since measurements within strata could be made to have a lower [[standard deviation]]. Randomizing divided strata are more manageable and cheaper in some cases than simply randomizing general samples.<ref name=":1" />
优势
分层随机化的优点包括：
分层随机化可以准确反映一般人群的结果，因为应用影响因素对整个样本进行分层并平衡样本在治疗组之间的重要特征。例如，采用分层随机化从人群中抽取 100 名样本可以保证每个治疗组的男女平衡，而使用简单随机化可能会导致一组只有 20 名男性，而另一组有 80 名男性。 [7] ]
分层随机化比其他抽样方法（例如整群抽样、简单随机抽样和系统抽样或非概率方法）的误差更小，因为可以使分层内的测量具有较低的标准偏差。在某些情况下，将分割的分层随机化比简单地随机化一般样本更易于管理且成本更低。 [11]
由于分层随机化性质的精确性，团队更容易接受培训以对样本进行分层。 [7]
由于这种方法的统计准确性，研究人员可以通过分析较小的样本量来获得非常有用的结果。
这种抽样技术涵盖了广泛的人口，因为已经对地层划分进行了完整的充电。
有时需要分层随机化来估计人口中各组的人口参数。 [11]
坏处
分层随机化的限制包括：
分层随机化首先参考预后因素将样本分成若干层，但有可能无法划分样本。在应用中，在某些情况下，预后因素的重要性缺乏严格的认可，这可能进一步导致偏差。这就是为什么在将因素纳入分层之前应该检查因素产生影响的潜力的原因。在某些因素对结果的影响无法得到批准的情况下，建议进行无分层随机化。 [18]
如果可用数据不能代表整个亚组人口，则认为亚组大小具有相同的重要性。在某些应用程序中，子组大小是根据可用数据量来决定的，而不是将样本大小缩放到子组大小，这会在因子效应中引入偏差。在某些需要对数据进行方差分层的情况下，子组方差差异显着，使得每个子组的抽样规模无法保证与整个子组总体成正比。 [19]
如果人口不能完全分配到层中，则不能应用分层抽样，这将导致样本大小与可用样本成正比，而不是与总体子组人口成正比。 [7]
如果受试者符合多层次的纳入标准，则将样本分配到亚组的过程可能涉及重叠，这可能导致总体的错误陈述。 [19]
# It is easier for a team to be trained to stratify a sample because of the exactness of the nature of stratified randomization.<ref name=":0" />
# It is easier for a team to be trained to stratify a sample because of the exactness of the nature of stratified randomization.<ref name=":0" />
# Researchers can get highly useful results by analyzing smaller sample sizes because of statistical accuracy of this method.
# Researchers can get highly useful results by analyzing smaller sample sizes because of statistical accuracy of this method.
# This sampling technique covers a wide range of population since complete charge over the strata division has been made.
# This sampling technique covers a wide range of population since complete charge over the strata division has been made.
# Sometimes stratified randomization is desirable to have estimates of population parameters for groups within the population.<ref name=":1" />
# Sometimes stratified randomization is desirable to have estimates of population parameters for groups within the population.<ref name=":1" />