我試圖批評研究論文,我不記得了 如何計算統計資料.几年前.樣本量為40.我也想讨論這个假設; 我不確定它是否正確。 我要寫出摘要:

This quasi-experimental and cross-sectional study was carried out to determine the efficacy of back massage, a nursing intervention, on the process of acute fatigue developing due to chemotherapy and on the anxiety level emerging in cancer patients receiving chemotherapy during this process. The study was conducted on 40 patients. To collect data, ... [reliability and validity seemed adequate].

In our study it was determined that mean anxiety scores decreased in the intervention group patients after chemotherapy. The level of fatigue in the intervention group decreased statistically significantly on the next day after chemotherapy (p=.020; effect size=0.84). At the same time, the mean anxiety scores of the patients in the intervention group decreased right after the massage was provided during chemotherapy (p=.109; effect size=0.37) and after chemotherapy.

In line with our study findings, it can be said that back massage given during chemotherapy affects anxiety and fatigue suffered during the chemotherapy process and that it significantly reduces state anxiety and acute fatigue.

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  • 2019-12-5
    1 #

    展望未来,似乎$ n = 40 $的樣本大小可能就足够了 在這項研究中,但没有更具體的資訊,這是不可能的 說四个肯定。

    在測試$ H_0時,無法进行功率計算来檢測二項式$ p $的變化: p = p_0 $$ H_a:p< p_0 $不知道具體的空值$ p_0 $以及效果的大小 被發現.

    在焦虑測試中你提到一个人可能会想到 研究人員希望檢測到$ 0.35,$的减少,但是對於精確的功率 計算,我们還需要知道$ p_0 $

    如果$ p_0 = .5 $且要檢測的目標值是$ 0.5 - 0.35 = 0.15,$< /跨度> 那麼為了在5%的測試中获得功率$ 0.95 $(檢測到變化的概率),那麼所需的樣本大小是$ n = 17,$,如下面的Minitab輸出所示.所以$ n = 40 $主题應足以檢測到$ 0.35。$

    Power and Sample Size 
    Test for One Proportion
    Testing p = 0.5 (versus < 0.5)
    α = 0.05
    
                  Sample  Target
    Comparison p    Size   Power  Actual Power
            0.15      14    0.90      0.913764
            0.15      17    0.95      0.958912
    

    在另一種情况下,如果$ p_0 = 0.8,n = 40,$$ \ alpha = 0.05,$以下輸出顯示目標值低於約 $ p = 0.6 $將被檢測到功率$ 0.9。$

    Power and Sample Size 
    Test for One Proportion
    Testing p = 0.8 (versus < 0.8)
    α = 0.05
    Sample
      Size  Power  Comparison p
        40   0.90      0.596562
        40   0.95      0.567110
    

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