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Nonparametric Statistics for Health Care Research: Statistics for Small Samples and Unusual Distributions最新文献

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Repeated Measures for More Than Two Time Periods or Matched Conditions 两个以上时间段或匹配条件的重复测量
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引用次数: 0
Tests for Two Independent Samples 两个独立样本的检验
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引用次数: 1
Overview of Nonparametric Statistics 非参数统计概述
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引用次数: 0
Epilogue 后记
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引用次数: 0
Assessing Differences Among Several Independent Groups 评估几个独立群体之间的差异
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引用次数: 1
Evaluating the Characteristics of Data 评估数据的特征
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引用次数: 1
“Goodness-of-Fit” Tests “拟合优度”测试
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引用次数: 0
The Process of Statistical Hypothesis Testing 统计假设检验的过程
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引用次数: 1
Tests for Two Related Samples: Pretest-Posttest Measures for a Single Sample 两个相关样本的测试:单个样本的前测后测措施
In Chapter 4, we examined nonparametric tests that could be used to determine the extent to which a single sample is similar to a hypothesized theoretical sample. In health care research, we frequently try to assess whether a particular intervention is effective with a certain population. In our hypothetical example from Chapters 1 and 3, we were interested in evaluating the effects of a staff-initiated intervention to reduce the number of sleep environment interruptions for hospitalized pediatric cancer patients on the frequency of their nocturnal awakenings, levels of fatigue, and distress compared to a usualcare group. In this study, there may be certain characteristics of the sample, such as type of cancer, age, or gender of the child, that are known from prior research to confound and potentially misrepresent the outcomes of the intervention. Two approaches that can be used to address this problem prior to the intervention are the following: (1) matching the subjects with regard to these extraneous confounding variables and then randomly assigning one of the pairs to the control and the other to the intervention group and (2) using each subject as his or her own control. Data being analyzed then become paired, either through the use of related samples or through repeated observations on a single sample. A third occurrence of matched pairs can occur when the researcher has sampled observations in pairs (e.g., husband and wife). Although each member of the pair may have separate scores on a dependent variable (e.g., marital satisfaction), there is reason to believe that knowing the scores of one member of the pair (e.g., wife’s marital satisfaction) will give information about the scores of the other member (husband’s marital satisfaction).
在第4章中,我们检查了非参数检验,可用于确定单个样本与假设理论样本相似的程度。在医疗保健研究中,我们经常试图评估一种特定的干预措施对特定人群是否有效。在我们第1章和第3章的假设示例中,我们感兴趣的是评估工作人员发起的干预措施的效果,以减少住院儿科癌症患者睡眠环境中断的次数,与常规护理组相比,他们夜间醒来的频率、疲劳程度和痛苦程度。在本研究中,可能存在样本的某些特征,如癌症类型、年龄或儿童的性别,这些从先前的研究中已知的特征会混淆并可能歪曲干预的结果。在干预之前,可以使用以下两种方法来解决这个问题:(1)将这些无关的混淆变量与受试者进行匹配,然后随机将其中一对分配给对照组,另一对分配给干预组;(2)将每个受试者作为他或她自己的对照组。然后,通过使用相关样本或通过对单个样本的重复观察,对分析的数据进行配对。当研究人员成对地(例如,丈夫和妻子)取样观察时,可能会出现配对的第三种情况。虽然这对夫妇中的每一个成员在因变量(例如,婚姻满意度)上可能有单独的分数,但有理由相信,知道这对夫妇中的一个成员的分数(例如,妻子的婚姻满意度)将提供关于另一个成员的分数(丈夫的婚姻满意度)的信息。
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引用次数: 0
Tests of Association Between Variables 变量间关联检验
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引用次数: 0
期刊
Nonparametric Statistics for Health Care Research: Statistics for Small Samples and Unusual Distributions
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