行为科学统计学精要（第6版）第一章

6月 22, 2010

行为科学统计学精要（第6版）
Essentials of Statistics for the Behavioral Sciences
by Frederick J Gravetter ,Larry B. Wallnau
作　　者： （美）格雷维特，（美）活尔塔 著
翻译：xht20040317 提交日期：2010年5月21日

第一章 统计学介绍

1.2总体和样本

他们是什么？科学研究通常开始于一个关于特殊的群体（或者是多个群体）的个体一般的问题。例如，研究者可能对离异对青春期前的儿童自尊心的影响感兴趣。或者研究者想测试男性的政治态度并与女性的来比较。在第一个例子中，研究者感兴趣的是青春期前的儿童这个群体。在第二个例子中，研究者想要比较男性和女性这两个群体。在统计术语中，研究者想要研究的整个群体称为总体。

定义 总体是特定研究所感兴趣的所有个体的集合。

像你所想象的那样，一个总体可以非常大—例如，地球上的所有女性的集合。研究者可能会更加详细的限制研究的总体，只研究那些在美国已经注册为选举人的女性。也许研究者只想研究包含州首脑的那些女性总体。总体在规模上可以有很明显的变化，从很大到很小，取决于研究者如何定义这个总体。被研究的总体应该总是由研究者决定。另外，总体不只是需要包含人—它可以是老鼠、公司、工厂生产的部件或者是其他研究者所想要研究的事物的总体。在实践中，总体是非常大的，比如美国四年级儿童的总体，或者是小企业的总体。

尽管研究问题关心的是整个总体，但是通常情况下，研究者不可能测验感兴趣总体中每一个个体。因此，研究者从总体中典型地选择一个小的、更可控的群体并且将他们的研究限制在这个所选择的群体里的个体。在统计学术语中，从（一个）总体中选择的个体的（一个）集合称为样（一个）样本。样本是拟其总体的代表，样本应该总是取决于选择它的那个总体的情况。

定义 样本是从总体中选择的个体的集合，在研究中它通常拟定代表总体。

就像我们看待总体一样，样本在规模上也可以变化。例如，一个研究将测验一个学前项目中的10个儿童的样本，另一个研究可能要用到多于1000个注册选举人来代表一个主要城市的总体。

到目前为止，我们已经阐述了从总体中选择出来的样本。然而，这实际上只是样本和它的总体之间关系的一半内容。尤其是当研究者完成了样本测试以后，其目的是把结论推广到整个总体。要知道研究是开始于关于总体的一般问题。为了回答这个问题， 研究者研究样本，并且把从样本得到的结论推广到总体中去。样本与总体的全部联系如图1.1所示。

变量与数据 通常，研究者感兴趣的是在总体（或是样本）中的个体的特殊特征，或者是影响个体的外在因素。例如，研究者可能对天气对人们的心情的影响感兴趣。当天气变化时，人们的心情会随之改变吗？那些可以变化的或者有不同值的事物叫做变量。

定义 变量是一个特征或者条件，是不同个体的变化或者是有不同的值。

再次，变量是使一个个体不同于其他个体的特征的集合，例如高度，体重，性别，或者人格。此外，变量可以是变化的环境条件，例如温度，时间，或者是研究控制的房间的大小。

为了说明变量的变化，有必要在变量测验中进行测量。每个个体测量得到的叫做数据，或者更一般的叫做分数或原始分数。分数的完整集合叫做数据集合或者简单的叫做数据。

定义 数据是测量值或者是观测值。数据集合是测量值和观测值的集合。一个数据是一个单一的测量值或者是观测值，一般称之为分数或者原始分数。

在我们继续学习之前，我们应该再多总结一些样本、总体和数据的要点。之前，我们从个体的角度定义了总体和样本。例如，我们讨论了一个未成年儿童的总体和一个学前儿童的样本。然而，预先提示，我们也涉及到了分数的总体或者样本。因为典型的研究包含了测量每个个体以得到分数，每个个体的样本（或者总体）产生了相关样本（或总体）的分数。

Chapter 1 Introduction to Statistics

1.2 Populations and samples

WHAT ARE THEY？

Scientific research typically begins with a general question about a specific group (or groups) of individuals. For example, a researcher may be interested in the effect of divorce on the self-esteem of preteen children. Or a researcher may want to examine the political attitudes for men compared to women. In the first example, the researcher is interested in the group of preteen children. In the second example, the researcher wants to compare the group of men with the group of women. In statistical terminology, the entire group that a researcher wishes to study is called a population.

DEFINITION A population is the set of all the individuals of interest in a particular study.

As you can imagine, a population can be quite large—for example, the entire set of women on the plant Earth. A researcher might be more specific, limiting the population for study to women who are registered voters in the United States. Perhaps the investigator would like to study the population consisting of women who are heads of state. Populations can obviously vary in size from extremely large to very small, depending on how the investigator defines the population. The population being studied should always be indentified by the researcher. In addition, the population need not consist of people—it could be a population of rats, corporations, parts produced in a factory， or anything else an investigator want to study. In practice, populations are typically very large, such as the population of fourth-grade children in the United States or the population of small businesses.

Although research questions concern an entire population, it usually is impossible for a researcher to examine every individual in the population of interest. Therefore, the researchers typically select a smaller, more manageable group from the population and limit their studies to the individuals in the selected group. In statistical teams, a set of individuals selected from a population is called sample. A sample is intended to be representative of its population, and a sample should always be identified in terms of the population from which it was selected.

DEFINITION A sample is a set of individuals selected from a population, usually intended to represent the population in a research study.

Just as we saw with population, samples can very in size. For example, one study might examine a sample of only 10children in a preschool program, and another study might use a sample of more than 1000 registered voters representing the population of a major city.

So far we have talked about a sample being selected from a population. However, this is actually only half of the full relationship between a sample and its population. Specifically, when a researcher finishes examining the sample, the goal is to generalize the results back to the entire population. Remember that the research started with the general question about the population. To answer the question, a researcher studied a sample and then generalizes the results from the sample to the population. The full relationship between a sample and a population is shown in Figure 1.1.

VARAIABLES AND DATA Typically, researchers are interested in specific characteristics of the individuals in the population (or in the sample), or they are interested in outside factors that may influence the individuals. For example, a researcher may be interested in the influence of the weather on people’s moods. As the weather changes, do people’s moods also change? Something that can change or have different values is called a variable.

DEFINITION A variable is a characteristic or condition that changes or has different values for different individuals.

Once again, variables can be characteristics that differ from one individual to another, such as height, weight, gender, or personality. Also, variables can be environmental conditions that change such as temperature, time of day, or the size of the room in which the research is being conducted.

In order to demonstrate changes in variables, it is necessary to make measurements of the variables being examined. The measurement obtained for each individual is called datum, or more commonly, a score or raw score. The complete set of scores is called the data set or simply the data.

DEFINITIONS Data (plural) are measurements or observations. A data set is a collection of measurements or observations. A datum (singular) is a single measurement or observation and is commonly called a score or a raw score.

Before we move on, we should make one more point about samples, populations, and data. Earlier, we defined populations and samples and samples in terms of individuals. For example, we discussed a population of preteen children and a sample of preschool children. Be forewarned, however, that we will also refer to populations or samples of scores. Because research typically involves measuring each individual to obtain a score, every sample (or population) of individuals produces a corresponding samples (or population) of scores.

相关推荐：前言

弗雷德里克·格雷维特
拉里·沃尔诺

作者简介

作者弗雷德里克·格雷维特（Frederick J.Gravetter）是纽约州立大学（Brockport）心理系教授，教授统计学、认知心理学和实验设计，教龄近40年，教学经验丰富。另一作者拉里·沃尔诺（Larry B.Wallnau）是同一所大学的心理系教授，著述颇丰，其教学研究成果多次获奖。

内容简介

统计学为行为科学研究者提供了客观、系统地描述和解释其研究成果的方法，是相关专业的必修课。本书不仅教会学生掌握统计学的工具和方法，而且传授了科学研究应遵循的客观性和逻辑性原理，是这一领域极受欢迎的教科书。第6版保持了清晰、准确的语言风格，细致地讲解重要概念，分步呈现具体统计过程，辅以大量图表，精心设计例题、练习题，附录还提供统计学公式汇编等，更加有利于教学。

出版信息：

出 版 社： 北京大学出版社 出版时间： 2008-1-1 I S B N ： 9787301128978 定价：￥62.00
购买链接：培文书系.心理学影印系列—行为科学统计学精要（第6版）
原版推荐：Essentials of Statistics for the Behavioral Sciences

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