Frederick J Gravetter，Larry B. WaLLnau《Statistics for the Behavioral Sciences 10e》

一， Statistics, Science, and Observations

Specifcally, statistics serve two general purposes:

1. Statistics are used to organize and summarize the information so that the researcher can see what happened in the research study and can communicate the results to others.2. Statistics help the researcher to answer the questions that initiated the research by determining exactly what general conclusions are justifed based on the specifc results that were obtained.

总体是在一个特定研究中所有感兴趣个体的集合。

样本是一个总体中选择出来的个体的集合，通常在研究中被期望代表总体。

变量是一种针对不同个体具有不同值的特性或条件。

描述性统计是用于总结、组织简化数据的统计过程。

The second general category of statistical techniques is called inferential statistics. Inferential statistics are methods that use sample data to make general statements about a population.

error, and it creates the fundamental problem inferential statistics must always address.

The dependent variable is the one that is observed to assess the effect of the treatment.

measurements

explain behavior. For example, we say that a student does well in school because he or she isintelligent. Or we say that someone isanxiousin social situations, or that someone seems to behungry. Variables like intelligence, anxiety, and hunger are calledconstructs, and because they are intangible and cannot be directly observed, they are often called hypothetical constructs. Although constructs such as intelligence are internal characteristics that cannot be directly observed, it is possible to observe and measure behaviors that are representative of the construct. For example, we cannot “see” intelligence but we can see examples of intelligent behavior. The external behaviors can then be used to create an operational defnition for the construct. Anoperational defnitiondefines a construct in terms of externalbehaviors that can be observed and measured. For example, your intelligence is measured

and defned by your performance on an IQ test, or hunger can be measured and defned by the number of hours since last eating.

2. When measuring a continuous variable, each measurement category is actually an intervalthat must be defned by boundaries. For example, two people who both claim to weigh 150 pounds are probably notexactlythe same weight. However, they are both around 150 pounds. One person may actually weigh 149.6 and the other 150.3. Thus, a score of 150 is not a specifc point on the scale but instead is an interval (see Figure 1.7). To differentiate a score of 150 from a score of 149 or 151, we must set up boundaries on the scale of measurement. These boundaries are calledreal limitsand are positioned exactly halfway between adjacent scores. Thus, a score ofX= 150 pounds is actually an interval bounded by alower real limit of 149.5 at the bottom and anupper real limit of 150.5 at the top. Any individual whose weight falls between these real limits will be assigned a score ofX= 150.

A nominal scale consists of a set of categories that have different names. Measurements on a nominal scale label and categorize observations, but do not make any quantitative distinctions between observations.

An ordinal scale consists of a set of categories that are organized in an ordered sequence. Measurements on an ordinal scale rank observations in terms of size or magnitude.

An interval scale consists of ordered categories that are all intervals of exactly the same size. Equal differences between numbers on scale reﬂect equal differences in magnitude. However, the zero point on an interval scale is arbitrary and does notindicate a zero amount of the variable being measured.

A ratio scale is an interval scale with the additional feature of an absolute zero point. With a ratio scale, ratios of numbers do reﬂect ratios of magnitude.

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