Quantitative research produces more precise (numerical) measures, but not necessarily more accurate ones. Reliability—different measurements of the same phenomenon yield the same results—is sometimes purchased at the expense of validity—the measurements reflect what the investigator is trying to measure. Qualitative researchers try to achieve accurate measures, but they generally have somewhat less precision.
注意reliability （信度）和 validity （效度）
测量的标准：人为的，需要研究者格外小心
Qualitative and quantitative measurements are similar in another way. For each, the categories or measures used are usually artifacts created by the investigator and are not “given” in nature.
所以：
But both kinds of researchers should provide estimates of the uncertainty of their inferences. Quantitative researchers should provide standard errors along with their numerical measurements; qualitative researchers should offer uncertainty estimates in the form of carefully worded judgments about their observations.
测量方式（nominal, ordinal, and interval）和研究问题息息相关。
Systematic Measurement Error
Qualitative data banks having standard categories may be constructed on the basis of shared expertise and discussion. They can then be used for evaluating hypotheses. If you are the first person to use a set of variables, it is helpful to let other informed people code
your variables without knowing your theory of the relationship you wish to evaluate. Show them your field notes and taped interviews, and see if their conclusions about measures are the same as yours.
Since replicability in coding increases confidence in qualitative variables, the more highly qualified observers who cross-check your measures, the better.
Nonsystematic Measurement Error
定义：
nonsystematic, or random, measurement error as having values that are sometimes too high and sometimes too low, but correct on average
dependent variable：
or even generally distinguishable from the usual random error present in the world as reflected in the dependent variable.Indeed, random measurement error in a dependent variable is not differentIndeed, random measurement error in a dependent variable is not different or even generally distinguishable from the usual random error present in the world as reflected in the dependent variable.
但是independent variable 的非系统测量错误会导致研究者低估因果关系
（2）EXCLUDING RELEVANT VARIABLES: BIAS
遗漏变量但不导致偏差的两种特例：
1、 irrelevant omitted variables cause no bias
2、The second special case, which also produces no bias, occurs when the omitted variable is uncorrelated with the included explanatory variable.
那该怎么办呢？根据理论模型考虑其他影响被解释变量的因素
Choosing when to add additional explanatory variables to our analysis is by no means simple. The number of additional variables is always unlimited, our resources are limited, and, above all, the more explanatory variables we include, the less leverage we have for estimating any of the individual causal effects. Avoiding omitted variable bias is one reason to add additional explanatory variables. If relevant variables are omitted, our ability to estimate causal inferences correctly is limited.
（3）INCLUDING IRRELEVANT VARIABLES: INEFFICIENCY
The inclusion of irrelevant variables can be very costly. Our key point is that even if the control variable has no causal effect on the dependent variable, the more correlated the main explanatory variable is with the irrelevant control variable, the less efficient is the estimate of the main causal effect
（4）ENDOGENEITY（内生性）
梦幻联动（不是）：
David Laitin outlines an example of an endogeneity problem in one of the classics of early twentieth century social science, Max Weber’s The Protestant Ethic and the Spirit of Capitalism. “Weber attempted todemonstrate that a specific type of economic behavior—the capitalist spirit — was (inadvertently) induced by Protestant teachings and doctrines. But . . . Weber and his followers could not answer one objection that was raised to their thesis: namely that the Europeans who already had an interest in breaking the bonds of precapitalist spirit might well have left the church precisely for that purpose. In other words, the
economic interests of certain groups could be seen as inducing the development of the Protestant ethic. Without a better controlled study, Weber’s line of causation could be turned the other way.” (Laitin
1986:187; see also R. H. Tawney 1935 who originated the criticism).
解决方案：
1、Parsing the Dependent Variable
将被解释变量中真正的被解释部分识别出来
2、Transforming Endogeneity into an Omitted Variable Problem
对遗漏变量进一步测量、控制
3、Selecting Observations to Avoid Endogeneity
This example illustrates how we can first translate a general concern about endogeneity into specific potential sources of omitted variable bias and then search for a subset of observations in which these
sources of bias could not apply.
4、Parsing the Explanatory Variable
将研究重点集中在外生上
（5）ASSIGNING VALUES OF THE EXPLANATORY VARIABLE
这里讲的比较简略，大样本和小样本存在区别
（6）CONTROLLING THE RESEARCH SITUATION
政治学的严格控制变量在现实中往往不可能，国家和国家之间不可能完全对应
Matching requires that we anticipate and specify what the possible relevant omitted variables might be. We then control by selecting observations that do not vary on them. Of course, we never know that we have covered the entire list of potential biasing factors. But for certain analytical purposes—and the evaluation of the adequacy of a matching selection procedure must be done in relation to some analytic purpose—the control produced by matching improves the likelihood of obtaining valid inferences.
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