Random and Systematic Error

(Reliability and Validity) Random and Systematic Error

The basic diffi culty in determining the effectiveness of a measured variable is that the measure will in all likelihood be infl uenced by other factors besides the conceptual variable of interest. For one thing, the measured variable will certainly contain some chance fl uctuations in measurement, known as random error. Sources of random error include misreading or misunderstanding of the questions, and measurement of the individuals on different days or in different places. Random error can also occur if the experimenter misprints the questions or misrecords the answers or if the individual marks the answers incorrectly. 

           Although random error infl uences scores on the measured variable, it does so in a way that is self-canceling. That is, although the experimenter may make some recording errors or the individuals may mark their answers incorrectly, these errors will increase the scores of some people and decrease the scores of other people. The increases and decreases will balance each other and thus cancel each other out. 

            In contrast to random error, which is self-canceling, the measured variable may also be infl uenced by other conceptual variables that are not part of the conceptual variable of interest. These other potential infl uences constitute systematic error because, whereas random errors tend to cancel out over time, these variables systematically increase or decrease the scores on the measured variable. For instance, individuals with higher self-esteem may score systematically lower on the anxiety measure than those with low self-esteem, and more optimistic individuals may score consistently higher. Also, as we have discussed in Chapter 4, the tendency to self-promote may lead some respondents to answer the items in ways that make them appear less anxious than they really are in order to please the experimenter or to feel better about themselves. In these cases, the measured variable will assess self-esteem, optimism, or the tendency to self-promote in addition to the conceptual variable of interest (anxiety). 

           Figure 5.1 summarizes the impact of random and systematic error on a measured variable. Although there is no foolproof way to determine whether measured variables are free from random and systematic error, there are techniques that allow us to get an idea about how well our measured variables “capture” the conceptual variables they are designed to assess rather than being influenced by random and systematic error. As we will see, this is accomplished through examination of the correlations among a set of measured variables.

Fundamentals of Measurement

(Measures) Fundamentals of Measurement

You will recall from Chapter 2 that the research hypothesis involves a prediction about the relationship between or among two or more variables—for instance, the relationship between self-esteem and college performance or between study time and memory. When stated in an abstract manner, the ideas that form the basis of a research hypothesis are known as conceptual variables. Behavioral scientists have been interested in such conceptual variables as self-esteem, parenting style, depression, and cognitive development.  

           Measurement involves turning conceptual variables into measured variables, which consist of numbers that represent the conceptual variables. 1 The measured variables are frequently referred to as measures of the conceptual variables. In some cases, the transformation from conceptual to measured variable is direct. For instance, the conceptual variable “study time” is straightforwardly represented as the measured variable “seconds of study.” But other conceptual variables can be assessed by many different measures. For instance, the conceptual variable “liking” could be assessed by a person rating, from one to ten, how much he or she likes another person. Alternatively, liking could be measured in terms of how often a person looks at or touches another person or the number of love letters that he or she writes. And liking could also be measured using physiological indicators such as an increase in heart rate when two people are in the vicinity of each other.