To operationalize these constructs means to define

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To operationalize these constructs means to define them in a way that other researchers could replicate your study. Now comes a question of professional judgment: How could we operationalize these constructs—that is, define what they mean in practice? Exhibit 2.1 Operationalizing Constructs
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Exhibit 2.1 shows some of the ways the two constructs could be operationalized or made measurable. We have taken ideas (mental constructions or “constructs”) and translated them into observable operations that can be measured. At the heart of many studies is a decision as to what measures will be used. Intuitively, some of the measures shown in Exhibit 2.1 appear to do a better job than others. Grade point average, for example, is a widely, though not totally, accepted measure of academic performance. On the other hand, membership in a gaming club or amount of money spent on games may have little or no relationship to whether an individual is an active game player. Of all the options, a best guess might be that time spent on gaming is the best measure of involvement as long as we can measure it accurately. (Note, however, the assumption that objective measurement of time spent is going to be the most relevant or useful measure. It could well turn out that gamers’ subjective ratings of time spent [“not much time,” “most of my time,” etc.] have greater explanatory power than an objective measure such as “hours per week.”) These constructs or concepts have now been operationalized into variables . Variables are the aspects of a construct that are capable of being measured or taking on a value. In other words, they can vary. The constructs “gaming” or “academic performance” cannot be measured; the variables “time spent on gaming” and “grade point average” can. Hypotheses: Making Predictions A statement about the relationship that we expect to find between variables is a hypothesis. Hypotheses can state simply that there will be a relationship, specify the direction of the relationship, or state that no relationship is expected. Two-tailed hypotheses state that there is a relationship between two variables but do not specify the direction of the relationship. For example: H 1 : There is a relationship between level of involvement in video gaming and academic performance. One-tailed hypotheses require extra confidence because you commit to predicting the direction of the relationship between the variables. For example: H 2 : As time spent in video gaming increases, academic performance decreases. Null hypotheses , usually symbolized as H 0 , specify that there is no relationship between variables. For example: H 0 : There is no relationship between level of involvement in video gaming and academic performance. Isn’t a null hypothesis self-apparent? Yes and no. Basically, the null hypothesis makes explicit the notion that we are always working with two hypotheses—the first that the relationship we suspect exists; the second that it does not (the null hypothesis). The null hypothesis proposes
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there is no relationship between variables other than what we would find by chance. The probability of getting the results we did can be calculated, as we shall see in Chapter 7 . Based on
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