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# lec8 - Chapter 8 Validity of Research Results(Reminder Dont...

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Chapter 8 Validity of Research Results (Reminder: Don’t forget to utilize the concept maps and study questions as you study this and the other chapters.) In this chapter we discuss validity issues for quantitative research and for qualitative research. Validity Issues in the Design of Quantitative Research On page 228 we make a distinction between an extraneous variable and a confounding variable. An extraneous variable is a variable that MAY compete with the independent variable in explaining the outcome of a study. A confounding variable (also called a third variable ) is an extraneous variable that DOES cause a problem because we know that it DOES have a relationship with the independent and dependent variables. A confounding variable is a variable that systematically varies or influences the independent variable and also influences the dependent variable. When you design a research study in which you want to make a statement about cause and effect, you must think about what extraneous variables are probably confounding variables and do something about it. We gave an example of "The Pepsi Challenge" (on p. 228) and showed that anything that varies with the presentation of Coke or Pepsi is an extraneous variable that may confound the relationship (i.e., it may also be a confounding variable). For example, perhaps people are more likely to pick Pepsi over Coke if different letters are placed on the Pepsi and Coke cups (e.g., if Pepsi is served in cups with the letter "M" and Coke is served in cups with the letter "Q"). If this is true then the variable of cup letter (M versus Q) is a confounding variable. In short we must always worry about extraneous variables (especially confounding variables) when we are interested in conducting research that will allow us to make a conclusion about cause and effect. There are four major types of validity in quantitative research: statistical conclusion validity, internal validity, construct validity, and external validity. We will discuss each of these in this lecture. Statistical Conclusion Validity Statistical conclusion validity refers to the ability to make an accurate assessment about whether the independent and dependent variables are related and about the strength of that relationship. So the two key questions here are 1) Are the variables related? and 2) How strong is the relationship? Typically, null hypothesis significance testing (discussed in Chapter 16) is used to determine whether two variables are related in the population from which the study data were selected. This procedure will tell you whether a relationship is statistically significant or not. For now, just remember that a relationship is said to be statistically significant when we do NOT believe that it is nothing but a chance occurrence, and a

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relationship is not statistically significant when the null hypothesis testing procedure says that any observed relationship is probably nothing more than normal sampling error or fluctuation.
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