Validity of Research Results in
Quantitative, Qualitative, and Mixed Research
Answers to Review Questions
10.1. What is a confounding variable, and why do confounding variables create problems in research studies?
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 a variable that DOES cause a problem because it is empirically related to both the independent and dependent variable. A confounding variable is a type of extraneous variable (it’s the type that we know is a problem, rather than the type that might potentially be a problem).
10.2. ...view middle of the document...
Note that when we reject the null hypothesis, the researcher says that the relationship is statistically significant.
• Effect size estimation involves the use of some type of effect size indicator (such as the percentage of variance explained, the size of the correlation, the size of the difference between two group means, etc.) to inform you of the size or strength of an observed relationship.
• In other words, null hypothesis testing tells us whether we have observed a real (i.e., non-chance) relationship, and an effect size indicator tells us how strong a significant relationship is.
10.4. What is internal validity, and why is it so important in being able to make causal inferences?
Internal validity is defined as the “approximate validity with which we infer that a relationship between two variables is causal” (Cook and Campbell, 1979, p.37). Often in research we want to be able to make causal inferences (i.e., state that two variables are causally related). To do this, we must have internal validity which is obtained through the use of design features and control techniques. The best designs are the strong experimental designs, and the best control technique is random assignment to groups. Note that it is essential for us to be able to make causal inferences because doing so helps us to know how to improve the world (e.g., find effective teaching practices, find ways to help people reach positive mental health, etc.). If you listen to your everyday language, you will see that cause and effect is embedded in your daily thinking.
10.5. What are the two types of causal relationships, and how do these two types of causal relationships differ?
1. Causal description involves describing the consequences of manipulating an independent variable.
2. Causal explanation involves more that just causal description; it involves explaining the mechanisms through which and the conditions under which a causal relationship holds. That is, causal explanation includes the use of mediating/intervening variables and/or moderating variables. (Definitions of these terms are in Table 2.2.)
10.6. What type of evidence is needed to infer causality, and how does each type of evidence contribute to making a causal inference?
The three necessary conditions for cause and effect are 1) Variable A and variable B must be related (the relationship condition), 2) Proper time order must be established (the temporal antecedence condition), and 3) The relationship between variable A and variable B must not be due to some confounding extraneous or third variable (the lack of alternative explanation condition). If you are going to argue that causation is occurring, then you must address each of the three conditions. You must also make sure that none of the threats to internal validity discussed in the chapter represents an alternative explanation for the research results. (A hand table showing these three necessary conditions for inferring causal...