- If sales and per capita income are positively related, classify all variables as dependent, independent, moderating, extraneous, or intervening.
Dependent variable – This refers to the variable or concept that a researcher can measure or predict in a study. Dependent variables are also known as criterion variables and are dependent on the independent variable. This implies that the dependent variable changes when the independent variable changes. In the automobile manufacturer scenario, the dependent variable includes the demand and sales of automobiles mainly because the demand is highly dependent on per capita income and other factors.
Independent variables – these are variables or constructs in research that cannot be influenced or changed by other variables. Rather, a researcher manipulates this variable so that it can affect the dependent variable. This variable is also known as predictor variable because it causes an effect on other variables (Flannelly, Flannelly & Jankowski, 2014). In the scenario of the automobile manufacturer, the independent variable is per capita income. This is because this variable can potentially impact demand for automobiles but it is not affected by other variables in the case scenario.
Moderating variables – These include the constructs or variables that affect the relationship between the independent and dependent variable. In other words, moderating variables are those concepts or factors that have significant contributory or contingent effect on the relationship between the dependent and independent variables (Namazi & Namazi, 2016). In the scenario, examples of moderating variables include the low interest rates, competitor advertising, competitor dealer discounts and introductions of new competitive models. These variables are moderators because the affect the relationship between the demand for automobiles and per capital income. For instance, competitor advertising could lead to a slight decrease in the automobile sales even if there is an increase in per capita income.
Extraneous variables – These are the constructs or concepts that have little effect on the independent and the dependent variables. While some extraneous variables are often ignored in research, some cannot be ignored because of the profound impact on the relationship between the independent and dependent variables. Most often, extraneous variables are unwanted because they could contribute to research errors. For instance, buyer age and buyer gender are extraneous variables in the scenario because they could impact the relationship between car sales and per capita income. However, this impact has little effect on the relationship.
Intervening variables – These are the variables that could theoretically affect the relationship between the independent and dependent variables, but cannot be seen or measured. Intervening variables are hypothetical in nature and researchers often infer its effects by considering the impacts of the independent and moderator variables on the dependent variable. In the scenario, an example of an intervening variable is the need for an automobile.
2. Comment on the utility of a model based on the hypothesis.
Automobile demand and sales (DV) are dependent on the rise in per capita income (IV). However, the demand and sales for cars (DV) could also be influenced by low interest rates, competitor advertising, competitor dealer discounts and introductions of new competitive models (MV). Other extra variables that influence car sales include buyer age and buyer gender, which could positively or negatively impact car sales. Given the numerous variables that affect car demand and sales, a model that is based on the discussed relationship between the independent and dependent variable is highly unlikely (Schindler & Cooper, 2014).