Types of Variables

Categorical Variable

A variable that has mutually exclusive ("named") groups that lacks intrinsic order. Gender, Ethnicity, or political affiliations are examples of categorical variables. The type of variable defines the test to be used to measure the variables. Categorical variable suggests using a t-test to measure the difference between group means. Any variable that is not quantitative is categorical. i.e. Results indicated a significant difference was found between genders and their mean transformational scores.

Continuous Variable

A variable that is "a number". Age, height, score on an exam, response on a Likert scale on a survey are all continuous variable. It can be ordinal, interval or ratio types. Examples of continuous variables are blood pressure, height, weight, income, and age. Rank-ordering data simply puts the data on an ordinal scale. Ordinal measurements describe order, but not relative size or degree of difference between the items measured. In this scale type, the numbers assigned to objects or events represent the rank order (1st, 2nd, 3rd, etc.) of the entities assessed. A Likert Scale is a type of ordinal scale and may also use names with an order such as: "bad", "medium", and "good"; or "very satisfied", "satisfied", "neutral", "unsatisfied", "very unsatisfied." An example of an ordinal scale is the result of a horse race, which says only which horses arrived first, second, or third but include no information about race times. In interval measurement the distance between attributes does have meaning. For example, when we measure temperature (in Fahrenheit), the distance from 30-40 is same as distance from 70-80. The interval between values is interpretable. Because of this, it makes sense to compute an average of an interval variable, where it doesn't make sense to do so for ordinal scales. But note that in interval measurement ratios don't make any sense - 80 degrees is not twice as hot as 40 degrees (although the attribute value is twice as large). A ratio variable, has all the properties of an interval variable, and also has a clear definition of 0.0. When the variable equals 0.0, there is none of that variable. Variables like height, weight, enzyme activity are ratio variables. Temperature, expressed in F or C, is not a ratio variable. A temperature of 0.0 on either of those scales does not mean 'no temperature'. However, temperature in degrees Kelvin in a ratio variable, as 0.0 degrees Kelvin really does mean 'no temperature'. Another counter example is pH. It is not a ratio variable, as pH=0 just means 1 molar of H+. and the definition of molar is fairly arbitrary. A pH of 0.0 does not mean 'no acidity' (quite the opposite!). When working with ratio variables, but not interval variables, you can look at the ratio of two measurements. A weight of 4 grams is twice a weight of 2 grams, because weight is a ratio variable. A temperature of 100 degrees C is not twice as hot as 50 degrees C, because temperature C is not a ratio variable. A pH of 3 is not twice as acidic as a pH of 6, because pH is not a ratio variable.

Independent Variable

A variable that is manipulated in an experiment whose changes are considered to be the cause of changes in other variables (the dependent variables); variables selected in a survey for the same purpose. Two Kinds of independent variables 1. Categorical - Ex. Gender, Ethnicity 2. Continuous - Ex. Age, Salary

Dependent Variable

A variable whose changes are the consequences of changes in other variables. We can define the DV as the variable that is being measured. It is this variable that we, as the researchers, look at for change. IF there is a change, we may conclude that the IV affected the DV. The ultimate here is to establish that the IV caused the change in the DV ("cause-effect" relationship).

Variable

Any quantity that may take on several points on a dimension. As a first step to gauge leader effectiveness across cultures, GLOBE empirically established nine cultural dimensions that make it possible to capture the similarities and/or differences in norms, values, beliefs -and practices—among societies. They build on findings by Hofstede (1980), Schwartz (1994), Smith (1995), Inglehart (1997), and others. They are: Power Distance, Uncertainty Avoidance, Humane Orientation, Collectivism I:(Institutional), Collectivism II:(In-Group), Asertiveness, Gender Egalitarianism, Future Orientation, and Performance Orientation.

Ratio Data

Data that are ordered so that we can make inferences regarding magnitude, have equal intervals between values, and contain an absolute zero point. Height is an example of ratio data: 60 inches is taller thatn 55 inches, the distance between 60 and 55 inches is the same as the distance between 30-25 inches, and a height of 0 inches implies no height at all.

Interval Data

Data that possess magnitude; one value can be judged greater than, less than, or equal to another and a constant distance between intervals such as units of measurement are the same on the scale regardless of where the unit falls. Example: In measuring temperature, the difference between 100 degrees and 99 degrees is the same as the difference between 40 degrees and 39 degrees.

Ordinal data

data whose values are ordered so that we can make inferences regarding magnitude, but which have no fixed interval between values. ex. response on a Likert survey question