In order to include a categorical predictor, it must be converted to a number of dichotomous variables, commonly referred to as dummy variables. Those familiar with regression may know that the predictors (or independent variables) must be metric or dichotomous. This is why this test is treated separately from the more general ANOVA in most text books. However, the independent variable holding only 2 distinct values greatly simplifies the calculations involved. Strictly, the independent-samples t-test is redundant because it's equivalent to a one-way ANOVA. In this test, the dichotomous variable defines groups of cases and hence is used as a categorical variable. Dichotomous Input VariablesДЄn example of a test using a dichotomous independent (input) variable is the independent samples t-test, illustrated below. They are among the most widely used (and simplest) stasticical tests around. The aforementioned tests -and some others- are used exclusively for dichotomous dependent variables. This question is answered with either a binomial test or a z-test for one proportion. We might want to know the percentage of people who do. For instance, people may or may consider buying a new car in 2017. If so, we use proportions or percentages as descriptive statistics for summarizing such variables. Some research questions involve dichotomous dependent (outcome) variables. This odd feature (which we'll illustrate in a minute) also justifies treating dichotomous variables as a separate measurement level. Dichotomous variables, however, don't fit into this scheme because they're both categorical and metric. The usual classification involves categorical (nominal, ordinal) and metric (interval, ratio) variables. Dichotomous Variables are both Categorical and MetricД¬hoosing the right data analysis techniques becomes much easier if we're aware of the measurement levels of the variables involved. Something similar holds for metric variables: if we know the average age of our sample (n = 100) is precisely 25 years old, then we don't know the variance, skewness, kurtosis and so on needed for drawing a histogram. That is, we can't describe the exact frequency distribution with one single number. Note that this doesn't hold for other categorical variables: if we know that 45% of our sample (n = 100) has brown eyes, then we don't know the percentages of blue eyes, green eyes and so forth. The point here is that -given the sample size- the frequency distribution of a dichotomous variable can be exactly described with a single number: if we've 100 observations on sex and 45% is male, then we know all there is to know about this variable. q2 is a dichotomous variable if we exclude the вЂњno answerвЂќ category from analysis and not dichotomous otherwise.Дichotomous Variables - What Makes them Special?Дichotomous are the simplest possible variables.

q1 is a dichotomous variable: since empty cells ( missing values) are always excluded from analyses, we have two distinct values left.

It would be dichotomous if we just distinguished between currently married and currently unmarried.

married is not a dichotomous variable: it contains 3 distinct values.

sex is a dichotomous variable as it contains precisely 2 distinct values.

It contains only one distinct value and we therefore call it a constant rather than a variable.

completed is not a dichotomous variable.

Next, we'll point out why distinguishing dichotomous from other variables makes it easier to analyze your data and choose the appropriate statistical test. Let's first take a look at some examples for illustrating this point. What is a Dichotomous Variable? By Ruben Geert van den Berg under T-Tests & Statistics A-ZДЄ dichotomous variable is a variable that contains precisely two distinct values.

Spss code dependent