qualitative variable

Examples of qualitative variable in the following topics:

• Qualitative Variable Models

  • Dummy, or qualitative variables, often act as independent variables in regression and affect the results of the dependent variables.
  • Dummy variables are "proxy" variables, or numeric stand-ins for qualitative facts in a regression model.
  • One type of ANOVA model, applicable when dealing with qualitative variables, is a regression model in which the dependent variable is quantitative in nature but all the explanatory variables are dummies (qualitative in nature).
  • This type of ANOVA modelcan have differing numbers of qualitative variables.
  • An example with two qualitative variables might be if hourly wages were explained in terms of the qualitative variables marital status (married / unmarried) and geographical region (North / non-North).

• Variables

  • An important distinction between variables is between qualitative variables and quantitative variables.
  • Qualitative variables are those that express a qualitative attribute such as hair color, eye color, religion, favorite movie, gender, and so on.
  • The values of a qualitative variable do not imply a numerical ordering.
  • Qualitative variables are sometimes referred to as categorical variables.
  • The variable "type of supplement" is a qualitative variable; there is nothing quantitative about it.

• Models with Both Quantitative and Qualitative Variables

  • A regression model that contains a mixture of quantitative and qualitative variables is called an Analysis of Covariance (ANCOVA) model.
  • A regression model that contains a mixture of both quantitative and qualitative variables is called an Analysis of Covariance (ANCOVA) model.
  • They are the statistic control for the effects of quantitative explanatory variables (also called covariates or control variables).
  • The regression relationship between the dependent variable and concomitant variables must be linear.
  • Demonstrate how to conduct an Analysis of Covariance, its assumptions, and its use in regression models containing a mixture of quantitative and qualitative variables.

• Measures of Variability of Qualitative and Ranked Data

  • Variability for qualitative data is measured in terms of how often observations differ from one another.
  • The study of statistics generally places considerable focus upon the distribution and measure of variability of quantitative variables.
  • A discussion of the variability of qualitative--or categorical-- data can sometimes be absent.
  • In such a discussion, we would consider the variability of qualitative data in terms of unlikeability.
  • It is the simplest measure of qualitative variation.

• Quantitative or Qualitative Data?

  • Different statistical tests are used to test quantitative and qualitative data.
  • Qualitative (categorical) research, on the other hand, asks broad questions and collects word data from participants.
  • Examples of qualitative variables are male/female, nationality, color, et cetera.
  • One of the most common statistical tests for qualitative data is the chi-square test (both the goodness of fit test and test of independence).
  • A common case for this test is where the events each cover an outcome of a categorical variable.

• Types of Variables

  • A continuous variable is a numeric variable.
  • A discrete variable is a numeric variable.
  • Therefore, categorical variables are qualitative variables and tend to be represented by a non-numeric value.
  • An ordinal variable is a categorical variable.
  • A nominal variable is a categorical variable.

• Graphing Bivariate Relationships

  • Measures of central tendency, variability, and spread summarize a single variable by providing important information about its distribution.
  • When one variable increases with the second variable, we say that x and y have a positive association.
  • The presence of qualitative data leads to challenges in graphing bivariate relationships.
  • We could have one qualitative variable and one quantitative variable, such as SAT subject and score.
  • If both variables are qualitative, we would be able to graph them in a contingency table.

• Describing Qualitative Data

  • When the categories may be ordered, these are called ordinal variables.
  • Categorical variables that judge size (small, medium, large, etc.) are ordinal variables.
  • Deciding what is a variable, and how to code each subject on each variable, is more difficult in qualitative data analysis.
  • Concept formation is the creation of variables (usually called themes) out of raw qualitative data.
  • It is more sophisticated in qualitative data analysis.

• Graphs of Qualitative Data

  • Recall the difference between quantitative and qualitative data.
  • Qualitative data are measures of types and may be represented as a name or symbol.
  • Variance and standard deviation require the mean to be calculated, which is not appropriate for categorical variables as they have no numerical value.
  • There are a number of ways in which qualitative data can be displayed.
  • The qualitative data results were displayed in a frequency table.

• Averages of Qualitative and Ranked Data

  • The central tendency for qualitative data can be described via the median or the mode, but not the mean.
  • In order to address the process for finding averages of qualitative data, we must first introduce the concept of levels of measurement.
  • Stevens proposed his typology in a 1946 Science article entitled "On the Theory of Scales of Measurement. " In that article, Stevens claimed that all measurement in science was conducted using four different types of scales that he called "nominal", "ordinal", "interval" and "ratio", unifying both qualitative (which are described by his "nominal" type) and quantitative (to a different degree, all the rest of his scales).
  • The nominal scale differentiates between items or subjects based only on their names and/or categories and other qualitative classifications they belong to.
  • In 1946, Stevens observed that psychological measurement, such as measurement of opinions, usually operates on ordinal scales; thus means and standard deviations have no validity, but they can be used to get ideas for how to improve operationalization of variables used in questionnaires.