Categorical Variables
Categorical variables are a type of qualitative data that represent categories or groups. They can take on a limited, fixed number of values and are often used to label or classify data. In statistical analysis, categorical variables are used to organize and group data for comparison and analysis.
8 Key excerpts on "Categorical Variables"
- Bruce B. Frey(Author)
- 2018(Publication Date)
- SAGE Publications, Inc(Publisher)
...Sara Tomek Sara Tomek Tomek, Sara Categorical Data Analysis Categorical data analysis 239 243 Categorical Data Analysis Categorical data analysis is a field of statistical analysis devoted to the analysis of dependent variables that are categorical in nature. Development of analytic techniques for inference utilizing categorical random variables began around 1900 when Karl Pearson introduced the chi-square statistic (χ 2). From this first introduction of tests of two-way contingency tables, the field has developed to include not only analyses of contingency tables but also more sophisticated analytic techniques such as the generalized linear mixed model. This entry defines Categorical Variables, outlines the most frequently utilized probability distributions for Categorical Variables, describes the most commonly used statistical analyses in the field of categorical data analysis, and discusses estimation methods for parameter estimates. Categorical Variables Categorical Variables are a class of random variables whose outcomes fall into discrete categories as opposed to a continuous range of numbers. Discrete Categorical Variables can be categorized based on their level of measurement, either nominal or ordinal. Nominal Categorical Variables contain categories of responses that have an arbitrary ordering. That is, variables measured on this scale cannot be ranked or ordered based on their observed outcomes. The categories are simply placeholders for the outcomes. As an example, gender is measured on a nominal scale, as the following two outcomes, male and female, cannot be ordered in a meaningful way. Ordinal Categorical Variables, in contrast, contain categories of responses that have a natural ordering to them. The observed outcomes can be ranked or ordered based on this natural ordering, which provides meaning to the categories...
eBook - ePubMarketing Research with IBM® SPSS Statistics
A Practical Guide
- Karine Charry, Kristof Coussement, Nathalie Demoulin, Nico Heuvinck(Authors)
- 2016(Publication Date)
- Routledge(Publisher)
...Furthermore, nominal and ordinal variables are considered as Categorical Variables. Nominal variables are variables containing categories that cannot be ordered. The variable gender is a nominal variable because it has two categories, male or female, and these two categories cannot be ordered. Ordinal variables are Categorical Variables where the categories can be ordered, e.g. a variable age group consisting of three age categories: young (less than 25 years), middle-aged (between 26 and 64 years old) and old (more than 65 years). Furthermore, continuous variables, i.e. interval-scaled or ratio-scaled variables, which do not satisfy the normality assumption, are also considered as ordinal variables in the remainder of this book. Finally, Likert- or semantic differential scales (having five or more than five points) are statistically considered as ordinal scales. However, previous research showed that using these scales as interval variables does not necessary produce unreliable results. In the remainder of the book, these scales with five or more points are considered as interval variables. This chapter is split into two large blocks, section 2.1. Categorical Variables and section 2.2. Continuous Variables. Besides the calculation of descriptive statistics for both categorical and continuous variables, each section examines how IBM SPSS Statistics deals with distribution analysis. Distribution analysis is the process by which the pattern of data points for a particular type of variable is summarized and visualized. Distribution analysis is another important step in discovering the quality and the characteristics of the data. For instance, it is an ideal tool to discover extreme variable values or outliers, or to verify whether a continuous variable is normally distributed. Dataset Description Throughout this chapter the dataset Descriptive_Analysis.sav is used to explain the use of descriptive statistics...
eBook - ePub- Frank J. Fabozzi, Frank J. Fabozzi(Authors)
- 2012(Publication Date)
- Wiley(Publisher)
...Categorical and Dummy Variables in Regression Models SERGIO M. FOCARDI, PhD Partner, The Intertek Group FRANK J. FABOZZI, PhD, CFA, CPA Professor of Finance, EDHEC Business School Abstract: In the application of regression analysis there are many situations where either the dependent variable or one or more of the regressors are Categorical Variables. When one or more Categorical Variables are used as regressors, a financial modeler must understand how to code the data, test for the significance of the Categorical Variables, and, based on the coding, how to interpret the estimated parameters. When the dependent variable is a categorical variable, the model is a probability model. There are many times in the application of regression analysis when the financial modeler will need to include a categorical variable rather than a continuous variable as a regressor. Categorical Variables are variables that represent group membership. For example, given a set of bonds, the rating is a categorical variable that indicates to what category—AA, BB, and so on—each bond belongs. A categorical variable does not have a numerical value or a numerical interpretation in itself. Thus the fact that a bond is in category AA or BB does not, in itself, measure any quantitative characteristic of the bond, though quantitative attributes such as a bond’s yield spread can be associated with each category. In this entry, we will discuss how to deal with regressors that are Categorical Variables in a regression model. There are also applications where the dependent variable may be a categorical variable. For example, the dependent variable could be bankruptcy or nonbankruptcy of a company over some period of time. In such cases, the product of a regression is a probability. Probability models of this type include linear probability, logit regression, and probit linear models. INDEPENDENT Categorical Variables Categorical input variables are used to cluster input data into different groups...
eBook - ePub- Keith F Punch, Alis Oancea(Authors)
- 2014(Publication Date)
- SAGE Publications Ltd(Publisher)
...This chapter therefore includes consideration of the general question of when measurement is appropriate in education research. To simplify, the discussion throughout this chapter assumes that we are measuring the traits (or characteristics) of people. It generalises to measuring the characteristics of things or events, as well as people. 12. 1 Types of variables Variables can be classified in several ways. One fundamental way is to distinguish between categorical and continuous variables. Categorical Variables (also called discrete variables and discontinuous variables) vary in kind rather than in degree or amount or quantity. Examples include eye colour, gender, religious affiliation, occupation and most kinds of treatments or methods. Thus, if an education researcher wants to compare computerised and non-computerised classrooms, the discrete variable involved is the presence or absence of computers (Wallen and Fraenkel, 1991). For a categorical variable, the variance is between different categories, and there is no idea of a continuum or scale involved. People (or groups, or things) are classified into mutually exclusive categories, of which there may be any number. A dichotomous variable has two categories, a trichotomous variable has three, and so on. Continuous variables (also called measured variables) vary in degree, level or quantity, rather than in categories. With differences in degree, we have first rank ordering, and then placing on a continuum, or scaling. Ordering people into ranks means identifying the first, second, third and so on among them, according to some criterion, but it does not tell us how far apart the rankings are. Introducing an interval of measurement tells us this, and lifts the level of measurement from ordinal to interval. When this is done, the variable is continuous – we have a continuum, with intervals, showing less and more of the characteristic. Examples of such differences in degree are height, weight and age...
eBook - ePubQuantitative Corpus Linguistics with R
A Practical Introduction
- Stefan Th. Gries(Author)
- 2016(Publication Date)
- Routledge(Publisher)
...The variable class with the lower information value of the two is that of Categorical Variables. If two entities A and B have different values/levels on a categorical variable, this means that A and B belong to different classes of entities. For example, you can code the two NPs the book and a table with respect to the variable Definiteness as definite and indefinite respectively, and the fact that the two NPs receive different levels on this variable means that, with regard to Definiteness, they are different. Other examples for Categorical Variables (and their possible levels) are • phonological variables: Stress (stressed vs. unstressed), StressPosition (stress on first syllable vs. stress on second syllable vs. stress elsewhere); • syntactic variables: NP-type (lexical vs. pronominal), Construction (V NP PP vs. V NP NP); • semantic variables: Animacy (animate vs. inanimate), Concreteness (concrete vs. abstract), LexicalAspect (activity vs. accomplishment vs. achievement vs. state). Given what you read in Section 3.3, you will not be surprised to read that Categorical Variables of this kind are usually stored as factors in R, and maybe sometimes as character vectors. The other variable class to be distinguished here is that of numeric. variables. For example, the syllabic length of an NP is a numeric variable; other examples are pitch frequencies in hertz, word frequencies in a corpus, number of clauses between two successive occurrences of two ditransitives in a corpus file, and the reaction time toward a stimulus in milliseconds...
eBook - ePubMeasurement, Design, and Analysis
An Integrated Approach
- Elazar J. Pedhazur, Liora Pedhazur Schmelkin(Authors)
- 2013(Publication Date)
- Psychology Press(Publisher)
...Chapter 19 A Categorical Independent Variable DOI: 10.4324/9780203726389-22 In Chapters 17 and 18, regression analysis was presented for designs with continuous independent variables. The present chapter is devoted to the application of regression analysis in designs with a categorical independent variable. Recall that a categorical variable consists of two or more mutually exclusive and exhaustive categories (e.g., treatments, marital status; see, for example, Chapters 2 and 8). Application of regression analysis in designs with more than one categorical variable is presented in the next chapter. At the conclusion of this and the next chapter, we comment on the equivalence between the approach we are presenting and the analysis of variance (ANOVA). Coding Categorical Variables Broadly speaking, application of regression analysis in designs with categorical independent variables is similar to that in designs with continuous independent variables. In both, the objective is to use information contained in the independent variables in an attempt to determine whether, and to what extent, they affect the dependent variable or help explain it. The nature of the information is, of course, different and has to do with the distinction between continuous and Categorical Variables. When the independent variable is categorical, the question addressed boils down to whether, and to what extent, being in the different groups, or categories, makes a difference so far as the dependent variable is concerned. Although the mechanics of the analytic approach to be presented are the same, regardless of how the categories were formed and regardless of substantive considerations, interpretation of the results is very much predicated on such matters...
- Helen M. Williams(Author)
- 2020(Publication Date)
- SAGE Publications Ltd(Publisher)
...4 Describing categorical data Chapter summary This chapter introduces you to the production and interpretation of frequency tables and crosstabs using categorical data. Categorical data is some of the most common data in social statistics and is often used to describe populations. This can be done through simple frequencies of occurrences of a single variable or through bivariate tables that show us pairings of options between two variables. The results can be expressed through counts of the number of times an answer or pair of answers occur, or through percentages that represent this as a proportion. Each of the options conveys the same data differently and helps us to make different points, so it’s very important to be able to produce and interpret the data correctly. Objectives In this chapter, you will learn: How to produce a table with one categorical variable How to produce a crosstabulation with two Categorical Variables How to produce and interpret a variety of percentages How to recode variables to create Categorical Variables or to combine into fewer categories How to customize the appearance of the output tables. Introduction Many politics datasets, like the ESS, are dominated by Categorical Variables and have very few continuous variables. Politics researchers frequently want to answer questions about voting intention, history and party identification; educational qualifications; religion; marital status; ethnicity and citizenship; and public opinion on a range of issues. All of these common variables are categorical...
eBook - ePubPractical Social Investigation
Qualitative and Quantitative Methods in Social Research
- Richard Lampard, Christopher Pole(Authors)
- 2015(Publication Date)
- Routledge(Publisher)
...An attitudinal question which uses the range of answers 'Strongly agree', 'Agree', 'Neither agree nor disagree', 'Disagree' and 'Strongly disagree' is often treated as generating an interval-level variable (see Chapter 5) when in fact this range of answers strictly speaking only constitutes an ordinal-level variable. Similarly, social class categories are in practice sometimes 'upgraded' from ordinal to interval level. Conversely, age-related variables are often collapsed into a set of age bands, which may then be treated as an ordinal-level variable, or even as a nominal-level variable. Variables with only two categories, which are often referred to as dichotomies or dichotomous variables, are simultaneously both nominal-level variables and also interval-level variables, as the single gap between the two categories means that these variables automatically have the property of being metric. In fact, by splitting a variable with any level of measurement into a set of categories and ignoring any natural ordering of these categories, the researcher can always obtain a nominal-level variable. Thus, after this simplification of the variables, all relationships between variables can be represented by cross-tabulations. There is, however, a price to be paid for this 'simplification' of the form of data analysed. Basically, the process of simplification involves throwing away information, and this loss of information makes it more difficult to assess whether an observed pattern or relationship in data from a sample is a genuine reflection of the situation in the broader population...
