3: Variable Types
- Page ID
- 48883
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)- Distinguish the difference between the terms variable and variance
- Understand how to determine if a variable is dependent or independent
- Understand the process for selecting a statistical test
Key Terms
- Variance
- Categorical Variables
- Continuous Variables
Statistics is the process of studying variation. What does that process look like? It starts with variables. Variables are terms we use to represent the different types of variation we observe.
- 3.1: Variables - Not Always Obvious
- This page explores the concept of variance in research, highlighting its impact on data accuracy and interpretation. It explains how variance arises from unrelated factors and measurement errors, emphasizing the importance of properly defining and categorizing variables. This understanding aids researchers in developing questions, choosing statistical analyses, and clarifying individual differences.
- 3.2: How to Organize Variation With Variables
- This page emphasizes the importance of identifying independent (IVs) and dependent variables (DVs) in research studies, highlighting their roles in manipulation and outcome measurement. It covers the distinction between categorical and continuous variables, alongside their subdivisions into ordinal, interval, or ratio scales.
- 3.3: Categorical Variables- Variables That Vary by Type
- This page explores the complexities of categorizing variables like gender, race, and cancer. It critiques binary classifications and urges a nuanced approach to gender identity, advocating for definitions based on research questions rather than arbitrary inclusivity. The importance of balancing inclusivity with data management is emphasized.
- 3.4: Continuous Variables- Variables That Vary by Level
- This page examines the categorization and measurement of variables in psychology, focusing on the differences between fixed effects and random effects, as well as ordinal, interval, and ratio variables. It highlights the subjective nature of ordinal and interval scales, emphasizing their context-dependent interpretations, particularly in psychological constructs like depression.
- 3.5: Nuances in Determining if a Variable Should be a Certain Type
- This page explores the continuum of sloppiness and precision in variable types, asserting that no classification (ratio, ordinal, interval) is superior; rather, it depends on the research context. Using age as an example, it illustrates how different divisions impact outcomes. It also suggests conceptualizing gender as two continuums (masculinity and femininity) instead of a binary. Ultimately, variable classification is tied to its relevance to research objectives.
- 3.6: Making Decisions About the Variables in Your Research Study
- This page emphasizes the crucial role of schematics in research design and statistical testing, focusing on the identification and operational definition of variables, including their types (independent or dependent) and scales (categorical or continuous). It underscores the importance of clear descriptions for accurate analysis and outlines the selection of suitable statistical tests, such as t-tests, ANOVAs, and MANOVAs, tailored to the variables' characteristics.
- 3.7: Lessons Learned About Variables
- This page explores the complexities of variables, particularly temperature as a potential ratio variable despite its non-definitive zero in measurement. It emphasizes the need to understand variable types (categorical or continuous and their subtypes) for proper statistical analysis. The author recommends cataloguing information from literature reviews regarding variable scaling in previous studies to enhance research design.
- 3.8: Discussion Questions
- This page discusses essential data analysis tasks such as creating a decision tree and a comparative table of variable types, their advantages and disadvantages, and relevant statistical methods. It emphasizes the identification and measurement of variables related to personal interests, understanding variance partitioning, and differentiating between independent and dependent variables.