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VARIABLES

VARIABLES. VARIABLES. Definition: Variables are properties or characteristics of people or communication phenomena that take on different values demographic characteristics personality traits communication styles or competencies constructs

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VARIABLES

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  1. VARIABLES

  2. VARIABLES • Definition: Variables are properties or characteristics of people or communication phenomena that take on different values • demographic characteristics • personality traits • communication styles or competencies • constructs • in order to be a variable, a variable must vary(e.g., not be a constant), that is, it must take on different values, levels, intensities, or states

  3. Variables Must Vary! • “Female” is not a variable; if everyone in a study is female, then “female” is a constant. • female versus male is a variable • degree of femininity is a variable • females’ sexual orientation is a variable

  4. DEFINITIONS • Variable: “any entity that can take on a variety of different values” (Wrench et al, 2008, p. 104) • gender • self-esteem • managerial style • stuttering severity • attributes, values, and levels are the variations in a variable (p. 106) • Attribute: political party: • Value: Democrat, Republican, Independent, etc. • Attribute: Self-esteem • Level: High, Medium, Low

  5. INDEPENDENT VARIABLE • the variable that is manipulated either by the researcher or by nature or circumstance (pp. 108-110) • independent variables are also called “stimulus” “input” or “predictor” variables • analogous to the “cause” in a cause-effect relationship

  6. Operationalization: translating an abstract concept into a tangible, observable form in an experiment (pp. 178-179) Operationalizations can include: variations in stimulus conditions (public schools versus home schooling) variations in levels or degrees (mild vs. moderate vs. strong fear appeals) variations based on standardized scales or diagnostic instruments (low vs. high self esteem scores) variations in “intact” or “self-selected” groups (single parent vs. dual parent households) “operationalization” of the independent variable

  7. TYPES OF VARIABLES • Discrete variables • Nominal variables: distinct, mutually exclusive categories (p. 111) • religion; Buddhists, Christians, Jews, Muslims, etc. • occupation; truck driver, teacher, engineer • marital status; single, married, divorced • Concrete versus abstract variables (p. 104) • concrete; relatively fixed, unchanging • biological sex • ethnicity • abstract; dynamic, transitory • mood, emotion • occupation • social media use

  8. Dichotomous variables: true/false, female/male, democrat/republican Ordered variables: mutually exclusive categories, but with an order, sequence, or hierarchy (p. 111-112) fall, winter, summer, spring K-6, junior high, high school, college Continuous variables (interval and ratio): include constant increments or gradations, which can be arithmetically compared and contrasted (pp. 113-115) IQ scores self-esteem scores age heart rate, blood pressure frequency of touch varieties and types of variables--continued

  9. Definition: The specific entity being examined (p. 105) individual; self esteem, fluency dyad: self disclosure, touch group: roles, norms organization: communication networks, upward-downward influence culture: individualism vs. collectivism What constitutes a specific score or measure on the outcome variable? marital satisfaction? one row of data in SPSS Ecological fallacy:drawing conclusions about individuals based on group data committing a “sweeping generalization” about participants in a research study all Asians are collectivistic all southerners are bigots all Catholics oppose gay marriage UNIT OF ANALYSIS

  10. OPERATIONALIZATION • definition: the specific steps or procedures required to translate an abstract concept into a concrete, testable variable • example: high versus low self-esteem (split-half or top vs. bottom third) • example: on-line versus traditional classroom (the amount of online instruction that constitutes an “on-line” class)

  11. credibility (high versus low) culture/ethnicity (self-report) type of speech therapy (in-clinic vs. at school, vs. at home) compliance-gaining strategy preferences (positive versus negative, self-benefit versus other benefit) “powerless” language style fear appeals (mild, moderate, strong) food server touch versus no touch examples of operationalizations

  12. DEPENDENT VARIABLE • a variable that is observed or measured, and that is influenced or changed by the independent variable • dependent variables are also known as “response” or “output” or “criterion” variables • analogous to the “effect” in a cause-effect relationship

  13. CONFOUNDING VARIABLE • also known as extraneous variables or intervening variables (p. 110, p. 267) • confounding variables “muddy the waters” • alternate causal factors or contributory factors which unintentionally influence the results of an experiment, but aren’t the subject of the study

  14. MEDIATING VARIABLE • a.k.a. moderating, intervening, intermediary, or mediating variables • a 2nd or 3rd variable that can increase or decrease the relationship between an independent and a dependent variable. • for example, whether listeners are persuaded more by the quality or quantity of arguments is moderated by their degree of involvement in an issue.

  15. interchangeability of independent and dependent variables • The same concept or construct could serve as the independent variable in one investigation, and the dependent in another. • example: “source credibility” • as an independent variable; RQ: Does source credibility (low versus high) have a significant effect on attitude change? • As a dependent variable; RQ: Does the amount of evidence contained in a speech affect listeners’ perceptions of the source’s credibility? • example: “fetal alcohol syndrome” (FAS) • As an independent variable: RQ: Does severity of FAS correlate positively with language delay in infants? • As a dependent variable: RQ: Does the amount of maternal alcohol use correlate positively with the severity of FAS in infancy?

  16. RELATIONSHIPS AMONG VARIABLES • Differences • Differences in kind, degree • Relationships (correlations) • Positive correlation • Negative correlation • No or neutral correlation

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