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## PowerPoint Slideshow about 'VARIABLES' - elmo

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Because things vary, research is conducted

- How or why things vary
- How differences in one variable are related to another

Types of Variables

- INDEPENDENT/DEPENDENT
- CRITERION/OUTCOME
- DICHOTOMOUS
- ATTRIBUTE/ACTIVE
- EXTRANEOUS
- INTERVENING
- CONFOUNDING
- UNCONTROLLED

Independent Variable

- The presumed cause
- The manipulated variable in an experiment
- The treatment
- The “active” variable

The Dependent Variable

- The variable that the researcher is interested in understanding, explaining or predicting
- The outcome variable
- The “critereon variable”

Independent /Dependent Variables

- Variables are not inherently one kind or the other, you must look at them in the context of the study.
- Causality is not necessarily implied, rather the directionality of the influence
independent variable changes in the dependent variable

Criterion variable

- The dependent variable--the outcome

Dichotomous variable

- A variable having two (and only two) values
e.g., male, female

smoker, nonsmoker

alive, dead

Attribute and Active variables

- Attribute variable--preexisting characteristic which researcher simply observes and measures, e.g. bloodtype, medical diagnosis, etc.
- Active Variable--researcher creates or manipulates this, e.g. metrology class, experimental drug, etc.

Intervening

Confounding

Uncontrolled

Variables which have an unwanted or irrelevant effect on the dependent variable under investigation. The kind of independent variable which may unexpectedly alter the results of the study

Variables to be controlled for:CONTROLLING EXTRANEOUS VARIABLES

- 1. HOMOGENEITY/ELIMINATION OF VARIABLE
- 2. INCLUDE IN DESIGN
- 3. MATCHING
- 4. STATISTICAL CONTROL
- 5. REPEATED MEASURES
- 6. RANDOMIZATION

Homogenity, Elimination of Var.

- Use subjects who are homogeneous for the variable which is suspected may have a confounding effect on the results of the study.
eg. If gender or age is thought to be a confounding factor use all one gender or all one age cohort as subjects, thus eliminating this as a factor

Include in the design

- Include the presumed intervening variable in the design of the study.
- For example: If age is a factor include several age groups in the design and analyze the results separately for each group.

Matching

- Match subject in two groups on relevant characteristics such as age, gender, diagnosis.

Statistical Control

- After study is completed perform an analysis of covariance to determine if the presumed extraneous variable had an effecrt.

Repeated Measures

- If the variable is subject to fluxuation, measure several times under several different circumstances.

Randomization

- Overall, the best way to control extraneous variables.
- Random assignment to groups means that all possible extraneous variables should be spread evenly between groups.

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