Chapter 5 variables and their types
- 1. CHAPTER 5 VARIABLES AND THEIR TYPES By Niranjan H N
- 2. Variables •Variables are things you measure, manipulate and control in statistics and research. All studies analyze a variable, which can describe a person, place, thing or idea.
- 3. TYPES i. Independent variables ii. Dependent variables iii. Intervening variables iv. Moderating variables v. Control variables vi. Extraneous variables vii. Quantitative variables viii. Qualitative variables ix. Confounding variables x. Composite variables
- 4. Independent variables • An independent variable is a singular characteristic that the other variables in your experiment cannot change. • Age is an example of an independent variable.
- 5. Dependent variables • A dependent variable relies on and can be changed by other components • A grade on an exam is an example
- 7. Control variables • Control or controlling variables are characteristics that are constant and do not change during a study. • They have no effect on other variables. • Prevents bias
- 8. Type of variable Definition Example (salt tolerance experiment) Independent variables (aka treatment variables) Variables you manipulate in order to affect the outcome of an experiment. The amount of salt added to each plant’s water. Dependent variables (aka response variables) Variables that represent the outcome of the experiment. Any measurement of plant health and growth: in this case, plant height and wilting. Control variables Variables that are held constant throughout the experiment. The temperature and light in the room the plants are kept in, and the volume of water given to each plant.
- 10. Intervening variables • An intervening variable, sometimes called a mediator variable, is a theoretical variable the researcher uses to explain a cause or connection between other study variables • Dependent and independent • If wealth is the independent variable, and a long-life span is a dependent variable, the researcher might hypothesize that access to quality healthcare is the intervening variable that links wealth and life span.
- 11. Moderating variables • A moderating or moderator variable changes the relationship between dependent and independent variables by strengthening or weakening the intervening variable's effect. • For example, in a study looking at the relationship between economic status (independent variable) and how frequently people get physical exams from a doctor (dependent variable), age is a moderating variable. That relationship might be weaker in younger individuals and stronger in older individuals.
- 12. Extraneous variables • EV are factors that affect the dependent variable but that the researcher did not originally consider when designing the experiment. • Changes studies result • Take, for example, a study assessing whether private tutoring or online courses are more effective at improving students' Spanish test scores. Extraneous variables that might unintentionally influence the outcome include parental support, prior knowledge of a foreign language or socioeconomic status.
- 13. Quantitative variables • Discrete • Continuous
- 14. Quantitative variables Type of variable What does the data represent? Examples Discrete variables (aka integer variables) Counts of individual items or values. •Number of students in a class •Number of different tree species in a forest Continuous variables (aka ratio variables) Measurements of continuous or non- finite values. •Distance •Volume •Age
- 16. Type of variable What does the data represent? Examples Binary variables Yes/no outcomes. •Heads/tails in a coin flip •Win/lose in a football game Nominal variables Groups with no rank or order between them. •Species names •Colours •Brands Ordinal variables Groups that are ranked in a specific order. •Finishing place in a race •Rating scale responses in a survey*
- 17. Confounding variables • A confounding variable is one you did not account for that can disguise another variable's effects. • supposed cause and the supposed effect of the study • Shows relationship between variables that dosent exits • For example, if you are studying the relationship between exercise level (independent variable) and body mass index (dependent variable) but do not consider age's effect on these factors, it becomes a confounding variable that changes your results.
- 18. Composite variables • A composite variable is two or more variables combined to make a more complex variable. • Overall health is an example of a composite variable if you use other variables, such as weight, blood pressure and chronic pain, to determine overall health in your experiment.