Sunday, 26 February 2017

WEEKLY TIPS FOR SPSS USERS: WHEN IS A HYPOTHESIS NOT A HYPOTHESIS?


A good theory should allow us to make statements about the state of the world. Statements about the world are good things: they allow us to make sense of our world, and to make decisions that affect our future. One current example is global warming. Being able to make a definitive statement that global warming is happening, and that it is caused by certain practices in society, allows us to change these practices and, hopefully, avert catastrophe. However, not all statements are ones that can be tested using science. Scientific statements are ones that can be verified with reference to empirical evidence, whereas non-scientific statements are ones that cannot be empirically tested. So, statements such as ‘Lindt chocolate is the best food’, and ‘This is the worst writ up in the world’ are all non-scientific; they cannot be proved or disproved. Scientific statements can be confirmed or disconfirmed empirically. ‘Watching Curb Your Enthusiasm makes you happy’, ‘having sex increases levels of the neurotransmitter dopamine’ and ‘Velociraptors ate meat’ are all things that can be tested empirically (provided you can quantify and measure the variables concerned). Non-scientific statements can sometimes be altered to become scientific statements, so ‘The Beatles were the most influential band ever’ is non-scientific (because it is probably impossible to quantify ‘influence’ in any meaningful way) but by changing the statement to ‘The Beatles were the best-selling band ever’ it becomes testable (we can collect data about worldwide record sales and establish whether The Beatles have, in
fact, sold more records than any other music artist). Karl Popper, the famous philosopher of science, believed that non-scientific statements were nonsense, and had no place in science. Good theories should, therefore, produce hypotheses that are scientific statements.


Some Important Terms

When doing research there are some important generic terms for variables that you will encounter:
Independent variable: A variable thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated.
Dependent variable: A variable thought to be affected by changes in an independent variable. You can think of this variable as
an outcome.
Predictor variable: A variable thought to predict an outcome variable. This is basically another term for independent variable (although some people won’t like me saying that; I think life would be easier if we talked only about predictors and outcomes).

Outcome variable: A variable thought to change as a function of changes in a predictor variable. This term could be synonymous with ‘dependent variable’ for the sake of an easy life.
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