To answer questions you need data, and to collect data you either collect it by numbers, this is called
quantitative methods or you can collect it by analysing language qualitative methods.
These two approaches are complementary.
The analysis of data may support your hypothesis or generate another one which in turn makes you revise
the theory, so they are linked.
A theory: an explanation or set of principles that is well substantiated by repeated testing and explains a
broad phenomenon.
A hypothesis: proposed explanation to a fairly narrow phenomenon or set of observations.
Theory and hypothesis are both conceptual. To move from the conceptual world to the the oberval domain
we need to operationalize our hypothesis in a way that makes us collect data.
We do this using predictions. These emerge from hypotheses and transform it from something
unobservable to something observable.
A variable that we think is a cause is called IV. The variable we think is an effect is the DV.
Sometimes you cannot manipulate variables thus you use correlational methods. There is no IV. all
variables are dependent. We rather call them predictor variable and outcome variable.
The relationship between what is being measured and the number that represents what is being measured
is called level of measurement. Variables can be categorical or numerical or can represent a different level
of measurement.
Types of categorical variables
binary : ex. male or female
When there are more than 2 possibilities, it is called a nominal variable.
When categories are ordered, they are called ordinary variables.
However, these data tell us nothing about the difference between values.
Continuous variable (type of an interval variable)
Can take on any value on the measurement scale we are using.
A type of continuous is interval, it is more preferred than the ordinal. We must be sure that equal intervals
on the scale represent equal differences in the property being measured.
Ratio variables (advanced interval)
This is really a step further than the interval and in addition that it meets the requirements of an interval
variable, the ratio of values among the scales is meaningful. How?
The scale has a true and meaningful 0 point.
It works when the thing measured has a true 0 point like time and 0 does not mean the absence of time.
Continuous variable
quantitative methods or you can collect it by analysing language qualitative methods.
These two approaches are complementary.
The analysis of data may support your hypothesis or generate another one which in turn makes you revise
the theory, so they are linked.
A theory: an explanation or set of principles that is well substantiated by repeated testing and explains a
broad phenomenon.
A hypothesis: proposed explanation to a fairly narrow phenomenon or set of observations.
Theory and hypothesis are both conceptual. To move from the conceptual world to the the oberval domain
we need to operationalize our hypothesis in a way that makes us collect data.
We do this using predictions. These emerge from hypotheses and transform it from something
unobservable to something observable.
A variable that we think is a cause is called IV. The variable we think is an effect is the DV.
Sometimes you cannot manipulate variables thus you use correlational methods. There is no IV. all
variables are dependent. We rather call them predictor variable and outcome variable.
The relationship between what is being measured and the number that represents what is being measured
is called level of measurement. Variables can be categorical or numerical or can represent a different level
of measurement.
Types of categorical variables
binary : ex. male or female
When there are more than 2 possibilities, it is called a nominal variable.
When categories are ordered, they are called ordinary variables.
However, these data tell us nothing about the difference between values.
Continuous variable (type of an interval variable)
Can take on any value on the measurement scale we are using.
A type of continuous is interval, it is more preferred than the ordinal. We must be sure that equal intervals
on the scale represent equal differences in the property being measured.
Ratio variables (advanced interval)
This is really a step further than the interval and in addition that it meets the requirements of an interval
variable, the ratio of values among the scales is meaningful. How?
The scale has a true and meaningful 0 point.
It works when the thing measured has a true 0 point like time and 0 does not mean the absence of time.
Continuous variable
