Statistical Concepts 1: Variables

Think of variables as questions and values as answers.

e.g. Two groups of participants (man and women). CCTV cameras should be used on public transport (Strongly agree, Agree, Neutral, Disagree, Strongly Disagree)

If all the values are the same then that variable must be discarded. (e.g. Are you willing to participate in this experiment?)

Independent Variables aka Factors

- Fixed factors - manipulated (varied) by the experimenter.
The different values the factor can take are known as
*values*,*levels*or*conditions*. - Subject variables - recorded by the experimenter, e.g. gender, age.
- Random factors - levels can't be repeated exactly, e.g. animal litter, household aka Randomised Blocks.

Dependent Variables

- What you measure, e.g. score, reaction time, number of errors, answer to a question.

Levels of Measurement

**Nominal aka Categorical**, e.g. colour, political party.**Ordinal**, e.g. Likert scales, ice dancing scores (subjective scales). [Nominal variables with 2 values]**Interval aka Scale**, e.g. Temperature Celcius, IQ.**Ratio**, e.g. reaction time, number correct. A subset of Interval.

Distributions

**Flat**, e.g. die**Binomial**, e.g. multiple coins**Bell curve**, e.g. sum of several dice**Poisson**, e.g waiting for a rare event to happen**Normal****Bi-modal**

Describing Distributions

**Central tendancy -**mean, median, mode**Variation**- range, interquartile range, standard deviation**Skewness**- how lop-sided is it? Normal distribution has a skewness of 0. Significantly skewed if |skewness| > 2 * SE skewness**Kurtosis**- how pointy is it? Normal distribution has a kurtosis of 0. Significantly skewed if |kurtosis| > 2 * SE kurtosis