STATISTICAL REPORTS Population Measures and Variables Requirements 1

STATISTICAL REPORTS Population Measures and Variables

Requirements 1. POPULATION MEASURES and VARIABLES • What are the population measures (or variables of interest ) i. e. what was measured or calculated in the investigation • What type of variables are they – identify and describe • Independent – one that isn’t changed by other variables you are trying to measure • Dependent – one that depends on other factors • Categorical – one that has two or more categories • Quantitative – one that can be expressed in numerical form • For experimental investigations you have explanatory and response variables - can you identify, describe, justify, and provide statistical insight?

Population measures and variables What are they? They refer to an object, event, idea, feeling etc. that was measured or calculated in the investigation or survey. All statistically based reports quote different types of statistical variables. These variables are items of interest that the researchers of the study are wishing to find more about.

Types of Variables Independent This kind of variable is one that is not affected or changed by other measurable variables. Example: Age, gender, ethnicity are all independent variables Dependent This kind of variable does depend on other factors or variables. This does depend on the type of contextual investigation or experiment. It is the output variable responding to changes in the independent variable. It is normally the variable that is measured.

Types of Variables Categorical (or nominal) This variable (non-numerical) has two or more categories which are not ordered. Example: Hair colour, Dominant hand, Type of Car Dichotomous A special example of a categorical variable. There is only two possible outcomes. Example: Gender (now days contentious), Questions (Y/N)

Types of Variables Ordinal Similar to categorical variable, but can be ordered. Example: Grades (N, A, M, E) Quantitative One that can be expressed in numerical form. Can be either discrete or continuous in form. Example: Height (c), Age (d/c), Salary (c)

Types of Variables Explanatory Used in experimental situations. Refers to the independent or predictor variable. Example: Having a target line Response Used in experimental situations. Refers to the dependent or predicted variable. Example: Height of jump (cm)

Special Variable – lurking/hidden These are variables which may affect the response variable, but cannot or have not been measured. They are sometimes hard to identify. These should always be considered, and controlled for within experimental studies, so as to minimise/negate their effect on the response variable. In a lot of studies involving humans, a person’s level of motivation is one that can be most difficult to control, along with many other human emotions.