Week 2/3 Flashcards
2 branches of statistics
Descriptive statistics - Organise, summarise, and communicate numerical information
Inferential statistics - Use representative sample data to draw conclusions about a population
Population
a collection of all possible members of a defined group
Could be any size
Sample
A set of observations drawn from a subset of the population of interest
A portion of the population
Sample results are used to estimate the population results
Operationalisation
Refers to the logical connection between the measure and the theoretical construct, or to the process by which we try to derive a measure from a theoretical construct.
Starting off with a vague concept and narrowing it down to come up with a precise way to measure it
Psychological measurement examples
Examples – age, intelligence, memory for an event, happiness levels, how often someone drinks alcohol
Theoretical construct
A thing you’re trying to take a measurement of
e.g. age, opinions, motives for drinking alcohol
Measure
Refers to the method or the tool that you use to make your observations
Variable
What we end up with when we apply our measure to something in the world. That is, variables are the actual “data” that we end up with in our data sets.
Types of variables - Continuous
Can take on a full range of values (usually decimals)
How tall are you?
Types of variables - discrete
Variables that can only take on specific values
e.g. Number of students
Can assign discrete values to things we’d normally consider words.
e.g. Political party
Classification of variables - Discrete
Nominal: category or name, frequency of belonging to a category – e.g., handedness
Ordinal: ranking of data; clear order to data but distance between points may vary – e.g., place in a race, 1st, 2nd, 3rd place etc
Classification of variables - Continuous
Interval: used with numbers that are equally spaced; Order to data points, fixed distance between points and negative values – e.g., temperature, 1º is always the same, negative temperatures possible
Ratio: like interval, order to data points, fixed distance between points, but has a meaningful 0 point (absence of the thing you are measuring); no negative values – e.g., height, always measured in cm, no negative heights
Generally described as scale variables
Examples of variables
Nominal: name of biscuit
Ordinal: ranking of favourite biscuits
Interval: temperature of biscuits
Ratio: how many biscuits are left?
Likert scales
(1) Strongly disagree
(2) Disagree
(3) Neither agree nor disagree
(4) Agree
(5) Strongly agree
Independent variables
For a true experiment: must be manipulated – meaning you changed it
Generally dichotomous variables (nominal) like experimental group versus control group
Variable you manipulate or categorise
For quasi experiment: used naturally occurring groups, like age; year of study
Still dichotomous, but you didn’t assign the group
when IVs are categorical, the groups are called levels
If political party is an IV, levels could be Conservative or Labour
+Dependant variables
The outcome information, what you measured in the study to find differences/changes based on the IV
Generally, these are interval/ratio variables, but you can use nominal ones too
Independent/dependent variables examples
Independent variable – Whether participants have 1 or 3 drinks
e.g. Group A – 1 drink
Group B – 3 drinks
Dependent variable – Participants’ reaction times on a driving simulator after 1 or 3 drinks
e.g. reaction time (ms)
Test-retest reliability
This relates to consistency over time.
If we repeat the measurement at a later date do we get the same answer