2 : MEASURES OF CENTRAL TENDENCY, HISTOGRAMS, VARIABLES AND RESEARCH DESIGN

CENTRAL THEORUM

• People tend to cluster in the same amount of scores

• Larger the sample - the more data points more normally distributed 

MEASURES OF CENTRAL TENDENCY

• Allows data to become more efficient

• Allows for statistical analysis 

CENTRAL TENDENCY: MEAN

• THE AVERAGE SCORE

• HOW TO: add all numbers together divide by data point

• PROPERTIES: mean can be influenced by extreme values ‘outliers’

CENTRAL TENDENCY: MEDIAN

• THE MIDDLE NUMBER OF RANKED DATA

• HOW TO: order the number in ascending order and see the middle number, if there is 2 middle numbers — add & divide by 2

• PROPERTIES: not influenced by extreme scores

CENTRAL TENDENCY: MODE

• THE MOST COMMON VALUE

• HOW TO: find the most common value within the data set

• PROPERTIES: represents the highest bar in bar chart, more than 1 mode = not normally distrubuted

• Bimodality = hint that data isnt normally distrubuted

MEASURES OF DISPERSION

• Statistical tools used to quantify the spread or variability of data points in a dataset. They include range, variance, and standard deviation.

DISPERSION: STANDARD DEVIATION

• S = √(Σ(x - μ)² / N-1)

  • To calculate the standard deviation in psychology, follow these steps:

  1. Calculate the mean (average) of the data set.

  2. Subtract the mean from each data point and square the result.

  3. Calculate the mean of the squared differences.

  4. Take the square root of the mean from step 3.

  This will give you the standard deviation, which is a measure of how spread out the data is from the mean in psychology.

• A statistical measure that quantifies the amount of variability or spread in a dataset. It calculates the average distance between each data point and the mean. A higher standard deviation indicates greater dispersion, while a lower standard deviation suggests less variability.

HISTOGRAMS

• A histogram is a graphical representation of the distribution of a dataset. It consists of a series of bars, where each bar represents a range of values and the height of the bar represents the frequency or count of values within that range.

• how many times someone got that specific score 

• Right/Positive skew: test is too hard 

  Left/Negative skew: test is too easy 

INDEPENDANT VARIABLE

• Manipulated by researcher - controlled

• e.g practice time, no of syllables 

DEPENDANT VARIABLES

• change accord to how the p response to IV

1. categorical data: e.g eye colour 

2. numerical data: e.g no of words recalled

   must be repped by number before analysis e.g convert to percentage

SUBJECT VARIABLES

• changes according to p’s characteristics - uncontrolled

• changes because is differs between people

  e.g gender, age 

EXPERIMENTAL

• IV is manipulated DV is effected - controlled conditions

QUASI

• uses at least one naturally occurring IV (subject variable)

CORRELATIONAL

• looking at relationships between DVs

OBSERVATIONAL

• ALL INVOLVE DV’S - We are always measuring something about our p’s