PSY1022 WEEK 11 DISC 5 Flashcards
DESCRIPTIVE STATISTICS
Techniques that help describe a set of data. eg. Graphs, tables, calculating an average score.
Creating a meaningful summary, so patterns and trends in the raw data can be seen.
INFERENTIAL STATISTICS
Methods that use the limited information from samples to answer general questions about populations.
- help researchers determine when it is appropriate to generalize from a sample to a population
STATISTIC
A summary value that describes a sample.
- eg. average age of students in a sample.
Two purposes:
- They describe or summarize the entire set of scores in the sample.
- They provide information about the corresponding summary values for the entire population.
PARAMETER
A summary value that describes a population.
- eg. average age of students in the population.
- symbolised by Greek letters.
FREQUENCY DISTRIBUTIONS
A method of simplifying and organising a set of scores by grouping them into an an organised display that shows the entire set.
- consists of a tabulation of the number of individuals in each category on the scale of measurement (X and f)
- displays set of categories and and number of individuals with scores in each category.
- advantage: allows researcher to view the entire set of scores
- disadvantage: can be tedious with large sets of data.
FREQUENCY DISTRIBUTION GRAPH
Scale of measurement, or categories, on X axis.
Frequencies on Y axis.
- histogram (boxy graph thing)
- polygon (line graph thing)
- when not numerical = bar graph. Similar to histogram, but bars don’t touch.
- first step in examining a set of data. Rarely used in published reports.
FREQUENCY DISTRIBUTION TABLE
Two columns of information
- first is scale of measurement, or categories. Labelled X.
- second is frequency or number of individuals
CENTRAL TENDENCY
A statistical measure that identifies a single score that defines the center of a distribution. The goal of central tendency is to identify the value that is most typical or most / best representative of the entire group.
- mean, median, mode
MEAN
Add all scores, divide by number of individuals.
Represented by M in research papers.
Mean for population represented by µ (mu).
- can be skewed by a few extreme scores
- can’t use with nominal scale
- ordinal is generally inappropriate.
MEDIAN
The score that divides a distribution in half.
Good when there are extremes.
Is the “middle” number. Write scores in order, pick the middle one.
If two in the middle add them together and divide by 2.
Also called 50th percentile.
- can be used for ordinal, ratio, and interval
- good alternative to mean when there are a few extreme scores.
MODE
Is the score or category with the greatest frequency. In a frequency distribution graph, the mode identifies the location of the peak (highest point) in the distribution.
- possible to be bimodal or multimodal.
- can be used for NOIR
VARIABILITY
Descriptive: describes the spread of scores in a distribution.
Inferential: provides a measure of how accurately any individual score or sample represents the entire population.
A primary value to describe a distribution of scores.
Small = clustered together (good representation)
Large - spread out (distorted picture)
Measured by range or standard deviation.
STANDARD DEVIATION
Uses the mean of the distribution as a reference point and measures variability by measuring the standard (average) distance between each score and the mean.
- averaged squared distance from the mean.
- represented by s or SD
- half will be positive and half with be negative
VARIANCE
Average squared distance from the mean.
= s^2
Measures variability.
CALCULATE STANDARD DEVIATION
- For each score, measure distance from mean (score = 84, mean = 80, deviation = 4)
- Find variance by summing all the squared distances
- find average squared distance (divide by n-1, not n).
- square root to find standard deviation
DEGREES OF FREEDOM
n-1
df
produces a variance for the sample that is an accurate and unbiased representation of the population variance
NORMAL CURVE
A symmetrical, bell-shaped frequency polygon representing a normal distribution.
NORMAL DISTRIBUTION
A theoretical frequency distribution that has certain special characteristics.
Also called Gaussian.
- bell-shaped, symmetrical
- mean, median, mode located at center. Only one mode.
- when any standard deviations are plotted on the x-axis the percentage of scores falling between the mean and any point on the x-axis is the same for all normal curves.
KURTOSIS
How peaked or flat a a normal distribution is.
MESOKURTIC
Peaks of medium height, distributions are moderate in breadth.
LEPTOKURTIC
Peak is tall and thin. Distribution is narrow.
PLATYKURTIC
Peak is broad and flat. Distribution is wide.
POSITIVELY SKEWED DISTRIBUTION
A distribution in which the peak is to the left of the center point, and the tail extends toward the right, or in the positive direction.
Mode = highest point
Median = divides in half
Mean = pulled in direction of tail (+ve) due to a few extremely high scores.
– most people have low scores, only a few have high.
NEGATIVELY SKEWED DISTRIBUTION
A distribution in which the peak is to the right of the center point, and the tail extends toward the left, or in the negative direction
Mode = highest point
Median = divides in half
Mean = pulled in direction of tail (-ve) due to a few extremely low scores.
– most people have high scores, only a few have low.
