7. Quantitative Data Analysis Flashcards
What is the aim of Quantitative Data Analysis?
To demonstrate validity of an argument
What is the purpose of surveys/sampling solutions?
To ensure all members are equally likely to be selected & sample is a good representative of the population
What are the different types of sampling?
Random Sampling
Stratified Random Sampling
Cluster Random Sampling
Quota Sampling
Define each type of sampling solution
Random sampling - inclusion/exclusion by chance
Stratified random sampling - divide population into smaller non-overlapping groups and sample each group separately
Cluster random sampling - randomly select participants when geographically spread out
Quota sampling - population divided into non-overlapping subgroups and judgement used to select proportion of each subgroup for analysis
What are the different types of quantitative data
Nominal/Categorical
Ordinal
Interval
Ratio
Define each type of quantitative data
Nominal/Categorical - groupings that can’t be ranked/ordered and each category is different
Ordinal - groupings that can’t be ranked/ordered and the difference between ranks isn’t clear/important
Interval - data that can be ranked/ordered, but the difference between rank/unit is clear and the same
Ratio - data that can be ranked/ordered and is measured on a continuous scale
How is each type of data best represented?
Nominal - pie or bar charts
Ordinal - histograms
Interval - line graphs or scattergrams
Ratio - line graphs or scattergrams
What are the categories for quantitative data?
Discrete data
Continuous data
Define each of the categories for quantitative data
Discrete - takes the form of a whole number (integer)
Continuous - not restricted to defined separate values but values over a continuous range
Give an example for for each category of quantitative data
Discrete - no of BIS students
Continuous - height of each BIS student
Which types of quantitative data fit into which quantitative data category?
Discrete - binary, nominal, ordinal
Continuous - interval, ratio
What are the different techniques for data analysis
Basic level - tables, charts or graphs allow some patterns to be revealed
Next level - simple descriptive statistical techniques reveals patterns such as averages
Complex level - powerful statistical techniques that determines whether patterns seen in data really do exist
What is the purpose of tables that features rates?
It captures occurrence of something per X (a fixed no) of a larger population
X = 1000 or 10,000
Enables comparison of occurrences between differently sized populations
