Data handling and analysis Flashcards
Quantitative data
Data that is in a numerical form, this data can be counted and is usually gathered by surverys
Strengths:
Findings can be generalised if selection process is well-designed and sample is representative of study population
Relatively easy to analyse
Data can be very consistent, precise and reliable
Limitations:
Related secondary data is sometimes not available or accessing available data is difficult/impossible
Difficult to understand context of a phenomenon
Data may not be strong enough to explain complex issues
Qualitative data
Data where there is less emphasis on counting numbers of people who think or behave in certain ways and more emphasis on explaining why people think and behave in certain ways.
Strengths:
Provide more detailed information to explain complex issues
Data collection is usually cost efficient
Limitations:
Findings usually cannot be generalised to the study population or community
More difficult to analyse; don’t fit neatly in standard categories
Data collection is usually time consuming
Mean
Arithmetic average of a set of scores calculated by dividing the sum of the scores by the number of scores.
Median
The middle value of a set of scores
Mode
The most frequently occuring value in a set of scores
Range
A measure of dispersion that is the difference between the highest and lowest score in a data set
Standard deviation
A measure of how spread out scores in a data set are.
Strengths:
Uses all scores in a data set in its calculation
Gives sensitive measure of how all scores are dispersed.
Limitations:
Harder to calculate than the range
It is distorted by extreme scores as it uses the mean
steps to calculate it
1- Find the mean od the data set
2- Calculate each score’s difference from the mean
3- To calculate the variance take each difference, square it then divide it by the total amount of scores in the set
4- To find the standard variation just square root the variance.
Histogram
A graph used to present data that is continuous (bars are always of the same width and have no gap in between) and occurs as a frequency.
Line graph
They can be used as an alternative to histograms. Line graphs are used to track changes over short and long periods of time. When smaller changes exist, line graphs are better to use.
Bar charts
Used for data in discrete categories rather than a continuous variable. Unlike in a histogram, the bars are separated by a gap.
Scattergrams
Used when there are two variables that pair well together. If there are two variables that pair well together, plotting them on a scatter diagram is a great way to see their relationship and see if it’s a positive or negative correlation.
Positive correlation
When one variable increases or decreases as the other variable increases or decreases. A perfect positive correlation has a coefficient of +1
A graph can have:
a perfect positive correlation
a less than perfect positive correlation
a positive correlation
Negative correlation
When one variable increases as the other decreases. A perfect negative correlation has a coefficient of -1
A graph can have:
a perfect negative correlation
a less than perfect negative correlation
a negative correlation
Zero correlation
There are times when no relationship is found between two variables.
Curvilinear correlation
A type of relationship between two variables where as one variable increases, so does the other variable, but only up to a certain point, after which, as one variable continues to increase, the other decreases.
