Dealing with data

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Quantitative data.

Strengths.
quick and easy to analyse and make statistical comparisons between groups.
can collect a large amount of data quickly.

Weaknesses.
lacks detail and does not explain why people behave as they do.
may force participants into categories using closed questions.

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Qualitative data.

Strengths.
Collects very rich and detailed data and gives a very valid view of the participant.
Can help answer why people do things rather than just how many times they do it.

weaknesses.
Difficult to analyse statistically and may require complicated coding of verbal responses.
can be influenced by researcher bias or misinterpretation of meanings to fit the research hypothesis.

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Descriptive statistics simply offer us a way to summarise and and describe our data but do not allow us to make a conclusion related to our hypothesis.

There are 2 main ways of summarising data using descriptive statistics.

the mean. this is calculated by addding all the scores together in each condition and then dividing by the number of scores.
this is most appropriate when the data range in roughly symmetrical and there are no anomalies or extreme scores. this is because the mean takes into account every piece of data, so it gives a good overall summary. 

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the median. this is calculated by finding the midpoint in a ordered list.
this is most appropriate when there are anomalies in the data. this is because any extreme or unusual scores are not taken into account.

the mode is the most common value in a set of values. 
this is most appropriate when dealing with category data. this is because it takes into account the frequency of data and can identify the most common score.

Another way of summarising and describing data is to calculate a measure of dispersion or the range.
this is most appropriate when finding out a distribution of the data. this is because the range shows how much the data varies e.g. the smaller the range the less the variation. 

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