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Data Collection - Coggle Diagram
Data Collection
Quantitative Data
Expressed in numerical form - descriptive statistics and statistical tests. Conclusions can be easily drawn.
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Secondary Data
Data that already exits from a previous study but is being used within the current investigation - researcher collects no new data
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(-) Lack of context and may lack detail: participants are not present during the research and they cannot be asked to expand their answers - lack detail. Decreases internal validity.
(-) The original studies might not be well conducted: the research may not be high quality - internal validity will decrease.
Qualitative Data
Non-numerical and rich in detail - can provide unexpected insights into thoughts and behaviour because the answers are not restricted.
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Collected by asking participants to state their opinions. Very subjective and can produce results that are not valid and/or reliable
Primary Data
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(+) Greater Insight: authentic - content of the research is more likely to match researchers needs and objectives
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Comparisons
Quantitative data involves numbers, where as qualitative data involves words.
Quantitative data can be measured objectively, whereas qualitative data is based on subjective interpretation of language
Quantitative data is immediately quantifiable, whereas qualitative data has to be transformed and is only quantifiable if data is put into categories and the frequency is counted.
Meta-analysis
Statistical analysis for analysing secondary data. Researcher reviews data from lots of smaller studied investigating the same aim. They identify recurring trends across the studies and provides an overview of findings. Helpful when a number of small studies have found contradictory or weak results.
(+) External Validity: results are based on a large number of participants and the different studies will use a different range of target populations - high population validity.
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