Types Of Data Flashcards
Quantitative data
Data in the form of numbers
Can then use descriptive stats (eg mean)
Qualitative data
Data in the form of words
Words are descriptions of beh, thoughts and feelings
Content analysis can turn qualitative data into quantitative data
To turn observations and interviews into quantitative data you can use behavioural categories and use tallies
When to use qualitative and quantitative data
- Quantitative data is used in experimental and observational research.
- Qualitative data is used in case studies, open-question interviews and questionnaires.
- Studies can collect a combination of both quantitative and qualitative techniques in research. If both methods agree, this increases credibility (Methodological triangulation)
Qualitative data strengths
- Qualitative data is seen as rich in detail; this is because qualitative researchers often collect more information, and the use of open-ended questions means participants are not limited in the responses they can give, meaning qualitative data has higher validity. (e.g. participants can give the answer they want, not limited to yes/no or out of 7)
Qualitative data weaknesses
X Qualitative data gathered by the researcher can be open to
interpretation and potentially biased.
X Due to the extensive range of data collected, it can be challenging to summarise.
X As the questions that produce qualitative data are open-ended, data tends to be more variable, reducing the reliability of qualitative research
Quantitative data strengths
Objectively measured, reducing the likelihood of bias. This increases scientific credibility.
/ Descriptive statistics allows quantitative data to be summarised and then displayed on graphs, charts and tables
Quantitative data tends to be more reliable.
Because of the limited number of responses, there is a higher chance of getting the same findings if the study is repeated.
Disadvantage of quantitative data
X The limited number of qualitative research responses results in data lacking depth and detail. Also, quantitative data collection can only focus on individual behaviours and what can be mathematically measured.
Primary vs secondary data
Primary data: The researcher is responsible for generating the data, also knowr
“first hand” or “original” data. Primary data is created to answer the research question. Common ways to collect primary data are the researcher conducting experiments, observations, interviews, questionnaires and case studies.
Secondary data: “second-hand” data, this is when researchers use information previously collected by a third party, such as another researcher or organisation. This Secondary data was initially collected for a reason other than to answer the current research question. Examples of secondary data are government or business statistics and records or previously published studies.
Primary data adv
Increased validity as the data is collected to answer the research question directly. The experiment or observation is designed to test the intended variable directly.
Increased validity as the researcher can control the data collection process carefully.
Primary data disadvantage
X Collecting original data from participants is both time-consuming for the researcher and potentially expensive. Costs include paying participants for their time and other researchers for their work. Setting up an experiment also includes paying for materials.
Secondary data adv
/ Secondary data already exists and is often already analysed; this can dramatically reduce both the time needed to conduct research and the costs involved in conducting a study involving participants.
Secondary data disadvantages
X Decreased validity as the data is not collected to answer the research question directly. The data may not be appropriate to answer the researcher’s research question.
X Decreased validity as the researcher had no role in the data collection process, so cannot ensure that the data was collected free from bias or is the result of variables.
Meta analysis
A process that collects and combines the results of a range of previously published studies asking similar research questions. The data collected is compared and reviewed together, and part of this review can include statistically combining all the data to produce an overall effect size and conclusion.
Strength of meta analysis
`/ The large sample size of meta-analysis produces results that are more statistically powerful than studies with a small number of participants.
As meta-analysis looks at the overall pattern of results across many studies, a small number of individual studies that are affected by bias or a lack of control will not change the overall pattern of results, making meta-analysis more trustworthy than any individual study.
Studies testing the same variable in various contexts (such as across cultures)
be compared, revealing unexpected relationships.
Weakness of meta analysis
X A meta-analysis has all the weaknesses of secondary data; the researcher has no control over the quality of the data collected. Also, included studies are conducted to answer particular research questions, so may not be comparat
X Studies that show a statistically significant result are more likely to be published (so included in a meta-analysis), while non-significant results are unlikely to be submitted for publication (the file draw problem)
X The choice of which studies to include/exclude could be biased.
