Since you've been using Vaia to revise physics, you've been feeling so relaxed about GCSEs that you're sleeping great. "That was my best night's sleep in years!" you exclaim as you wake up. However, as the excellent physicist you are, you know this statement is merely a hypothesis. To use the scientific method to test your hypothesis that last night's sleep was indeed record-breaking, you'll need to collect some data. This article introduces the basic scientific principles of collecting data and why these are important to ensure reliable results.
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Jetzt kostenlos anmeldenSince you've been using Vaia to revise physics, you've been feeling so relaxed about GCSEs that you're sleeping great. "That was my best night's sleep in years!" you exclaim as you wake up. However, as the excellent physicist you are, you know this statement is merely a hypothesis. To use the scientific method to test your hypothesis that last night's sleep was indeed record-breaking, you'll need to collect some data. This article introduces the basic scientific principles of collecting data and why these are important to ensure reliable results.
The scientific method has enabled countless aspects of modern life, being responsible for the development of medicines, aeroplanes and computers. It defines the process of making a hypothesis, experimentally testing it, analysing results, and using this to iteratively refine hypotheses. The key activity needed to test the hypothesis is clearly the testing - however, to effectively analyse the results of any experiment that we run, we need to collect data from it.
The purpose of any data collection is to collect high-quality evidence that can be analysed to come up with convincing and trustworthy responses to the questions or hypotheses proposed.
Data collection is the process of gathering data on certain variables in a structured and controlled manner, allowing one to analyse the data collected to answer relevant questions and assess consequences.
Imagine you are ordering a custom-made suit or dress. When the tailor asks you how long you would like the legs to be, you have two options: you could say "long" and hope that the tailor is able to get the length about right for you, or give them a measurement, "30-inch inner leg" and know that they now have your exact size. While both these approaches could get you a great outfit, there is a key difference - the first approach uses qualitative data, while the second approach uses quantitative data.
Type of Data | Collecting methods | Analysis methods |
Quantitative | Measuring and counting are used to collect quantitative data, with data stored in numerical form. Measurements can be made manually or using sensors. | Quantitative data is analysed using statistical analysis. In some applications, the data may be analysed using more advanced techniques such as machine learning and artificial intelligence. |
Qualitative | Interviews and observation are used to gather qualitative data. Data is usually descriptive, meaning there is no set range that results are limited to. | Qualitative data is studied by organizing it into meaningful groups or themes, which can then be further analysed. The data collected is non-numerical. |
Here are some example comparisons of types of qualitative and quantitative data.
The differences between qualitative and quantitative data are sometimes subtle - describing a dress as pink and white is qualitative, but counting the number of pink and white dresses in a store would be quantitative.
Returning to our example of a scientific study on the quality of sleep, you might gather some qualitative data about this by conducting interviews with people who have just woken up. You could ask questions like: "did you sleep well last night?" or "how rested do you feel?". These leave the answer open-ended, so the sleepy interviewee is free to answer however they like.
Qualitative data is information defined using descriptive language, allowing open-ended, detailed answers that are non-numerical in nature.
In addition to collecting qualitative data about how people's self-reported quality of sleep, you may also want to record or measure some quantitative data. While this would lack the depth and detail of subjective information, a more controlled set of data gathered quantitatively allows for more straightforward numerical analysis and comparison of results. Some data you could record to quantitively measure the quality of someone's sleep could be the duration of the night's sleep, or asking participants to rate how rested they feel on a 1-10 scale.
In the case of quantitative data collection, most measured parameters have some unit assigned to them. In the examples above, the duration of sleep would be measured in minutes, while the scale would have descriptive guidelines such as 1 = no sleep, 10 = most rested possible.
A major benefit of using quantitative data is that it is objective, and not susceptible to external influences such as bias. The objective nature of quantitative data also allows us to directly compare different results. While two people may have different qualitative descriptions of what makes a good night's sleep, we can directly compare the quantifiable durations that were asleep. Finally, if data is collected in quantitative terms it is much more straightforward to statistically analyse.
There are two main categories of data collection - primary data and secondary data collection.
Both qualitative and quantitative data can be primary or secondary. The type of data defines if it is qualitative or quantitative, while the source of the data determines if it's primary or secondary.
This is data directly gathered by the person or organisation conducting the investigation, for the purpose of conducting the investigation. For example, in our sleep quality study, a source of primary data would be you running an experiment which timed the duration of people's sleep. Alternatively, qualitative data collected through one-to-one interviews would also be primary data.
Secondary data is information that was not gathered for the specific investigation it is being used for, even if it is still useful. Datasets created by a different study or group can be used as a source of data for another investigation, and sometimes this is the best source of data for certain information - for example, if you wanted to find out the average height of 15-year-olds in the UK to compare to the participants in your sleep investigation, this data would be better obtained from a secondary source such as national anthropometry statistics. It's unrealistic to conduct a huge experiment for every study that may need the same dataset, so by conducting a single large-scale study other investigations can use the results as a high-quality source of secondary data.
Derived data is data that has been created or calculated using other data, rather than directly collected or measured. An example would be a value for average height - this figure will have been calculated using an original dataset of many individual height Measurements.
One feature of derived data is that it's hard (or impossible) to retrieve the original data from the derived data. For example, if given the average age of a population, it's impossible to determine the original set of individual lifespans data that was used to calculate the average.
We have already discussed several examples of different types of data collection throughout this article, but here are some more to provide even more context to your understanding of data collection.
Data collection is the process of obtaining data on certain variables in a structured manner, allowing one to answer questions relevant to a hypothesis or theory and assess consequences.
The purpose of any data collecting is to collect high-quality evidence that can be analysed to come up with convincing and trustworthy responses to the questions addressed. Data collection is an essential part of the scientific method, which bases conclusions on data-driven evidence.
Methods often used for qualitative data collection include; one-on-one interviews, focus groups, record keeping and case studies. On the other hand, surveys, questionnaires and experiments can be used to collect quantitative data.
Some examples of different types of data collection include:
There are two types of data collected: qualitative and quantitative data. These can be categorised into primary or secondary data, depending on who it was collected by.
What is the purpose of data collection?
The purpose of any data collecting is to collect high-quality evidence that can be analysed to come up with convincing and trustworthy responses to the questions posed or to test a hypothesis.
What are the two types of data that we can collect?
Quantitative and Qualitative data. Quantitative data is usually numerical and exists on a defined range. Qualitative data is descriptive and open-ended, without any defined range.
Is numerical data quantitative or qualitative?
Quantitative
What collecting methods are used for quantitative data?
Measuring and counting. This may be done manually or using techniques such as a questionnaire or sensors.
Is the data recorded using sensors in the lab quantitative or qualitative?
Quantitative, as data collected by sensors is always numerical.
Is statistical analysis used to analyse quantitative or qualitative data?
Quantitative. If qualitative data is to be analysed statistically, it first needs to be grouped or categorised. to allow it to be handled numerically.
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